Thèse De Doctorat D'avignon Université

Mehrnaz Zardari

DR, INRAE M Yves Brunet

Bordeaux Président

DR/ Mme Anne Oppliger

HDR, IMT Mme Sandrine Bayle

Mines Alès

DR, INRAE Jean-Paul Douzals

Montpellier Examinateur

DR, INRAE Mme Dominique Courault

HDR Mme Séverine Tomas

M Bruno Molle

Keywords: Eaux usées traitées, Echantillonnage d'aérosols, Irrigation par aspersion, Modèle empirique, Soufflerie, Transport advectif Air sampling, Airborne transport, Empirical modeling, Sprinkler irrigation, Treated wastewater, Wind tunnel

La réutilisation d'eaux usée traitée représente une alternative intéressante pour pallier le manque d'eau et contourner les périodes de pénuries de plus en plus fréquentes ces dernières années. On constate cependant que cette pratique est peu utilisée en France, en raison d'une réglementation contraignante, et une crainte que ces eaux puissent contenir encore des contaminants ou pathogènes pas complètements éliminés par des traitements standards. Il manque de données sur ce dernier point en particulier concernant les bioaérosols qui sont émis suite à l'irrigation par aspersion. Très peu de mesures existent et les protocoles ne sont pas standardisés. L'irrigation par aspersion utilisant des eaux usées traitées peut présenter des risques sanitaires potentiels liés aux bioaérosols émis dans l'atmosphère. Le transport de ces aérosols est un phénomène complexe sur lequel on manque encore de connaissances sur les processus de dispersion. Il dépend de l'effet combiné de plusieurs paramètres dont la taille et la proportion de fines gouttelettes (<0,15 mm) et la vitesse du vent, qui sont les vecteurs les plus importants pour la transmission potentielle de pathogènes. L'objectif de notre étude a été de mieux comprendre les mécanismes de dispersion atmosphérique des contaminants émis dans l'air par les eaux usées lorsque l'on irrigue par aspersion. Les principales questions auxquelles nous avons cherché à répondre ont été : -Quels sont les principaux facteurs et conditions affectant la dispersion des contaminants lors de l'irrigation par aspersion ? -Quelles techniques et méthodes doivent être utilisées pour la détection et l'échantillonnage des (bio) aérosols générés par l'irrigation par aspersion ? -Comment modéliser le transport et la dispersion des aérosols provenant de l'irrigation par aspersion des eaux usées traitées ? Pour répondre à ces questions, différentes expériences ont été réalisées en conditions contrôlées et en extérieur pour diverses conditions environnementales. Des comparaisons entre différents types de collecteurs (actifs (AGI-4 et SKC BioSampler) et passifs (fils de PVC)) ont été faites en utilisant deux traceurs. Avec un colorant fluorescent (Acid Brilliant Flavine), les résultats ont montré que l'efficacité physique de l'AGI-4 était presque 16,3 % plus élevée que celle du SKC BioSampler dans la plage de 5 à 30 minutes. Des expériences ont également été menées avec E.coli. L'efficacité du SKC est apparue supérieure à celle de l'AGI-4 pour les différentes conditions étudiées. L'augmentation du temps d'échantillonnage de 5 min à 15 min, montre que la viabilité d'E.coli est réduite dans les deux AGI-4 et SKC d'un facteur de 37% et 14%, respectivement. Des expériences en soufflerie ont permis d'analyser le comportement d'un nuage de fines gouttes. En conditions extérieures, neuf expériences ont été menées avec différentes conditions environnementales. En injectant de l'ABF dans l'eau, la distribution des aérosols produite à partir d'un asperseur soumis à une pression de fonctionnement de 350 kPa a été mesurée verticalement (1-10 m au-dessus du sol) sous le vent et horizontalement dans un périmètre équivalent à 4 fois la portée de l'asperseur (20-40 m). La distribution transversale au vent du panache de gouttelettes provenant d'un asperseur suit une distribution gaussienne, et le taux de dispersion dépend de la vitesse du vent et de l'écart type de la direction du vent. Différents modèles ont été évalués en faisant varier les facteurs climatiques mesurés. Deux modèles empiriques combinant des variables spatiales et les principaux paramètres météorologiques, notamment la vitesse du vent, l'écart type de la direction du vent, la température et l'humidité relative (pour le transport advectif), fournissent les meilleures corrélations entre les valeurs de transport simulées et les données observées (R 2 =0,9). Ces modèles peuvent être utilisés pour de futures études visant à évaluer les risques liés à la réutilisation des eaux usées traitées pour l'irrigation.

140

To my Mum and Dad, To my Sisters, To Behzad

Acknowledgment

The research included in this thesis could not have been performed if not for the assistance, patience and support of many individuals. I should express my thanks to the members of jury, Dr. Yves Brunet and Dr. Anne Oppliger, who agreed to make reports on this thesis and for their fruitful comments and input to this work. I would also like to thank the members of my thesis monitoring committees, Dr. Sandrine Bayle and Dr. Jean-Paul Douzals, for their wise scientific advices throughout my PhD thesis, and also for agreeing to participate in the jury as examiners of this work. My sincere thanks also go to Pr. Fabien Anselmet, Dr. Muriel Amielh, and Dr. Carole Bedos for valuable suggestions to overcome difficulties encountered during my PhD thesis, for their insightful comments and questions that helped me to improve this thesis. Special thanks are directed, of course, to Dr. Laurent Aprin. Without his permanent support, unquestionable skills, understanding and helpfulness the experimental part of this work in the laboratory would definitely not have been possible. Many thanks also go to the Laboratory of Engineering of the Industrial Environment and Industrial and Natural Risks (LGEI) and the Laboratory for the Science of Risks (LSR) teams at IMT Mines Alès.

Appreciation is extended to Dr. Clément Chanut for the detailed explanations and guidance on setting up the PDPA system, and all the technical support. Also, to Christian Lopez, the formidable technician, who helped me a lot for the installation of all optical systems. Thank you also to my fantastic friends and office mates, who supported and encouraged me through this research, especially Hind Oubanas, Alix Bartoli, Ivano Cornacchia, Claire Richert, Nancy Rizk, Claire Dedieu and Yasmine Razavi Ebrahimi.

My deepest thanks goes to my parents for all the unconditional support, love and believing in me through all the years. I find it difficult to express my appreciation because it is so boundless. My sisters, Rozita, Shiva and Sanaz thanks for their continuous encouragement, for endless love through all my life. I would like to thank my brother-in-law, Arman Rokhzadi for fruitful discussions on fluid mechanics and his moral support. 190 Last but not the least, I give my sincere gratitude to my lovely husband, Behzad, who was more than a husband, he was a real friend rather. He has seen me through the ups and downs of the dissertation process and he was able to bring me comfort and love during all these years, even though we were several hundred kilometers apart. Without his support and encouragement, I could not have dared to begin and finish this work. [START_REF] Grinshpun | A general equation for aerosol aspiration by thin-walled sampling probes in calm and moving air[END_REF] 5.11. The simulated results of (a): horizontal dispersion of aerosol plume at a height of 1 m above the ground and a distance of 20 m from the sprinkler head (b): vertical dispersion of aerosol plume at a distance of 40 m from the sprinkler head through both models (M1: Black lines, M2: Red dashed lines) and experimental data from PVC strings (black dot) related to Exp.9. The unit of RMSE: mL.m -2 .h -1 .........142 Figure 5.12. Correlation between observed and simulated data from Petri dishes (sedimented transport), y=ax, (a): Power-crosswind Gaussian model, M1, (b): Exponential-crosswind Gaussian model, M2 ...143 Figure 5.13. Examples of sedimented transport simulation (line/dashed line) downwind from the sprinkler head and experimental data (dots) using Petri dishes from Exp.3 (black) and Exp.5 (blue), see Table 5.1 Table 1.1. Ranking of the top 22 countries reusing treated municipal wastewater for irrigation purposes [10 9 m 3 . Year -1 ] and two important countries in agriculture as well as our destination France ..................33 Table 1.2

List of Figures

List of Tables

Introduction Context

The reuse of treated wastewater is a common practice in various countries such as Israel, Tunisia, Spain, Australia or the USA. Depending on the region, wastewater reuse is considered controlled (i.e. the wastewater is treated to minimize risks) or uncontrolled (i.e. the wastewater is discharged directly in water bodies and reuse, or directly reused). [START_REF] Thebo | A global, spatially-explicit assessment of irrigated croplands influenced by urban wastewater flows[END_REF] estimated the worldwide surface area irrigated with controlled treated wastewater to 6 million hectares, when the area receiving partially or untreated wastewater represents up to 30 million hectors. This, in total, represents approximately 12% of irrigated areas. The practice is not a lot developed in France, partly because of the relative abundance of resources, and partly because of the strict regulation, mainly due to the lack of knowledge of the potential dissemination of contaminant pathogens remaining in the treated wastewater.

The French regulation prepared by ANSES and soon replaced by a European text, even more restrictive, considers the specificity of sprinkler, that disperses water in the air to form a kind of rainfall. As such irrigation can occur under windy conditions, the pathogens contained in the dispersed droplets that can be released into the air may induce risks of contamination. Thus, managing risks requires a good knowledge of droplets formation and path in real conditions through dedicated measurement methods.

Sprinklers disperse water in the air in the form of one or several jets that generates droplets. Depending on their size these droplets may drift under the influence of wind and induce the formation of aerosols.

These could be bio-aerosols as they may contain various contaminants some of them being harmful. But, today there is no accepted standard sampling method for aerosolized particles issued from a sprinkling system and the contaminants they may contain.

Very few in field characterizations of bioaerosols from irrigation with wastewater are reported in the literature. Transport of aerosol induced by sprinkler irrigation of treated wastewater is a multi-parameters

phenomenon not yet sufficiency analyzed .

Objectives of the thesis

In this context, the present work will investigate knowledge to address the following questions:

• What are the factors and conditions affecting the dispersion of contaminants during sprinkler irrigation?

• What techniques and methods should be used for the detection and sampling of (bio)aerosols generated by sprinkler irrigation ? 610

• How to model the transport and dispersion of aerosols issued from irrigation sprays of the treated wastewater?

To address these questions, we have carried out different experiments that will be described in Parts II and III of this report. This will be preceded by a Part I that presents a literature review, with the Chapter 1 dedicated to an overview of the experiences of reuse of treated wastewater for irrigation, and the Chapter 2 focusing on the description of air sampling methods developed.

Part II was devoted to the laboratory experiments and divided in two chapters. Chapter 3 lists and describes the different sampling methods that have been developed to date to measure the quantity of airborne particles in controlled and low wind conditions, and the impact of measurement parameters on the survival of E.Coli. Chapter 4 focusses on the characterization of fine droplets spray in controlled conditions 620 (wind tunnel experiment).

The part III was dedicated to the in-situ experiments (Chapter 5) evaluating the aerosol losses to which an agricultural impact sprinkler is subject to, using passive samplers covering a distance corresponding to approximately 4 times the sprinkler radius of throw. [START_REF] Dehghani | Towards an integrated system modeling of water scarcity with projected changes in climate and socioeconomic conditions[END_REF][START_REF] He | Future global urban water scarcity and potential solutions[END_REF].

The reuse of treated wastewater (TWW) in agricultural and landscape irrigation has therefore become an important solution worldwide to mitigate the impacts of water scarcity and the degradation of conventional water resource quality [START_REF] Nikolaou | Implementing Sustainable Irrigation in Water-Scarce Regions under the Impact of Climate Change[END_REF][START_REF] Shevah | Water scarcity, water reuse, and environmental safety[END_REF]. 640

Water consumption and water scarcity in the world

Worldwide, 70 percent of total freshwater withdrawals is destined for the agriculture sector, which tends to increase [START_REF] Fao | The state of the world's land and water resources for food and agriculture_Systems at breaking point[END_REF][START_REF] Ritchie | Water Use and Stress[END_REF]. Rising temperature on the growing season driven by climate change, much visible in the last decades, could cause more variable water availability and exacerbate evapotranspiration and crop water requirement and subsequent the risks of agricultural production losses [START_REF] Denicola | Climate Change and Water Scarcity: The Case of Saudi Arabia[END_REF][START_REF] Hristov | Reuse of treated water in European agriculture: Potential to address water scarcity under climate change[END_REF]. An estimated 3.2 billion people live in agricultural regions with high to very high water stress or scarcity, of whom 1.2 billion people (about onesixth of the world's population) live in severely water-constrained agricultural areas (FAO, 2020). Figure 1.1a shows that the countries of Central Asia, the Middle East-West Asia and North Africa are at the highest levels of water stress for the agricultural sector [START_REF] Fao | The state of the world's land and water resources for food and agriculture_Systems at breaking point[END_REF]. It was projected that by 2040 (Figure 1.1b), 650 the number of countries exposed to severe water stress in all sectors will generally increase due to climate change, bio-energy demands and expected population growth [START_REF] Gosling | A global assessment of the impact of climate change on water scarcity[END_REF][START_REF] Stenzel | Irrigation of biomass plantations may globally increase water stress more than climate change[END_REF].

Reuse of treated wastewater for irrigation

Reuse of treated wastewater can alleviate water stress on the agricultural sector and constitute a 660 significant component when operating integrated water resources management [START_REF] Intriago | Agricultural reuse of municipal wastewater through an integral water reclamation management[END_REF][START_REF] Stathatou | Treated wastewater reuse potential: mitigating water scarcity problems in the Aegean islands[END_REF]. The practice of domestic/municipal wastewater reuse (both treated and untreated) for agricultural and landscape irrigation has a long development history [START_REF] Jones | Country-level and gridded estimates of wastewater production, collection, treatment and reuse[END_REF][START_REF] Qadir | Non-conventional water resources and opportunities for water augmentation to achieve food security in water scarce countries[END_REF]. Today, advanced techniques for treatment of wastewater and reuse projects are being planned and implemented more rapidly throughout the world [START_REF] Angelakis | Water Reuse: From Ancient to Modern Times and the Future[END_REF]. An estimated at least 300 million hectares in 50 countries, or 10% of the total irrigated land, which is 17% of the total arable land, are irrigated with partially treated wastewater worldwide [START_REF] Jiménez | Irrigation in Developing Countries Using Wastewater[END_REF][START_REF] Ungureanu | Water Scarcity and Wastewater Reuse in Crop Irrigation[END_REF]. [START_REF] Drechsel | Wastewater use in irrigated agriculture[END_REF] stated that estimates of the total area that is being irrigated with wastewater (both treated and untreated) are still fragmentary, the numbers mentioned are likely to range between 5 and 20 million hectares (WWAP, 2017). In addition, the use of treated wastewater for irrigation purposes has further 670 increased in recent years. Based on the last data obtained by AQUASTAT (FAO AquaStat, 2022), approximately 8.5 million hectares in 38 countries (recorded data from 2019) are equipped for irrigation by direct use of treated municipal wastewater. Table 1.1 also displays the top 22 countries that reuse treated municipal wastewater for irrigation purposes (10 9 m 3 .yr -1 ), placing Mexico in first place (with 1.38×10 9 m 3 .yr -1 ), followed by China (with 1.26 ×10 9 m 3 .yr -1 ). Note that the data was not available for all countries, including two important agricultural producing countries (India and USA).

FAO (FAO Statistics, 2021) estimates that in the entire world 342 million hectares are equipped for agricultural irrigation in 2019 (Figure 1.2). Asia accounts for 70 percent of the total area of the world among which a large part corresponds to paddy rice. Among the three main types of irrigation (surface irrigation, sprinkler irrigation and localized irrigation), sprinkler irrigation is applied on about 11 % of area 680 equipped for irrigation, followed by localized irrigation about 4 % in worldwide. As shown in Table 1.1, the type and diffusion of irrigation technologies are quite diverse in the countries. In France, sprinkler irrigation is the most widely-used irrigation system [almost 90%]. Irrigation is also practiced in urban dwellings, if the shrubs and trees are mostly irrigated with localized irrigation techniques, the surfaces covered by grass (public parks, private lawn, and sport fields) are mostly sprinkler irrigated.

Sprinkler irrigation

Sprinkler irrigation is a method in which water discharges from nozzle(s) or small orifices under pressure into the air in the form of a water jet. The water jet then breaks up in droplets following the process of atomization (divided into primary and secondary atomization), which fall on the soil or crop canopy like natural rainfall. Note that in the case of the sprinkler irrigation, injection velocity is high (around 10-35 m.s -1 ), so the breakup regime is that of atomization: a complete jet disruption, producing ligaments that divide in droplets out of the nozzle [START_REF] Lefebvre | Atomization and Sprays[END_REF].

The primary step of the atomization regime, initial flow deformation and the subsequent production of liquid fragments, is strongly influenced by geometry of the nozzle, liquid viscosity, liquid cavitation at the nozzle entrance, jet velocity and turbulence, and surface tension [START_REF] Dumouchel | On the experimental investigation on primary atomization of liquid streams[END_REF][START_REF] Stevenin | Étude de l'atomisation d'un jet d'eau haute vitesse Application à l'irrigation par aspersion et à la pulvérisation[END_REF][START_REF] Wu | Effects of initial flow conditions on primary breakup of nonturbulent and turbulent round liquid jets[END_REF]. Aerodynamic drag forces, air turbulence and surface tension dominate the secondary one:

continued distortion and disintegration of liquid fragments until the production of stable droplets. [START_REF] Diemuodeke | Mechanistic Model for the Breakup Length in Jet Atomization[END_REF][START_REF] Dumouchel | On the experimental investigation on primary atomization of liquid streams[END_REF]. Many kinds of sprinkler heads are available, some are shown in Figure 1.6. The selection of an appropriate sprinkler and nozzle combination requires knowledge on system types, sprinkler spacing, 730 operating pressures, soil, topography (e.g., slope), the type and stage of growth of the crop, and meteorological conditions. Until now, various studies have been carried out on the performance of the sprinklers to improve their water distribution uniformity and to limit run-off and drift losses [START_REF] Bartoli | Liquid rotary jet fragmentation of a pressure atomizer[END_REF][START_REF] Ge | Comparisons of spray characteristics between vertical impact and turbine drive sprinklers-A case study of the 50PYC and HY50 big gun-type sprinklers[END_REF][START_REF] Kadem | Experimental and numerical modeling study for irrigation gun water jet[END_REF][START_REF] Kincaid | Drop Size Distributions for Irrigation Sprinklers[END_REF][START_REF] Molle | Caractérisation du risque de dérive et d'évaporation d'une gamme d'asperseurs d'irrigation application à la définition des limites d'utilisation de l'aspersion en réutilisation d'eaux usées traitées[END_REF]

Sprinkler irrigation with treated wastewater

As a sprinkler reproduced a rainfall the use of such irrigation for distributing treated wastewater may cause potential health risks linked to human contact with contaminants with TWW through inhalation, dermal contact or ingestion because of exposure to aerosols [START_REF] Adegoke | Epidemiological Evidence and Health Risks Associated With Agricultural Reuse of Partially Treated and Untreated Wastewater: A Review[END_REF][START_REF] De Matos Nascimento | Quantitative microbial risk assessment of occupational and public risks associated with bioaerosols generated during the application of dairy cattle wastewater as biofertilizer[END_REF][START_REF] Fattal | Health risks associated with wastewater irrigation: an epidemiological study[END_REF]. The investigations into the dispersion of aerosolized pathogens such as viruses and bacteria during and after the sprinkler irrigation of TWW have shown their decreased viability and airborne concentration with distance from irrigation source [START_REF] Fattal | Viruses in wastewater aerosols[END_REF][START_REF] Katzenelson | Dispersion of Enteric Bacteria by Spray Irrigation[END_REF][START_REF] Kritana | Atmospheric pathway: A possibility of continuous outbreaks of foot-and-mouth disease in South Korea in 2010-2011[END_REF][START_REF] Linnemann | Risk of Infection Associated with a Wastewater Spray Irrigation System Used for Farming[END_REF][START_REF] Margalith | Antibodies to polioviruses in an Israeli population and Overseas volunteers[END_REF]. A study by [START_REF] Shuval | Aerosolized Enteric Bacteria and Viruses Generated by Spray Irrigation of Wastewater*[END_REF] detected a low concentration ranging from 1-100 (CFU.m -3 ) for bacteria and 0.03-2 (PFU.m -2 ) for 750 viruses over distances up to 730 m downwind of wastewater (partially treated) irrigated field.

Aerosol definition

The term "aerosol" refers to suspension of liquid droplets or fine solid particles in the air that are generated by spray irrigation of TWW. Bioaerosols are also defined as biological particles containing living and dead organisms (such as protozoa and helminths, bacteria, and viruses) and various fragments or excretions in aerosols (Figure 1.7) and are typically surrounded by a thin layer of water.

Factors affecting the fate and viability of aerosolized TWW microorganisms

Exposure via sprinkler irrigation strongly depends from the factors that influence the fate and viability of pathogen-containing aerosols. These primary factors can fall into the following categories [START_REF] Brooks | Estimation of bioaerosol risk of infection to residents adjacent to a land applied biosolids site using an empirically derived transport model[END_REF]: the physical characteristics of aerosol [START_REF] Maier | Environmental microbiology[END_REF][START_REF] Rojas-Valencia | Analysis of allergenic bioaerosols distributed through sprinkling irrigation systems and mitigating strategies 9[END_REF], their aerodynamic behavior [START_REF] Derkachov | Drying of a Microdroplet of Water Suspension of Nanoparticles: from Surface Aggregates to Microcrystal[END_REF], their chemical composition related to the nature of wastewater, the level of treatments (Harris et al. 2012), and more important, atmospheric parameters:

relative humidity, solar radiation and temperature [START_REF] Brown | Fate and Transport of Microorganisms in Air[END_REF][START_REF] Teltsch | Airborne Enteric Bacteria and Viruses from Spray Irrigation with Wastewater[END_REF]. All other stresses are defined as secondary factors, like initial shock associated with the atomization process (internal geometrical spray nozzle characteristics and pressure behind them), sampling and analysis methods [START_REF] Cox | Bioaerosols Handbook[END_REF][START_REF] Donnison | Bacterial survival and dispersal in spray irrigation aerosols[END_REF][START_REF] Ghosh | Review of bioaerosols in indoor environment with special reference to sampling, analysis and control mechanisms[END_REF].

Furthermore, the extent of resistance or die-off due to these factors also depends on the type of pathogens [START_REF] Bertrand | The impact of temperature on the inactivation of enteric viruses in food and water: a review[END_REF][START_REF] Courault | Assessment and risk modeling of airborne enteric viruses emitted from wastewater reused for irrigation[END_REF][START_REF] Cox | The Survival of Escherichia coli sprayed into Air and into Nitrogen from Distilled Water and from Solutions of Protecting Agents, as a Function of Relative Humidity[END_REF][START_REF] De Giglio | Occurrence of Legionella in groundwater used for sprinkler irrigation in Southern Italy[END_REF][START_REF] Hamilton | Health risks from exposure to Legionella in reclaimed water aerosols: Toilet flushing, spray irrigation, and cooling towers[END_REF]. For example, experimental studies involving aerosols of infectious samples implemented by van Doremalen et al. ( 2020) and [START_REF] Fears | Persistence of Severe Acute Respiratory Syndrome Coronavirus 2 in Aerosol Suspensions[END_REF] showed that SARS-CoV-2 can remain viable for up to several hours.

Additionally, viruses and other pathogens which are not completely eliminated by common water treatment and transported into the soil by irrigation water could also be aerosolized later during windy conditions [START_REF] Girardin | Viruses carried to soil by irrigation can be aerosolized later during windy spells[END_REF]. In another study, [START_REF] Jones | The effects of meteorological factors on atmospheric bioaerosol concentrations-a review[END_REF] found that minimum wind speeds of approximately 0.4 m.s -1 are required for microbial aerosol emission from plant canopies.

Bioaerosol dispersion via spray irrigation of wastewater: Field studies

A review on field studies conducted to evaluate the factors affecting the dispersion of bio-aerosols from sprinkler irrigation of (partially or untreated) wastewater has been realized, where their results have been summarized in Table 1.2. The majority of these studies established that low humidity and high solar radiation reduce bacteria survival in bioaerosols. Therefore, the highest downwind concentrations were observed when irrigation occurred at night. [START_REF] Hangal | Technical Background Document: Microbial Risk Assessment and Fate and Transport Modeling of Aerosolized Microorganisms at Wastewater Land Application Facilities in Idaho[END_REF] also stated that worst-case conditions that lead to the greatest exposure and potential risk of infection are nighttime low-wind stable conditions, which maximize the inhalation pathway, and high-wind conditions, which maximize the deposition and produce ingestion pathway.

It is important to note that the wastewater in use, in most of the studies, was untreated or partially (poor) treated. In addition, apart from the specific configurations, most of the experiments were carried out in a restricted number of regions (United states and Israel), so the results may not be generalized in other ones because of differences in meteorological conditions. [START_REF] Devaux | Infectious risk associated with wastewater reuse: an epidemiological approach applied to the case of Clermont-Ferrand, France[END_REF] found no health risks for farmers associated with spray irrigation using treated wastewater with water concentrations equal to 10 3 CFU/100mL in Clermont-Ferrand, France, compared with non-exposed family members or farmers not using treated wastewater.

As can also be seen from the table below, in recent years, very few data have been published on pathogen or faecal indicator transport by spray and aerosols resulting from aspersion with treated waste water [START_REF] Brissaud | Water reuse for urban landscape irrigation: aspersion and health related regulations[END_REF]. Although, a number of studies on the exposure and risks for spray irrigated livestock manure have been recently presented [START_REF] Burch | Quantitative Microbial Risk Assessment for Spray Irrigation of Dairy Manure Based on an Empirical Fate and Transport Model[END_REF][START_REF] Dungan | Estimation of Infectious Risks in Residential Populations Exposed to Airborne Pathogens During Center Pivot Irrigation of Dairy Wastewaters[END_REF][START_REF] Dungan | Technical Note: Effect of Sprinkler Pressure and Spray Plate on Culturable Microorganism Concentrations During Simulated Irrigation of Dairy Wastewater[END_REF][START_REF] Nag | Risk assessment of Escherichia coli in bioaerosols generated following land application of farmyard slurry[END_REF], but human pathogens do not find in livestock manure and its treatment process differs from municipal wastewater. Moreover, the SARS-CoV-2 (COVID-19) problem has exploded the number of papers relating to the dispersion of aerosols in indoor and outdoor air [START_REF] Asdaq | Influence of environmental factors on the spread of COVID-19 in Saudi Arabia[END_REF][START_REF] Azuma | Environmental factors involved in SARS-CoV-2 transmission: effect and role of indoor environmental quality in the strategy for COVID-19 infection control[END_REF][START_REF] Kifer | Effects of Environmental Factors on Severity and Mortality of COVID-19[END_REF][START_REF] Li | Dispersion of evaporating cough droplets in tropical outdoor environment[END_REF][START_REF] Peng | Practical Indicators for Risk of Airborne Transmission in Shared Indoor Environments and Their Application to COVID-19 Outbreaks[END_REF]. Albeit, the results gave extremely useful contribution in understanding the effect of the various factors on the airborne transmission of the virus via respiratory droplets and the fate of the virus in the atmosphere, but the data cannot be generalized for the case of TWW spray irrigation. That is because the atomization process of sprinklers, and subsequently, the initial shock to the viruses (presented earlier in section 1.3.2), the ballistic behaviors of airborne droplets and their dehydration differ completely from the spread of droplets containing SARS CoV-2 virus, which have been released in the air by respiratory activities [e.g., breathing (UInitial =1 m.s -1 ), coughing (UInitial =10 m.s -1 ), sneezing (UInitial =100 m.s -1 )]( [START_REF] Katre | Fluid dynamics of respiratory droplets in the context of COVID-19: Airborne and surfaceborne transmissions[END_REF]. Comments (Factors affecting the fate and viability of bioaerosols) [START_REF] Bausum | Microbiological Aerosols from a Field-Source Wastewater Irrigation System[END_REF] 96 Sprinklers (For a single head: P = 308 -406 kPa, Q = 18.9 L.min -1 , Height of sprinklers = 0.6 m AGL, Radius of throw = 25.9 m, spray arc height = 2.7 m AGL)

Quantitative Microbial Risk Assessment

Quantitative Microbial Risk Assessment (QMRA) is a risk assessment approach, consisting in the following four steps: i) hazard identification; ii) exposure assessment; iii) dose-response modeling; and iv) risk characterization. Several studies have employed the QMRA models to investigate the infection (enteric or respiratory pathogens) risks associated with sprinkler irrigation with treated wastewater for farmworkers or individuals inhabiting downwind from these agricultural plots. They used Gaussian models (see 1.5.1)

with specific site, meteorological data or computational fluid dynamics based simulation models, CFD (see section 1.5.4). Some of these researches have been discussed here, but given that this assessment approach is outside the scope of our thesis, the procedure for each step was not elaborated. The results of the present work will contribute to such risk evaluation.

The research of [START_REF] Courault | Assessment and risk modeling of airborne enteric viruses emitted from wastewater reused for irrigation[END_REF], which have been presented in Table 1.2, consisted in an assessment of the health risk associated with inhaled norovirus using a Bayesian QMRA approach that included an atmospheric dispersion model. It was determined that if the annual probability of infection at 500 m and a constant virus emission rate of 8 GC.m -3 .s -1 is high, i.e., 6.3E-04 at the 95th percentile (a wind speed of 1 m.s -1 ), the risk is increased by 2 log at 6 m.s -1 . This probability decreases by 3 log for a wind speed of 1 m.s -1 when the distance to the emission source is doubled (1000 m). pneumophila from a single exposure to agricultural spray irrigation, over the growing seasons. Each unique combination of receptor distance (1-7 km) and a spray irrigator with two operating pressure of 10 psi or 69 kPa (low) and 30 psi or 207 kPa (high) with the aerosol concentrations of 0.2506 and 0.495 µg.m -3 air (median values), respectively, have been referred to as a "scenario". It was found that the mean risk of infection for a single exposure exceeded the safety threshold of 10 -6 infections/exposure up to 1 km from a low-pressure irrigator and up to 2 km from a high-pressure irrigator, although no median risk exceeded the threshold for any distance or irrigator pressure.

This is an important consideration for irrigation management as when calculating the risks for reclaimed water reuse in irrigation many QMRA studies only consider the crop consumption scenario, which may not be the scenario entailing the highest risk. However, the risks for accidental aerosol ingestion were calculated using very conservative estimates, considering a high number of exposures per year and that the irrigation was via sprinklers. This is often not the case in irrigation management, for example, at Shafdan and the Bolivar sites, drip irrigation is more frequently used than sprinkler irrigation [START_REF] Ayuso-Gabella | Quantifying the effect of Managed Aquifer Recharge on the microbiological human health risks of irrigating crops with recycled water[END_REF].

Regulations and politics

Regulations and guidelines were defined by international organizations such as World Health Despite these guidelines, there is still lack of knowledge about 1) health risk due to dispersion of pathogens by sprinkler irrigation because of too little measurements of bioaerosols and experimental data, and 2) robust methods to evaluate bioaerosol transports [START_REF] Zardari | Assessment of Sampling Techniques to Investigate Airborne Transport of Contaminants in Treated Wastewater from Sprinkler Irrigation Fine Droplet[END_REF].

Unlike a subsurface dispersal system, a spray distribution system requires the greatest level of wastewater pretreatment because of the risk of human contact. This in turn increases the cost of the advanced pretreatment system, because it must produce a high-quality effluent and includes a disinfection component.

Transport and wind drift 900

There is a wide variety of pathogens in reclaimed wastewater, which often go undetected or underestimated in aerosol from sprinkler irrigation. The simplest and most practical way of estimating microbial risks is to provide relevant information on the fraction of TWW spray flow that becomes aerosolized, and their dynamic movement [START_REF] Hangal | Technical Background Document: Microbial Risk Assessment and Fate and Transport Modeling of Aerosolized Microorganisms at Wastewater Land Application Facilities in Idaho[END_REF][START_REF] Molle | Experimental Approach to Assessing Aerosol Dispersion of Treated Wastewater Distributed via Sprinkler Irrigation[END_REF]. This spray volume component is defined by the term of "transport", which in the present study corresponds to aerosolization efficiency. Transport is therefore the volume of water that escapes from the wetted radius under windy conditions in the form of aerosols (Figure 1.8). For irrigation purposes, it corresponds to an application rate of less than 0.26 mm.h -1 that cannot be quantified directly using rain-gauges (ISO 15886-3, 2012).

Note that the term of "wind drift" defines as the volume of water that escapes from the wetted perimeter under the influence of the wind, which corresponds to an application rate greater than 0.26 mm.h -1 (see 910

Factors affecting the aerosol transport

Transport is an intricate phenomenon combining the effect of several parameters (Figure 1.9), among others: i) the proportion of droplets inferior to 0.1-0.15 mm, which can completely evaporate [START_REF] Hangal | Technical Background Document: Microbial Risk Assessment and Fate and Transport Modeling of Aerosolized Microorganisms at Wastewater Land Application Facilities in Idaho[END_REF][START_REF] Molle | Evaporation and wind drift losses during sprinkler irrigation influenced by droplet size distribution: sprinkler losses and droplet size[END_REF][START_REF] Xie | How far droplets can move in indoor environmentsrevisiting the Wells evaporation-falling curve[END_REF], and become aerosolized under moderate humidity conditions, ii) wind that plays a major role in droplet transport [START_REF] Courault | Bioaerosol Dispersion in Relation with Wastewater Reuse for Crop Irrigation. (Experiments to understand emission processes with enteric virus and risks modeling)[END_REF][START_REF] Molle | Experimental Approach to Assessing Aerosol Dispersion of Treated Wastewater Distributed via Sprinkler Irrigation[END_REF] and that can cancel out gravity effects for smaller particles. Thus, these two parameters with other meteorological 920 parameters (temperature, relative humidity, air stability and solar radiation) which have been studied in this thesis (presented in Chapter 5) have been explained as follows. The size of droplets is the critical parameter in the transport phenomenon [START_REF] Pepper | Risk of infection from Legionella associated with spray irrigation of reclaimed water[END_REF][START_REF] Wang | Field evaluation of spray drift and environmental impact using an agricultural unmanned aerial vehicle (UAV) sprayer[END_REF]. The airborne infection is relevant to dried-out infectious droplet nuclei [< 10 µm (Xie 930 et al., 2007)] derived directly from droplet less than 0.15 mm [START_REF] Hangal | Technical Background Document: Microbial Risk Assessment and Fate and Transport Modeling of Aerosolized Microorganisms at Wastewater Land Application Facilities in Idaho[END_REF][START_REF] Molle | Evaporation and wind drift losses during sprinkler irrigation influenced by droplet size distribution: sprinkler losses and droplet size[END_REF][START_REF] Xue | Longitudinal drift behaviors and spatial transport efficiency for spraying pesticide droplets[END_REF], which remain suspended in the air for a long time and could be carried over long distances by the air current (Figure 1.10). Although there is no specific droplet size range that is likely to totally evaporate before falling under all conditions, certain researchers suggest droplets smaller than < 0.1 mm [START_REF] Gil | Determination of drift potential of different flat fan nozzles on a boom sprayer using a test bench[END_REF]Li et al., 2018;[START_REF] Wells | On Air-borne Infection. Study II. Droplets and Droplet Nuclei[END_REF][START_REF] Yin | Investigation on the evaporation and dispersion of human respiratory droplets with COVID-19 virus[END_REF].

Droplet evaporation

During the process of droplet movement, droplet evaporation take place under several physical and 940 environmental factors including low relative humidity and high air temperature that cause a rapid reduction in the size and the weight of droplets, and consequently increase losses due to transport [START_REF] Tobi | Determination of accuracy level of agricultural spraying application in Sanliurfa/Turkey[END_REF]. [START_REF] Goering | Mathematical Modeling of Spray Droplet Deceleration and Evaporation[END_REF] presented the following equation to model the decrease in diameter D of a spherical droplet in the air due to in-flight evaporation (so called D 2 law), based on heat and mass transfer theory [START_REF] Marshall | Atomization and Spray Drying[END_REF]Ranz and Marshall, 1952a): Where 𝐷 𝑝 is droplet diameter in m; 𝑡 is time of flight in s; 𝑀 𝑣 is the molecular weight of water vapor in g; 𝑀 𝑚 is the molecular weight of air in g; 𝐾 is the diffusivity of vapor in air in m 2 .s -1 ; 𝜌 𝑎 is the density of air in kg.m -3 ; 𝑝 𝑤 is the density of water droplet in kg.m -3 ; ∆𝑃 is the difference between saturation pressure at the wet bulb temperature of air and vapor pressure at the dry bulb temperature in Pa; 𝑃 𝑓 is the partial 950 pressure of air in Pa. Classical correlation expressing 𝑁 𝑢 number (Nusselt number) as a function of particle/ droplet Reynolds, 𝑅𝑒 𝑝 (Eq. A-3) and Schmidt number, 𝑆𝑐; and finally 𝜈 𝑎 is kinematic viscosity of air in m 2 .s -1 [START_REF] Yan | Model of droplet dynamics and evaporation for sprinkler irrigation[END_REF].

𝑑𝐷 𝑝 𝑑𝑡 = -
Note that the evaporation of droplets containing particles involves complicated multiphase heat and mass transport. The morphology of the dry aggregate particles are controlled by the Peclet number, 𝑃𝑒 (defined as the ratio of evaporation rate constant of pure water droplet to the particle mass diffusivity in the liquid phase [see Eq. A-23 and Eq. A-25]). Figure 1.11 shows the evolution of morphology during the evaporation process of a water suspension droplet. As evaporation proceeds, the particles are trapped at the droplet surface due to the droplet shrinking (Figure 1.11b). Then islands of particles are formed at the droplet surface (see Figure 1.11c). For longer time, large islands and high-coverage patterns form a layer 960 or a shell of nanoparticles (Figure 1.11d). Further evaporation leads to collapse of the shell and a solid structure is formed [START_REF] Weber | A comparison of different estimators for the standard deviation of wind direction based on persistence[END_REF].

Figure 1.11. Morphological evolution from numerical simulation illustrating the evaporation dynamics of an aqueous droplet containing 200 nm polystyrene particles [START_REF] Derkachov | Drying of a Microdroplet of Water Suspension of Nanoparticles: from Surface Aggregates to Microcrystal[END_REF] A variety of physical-mathematical modelling approaches have been proposed in the literature to describe droplet motion dynamics [START_REF] Zerihun | Sprinkler Irrigation Droplet Dynamics. I: Review and Theoretical Development[END_REF]. The models are used to predict the uniformity and water distribution patterns of sprinklers within the wetted area. Moreover, these models are used to predict the kinetic energy influences of large droplets (> 3 mm) on runoff and soil erosion, as well as to predict the wind drift and evaporation losses of fine droplets (< 0.6-1 mm in diameter) [START_REF] Edling | Kinetic energy, evaporation and wind drift of droplets from low pressure irrigation nozzles[END_REF][START_REF] Kincaid | Drop Size Distributions for Irrigation Sprinklers[END_REF]. [START_REF] Lorenzini | Simplified Modelling of Sprinkler Droplet Dynamics[END_REF] and [START_REF] Thompson | A Sprinkler Water Droplet Evaporation and Plant Canopy Model: II[END_REF] developed two reliable models based on travel distance and "time of flight" of sprinkler droplets in turbulent flow conditions. The results of these studies showed reasonable agreement for cases in which the droplets have intermediate diameters in the range of 970 0.9-1.8 mm, while poor agreement was found for a droplet diameter of 0.3 mm. Therefore, for studies focusing on irrigation efficiency or water saving, volume percentages of sprayed droplets smaller than 0.1-0.15 mm has been rarely estimated and analyzed in terms of their potential for bioaerosol transport [START_REF] Hangal | Technical Background Document: Microbial Risk Assessment and Fate and Transport Modeling of Aerosolized Microorganisms at Wastewater Land Application Facilities in Idaho[END_REF]. For example, the model proposed by [START_REF] Pepper | Risk of infection from Legionella associated with spray irrigation of reclaimed water[END_REF] for assessing the inhalation volume of recycled wastewater droplets from a sprinkler was derived from: i) the extrapolation of the exponential function and parameters provided by [START_REF] Kincaid | Drop Size Distributions for Irrigation Sprinklers[END_REF] to determine the statistical distribution of the droplets smaller than 10 µm; ii) an empirical drift distance-droplet size model, which was only calibrated for a unique set of meteorological parameters.

Wind speeds and direction

Upon a droplet released in the atmosphere from the sprinkler head, wind direction and its fluctuation is an important factor that dictates the fluctuation of direction of the transport. Regarding wind speed, it may affect the production of aerosols, in particular when the rotating jet is upwind and crosswind, and significantly contributes in the transport of aerosols as well as larger droplets [START_REF] Fan | Numerical investigation of the effects of environmental conditions, droplet size, and social distancing on droplet transmission in a street canyon[END_REF][START_REF] Molle | Experimental Approach to Assessing Aerosol Dispersion of Treated Wastewater Distributed via Sprinkler Irrigation[END_REF][START_REF] Xie | The source and transport of bioaerosols in the air: A review[END_REF]. [START_REF] Cornacchia | Assessment of Airborne Transport of Potential Contaminants in a Wind Tunnel[END_REF] used a fogger as a spray generator with the volume median diameter of 0.11 mm to investigate the effect of wind speed on airborne transport of the spray under saturated conditions (above 90%) using a fluorescent tracer in a wind tunnel. The results showed that the mean transport increases from 0.13 to 1.18 L.h -1 m -2 at 7.7 m from the source as the wind velocity increases from 1 to 3 ms -1 .

The most recommendations are to stop spraying if wind speeds are exceed 4 m.s -1 [START_REF] Anses | Réutilisation des eaux usées traitées pour l'irrigation des cultures, l'arrosage des espaces verts par aspersion et lavage des voiries[END_REF][START_REF] Molle | Experimental Approach to Assessing Aerosol Dispersion of Treated Wastewater Distributed via Sprinkler Irrigation[END_REF].

Atmospheric stability

The stability of the atmosphere is the major determinant of small droplet movement, on long-range transport (Miller and Stoughton, 2000). Atmospheric stability classifications are defined from extremely unstable (A) to the moderately stable (F) regarding wind speed, surface roughness, incoming solar radiation and cloud cover [START_REF] Epa | EPA -Meteorological Monitoring Guidance for Regulatory Modeling Applications[END_REF][START_REF] Turner | The Long Lifetime of the Dispersion Methods of Pasquill in U.S. Regulatory Air Modeling[END_REF].

Although, the effect of the atmospheric stability is rarely taken into account in transport/ spray drift models such as CFD [START_REF] Hong | Fluid Dynamic Approaches for Prediction of Spray Drift from Ground Pesticide Applications: A Review[END_REF], and is mostly considered in horizontal and vertical a Gaussian plume model.

One of the main limitations of wind energy research is that all extrapolation of data generally assumes a neutral, uniform wind profile.

Stable conditions occur mostly at night and early in the morning; unstable conditions occur during daytime periods with low winds; and neutral conditions occur during periods of relatively high wind speeds or overcast conditions. In fact, temperature inversion (stable atmospheric condition) can occur when solar radiation is no longer strong enough to heat the Earth's surface to a temperature equal to or greater than that of the surrounding atmosphere [START_REF] Stull | Practical Meteorology : An Algebra-based Survey of Atmospheric Science[END_REF]. This can result in a layer of cool and still air being trapped below warmer air, and the fine droplets can be concentrated in the cool layer close to the ground where they remain isolated from the surrounding weather conditions [START_REF] Thistle | The role of stability in fine pesticide droplet dispersion in the atmosphere: A review of physical concepts[END_REF]. Miller et al., (2000) addressed the interactions of the atmospheric stability and spray drift applied by an orchard air-blast sprayer. The static and dynamic stability was measured with fast response turbulence sensors. It was found that on average, the total amount of drift collected by the samplers in very stable conditions was 5.9, 3.6, and 2.1 times the amount of drift in unstable conditions for the plates, the high volume air samplers, and the strings, respectively. [START_REF] Gil | Atmospheric loss of pesticides above an artificial vineyard during air-assisted spraying[END_REF] found that in stable atmospheric conditions, the spray droplets preferentially drifted along the mean wind direction, whereas in unstable conditions, the sprayed plume was wider and more dispersed to higher altitudes during air-assisted spraying. [START_REF] Tang | Influence of the atmospheric boundary layer stability on aerial spraying studied by computational fluid dynamics[END_REF] presented the results of CFD simulations of the velocity field and droplet movement (water droplets with 3% non-volatile) with different atmospheric stabilities in aerial application (release height = 5 m). The results indicated that droplets (most of them < 100 µm) in the stable conditions move more rapidly downwind than those in the unstable case by nearly 300% around at downwind distances of 1020 100 m, 180 m, 700 m, and ∼200% at around 220 m and 550 m. However, droplets dispersed more easily in unstable conditions because of the greater turbulence kinetic energy. These aspects will be discussed in more detail later in Chapter 5 (see 5.2.4).

Solar radiation

Many parameters related to atmospheric dispersion (e.g., atmospheric stability and heat flux) depend on the incoming solar radiation and the radiation balance. It is therefore important to understand the radiation processes in the atmosphere which affect such parameters. As can be seen in Table 1.4, a mix of different wind conditions and solar radiation/vertical temperature gradient (for nighttime conditions) results into three basic states of the atmosphere: unstable, stable, and neutral, that largely influence the concentration of aerosols. Besides, solar UV radiation can potentially sterilize the ambient air from 1030 bioaerosols, a phenomenon more effective for airborne bacteria [START_REF] Xie | The source and transport of bioaerosols in the air: A review[END_REF].

Previous field experiments of transport

During the 1970s and 1980s, several field measurements were conducted, mostly in Israel [START_REF] Applebaum | Dispersion of Aerosolized Enteric Viruses and Bacteria by Sprinkler Irrigation with Wastewater[END_REF][START_REF] Bausum | Microbiological Aerosols from a Field-Source Wastewater Irrigation System[END_REF][START_REF] Bausum | Comparison of coliphage and bacterial aerosols at a wastewater spray irrigation site[END_REF] and in the USA [START_REF] Camann | Microorganism Levels in Air near Spray Irrigation of Municipal Wastewater: The Lubbock Infection Surveillance Study[END_REF][START_REF] Raynor | Aerosol production by irrigation equipment used for land application of wastewater[END_REF][START_REF] Sorber | A Study of Bacterial Aerosols at a Wastewater Irrigation Site[END_REF] to estimate aerosolization by injecting a fluorescent dye into the irrigation system. Air sampling was performed at predetermined location downwind by active samplers such as AGI-30 or Andersen sampler (presented in sections 2.2.2.1 and2.2.2.2) compared to calculations of the atmospheric diffusion model [START_REF] Craig | Collection Efficiency of Surfaces used for Spray Drift Sampling 8[END_REF] to predict the dye air concentration at each location, assuming complete aerosolization of sprayed water. The ratio between the measured and predicted dye concentrations gave a point estimate of the production of aerosols. In the case of sprinklers, depending on its type, configuration (nozzle size and operating pressure) and sampling position, the authors found that 0.1% to 2% and in one case up to 4.16% of sprayed wastewaters was aerosolized. So, pathogenic microorganisms encased in these airborne water may be spread and transported for hundreds of meters (Teltsch et al., 1980b) or end-up in dry aerosols under the influence of air evaporation potential.

In recent years, numerous studies have been carried out to develop field measurement of "pesticide spray drift", that defines the off-target movement of pesticides by the action of air flux during an application using robust airborne sampling methods, currently standard polyethylene or polytetrafluoroethylene lines on which the pesticide deposits following ISO 22866 (2005) presented in the next chapter (section 2.2.1). This method is a good alternative to the transport measurements with dye tracers on a wide scale simultaneously. [START_REF] Molle | Experimental Approach to Assessing Aerosol Dispersion of Treated Wastewater Distributed via Sprinkler Irrigation[END_REF] characterized the transport from an irrigation rotating sprayer Rainbird 5000+ through this method, at two sampling heights (1 and 2 m) from the ground at a distance up to 4 times the sprinkler radius of throw, downwind of wetted area.

The study continued by [START_REF] Tomas | Transport Modeling in Sprinkler Irrigation[END_REF] under a wider range of operating pressure and meteorological conditions proposed an empirical modeling depending on meteorological parameters and dimensionless numbers that characterize atomization.

Atmospheric dispersion models

Gaussian plume

Gaussian plume model equations are widely used in atmospheric dispersion (diffusion+ transport) modelling, in particular to estimate three-dimensional concentration of microorganisms in aerosol from spray irrigation of wastewater [START_REF] Courault | Bioaerosol Dispersion in Relation with Wastewater Reuse for Crop Irrigation. (Experiments to understand emission processes with enteric virus and risks modeling)[END_REF][START_REF] Hamilton | Comparing microbial risks from multiple sustainable waste streams applied for agricultural use: Biosolids, manure, and diverted urine[END_REF][START_REF] Hamilton | Health risks from exposure to Legionella in reclaimed water aerosols: Toilet flushing, spray irrigation, and cooling towers[END_REF][START_REF] Katzenelson | Dispersion of Enteric Bacteria by Spray Irrigation[END_REF][START_REF] Nag | Risk assessment of Escherichia coli in bioaerosols generated following land application of farmyard slurry[END_REF][START_REF] Sorber | A Study of Bacterial Aerosols at a Wastewater Irrigation Site[END_REF] to subsequently perform a quantitative microbial risk assessment, QMRA. The distribution of aerosol plume in both horizontal and vertical directions is assumed to be Gaussian or normal (Figure 1.12). Assuming steady-state approximation (i.e. no parameter is timedependent) with a negligible wind shear effect on horizontal diffusion, steady state point source (sprinkler), homogenous, a negligible deposition rate of aerosols and background pollution (Abdel-Rahman, 2008), the standard Gaussian plume equation (with the term of the ground reflection) is expressed as :

𝐶(𝑥, 𝑦, 𝑧) = 𝑄 2𝜋𝑈𝜎 𝑦 (𝑥)𝜎 𝑧 (𝑥) . [𝑒𝑥𝑝 -( 𝑦 2 𝜎 𝑦 (𝑥) 2 )] . {𝑒𝑥𝑝 [ -(𝑧 -𝐻) 2 2𝜎 𝑧 (𝑥) 2 ] + 𝑒𝑥𝑝 [ -(𝑧 + 𝐻) 2 2𝜎 𝑧 (𝑥) 2 ]} Eq. 1-4
where 𝑄 is the emission rate, 𝑈 is the mean wind speed [m. Until now, the various commercial advanced Gaussian dispersion models have been developed, some of them are :

• U.S. Environmental Protection Agency (EPA):

AERMOD (Preferred/Recommended Model), includes several improvements compared to standard

Gaussian models such as, use of vertical continuous profiles of horizontal and vertical turbulence that are either based on measurements or computed based on similarity theory; simulation of buoyant or nonbuoyant plumes; simulation of dispersion under convective and stable conditions; adapted to flow in the 1090 complex terrain. The plume is modeled as a combination of terrain-following and terrain-impacting states [START_REF] Cimorelli | AERMOD: Description of model formulation[END_REF]. [START_REF] Dungan | Estimation of Infectious Risks in Residential Populations Exposed to Airborne Pathogens During Center Pivot Irrigation of Dairy Wastewaters[END_REF] modeled the dispersion of the of the pathogens with AERMOD. [START_REF] Mori | Quantification and Comparison of Risks Associated with Wastewater Use in Spray Irrigation[END_REF] applied AERMOD model to generate air concentrations of water aerosols during the irrigation season (May-September) for the years 2013-2018.

Screening Procedures, containing generated meteorological conditions which were not based on measured data such as, SCREEN3 (provide ground-level concentrations for point, area, and volume sources and concentrations due to inversion break-up), TSCREEN (for toxic emissions), VISCREEN (calculation of the potential visual impact of a plume of specified emissions for specific transport and dispersion (meteorological) conditions), CTSCREEN (a screening technique for regulatory application to plume impaction assessments in complex terrain) (cited by US EPA (2017) website).

Note that SCREEN3 can only simulate a single emission source and is most common model used for simulate worst-case scenarios (e.g. high wind speed) at a specified downwind distance from the source [START_REF] Hangal | Technical Background Document: Microbial Risk Assessment and Fate and Transport Modeling of Aerosolized Microorganisms at Wastewater Land Application Facilities in Idaho[END_REF].

• UK-Cambridge Environmental Research Consultant (UK-CERC):
UK-ADMS, advanced steady state Gaussian-based dispersion model, comprises a number of individual models, simulates the dispersion of buoyant or neutrally buoyant gaseous and particulate emissions to the atmosphere. Each individual model deals with either one aspect of the dispersion process or data input and output and can be modified independently to keep it, and hence the whole model, scientifically up-to-date [START_REF] Carruthers | UK-ADMS: A new approach to modelling dispersion in the earth's atmospheric boundary layer[END_REF]. It uses current knowledge of the structure of the atmospheric boundary layer, incorporating the Monin-Obukhov length and boundary layer depth [START_REF] Cerc | Atmospheric Dispersion Modelling System[END_REF]. This model includes algorithms which take account for the following: non-point sources, continuous plumes, plume rise as a function of distance, jets and directional releases, gravitational settling and dry deposition, decay dose, short duration releases (< one hour) and transport over complex terrain [START_REF] Douglas | Sensitivity of predicted bioaerosol exposure from open windrow composting facilities to ADMS dispersion model parameters[END_REF][START_REF] Gloster | Re-assessing the likelihood of airborne spread of foot-and-mouth disease at the start of the 1967-1968 UK foot-and-mouth disease epidemic[END_REF][START_REF] Taha | Improving bioaerosol exposure assessments of composting facilities -Comparative modelling of emissions from different compost ages and processing activities[END_REF]. The model is applicable up to about 60 km downwind of the source and provides useful information for distances up to 100 km.

Generally, the Gaussian approach does not provide much resolution in the representation of near-field dynamics. More information about Gaussian plume models and their limitations at short length and time scales can be found in [START_REF] Stocker | A review of dispersion modelling of agricultural emissions with non-point sources[END_REF] and [START_REF] Woodward | A review of the applicability of Gaussian modelling techniques to near-field dispersion[END_REF].

Lagrangian

The Lagrangian approach, as discrete phase tracking, has been widely employed to simulate two phase flows, covering spatial scales up to thousands of kilometers from the release points. It assumes that each source emits a large number of particles/droplets into the air, and each particle follows a random path around the mean wind vector. In fact, the models calculate the individual trajectory of each particle/droplet with its own aerodynamic characteristics driven by deterministic (wind field and buoyancy) and stochastic (turbulence) effects [START_REF] Leelőssy | Dispersion modeling of air pollutants in the atmosphere: a review[END_REF].

The path of particles is updated on each simulation time step as the particles evolve with time under various atmospheric processes. Either running forward or backward in time, this method is a powerful tool for tracking and understanding the fates, or origins, of particles in atmosphere. Hence, the Lagrangian models are more determinist than Gaussian models, which do not incorporate wind shear or inhomogeneity of the turbulence field, operate within a static spatial grid, as well as do not include evaporation as a factor.

In addition, they are not confined by stability classes or sigma curves, as in the case of Gaussian models and do not require artificial diffusivity, as do the Eulerian models for convective transport. However, a large amount of droplets/particles implies that the individual equations of each particle must be solved, involving significant computational effort and cost of this approach [START_REF] Leelőssy | Dispersion modeling of air pollutants in the atmosphere: a review[END_REF]. In the other hand, if the number of particles/droplets is not sufficient the models can produce discontinuous results and can become unreliable predictors of the major parameters influencing airborne transport according to droplet diameter. This approach has mostly used to simulate downwind deposition of spray drift in pesticide study purposes [START_REF] De Cock | Investigation on optimal spray properties for ground based agricultural applications using deposition and retention models[END_REF]Gil and Sinfort, 2005;[START_REF] Teske | AgDrift®: A model for estimating near-field spray drift from aerial applications[END_REF].

A new version of the Lagrangian dispersion modelling system Micro-SWIFT-SPRAY (MSS developed by ARIA Technologies and ARIANET) has been developed in order to allow simulation of spray plume, microorganisms, insecticide or pesticide dispersion. [START_REF] Lacome | New lagrangian approach for wet plume modelling[END_REF].

Puff models

Puff models are the intersection between Gaussian and Lagrangian models, assuming that the emission of a stack can be subdivided in a series of puffs with a given volume. Each puff behaves according to the Gaussian dispersion equation and follows the local wind speed and wind direction independently of other puffs, while above the puff size, Lagrangian trajectories are calculated [START_REF] Holmes | A review of dispersion modelling and its application to the dispersion of particles: An overview of different dispersion models available[END_REF][START_REF] Hong | Fluid Dynamic Approaches for Prediction of Spray Drift from Ground Pesticide Applications: A Review[END_REF]. The most commercial Puff models used in long-range atmospheric dispersion and include the DERMA, RIMPUFF and NAME in Europe and the Fluent, CALPUFF, INPUFF, OMEGA in the USA [START_REF] Van Leuken | Atmospheric dispersion modelling of bioaerosols that are pathogenic to humans and livestock -A review to inform risk assessment studies[END_REF].

Eulerian

Eulerian models are based on a grid that is fixed in space. In each grid point the advection (i.e., wind speed) and dispersion of pollutants is calculated based on local concentration gradients. To illustrate the concept of Eulerian models, a three-dimensional atmospheric transport equation (advection-diffusion) can be expressed as:

𝜕𝐶 𝜕𝑡 = 𝑄 -𝑢 𝜕𝐶 𝜕𝑥 -𝑣 𝜕𝐶 𝜕𝑦 -𝑤 𝜕𝐶 𝜕𝑧 ⏟ 𝑎𝑑𝑣𝑒𝑐𝑡𝑖𝑜𝑛 + 𝜕 𝜕𝑥 [𝑘 𝑥 𝜕𝐶 𝜕𝑥 ] + 𝜕 𝜕𝑦 [𝑘 𝑦 𝜕𝐶 𝜕𝑦 ] + 𝜕 𝜕𝑧 [𝑘 𝑧 𝜕𝐶 𝜕𝑧 ] ⏟ 𝑑𝑖𝑓𝑓𝑢𝑠𝑖𝑜𝑛 Eq. 1-5
This equation represents the change of the concentration at a specified point in time as the sum of the emission rate (source term, 𝑄) [m 3 .s -1 ], the transport via advection by the mean wind speed [m.s -1 ] (𝑢, 𝑣, 𝑤) in directions [𝑥, 𝑦, 𝑧], and the transport via diffusion by turbulent eddy motions where 𝑘 is the eddy diffusivity in m 2 .s -1 .

In this approach, the plume is not modeled as a single entity, so that transport over long distance can be modeled with relative accuracy. Although these models have low resolution, often 10-km cell size or more, and the computational resources needed increases very rapidly with increasing resolution. Furthermore, the physics underlying Eulerian dispersion models, gradient transport theory, breaks down at short distances (a few kilometers) from the source, where the size of the largest turbulent eddies is not negligible in comparison with the distance between the source and the receptor [START_REF] De | Air dispersion modeling : foundations and applications[END_REF].

Eulerian descriptions are complicate at the beginning of the simulation, due to an increased analytical complication, but prove to be more practical afterwards as the grid does not change with time [START_REF] De Wrachien | Water drops kinematic analysis: the classic-quantum and singlemultiparticle viewpoints[END_REF].

Computational fluid dynamics

Computational fluid dynamic (CFD) models provide complex analysis of fluid flow based on conservation of mass and momentum by resolving the Navier-Stokes equation using finite difference and finite volume methods in three dimensions. CFD methods are usually used in combination with Lagrangian aerosol tracking to simulate the transport of particles at smaller scales.

For the particulate/droplet phase, the governing equation for the simplified drift-flux model can be written as:

𝜕𝐶 𝑖 𝜕𝑡 + 𝛻. [(𝑈 + 𝑉 𝑡𝑠𝑖 )𝐶 𝑖 ] = 𝑄 𝐶 𝑖 + 𝛻. [(𝐷 𝐵𝑖 + 𝜀 𝑝 )𝛻𝐶 𝑖 ] Eq. 1-6
where 𝐶 𝑖 is the particle mass concentration [kg. m -3 ] (or number concentration, m -3 ) of particle size group i, 𝑉 𝑡𝑠𝑖 is the particle settling velocity, 𝜀 𝑝 is the particle eddy diffusivity and 𝐷 𝐵𝑖 is the Brownian diffusion coefficient.

It should be mentioned that CFD can in principle calculate both the full flow field and droplet movement from spray break-up to deposition. Droplet movement can be calculated using either a Eulerian or a Lagrangian approach. However, the cost of resolving all the required scales in a single calculation would be very high.

Empirical modeling

Considering the multivariable nature of the question addressed, it is so complicated to approach deterministic answers, that an empirical approach, based on statistics inference, has been sometimes preferred [START_REF] Tomas | Transport Modeling in Sprinkler Irrigation[END_REF]US-EPA, 1982). The empirical model proposed by [START_REF] Tomas | Transport Modeling in Sprinkler Irrigation[END_REF] is based on the use of the environmental variables. It will be presented in the Chapter 5.

Conclusion

In this chapter, several researches concerning the reuse of treated wastewater distributed via spray/sprinkler irrigation have been briefly reviewed. We have discussed on the impact of operating and field parameters on the possible dispersion of contaminant in the case of sprinkler irrigation.

A number of numerical and experimental approaches based on ballistics or fluid dynamics are concerned with dispersion mechanisms, but the volume of droplets smaller than 0.15 mm that escapes from the wetted radius with an application rate of less than 0.26 mm.h -1 are rarely estimated and analyzed. In fact, the atomization process and the movement of aerosol droplets generated from a sprinkler jet, in field experiments under unstable meteorological conditions, generally appears different from what was predicted by simple ballistic models. The same applies when measuring the droplet size distribution in calm/laboratory conditions to precisely understand what happens in the field.

Most of the previously cited field studies focused on punctual measurements of airborne transport by injecting a fluorescent dye into the irrigation system, thus were limited in terms of number of measurement points. The same appears for measurement heights (1 or 2 meters above the ground) when using passive samplers (PVC Strings, see 2.2.1).

Among the different models applicable to reproduce aerosols dispersion, the Gaussian steady-state models is the most widely used to simulate aerosol atmospheric dispersion (diffusion+ transport). In the case of aerosols generated by irrigation sprinklers such models are not adapted to low wind conditions or distances inferior to 100 m from the emission area (sprinkler wetted perimeter).

Thus, one of the objectives of this thesis is to review methods and conduct field experimentations with different sampling methods to qualify fine droplets dispersion in atmospheric conditions at distances inferior to 100 m from an impact sprinkler, and propose empirical models validated against experimental data to estimate airborne spray transport and surface sedimentation (see Chapter 5) Various atmospheric dispersion models were investigated in this chapter. In recent years, many efforts were also made to simulate the particles transport using the computation fluid dynamic (CFD), in particular, for different COVID-19 problems [START_REF] Mohamadi | A Review on Applications of CFD Modeling in COVID-19 Pandemic[END_REF]. For the case of sprinkler irrigation, we suggest further investigations applying the Eulerian Drift-Flux model (considering a spray plume in the size range of 1E-04-0.15 mm) to simulate airborne transport and distribution.

Air sampling methods-Overview Introduction

Accurate and reliable quantification and detection of aerosols from TWW distributed by sprinkler irrigation require air sampling methods that efficiently capture representative samples, match the targeted bio/aerosol (e.g., particle size, stress-sensitivity, and concentration level) as well as appropriate analysis techniques [START_REF] Alvarez | PCR for bioaerosol monitoring: sensitivity and environmental interference[END_REF].

The purpose of this chapter was to review the common sampling methods developed in the different domains and to examine the possibility of using them for quantifying the amount of aerosols from sprinkler irrigation with TWW.

Note that some fundamentals are given in Appendix. A to better understand the aerosol motions.

Principal aerosol sampling techniques

Aerosols samplers are generally classified into two categories: passive (deposit on a surface without an air pump or external power) and active (involving a mechanical component such as vacuum pump) samplers as described in the following sub-sections. It should be highlighted that the results from passive and active samplers should not be assumed comparable. Among other techniques, we can mention the optical sampling captures such as aerodynamic particle sizer, scanning mobility particle counter, which are capable of detecting total count of viable bacterial particles or viable + non-viable in real time as well as LIDAR technique for pesticide spray drift studies.

Passive samplers

Passive samplers are the most readily available, inexpensive and easy to set up that allow covering very large spatial scales [START_REF] Lindsley | Sampling and characterization of bioaerosols, in NIOSH Manual of Analytical Methods (NMAM)[END_REF][START_REF] Manibusan | Passive bioaerosol samplers: A complementary tool for bioaerosol research[END_REF].

Passive samplers have been designed/reported for measuring Particulate Matter (PM, includes microscopic matter suspended in air) concentration in environmental exposure studies (see Figure 2.1).

Agar settling plate [START_REF] Phillips | Biomedical Applications of Laminar Airflow[END_REF][START_REF] Rodriguez | 144 -Growth promotion efficiency of dehydrated Agar settle plates used for monitoring viable particles in grade A environments[END_REF], Vinzents [START_REF] Jacobsen | Cross-shift and longitudinal changes in FEV1 among wood dust exposed workers[END_REF][START_REF] Vinzents | A passive personal dust monitor[END_REF], UNC-PAS sampler [START_REF] Arashiro | Precision of PM measurements with the UNC passive aerosol sampler[END_REF][START_REF] Castillo | Field testing a low-cost passive aerosol sampler for long-term measurement of ambient PM2.5 concentrations and particle composition[END_REF]Wagner andLeith, 2001a, 2001b), and Electrostatic dust fall collector, EDC (Delort and Amato, 2017;[START_REF] Kilburg-Basnyat | Electrostatic dust collectors compared to inhalable samplers for measuring endotoxin concentrations in farm homes[END_REF][START_REF] Zahradnik | Passive airborne dust sampling to assess mite antigen exposure in farming environments[END_REF] are among the common devices that can be mentioned (Figure 2.1). Ideally, these techniques would be useful in large epidemiological and ecological studies with high replication necessary (especially for industrial and urban areas) [START_REF] Adams | Passive dust collectors for assessing airborne microbial material[END_REF]. However, it should be noted that most of these sampling methods capture a priori airborne PM after the gravitational settling (e.g. UNC-PAS) or an electrostatic precipitator (EDC), while ambient aerosol particles are generally heterogeneous and depending on physical properties, as discussed in the Appendix. A. Besides, the environmental 1250 conditions such as air velocity, humidity, and temperature also influence their behavior. Thus, the methods are still under development in particular for the detection of ultrafine particles [START_REF] Nash | Ultrafine Particle Sampling with the UNC Passive Aerosol Sampler[END_REF] and bioaerosols inferior to 5 µm such as viruses, bacteria, most fungi, and smaller fragments or byproducts of biological particles [START_REF] Reponen | Biological Particle Sampling[END_REF][START_REF] Therkorn | Design and development of a passive bioaerosol sampler using polarized ferroelectric polymer film[END_REF]. -Strings: Cotton strings or cord [START_REF] Martin | Effect of Application Height and Ground Speed on Spray Pattern and Droplet Spectra from Remotely Piloted Aerial Application Systems[END_REF][START_REF] Whitney | String Collectors for Spray Pattern Analysis[END_REF], nylon string [START_REF] Fritz | Aerial Application Methods for Increasing Spray Deposition on Wheat Heads[END_REF][START_REF] Kirk | Aerial spray drift from different formulations of glyphosate[END_REF], Soda straws (Fritz et al., 2006), wire (Riley andWiesner, 1990), and standard polyethylene or polytetrafluoroethylene strings, (Ø1.98 or 2 mm) [START_REF] Akrour | Influence of spray characteristics on potential spray drift of field crop sprayers: A literature review[END_REF][START_REF] Gil | Atmospheric loss of pesticides above an artificial vineyard during air-assisted spraying[END_REF][START_REF] Nuyttens | Comparison between indirect and direct spray drift assessment methods[END_REF][START_REF] Torrent | Comparison between standard and drift reducing nozzles for pesticide application in citrus: Part I. Effects on wind tunnel and field spray drift[END_REF]; -Flat tapes [START_REF] Fox | Downwind residue from air spraying of a dwarf apple orchard[END_REF]SDTF, 1994); -Nylon screens [START_REF] Salcedo | Reducing ground and airborne drift losses in young apple orchards with PWM-controlled spray systems[END_REF] -Water-sensitive papers [START_REF] Panneton | Effect of Air-jet Configuration on Spray Coverage in Vineyards[END_REF][START_REF] Chen | Spray drift and off-target loss reduction with a precision air-assisted sprayer[END_REF][START_REF] Xue | Longitudinal drift behaviors and spatial transport efficiency for spraying pesticide droplets[END_REF]Gregorio et al., 2016a); -Filter papers [START_REF] Qi | The classification of the drift risk of sprays produced by spinning discs based on wind tunnel measurements[END_REF][START_REF] Bueno | Assessment of spray drift from pesticide applications in soybean crops[END_REF][START_REF] Brusselman | Wind tunnel evaluation of several tracer and collection techniques for the measurement of spray drift[END_REF]Gregorio et al., 2016a), and filter cloths [START_REF] Brusselman | Wind tunnel evaluation of several tracer and collection techniques for the measurement of spray drift[END_REF]; 1270 -Blotting papers [START_REF] Garcerá | Spray pesticide applications in Mediterranean citrus orchards: Canopy deposition and off-target losses[END_REF] ; -Paper cleaner [START_REF] Kasner | Spray Drift from a Conventional Axial Fan Airblast Sprayer in a Modern Orchard Work Environment[END_REF].

There is a complex interaction between the size of the droplets, the passive samplers in its path, and their relative velocity [START_REF] Matthews | Pesticide Application Methods[END_REF]. Several studies have been conducted across the world in order to evaluate collection efficiency (𝜂 𝑐 ) of passive samplers with different shape and physical characteristics for small droplets (<100 μm) traveling in different air velocities as well as their recovering efficiency for tracer materials captured from their surface [START_REF] Miller | The measurement of spray drift[END_REF]. Note that for small droplets (<100 μm) suspended in air, and ignoring gravitational, electrostatic, and any other external forces, the collection efficiency (𝜂 𝑐 ) of a passive sampler is a combination of inertial impaction efficiency, interception efficiency, and diffusion efficiency (only for droplets less than 1 micron in size). 1280

Most of the theoretical and experimental studies of collection efficiency of passive samplers have been focused on inertial impaction. Table 2.1 represents some of the empirical equations to estimate airborne droplet collection efficiency of passive samplers.

𝜂 𝑐 = 𝛼 + 𝛾. 𝑒𝑥𝑝{-exp [-𝛽. (𝑆𝑡𝑘 𝑟𝑒𝑓 -𝑘)]}
where 𝛼 = 0.88𝑑 + 5.95; 𝛾 = 75.9 -2.56𝐷; 𝛽 and 𝑘 are constant: 1.60 and 0.49, respectively.

𝑆𝑡𝑘 𝑟𝑒𝑓 : the reference Stokes number (see A. 5 and Eq. A-22) [START_REF] Miller | A simulation model of the spray drift from hydraulic nozzles[END_REF] concluded that passive collectors (for pesticide spray drift studies) should not be used if local wind speeds are less than 2 m.s -1 . [START_REF] Parkin | Measurements and computational fluid dynamic simulations of the capture of drops by spray drift samplers[END_REF] evaluated the collection efficiency of cylindrical lines of various diameters

(1 to 10 mm) in a wind tunnel, exposing the lines to the plumes of electrically neutral monosized droplets of various diameters (10 to 63 µm) at a range of known air velocities (1 to 4 m.s -1 ). They found that when the collection efficiency of the lines was plotted against Stokes number (𝑆𝑡𝑘 𝑟𝑒𝑓 ) the results did not collapse to a single sigmoidal curve above 𝑆𝑡𝑘 𝑟𝑒𝑓 > 1, which did not follow the May and Clifford's model. In addition, the results indicated the collection efficiency reached a series of limiting values that decreased with line diameter. They used also a time-dependent computational fluid dynamics (CFD) to examine collection efficiency if the adhesion of a droplet to a surface can be predicted or discounted (Figure 2.2). Herbst and Molnar ( 2002) concluded that cylindrical collectors with a diameter of 2 mm, characterized by a smooth and well-defined surface, were the most suitable collectors for aerial spray drift. [START_REF] Fox | Airborne spray collection efficiency of nylon screen[END_REF] found that the collection efficiency of monofilament nylon screens depends on droplet size and wind speed. Gil et al. (2005) concluded that under a wind speed of less than 3.5 m.s -1 , the efficiency of 2-mm PVC plastic lines was higher than 77%.

Fritz and Hoffmann (2008) reported a comparison of soda straw collectors and a monofilament line, with the latter having a collection efficiency increased from 52.3% to 82.6% with an increase in air velocity from 0.45 to 4.0 m.s -1 . This protocol was tested by comparing the theoretically and experimentally derived of soda straw collectors. Results showed no statistical difference between the theoretical and experimental 𝜂 𝑐 at each air velocity tested. As the air velocity increased from 0.45 to 4.0 m.s -1 , the 𝜂 𝑐 for the monofilament line increased from 52.3% to 82.6%, respectively, while the 𝜂 𝑐 of the soda straw also increased from 6.7% to 45%, respectively.

Therefore, 2 mm standard polyethylene or polytetrafluoroethylene lines (ISO 22856, 2008;ISO 22866, 2005) is a good alternative to the pesticide spray drift and transport measurements with dye tracers on a wide scale simultaneously.

It should be mentioned that under still or low-windy conditions, even small droplets will fall by gravity on to a horizontal surface after traveling a distance [START_REF] Pulley | The effect of interception on particle collection by spheres and cylinders[END_REF]). Among the passive samplers commonly used to measure the sedimented spray on the ground and mounted horizontally that can be cited are: Petri dishes, filter papers, paper surface and microscope slides (ISO 22856, 2008).

Active samplers

Currently, numerous types of active samplers are available for collection of bio/aerosols from both outdoor and indoor environments [START_REF] Cage | Evaluation of Four Bioaerosol samplers in the Outdoor Environment[END_REF][START_REF] Verreault | Methods for Sampling of Airborne Viruses[END_REF]. They employ various collection mechanisms, including impaction, impingement, filtration, cyclone, Condensation technique, and electrostatic precipitation [START_REF] Ghosh | Review of bioaerosols in indoor environment with special reference to sampling, analysis and control mechanisms[END_REF][START_REF] Haig | Bioaerosol sampling: sampling mechanisms, bioefficiency and field studies[END_REF][START_REF] Lindsley | Sampling and characterization of bioaerosols, in NIOSH Manual of Analytical Methods (NMAM)[END_REF], some of them are illustrated in Figure 2.

Impaction

The impaction method separates particles from the air by utilizing the inertia of the particles to force their deposition onto a solid or semi-solid surface. The collection surface for bioaerosol sampling is usually an agar medium [START_REF] Yates | Manual of Environmental Microbiology[END_REF]. It changes the flow into an abrupt 90° turn and particles with sufficient inertia are not capable of following the change in the direction of streamlines and collide with the surface.

The cross-section of a stage of impactor is shown in Figure 2.4. The history of the impactor development has been reviewed by [START_REF] Marple | History of Impactors-The First 110 Years[END_REF]. Slit impactors with a single suction nozzle in a rectangular shape have been widely used in agricultural and domestic environments with high contamination, and employed for detecting large airborne particles such as large fungal spores and pollen grains [START_REF] Ghosh | Review of bioaerosols in indoor environment with special reference to sampling, analysis and control mechanisms[END_REF].

Two criteria are considered to design a conventional single slit sampler: 1) the jet-to-plate (agar) distance divided by nozzle diameter, S/W, should be larger than 1.5; 2) The Reynolds number of the air flow in the jet nozzle throat should be between 500 and 3000 [START_REF] Grinshpun | A small change in the design of a slit bioaerosol impactor significantly improves its collection characteristics[END_REF]. Although, unlike Sieveimpactors and Cascade impactors, slit impactors do not provide information about the particle size.

Cascade impactors such as six-stage Andersen cascade impactor [START_REF] Andersen | New Sampler for the Collection, Sizing, and Enumeration of Viable Airborne Particles[END_REF] are most commonly used to measure particle size distribution [START_REF] Nichols | Good Cascade Impactor Practice (GCIP) and Considerations for "In-Use" Specifications[END_REF] and to simulate cultivable bioaersol concentrations in different region of human respiratory system. The stages have progressively smaller nozzles, which create higher particle impaction velocities onto the culture plate, "nasal cavity" = stage 1: 7 µm and above, "Pharynx" = stage 2: 4.7-7 µm, "Trachea and Primary Bronci"= stage 3: 3.3-4.7 µm, "Secondary Bronchi" = stage 4: 2.1-3.3 µm, "Terminal Bronchi" = stage 5: 1.1-2.1 µm, and "Alveoli" = stage 6: 0.6-1.1 µm (Xu and Yao, 2013). The impactor MOUIDI (Micro orifice Uniform Deposit Impactor) collect the particles down to 0.056 μm in aerodynamic diameter, and gives a uniform particle deposit on 1350 the plates [START_REF] Marple | A Microorifice Uniform Deposit Impactor (MOUDI): Description, Calibration, and Use[END_REF].

The dominant parameter describing the collection efficiency of an impactor is the Stokes number [START_REF] Yao | Investigation of Cut-Off Sizes and Collection Efficiencies of Portable Microbial Samplers[END_REF]. The cut-off size (𝑑 50 ) is related to the Stokes number (i.e., particle inertia), 𝑆𝑡𝑘 50 , that gives 50% collection efficiency. It means that the mass of particles below cut-off size collected equals the mass of particles getting through larger than the cut-off. [START_REF] Marple | Sampling and Measurement Using Inertial, Gravitational, Centrifugal, and Thermal Techniques[END_REF] determined the cutoff size, 𝑑 50 , using the following equation,

𝑑 50 = √ 9𝜇 𝑎 𝑊 𝜌 𝑝 𝑈 𝑛 𝐶 𝑐 √𝑆𝑡𝑘 50 Eq. 2-1
where 𝑈 𝑛 is the average air velocity from "n" separate nozzles at the same stage (multiple-stage impaction), and as mentioned above, 𝑊 is average nozzle diameter (or width) for a given stage. The recommended values for √𝑆𝑡𝑘 50 is 0.49 for circular jet [START_REF] Hinds | Aerosol technology : properties, behavior, and measurement of airborne particles[END_REF]. Note that, the shape and the density of particles are unknown in most cases for a given aerosol, in particular, in field studies. Particle size at 1360 each stage of the impactor is therefore expressed through an aerodynamic diameter. The density of particles is also considered equal to one. or less of 0.5 µm particles (bacterial cells) onto the agar medium, but they would be more efficient when collecting larger fungal spores.

Despite the advantages of the inertial impactors as a particle size classifier and direct collection of bioaerosols onto culture plates that results in reduction of post-sampling processing, there are also several limitations. The bioaerosol stress during the samplings such as shear forces on the bioaerosols within the nozzle and on impaction with the agar plates [START_REF] Cox | Field sampling of indoor bioaerosols[END_REF], the desiccation of pathogens on solid surface in high velocity impactions or sampling time [START_REF] Mainelis | The Effect of Sampling Time on the Overall Performance of Portable Microbial Impactors[END_REF], overloading of agar plates with colonies can lead to counting errors and microbial growth inhibition effects [START_REF] Marple | Sampling and Measurement Using Inertial, Gravitational, Centrifugal, and Thermal Techniques[END_REF][START_REF] Reponen | Biological Particle Sampling[END_REF], influence the bio-efficiency of impactors. The losses of re-entrainment and particle bounce after impaction, especially for particles larger than the cut-off diameter affect the collection efficiency [START_REF] Lindsley | Sampling and characterization of bioaerosols, in NIOSH Manual of Analytical Methods (NMAM)[END_REF]. The particle bounce is particularly a problem with dry solid collection surfaces, which can be overcome by mineral-oil-spread on agar plates (Xu et al., 2013).

A virtual impactor is a type of inertial impactor, but with the difference that instead of an impaction plate, it has a collection probe that divides the minor flow and the major flow inside the virtual impactor [START_REF] Lim | Development of a multi-slit virtual impactor as a high-volume bioaerosol sampler[END_REF]. The presence of collection probe prevents issues of particle bounce, overload, and reentrainment. Furthermore, the minor flow concentrate the particles of a certain size, which aspirate from the impactor inlet. However, virtual impactors are subjected to collection losses near to the size of the cutoff diameter at the inlet of the probe [START_REF] Haig | Bioaerosol sampling: sampling mechanisms, bioefficiency and field studies[END_REF]. The researches for improving the performance the virtual impactors have been investigated in [START_REF] Lim | Development of a multi-slit virtual impactor as a high-volume bioaerosol sampler[END_REF].

Impingement

The impingement technique is similar to impaction expect that the aerosols are collected into a liquid medium (Figure 2.5), which make it more effective at preserving the viability of sensitive viruses [START_REF] Harstad | Sampling Submicron T1 Bacteriophage Aerosols[END_REF][START_REF] Verreault | Methods for Sampling of Airborne Viruses[END_REF]. Most impingers have a curved inlet tube designed to mimic human nasal passage, making these samplers useful for studying the respiratory infection potential of bioaerosols [START_REF] Yates | Manual of Environmental Microbiology[END_REF]. The sample can be directly analyzed with culture and analytical methods without the need to extract the microorganisms from a surface or filter [START_REF] Xu | Bioaerosol Science, Technology, and Engineering: Past, Present, and Future[END_REF]. [START_REF] Stephenson | Side-by-side comparison of three sampling methods for aerosolized endotoxin in a wastewater treatment facility[END_REF] found that the impingers appear to detect 2-10 times greater airborne endotoxin than glass fiber or polycarbonate filters, when comparing the sampling methods in a wastewater treatment plant.

However, the evaporation of collection liquid (buffered water medium) during the sampling can have an important impact on viability or inactivation of microorganisms. This is the main problem of most currently available impingers [START_REF] Henningson | Collection efficiency of liquid samplers for microbiological aerosols[END_REF]. Re-aerosolization also occurs when collected airborne particles in the collection liquid are re-entrained into the airflow, leading to the loss of collection efficiency. Turbulent motion of collection liquid during sampling provides enough energy for the already collected particles to become re-entrained into rising bubbles [START_REF] Grinshpun | Effect of Impaction, Bounce and Reaerosolization on the Collection Efficiency of Impingers[END_REF][START_REF] Han | Investigation of inherent and latent internal losses in liquid-based bioaerosol samplers[END_REF][START_REF] Lin | Collection efficiency and culturability of impingement into a liquid for bioaerosols of fungal spores and yeast cells[END_REF][START_REF] Riemenschneider | Characterization of reaerosolization from impingers in an effort to improve airborne virus sampling: Reaerosolization from impingers[END_REF][START_REF] Lin | Improved Aerosol Collection by Combined Impaction and Centrifugal Motion[END_REF]. In addition, the particles (large particles with sufficient inertia) that impact onto bottom of the impinger may be rebound into the airflow because of low collection liquid volume or high sampling flow rate. Therefore, for preventing particle bounce, [START_REF] Lin | Effect of Sampling Time on the Collection Efficiency of All-Glass Impingers[END_REF] proposed to coat the bottom surface with 0.1 to 0 .2 mL of petroleum jelly/ hexane solution.

Chen et al. ( 2021) and [START_REF] Hermann | Optimization of a Sampling System for Recovery and Detection of Airborne Porcine Reproductive and Respiratory Syndrome Virus and Swine Influenza Virus[END_REF] have discussed on the parameters affecting the impinger collection efficiency. Among these parameters, the following could be mentioned: impinger design [START_REF] Kesavan | Sampling and Retention Efficiencies of Batch-Type Liquid-Based Bioaerosol Samplers[END_REF], sampling flow rate [START_REF] Lin | The Effect of Sampling Time and Flow Rates on the Bioefficiency of Three Fungal Spore Sampling Methods[END_REF], composition of collection medium (liquid) [START_REF] Chang | Assessment of Bioaerosol Sampling Techniques for Viable Legionella pneumophila by Ethidium Monoazide Quantitative PCR[END_REF], sampling time [START_REF] Mainelis | The Effect of Sampling Time on the Overall Performance of Portable Microbial Impactors[END_REF], aerodynamic diameter of airborne particles [START_REF] Dart | Collection efficiencies of bioaerosol impingers for virus-containing aerosols[END_REF][START_REF] Yu | Improving the collection efficiency of the liquid impinger for ultrafine particles and viral aerosols by applying granular bed filtration[END_REF], morphology of pathogens [START_REF] Tseng | Collection efficiencies of aerosol samplers for virus-containing aerosols[END_REF], and ambient or collection medium temperature [START_REF] Springorum | A Temperature-Controlled AGI-30 Impinger for Sampling of Bioaerosols[END_REF]. outlet and the surface of the liquid as well as the airflow influence the diameter of the particles and the viability of microorganisms that will be collected [START_REF] Haig | Bioaerosol sampling: sampling mechanisms, bioefficiency and field studies[END_REF]. [START_REF] May | The Efficiency of Various Liquid Impinger Samplers in Bacterial Aerosols[END_REF] found that AGI-30 (its jet nozzle placed at 30 mm from the bottom of the collection vessel) preserved collected viable bacteria (S. marcescens) better than AGI-4 (at 4 mm from the collection vessel). [START_REF] Henningson | Collection efficiency of liquid samplers for microbiological aerosols[END_REF] likewise reported that when only collection efficiency is considered, AGI-4 is more preferable than AGI-30 (> 95% and = 97 compared to > 70% and = 85%, if Dp > 0.5 µm and Dp=0.9 µm, respectively).

Nevertheless, a significantly lower survival Escherichia coli (0.6 ± 0.2) was found in the AGI-4 compared to the AGI-30. [START_REF] Lin | Effect of Sampling Time on the Collection Efficiency of All-Glass Impingers[END_REF] confirmed this fact that the AGI-30 collects sub-micrometer particles considerably less efficiently than the AGI-4, but may causes less biological stress on the collected microorganisms. Either way, the bubble bursts is the most detrimental to bacterial viability when tested with both impingers.

BioSampler that combined the forces of impingement and centrifugation was developed to generate very few bubbles and therefore causes minimal re-aerosolization of particles and impact on viability of already collected microorganisms [START_REF] Kesavan | Sampling and Retention Efficiencies of Batch-Type Liquid-Based Bioaerosol Samplers[END_REF][START_REF] Lin | Survival of Airborne Microorganisms During Swirling Aerosol Collection[END_REF][START_REF] Lin | Improved Aerosol Collection by Combined Impaction and Centrifugal Motion[END_REF]. Therefore, it frequently used as a reference when investigating other samplers for collection of viruses [START_REF] Anderson | Bioaerosol Sampling in Modern Agriculture: A Novel Approach for Emerging Pathogen Surveillance?[END_REF][START_REF] Cao | Development of an improved methodology to detect infectious airborne influenza virus using the NIOSH bioaerosol sampler[END_REF][START_REF] Fabian | Airborne influenza virus detection with four aerosol samplers using molecular and infectivity assays: considerations for a new infectious virus aerosol sampler[END_REF][START_REF] Kasner | Spray Drift from a Conventional Axial Fan Airblast Sprayer in a Modern Orchard Work Environment[END_REF]Li et al., 2018;[START_REF] Su | Re-evaluation of BioSampler and its improvement for on-line, time-resolved monitoring of environmental coarse aerosol[END_REF][START_REF] Zheng | Liquid impinger BioSampler's performance for size-resolved viable bioaerosol particles[END_REF]. Most recently, the presence of SARS-CoV-2 in air was determined through SKC BioSampler [START_REF] Ahn | Environmental contamination in the isolation rooms of COVID-19 patients with severe pneumonia requiring mechanical ventilation or high-flow oxygen therapy[END_REF][START_REF] Kenarkoohi | Hospital indoor air quality monitoring for the detection of SARS-CoV-2 (COVID-19) virus[END_REF]. 1440 [START_REF] Hermann | Optimization of a Sampling System for Recovery and Detection of Airborne Porcine Reproductive and Respiratory Syndrome Virus and Swine Influenza Virus[END_REF] investigated the collection of aerosolized porcine reproductive and respiratory syndrome virus (PRRSV) in air-sampling systems, and analyzed the samples by quantitative reverse transcriptase PCR (QRT-PCR). As estimated by QRT-PCR, the mean collection efficiency of PRRSV were 0.022, 0.016 and 0.009 for the SKC BioSampler (12.5 L.min -1 ), AGI-30 (12.5 L.min -1 ) and AGI-4 (6.0 L.min -1 ), respectively.

Aerodynamic diameter of airborne particles-

The lowest cutoff diameter of the most common impingers was about 0.3 µm [START_REF] Dart | Collection efficiencies of bioaerosol impingers for virus-containing aerosols[END_REF]. For the common liquid impingers (AGI-30 1450 impinger, SKC BioSampler and a frit bubbler), [START_REF] Hogan | Sampling methodologies and dosage assessment techniques for submicrometre and ultrafine virus aerosol particles[END_REF] indicated that the collection efficiency of particles in the range of 30-100 nm, is inferior to 10 % because of their insufficient inertia to be collected in liquid and high diffusion and small inertial requirement to transfer into bubbles from liquid. It should be mentioned that the common size of a virus is fallen into this range. The following solutions were therefore provided to enhance the efficiency of the impingers for the collection of nanoparticles:

- [START_REF] Yu | Improving the collection efficiency of the liquid impinger for ultrafine particles and viral aerosols by applying granular bed filtration[END_REF] found that applying 3 mm packed glass beads in vessel collection can be successfully improved the efficiency of AGI30 sampler for the collection of ultrafine particles in the size range of 20-400 nm and viral aerosols (MS2). [START_REF] Chen | Effect of selected sampling media, flow rate, and time on the sampling efficiency of a liquid impinger packed with glass beads for the collection of airborne viruses[END_REF] also reported that the tracer (0.15% Uranine as a tracer) collection efficiency of the AGI-30 samplers without and with packing glass beads ranged from 1.20 ± 0.46 to 14.09 ± 3.41% and from 1.91 ± 1.27 to 44.49 ± 15.86%, respectively, under various sampling times, collection liquid, and sampling flowrate. While the PFU (viable Bacteriophages MS2) collection efficiency of the AGI-30 sampler without and with packing ranged from 0.62 ± 0.06 to 6.41 ± 1.04% and from 0.59 ± 0.05 to 11.57 ± 3.55%, respectively. The lower recovery of viable MS2 compared to the tracer could be due to the higher-pressure drop across the impinger.

- [START_REF] Lin | Development of an efficient viral aerosol collector for higher sampling flow rate[END_REF] paired the steam-jet aerosol collector (SJAC), which is based on condensation growth method, with AGI-30 and SKC BioSampler to collect viral aerosol (MS2 bacteriophage around 28 nm). The results indicated that the system was 7 and 22 times more efficient for viability preservation of MS2 bacteriophage than the commonly used AGI-30 and BioSampler, respectively.

In addition, the system applied at a low aerosol concentration (10 5 PFU.m -3 ) was 4.7 times more efficient for viability preservation than using AGI-30 alone.

Note that the size of a virus does not represent the size of the bioaerosol sprayed from irrigation of treated wastewater, which contains many components such as proteins and cellular debris and larger particles in addition to the virus [START_REF] Cox | Field sampling of indoor bioaerosols[END_REF]. [START_REF] Stobnicka-Kupiec | Detection and identification of potentially infectious gastrointestinal and respiratory viruses at workplaces of wastewater treatment plants with viability qPCR/RT-qPCR[END_REF] cited that the highest concentrations of airborne noroviruses and rotaviruses at workplaces in wastewater treatment plants were noted for particles with diameters of > 4.5 µm and 9 µm, respectively.

Sampling time-The impingers are not sensitive to under or overloading during sampling because the liquid sample can be diluted or concentrated after sample collection, but due to evaporation of collection liquid resulting in loss of viability and reaerosolization of already collected particles, sampling time should be carefully selected [START_REF] Chattopadhyay | Sample Collection Information Document for Pathogens, Companion to Selected Analytical Methods for Environmental Remediation and Recovery (SAM)[END_REF]. [START_REF] Lin | The Effect of Sampling Time and Flow Rates on the Bioefficiency of Three Fungal Spore Sampling Methods[END_REF] found that the relative survival of fungal spores collected in AGI-30 impingers became lower as the sampling time and flow rates increased. Total recovery of the AG1-30 impinger at the flow rate of 12.5 L.min -1 , was 24 ± 9% for 1min samples and decreased to 3.9 ± 2.5% for 60 min samples.

In fact, the selection of appropriate sampling time depends on the resistance of the microorganisms to stress and the following parameters: composition of collection medium, sampling flow rate, temperature and relative humidity.

Composition of collection medium-Several liquid media have been used for sampling aerosols in impingers. The choice of suitable sampling liquid is important because each liquid has its own specifics and subsequently, its advantages and drawbacks. The choice of the medium is also dependent on the targeted pathogen. Most used liquid media in previous experiments are as follow:

-Filtered/ sterilized deionized water as a common collection medium for microorganisms [START_REF] Chang | Evaluation of bioaerosol sampling techniques for Legionella pneumophila coupled with culture assay and quantitative PCR[END_REF][START_REF] Chang | Assessment of Bioaerosol Sampling Techniques for Viable Legionella pneumophila by Ethidium Monoazide Quantitative PCR[END_REF][START_REF] Miljevic | On the efficiency of impingers with fritted nozzle tip for collection of ultrafine particles[END_REF][START_REF] Zheng | Liquid impinger BioSampler's performance for size-resolved viable bioaerosol particles[END_REF] -Mineral oil as a non-evaporating liquid which can be used for long-term sampling, and because of its high viscosity and low freezing point [START_REF] Springorum | A Temperature-Controlled AGI-30 Impinger for Sampling of Bioaerosols[END_REF][START_REF] Lin | Improved Aerosol Collection by Combined Impaction and Centrifugal Motion[END_REF], -Peptone water as a nutrient broths for stabilizing the collected airborne viruses, maintaining the viability of the microbial cells and increasing their detectability [START_REF] Hemmes | Virus Survival as a Seasonal Factor in Influenza and Poliomyelitis[END_REF][START_REF] Lin | The Effect of Sampling Time and Flow Rates on the Bioefficiency of Three Fungal Spore Sampling Methods[END_REF], -Phosphate-buffered saline based surfactant [START_REF] Dybwad | Comparative Testing and Evaluation of Nine Different Air Samplers: End-to-End Sampling Efficiencies as Specific Performance Measurements for Bioaerosol Applications[END_REF][START_REF] Fabian | Airborne influenza virus detection with four aerosol samplers using molecular and infectivity assays: considerations for a new infectious virus aerosol sampler[END_REF][START_REF] Veillette | Preferential aerosolization of Actinobacteria during handling of composting organic matter[END_REF], and -Tween 80 mixture (deionized water with 1% peptone, 0.01% Tween 80 and 0.005% antifoam A) [START_REF] Chang | Assessment of Bioaerosol Sampling Techniques for Viable Legionella pneumophila by Ethidium Monoazide Quantitative PCR[END_REF][START_REF] Chen | Sampling Performance for Bioaerosols by Flow Cytometry with Fluorochrome[END_REF][START_REF] Lange | Application of Flow Cytometry and Fluorescent In Situ Hybridization for Assessment of Exposures to Airborne Bacteria[END_REF]. Tween 80 could reduce the surface tension and decrease the impact on microorganisms [START_REF] Chen | Effect of selected sampling media, flow rate, and time on the sampling efficiency of a liquid impinger packed with glass beads for the collection of airborne viruses[END_REF]. Besides, antifoam is generally added to prevent excessive liquid losses and in dampening foam formation [START_REF] Lembke | Precision of the all-glass impinger and the andersen microbial impactor for air sampling in solid-waste handling facilities[END_REF].

Concerning the deionized water, [START_REF] Rule | Assessment of Bioaerosol Generation and Sampling Efficiency Based on Pantoea agglomerans[END_REF] observed that PBS yields higher (p <= 0.01) viable counts compared to sterile deionized water. However, [START_REF] Chang | Evaluation of bioaerosol sampling techniques for Legionella pneumophila coupled with culture assay and quantitative PCR[END_REF] demonstrated that deionized water obtained more culturable L. pneumophila than Tween mixture by a factor of 4.1-12.5 in SKC BioSampler and 2.2-6.8 in AGI-30 during 3-60 min sampling (P <0.0001). Moreover, deionized water replenishment every 15 min during sampling significantly improved the recovery of culturable L. pneumophila in both AGI-30 and BioSampler by a factor of 3.3 and 7.1, respectively (P=0.001). [START_REF] Tseng | Collection efficiencies of aerosol samplers for virus-containing aerosols[END_REF] also found that among the three evaluated collection media (sterile deionized water, nutrient broth, and peptone broth), deionized water provided better virus infectivity retention at 4 and 25 °C.

The use of mineral oils are suitable due to its minimal evaporation, but, willeke et al., (1998) reported that the AGI-30 impinger cannot be used with high viscosity liquids due to formation of large bubbles.

Therefore, the collection efficiency of samplers such as SKC BioSampler (which can use glycerol) in conditions with high ambient temperature and for long-term sampling may be more suitable than AGI-30.

Nevertheless, [START_REF] Springorum | A Temperature-Controlled AGI-30 Impinger for Sampling of Bioaerosols[END_REF] evaluated the effect of temperature and relative humidity on the final volume of three liquid media (mineral oil, peptone water, and PBS) during 30 min of sampling with AGI-30. The results showed that mineral oil has the lowest losses, followed by PBS and peptone water.

Sampling flow rate-The change of impinger flow rates could change the cut-off diameter [START_REF] Asking | Calibration at Different Flow Rates of a Multistage Liquid Impinger[END_REF], bubble formation and re-aerosolization of collected particles [START_REF] Grinshpun | Effect of Impaction, Bounce and Reaerosolization on the Collection Efficiency of Impingers[END_REF]. The results of [START_REF] Riemenschneider | Characterization of reaerosolization from impingers in an effort to improve airborne virus sampling: Reaerosolization from impingers[END_REF] showed that re-aerosolization of MS2 bacteriophage (around 28 nm in diameter) from AGI-30 increases as flow rate increases.

For viral sampling, [START_REF] Zheng | Liquid impinger BioSampler's performance for size-resolved viable bioaerosol particles[END_REF] showed that higher sampling flow rate, i.e., 30 L.min -1 , together with 5 mL non-evaporating liquid may perform much better than the recommended standard operating conditions (12 L.min -1 ) when using the BioSampler. In contrast, for fungal spores, lower sampling flow rate with smaller amount of collection liquid could be a better option. For bacterial aerosols, the recommended standard option could be the choice. [START_REF] Raynor | Comparison of samplers collecting airborne influenza viruses: 1. Primarily impingers and cyclones[END_REF] reported that high flow rate generally collected greater quantities of virus than low flow rate when using six impinger/cyclone air samplers. However, low flow rate samplers generally measured higher and likely more accurate, airborne concentrations of infectious virus and viral RNA than high flow samplers. To assess airborne viruses in the field, a two-sampler approach with different flow rates may work well.

Temperature or relative humidity-controlled -High relative humidity is helpful to improve the collection process of airborne nanoparticles [START_REF] Su | Re-evaluation of BioSampler and its improvement for on-line, time-resolved monitoring of environmental coarse aerosol[END_REF]. [START_REF] Springorum | A Temperature-Controlled AGI-30 Impinger for Sampling of Bioaerosols[END_REF] insulated the AGI-30 with an holder and controlled the temperature of the collection fluid by means of a Peltier element, under laboratory and field conditions. The results showed that tempering strongly affected the residual volume of the liquid (i.e. reduced evaporation) after sampling and the number of culturable bacteria collected in the sampling liquid. In the tempered impingers, three times higher CFU (colony-forming unit) per cubic meter of air were found than in the untempered ones. [START_REF] Su | Re-evaluation of BioSampler and its improvement for on-line, time-resolved monitoring of environmental coarse aerosol[END_REF] evaluated the volume loss rate and the temperature of collection liquid of SKC BioSampler as a function of time under conditions of 𝑇 𝑎 = 19°C, 𝑅𝐻 = 0%, 𝑄 𝑠 = 11.9 L.min -1 and 𝑃 = 48 kPa (pressure drop). The results showed that the liquid temperature drops rapidly from air temperature to around 4.3 °C in the first 15 min and becomes stable later, having the same trend with volume loss rate (with the opposite symbol).

Filtration

Filters are widely used because of their simplicity of handling, transport, and low cost [START_REF] Paba | Indoor exposure to airborne endotoxin: a review of the literature on sampling and analysis methods[END_REF]. As presented in Figure 2.7 [START_REF] Lindsley | Filter pore size and aerosol sample collection. NIOSH Man[END_REF], the efficiency of filter media for the filtration of airborne particles consists of five mechanisms: impaction, interception, diffusion, sedimentation and electrostatic attraction. The last occurred when filters carry an electrostatic charge, which can attract charged airborne particles. They could provide high physical collection efficiency (superior to 85%) for a broad range of airborne particles sizes from viruses (<0.3 µm) to clusters of spores (>7 mm) [START_REF] Walls | Long-term viable bioaerosol sampling using a temperature-and humiditycontrolled filtration apparatus, a laboratory investigation using culturable E. coli[END_REF]. However, Filters are not recommended for collecting viable aerosols due to desiccation of the sample during air sampling as well as damage and reduce the amount of detectable genetic material [START_REF] Verreault | Methods for Sampling of Airborne Viruses[END_REF]. The sampling time should therefore be short, around 15-20 min or even shorter for microorganisms [START_REF] Pan | Collection, particle sizing and detection of airborne viruses[END_REF][START_REF] Walls | Long-term viable bioaerosol sampling using a temperature-and humiditycontrolled filtration apparatus, a laboratory investigation using culturable E. coli[END_REF]. Note that the type, size, and porosity of the filters determine typically the required power (or pressure drop) to supply a particular flow rate and consequently to define the sampling time to collect the airborne particles [START_REF] Mainelis | Bioaerosol sampling: Classical approaches, advances, and perspectives[END_REF]. Numerous type of filters such as fibrous filters (glass fiber filter), porous membrane (mixed cellulose esters or polytetrafluoroethylene (Teflon, PTFE), nucleopore, capillary pores (Polycarbonate, PC), and gelatin filters have been investigated, based on the characteristics of bio/aerosols and analysis technique [START_REF] Fabian | Airborne influenza virus detection with four aerosol samplers using molecular and infectivity assays: considerations for a new infectious virus aerosol sampler[END_REF][START_REF] Herrmann | Field studies measuring the aerosolization of endotoxin during the land application of Class B biosolids[END_REF][START_REF] Jeong | Comparison of five membrane filters to collect bioaerosols for airborne microbiome analysis[END_REF][START_REF] Wang | Field evaluation of personal sampling methods for multiple bioaerosols[END_REF].

The glass fiber filter has been applied in various fields because its high physical collection efficiency [START_REF] Kesavan | Bioaerosol concentrator performance: comparative tests with viable and with solid and liquid nonviable particles[END_REF]J. Li et al., 2018), in particular for particles in sub-micron (e.g. > 93% for 0.3 µm dioctyl phthalate) [START_REF] Vanosdell | Experimental Study of Submicrometer and Ultrafine Particle Penetration and Pressure Drop for High Efficiency Filters[END_REF].

For bioaerosol sampling in survivability studies, among others, the use of high moisture content of gelatin filters causes minimum sampling stress [START_REF] Pan | Collection, particle sizing and detection of airborne viruses[END_REF][START_REF] Zuo | Survival of Airborne MS2 Bacteriophage Generated from Human Saliva, Artificial Saliva, and Cell Culture Medium[END_REF], and it can be completely dissolved in water. Although, [START_REF] Lin | Characterization of a Piezoelectric Inkjet Aerosol Generator for the Study of Bioaerosol Survivability[END_REF] observed that even with the use of high moisture content of gelatin filters, the filtration-based technique still causes detrimental effects on yeast survivability. [START_REF] Fabian | Airborne influenza virus detection with four aerosol samplers using molecular and infectivity assays: considerations for a new infectious virus aerosol sampler[END_REF] also reported that the infectious assayed and total concentration of virus (Influenza virus RNA) recovered from gelatin were 10 and 63 %, respectively of the number assayed and total recovery from SKC BioSampler. Nevertheless, the results of [START_REF] Wu | Use of gelatin filter and BioSampler in detecting airborne H5N1 nucleotides, bacteria and allergens[END_REF] showed that the physical collection efficiency of gelatin filter can be outperformed the SKC BioSampler for the influenza A virus when sampling time is less than two minutes.

Filters usually have a diameter of 25, 37, or 47 mm, and are loaded in various sampling heads or filter holders such as Button Aerosol Sampler, IOM cassettes, and Cassette blanks with two or three section housings for filters (see Figure 3.3b). The use of appropriate filter size as well as filter holder with specific inlet characteristics allows to collect the interesting bio aerosol size and to control the aspiration and transmission losses. More information about the characteristics and performance of filter holders and sampling head as well as aerosol filtration efficiency could be found in the literatures [START_REF] Bulejko | Numerical Comparison of Prediction Models for Aerosol Filtration Efficiency Applied on a Hollow-Fiber Membrane Pore Structure[END_REF][START_REF] Görner | Laboratory Study of Selected Personal Inhalable Aerosol Samplers[END_REF][START_REF] Jeong | Comparison of five membrane filters to collect bioaerosols for airborne microbiome analysis[END_REF][START_REF] Lindsley | Filter pore size and aerosol sample collection. NIOSH Man[END_REF][START_REF] Paik | Aspiration efficiency for thin-walled nozzles facing the wind and for very high velocity ratios[END_REF].

Efficiency of active samplers

As shown in Figure 2.8, regardless of fundamental design differences, the overall sampling efficiency of an active sampler is product of the aspiration (𝜂 𝑎𝑠𝑝 ), transmission (𝜂 𝑇 ), collection ( 𝜂 𝑐 )/retention (𝜂 𝑅𝐸𝑇 ), recovery efficiency (𝜂 𝑅𝑒𝑐 ), and biological efficiency (𝜂 𝐵𝑖𝑜 , for microorganisms) [START_REF] Kesavan | Evaluation criteria for bioaerosol samplers[END_REF], defined as: 1590

𝜂 𝑜𝑣𝑒𝑟𝑎𝑙𝑙 = 𝜂 𝑎𝑠𝑝 × 𝜂 𝑇 × 𝜂 𝑐 × 𝜂 𝑅𝐸𝑇 × 𝜂 𝑅𝑒𝑐 ⏟ 𝜂 𝑃ℎ𝑦𝑠 × 𝜂 𝐵𝑖𝑜 Eq. 2-2
The overall sampling efficiency of each active sampler is examined by two critical parameters:

• Physical collection efficiency, 𝜼 𝑷𝒉𝒚𝒔 , describes how many particles can be extracted from the collection medium (solid, semi-liquid, or liquid) or filters and be measured, regardless of whether these particles are still viable and infectious.

• Biological efficiency, 𝜼 𝑩𝒊𝒐 , is expressed as the ratio of viable microorganisms to total (inactivated or viable) microorganisms penetrated into collection medium/filter at the completion of a test. As mentioned earlier, preserving the microorganism's viability depends on a set of factors, including the most efficient sampling to minimize the stress and desiccation on microorganisms during sampling, the extraction method and selected analysis technique. 1600 Figure 2.8. Schematic of the examinable efficiencies of an active sampler during physical aerosol sampling steps [START_REF] Kesavan | Evaluation criteria for bioaerosol samplers[END_REF].

The physical efficiency parameters have been discussed in the following subsections.

Aspiration efficiency (𝜼 𝒂𝒔𝒑 )

The aspiration efficiency, 𝜂 𝑎𝑠𝑝 , is expressed as the ratio of the particle flow concentration, 𝐶 𝑎 , aspirated through the sampler's inlet to the particle flow concentration in the undisturbed air environment, 𝐶 0 .

Grinshpun et al. (1993) introduced a general equation being valid for all air flow conditions ranging

from calm-air to high-air velocities, as follow:

𝜂 𝑎𝑠𝑝 = 𝐶 𝑎 𝐶 0 = 𝑆 0,𝑝 . 𝑈 𝑝 𝑆 𝑖 . 𝑈 𝑠 Eq. 2-3
where, 𝑆 0,𝑝 denotes the particle flow cross-sectional area, also known as the upstream critical area : the location containing all particles that terminate inside the sampler, 𝑆 𝑖 denotes the sampler's inlet area, 𝑈 𝑝 1610 denotes the particle's initial velocity, and 𝑈 𝑠 is air sampling velocity (see Figure 2.9). 𝑆 𝑖 and 𝑈 𝑠 can be determined by inlet geometry and flow rate of samplers. 𝑆 0,𝑝 and 𝑈 𝑝 may be achieved theoretically or experimentally [START_REF] Chattopadhyay | Sample Collection Information Document for Pathogens, Companion to Selected Analytical Methods for Environmental Remediation and Recovery (SAM)[END_REF][START_REF] Grinshpun | Effect of Impaction, Bounce and Reaerosolization on the Collection Efficiency of Impingers[END_REF]. Based on the above physical parameters and assuming an inlet of simple geometry (circular area), and negligible settling velocity of the particles compared to flow velocities, the aspiration efficiency of an inlet may be a function of four key dimensionless variables:

• Air velocity ratio 𝑅 = 𝑈 0 𝑈 𝑠 in which 𝑈 0 is the free air streamline velocity and 𝑈 𝑠 is air sampling velocity;

• Dimensional ratio 𝑟 = 𝐷 𝑂𝑢𝑡 𝐷 𝑖𝑛
in which 𝐷 𝑂𝑢𝑡 and 𝐷 𝑖𝑛 are outlet and inlet orifice diameter, respectively;

• Reynolds number (see Eq. A-2 ) in which the parameters of characteristic air velocity and length-scale and are replaced by 𝑈 0 and 𝐷 𝑜𝑢𝑡 respectively;

• Stokes number of particles near the active samplers can be calculated using the Eq. A-22 ( 𝑆𝑡𝑘 𝑟𝑒𝑓 = 𝜏 𝑝 .𝑈 0 𝐷 𝑖𝑛 ).

Before processing, it should be mentioned that in this part of the thesis, only aspiration efficiency of a thin-walled ( 𝐷 𝑂𝑢𝑡 𝐷 𝑖𝑛 ⁄ < 1.1), sharp-edged tube in isoaxial sampling conditions from moving air have been reviewed.

Figure 2.11 shows the typical limiting streamline patterns near the aspirating sampler's inlet. Note that, the limiting streamline divides the sampled from the unsampled air:

1) Isokinetic sampling conditions, in which the sampler face directly into the laminar air flow, and the air velocity ratio is equal to one (𝑈 0 = 𝑈 𝑠 ). Thus, there is no divergence of streamlines near the inlet, and a stagnation could be only found at the edges of the entry. Hence, the isokinetic sampling provides a representative assessment of the particulate matter that is in the upstream, and minimize the sampling error (Figure 2.11a).

2) Super-isokinetic sampling occurs when the air velocity ratio is lower than one (𝑈 0 < 𝑈 𝑠 ) and the sampler draws the air from a greater area than its inlet own. A constriction force the gas to increase in velocity and be focused in the center of the entrance region of sampler. This affect is referred to as a vena contracta, and after that, the air flow expands again to establish an equilibrium pattern and the value of the air velocity thereafter remain constant (Figure 2.11c) [START_REF] Brockmann | Aerosol Transport in Sampling Lines and Inlets[END_REF]. In this case, the small particles with insufficient inertia converge into the sampler's inlet, but the inertia of large particles keep them from following the streamline that converge into the sampler [START_REF] Wilcox | Isokinetic Flow and Sampling[END_REF].

3) Sub-isokinetic sampling conditions corresponds to air velocity ratio higher than one (𝑈 0 > 𝑈 𝑠 ), in which air the streamlines diverge from the edge of the inlet, and a stagnation occurs inside the sampler inlet [START_REF] Addlesee | Anisokinetic sampling of aerosols at a slot intake[END_REF]. The air velocity in the approaching air freestream along the axis of the inlet decreases continuously, only become steady at some distance into entrance region of sampler [START_REF] Vincent | Aerosol Sampling[END_REF]. In this condition, the large particles with sufficient inertia can cross the limiting streamline to be collected by the sampler, while the small particles follow the streamlines (Figure 2.11b). Therefore, the aspiration efficiency increase from 1 to 𝑅 for large particles [START_REF] Brockmann | Aerosol Transport in Sampling Lines and Inlets[END_REF]. 2.2 presents a series of equations developed to predict the aspiration efficiency for isoaxial sampling with a thin-walled nozzle.

According to the empirical equations, for particles with an aerodynamic diameter of < 1 µm, the isokinetic sampler is no longer an issue (as long as 𝑈 0 𝑈 𝑠 is kept within 0.5 and 2).

Transmission efficiency (𝜼 𝑻 )

Transmission efficiency (𝜂 𝑇 ) is the efficiency of transport of particles aspirated from the inlet of sampler to the filter or collection medium through nozzle exit (see Figure 2.6 and Figure 2.8). Transmission and 1670 deposition behavior of aerosols inside the inlet is influenced by a large amount of factors including Reynolds number (turbulence or laminar flow cases), Stokes number, sampling angle, wall material, bluntness ratio, and conventional gravitational deposition parameter. [START_REF] Okazaki | Transmission and Deposition Behavior of Aerosols in Sampling Inlets[END_REF] developed un model from the experimental data to estimate the gravitational settling transmission efficiency for thin-wall tubular inlets. [START_REF] Hangal | Technical Background Document: Microbial Risk Assessment and Fate and Transport Modeling of Aerosolized Microorganisms at Wastewater Land Application Facilities in Idaho[END_REF] introduced an inertial transmission efficiency model for super-isokinetic sampling. Several theoretical and numerical models have also been made to predict the inertial deposition of aerosol particles in 90° bends [START_REF] Nicolaou | Characterization of aerosol Stokes number in 90° bends and idealized extrathoracic airways[END_REF][START_REF] Pui | Experimental Study of Particle Deposition in Bends of Circular Cross Section[END_REF][START_REF] Sun | Particle flow behavior of distribution and deposition throughout 90° bends: Analysis of influencing factors[END_REF]. Generally, particle wall losses increase with particle size (US-EPA, 2012; [START_REF] Zhao | Numerical simulations of the sampling performance of a large particle inlet[END_REF]. [START_REF] Seshadri | Application of ATP bioluminescence method to characterize performance of bioaerosol sampling devices[END_REF] pointed out that sampling flow rate will significantly influences the particle wall loss of the BioSampler using (adenosine triphosphate) ATP bioluminescence method. 1680 However, the value of 𝜂 𝑇 is difficult to predict and almost all practical recommendations for aerosol sampling have as a goal to minimize or completely avoid the uncertainties caused by transmission aspiration, when particles of unknown bounce properties are sampled. for instance, the internal wall loss effect can be minimized by use of a short sampling line [START_REF] Grinshpun | A general equation for aerosol aspiration by thin-walled sampling probes in calm and moving air[END_REF].

Collection efficiency (𝜼 𝑪 )

Collection efficiency by collection medium/filter represents the ratio of the particles drawn into the sampler from nozzle exit during a test and the fraction of the particles that successfully spike into collection medium/filter at the completion of a test.

𝜂 𝐶 =

𝐶 𝐿 𝜂 𝑎𝑠𝑝 . 𝜂 𝑇 . 𝐶 𝑢𝑝 Eq. 2-4

Retention efficiency (𝜼 𝑹𝑬𝑻 )

Retention efficiency indicates how well collected particles are retained by the collection medium as a function of time, and defined as follows [START_REF] Kesavan | Sampling and Retention Efficiencies of Batch-Type Liquid-Based Bioaerosol Samplers[END_REF]:

𝜂 𝑅𝐸𝑇 = 𝑁 𝐿,𝑡 𝑁 𝐿,0
Eq. 2-5

where 𝑁 𝐿,0 and 𝑁 𝐿,𝑡 are the number of particles introduced into the collection medium at the start of sampling and recovered from medium at time t of sampler operation, respectively.

Recovery efficiency (𝜼 𝑹𝐞𝐜 )

The Recovery efficiency demonstrates how effective sample can be recover as a function of proper adequacy of the method of extraction, transport and storage of the particles and viable microorganisms between collection and analysis, as well as, of analysis method. For instance, [START_REF] Li | Storage effects on bacterial concentration: determination of impinger and filter samples[END_REF] recommended a sample collected by impingement methods should be refrigerated and processed as soon as possible to avoid an increase of bacterial culturability (E.coli and B. subtilis). Moreover, it was demonstrated that the storage effect by Nuclepore filtration depends on the type of bioaerosol, so that for B. subtilis was insignificant, but E.coli recovery decreased as storage time increased. [START_REF] Paba | Indoor exposure to airborne endotoxin: a review of the literature on sampling and analysis methods[END_REF] reviewed the sampling and analysis methods for the assessment of exposure to airborne endotoxins and reported that the main factor affecting the endotoxin measurements is the extraction method but only few studies (from 82 papers) have focused on the standardization of recovery and analysis methods.

The subsection 2.2.4 was briefly mentioned the various analysis technique that generally used to quantify or detect the collected bioaerosols and their compatibility with bioaerosol samplers. The more information about the characteristics of the analytical techniques and their efficiency to identify the selective species under different conditions (e.g. type of collection medium and environment) could be found in literatures [START_REF] Dungan | Qualitative and quantitative methodologies for determination of airborne microorganisms at concentrated animal-feeding operations[END_REF][START_REF] Ghosh | Review of bioaerosols in indoor environment with special reference to sampling, analysis and control mechanisms[END_REF].

Other techniques

Optical particle counters (OPC) are very widely used for real time aerosol analysis due to their ability to deliver particle-size distribution over a wide range. The OPCs detect and size particles by measuring the amount of light scattered by individual particles as they pass through a beam of light, so, they measure the optical diameter rather than the aerodynamic diameter. Consequently, these instruments need to be calibrated for each targeted particle type. This makes them poorly adapted for atmospheric pollution as it contains particles of various natures [START_REF] Djoumi | Real Time Cascade Impactor Based On Surface Acoustic Wave Delay Lines for PM10 and PM2.5 Mass Concentration Measurement[END_REF].

BioTrak ® is a real-time viable particle counter that combines an optical particle counter (OPC) system with a fluorescence detector to determine if a particle is biological based on a built-in discrimination algorithm (see 3.3.4). The counter's utilization has been validated by the USFDA [START_REF] Kirschman | Ultraviolet in-room air disinfection and recirculation greatly reduces viable airborne particle concentrations in the operating room setting[END_REF] and allows an easy and fast monitoring of total viable airborne bioaerosol with a cut-off size of 2 μm [START_REF] Soret | Investigation of Removal Capacities of Biofilters for Airborne Viable Micro-Organisms[END_REF]. However, further investigations are needed to better understand the response of this new technique to different types of biogenic aerosol particles and to quantity potential interferences with non-biogenic particles. Therefore, this instrument can be compared to other bioaerosol monitoring instruments.

An alternate to the OPC is the aerodynamic particle sizer, which provides real-time measurement of aerodynamic diameter, and is especially useful where the filtration, deposition, and inhalation properties of the particles are important. The size, shape, and density of the particles can be measured with the electronic aerodynamic particle sizer. In this instrument, the transit time of a particle traveling through a pair of parallel illuminated planes is measured. The transit time is a function of the aerodynamic diameter of the particles. The aerodynamic particle sizer has been found to correlate well to cascade impactors [START_REF] Welker | Chapter 1 -Basics and Sampling of Particles for Size Analysis and Identification[END_REF].

LIDAR (light detection and ranging) is a powerful tool for quantifying aerosols' (both solid and condensed) backscatter and extinction coefficients (extinction is defined as the radiation that does not reach the sensor due to scattering and absorption), and operates at the UV-VIS-NIR range [START_REF] Gil | Use of a Terrestrial LIDAR Sensor for Drift Detection in Vineyard Spraying[END_REF][START_REF] Kira | Estimating drift of airborne pesticides during orchard spraying using active Open Path FTIR[END_REF]. LIDAR has recently been developed by [START_REF] Gregorio | LIDAR as an alternative to passive collectors to measure pesticide spray drift[END_REF] for monitoring of spray drift in real time, with high range and temporal resolution (Gregorio et al., 2016). The lidar system was also used for monitoring the evolution of the spray flux over the canopy and to generate 2-D images of these plumes (Gregorio et al., 2016a). However, spray drift measurements using LIDAR can be affected by droplet size and the prototype is under development. In addition, more spray drift measurements are needed to compare its results with those obtained using passive collectors.

Enumeration technique

Conventional methods for evaluating performances of bioaerosol samplers frequently rely on culturing microorganisms or counting them with a microscope. Culture techniques are widely used to enumerate fecal indicators such as E. coli in irrigation water [START_REF] Becerra-Castro | Wastewater reuse in irrigation: A microbiological perspective on implications in soil fertility and human and environmental health[END_REF][START_REF] Holvoet | Relationships among hygiene indicators and enteric pathogens in irrigation water, soil and lettuce and the impact of climatic conditions on contamination in the lettuce primary production[END_REF][START_REF] Truchado | Suitability of different Escherichia coli enumeration techniques to assess the microbial quality of different irrigation water sources[END_REF]. However, the results obtained from culturing techniques can only represent the culturable fraction of the microorganisms, which often significantly underestimates the microbial loads in the environments [START_REF] Wu | Use of gelatin filter and BioSampler in detecting airborne H5N1 nucleotides, bacteria and allergens[END_REF]. In recent years, there is an increased interest in applying cultureindependent approaches such as enzyme-linked immunosorbent assay (ELISA), Flow cytometry (FCM),

Epifluorescence microscopy (capable to count both culturable and nonculturable cells), and quantitative polymerase chain reaction (qPCR)-based technologies for analyzing bio-aerosols. These techniques have high levels of sensitivity and specificity [START_REF] Ghosh | Review of bioaerosols in indoor environment with special reference to sampling, analysis and control mechanisms[END_REF]. Although, the major disadvantage to qPCR is that it cannot distinguish between viable and dead cells [START_REF] Tseng | Optimization of Propidium Monoazide Quantitative PCR for Evaluating Performances of Bioaerosol Samplers for Sampling Airborne Staphylococcus aureus[END_REF]. Propidium monoazide quantitative polymerase chain reaction (PMA-qPCR) is one of the promising methods for timely detection of Viable but non-culturable (VBNC) non pathogens in environmental samples [START_REF] Golpayegani | Propidium monoazide-quantitative polymerase chain reaction (PMA-qPCR) assay for rapid detection of viable and viable but non-culturable (VBNC) Pseudomonas aeruginosa in swimming pools[END_REF].

Conclusion

In this part a brief review of air sampling methods from various fields and the sampler efficiencies have been presented. Various samplers have been used to quantify bioaerosols. Active samplers with pumps are the most popular in various environmental conditions. The use of samplers with higher collection flow rate, that results in higher power consumption, which would shorten sampling time capacity, are generally 1790 preferred. Higher flow rate also means more intense desiccation of the collecting medium which may lead to a lower recovery of the sensitive organisms.

The accurate quantification of the volume of airborne droplets containing pathogens is of critical importance to estimate the health risks due to sprinkler irrigation with treated wastewater. Despite numerous studies on performance of the bioaerosol samplers and peer-reviewed literature, a standard bioaerosol sampling protocol has not yet been provided.

It is difficult to conclude on the best samplers or on the best protocols to collect bioaerosols. Our review has shown results obtained in different experiments, with various sampling times, collect medium and targeted pathogens monitored in different environments. There are not a lot of studies comparing the most common samplers in the same conditions thus no standard design or protocol to refer on. 1800

This analysis is the object of the following chapter .

Part II. Laboratory Experiments

Contribution to the thesis project: This part was divided in two chapters (Chapter 3 andChapter 4).

The aims were:

• To compare different active sampling methods of airborne particles in controlled conditions in order to assess which one is the most suitable for monitoring aerosols from sprinkler irrigation of TWW.

• To quantify the physical collection efficiency in low wind velocity, using a fluorescent tracer.

• To investigate the effect of sampling time on the collection efficiency and on the culturability of the E.coli cells sampled by the impingers

• To characterize the effect of air velocity and nozzle height on the airborne droplet size distributions downwind from the source using Phase Doppler Particle Analysis, in a small scale wind tunnel (Chapter 4)

The results of Chapter 3 have been published in the proceedings of the World Environmental and Water Resources Congress, held in Henderson, Nevada, May 17-21, 2020[START_REF] Zardari | Assessment of Sampling Techniques to Investigate Airborne Transport of Contaminants in Treated Wastewater from Sprinkler Irrigation Fine Droplet[END_REF])

Performance assessment of active samplers in calm air

Introduction

This chapter was firstly focused on evaluation of the physical collection efficiency of two impingers, SKC BioSampler and AGI-4 for tiny droplets containing a fluorescent dye, ABF (see 3.5.1.1) under various sampling time. We selected this tracer because it has been used in previous experiments of our team (Molle 1810[START_REF] Molle | Experimental Approach to Assessing Aerosol Dispersion of Treated Wastewater Distributed via Sprinkler Irrigation[END_REF][START_REF] Tomas | Transport Modeling in Sprinkler Irrigation[END_REF] to determine the airborne spray transport.

Moreover, as previously mentioned in the Chapter 1 (see Table 1.3), Escherichia coli (E. coli) is a member of the faecal coliform group and is a more specific indicator of faecal pollution than other faecal [START_REF] Francy | Escherichia Coli and Fecal-coliform Bacteria as Indicators of Recreational Water Quality, Waterresources investigations report[END_REF]. It is adopted in most European regulations as a basic tool to assess the microbiological quality of treated municipal wastewater [START_REF] Kellis | Review of wastewater reuse in the mediterranean countries, focusing on regulations and policies for municipal and industrial applications[END_REF][START_REF] Vergine | Fate of the fecal indicator Escherichia coli in irrigation with partially treated wastewater[END_REF], because of the availability of affordable, fast, sensitive, specific and easier to perform detection methods for E. coli [START_REF] Odonkor | Escherichia coli as an indicator of bacteriological quality of water: an overview[END_REF]. It is also used as one of the standards for testing bioaerosol sampling and air purification devices [START_REF] Macher | Evaluation of Bioaerosol Sampler Performance[END_REF][START_REF] Simon | Aerosolisation of Escherichia coli and associated endotoxin using an improved bubbling bioaerosol generator[END_REF]. Furthermore, many studies focused on the generation and transportation of indicator microorganisms, in particular, E.coli in bioaerosols [START_REF] Carducci | Assessing airborne biological hazard from urban wastewater treatment[END_REF][START_REF] Chan | Airborne Survival of Escherichia coli under Different Culture Conditions in Synthetic Wastewater[END_REF][START_REF] Han | Characterization of the airborne bacteria community at different distances from the rotating brushes in a wastewater treatment plant by 16S rRNA gene clone libraries[END_REF][START_REF] Nag | Risk assessment of Escherichia coli in bioaerosols generated following land application of farmyard slurry[END_REF]. Therefore, in this study, a non-pathogenic E. coli strain 1820 was selected as a model bacterium for investigating the bioaersol sampling of AGI -4, SKC BioSampler, and BioTrak ® , under two sampling time of 5 and 15 min.

Note that this chapter started with the presentation of a series of the preliminary experiments to understand necessary precautions, strengthen the interpretations and to ensure the repeatability of the experiments.

Description of aerosol generation and test facility

To address the objective of this chapter, it was necessary to work in controlled conditions with a system that generates controlled size and discharge of aerosols, to further benchmark four different collection techniques.

Our experiments have been conducted in laboratory of Engineering of the Industrial Environment and 1830

Industrial and Natural Risks (LGEI in IMT Mines Alès), which has developed in partnership with the companies Europe Environment and Bruk'Air, a sampling chamber of 1.234 × 0.63 × 0.8 m 3 dimension presenting a laminar flow with a range of air velocity (𝑈) of 0-0.09 m.s -1 . In this chapter, only 𝑈 = 0.09 m.s -1 (dry airflow Q_DRY=400 L.min -1 ) was investigated (Figure 3.1).

The aerosol generator was composed of an ultrasonic humidifier (U7146, Boneco) that has been installed in a 40 L tank. The liquid suspension of each test tracer was aerosolized by the ultrasonic humidifier. A 60 L.min -1 airflow (Q_NEB) controlled by a mass flowmeter (Brooks, SLA 5800 series, USA) transported the synthetic aerosol outside the generator where it was diluted by a dry airflow (Q_DRY=400 L.min -1 ). Afterward, the aerosols passed through a mixing zone and entered the sampling chamber (Figure 3.2). The temperature and relative humidity were recorded in the sampling chamber by 1840 the Thermo-Hygrometer (HD50, KIMO ® INSTRUMENTS, France).

Description of the aerosol samplers AGI-4

The All-Glass Impinger, AGI-4 (Ace Glass Inc., Vineland, NJ, USA) has a nominal air sampling flow rate of 6 L.min -1 passing through a 59.44 mm² aperture area when the pressure drop across the nozzle is at least 16.14 inHg (~0.54 bar) for achieving sonic velocity (Figure 3.3a(left)). The nozzle is directed 1850 perpendicularly to the bottom of the bottle, with a 4 mm distance from the nozzle exit to the bottom surface.

Typically, the initial liquid volume (demineralized water (DW) or sterile DW) in the glass bottle is 20 mL.

As previously mentioned (see 2.2.2.2), sampling duration is usually 10-30 min. The AGI-4 and similar conventional impingers are commonly used with low viscosity liquids such as water (Lin et al., 1999).

SKC BioSampler

SKC BioSampler (SKC Inc., Eighty Four, USA) consists of a 90° curved inlet section with approximately 70.88 mm 2 aperture area, a nozzle section with a three tangential 0.63 mm nozzles (sonic orifices) at a distance of 30 mm from to the bottom surface of the liquid reservoir, and a collection reservoir. Each nozzle permits 4.2 L.min -1 of ambient air to pass through for a total nominal flow rate of approximately 12.5 L.min -1 at a pressure drop of 15 in Hg (~0.5 bar). This device has been developed to 1860 prevent the loss of sampling liquid during operation and to prevent damage to bacterial cells during collection (Figure 3.3a(right)) and can be operated continuously for up to 8 h without a decrease in collection liquid when a non-evaporating liquid (mineral oil) is used (Lin et al., 1999). The SKC BioSampler has been used previously in bioaerosol field studies [START_REF] Fabian | Ambient bioaerosol indices for indoor air quality assessments of flood reclamation[END_REF] and has a collection efficiency of 79% for particles larger than 0.3 μm and 100% for particles larger than 2 μm (Willeke et al. 1998). 3.1 indicates the aspiration features of the AGI-4 and SKC BioSampler. Note that the nominal flow rate are calibrated by the manufacturer, at standard conditions, at normal temperature and pressure (NTP), which refers to 101 kPa and 293 K [1 atmosphere and 20 °C] and these values could be varied based on the atmospheric pressure (Pa) and environmental temperature (Ta). [START_REF] Su | Re-evaluation of BioSampler and its improvement for on-line, time-resolved monitoring of environmental coarse aerosol[END_REF] carefully evaluated the pressure drop, mass loss rate and temperature of the collection liquid of SKC BioSampler through literature review and theoretic analysis.

Summary Table

Glass fiber filter

Glass fiber filter (A/E, SKC Ltd, UK) with 1µm pore size and 37 mm diameter supported by cellulose pads are loaded into two piece plastic cassettes (Figure 3.3b). It maintains high physical collection efficiency across all measured particle sizes, sampling flowrates, and sampling times. It typically provides more than 85% physical collection efficiency for particle sizes from viruses (< 30 nm) to clusters of spores (>7 µm) [START_REF] Walls | Long-term viable bioaerosol sampling using a temperature-and humiditycontrolled filtration apparatus, a laboratory investigation using culturable E. coli[END_REF]. Sampling times must be short (≤ 20 min) for some vegetative bacteria that are sensitive to stress. In this study, this glass fiber filter was used as reference when using fluorescent dye to estimate physical efficiency. A pump is used to force air through the filter with a discharge that reproduce the same suction velocity as for the sampler (section 3.5.1.2).

BioTrak ®

The In this study, this device was used as a reference for normalization of the number of bacterial colonies collected by the impingers. In addition, it allowed to determine droplet size distribution in the sampling chamber.

Preliminary experiments

Droplet size distribution in the sampling chamber

BioTrak ® was used to detect the size distribution of droplets in the sampling chamber. As described earlier, the system was designed to discharge dry air at 400 L.min -1 of air with a constant relative humidity inside the chamber at about 5%. As soon as the aerosol generator was turned on, the humidity relative reached 10 to 15%. Given this range of low relative humidity and consequently evolution of the size of initial droplets in the system, BioTrak ® characterized only the final droplet size distribution in the sampling chamber. It means the one captured by collecting devices. Since the intervals of the particle channel sizes 1910 of BioTrak ® were not uniform, we assumed that all the droplets in each size range have a diameter equal to midpoint of the size interval. In addition, we normalized the number and volume fraction per the difference of the logarithms of the upper and lower size limits of the size range, as shown in Table 3.2.

The cumulative distribution function, F(Du), of discretized number and volume size distribution of droplets data from BioTrak ® was calculated, as follow:

𝐹(𝐷 𝑢 ) = 1 -∑ 𝑁 𝑖 (𝑜𝑟 𝑉 𝑖 ) 𝑁 𝑡𝑜𝑡 (𝑜𝑟𝑉 𝑡𝑜𝑡 ) 6 𝑖=1 Eq. 3-1
where 𝐷 𝑢 is the upper size limit of the size range. These values were also fitted with cumulative Lognormal distribution (in number and volume), which is the most widely used for aerosol size distribution [START_REF] Kulkarni | Aerosol Measurement: Principles, Techniques, and Applications[END_REF], and defined by:

𝐹(𝐷) = 1 𝑙𝑛𝜎 𝑔 √2𝜋 ∫ 𝑒𝑥𝑝 [- (𝑙𝑛𝐷 -𝑙𝑛𝐷 ̅ 𝑔 ) 2 2(𝑙𝑛𝜎 𝑔 ) 2 ] 𝐷 0 𝑑𝑙𝑛𝐷 Eq. 3-2
where,

𝑙𝑛𝜎 𝑔 = √ ∑ 𝑁 𝑖 (𝑙𝑛𝐷 𝑖 -𝑙𝑛𝐷 ̅ 𝑔 ) 2 6 𝑖=1 ∑ 𝑁 𝑖 6 𝑖=1
Eq. 3-3 and geometric mean diameter, 𝐷 ̅ 𝑔 , is given by: 1920

𝑙𝑛𝐷 ̅ 𝑔 = 1 - ∑ 𝑁 𝑖 𝑙𝑛𝐷 𝑖 6 𝑖=1 ∑ 𝑁 𝑖 6 𝑖=1
Eq. 3-4

For a Lognormal distribution, the number median diameter, NMD, is equal to 𝐷 ̅ 𝑔 , so the volume cumulative Log-normal distribution can be calculated by replacing NMD by volume median diameter 3.2, the 90% of the total volume could be estimated to be inferior to about 2.5 micron.

(𝑉𝑀𝐷 = 𝑁𝑀𝐷 × 𝑒𝑥𝑝 (3𝑙𝑛 2 𝜎 𝑔 )).
In addition, the data were moderately mono-dispersed considering their narrow size distribution (0.5-0.7), as well as the geometric standard deviation of 1.39.

Estimation of the aspiration efficiency of active samplers in calm air

When air velocity →0 (calm air), the particle inertia and its stokes number 𝑆𝑡𝑘 𝑖 depend exclusively on air sampling velocity (𝑈 𝑠 ), as follow:

𝑆𝑡𝑘 𝑖 = 𝜌 𝑝 𝐷 𝑝 2 𝐶 𝑐 𝑈 𝑠 18𝜇 𝑎 𝐷 𝑖𝑛 Eq. 3-5
Note that the parameters were already defined (see page 72 and Appendix. A).

Furthermore, [START_REF] Grinshpun | A general equation for aerosol aspiration by thin-walled sampling probes in calm and moving air[END_REF] developed the following empirical equation for aerosol aspiration into thin-walled, sharp-edged ( 𝐷 𝑂𝑢𝑡 𝐷 𝑖𝑛 ⁄ < 1.1) from calm air, which is the based on the numerical 1940 calculations:

𝜂 𝑎𝑠𝑝(𝑐𝑎𝑙𝑚) = exp [- 4𝑆𝑡𝑘 𝑖 {(R 𝑐𝑎𝑙𝑚 ) 0.5 +1} 1 + 2𝑆𝑡𝑘 𝑖 ] + R 𝑐𝑎𝑙𝑚 . 𝑐𝑜𝑠𝜑 𝑔 Eq. 3-6
where 𝜑 𝑔 is inlet orientation in gravitational field (in this study, 𝜑 𝑔 = 90°), and 𝑅 𝑐𝑎𝑙𝑚 is the velocity ratio in calm air, expressed as:

𝑅 𝑐𝑎𝑙𝑚 = 𝑈𝑡𝑠 𝑈 𝑠 Eq. 3-7
where 𝑈𝑡𝑠 is the terminal settling velocity of the particle.

It should be mentioned that 𝐷 𝑂𝑢𝑡 𝐷 𝑖𝑛 ⁄ of AGI-4 and SKC BioSampler were slightly higher than the required assumption for entrance region (1. 26 and 1.35, respectively). Although, [START_REF] Belyaev | Techniques for collection of representative aerosol samples[END_REF] have shown that the effect of bluntness ratio can be still negligible for 𝐷 𝑂𝑢𝑡 𝐷 𝑖𝑛 ⁄ superior to 1.1.

Concerning the reference sampler (Glass fiber filter), given that the ratio of 𝐷 𝑂𝑢𝑡 𝐷 𝑖𝑛 ⁄ of two piece plastic cassettes including the filter was about 1.75 and also because of its small inlet diameter (Ø 4.9 mm), 1950 a crystal tube (Ø 9 × 12 mm) of 20 mm long have been connected to the entrance. It allowed to reduce the ratio of 𝐷 𝑂𝑢𝑡 𝐷 𝑖𝑛 ⁄ by 1.33 and also, to produce an air sampling velocity almost similar to the one of impingers for a given flow rate. The results showed that for droplets up to 2 µm, which correspond to the range of droplet size distribution of this study, the stokes numbers were sharply lower than 1. Therefore, the droplets perfectly followed the streamlines, and the aspiration efficiencies of both impingers were closely to unity. Note that the red color indicates the aspiration efficiencies less than 0.9

To visualize the trajectory of airflow in the vicinity of the aspirating impingers in calm air conditions Regarding the literature [START_REF] Belyaev | Techniques for collection of representative aerosol samples[END_REF][START_REF] Grinshpun | Inlet characteristics of bioaerosol samplers[END_REF][START_REF] Hangal | Technical Background Document: Microbial Risk Assessment and Fate and Transport Modeling of Aerosolized Microorganisms at Wastewater Land Application Facilities in Idaho[END_REF], when the size of single particle or bacteria are less than 1-3 µm, assuming no bacteria/particle agglomerates in sampling lines, the transmission efficiency of the active samplers along inlet and nozzle sections could be negligible. Although, in this study, according to the values of physical collection and sampling efficiencies, it was possible to estimate the transmission losses in the inner walls of the sampling lines.

Evaporation/aerosolization rate of the collection liquid

To investigate the impact of temperature and sampling time on the evaporation/re-aerosolization rate of the liquid collection (DW), the impingers were filled with 20 mL of DW and then, turned on at different time intervals without Aerosol. The samplers (Impinger+ collection liquid) were weighed before and after each test using a balance (KERNPKS, accuracy: 0.01 g, KERN & Sohn GmbH, Germany) .The measurement were repeated three times in order to increase the precision.

As shown in Figure 3.6a, the liquid evaporation/aerosolization rate for impingers, SKC BioSampler and AGI-4 at approximately 25°C was respectively, about 0.19 and 0.14 mL.min -1 . Whereas previous studies [START_REF] Lin | Effect of Sampling Time on the Collection Efficiency of All-Glass Impingers[END_REF][START_REF] Lin | Improved Aerosol Collection by Combined Impaction and Centrifugal Motion[END_REF] observed an evaporation rate of 0.2 mL.min -1 for AGI-4 at the flow rate of 12.5 L.min -1 and at a temperature of 25 °C, it can be concluded that it would be necessary to calibrate the liquid evaporation/ aerosolization rate against the experimental impingers.

The experimental results in the aerosol test system show that when impingers operated in their most common mode, with 20 mL of DW at nominal flow rate and sampling time of 15 min, for every 1-degree increase in ambient temperature, 0.1 mL of water evaporates (Figure 3.6b).

Materials and methods

Physical collection efficiency set-up

Specification of the tracer

To study the effect of sampling time on the physical collection efficiency, 𝜂 𝑝ℎ𝑦𝑠 , fluorescent dye Acid Brilliant Flavine, ABF (WALDECK, Münster) has been applied as a tracer. This tracer with excitation and emission wavelength of 450 nm and 500 nm respectively is commonly used in quantification of pesticide spray drift analysis both in field and in wind tunnels studies [START_REF] Cornacchia | Assessment of Airborne Transport of Potential Contaminants in a Wind Tunnel[END_REF][START_REF] Gil | Atmospheric loss of pesticides above an artificial vineyard during air-assisted spraying[END_REF][START_REF] Holterman | Evaluation of drift reducing effects of end nozzles using the IDEFICS spray drift model[END_REF][START_REF] Molle | Experimental Approach to Assessing Aerosol Dispersion of Treated Wastewater Distributed via Sprinkler Irrigation[END_REF]. The advantages of this fluorescent dye are: moderate UV degradation, 7% on average between 30 and 180 min of exposure, no impact on the movement of droplet [START_REF] Schleier | The effect of fluorescent tracers on droplet spectrum, viscosity, and density of pesticide formulations[END_REF], nearly constant fluorescent intensity over the pH range from 6.9-10.4 [START_REF] Zhu | Effect of solution pH conditions on fluorescence of spray deposition tracers[END_REF], and its non-toxicity.

The quantification of the ABF concentration (µg.L -1 ) from all samplers was performed using a fluorescence Spectrometer (Perkin Elmer LS 45, Waltham, USA) with the optimal range of accuracy in the linear trend between 5 and 450 μg.L -1 of the ABF concentration, and verified using a fluorescenceconcentration calibration curve. The limit of quantification was considered as twice the standard deviation 2010 of a blank sample, a sample containing only demineralized water, which lies approximately in a range between 1.5 μg.L -1 and 2.5 μg.L -1 . At greater than 450 μg.L -1 , a dilution of the sample was therefore required to bring the read value within the optimal range of accuracy.

Characterization of Colloidal particles of the ABF in the solvent

The ABF is highly hydrosoluble, however, in a separate experiment, the size of colloidal particles of ABF in the solvent (demineralized water) was determined by a nanoparticle size analyzer (VASCO γ , Cordouan Technologies, France), in SQPQV-EMMAH soil laboratory of INRAE Avignon. This instrument is intended for measuring size distribution of colloidal and suspension nanoparticles from 1 nm to 10 µm, based on dynamic light scattering (DLS). Briefly, when sample suspension is irradiated by a stabilized laser with 657 nm wavelength, the intensity scattered by the nanoparticles fluctuates over time. 2020

This phenomenon is related to the thermal agitation, to the Brownian movement of the particles, which induces a loss of coherence of scattered /diffused light. The smaller the particle, the faster the its motion also the higher the frequency of intensity fluctuations. The autocorrelation function (ACF) of the diffused signal highlights a characteristic decay time, 𝛤, which depends on the temperature, the viscosity of the fluid, and the linear diffusivity coefficient of the particles, 𝐷 𝑡 . Then the size of particles can be calculated via the Stokes-Einstein formula (see Figure 3 For this purpose, a sample suspension was prepared by dissolving 50 mg of ABF in 100 mL 2030 demineralized water (concentration of 0.5 g.L -1 ) and mixed them using a magnetic mixer for 30 minutes at To measure this efficiency, the ultrasonic humidifier was filled with 300 mL demineralized water (DW) to which the ABF was added at a concentration of 0.5 g.L -1 . The physical collection efficiency, ηphys, defined in Eq. 3-8, was determined through measurement of the mass of the ABF recovered from two glass fiber filters situated: next to the impinger with the interval of 0.1 m (upstream, Mup), and downstream the impinger's suction line (downstream, Mdown).

𝜂 𝑝ℎ𝑦𝑠 = 1 - 𝑀 𝑑𝑜𝑤𝑛 𝑀 𝑈𝑝 Eq. 3-8
Moreover, the sampling efficiency, 𝜂 𝑐(𝑠𝑎𝑚𝑝𝑙𝑒) , that represents the ratio of the mass of ABF in the final sampling liquid, MC, to that recovered by the glass fiber filters as a reference, Mup was determined as: 2060

𝜂 𝑐(𝑠𝑎𝑚𝑝𝑙𝑒) = 𝑀 𝐶 𝑀 𝑈𝑝
Eq. 3-9

Thus, the residual efficiency, 𝜂 𝑅 , represents the ratio of the mass total of ABF in the final sampling liquid, MC, added to the downstream filter, Mdown to that of the ABF recovered by glass fiber filter, as a reference, Mup. It is determined as follow:

𝜂 𝑅 = 𝑀 𝐶 + 𝑀 𝑑𝑜𝑤𝑛 𝑀 𝑈𝑝

Eq. 3-10

Note that all tests related to this study have been repeated at least three times and their arithmetic averages have been calculated. It should be mentionned that in addition to recording the flow rate during the tests (once per 30 seconds), before each test, the flow rate of the upstream filter has been adjusted based on the average value measured 2070 for the corresponding impinger.

In addition, to evaluate possible losses of collected ABF on inner walls of two piece cassettes, after removing the filter, the cassettes were washed with 5 mL of DW and the samples were analyzed using the fluorescence Spectrometer. However, the concentration of all samples was lower than the detection limit of the device. So, in this study, this loss was negligible.

Recovery of the ABF from the filters

To recover the totality of the ABF from the glass fiber filters, two methods were compared, the method inspired from the study of [START_REF] Robinson | a Meteorological Tracer Technique Using Uranine Dye[END_REF], M1, and the methods of [START_REF] Kesavan | Factors That Affect Fluorescein Analysis[END_REF], M2.

For the two methods, a measured amount of a mixture of DW and ABF was added to six Glass fiber Viability and culturability assessment tests

Preparation of bacterial suspension

The E.coli (ATCC 15597) strain has been grown in 5 mL nutrient Luria Broth, LB (CONDA S.A, Madrid, Spain) in a shaking incubator at 180 rpm and 37 °C for 18h. Precultured cells were transferred into 100 mL LB, followed by an incubation for 1.5-2 h. The cell suspension LB was tested by spectrometer (DOP600 = 0.5-0.7), then E.coli was harvested from the broth by centrifugation at 8000 rpm for 5 min at 15°C and washed in sterile DW. The cell suspension was diluted and transferred to the ultrasonic humidifier at a concentration of 6.68E+08 to 1.24E+09 CFU. mL -1 depending on the culture method. The preparation steps of bacterial suspension have been listed and presented in detail in Appendix. C.

Bacterial collection efficiency set-up

The schematic of the experimental setup to evaluate the effect of sampling time on bacteria collection efficiency and according to the type of impingers is shown in Figure 3.12a. The impingers were filled with 20 mL of sterile DW. Two SKC BioSampler and/or AGI-4 were run in parallel at their nominal flow rates: one for 5 min and the other for 15 min. This minimized temperature and relative humidity as well as concentration fluctuations during sampling.

After sampling, the bacterial plates were incubated for 24-48 h hours at 37 °C (Appendix. C, C. 5). The number of the bacterial colonies, Ncul (CFU ) were then counted, and the results are used to calculate the airborne concentrations of the culturable cells, 𝐶 𝑠𝑎𝑚𝑝𝑙𝑒 (CFU.m -3 ):

𝐶 𝑠𝑎𝑚𝑝𝑙𝑒 = 1000 × 𝑁 𝑐𝑢𝑙 𝑄 𝑠 × 𝑡 𝑠 × 𝑉 𝑓𝑖𝑛 𝑉 𝑀𝑙𝑖𝑞

Eq. 3-11

Where: 𝑄 𝑠 = the sampling flow rate (L.min -1 ); 𝑡 𝑠 = sampling time (min); 𝑉 𝑓𝑖𝑛 = Volume of the final sampling liquid in mL; and 𝑉 𝑀𝑙𝑖𝑞 = volume of the liquid used for culturable analysis (µL).

The BioTrak ® has been chosen as a reference (Figure 3.12b). Given that the flow rate of the BioTrak ® was 28.3 L.min -1 , it has been run before and after each test for at least 15 min. The results of the impingers were therefore normalized based on the viable E.coli, CRef (viable), and the total aerosols, CRef(total) detected by BioTrak ® . These normalizations are expressed as:

𝐶𝐵 𝑣𝑖𝑎𝑏𝑙𝑒 = 𝐶 𝑆𝑎𝑚𝑝𝑙𝑒 𝐶 𝑅𝑒𝑓 (𝑣𝑖𝑎𝑏𝑙𝑒)
Eq. 3-12 and,

𝐶𝐵 𝑡𝑜𝑡𝑎𝑙 = 𝐶 𝑆𝑎𝑚𝑝𝑙𝑒 𝐶 𝑅𝑒𝑓 (𝑡𝑜𝑡𝑎𝑙)
Eq. 3-13

Results and discussion

Effect of sampling time on physical collection efficiency

By using the ABF as the tracer, the physical collection efficiency of the AGI-4 was 14.7, 17.2, 17 and 25.3% higher than the one of SKC BioSampler for 5, 15, 30, and 60 min sampling time, respectively (Figure 3.13a). However, the increase of the sampling time from 5 to 60 min had no significant influence on the physical efficiency of AGI-4 that could be considered constant (Figure 3.13a). For SKC Biosampler, a 8.3% reduction in physical collection efficiency was found from 30 min to 60 min. Figure 3.13b shows that with increasing the sampling time of the impingers, the sampling efficiency corresponding the ratio of the mass of ABF extracted by the sampling liquid, MC, to that recovered by the glass fiber filters as a reference, decreased. This reduction was significant for AGI-4 only from 5 to 15 2130 min, approximately 11.2%. For SKC BioSampler, we observe small variations between 5 to 30 min and a more significant decrease at 60 min (6.9%).

Assuming that the transmission efficiencies of the impingers were close to one, it was expected that the physical collection efficiency and sampling efficiency would be equal, but the results did not confirmed this fact. For this reason, Figure 3.13c shows the ratio (Eq. 3-10) of the mass of the ABF drawn into the impingers and that of the ABF detected through the reference, which represent the percentage of residual efficiency. These results were globally similar between the two impingers. Although, from 15 to 60 min, the residual efficiency of SKC BioSampler was slightly higher than the one of AGI-4 (about 6). The effect of the sampling time seems be more important at the beginning (the first 5 min) for AGI 4, and then the losses seem keep almost constant from 15 to 30 min around (6.65%) compared to the initial solution. These 2140 losses can be due to as the adherence of the ABF on the inner walls of inlet tube (transmission losses) or the collection reservoir of the impingers, which were in accordance with the conclusion of Han and Mainelis (2012) about latent losses.

Characterization of the total losses

A brief statistical analysis has been done by calculating the measurement errors (1-efficiency = loss), 2150 their averages and standard deviations. It has been mentioned that standard deviation was calculated as the square root of variance by determining the variation between each measurement error (%) relative to the average. Table 3.4 shows that average of the measurement errors for the investigated AGI-4 was approximately 7.45%, considering ABF as the reference tracer and that the sampling time had no effect on physical collection efficiency. Comparison of the physical efficiency, the sampling efficiency, and the residual loss reveals that for sampling times of 5 min, the sampling efficiency of AGI-4 was higher than the glass fiber filter (as a reference). Therefore, when the concentration of the ABF in the air was high, the sampling time of 5min is recommended. SKC BioSampler had the highest measurement error for the sampling times of 60 min. So, when using 2160 DW as a collection liquid, the sampling times of between 5 and 30min was recommended. Moreover, the residual loss of SKC (3.76%) was less than AGI-4 (6.65%). The red color indicates that these values were not considered in calculation of averages and standard deviations of measurement losses.

Effect of sampling time on the culturability of airborne Escherichia coli cells sampled by impingers

The effect of sampling time on the culturability maintenance of impingers is shown in Figure 3.14. As mentioned earlier (see sections 3.3.4 and 3.5.2.2), in order to simultaneously compare the culturability maintenance of the impingers, the number of the culturable E.coli recovered by impingers (CFU.m -3 ) was normalized by dividing it by the total airborne bacteria detected by BioTrak ® (#.m -3 , with a counting efficiency of 100% for particles > 0.75 µm) in sampling chamber.

Figure 3.14 shows that by increasing the sampling time from 5 min to 15 min, the culturability of SKC BioSampler and AGI-4 decreased. This reduction for SKC and AGI-4 sampling was 14% and 37%, respectively when using total aerosols detected by BioTrak as the reference. [START_REF] Henningson | Collection efficiency of liquid samplers for microbiological aerosols[END_REF] found that when only collection efficiency was targeted, AGI-4 was preferable, but particle rebound greatly affected the viability of the bacteria in the AGI-4 test. [START_REF] Lin | Survival of Airborne Microorganisms During Swirling Aerosol Collection[END_REF] stated that the swirling motion of the collection liquid in the SKC BioSampler generates very few bubbles compared to AGI-4 and therefore causes less re-entrainment of the microorganisms collected. It is in accordance with our SKC BioSampler tested for 15 min sampling duration.

Given that SKC BioSampler has the highest culturability maintenance for E.coli cells at 15 min, the ratio of the culturability obtained by AGI-4 and SKC BioSamplerthe is 0.93. It can therefore be inferred that culturability of AGI-4 is equal to culturability of SKC multiplied by 0.93.

Comparison of samplers collecting/detecting the airborne live Escherichia coli: Impingers and BioTrak ®

Figure 3.15 represents the ratio of the culturable E.coli cells collected by impingers (CFU.m -3 ) to the number of viable airborne bacteria counted by BioTrak ® (vcnt.m -3 ). Given that all the viable Escherichia coli cells cannot be grown using conventional culture media, it was expected that regardless of the sampling duration, the values of these ratios were higher or at least equal to one (Figure 3.15). It can therefore be concluded that BioTrak ® provides a fast but less accurate number of viable particles compared to the impingers.

Besides, as can be seen in Figure 3.15, if the number of viable bacteria counted by BioTrak ® was taken as the reference, similar to the previous section (see 3.6.2), from 5 to 15 min, the culturability of SKC 2200 BioSampler fell by 22%, while the reduction value for AGI-4 remained constant (37%).

Conclusion

The objective of this study was to investigate the physical collection efficiency of active samplers when using different tracers (fluorescent ABF and E.coli) at different sampling times (5-60 min). Furthermore, 2210 the effect of impinger types and sampling times (5 and 15min) on the culturability maintenance of the E.coli cells were assessed.

A comparison of the physical efficiency of impingers revealed that the efficiency of AGI-4 was almost 16.3% higher than the SKC Biosample in the range of 5-30 min. As the sampling time is an important parameter in field studies, where the concentration of ABF in aerosol (as a tracer of the droplet dispersion) is limited, it is therefore recommended to use the AGI-4 as an active sampler. Note that this is in the case

Droplet size distribution in moving air Introduction

In cases where the spray nozzle has been mounted in a downward direction, such as some types of spray nozzles used for center pivot and linear irrigation systems or fogger mist sprinklers, apart from all the factors mentioned in the Chapter 1, the height of nozzle from the target/soil and spray angle can play an important role in the control of downwind dispersion of droplets/aerosols.

In this chapter, the factors affecting the spread of small droplets (<150 μm) from the spray nozzle mounted in downward direction including nozzle height, at least over short distance have been evaluated.

They would be of help to healthcare farmers in close proximity of sprinkler irrigation.

Materials and methods

Description of Wind Tunnel

The experiments of this chapter have been conducted in an open circuit wind tunnel, situated in the Laboratory for the Science of Risks (LSR) at 'Institute Mines Telecom (IMT)' Mines Ales, in the south of France (Figure 4.1). Air was drawn into the tunnel from the laboratory environment through an aerodynamically designed cone with the honeycomb wall that provide an almost uniform velocity profile throughout the working section. It then entered the working section and moved through a diffuser before passing through a second honeycomb and then to a variable-speed axial fan. The air leaved the fan, passed through a cylindrical unit and then backed out to the laboratory. The rectangular working section was 0.35 (height) × 0.5 (width) m 2 in cross section and 2 m long. The roof and sides of the working section were made of transparent Plexiglas sheets, which allowed executing the experiment with optical techniques. However, in order to install the equipment, described in the following chapters, it was necessary to drill holes in the roof at several points, causing the Plexiglas sheet to crack, which we replaced it with a wooden plate. The floor of the working section was also changed into the removable puzzle pieces, which allowed to place the samplers with the variable heights as well as a container beneath the spray nozzle, in different positions within the working section (see section 4.2.7). Besides, it was covered with an artificial grass (6 mm thickness) to adsorb the deposited water on the floor surface and so to prevent water runoff when using the spray nozzle (see section 4.2.4).

Air velocities measurements

The wind tunnel was able to generate the wind speed up to 15 m.s -1 , which was controlled by a housing of electronic variable speed drive (Digidrive SK, Lorey Somer) by changing the frequency (accuracy: 0.1 2260 Hz). The frequency values were converted to mean air velocities (Curve-fitting: Fr-U) through a series of the measurements by a hot wire anemometer (Kimo VT 100, maximum error of ± 0.1 m. s -1 ) across two cross sectional area (Y-Z axis) located 0.95 and 1.80 m downwind from the inlet of the working section.

The PIV measurements were also carried out in a field of 91 × 91 mm 2 along the X-Y axis (its center position: Y=0.175 m, Z= 0.25 m) in which the flow field was laden with smoke to characterize the airflow velocity in the center plane of the working section. The scheme of measurement points and the details of the characterization of the wind tunnel airflow have been presented in Appendix. D. As shown in the synthesis Table 4.1, air velocity measurements were performed at the locations where samplers or optical systems would be placed during the planned experiments. The values of mean air velocities in Y direction (𝑣̅ ) revealed that regardless the airflow velocity, the deviation of the local air velocity from the mainstream 2270 velocity was generally less than 0.6 degrees. Given the limitations due to the edge of upper boundary layers, ULB (Appendix. D) , the passive samplers have to be placed up to a maximum height of 0.275 m above the working section floor. The mean airflow velocities denoted by 𝑈, were defined based on the measurements in the center plane. The further experiments were therefore made with wind tunnel operating at 𝑈 of 1.2, 2.25, 3.3 and 5.0 m.s -1 . Although, under the air velocities ranged from 1.2 to 5.0 m.s -1 , the turbulent flow (Re > 4000) took place in the working section.

Y X Z Flow direction

Laboratory control conditions

The laboratory was equipped with a heat reclaim ventilation unit (Daikin HRV Europe, Belgium) and the air temperature was maintained at 20±1°C. Nevertheless, it was not possible to control the relative humidity in the wind tunnel. The relative humidity and temperature were recorded, respectively, by two Hygrometers (testo 48H) and the wire thermocouples at inlet and outlet of the working section (Figure 4.2). The temperature from the thermocouple was read through a background analog input channel 2290 (National instruments 9211, CompactDAQ "cDAQ3Mod2", Hungary), using LabVIEW software to configure the measurements and acquire the data with a sampling rate of once per second. Table 4.3 shows some of properties of air at a temperature of 20 °C.

Characteristic of spray nozzle

As it was not possible with a sprinkler in controlled conditions, an axial-flow hollow cone nozzle sprayer (Type: 220.085, Lechler GmbH, Germany) with a spray angle of 80° was considered in this part of thesis. 2300

This type of the spray nozzle with integrated strainer was selected due to its narrow spray pattern, and extremely fine droplets (fog-like) produced by spiral grooves in the swirl insert (Figure 4. • Transmitter which uses a He-Ne (Helium-Neon) laser to produce a collimated laser beam that come through the spray atomized to measurement zone;

• Receiver module which holds the lens assembly and photodiodes detectors to detect the light diffraction and converting the light detected into electrical signals;

• Analogue and the Digital electronics boards for processing the signals and

• the Software to analyze the light diffraction patterns by a scattering model to calculate the spray size distribution (Malvern Instruments Ltd., 2007).

The fundamentals of Spraytec spray measurement is explained in detail in [START_REF] Lefebvre | Atomization and Sprays[END_REF] and Malvern Instruments Ltd. (2007).

Malvern Spraytec, which is considered as a spatial sampling device, has been widely used for the studies on atomization and spray drift application process [START_REF] Fritz | Measuring Spray Droplet Size from Agricultural Nozzles Using Laser Diffraction[END_REF][START_REF] Kippax | Particle size characterisation of agricultural sprays using laser diffraction[END_REF][START_REF] Sirmour | Droplet Size characterization of agricultural sprays using laser diffraction[END_REF], and on the aerosol properties [START_REF] Dayal | Evaluation of different parameters that affect droplet-size distribution from nasal sprays using the Malvern Spraytec[END_REF]; Z. Y. Han et al., 2013;[START_REF] Haynes | Evaluation of the Malvern Spraytec with inhalation cell for the measurement of particle size distribution from metered dose inhalers[END_REF] thanks to its ability to produce an overall view of size distribution.

However, the results of the laser diffraction technique can be influenced by a large range of factors, including, correct alignment, sufficient density to detect, fitting method to obtain the droplet size distribution, the droplets acceleration and deceleration [START_REF] Chin | Influence of Downstream Distance on the Spray Characteristics of Pressure-Swirl Atomizers[END_REF] and the probability of overestimation of the number of small droplets, particularly, close to nozzle, due to their low velocity and thus higher concentration in the sample volume [START_REF] Sijs | Drop size measurement techniques for sprays: Comparison of image analysis, phase Doppler particle analysis, and laser diffraction[END_REF] or inversely, the increased errors due to droplet evaporation at longer distances [START_REF] Wang | Mean drop sizes from pressure-swirl nozzles[END_REF], differences in spray breakup mechanisms away from the centerline [START_REF] Dodge | Calibration of the Malvern particle sizer[END_REF][START_REF] Kooij | What Determines the Drop Size in Sprays?[END_REF]. According to [START_REF] Emekwuru | Analysis of a two-fluid sprayer and its use to develop the number size distribution moments spray model, part I: Experimental analysis[END_REF] and [START_REF] Wang | Mean drop sizes from pressure-swirl nozzles[END_REF], a distance of 50 mm is the best minimum axial position beyond which the results were consistent. [START_REF] Wang | Mean drop sizes from pressure-swirl nozzles[END_REF] estimated that the 150 mm was the best downstream distance. Therefore, the application of the device inside the wind tunnel at different distances downwind is not indeed pertinent. The nozzle body was attached to a traverse system, which allowed the spray to pass through the laser beam with travel speed of 2.5 mm.s -1 . We used the Malvern Spraytec with a 750 mm lens that can detect 2350 droplets from 2 μm to 2000 μm. The diameter of the laser beam was about 15 mm. The Spraytec was set to measure the droplet size distribution at axial distances of 50 mm, 100 mm and 150 mm from the nozzle tip, and radial distances from the spray centerline with one sampling every second assuming that the spray was almost symmetrical. At each distances from the tip nozzle, six full passages have been conducted along the diameter of the spray. The software of the Spraytec also employs the Lorentz-Mie theory and Fraunhofer approximation to account for the contribution to the angular light energy distribution of scattering through small droplets.

Hydraulic circuit

To evaluate the size of airborne droplets generated by the axial-flow hollow cone nozzle under controlled conditions in the wind tunnel, as shown in Figure 4.5, the demineralized water was delivered to 2360 the spray nozzle from a pressure vessel (Sartorius Stedim 5L, Germany). It was run at 200 kPa by feeding air compressor unit (BLACK & DECKER ® , Mauk). The pressure was controlled through a regulator (SMC, China), installed between the vessel and the compressor by a pressure hose. Besides, the pressure was recorded at 10 Hz frequency using a pressure transmitter (HD 9408T, accuracy: ± 0.25%) at the end of the outlet tube of vessel. Two valves at the two ends of outlet tube controlled the start and stop of experiments runs. Finally, the nozzle tap was connected to a brass bushing at the end of a copper pipe. The copper pipe held by a tripod clamp, then passed from the top of the working section through the pre-drilled holes, by which spray plume downwards into the working section and could be moved up or down at the desired points (see Figure 4.7).

Note that the temperature of demineralized water within the vessel was maintained at 20±1 °C by a heat mat connected with a temperature regulator.

Configuration of the PDPA experiments

The experimental set-up of PDPA is shown in Figure 4.7 with an overall view of the placements of spray nozzle along the X and Y directions. In the present case, the measurement point of PDPA was fixed in the center of cross section (Height = 0.175 m, width = 0.25 m), while the spray nozzle was situated at four axial distances upstream of the measurement point, 0.45, 0.85, 1.25 and 1.65 meters. Note that these 2410 measurement distances were selected according to the standard ISO 22856 (2008), and due to the limitations imposed by the presence of the metal frames/ rods on the roof of the working section (at the distances of 0.5, 1 and 1.5 m from the entrance). The nozzle tip was set in vertical position, at three heights of 0.1, 0.175 and 0.275 m above the working section floor. At each position, the droplet size distribution was measured at the air velocities of 1.2, 2.25, 3.3 and 5.0 m.s -1 . At least 5,000 droplets were measured at each position except in the cases where the number of the airborne spray droplets that reached to the measurement point were too limited or negligible. To ensure the stability of measurements, the acquisition time taken in these cases was at least 15 minutes. Each experiment was repeated three times. However, as previously mentioned, the temperature was kept at 20 ± 1 °C and only the data obtained under a relative humidity of about 40-50% have been retained for the analysis. 2420

Data analysis

For each configuration, several parameters were determined from the cumulative volume (number) of droplet size distribution following percentile characteristics calculated: The aerodynamic mean diameter, 𝐷 𝑎 ; volume mean diameter, 𝐷 𝑣30 ; Sauter mean diameter, 𝐷 𝑣32 ; volume (numeric) median diameter, 𝐷 𝑣50 (𝐷 𝑛50 ), also known as VMD (NMD). 𝐷 𝑣10 (𝐷 𝑛10 ), and 𝐷 𝑣90 (𝐷 𝑛90 ), which represent the droplet diameter values whereby 10% and 90% of the volume (total number) of spray are in droplets smaller than these values, respectively, were also calculated. 2430

The Sauter mean diameter is commonly used as an indicator of important processes involving the ratio of inertia to aerodynamic drag forces [START_REF] Bayvel | Liquid Atomization[END_REF]. It is defined as,

𝐷 32 = ∑ 𝐷 𝑖 3 𝐷 𝑖 2 𝑖=𝑛 𝑖=1 Eq. 4-1
where, 𝐷 𝑖 is the diameter of the ith droplet and n is the number of droplets.

Furthermore, the relative span factor (RSF) is found to be one of the dominant parameters indicating the homogeneity of the droplet size, which is expressed as,

𝑅𝑆𝐹 = 𝐷 𝑣90 -𝐷 𝑣10 𝐷 𝑣50
Eq. 4-2

In the present study, the cumulative volume of the droplet distribution was also fitted by Rosin-Rammler function (Weibull, 1951) which is widely used in spray studies [START_REF] González-Tello | A modified Nukiyama-Tanasawa distribution function and a Rosin-Rammler model for the particle-size-distribution analysis[END_REF][START_REF] Jain | Characterization of the full cone pressure swirl spray nozzles for the nuclear reactor containment spray system[END_REF] as defined,

𝐹(𝐷) = 1 -exp [-( 𝐷 𝑋 ) 𝛿 ]
Eq. 4-3

where, 𝐹(𝐷) denotes fraction of the total volume contained in droplets of diameter less than 𝐷, 𝑋 denotes the characteristic size parameter, defined as the size at which 63.2% [1 -𝑒 -1 ] of droplets are smaller than 𝑋, and 𝛿 is the droplet uniformity parameter, whose higher value represents less nonuniformity of droplet size.

Moreover, by deriving the Eq. 4-3, a function of the volume distribution 𝑓(𝐷) is yielded as follow,

𝑓(𝐷) = 𝛿 𝑋 𝛿 𝐷 𝛿-1 exp [-( 𝐷 𝑋 ) 𝛿 ]
Eq. 4-4 The results show that all droplet size distributions were similar and quasi-parabolic, symmetrical in respect to the centerline. Regardless of the axial distance from the nozzle tip, the range of the droplets size at the center of the spray was higher than towards the edge of the spray. It was expected that the number of droplets in the center of a hollow cone nozzle sprayer would be limited. But the profile obtained in this case is completely different, which can be explained by the selection of the lowest working pressure for this type of sprayer.

Results

Initial droplet size distribution

As the axial distance increased from 50 mm to 150 mm, the number of droplets inferior to 50 µm decreased sharply. Although, this increase has occurred at the edges of the spray cone. For this reason, it is seen that the trends of droplet size in term of 𝐷 𝑣50 and 𝐷 𝑣90 at the distance of 50 mm sloped in the opposite direction from these trends at a distance of 150 mm. In this case, [START_REF] He | Quantitative Analysis of Droplet Size Distribution in Plant Protection Spray Based on Machine Learning Method[END_REF] pointed out that, during the descending movement of the droplets in a spray, small droplets tend to move to the centerline due to the air entrainment effect caused by the interaction of droplets with the surrounding gaseous phase with speed difference. The larger droplet was less affected by the air entrainment effect for its large initial kinetic energy, so it had a strong diffusion ability to reach the edge of the spray. First, increasing the axial distance from 50 mm to 150 mm shifted the cumulative volume curve towards the larger droplets. The uniformity of the spray (narrower distribution) is also enhanced as it moves away from the nozzle. In fact, as the spray moves axially downward, the frequency (probability) of small droplets experiencing an effective collision force due to collisions between neighboring droplets increases. Moreover, by increasing the axial distances from the nozzle tip the other forces as aerodynamic drag and gravity become more predominant than the inertia force, as mentioned in the literature [START_REF] Santangelo | Experiments and modeling of discharge characteristics in water-mist sprays generated by pressure-swirl atomizers[END_REF][START_REF] Shrigondekar | Characterization of solid-cone simplex mist nozzles[END_REF]. Table 4.6 shows the values of parameters representing the droplet size distribution of the spray nozzle at three axial distances from nozzle. The values of RSF also confirm that the most uniformity of droplet size distribution is related to the distance of 150 mm. Furthermore, the volume diameter of almost 90 2480 percent of the droplets was less than 150 microns in all three distances. At a nozzle height of 0.1 m above the working section floor ), and at two air velocities of 1.2 and 2.25 m.s -1 , no droplet was observed at any examined distance. For the same nozzle height, increasing the air velocity from 3.3 to 5.0 m.s -1 did not change the droplet parameters. The effect of distance on droplet characteristics was not significant, apart for the larger droplets which constitute 90% of the total volume (𝐷𝑣 90 ) that decreased from 56 µm at a distance of 0.85 m downwind to 41 µm at a distance of 1.65 m downwind when the air velocity increased.

Effect of air velocity and nozzle height on droplet size distribution along the centerline of the cross-section using PDPA

Comparison of the downwind airborne droplet size spectrum

At a nozzle height of 0.175 m above the working section floor (Figure 4.10 [e-h]), and for an air velocity of 1.2 m.s -1 , no droplet size was detected by PDPA at a distance of 1.25 m from the source. Similar to the results obtained from a nozzle height of 0.1 m, the large droplets proportion, which are expected to be more affected by gravity force, decreased with distances. Although the almost constant 𝐷𝑛 90 values from a distance of 0.85m to 1.65 m confirms that the contribution of the large droplets from the total droplets is few, but this small number plays an important role in the total volume. And, at a nozzle height of 0.275 m above the working section floor ), regardless of the air velocity values, increasing the distance from 0.45 m to 1.65 m, decreased the all the airborne droplet parameters values. Comparison of the initial droplet size distribution at axial distance of 100 mm from the nozzle tip (see Table 4.6) and ) indicates that an air velocity of 5 m.s -1 caused the direct transport of all the droplets inferior to around 135 µm (𝐷𝑣 90 ) to a distance of 0.45 m, and the droplets below around 124 µm to a distance of 0.85 m.

Finally, Table 4.7 presents the Pearson correlation coefficients, which estimate the binary linear association between variables of air velocity (𝑈), distance (𝐷𝑖𝑠𝑡), and nozzle height and 𝐷𝑣 50 and 𝐷𝑣 90 of downwind airborne spray. It estimates that the effect of 𝑈 and nozzle height on both 𝐷𝑣 50 and 𝐷𝑣 90 was statistically significant at 95 % (even 99%) level of confidence, but the distance had no impact on 𝐷𝑣 50 at least up to 1.65 m from the source. Considering the results obtained by installing the nozzle at a height of 0.1 m above the floor and the lack of sufficient number of downwind airborne droplets, under the examined air velocities, the results related to this nozzle height were not presented in the following 2530

(a) (b) (c) (d) (e) (f) (g) (h) (i) (j) (k) (l)

Downwind airborne droplet size distribution

Conclusion

In this chapter, the effect of air velocities (1.2, 2.25, 3.3 and 5.0 m.s -1 ) and spray nozzle heights (0.1, 0.175 and 0.275 m) on airborne spray transport and droplet size have been evaluated using PDPA. The plume is generated by a spray nozzle producing extremely fine droplets (< 150 μm, supposed to induce to health risks).

The first purpose of this part of the study was to introduce an optimal configuration (combination of spray nozzle height, distances, and air velocities) for evaluating the performance of the active samplers 2590 (see Appendix. E), which was as follows: spray nozzle positioned at a height of 0.275 m above the floor, and applying an air velocity of 2 m.s -1 at a distance of 0.45 m, 3 m.s -1 at 0.85 m and 4.5 m.s -1 at 1.25 m (despite different airborne spray volume).

The results showed that the nozzle height is an important factor influencing airborne dispersion.

Regardless of the air velocity values and downwind distances from the nozzle tip, decreasing the nozzle height from 0.275 to 0.175 m, in other word, by bringing the nozzle to the height of the measurement points (i.e. inlet of the samplers), decreases the amount of airborne transport by 99%.

For the nozzle height of 0.175 m above the floor, except for the air velocity of 1 m.s -1 , the total droplet size distribution in airborne spray varied very little with air velocity and distances, but the factor of air velocity significantly impacts (at 95%) on the % of transported volume due to the direct transport of larger 2600 droplets in the last 10% of the distribution range (𝐷 𝑉90 ).

For the nozzle height of 0.275 m above the floor, except for the air velocity of 1 m.s -1 , the droplet size distribution and consequently airborne spray transport varied according the air velocities, but this variation decreases with distance.

Perspective of Part II

A number of experiments were performed to develop a protocol for characterizing the physical collection efficiency of the samplers when using Polystyrene Latex Spheres in the range of 0.3 to 2 μm under various concentrations and sampling times, in the laboratory of LGEI (Chapter 3). However, the studies have shown that the ultrasonic humidifier was not capable to aerosolize these beads. Therefore, we replaced a Collison Nebulizer 6 jets (CH Technologies, USA), but because an electrostatic charge neutralizer was needed and due to time constraints, it was not possible to perform these experiments as a part of the present thesis project.

For future experiments in this facility where Glass fiber filter (A/E, SKC Ltd, UK) will be used as a reference sampler next to each of the impingers, it is recommended to modify the head filter holder (twopiece plastic cassettes). It could be designed in such a way that a 70-110 mm long stainless steel probe with a cross sectional area corresponding to the inlet diameter and wall thickness of the impinger will be set to a cone (tangential design) to minimize aerodynamic blockage. It will be used by inserting the cone into the middle ring of an open-face three-piece cassette containing the glass fiber filter. Note that the exact length of the tube should be calculated according to particle size and required flow rate. In other cases, and further, in moving air, a sharp-edged, thin-walled probe will be used applying isokinetic conditions to measure the aerosol concentrations. This method can be carried out based on models described in the literature [START_REF] Görner | Laboratory Study of Selected Personal Inhalable Aerosol Samplers[END_REF][START_REF] Paik | Aspiration efficiency for thin-walled nozzles facing the wind and for very high velocity ratios[END_REF]. After each experiment, any particles/ ABF tracer that may have deposited on the inner tube wall will be rinsed and removed passing the rinsing liquid through a second Glass fiber filter or washing the head filter with demineralized water. The sample will be then measured using a fluorescence Spectrometer for ABF tracer or other techniques such as flow cytometry.

This study was limited to the impingers with flow rates of 6 and 12.5 L.min -1 that can simulate the particles entering either the mouth or nose (worst case situation is the oral breathing of 20 L.min -1 ). Future studies will focus on even higher flow rates sampling, preferably over 100 L.min -1 , using a liquid-based sampler such a BioGuardian Air Sampler that are particularly adapted for our understanding toward the dispersion of low bioaerosol concentrations from spray irrigation of TWW. In addition, application of other methods such as qPCR or epifluorescent for assessment of the viability of bioaerosols are also needed to strengthen the study.

Due to limitations caused by the dimension of wind tunnel (Chapter 4), it was not possible to executer a physical model to characterize and simulate short-range airborne transport downwind of a sprinkler irrigation such as the center pivot and linear systems in control conditions (provide a similar operating conditions: initial air velocity, operating pressure, etc.). Nevertheless, given the results from the impact of nozzle height on airborne spray transport, it is possible, as a suggestion, to reduce health risks for the farmers in proximity of radial irrigation by applying a riser height shorter than the height of personnel (1.5 m).

Furthermore, apart from the case of sprinkler irrigation, the data base created in this chapter could be useful for big data analysis of droplet transmission of pathogens such as SARS-CoV-2 (COVID-19) from the sources of cough [droplet size between 0.62 and 15.9 µm [START_REF] Dhand | Coughs and Sneezes: Their Role in Transmission of Respiratory Viral Infections, Including SARS-CoV-2[END_REF]], sneeze [ droplets between 0.5-12 µm in size [START_REF] Tang | Factors involved in the aerosol transmission of infection and control of ventilation in healthcare premises[END_REF]] or normal speaking [1 to 500 µm in diameter [START_REF] Stadnytskyi | The airborne lifetime of small speech droplets and their potential importance in SARS-CoV-2 transmission[END_REF]] in moving air.

Further study (presented in Appendix. E) will focus on characterizing the collection efficiency of two common passive samplers: PVC lines and plastic spheres, and two active samplers: AGI-4 and SKC BioSampler in wind tunnel. The aim will be to select the most appropriate sampler for collecting airborne transport under field conditions. This work will define a methodology for evaluating collection efficiency using a combination of small-scale wind tunnel and mathematical evaluations for active and passive samplers under various air velocities.

In addition, a preliminary study has been conducted in wind tunnel conditions using Particle Image Velocimetry (PIV) technique. The aims were to determine the length of disturbance of air stream upstream the sampler inlet under non-isokinetic, and to estimate the Stokes numbers of defined airborne droplets projected into the air upstream the sampler inlet using PIV experiments. However, the results were not presented in this thesis, because our perspective is to complete the experiments in a closed circuit wind tunnel in order to i) evaluate the effect of aerosol size (microspheres with diameters between 0.3 and 10 µm) on the aspiration efficiency of active samplers under non-isokinetic conditions and turbulent airflow (Re >2300) and ii) compare the results with validated empirical models for the laminar flow regime.

Part III. In-situ Experiments

Contribution to the thesis project: The objectives of the work presented in this part of thesis were :

• to characterize the distribution of the fine droplet plume emanating from a sprinkler head in the horizontal and vertical axes under different meteorological conditions and

• to model i) the short-range airborne and ii) sediment transport based on the combination of meteorological and spatial parameters. Nine experiments have been conducted outdoor in various environmental.

The main part of this chapter has been published in the journal of Irrigation Science [START_REF] Zardari | Empirical modeling of aerosol transport from sprinkler irrigation[END_REF]: Zardari, M., [START_REF] Zardari | Empirical modeling of aerosol transport from sprinkler irrigation[END_REF]. Empirical modeling of aerosol transport from sprinkler irrigation. Irrig. Sci. https://doi.org/10.1007/s00271-022-00822-x .

Transport and distribution of aerosols during sprinkler irrigation of treated wastewater-Field study Introduction

Transport of aerosols resulting from sprinkler irrigation of treated wastewater is a complex phenomenon due to the combined effect of several parameters including fine droplets (<0.15 mm) and wind speed, which are the most important vectors for pathogen transmission.

As explained in Chapter 1, numerous experimental and physical-mathematical modeling approaches based on ballistics and fluid mechanics have been proposed to predict the dispersion mechanism of droplets that have the most significant volume. However, the small droplets (<0.15 mm) were not considered thanks to their negligible volume (transported volume).

Besides, most of the previously cited field studies focused on measurements of airborne transport through active samplers or were limited in terms of heights: 1 or 2 m above the ground using passive samplers (PVC Strings). Additionally, the Gaussian steady-state models, which are widely used in atmospheric dispersion (diffusion + transport) of aerosols generated by sprinkler irrigation, were not designed to model dispersion under low wind conditions or distances inferior to 100 m from the sprinklers. Therefore, in this study, nine experiments have been conducted outdoor to characterize the short-range transport horizontally (up to 4 times the sprinkler range) and distribution of the airborne droplet/droplet nuclei plume in the crosswind, and vertically, up to 10 m above the ground, from an impact sprinkler under different meteorological conditions. The approach was based on the evaluation of a combination of methods to quantify the airborne transport (Ta) and sedimented transport (Ts) on the soil surface. Note that in this study, the expression of "sedimented transport" refers to the droplets escaping from the wetted radius under windy conditions and deposited on Petri dishes.

A second objective was to propose empirical models validated against experimental data to estimate airborne spray transport and surface sedimentation.

Material and Methods

Characteristics of sprinkler irrigation

A dual-nozzle impact sprinkler (type 5022SD-U, 2.5×1.8 mm "purple", NaanDanJain Irrigation Ltd) was used for the present study (Figure 5.1). This sprinkler is commonly used for small or irregularly shaped plots for different irrigation purposes such as seed germination, as well as for the watering of vegetable, flowers, horticultural and nursery crops.

An operating pressure of 350 kPa was set, resulting in a flow rate and radius of throw of 0.752 m3.h-1 and 10.3 m respectively. This pressure was selected to maximize the production of droplets subjected to transport and evaporation due to their relatively small size distribution. The height of nozzles above the ground level/soil level collectors was 1m. This work was conducted in accordance with the standard ISO 15886-3 (2012) in windless conditions, and the trajectory height and duration of rotation through each quadrant were measured at approximately 3.08 m above the ground and 8.61s, respectively.

Water and droplet size distribution under indoor environment conditions

Indoor experiments were carried out to characterize the water application rate, in accordance with ISO 15886-3 (ISO, 2012), as well as droplet size distribution, which are the key parameters governing the evaporation rate, wind losses and transport [START_REF] Li | Development and validation of a modified model to simulate the sprinkler water distribution[END_REF].

As shown in Figure 5.2, the impact sprinkler was mounted perpendicularly on a pipe riser with a nozzle height of 1 m above the measurement instruments and positioned inside a round shelter with enough cutout dimensions for unrestricted jet operations in the direction of the collectors so as to not to affect their measurement accuracy. A pressure gauge was installed upstream of the sprinkler, and at the height of the main nozzle to maintain operating pressure at 350 kPa. Discharge through the sprinkler was measured by means of an electromagnetic flowmeter (Endress+Hauser Progmag 33FT40, Sweden) offering an accuracy rate of ± 0.2%.

Water application rate:

A row of collectors (0.4 × 0.25 m = 0.1 m 2 surface area and at a 0.43 m height) were placed along the radius of throw to create a continuous sampling line. The center of the first collector was placed at a 0.65 m distance from the sprinkler head [Figure 5.2 (left)]. This distance is necessary to avoid the influence of the shelter. In accordance with the ISO 15886-3 (2012) measurement standard, the collected water values considered, are those superior to 0.26 mm.h -1 . The experiment duration was 180 min. The distribution was measured along a single collector radius, and repeated three times to obtain an average radial distribution curve.

Droplet size distribution:

A dual-beam Spectropluviometer, DBS [START_REF] Delahaye | A dual-beam spectropluviometer concept[END_REF] was used to measure the equivalent diameter of droplets ranging from 0.1 mm [START_REF] Akrour | Influence of spray characteristics on potential spray drift of field crop sprayers: A literature review[END_REF]) to more than 5mm, at six points within the sprinkler range: 30, 40, 60, 70, 80, and 90% of the maximum radius (Figure 5.2). These distances were chosen based on results from previous experiments conducted in our laboratory. In general, the 3 first points are best used to characterize the distributed volume, while the 3 2730 others are essential for obtaining a better understanding of phenomena related to the water application rate. At least 5,000 droplets were measured at each position. It should be mentioned that the calibration of the droplet sizer was carried out using steel balls with known diameters in the range of 0.3 to 6 mm.

From the droplet size distribution at each position, a set of parameters are calculated: number median diameter (DN50), Volume Median Diameter (Dv50), the Sauter mean diameter (D32), and the percentage of volume in droplets above the predefined thresholds. The Sauter diameter (𝐷 32 = ∑ 𝐷 𝑖 3 𝐷 𝑖 2) is commonly used as an indicator of important processes involving the ratio of inertia to aerodynamic drag forces [START_REF] Bayvel | Liquid Atomization[END_REF]. Additionally, the total droplet size distribution in volume was built by weighting the application rate with the water volume reconstituted from the sampled droplet volumes.

The cumulative volume of the total droplet distribution was also fitted by the Rosin-Rammler function 2740 (see Eq. 4-3). Campbell Scientific, accuracy: typically < ±3%) at 1 m height. All the meteorological parameters were sampled at a rate of 0.2 Hz with data being averaged every 30 seconds during a 30 min experimentation period coinciding with the initiation of sprinkler irrigation.

Then, the vapor pressure deficit (VPD) in the air was calculated using the Tetens equation [START_REF] Tetens | Uber einige meteorologische Begriffe[END_REF] and is computed by estimating the difference between the saturation vapor pressure (es) and the actual vapor pressure (ea) [START_REF] Allen | Crop Evapotranspiration-Guidelines for Computing CropWater Requirements-FAO Irrigation and Drainage Paper 56[END_REF]].

In addition, the standard deviation of wind direction was computed using the equation of [START_REF] Yamartino | A Comparison of Several "Single-Pass" Estimators of the Standard Deviation of Wind Direction[END_REF] to estimate the crosswind/horizontal transport and aerosol dispersions (detail of the calculations are available in the Appendix G. 1. 3).

Based on the measured meteorological parameters (Figure G. 1), it was found that wind speed appeared to be 31 % higher at 10 m compared to 2 m while wind direction deviates by 7.2 degrees on average between the same heights (for eight experiments). Wind direction fluctuations are on average 8% higher at 2 m than 10 m (for six over the eight measurements). As these values at 10 m are steadier, and more representative of the average movement of the air mass, we chose its related standard deviation of wind direction (σƟ) for our models. The same procedure was applied to the other meteorological parameters using the 10 m height [START_REF] Epa | EPA -Meteorological Monitoring Guidance for Regulatory Modeling Applications[END_REF] reference. At this height, the average temperature recorded was between 18.9 and 32.5 °C. The average relative humidity and solar radiation ranged from 27.2 to 96.5% and 60.5 to 740.2 w.m 2 , respectively.

Atmospheric stability characterization

Atmospheric stability is an important meteorological parameter when assessing fine droplet dispersion due to its role in suppressing or enhancing the ambient turbulence. We have evaluated different criteria to quantify this atmospheric state.

Thus, at the first step, the atmospheric stability class of the nine experiments at the 10 m reference height was determined based on the Solar radiation/delta T (SRDT) method for estimating Pasquill-Gifford stability categories [START_REF] Epa | EPA -Meteorological Monitoring Guidance for Regulatory Modeling Applications[END_REF].

As shown in Table 5.1, atmospheric stability classes ranged from A, the most unstable condition, to D describing neutral conditions. The conditions are considered similar as 6 of the 9 experiments have been conducted in stability C conditions, while one was attributed to class A, i.e., low wind conditions (1.0 m.s -1 ), another in class B with 1 m.s -1 difference between 2 and 10 m measurements, and the last is found in class D, mainly as a result of early morning measurements (with low solar radiation).

In order to use atmospheric stability in the empirical modeling, the Bulk Richardson number, as a numerical representation of each class, has been computed. It is a nondimensional stability ratio that is widely used to approximate the ratio of buoyancy to vertical wind shear, and is defined as, 𝐵 = (-𝑔 𝜕𝜃 𝑝 𝜕𝑧 ⁄ 𝜃 𝑝 . 𝑈 2 𝑧 2 ) (Eq. where, 𝜕𝜃 𝑝 𝜕𝑧 ⁄ is the gradient potential temperature (at two heights), U is the wind speed at the computation height z, which is taken as the geometric mean height between 𝑧 1 = 2 m and 𝑧 1 = 10, g is the gravitational acceleration [START_REF] Zoumakis | The dependence of the bulk Richardson number on stability in the surface layer[END_REF]. To determine the air stability as a function

Sampler layout

The measurement points were placed within a perimeter extending to four times the sprinkler range (Figure 5.4). Aerosol plume assessment was carried out using a set of 2 mm diameter PVC strings (PLASTTUB SAS, France). As mentioned earlier (see section 2.2.1), these strings continue to be the standard methodology [START_REF] Akrour | Influence of spray characteristics on potential spray drift of field crop sprayers: A literature review[END_REF][START_REF] Nuyttens | Comparison between indirect and direct spray drift assessment methods[END_REF][START_REF] Torrent | Comparison between standard and drift reducing nozzles for pesticide application in citrus: Part I. Effects on wind tunnel and field spray drift[END_REF], even though the experimental process is time-consuming and labor-intensive [START_REF] Torrent | Assessment of spray drift potential reduction for hollow-cone nozzles: Part 1. Classification using indirect methods[END_REF]. In addition, this method can be used to evaluate very large sampling areas for liquid aerosols that evaporate at a very slow rate.

To characterize aerosol plume distribution in the vertical axis, 7 m long PVC strings were stretched horizontally between a pair of 12 m pneumatic telescopic masts (SQT12-8/HP, CLARK MASTS) at a 20 m distance from the sprinkler. A pair of 6 m telescopic masts were positioned at a 30 m distance, and a pair of 10 m pneumatic telescopic masts (QTX15-9/HP 37212, CLARK MASTS) at a 40 m distance from the sprinkler. Therefore, depending on the mast system used, air samples were taken from 1 to 10 m above the ground, in 1m increments, at the distances of 20 and 40 m from the sprinkler head, and up to 6 m above the ground at a distance of 30 m from the sprinkler.

The PVC strings were positioned as perpendicularly as possible to the estimated mean wind direction at the beginning of the experiments. Due to the fluctuations in wind direction during the experiments, complementary PVC strings were also placed on both sides of the mean wind direction at an angle of 20 degrees, along the previous locations. The latter were positioned at 1 and 2 m heights above ground.

Note that the previous studies, in particular, in wind tunnel experiments [START_REF] Cornacchia | Assessment of Airborne Transport of Potential Contaminants in a Wind Tunnel[END_REF] confirmed that using consecutive PVC strings (with 2mm diameter) at sufficient intervals have no obstacle effect on wind direction fluctuation.

In the same longitudinal and angular directions, the volume of the sediment/ground deposition (sedimented transport, Ts) was quantified using Petri dishes (6361.72 mm 2 ) installed horizontally 0.25 m above ground at 2 m intervals from 14 (1.5 times) to 40 m (4 times) distances from the sprinkler head.

Our goal was to evaluate sedimented transport beyond the wetted perimeter over the field including locations 20 to 40 m downwind of the sprinkler head [distances recommended by ANSES ( 2012)].

Additionally, a 120-degree arc of PVC strings (for tests 1 and 5-9), placed 20 m downwind from the source, and at the heights of 1 and 2 m from the ground allowed us to quantify the aerosol plume distribution in the crosswind. To estimate the sedimented transport profile in the crosswind direction, seven Petri dishes were positioned on the arc at 20 degree intervals.

Spray volume estimation

After each experiment, samplers were retrieved immediately after drying. Petri dish lids were closed and the PVC strings were placed in bottles of 50 mL. The collectors were stored in a dark bag until collection was completed (about 2 hours), and then directly placed in the laboratory refrigerator.

Once in the laboratory, quantification of the ABF concentration (µg.L -1 ) was performed for all the 2870 samplers using a fluorescence Spectrometer (Perkin Elmer LS 45, Waltham, USA) with the optimal range of accuracy between 5 and 450 μg.L -1 of the ABF concentration. Airborne transport (Ta) based on PVC string samples and sedimented transport (Ts) using Petri dishes, expressed in mL.m -2 .h -1 , were calculated as follows:

𝑇 𝑎 𝑜𝑟 𝑇 𝑠 = 𝐶 𝑠 . 𝑉 𝑑𝑖𝑙 𝐶 0 . 𝐴. 𝑡 (Eq. where, 𝐶 𝑠 and 𝐶 0 are the fluorescent concentration of each sample and the tank mixture, respectively, in μg ABF L -1 ; 𝑉 𝑑𝑖𝑙 is the sample dilution volume (20 mL demineralized water) in mL, t is sampling duration in hours. In addition, A is the effective sample area in m 2 (for PVC strings and Petri dishes), considering 80% of the collection efficiency for PVC strings.

This value was applied according to the results of theoretical and experimental studies carried out in wind tunnels investigating the effect of various parameters such as Stokes number, Reynolds of droplets, 2880 droplet diameter and the relative velocity between the droplets and the air on the collection efficiency of 2 mm diameter cylindrical passive samplers [START_REF] Aylor | Modeling spore dispersal in a barley crop[END_REF]Gil et al. 2005[START_REF] Gil | Atmospheric loss of pesticides above an artificial vineyard during air-assisted spraying[END_REF][START_REF] Parkin | Measurements and computational fluid dynamic simulations of the capture of drops by spray drift samplers[END_REF][START_REF] Walklate | A simulation study of pesticide drift from an air-assisted orchard sprayer[END_REF]).

It should be mentioned that the concentrations below the detection limit of the fluorescence Spectrometer (< 5μg.L -1 or <0.0019-0.0025 mL.m -2 .h -1 ) have been considered as zero.

Statistical analysis

At the first step, bivariate correlations between the different variables (meteorological data, deposits and airborne sampling from PVC lines, distances from the emission source) were analyzed using the Pearson correlation coefficient methods with the R statistical package, at a significance level (p-values) of 0.05. Where appropriate, the Pearson method gives the significance level of inter-correlation between input variables used to calibrate the empirical models.

The results of bivariate correlations between meteorological variables measured from eight experiments highlighted that the meteorological parameters can be considered independent (see

supplementary Figure G. 2 and Table G. 3).
Two empirical models (power and exponential) from a set of 80 combinations were retained to adjust airborne and sedimented transport measurements based on the following criteria:

• Level of correlation between the observed and simulated distributions of aerosol plume in the horizontal axis and in the vertical direction at different distances;

• Previous empirical models proposed by [START_REF] Molle | Experimental Approach to Assessing Aerosol Dispersion of Treated Wastewater Distributed via Sprinkler Irrigation[END_REF] and [START_REF] Tomas | Transport Modeling in Sprinkler Irrigation[END_REF], and the Gaussian dispersion models;

• The previous models developed for estimating downwind pesticide spray drift as a function of distance (Dist): German drift Model (%𝑑𝑟𝑖𝑓𝑡 = 𝑎𝐷𝑖𝑠𝑡 -𝑏 ) and Dutch drift Model (%𝑑𝑟𝑖𝑓𝑡 = 𝑎 0 𝑒 -(𝐷𝑖𝑠𝑡×𝑎 1 ) + 𝑏 0 𝑒 -(𝐷𝑖𝑠𝑡×𝑏 1 ) ).

For each combination, the observed and simulated data are fitted through a recursive loop until the residues between the observed and the calculated value are lower than 1.5 times the standard deviation of the observed values. At each iteration, Cook's distance is calculated for each observation, and if it is found to be greater than three times the mean Cook's distance the observation is considered as an outlier and omitted. Such a procedure is necessary as some of the measurements may be accidentally "polluted" by dye. The estimated coefficients were validated through the evaluation of t-statistic (ratio of Estimate to Standard error of the coefficient) and the level of significance (p-value that shall be <0.05).

To select the most appropriate models we have progressively added each parameter (1. spatial parameters, 2. Standard deviation of wind direction, 3. Wind speed or Bulk Richardson number, 4. Temperature, 5. Relative humidity, 6. Solar radiation, and 7. Vapor pressure deficit) until the inclusion of a new parameter, or combination of the parameters, did not improve the coefficient of determination (R 2 ) while retaining a maximum number of observations. In addition, the Root Mean Square Error (RMSE) was calculated. It has the advantage of expressing the prediction error in the units as the variable, and therefore it provides more information about model accuracy [START_REF] Legates | Evaluating the use of "goodness-of-fit" Measures in hydrologic and hydroclimatic model validation[END_REF].

Thus, for each of the two principal models (power and exponential) described in section 5.3.5, twenty-one models, combining the parameters were tested, of which ten of the best at predicting airborne transport, and nine models for sedimented transport, are highlighted in green and are available in the supplementary Table G. 5 andTable G. This demonstrates that droplet volumes which were supposed to aerosolize, decrease in line with the increase of the radius of throw [START_REF] Yan | Model of droplet dynamics and evaporation for sprinkler irrigation[END_REF]. [START_REF] Montero | Sprinkler droplet size distribution measured with an optical spectropluviometer[END_REF] stated that the droplet diameter formed in the jet break-up process is inversely proportional to air/water relative velocity. This process produces small droplets that escape from the water jet during its breakup. Conversely, large droplets are produced from the core of the jet where the relative water air velocity is lowest. As shown in Figure 5.5b, the Rosin-Ramler model fits very well the distribution of the cumulated droplet volume.

Results and discussions

Water distribution radial curve and Distribution of Droplet Sizes in Laboratory Conditions

Nevertheless, the model tends to overestimate the proportion of droplets smaller than 0.5 mm (Table 5.2), which is in agreement with previous studies [START_REF] Kincaid | Drop Size Distributions for Irrigation Sprinklers[END_REF] and with the capacities of the droplet sensor that tends to underestimate small (<0.4 mm) droplets. Therefore, using the Rosin-Ramler approach is appropriate for the present study as it results in an overestimation of the droplets that present the highest pollutant and pathogen dissemination risks.

Moreover, as discussed by [START_REF] Molle | Experimental Approach to Assessing Aerosol Dispersion of Treated Wastewater Distributed via Sprinkler Irrigation[END_REF], the estimation of droplet size distribution is measured in calm/laboratory conditions while in field experiments the wind amplifies spray atomization (Richards and Wetherhead 1983). Effect of wind speed on aerosol transport 2970

The effect of wind speed on transport was investigated based on four of the experiments carried out in wind speeds ranging between 0.16 and 3.6 m.s -1 . The values ranged respectively from zero (<0.0025 mL.m -2 .h -1 ) to 20.8 mL.m -2 .h -1 , for airborne transport and zero to 2.09 mL.m -2 .h -1 for sedimented transport. The higher the wind speed, the higher the transport. The samples taken at a 1m height contain the highest aerosol transport concentrations, while the maximum deposit is recorded at 18m from the sprinkler.

Figure 5.7 illustrates airborne transport deposits measured at the height of 2 m above the ground as a function of wind speed, at locations 20 to 40 m downwind from the sprinkler head following the wind axis. For a given distance, airborne transport concentration rose with increased wind speed, particularly under the wind speed of 3.6 m.s -1 . There is a sharp increase in transport values for 3.6 m.s -1 probably 2980 due to increased airflow turbulence. Whatever the wind speed, transport values decreases with distance.

Airborne transport concentrations obtained at the distance of 20 m (twice the sprinkler range) were measurable even for wind speed of 1.1 m.s -1 (2.35 mL.m -2 .h -1 ). At 30 and 40 m from the sprinkler head, this quantity falls below 0.2 and 0.1 mL.m -2 .h -1 , respectively. In order to simultaneously compare the effect of wind speed on aerosol plume distribution in a vertical and downwind direction, the airborne and sedimented transported volumes were normalized by dividing them by their respective highest values (20.8 for Figure 5.8a to 5.8c and 2.9 mL.m -2 .h -1 for 2990 Figure 5.8 d). Note that the concentrations obtained from the Petri dishes positioned at 14 and 16 m from the sprinkler head were very high. Thus, these points were not considered in downwind sedimented transport (Figure 5.8d). At a wind speed of 0.16 m.s -1 , no airborne or ground deposition was observed at any examined distance. At a distance of 20 m, for the three windy conditions (1.1, 2.4 and 3.6 m.s -1 ), the quantities of measured airborne transport were higher than 1 mL.m -2 .h -1 up to the height of 6 m above the ground (Figure 5.8a). At a distance of 40 m from the sprinkler head, and at a height of 6 m above the ground, for the same windy conditions the airborne transport falls below 0.05, 0.1 and 0.71 mL.m - 2 .h -1 , respectively. Figure 5.8d shows that the sedimented transport values decrease downwind according to distance for the three wind speeds, which illustrates the flattening and widening of the aerosol/droplet plume distribution as the downwind distance increases [START_REF] Turner | Workbook of Atmospheric Dispersion Estimates: An Introduction to Dispersion Modeling[END_REF] [see Figure G. 4]. 3000

As shown in Figure 5.8a, for unstable conditions (B<0, Table 5.1), the vertical dispersion of aerosols produced by the sprinkler, installed at a height of 1m above the ground, decreases with wind speed (see Figure G. 5). This agrees with the observations of [START_REF] Gil | Atmospheric loss of pesticides above an artificial vineyard during air-assisted spraying[END_REF], who concluded that in low wind speed conditions, vertical drift of very fine and fine droplets is greater than the horizontal one. This induces that the measurement method is not significantly affected by possible/potential evaporation, which is the cause of a significant proportion of dry aerosol production that cannot be collected by this measurement method. Exp.2: 32.5 °C; red dots: Exp.9: 22.9 °C)

According to the results of vertical distribution of aerosol plume, the Gaussian models that widely have been used for long-range (>100 m) dispersion of aerosols from wastewater treatment plant [START_REF] Fileni | Analysis of air polluant emissions in a wastewater treatment plant using dispersion models[END_REF][START_REF] Pasalari | Assessment of airborne enteric viruses emitted from wastewater treatment plant: Atmospheric dispersion model, quantitative microbial risk assessment, disease burden[END_REF] and prediction of pesticide spray drift [START_REF] Hong | Fluid Dynamic Approaches for Prediction of Spray Drift from Ground Pesticide Applications: A Review[END_REF] could not be reliable for estimating short-range airborne transport.

Empirical modeling

Based on the trends obtained from the aerosol plume distribution in the vertical and horizontal directions, and the sedimented transport at different distances from the sprinkler, presented in the previous sections, the two following principal empirical models were selected as the most effective (among 40 others) in reproducing the experimental results. Note that these models combine the most 3040 significant parameters presented below.

-M1: Power-crosswind Gaussian

𝑇 𝑎/𝑠 = 𝐷𝑖𝑠𝑡 𝑑𝑖𝑠𝑡 × 𝐻 ℎ × 𝑈 𝑢 × 𝑇𝑒𝑚𝑝 𝑡𝑒𝑚𝑝 × 𝑅𝐻 𝑟ℎ × 𝑉𝑃𝐷 𝑣𝑝𝑑 × 𝑅𝑠 𝑟𝑠 × 𝑎𝑏𝑠(𝐵) 𝑏 × 𝑒 {-0.5×( ∆𝜃 𝜎 𝜃 ) 2 }
(Eq. 5-3) where, the coefficients/exponents: 𝑑𝑖𝑠𝑡 (distance), ℎ (height, for airborne transport model), 𝑢 (wind speed), 𝑡𝑒𝑚𝑝 (temperature), 𝑟𝑠 (solar radiation), 𝑟ℎ (relative humidity) and 𝑏 (bulk Richardson number) are associated to each input variable, and were estimated using the non-linear regression method supplied by Matlab ® R2020a.

The models that meet all the validation criteria can be found in the supplementary information section, in Table G. 5-6 (highlighted in green). Model validity was based on the significance level of each coefficient (<0.001 to 0.01), the high number of total valid observation, the coefficient of determination (R 2 > 0.8) and the smallest RMSE as possible (see Appendix G. 3. 1).

In these analyses, the effects of spatial (distance, height, angle) and meteorological parameters (wind speed and standard deviation of wind direction) were always considered. The other parameters were only considered if they improved the level of accuracy of the final model.

Airborne transport assessment

Sedimented transport

As previously mentioned, the relative humidity has minor or no impact on sedimented transport (Table G. 7). Consequently, in the final selected model, relative humidity was not considered. The formulas of the final models are:

𝑀 1 . 𝑇 𝑠 = 𝐷𝑖𝑠𝑡 -3.06 × 𝑈 (Eq. 5-8) Contrary to airborne transport, except for the first two distances, i.e., 16 and 18 m, there is no significant difference between Exp.3 and 5. Therefore, this result confirms that relative humidity had little or noimpact on the sedimented transport in our experimental conditions. In fact, sedimented transport values were obtained from the collection of the larger droplets that were transported by the wind out of the wetted area and that fell on the ground. Since the settling time of the larger droplets (>100 μm) is very short (~0.5 s) and the time of evaporation rises approximately by 𝐷 𝑖 2 (Ranz andMarshall, 1952b, 1952a), relative humidity effects on sedimented transport appears to be statistically lower than for airborne transport (resulted from small droplets). Thus, in the experimental conditions we faced, wind was the dominant parameter governing sedimented transport. However, to explain the role of relative humidity, more experiments would need to be performed; preferably in controlled conditions that would allow the

Interpretation of results to estimate the potential presence of Escherichia coli bacteria

In this study, we were able to determine the size of ABF colloidal particles in the solvent, which were around 1 µm in diameter and similar to the size of bacteria such as E.coli (or other pathogens with the same size) for aerodynamic behavior. Obviously, if we want to study the risk of infection by inhalation, we must take into account the infectivity of the pathogen, which we cannot address here. As explained in the introduction, our experiments can be used to offer useful information on the fraction of TWW spray that becomes aerosolized and the aerodynamic transport, which is a first step in the quantification of the microbial risk linked to TWW reuse. Thus, given the results of the experiments in the sub section 5.3.3, regardless of the viability and dieoff, or the inactivation rate of the microorganisms in the aerosol form, the following conditions are taken as an example: wind speed 2.4 m.s -1 , 1 m above the ground, and 40 m from the sprinkler head (four times the sprinkler range). In addition, the sprinkler is assumed to spray treated wastewater with a concentration of 250 CFU/100 mL of Escherichia coli bacteria under the average wind direction for a 60 min exposure. Note that, according to French decree (ANSES 2012), this treated wastewater can be used for the irrigation of food crops that are not destined for industrial food processing. Thus, the potential of presence of Escherichia coli bacteria could be estimated at 1 CFU.m -2 .h -1 . This value reaches about 7 CFU.m -2 .h -1 at wind speed of 3.6 m.s -1 .

Likewise, the models developed in this study can be used to give a primary estimation on the potential presence of contaminants in aerosols resulting from the sprinkler irrigation of treated waste water, under different meteorological and spatial conditions. Figure 5.15 shows the simulation of downwind E.coli transport as a function of wind speed using model 1 (M1), under constant meteorological conditions (at normal temperature (20°C) and Pressure (1 atm), NTP, RH: 40%, σθ: 10 degrees and under the average wind direction), at a height of 1.5 m above the ground for simulating aspiration from the human breathing zone (Figure 5.15a), and on the ground surface (Figure 5.15b). The results showed that from the wind speeds of 1.4, 2.1 and 2.7 m.s -1 , it is possible to detect at least one E.coli bacterium (1 CFU.m -2 . h -1 ) at the distances of 20, 30 and 40 m from the sprinkler head, respectively. Up to 0.25 m above the ground, where there is the risk of dermal/skin contact or the deposit of the E.coli on plants, these wind speed thresholds reach 2.6, 4, and 5.4 m.s -1 , at the same distances, respectively.

Overall, the most suitable irrigation management strategy when using treated wastewater would be to favor low winds, which will minimize dispersion, i.e. a wind speed < 2 m.s -1 (up to a maximum temperature of 40 °C, in summer), when the buffer zone or distance between the sprinkler head and residential areas (for example) is 40 m. Moreover, when assessing pathogen dispersion risks linked to sprinkler irrigation, pathogen reaerosolization from the soil or the plants, and their resulting multiplication in the air should also be considered. Therefore, in addition to monitoring meteorological and spatial parameters, other effective management controls such as tertiary storage and/or advanced treatment wastewater (ultrafiltration, etc.) prior to sprinkler irrigation could help reduce microorganism concentrations [START_REF] O'hara | Reducing bioaerosol dispersion from wastewater treatment and its land application: a review and analysis[END_REF]. The results have also shown that the crosswind distribution of aerosol plume is Gaussian and its dispersion rate is highly dependent on the standard deviation of the wind direction and wind speed. The distribution of the aerosol plume in the vertical axis with regards to the sprinkler head presents the highest airborne transport values at heights of 1 to 2 m above the ground except for one case, i.e., Exp.

2: U = 2.7 m.s -1 , Temp = 32.5°C, RH = 46.1%, RS = 279.1 w.m -2 and σθ = 35.7 degrees. The ranking analysis of parameter impacts on the transport showed that wind speed, followed by temperature, play a major role in droplet transport. Regarding sedimented transport, it was observed that the temperature effect is much lower than for airborne transport.

From these analyses, two semi-empirical models (M1: power and M2: exponential) were found to be the fittest for the estimation of both airborne and sedimented transport when considering both spatial variables (distance, height, angle from the average wind direction) and meteorological (wind speed, standard deviation of wind direction, temperature and relative humidity only for airborne transport) parameters. Both models were found to predict transport well using a coefficient of determination (R 2 ) higher than 0.9. However, at long distances (>30 m from the sprinkler head), model 1 performance exceeds that of 2.

These empirical models have been developed for an impact sprinkler at a single pressure, which results in a specific droplet distribution. However, their application to another dataset acquired with a different configuration has shown their genericity as they gave satisfactory results.

To complete this study, supplementary measurements using different sprinkler types are in progress:

experiments using a big gun are currently being conducted by the same team in order to evaluate the genericity of the models.

The selected tracer (ABF) can be considered simulate transport of droplet nuclei containing particles around 1µm in diameter, and similar to the size of bacteria such as E.coli. Thus, the proposed approach can be used to assess pathogen exposure risks linked to sprinkler irrigation using TWW.

General conclusion

Transport of aerosols resulting from sprinkler irrigation of treated wastewater (TWW) is a complex phenomenon due to the combined effect of various parameters including fine droplets (<0.15 mm) and wind speed, which are the most important vectors for pathogen transmission. Our literature review has shown very few bioaerosols measurements from TWW reuse for spray irrigation. Various samplers exist but there is no consensus on the most efficient for this or that pathogen, with a standardized protocol.

Atmospheric models have been used to simulate bioaerosol transport. Most of them are based on gaussian approaches. In the case of aerosols generated by irrigation sprinklers such models are not adapted to low wind conditions or distances inferior to 100 m from the wetted radius. 3230

In our work, we have tried to address the three following questions:

-What are the factors and conditions affecting the dispersion of contaminants during sprinkler irrigation?

-What techniques and methods should be used for the detection and sampling of (bio) aerosols generated by sprinkler irrigation? -How to model the transport and dispersion of aerosols from spray irrigation of the treated wastewater?

Different experiments were conducted to address these questions, done in both controlled and natural conditions.

First a brief review of air sampling methods has been done (Chapter 2). Various samplers were used 3240 to quantify bioaerosols. Aerosols samplers are generally classified into two categories: passive (deposit on a surface without an air pump or external power) and active (involving a mechanical component such as vacuum pump) samplers. Passive and active samplers are not comparable. Other techniques exist such as optical sampling which quantify aerodynamic particle size, scanning mobility particle counter, and are capable of detecting also total count of viable particles or viable + non-viable in real time as well as LIDAR technique. Active samplers with pumps are the most applied in various environmental conditions. The overall sampling efficiency of an active sampler is product of the aspiration (𝜂 𝑎𝑠𝑝 ), transmission (𝜂 𝑇 ), collection (𝜂 𝑐 )/retention (𝜂 𝑅𝐸𝑇 ), recovery efficiency (𝜂 𝑅𝑒𝑐 ), and biological efficiency (𝜂 𝐵𝑖𝑜 , for microorganisms). The review has shown that many authors agree on the total lack of standardization for bioaerosol sampling methods and sample treatment. The use of higher sampling flow rate results in higher power consumption, which would shorten portable samplers' battery life. Higher flow rate also means more intense desiccation of the collect medium which may lead to a lower recovery of the sensitive organisms. The accurate quantification of airborne volume droplets containing pathogens is of critical importance to estimate the health risks due to sprinkler irrigation with treated wastewater. There are not a lot of studies comparing the most common samplers in the same conditions.

The experiments of the part II were carried out under controlled conditions in order to assess the most suitable method for aerosol monitoring in the case of sprinkler irrigation (Chapter 3 ), and to evaluate the factors affecting the spread of small droplets (<150 µm) from sprinklers (Chapter 4). In Chapter 3, the experiments were conducted in laboratory of Engineering of the Industrial Environment and Industrial and Natural Risks (LGEI in Ales), which has developed, a sampling chamber of 1.234 × 0.63 × 0.8 m 3 dimension presenting a laminar flow with a range of air velocity (𝑈) of 0-0.09 m.s -1 . We have worked with a windspeed U = 0.09 m.s -1 (and dry airflow Q_DRY=400 L.min -1 ). A fluorescent ABF was used a tracer to assess the physical collection efficiency of active samplers at different sampling times (5-60 min). A comparison of the physical efficiency of impingers revealed that the efficiency of AGI-4 was almost 18.5% higher than the SKC Biosample in the range of 5-30 min.

To respond the third objective, the experiments were conducted with a non pathogenic E. coli strain to evaluate the effect of sampling time on culturability of airborne E.coli cells sampled by impingers. The results showed that increasing sampling time from 5 min to 15 min, reduced the culturability of E.coli in both AGI-4 and SKC by a factor of 37% and 14%, respectively. It can therefore be inferred that culturability of AGI-4 is equal to culturability of SKC multiplied by 0.93. Also, it was showed that the BioTrak ® can count total particles in real-time, which can be useful as a rapid assessment technique, especially, for quantifying the potential presence of contaminants, but its accuracy in measuring the viable particles was lower than accuracy obtained with the impingers, particularly in low relative humidity conditions. In addition, it physically detects the presence of viable bacteria but does not identify its type.

In Chapter 4, the experiments have been conducted in an open circuit wind tunnel with a working section of 0.5 m by 0.35 m and 2 m long (laboratory of Risk Sciences, IMT Mines Ales). The wind tunnel was controlled by a housing of electronic variable speed drive and the wind speed can be adjusted up to 15 ms -1 . The air temperature was maintained at 20±1°C and only the data obtained under a relative humidity of about 40-50% have been retained for the analysis. In this study, the effect of air velocity and nozzle height on airborne droplet size distribution was investigated using PDPA. The results showed that the nozzle height is an important factor influencing airborne dispersion. Regardless of the distance from the nozzle tip, by decreasing a nozzle height from 0.275 to 0.1 m above the working section floor, 90% of the overall volume of droplets being less than or equal to 138 µm (𝐷 𝑉90 = 138 µm ) for 0.275 m reached 58 µm (𝐷 𝑉90 = 58 µm) for 0.1 m. The results also showed that air velocity plays an important role in the direct transport of small droplets, at short-range distance. At a distance of 1.65 m and a nozzle height of 0.27m above the floor, from air velocity of 1.2 to 5.0 m.s -1 , 𝐷 𝑉90 increased from 34 µm to 84 µm, respectively.

In the last part of thesis (Chapter 5), nine field experiments were conducted to quantify the airborne and sedimented transport of the droplets released from a sprinkler irrigation under different meteorological conditions. An accurate characterization of the fraction of spray flow spread by a sprinkler (usually applied over various crops) was carried out thanks to our tried and tested experimental protocol. The quantification of the aerosolized component was analyzed over a horizontal plane (up to 40 m from the emission source) and vertically (up to 10 m above the ground) in a downwind direction, beyond the irrigated area (i.e. wetted radius). The measurements conducted in laboratory conditions have shown that droplets smaller than 0.15 mm (subjected to aerosolization) represented 0.15% of the total irrigated volume.

The results have also shown that the crosswind distribution of aerosol plume is Gaussian and its dispersion rate is highly dependent on the standard deviation of the wind direction and wind speed. The distribution of the aerosol plume in the vertical axis with regards to the sprinkler head presents the highest airborne transport values at heights of 1 to 2 m above the ground except for one case, i.e., Exp. 2: u = 2.7 m.s -1 , Temp = 32.5°C, RH = 46.1%, RS = 279.1 w.m -2 and σθ = 35.7 degrees. The ranking analysis of parameter impacts on the transport showed that wind speed, followed by temperature, play a major role in droplet transport. Regarding sedimented transport, it was observed that the temperature effect is much lower than for airborne transport.

From these analyses, two semi-empirical models (M1: power and M2: exponential) were found to be the fittest for the estimation of both airborne and sediment transport when considering both spatial variables (distance, height, angle from the average wind direction) and meteorological (wind speed, standard 3320 deviation of wind direction, temperature and relative humidity only for airborne transport) parameters.

Both models were found to predict transport well using a coefficient of determination (R 2 ) higher than 0.9.

However, at long distances (>30 m from the sprinkler head), model 1 performance exceeds that of 2. These empirical models have been developed for an impact sprinkler at a single pressure, which results in a specific droplet distribution. However, their application to another dataset acquired with a different configuration has shown their genericity as they gave satisfactory results.

Perspective

To complete this study, supplementary measurements using different sprinkler types are in progress: experiments using a big gun are currently being conducted by the same team in order to evaluate the genericity of the models.

For future experiments where Glass fiber filter (A/E, SKC Ltd, UK) will be used as a reference sampler next to each of the impingers, it is recommended to modify the head filter holder (two-piece plastic cassettes). It could be designed in such a way that a 70-110 mm long stainless steel probe with a cross sectional area corresponding to the inlet diameter and wall thickness of the impinger will be set to a cone (tangential design) to minimize aerodynamic blockage. It will be used by inserting the cone into the middle ring of an open-face three-piece cassette containing the glass fiber filter. Note that the exact length of the tube should be calculated according to particle size and required flow rate. In other cases, and further, in moving air, a sharp-edged, thin-walled probe will be isokinetically applied to measure the aerosol concentrations. This method can be carried out based on models described in the literature [START_REF] Görner | Laboratory Study of Selected Personal Inhalable Aerosol Samplers[END_REF][START_REF] Paik | Aspiration efficiency for thin-walled nozzles facing the wind and for very high velocity ratios[END_REF]. After each experiment, any particles/ ABF tracer that may have deposited on the inner tube wall will be removed through a second Glass fiber filter or washing the head filter with demineralized water. The sample will be then measured using a fluorescence Spectrometer for ABF tracer or other techniques such as flow cytometry.

Future studies will focus on even higher sampling flow rate, preferably over 100 L.min -1 , using the liquid-based samplers such a BioGuardian Air Sampler that are particularly needed for our understanding toward the dispersion of low bioaerosol concentrations from spray irrigation of TWW.

In addition, application of other methods such as qPCR or epifluorescent for assessment of the viability of bioaerosols are also needed to strengthen the study.

Appendix. A. Fundamentals of the behavior of aerosol particles/droplets

Droplet/particle motion is governed by physical forces including (i) drag which usually tends to slow a particle from the bulk air velocity and changes with droplet size and shape; (ii) gravity, which usually accelerates a particle to its terminal settling velocity; (iii) electrostatic forces, which attracts particles to surfaces ; and (iv) Brownian, random motion of small particle (𝐷 𝑝 < 0.1µm) [START_REF] Baron | Aerosol Measurement: Principles, Techniques, and Applications[END_REF].

𝑚 𝑝 𝑑𝑈 𝑝 𝑑𝑡 = 3𝜋𝜇 𝑎 𝐷 𝑝 𝐶 𝑐 (𝑈 𝑎 -𝑈 𝑝 ) + ∑ 𝐹𝑒𝑖 𝑖 Eq. A-1
where 𝐹𝑒𝑖 denotes the external force i (such as gravity and electrostatic force), and the other parameters are explained in the following.

A. 1. Drag force acting on aerosols in motion

The dynamical behavior of aerosols in motion is affected by the inertial force of aerosols as defined by the Newton Resistance Law and the viscous or drag force of the atmosphere as defined by Stokes Law 4470 [START_REF] Lighthart | Atmospheric microbial aerosols : theory and applications[END_REF].

The general Reynolds number, Re, defined in Eq. A-2 is a dimensionless number that gives a measure of the ratio of inertial forces to viscous forces and consequently determines the relative importance of these two types of forces for given flow conditions in aerosol systems.

𝑅𝑒 =

𝜌 𝑎 𝑈𝑙 𝜇 𝑎 Eq. A-2

where 𝑈 is characteristic air velocity, 𝜇 𝑎 is dynamic viscosity of the air, 𝜌 𝑎 is air density, and 𝑙 is characteristic length-scale (dimension). Note that in the cases of the flow that passes, for instance, through the inlet tube of active samplers, the fibrous filter medium and working section of wind tunnel is of interest, then the cross-sectional diameter for circular tubes or hydraulic diameter (𝐷 𝐻 = 4𝐴 𝑃 ) for non-circular sections is applied as characteristic length-scale. If the dimensionless Reynolds number varies from 0 to 2000 then the flow is laminar; the Reynolds number is bigger than 4000 then the flow is turbulent and if 4480 the Reynolds number varies between 2000 < 𝑅𝑒 < 4000, then there is a transitional situation for the air flow.

In the case of the air flowing around a droplet/particle (aerosol), the particle Reynolds number, 𝑅 𝑃 , is demonstrated by the following equation,

𝑅𝑒 𝑝 = 𝐷 𝑝 𝑈 ∞ 𝜌 𝑎 𝜇 𝑎 Eq. A-3
where 𝐷 𝑝 is the particle diameter, and 𝑈 ∞ is the local relative velocity between gas (air) and aerosol, defined as |𝑈 ⃗ ⃗⃗ 𝑝 -𝑈 ⃗ ⃗⃗ 𝑎 |. It is evident that if the particle and the air move in the same direction, the magnitude of 𝑈 becomes 𝑈 ̅ 𝑝 -𝑈 𝑎 . If the particle and the air move in opposite directions, magnitude of 𝑈 ∞ becomes 𝑈 ̅ 𝑝 + 𝑈 𝑎 , and for the rests it would be assumed that |𝑈 ⃗ ⃗⃗ 𝑎 | = 0 m.s -1 .

Laminar flow at very small Reynolds number ≪ 1, which is the case with most aerosol is called creeping flow. As an example, for very small organism, e.g., bacteria, the particle Reynolds number is very 4490 small, typically in the range of 1 × 10 -6 .

A. 1. 1. Laminar flow: Stokes' law

When viscous forces dominate inertial forces, 𝑅𝑒 𝑝 ≪ 1, and the type of air flow is laminar or creeping flow, the drag force predicted by Stokes' law is defined by :

𝐹 𝑑𝑟𝑎𝑔,𝑆𝑡𝑘 = 3𝜋𝜇 𝑎 𝐷 𝑝 U ∞ 𝐶 𝑐 Eq. A-4
where 𝐷 𝑝 is the spherical particle diameter moving with relative particle velocity, 𝑈 ∞ . For particles of other shapes, the volume equivalent diameter, 𝐷 𝑣 , and a dynamic shape factor, ꭓ, are introduced to Eq. A- et al., 2011).

4 [𝐹 𝑑𝑟𝑎𝑔,𝑆𝑡𝑘 = 3𝜋𝜇 𝑎 𝐷 𝑣 ꭓ 𝑈 ∞ ] (Kulkarni
Additionally, 𝐶 𝑐 is a correction factor, referred to as the Cunningham slip correction factor, given by [START_REF] Davies | Definitive equations for the fluid resistance of spheres[END_REF] :

𝐶 𝑐 = 1 + 𝐾𝑛 [1.257 + 0.4𝑒𝑥𝑝 ( -1.1 𝐾𝑛 )] Eq. A-5
where 𝐾𝑛 = 2γ/𝐷 𝑝 is the Knudsen number, a dimensionless number defined as the ratio of the mean 4500 free path, γ, to particle radius, 𝐷 𝑝 /2. In fact, the Stoke's law is based on the solution of equations of continuum regime and therefore is applicable to the limit 𝐾𝑛 ≪ 1. In non-continuum effects, the drag force exerted by the air is smaller than predicted by Stokes law that can be described as a result of slip on the particle surface. The slip correction factor is generally neglected for particles exceeding 10 µm in diameter, as the correction is less than 2% [START_REF] Seinfeld | Atmospheric chemistry and physics : from air pollution to climate change[END_REF].

A. 1. 2. Drag coefficient

Beyond the laminar flow and for particle Reynolds number greater than 0.1, Stokes' law does not give a good description of the drag force and a correction for the inertial force is needed. A general equation for the drag force, which modifies Stokes drag, is:

𝐹 𝑑𝑟𝑎𝑔 = 𝐶 𝐷 𝜋𝜌 𝑎 𝐷 𝑝 2 𝑈 ∞ 2 8 Eq. A-6
where 𝐶 𝐷 is drag force coefficient for a spherical particle. Various empirical models for calculation of 4510 drag coefficient calculation have been reported in different fields of study [START_REF] Yan | Model of droplet dynamics and evaporation for sprinkler irrigation[END_REF]. The expression for the drag coefficient, according to [START_REF] Shiller | A Drag Coefficient Correlation[END_REF] cited by [START_REF] Lefebvre | Atomization and Sprays[END_REF]

A. 2. Gravitational Settling

A settling particle quickly reaches a constant velocity (known as the terminal velocity) as a result of balancing between the gravitational force and drag force. Equating the drag force to the force of gravity give the following equation [START_REF] Wang | Chapter 4 Behavior of aerosol particles[END_REF],

𝐹 𝑑𝑟𝑎𝑔 = 𝐹 𝑔 = 1 6 𝜋(𝜌 𝑝 -𝜌 𝑎 )𝑔𝐷 𝑝 3 ≅ 1 6 𝜋𝜌 𝑝 𝑔𝐷 𝑝 3 Eq. A-8
where 𝜌 𝑝 is droplet/particle density, 𝑔 is the gravitational acceleration vector.

• Terminal settling velocity 4520

The terminal settling velocity for a spherical droplet/particle in still air is obtained from Stokes drag force to give:

𝑈𝑡𝑠 = 𝜌 𝑝 𝑔𝐷 𝑝 2 𝐶 𝑐 18𝜇 𝑎 𝑅𝑒 𝑃 < 0.1 (Stokes' law) Eq. A-9 𝑈𝑡𝑠[𝑚. 𝑠 -1 ] ≅ 3 × 10 -8 𝜌 𝑝 [𝐾𝑔. 𝑚 -3 ]𝐷 𝑝 2 [µ𝑚]
at NTP, [𝐶 𝑐 = 1] and 1 µ𝑚 < 𝐷 𝑝 < 100 µ𝑚 Eq. A-10

Similarly, extending the gravitational settling concept to the non-Stokes regime, the drag force given by Eq. A-6 can be equated with Eq. A-8. This results in the following terminal settling velocity:

𝑈𝑡𝑠 = √ 4𝜌 𝑝 𝑔𝐷 𝑝 3𝐶 𝐷 𝜌 𝑎 Eq. A-11
Note that in the previous equation, 𝐶 𝐷 is the drag coefficient at sedimentation and depends on 𝑅𝑒 𝑝 (Eq. A-7) and therefore on sedimentation velocity itself.

A. 3. Relaxation time (𝝉 𝒑 )

The time required for an aerosol particle or droplet to respond the sudden changes in air velocity, air temperature or to go from its initial velocity coming out of a nozzle to its final velocity (its terminal settling 4530 velocity or the local air velocity), is called the relaxation time (𝜏 𝑝 ) or the time response of aerosol droplets/particles. This parameter would be obtained by the succession of the equations below.

The equation of motion for a spherical particle in a gas is given by:

𝑚 𝑑𝑈 𝑝 𝑑𝑡 = 𝐹 𝑑𝑟𝑎𝑔 = 𝐶 𝐷 𝜋𝜌 𝑎 𝐷 𝑝 2 𝑈 ∞ 2 8 Eq. A-12
where m is the mass of particle/ droplet and the ratio

A. 4. Stopping distance (S)

In the absence of external forces, the maximum distance a particle with an initial velocity will travel in the still air is defined as stopping distance. In Stokes region, the stopping distance, 𝑆 𝑆𝑡𝑘 , can be calculated through the following equations :

𝑑𝑈 𝑝 𝑑𝑡 = 1 𝜏 𝑝 (𝑈 𝑎 -𝑈 𝑝 ) Eq. A-17
The solution to this equation for a constant air velocity is :

𝑈 𝑝 (𝑡) = 𝑈 𝑎 (1 -𝑒 -𝑡 𝜏 𝑝,𝑆𝑡𝑘 ⁄ ) Eq. A-18
where

𝑈 𝑝 = 𝑈 0 (𝑒 -𝑡 𝜏 𝑝,𝑆𝑡𝑘 ⁄ ) Eq. A-19 𝑆 𝑆𝑡𝑘 = 𝑈 0 𝜏 𝑆𝑡𝑘 Eq. A-20
Above equation is valid only for Stokes region. [START_REF] Mercer | 2 -Properties of Aerosols[END_REF] proposed an empirical equation for particles/doplets having an initial 1 < 𝑅𝑒 𝑝 < 400, which can be found in Eq. E-4.

A. 5. Stokes Number

The Stokes number, 𝑆𝑡𝑘 𝑉 , is an important parameter in fluid (air)-particle flows, which characterizes the ability of particles to adapt to air flow changes and that the air flow and particle trajectory pattern may differ to an extent dictated largely by 𝑆𝑡𝑘 𝑉 magnitude [START_REF] Vincent | Aerosol Sampling[END_REF]. In other words, the inertia of a particle in a flow is characterized by 𝑆𝑡𝑘 𝑉 .Therefore, the Stokes number expresses the ratio of particle's response time, 𝜏 𝑝 , to a characteristic fluid time scale, 𝜏 𝐹 [START_REF] Crowe | Multiphase Flows with Droplets and Particles, Second Edition[END_REF],

𝑆𝑡𝑘 𝑉 = 𝜏 𝑝 𝜏 𝐹 Eq. A-21
For the simplest case where the 𝑅𝑒 𝑝 < 1 , the value of 𝜏 𝑝 would be calculated through (

𝜌 𝑃 𝐷 𝑃 2 𝐶 𝑐 18𝜇 𝑎
) and the value of 𝜏 𝐹 is inferred from dimensional analysis as a ratio between a characteristic dimension, 𝑙, and a characteristic velocity 𝑈. In this case, the Stokes number that typically referred to the reference Stokes number, 𝑆𝑡𝑘 𝑟𝑒𝑓 can be rewritten as,

𝑆𝑡𝑘 𝑟𝑒𝑓 = 𝜏 𝑝 𝑈 𝑙 Eq. A-22
Note that the definition of the characteristic velocity will be detailed in function to its application.

Furthermore, in the present study, the width of body for passive samplers or inlet diameter for active samplers are taken as the characteristic dimension of sampler.

As shown in Figure A. 1, if 𝑆𝑡 𝑉 << 1, the particle relaxation time is sufficiently small compared with the time scale of the flow, and therefore the particle/droplet reaches dynamic equilibrium with its surroundings and acts as gas tracers in the flow field. On the other hand, if 𝑆𝑡 𝑉 >> 1, the particles will separate from any curved streamlines and continue in their original direction [START_REF] Tu | Computational Fluid and Particle Dynamics in the Human Respiratory System[END_REF]. The physical significance of 𝑆𝑡 𝑉 becomes apparent for situations where the flow is distorted (divergent and/or 4580 convergent), for example, close to aerosol sampler. [START_REF] Vincent | Aerosol Sampling[END_REF].

It should be mentioned that the theories of particle motion and trajectories based the term 𝑆𝑡𝑘 𝑟𝑒𝑓 (Eq.

A-22) have well predicted the performance of inertial impactors, and the transition of aerosols in curvedtube.

A. 6. Brownian motion and diffusion

For aerosols < 0.5-1 µm, the gravity becomes insignificant and the time scales for instance, in the common meteorological conditions relevant to sprinkler irrigation with TWW (U < 4.0 m.s -1 ) are much 4590 longer than the particle relaxation times, which are on the order of micro or pico seconds for ultrafine particles in air [START_REF] Tsuda | Particle transport and deposition: basic physics of particle kinetics[END_REF]. So, the submicron particles are subjected to Brownian motion, a key mechanism of transmitting the infectious bioaerosols, such as viruses. Briefly, Brownian motion is the characteristic random wiggling motion of nanometer-sized aerosol particles as a result of collisions with surrounding gaseous molecules. If the spatial distribution of particles is not uniform, Brownian motion will lead to a net movement of particles from a region of higher concentration to a lower one. The net movement is known as Brownian diffusion [START_REF] Wang | Chapter 4 Behavior of aerosol particles[END_REF] ) is the mechanical mobility (inverse the Stokes drag coefficient).

It should be mentioned that the above equation estimate the Browonian motion of the small particles assuming the air flow is still. As the rate of deposition of small particles on surface collecting (often the filters) by Brownian motion from laminar or turbulent flow are higher than from still air, the movement process is defined by the expression "convective Brownian diffusion" [START_REF] Wang | Chapter 4 Behavior of aerosol particles[END_REF]. So, the dimensionless Peclet number, 𝑃𝑒, is used to measure the relative magnitude of the diffusional motion of the particles and the convective motion of the air toward the surface collecting, in particular the deposition on the fiber filters [START_REF] Dunnett | Chapter 5: Filtration Mechanisms[END_REF],

𝑃𝑒 = 𝑈𝑙 𝐷 𝐵

Eq. A-24

The parameters are already defined. As an example, for fibrous filter, the characteristic length (𝑙) is the diameter of fiber. According to the equation above, a large number of Peclet indicates an advectively 4610 dominated distribution, and a small number indicates a diffuse flow. For this, [START_REF] Wang | Modeling of filtration efficiency of nanoparticles in standard filter media[END_REF] suggested a relation between diffusion efficiency of a filter media, 𝜂 𝐷 , and Peclet number (𝜂 𝐷 = 0.84𝑃𝑒 -0.43 ).

Furthermore, the Peclet number can also be defined as follow,

𝑃𝑒 = 𝑆𝑐 × 𝑅𝑒

Eq. A-25

where Schmidt number, 𝑆𝑐, is the ratio of the momentum diffusivity to mass diffusivity (𝑆𝑐 = In this study, the performance of two common passive samplers (PVC strings and plastic spheres), and two active samplers (AGI-4 and SKC BioSampler) was compared in the wind tunnel.

As explained previously (Chapter 2), due to the specific characteristics of the different samplers (e.g. shape, flow rate (for active samplers), and physical characteristics), each sampler has a unique collection efficiency depending various parameters including particle size and meteorological conditions. The work herein describes a methodology for evaluating the performance of samplers in collecting airborne spray droplets in the size range of sub microns to 200 µm (𝐷 𝑣90𝑚𝑎𝑥 = 138 µm), using a combination of experiments in a small-scale wind tunnel and theoretical calculations under various air velocities.

E. 2. Material and methods

E. 2. 1. Preparation of spray solution

In this study, the spray solution was prepared by adding 1g Acid Brilliant Flavine (ABF) and 2 L demineralized water and stir the mixture with a magnetic Stirrer (Thermo Scientific VARIOMAG ® , Mono direct, USA) for at least 8 hours. The spray solution at a concentration of approximately 0.5 g.L -1 was then transferred into the pressure vessel. The features of spray nozzle, hydraulic circuit, and how to keep the spray solution at a temperature of 20±1 °C were described in 4.2.5. To measure the concentration of the spray solution, the samples were regularly taken at the spray nozzle outlet.

E. 2. 2. Active samplers experimental setup

Based on the results obtained using PDPA (in Chapter 4), the experiments with AGI-4 and SKC BioSampler were divided into parts, 1) to assess the effect of air velocity on airborne spray transport; 2) to compare the performance of the impingers to collect a given droplet size distribution under various air velocities.

In the first set of experiments, the impinger was filled with 20 mL demineralized water (DW) and the mass And in the second set, the impinger was in the same position as before, and the spray nozzle was moved to 0.45, 0.85 and 1.25 m upstream of the inlet of the aspirating impinger (at its nominal air sampling velocity of 1.68 (AGI-4) or 2.94 m.s -1 (SKC BioSampler)) wherein the air velocities of 2.25, 3.3 and 5.0 m.s -1 were drawn into the working section, respectively (Figure E. 1). These were the selected optimal configurations in the previous chapter, which in downwind airborne droplet size distribution was approximately equal (𝐷 𝑣32 ≃ 67 µm, 𝐷 𝑣50 ≃ 74 µm, and 𝐷 𝑣90 = 104 µm) (see 4.3.2.2).

In addition, in order to evaluate the rate of re-aerosolization/evaporation of the collected airborne droplets containing the ABF in collection liquid of samplers during sampling, two pieces cassette including glass fiber filter was positioned downstream of the impinger's suction line, as already explained in 3.5.1.2.

The sampling time was 15 min, and each experiment was performed in triplicate. This sampling time is often used for both AGI-4 and SKC BioSampler [START_REF] Riemenschneider | Characterization of reaerosolization from impingers in an effort to improve airborne virus sampling: Reaerosolization from impingers[END_REF]). In addition, the effect of 15 min sampling time on physical collection efficiency of both impingers was examined in chapter 3 (see 3.

E. 2. 2. 1. Recovery of the collection liquid

After sampling, the body of the impinger was completely dried with a napkin and the final mass of the collection reservoir of sampler containing the DW was measured by the balance and poured in bottles of 50 mL.

The filters were recovered using the same technique as employed in 3.5.1.3.

To determine the transmission losses, the volume of deposited droplets on the inner surfaces of impingers, including the inside walls of the nozzles and curved inlet section), the impinger was remounted in other clean local and 30 mL demineralized water was progressively injected into inlet's sampler through a sterile syringe to rinse the curved inlet section. The sample directly drawn into a bottle positioned underneath the nozzle exit using an air compressor, 𝑉 𝑇𝐿 .

The recovered impinger samples were stored in the refrigerator, their ABF concentration (µg.L -1 ) was then quantified by fluorescence spectrometer, as explained previously (see 3. Eq. E-2

where 𝐶 𝑠 and 𝐶 0 are the fluorescent concentration of each sample and the spray at the outlet of the nozzle, respectively, in μg ABF L -1 ; and 𝑉 𝑓𝑖𝑛𝑎𝑙 is the volume of the final sampling liquid in mL.

E. 2. 2. 2. Estimation of aspiration efficiency of the active samplers for droplets in moving air

To estimate the aspiration efficiency of the impingers for airborne droplets, the following steps were taken for each droplet size bin (intervals: 5 microns) from a given airborne droplet size distribution (as measured by PDPA and related to a specific configuration.

1) The droplet Reynolds number (𝑅𝑒 𝑝 ) was calculated for each size bin using Eq. A-3;

2) For 𝑅𝑒 𝑝 < 1 (Stokes' law), the Stokes number (𝑆𝑡𝑘 𝑟𝑒𝑓 = 𝜏 𝑝 .𝑈 0 𝑙 , see A. 5) was calculated for each droplet size bin upstream of the aspirating impingers, based on the following expressions, 𝑆𝑡𝑘 𝑟𝑒𝑓 = 𝜌 𝑝 𝐷 𝑝 2 𝐶 𝑐 𝑈 0 18𝜇 𝑎 𝐷 𝑖𝑛 Eq. E-3

The parameters have already been presented, and here, the characteristic dimension (𝑙) is the inlet diameter of the impingers 𝐷 𝑖𝑛 .

Since the Stokes number can be defined as the ratio of the stopping distance (𝑆, see A.

)]

Eq. E-4

where 𝑅𝑒 0 = 𝜌 𝑎 𝐷 𝑝 𝑈 0 µ 𝑎 and 𝑈 0 is the initial velocity of particle/droplet. Note that this equation derived from Klyachko's formula is accurate within 3% for (1 < 𝑅𝑒 𝑝 < 400).

The 𝑆𝑡𝑘 𝑟𝑒𝑓 values ranged from 0.0098 to 18.96 as a function of droplet size (5-195 µm), free stream air velocity (1.2 to 5.0 m.s -1 ) and the inlet diameter of the impingers (8.7 and 9.5 mm, for AGI-4 and SKC BioSampler, respectively);

3) Based on the range of the 𝑆𝑡𝑘 𝑟𝑒𝑓 values and the ratio of the free stream air velocity to the nominal air sampling velocity (𝑅), aspiration efficiency of the impingers was therefore estimated for each droplet size bin using the empirical equation of [START_REF] Liu | A numerical study of inertial errors in anisokinetic sampling[END_REF] and [START_REF] Zhang | On the empirical fitting equations for aspiration coefficients for thinwalled sampling probes[END_REF], 𝜂 𝑎𝑠𝑝,𝑖 (see Table 2.2); 4) Finally, the overall aspiration efficiency (overall droplet size bins), 𝜂 𝑎𝑠𝑝 , for each configuration is the sum overall size bins of fractional efficiency, 𝜂 𝑎𝑠𝑝 (𝑖) , multiplied by the volume fractions, 𝑉 (𝑖) [-], of total airborne droplet size distribution as measured by PDPA as:

𝜂 𝑎𝑠𝑝 = ∑ 𝜂 𝑎𝑠𝑝,𝑖 𝑖𝑚𝑎𝑥=39 𝑖=1
× 𝑉 𝑖 Eq. E-5

Note that these steps have been repeated for all selected configurations.

E. 2. 3. Passive samplers experimental setup

To quantify airborne spray transport using two passive samplers of PVC strings and spheres, the single spray nozzle was situated in vertical position and tested at two different heights (0.175 and 0.275 m above the floor), and also, 0.15 m downwind inside the working section. The spray solution was exposed to the air stream with a velocity range of 1.2 to 5.0 m.s -1 for 10s, a long enough time to detect fluorescent concentration of the samples by Fluorescent spectrometer but without saturating any samplers. Replicate measurement were performed for 3 times.

E. 2. 3. 1. PVC Strings

As shown in

E. 2. 3. 3. Recovery of tracers from the passive samplers

After each experiment, samplers were retrieved immediately after drying. The PVC strings and the spheres were placed in bottles of 50 mL and 20 mL respectively, and their ABF concentration in µg.L -1 was then quantified by fluorescence spectrometer.

To compare the total airborne spray transport potential, 𝑇𝑃 𝑎 𝑡𝑜𝑡𝑎𝑙 , collected on PVC strings and spheres in % (with different shapes and dimensions) at each position downwind from the spray nozzle, the following expression, in accordance with ISO 22856 (2008) was used:

𝑇𝑃 𝑎 𝑡𝑜𝑡𝑎𝑙 = ∑ 𝑇𝑃 𝑎 𝑗 𝑗=6 𝑗=1
Eq. E-6

where 𝑇𝑃 𝑎 𝑗 is the airborne spray transport potential at the vertical collector line j in % (there were 6 vertical collector lines), which was calculated for PVC strings using the following expression:

𝑇𝑃 𝑎 𝑗 = ( 𝑉 𝑗 . 𝑑 𝑐 𝐷 𝑐 . 10 -3 𝑞 𝑁 . ( 𝑡 𝑠 60 ⁄ )
) . 100

Eq. E-7

where 𝑑 𝑐 is the interval between collectors (𝑦 𝑗+ 1 2 -𝑦 𝑗-1 2 = 0.05 𝑚), 𝐷 𝑐 is the collector diameter (0.002 m), and 𝑞 𝑁 is nozzle flow rate in L.min -1 , and 𝑡 𝑠 here is in second. 𝑉 𝑗 is the airborne spray volume deposited in the collector line j in mL, and given by Eq. E-9.

When using the spheres as a sampler, the airborne spray transport potential at the vertical line j (𝑇𝑃 𝑎 𝑗 ), was calculated as follows:

𝑇𝑃 Eq. E-9

where 𝑉 𝑑𝑖𝑙 is the sample dilution volume in mL (20 mL demineralized water for the PVC string samples and 5 or 10 mL for the spheres). Other parameters have already been presented.

Note that for the spheres, the total airborne spray transport potential was also calculated with kriging interpolation method using Surfer 8.0 Software.

E. 2. 3. 4. Estimation of collection efficiency of droplets on passive samplers

As downwind droplet size distributions were only measured along the centerline of wind tunnel cross section, it was not possible to accurately estimate the collection efficiency for the PVC strings and spheres in moving air and to compare them with the experimental results. However, a rough comparison was made with the assumption that when the nozzle is situated at a height of 0.275 m above the floor, the droplet size distribution at the midpoint (Y = 0.175 m, Z = 0.25 m) is representative of the distribution on 0.5 m-long line at the height of 0.175 m above the floor. Regarding the initial distribution of the spray plume in no wind conditions at a height of 100 mm, it can be found that the 𝐷 𝑣50 throughout the spray plume ranged between 86 and 100 microns and is almost uniform. Therefore, the results will not be far from the reality.

To estimate the droplet collection efficiency of passive samplers, the same steps of 1 to 3 as described in E.

2. 2. 2 for aspiration efficiency of the active sampler were followed for each droplet size bin from a downwind droplet size distribution, except that in the Stokes number expression, the characteristic dimension (𝑙) is the diameter of the passive samplers (2 mm for the PVC strings and 5 mm for the spheres).

Then, the collection efficiency of droplet size bin have been determined using mean curves of 𝑆𝑡𝑘 𝑟𝑒𝑓 -𝜂 𝑐 from the experimental data of [START_REF] May | The Impaction of Aerosol Particles on Cylinders, Spheres, Ribbons and Discs[END_REF], as shown in Eq. E-10

And we found a cubic function of the logarithm that fitted very well to these data for the collection efficiency Eq. E-11 R 2 = 0.999, MSE=0.744, RMSE=0.863

Finally, the overall droplet collection efficiency (overall droplet size bins) for each configuration was calculated using Eq. E-5 in the sub-section E. 2. 2. 2.

E. 3. Results and discussions

E. 3. 1. Comparison of performance of the active samplers in moving air

In the present study, the performance of the AGI-4 and SKC BioSampler were tested under various air velocities (Table E. 1). The discussion starts with the evaluation of the predicted aspiration efficiency values by the empirical equation of [START_REF] Liu | A numerical study of inertial errors in anisokinetic sampling[END_REF] and [START_REF] Zhang | On the empirical fitting equations for aspiration coefficients for thinwalled sampling probes[END_REF]. At a constant droplet size distribution (2m.s -1 at 0.45 m, 3.3 m.s -1 at 0.85 m and 5.0 m.s -1 at 1.25 m), the predicted aspiration efficiency values rose with increasing the air velocity. According to the theory of [START_REF] Belyaev | Techniques for collection of representative aerosol samples[END_REF], by increasing the Stokes number and sub-isokinetic ratio ( 𝑈 0 𝑈 𝑠 > 1), the aspiration efficiency increases and exceeds unity. In contrast, in Regardless of the air velocity values or downwind distances, airborne spray volume decreased with increasing nozzle height, which agrees with previous researches [START_REF] Hassen | Effect of cross wind, nozzle angle and height on the performance of broadcasting spraying system[END_REF][START_REF] Zhao | Effects of sprayers and nozzles on spray drift and terminal residues of imidacloprid on wheat[END_REF]. As demonstrated in Table E. 2, at the air velocity of 2.25 m.s -1 , close to the reference air velocity employed for spray drift studies in wind tunnel [2.0 m.s -1 , (ISO 22856, 2008)], from a nozzle height of 0.275 to 0.175 m, the total airborne spray transport potential (Eq. E-7, for PVC strings) fell by about 67.7% at both distances from the source.

For a given distance and adjusted release height, airborne spray volume rose with increasing air velocity. At a distance of 0.85 m downwind from a spray nozzle height of 0.275 m, the highest volume of airborne spray under an air velocity of 5.0 m.s -1 was distributed vertically within the height of 0.125 m. Furthermore, at the same distance, the volume of airborne spray transported to the height of 0.025 under air velocity conditions of 5 m.s -1 was even lower than the value for 2 m.s -1 . Given a neutral stability in air and almost uniform wind profile developed in the wind tunnel, it can therefore be concluded that for an air velocity of 5.0 m.s -1 , the spray droplet has completely transported by the air flow before it reaches the ground/floor. At 1.65 m from the source, for the air velocity of 5.0 m.s -1 , this highest volume of airborne spray shifts to height of 0.075, due to the other predominant forces (e.g. gravity force) that affect, particularly the trajectory of larger droplets in airborne spray plume.

Another issue to consider is that, as shown in Figure E. 6, with decreasing spray nozzle height from 0.275 to 0.175 m, at the same air velocity and vertical interval between the nozzle tip and downwind measurement point, the transported airborne spray volume decreases up to 30 %, particularly under high air velocity conditions. It could be caused by the effect of entrained air and vortex generated in spray jet that resulted in modification of initial droplet size distribution characterized at a specific axial downward distance under calm air conditions, and certainly due to ground boundary layer effect on droplet dynamic behaviors.

In fact, for short range airborne spray transport studies, their results are strongly influenced by the initial release conditions. In cases where the spray nozzle mounted in downward direction, such as some types of spray nozzles used for center pivot and linear irrigation systems, fogger mist sprinklers, and pesticide sprayer, vertical profile of airborne spray transport near the source depend largely on the ratio of the air velocity to the induced air jet speed (𝑈 𝑎 /𝑈 𝑗 ), spray angle and initial droplet size and velocity. [START_REF] Ghosh | Spray jets in a cross-flow[END_REF] found that for the case of a weak air velocity (𝑈 𝑎 /𝑈 𝑗 ≤ 0.1), in the zone immediately below the nozzle, the spray centerline acts as a line sink for the external flow, and small droplets (50 µm) tend to aggregate towards the spray As can be seen in Table E. 3, whatever the type of the samplers, as the air velocity increased, this ratio got closer to one, which can be related to the transport of larger droplets with greater inertia (the increase in the proportion of larger droplets in droplet size distribution) at higher air velocities, and consequently higher Stokes number. It should be rappelled that at 0.85 m downwind from the spray nozzle, as the air velocity increased from 1.2 to 5.0 m.s -1 , the 𝐷 𝑣90 increased from 90 µm to 124 µm, respectively. This parameter rose from 37 to 84 µm at 1.65 m, respectively [see Figure 4.10]). This result is in agreement with the results of [START_REF] Parkin | Measurements and computational fluid dynamic simulations of the capture of drops by spray drift samplers[END_REF].

E. 3. 3. Graphical presentation of downwind airborne spray volume using spheres

However, as shown in Figure E. 8, the ratio of observed collection efficiency of spheres to PVC strings (a ratio smaller than one , based on the data presented in Table E. 4) was lower than the ratio of the estimated ones by [START_REF] May | The Impaction of Aerosol Particles on Cylinders, Spheres, Ribbons and Discs[END_REF] data for all of the air velocities. Among several equations to compute the standard deviation of wind direction, 𝜎 𝜃 [START_REF] Essenwanger | General climatology. 1B[END_REF][START_REF] Ibarra | A new approach for the determination of horizontal wind direction fluctuations[END_REF][START_REF] Mori | Methods for Estimating the Mean and the Standard Deviation of Wind Direction[END_REF][START_REF] Nelson | A Simple and Accurate Method for Calculation of the Standard Deviation of the Horizontal Wind Direction[END_REF][START_REF] Turner | Comparison of Three Methods for Calculating the Standard Deviation of the Wind Direction[END_REF][START_REF] Weber | A comparison of different estimators for the standard deviation of wind direction based on persistence[END_REF], [START_REF] Yamartino | A Comparison of Several "Single-Pass" Estimators of the Standard Deviation of Wind Direction[END_REF] was found to acquire the best results for most cases [START_REF] Epa | EPA -Meteorological Monitoring Guidance for Regulatory Modeling Applications[END_REF], which is defined as: Eq. G-3 and 𝑈 𝑣 is the vector mean wind speed; 𝑈 𝑎 is average wind speed, and 𝑈 𝑖 , 𝜃 𝑖 , n are denoted the wind speed, wind direction, and number of recorded data, respectively.

𝜎 𝜃 =

G. 1. 4. Atmospheric stability

As shown in Table 5.1 (in see 5.2.4) , the highest standard deviations of wind direction are encountered in the experiments with the lower wind speed which is somewhat consistent with the results of [START_REF] Mahrt | Surface Wind Direction Variability[END_REF], who stated that the wind direction variability is inversely related to the average wind speed. It should be mentioned that Exp.4 had a minimum wind speed (0.16 m.s -1 ). However, due to recording zero wind speed in most cases, the probability of inaccuracy of the estimated value for σƟ is very high. It is worth mentioning that Takeuchi (1963) concluded that the standard deviation of wind direction at the same height decreases with stability. Note that this conclusion is more consistent with our results than the previous fact (see Table 5.1).

G. 1. 5. Correlation analysis between input variables: Pearson method

The bivariate correlation analysis between all input variables was carried out using Pearson correlation coefficients (Figure G. 2) without experiment 4 conducted in calm air (0.16 m.s -1 ) that confuse the correlation analysis between meteorological parameters. The Pearson test for meteorological variables indicated that:

-Wind speed and temperature were not correlated (R 2 < 0.35);

-Bulk Richardson number was negatively correlated with solar radiation and standard deviation of wind direction, in accordance with Takeuchi (1963);

-Relative humidity and solar radiation and also temperature show R²=-0.38 and -0.30, respectively;

-Other correlation coefficients were smaller than ±0.3.

Consequently, the meteorological parameters can be considered as independent variables for application in airborne and sedimented transport models.

Additionally, The results shows that the airborne transport (Ta) was positively correlated with wind speed, Bulk Richardson number (unstable conditions), temperature, and negatively correlated with distance and spatial variables (height, distance, and angle). The sedimented transport (Ts) was also negatively correlated with relative humidity and positively with vapor pressure deficit. Although, the significance level of relative humidity is quite small (Table G. 3). Evidently, solar radiation is a determinant parameter for classifying air stability, but it has no impact on transport or at least their correlation is not linear.

It should be noted that the global regressions of Ta or Ts in function of the independent variables are basically non-linear which are presented in the next section. Consequently, the Pearson correlation coefficients, which estimate the binary linear association between each input and transport, were not high. -value<0.001 (highly significant);** p-value<0.01 (significant); *p-value<0.05; and no asterisk (not significant). The unit of the RMSE: mL.m2.h -1 . The light green color indicates that these models met all the validation criteria. The green grass color indicates the selected models (the best airborne transport models). The red color highlighted the models include both wind speed and Bulk Richardson number or have a p-value≥0.05 -value<0.001 (highly significant);** p-value<0.01 (significant); *p-value<0.05; and no asterisk (not significant). The unit of the RMSE: mL.m -2 .h -1 . The light green color indicates that these models met all the validation criteria. The green grass color indicates the selected models (the best airborne transport models). The red color highlighted the models include both wind speed and Bulk Richardson number or have a p-value≥0.05. -value<0.001 (highly significant);** p-value<0.01 (significant); *p-value<0.05; and no asterisk (not significant). The unit of the RMSE: mL.m -2 .h -1 . The light green color indicates that these models met all the validation criteria. The green grass color indicates the selected models (the best sedimented transport models). The red color highlighted the models include both wind speed and Bulk Richardson number or have a p-value≥0.05. -value<0.001 (highly significant);** p-value<0.01 (significant); *p-value<0.05; and no asterisk (not significant). The unit of the RMSE: mL.m -2 .h -1 . The light green color indicates that these models met all the validation criteria. The green grass color indicates the selected models (the best sedimented transport models). The red color highlighted the models include both wind speed and Bulk Richardson number or have a p-value≥0.05.

First

  and foremost, I feel honored to express my most sincere gratitude to my supervisor, Dr. Dominique Courault for her scientific discussions and advices, for support and encouragement throughout my doctoral study. I am also deeply thankful to my co-supervisors, Dr. Séverine Tomas and Dr. Bruno Molle for their great scientific qualities, problem-solving skills, constructive suggestions and flexibility during the dissertation process. I am highly indebted to the supervision team for giving me ample freedom to work my way and simultaneously guiding me to the completion of this work.

I

  would like to express my appreciation to the National Research Institute for Agriculture, Food and the Environment (INRAE) and French Agency for Food, Environmental and Occupational Health & Safety (ANSES) for funding this PhD thesis. I would like to thank the OPTIMIST team (UMR-GEAU) at INRAE Montpellier. Special thanks to Geoffrey Froment, Cyril Dejean, Antoine Le Guillou, Gaël Hermet, Marine Muffat-Jeandet, François Liron, Joahn-David Dominguez and Margot Lagier for their contributions during field experiments. Thanks to Hugo Lottin for his internship under my supervision and for the invaluable help he has given in implementing the field-study. I give my thanks to Dr. Eric Michel from UMR 1114 EMMAH for useful discussions on the colloid characterization by Dynamic Light Scattering. Also, to Eric Cotteux and Cyril Tinet from UMR ITAP at INRAE Montpellier.

Mehrnaz 200 "

 200 Let us fight to free the world, to do away with national barriers, to do away with 210 greed, with hate and intolerance. Let us fight for a world of reason, a world where science and progress will lead to all men's happiness" ......................................................................................................................................................3

Figure 1 .

 1 Figure 1.1. (a) Level of water stress due to the agricultural sector by basin, 2018 (Source: FAO, 2021); (b) Water stress by country: 2040. Note: Projections are based on a business-as-usual scenario using SSP2 and RCP8.5 (Source: World Resources Institute, 2015). ..................................................................................31 Figure 1.2. Area equipped for irrigation by region. Note: Percentages on the figure indicate the shares in the total; they may not tally due to rounding. (Source: FAO Statistics, 2021). ..........................................32 Figure 1.3. A set of shadow images at the jet's centerline from 𝑥/𝑑𝑛 = 0 to 𝑥/𝑑𝑛 = 800, where 𝑥 is the distance from a model nozzle exit in mm, and 𝑑𝑛 is the diameter of the nozzle (𝑑𝑛 = 1.2 mm) from Felis-Carrasco (2017). .........................................................................................................................................34 Figure 1.4. Classification of the major types of Sprinkler irrigation systems based on their movement. ..35 Figure 1.5. Examples of several types of sprinkler irrigation systems. ......................................................35 Figure 1.6. Examples of the types of sprinkler heads available. *LEPA: Low Energy Precision Application (Sources: Big Gun-https://www.kometirrigation.com/; Rotator ® -https://nelsonirrigation.com/; Bubbler-LEPA-https://www.senninger.com; Impact sprinkler and Rotor-https://www.rainbird.com/; Micros prinkler-https://www.antelco.com/; Fogger-https://www.rivulis.com/) ....................................................36 Figure 1.7. Characteristic size ranges of atmospheric particles and bioaerosols (A) protein (B) virus, (C) bacteria, (D) fungal spore and (E) pollen grain (Fröhlich-Nowoisky et al., 2016).....................................37 Figure 1.8. Schematic representation of the notion of transport, wind drift and evaporation losses ..........47 Figure 1.9. Factors affecting the transport from sprinkler irrigation ..........................................................48 Figure 1.10. Lateral movement of water droplets (Hofman and Solseng, 2004) .......................................49 Figure 1.11. Morphological evolution from numerical simulation illustrating the evaporation dynamics of an aqueous droplet containing 200 nm polystyrene particles (Derkachov et al., 2008) .............................50 Figure 1.12. Gaussian distribution of aerosol plume in crosswind and vertical direction downwind from a sprinkler. h and d are the sprinkler height and downwind distance from sprinkler head to receptor, respectively. Other parameters have already been presented (US-EPA, 1982). .........................................54

  Figure 1.1. (a) Level of water stress due to the agricultural sector by basin, 2018 (Source: FAO, 2021); (b) Water stress by country: 2040. Note: Projections are based on a business-as-usual scenario using SSP2 and RCP8.5 (Source: World Resources Institute, 2015). ..................................................................................31 Figure 1.2. Area equipped for irrigation by region. Note: Percentages on the figure indicate the shares in the total; they may not tally due to rounding. (Source: FAO Statistics, 2021). ..........................................32 Figure 1.3. A set of shadow images at the jet's centerline from 𝑥/𝑑𝑛 = 0 to 𝑥/𝑑𝑛 = 800, where 𝑥 is the distance from a model nozzle exit in mm, and 𝑑𝑛 is the diameter of the nozzle (𝑑𝑛 = 1.2 mm) from Felis-Carrasco (2017). .........................................................................................................................................34 Figure 1.4. Classification of the major types of Sprinkler irrigation systems based on their movement. ..35 Figure 1.5. Examples of several types of sprinkler irrigation systems. ......................................................35 Figure 1.6. Examples of the types of sprinkler heads available. *LEPA: Low Energy Precision Application (Sources: Big Gun-https://www.kometirrigation.com/; Rotator ® -https://nelsonirrigation.com/; Bubbler-LEPA-https://www.senninger.com; Impact sprinkler and Rotor-https://www.rainbird.com/; Micros prinkler-https://www.antelco.com/; Fogger-https://www.rivulis.com/) ....................................................36 Figure 1.7. Characteristic size ranges of atmospheric particles and bioaerosols (A) protein (B) virus, (C) bacteria, (D) fungal spore and (E) pollen grain (Fröhlich-Nowoisky et al., 2016).....................................37 Figure 1.8. Schematic representation of the notion of transport, wind drift and evaporation losses ..........47 Figure 1.9. Factors affecting the transport from sprinkler irrigation ..........................................................48 Figure 1.10. Lateral movement of water droplets (Hofman and Solseng, 2004) .......................................49 Figure 1.11. Morphological evolution from numerical simulation illustrating the evaporation dynamics of an aqueous droplet containing 200 nm polystyrene particles (Derkachov et al., 2008) .............................50 Figure 1.12. Gaussian distribution of aerosol plume in crosswind and vertical direction downwind from a sprinkler. h and d are the sprinkler height and downwind distance from sprinkler head to receptor, respectively. Other parameters have already been presented (US-EPA, 1982). .........................................54

  Figure 2.1. Passive samplers for measuring Particulate Matter. (a) Vinzents sampler, (b) UNC-PAS sampler, and (c) Electrostatic dust fall collector ........................................................................................60 Figure 2.2. Droplet trajectories upstream of a 10 mm diameter cylinder in a 4 m.s -1 free airstream simulated using computational fluid dynamics. (a) 1 μm droplets. (b) 35 μm droplets. Data from Parkin and Young (2000) [Bateman et al. 2014]. .....................................................................................................................62 Figure 2.3.(a) Cascade impactor, (b) Slit sampler (Impactor), (c) SKC BioSampler (Impinger), (d) Coriolis μ air sampler (cyclone), (e) Cassette samplers and filters (filter), (f) Electrostatic precipitator. ...............63 Figure 2.4. Schematic of cross-section of a stage of impactor depicting the trajectories of particles of different sizes; W is the nozzle exit diameter, and S is the distance from the nozzle exit to collection surface (Drawn by Zardari). ....................................................................................................................................64 Figure 2.5. Airborne particles collected by impinger into the liquid (Drawn by Zardari) .........................66 Figure 2.6. Schematic diagram of the impingers: (a) SKC BioSampler, (b) AGI-4 or -30 (Drawn by Zardari). ......................................................................................................................................................67 Figure 2.7. Schematic presentation of collection mechanisms at a filter (Lindsley, 2017). .......................71 Figure 2.8. Schematic of the examinable efficiencies of an active sampler during physical aerosol sampling steps (Kesavan and Sagripanti, 2015). ........................................................................................................72 Figure 2.9. Schematic diagram of the limiting particle trajectory (Grinshpun et al., 1993) .......................73 Figure 2.10. Diagram representation of the physical parameters affecting the aspiration efficiency.........73 Figure 2.11. Schematic diagram of isoaxial sampling with a thin-walled nozzle, with sample flow gas velocity 𝑈𝑠 and free stream ambient gas velocity 𝑈0 under (a) isokinetic (𝑈0 = 𝑈𝑠) sampling conditions,

Figure 4 . 7 .

 47 Figure 4.7. Schematic of the spatial location of the PDPA set-up and placing the nozzle tip at different positions upstream of the measurement point of PDPA. ..........................................................................111 Figure 4.8. Trend of 𝐷𝑣10, 𝐷𝑣50, and 𝐷𝑣90 variation along the diameter of spray plume at axial distances of, (a): 50 mm, (b):100 mm, and (c): 150 mm from the nozzle tip. .........................................................113 Figure 4.9. Droplet volume distribution at axial distance z= (a) 50 mm; (b) 100 mm and (c)150 mm from the tip of the nozzle. .................................................................................................................................114 Figure 4.10. Effect of air velocity on characteristics of airborne droplet generated from a nozzle tip positioned at a height of (a-d) 0.1 m, (e-h) 0.175 m, and (e-h) 0.275 m above the working section floor, and at the distances of 0.45 to 1.65...........................................................................................................117 Figure 4.11. Effect of air velocities of 1.2, 2.25, 3.3 and 5.0 m.s -1 on the function and cumulative downwind airborne droplet size distribution at a distance of (a) 0.45, (b) 0.85, (c) 1.25, and (d) 1.65 m from the nozzle positioned at a height of 0.175 m above the floor. ...................................................................................119 Figure 4.12. Effect of air velocities of 1.2, 2.25, 3.3 and 5.0 m.s -1 on the function and cumulative downwind airborne droplet size distribution at a distance of (a) 0.45, (b) 0.85, (c) 1.25, and (d) 1.65 m from the nozzle positioned at a height of 0.275 m above the floor. ...................................................................................119 Figure 4.13. Downwind airborne spray transport measured using PDPA and normalized by the maximum value. The nozzle head was positioned at heights of: (a) 0.175 m, and (b) 0.275 m above the working section floor. For better comparison of the values, the y-axis were presented in logarithmic scale. .......120

Figure 5 .

 5 Figure 5.1. Photo of 5022SD-U. NaanDanJain impact sprinkler irrigation, used in the present study ...126 Figure 5.2. Experimental setup used for evaluation of: (left) application rate, (right) droplet size distribution ..................................................................................................................................................................127 Figure 5.3. Classification scheme for the bulk Richardson number, B, as a function of the height z, in terms of the Pasquill A-F stability classes (Zoumakis and Kelessis, 1991). ......................................................130 Figure 5.4. Scheme of sampler and meteorological station downwind from the sprinkler ......................133 Figure 5.5.(a): Water application curve, (b): Cumulative distribution in volume [%] .............................136 Figure 5.6. Crosswind distribution of aerosol plume at a distance of 20 m at two heights of (a):1 m, (b): 2 m above the ground, and (c): deposited on the ground/surface, for the Exp.5 and Exp.6 ........................137 Figure 5.7. Downwind airborne transport as a function of wind speed, at the height of 2 m above the ground ..................................................................................................................................................................138 Figure 5.8. Vertical profiles of normalized airborne transport as a function of wind speeds at distances of (a): 20 m, (b): 30 m, and (c): 40 m from the sprinkler. (d): Normalized sedimented transport as a function of wind speed at distances of 18 to 40 m from the sprinkler head ...........................................................139 Figure 5.9.(a): Vertical profile of normalized airborne transport and (b): normalized sedimented transport as function of temperature at the different distances from the sprinkler head. (Black dots: Exp.2: 32.5 °C; red dots: Exp.9: 22.9 °C) ..........................................................................................................................140 Figure 5.10. Correlation between observed and simulated data from PVC strings (Airborne transport), y=ax, (a): Power-crosswind Gaussian model, M1, and (b): Exponential-crosswind Gaussian model, M2 ..................................................................................................................................................................142 Figure 5.11. The simulated results of (a): horizontal dispersion of aerosol plume at a height of 1 m above the ground and a distance of 20 m from the sprinkler head (b): vertical dispersion of aerosol plume at a distance of 40 m from the sprinkler head through both models (M1: Black lines, M2: Red dashed lines) and experimental data from PVC strings (black dot) related to Exp.9. The unit of RMSE: mL.m -2 .h -1 .........142 Figure 5.12. Correlation between observed and simulated data from Petri dishes (sedimented transport), y=ax, (a): Power-crosswind Gaussian model, M1, (b): Exponential-crosswind Gaussian model, M2 ...143 Figure 5.13. Examples of sedimented transport simulation (line/dashed line) downwind from the sprinkler head and experimental data (dots) using Petri dishes from Exp.3 (black) and Exp.5 (blue), see Table 5.1. ..................................................................................................................................................................144

  . Summary of the field studies examining the factors affecting microorganism concentrations in aerosol from sprinkler irrigation of (treated or untreated)wastewater ........................................................39 Table 1.3. Guidelines for the safe use of spray irrigation with treated wastewater ....................................44 Table 1.4. Key to Solar Radiation Delta-T (SRDT) Method for Estimating Pasquill-Gifford (P-G) Stability Categories (EPA, 2000) ..............................................................................................................................52 Table 2.1. Some of empirical equations of collection efficiencies of airborne droplets on various types of passive samplers .........................................................................................................................................61 Table 2.2. Aspiration efficiency equations for isoaxial sampling ..............................................................75Table 3.1. Aspiration characteristics of the studied impingers at NTP ......................................................85 Table 3.2. Calculations for normalization of number and volume Fraction : 𝑑𝑁𝑁𝑡𝑜𝑡. 𝑑log𝐷 or 𝑑𝑉𝑉𝑡𝑜𝑡. 𝑑log𝐷............................................................................................................................................87 Table 3.3. Calculations of Stokes number and aspiration efficiency of AGI-4 and SKC BioSampler in calm air ................................................................................................................................................................89 Table 3.4. Measurement errors for the investigated impingers, using ABF as a tracer ..............................98 Table 4.1. Mean air velocities at the locations of the samplers (active\passive) or optical systems ........104 Table 4.2. Reynolds number under the different air velocities .................................................................104 Table 4.3. Some of properties of air at 20 °C and pressure of 101.3 kPa [1atm] .....................................105 Table 4.4. Spray nozzle specification .......................................................................................................106 Table 4.5. Configuration of PDPA-system for measurements in wind-Tunnel........................................109 Table 4.6. Synthesis of droplet size distribution of the spray nozzle under a pressure of 200 kPa in laboratory conditions. ...............................................................................................................................114 Table 4.7. P-Value (Pearson) between variables and 𝐷𝑣50 and 𝐷𝑣90 ....................................................117

Figure

  Figure Int. 1. Schematic representation of the outline of this thesis

  1. (a) Level of water stress due to the agricultural sector by basin, 2018 (Source: FAO, 2021); (b) Water stress by country: 2040. Note: Projections are based on a business-as-usual scenario using SSP2 and RCP8.5 (Source: World Resources Institute, 2015).

Figure 1 . 2 .

 12 Figure 1.2. Area equipped for irrigation by region. Note: Percentages on the figure indicate the shares in the total; they may not tally due to rounding. (Source: FAO Statistics, 2021).

  Figure 1.3 illustrates the break up process at a jet's centerline (average jet velocity=35 m.s -1 ) ranging from 𝑥/𝑑𝑛 = 0 (model nozzle point) to 𝑥/𝑑𝑛 = 800 (Felis-Carrasco, 2017).

Figure 1

 1 Figure 1.3. A set of shadow images at the jet's centerline from 𝑥/𝑑𝑛 = 0 to 𝑥/𝑑𝑛 = 800, where 𝑥 is the distance from a model nozzle exit in mm, and 𝑑𝑛 is the diameter of the nozzle (𝑑𝑛 = 1.2 mm) from Felis-Carrasco (2017). Sprinklers irrigation systems are classified according to each of the their special features, based on spraying mechanism, portability, precipitation rates, radius of throw (low [<13 m], medium [13-25 m] or high [> 25 m]), principle of operation, and movement. Based on their movement, sprinkler irrigation systems are classified into following two general categories:

Figure 1 .

 1 Figure 1.4 presents a diagram of major types of sprinkler irrigation systems related to each category, some of which are shown in Figure 1.5. The specifications and application conditions of each of these systems have been well explained in most of the irrigation books and manuals, such as Irrigation

Figure 1 . 4 .

 14 Figure 1.4. Classification of the major types of Sprinkler irrigation systems based on their movement.

Figure 1 . 5 .

 15 Figure 1.5. Examples of several types of sprinkler irrigation systems (source: https://images.google.com).

Figure 1 . 7 .

 17 Figure 1.7. Characteristic size ranges of atmospheric particles and bioaerosols (A) protein (B) virus, (C) bacteria, (D) fungal spore and (E) pollen grain (Fröhlich-Nowoisky et al., 2016)

✓✓✓✓

  Size of 50% of the bacteria sampled in the air <7 µm in diameter. ✓ Relative humidity (RH) and solar radiation (Rs) → survival of aerosolized bacteria  with a correlation of 0.8 and -0.5, respectively. ✓ Bacterial aerosol levels at night (higher atmospheric stability and RH, and lack of Rs) 10 times higher than during daytime → E.coli 60/m 3 (day) and 710/m 3 (night) ✓ Detection of echovirus 7, in 4 of the 12 air samples collected 40 m downwind from the sprinkler ✓ When tracer bacteria < 3.4 to 5.7E+03/mL→ No bioaerosols detected 20 m downwind from the sprinkler Teltsch et al. Survival of airborne coliform bacteria (indicator) < that of airborne salmonellae and enteroviruses ✓ Hardiness of Enteroviruses > Salmonella in wastewater aerosols→ only presence of enteroviruses over 100 m from the sprinkler ✓ Only one positive Salmonella air sample was from a distance of 100 m, that was when the sprinkler with a very large discharge was Mean die-away rate of aerosolized E.coli for very early morning runs (8.8× 10 -3 s -1 ) with RH=98% and Rs = 0 J.cm -2 < for afternoon runs (6.6 × 10 -3 s -1 ) with RH= 50% and Rs = 293 J.cm -2 → 90% reduction in bacterial survival (T9o) of about every 100 s and 10 s in the early morning and in the afternoon, Median aerodynamic particle size of the microbial aerosols ~5 µm ✓ Effect of chlorination: reduction of downwind aerosolized bacterial concentrations to near background levels, while detection of coliphage at distances of 137 m downwind ✓ Survival of aerosolized bacteria [standard plate count] (5.2%) > that of coliphage f2 (4.3%) in the range of 46 to 76 m ✓ Relatively greater aerosol strengths at night were predicted by the volume source diffusion model.

  Hamilton et al. (2018) develop a generalized model for long-range transport with reclaimed water containing Legionella under a range of meteorological conditions that does not rely upon intensive site specific information. Risks (infection or clinical severity infection) were compared using data from nineteen United States reclaimed water utilities measured with culture-based methods, quantitative PCR (qPCR), and ethidium-monoazide-qPCR (EMA-qPCR based). A maximum trajectory height of 6 m and a distance of 24.4 m to reach the apex of the sprinkler stream was added to estimates for sprinkler (sprinkler height <1 m) setback distances. The results showed that for annual infection residential population risks, setback distances would be between <75 m (culture based) and about 625 m (qPCR-based) based on median values for comparison. 95th percentile annual clinical severity residential population risks were <10 -4 for culture-based and EMA-qPCR methods, but not for qPCR (corresponding to about 225 m setback distance). Occupational annual infection risks indicate a setback distance almost 5025 m (culture-based) or ≥10,000 m (EMA-qPCR or qPCR based) and occupational clinical severity risks correspond to distances ranging from <75 m (culture-based) to around 1225 m (qPCR based) using a 95th percentile comparison and <75 m using a median comparison. Mori and Smith (2022) conducted a QMRA to determine an individual adult's risk of infection with L.

Figure

  Figure 1.8).

Figure 1

 1 Figure 1.8. Schematic representation of the notion of transport, wind drift and evaporation losses

Figure 1 . 9 .

 19 Figure 1.9. Factors affecting the transport from sprinkler irrigation

Figure 1 . 10 .

 110 Figure 1.10. Lateral movement of water droplets[START_REF] Hofman | Spray Equipment and Calibration[END_REF]

  s -1 ], 𝜎 𝑦 (𝑥) and 𝜎 𝑧 (𝑥) are the standard deviation of plume concentration distribution in the crosswind and vertical direction [m], 𝑥, 𝑦 are downwind direction along plume centerline, the distance in crosswind, and vertical direction, respectively, and 𝐻 is the height of plume centerline [m]. To predict the fraction of microorganisms that remain viable at a given distance from the source, the term of microorganism die-off factor [𝑒𝑥𝑝 -(𝜆. 𝑥 𝑈 )] is multiplied by the Eq. 1-4. The parameter 𝜆 denotes the decay (inactivation) rate of the viable microorganisms [s -1 ]. 1080 Figure 1.12. Gaussian distribution of aerosol plume in crosswind and vertical direction downwind from a sprinkler. h and d are the sprinkler height and downwind distance from sprinkler head to receptor, respectively. Other parameters have already been presented (US-EPA, 1982).

  Passive samplers for measuring Particulate Matter. (a) Vinzents sampler, (b) UNC-PAS sampler, and (c) Electrostatic dust fall collector Numerous studies have been carried out to obtain field measurements of "pesticide spray drift" that define the off-target movement of pesticides by the action of air flux during an application using a wide range of passive samplers such as: 1260

Figure 2 . 2 .

 22 Figure 2.2. Droplet trajectories upstream of a 10 mm diameter cylinder in a 4 m.s -1 free airstream simulated using computational fluid dynamics. (a) 1 μm droplets. (b) 35 μm droplets. Data from Parkin and Young (2000) [Bateman et al. 2014].

  3 and three of the most common ones have been reviewed in this chapter. Mainelis (2020) also summarized the current state of the art in bioaerosol research with emphasis on bioaerosol sampling, and the need to develop a more standardized set of guidelines for protocols used in bioaerosol research. Moreover, more information about the several commercial samplers have been analyzed in Yates et al., (2016), which can be found in (Appendix. B). Verreault et al. (2008) has also reviewed the different samplers applied to detect viruses in bioaerosols. 3.(a) Cascade impactor, (b) Slit sampler (Impactor), (c) SKC BioSampler (Impinger), (d) Coriolis μ air sampler (cyclone), (e) Cassette samplers and filters (filter), (f) Electrostatic precipitator.

Figure 2 . 4 .

 24 Figure 2.4. Schematic of cross-section of a stage of impactor depicting the trajectories of particles of different sizes; W is the nozzle exit diameter, and S is the distance from the nozzle exit to collection surface (Drawn by Zardari). Yao and Mainelis (2006) evaluated the cut-off size of seven impactors (SMA, BioCulture, MAS-100, Microflow, SAS Super 180, Millipore Air Tester, and RSC High Flow). The results showed that for most samplers, the theoretical 𝑑 50 was lower than the experimental value, which was likely due to the dissipation

Figure 2 . 5 .

 25 Figure 2.5. Airborne particles collected by impinger into the liquid (Drawn by Zardari) Impinger design-Among the various type of the devices for air sampling into liquid (see Appendix. B), all-glass impingers (Ace Glass Inc., Vineland, NJ) and BioSampler (SKC Inc., Eighty Four, PA) are two types of the most used samplers (Figure 2.6). All glass impingers (AGI) collects airborne particles by their impingement vertically through a nozzle into a liquid collection. The distance between the jet nozzle

Figure 2 .

 2 Figure 2.6. Schematic diagram of the impingers: (a) SKC BioSampler, (b) AGI-4 or -30 (Drawn by Zardari).

Figure 2 . 7 .

 27 Figure 2.7. Schematic presentation of collection mechanisms at a filter (Lindsley, 2017).

Figure 2 . 9 .

 29 Figure 2.9. Schematic diagram of the limiting particle trajectory[START_REF] Grinshpun | A general equation for aerosol aspiration by thin-walled sampling probes in calm and moving air[END_REF]

Figure 2 .

 2 Figure 2.10. Diagram representation of the physical parameters affecting the aspiration efficiency 1620

Figure 2 .

 2 Figure 2.11. Schematic diagram of isoaxial sampling with a thin-walled nozzle, with sample flow gas velocity 𝑈 𝑠 and free stream ambient gas velocity 𝑈 0 under (a) isokinetic (𝑈 0 = 𝑈 𝑠 ) sampling conditions, (b) sub-isokinetic (𝑈 𝑠 < 𝑈 0 ) sampling conditions, and (c) super-isokinetic (𝑈 𝑠 > 𝑈 0 ) sampling conditions (Kulkami et al.2011). 1660 Table2.2 presents a series of equations developed to predict the aspiration efficiency for isoaxial

  [START_REF] Kesavan | Sampling and Retention Efficiencies of Batch-Type Liquid-Based Bioaerosol Samplers[END_REF] evaluated directly the retention efficiency of four active samplers by spiking a batch of liquid with solid polystyrene spheres (PS with size of 1 or 10 µm in diameter) into the sampler, and measurement of the tracer after running and operating the sampler for 30 s as a 𝑁 𝐿,0 from Eq. 2-5. The values of 𝑁 𝐿,𝑡 were measured based on analyses of subsequent batches of liquid at time-intervals of 15, 30, 45, and 60 min. For the AGI-30 and the batch-type wetted wall cyclone with compensation for evaporation (BWWC-EC), the retention efficiency of 1 µm PS particles after 60 min was less than 30%, while that of the SKC BioSampler was 59%. This value for SKC BioSampler with 10 µm PS particles was greater than 90% even at 60 min sampling time. They continued the experiments with two set of samplers (AGI-30 and SKC BioSampler) for 1 μm Bacillus atrophaeus spores, and 0.7 μm Pantoea agglomerans vegetative cells. The results for the spores were about the same as those for 1 µm PS and show lower retention efficiencies for the vegetative bacteria.Han and Mainelis (2012) investigated the effect of particle type (polystyrene latex particles of 0.9 and 3.2 mm, B. subtilis bacteria and C. cladosporioides fungal spores), particle concentration, and collection liquid volume (for SKC BioSampler) on retention efficiency of AGI-30 and SKC BioSampler after 15 and 30 min sampling time. The results indicate that the average retention efficiency for BioSampler with 5 mL liquid, BioSampler with 20 mL liquid and AGI-30 were 71.5%, 74.4% and 81.6%, respectively. Longer sampling time led to lower retention efficiency and higher concentration resulted in higher retention efficiency compared to lower one: 83.6 ± 12.4% compared to 70.6 ± 26.0%, on average.[START_REF] Su | Re-evaluation of BioSampler and its improvement for on-line, time-resolved monitoring of environmental coarse aerosol[END_REF] too tested the retention efficiency of a Continuous-Extraction BioSampler (CEBS) for different fluorescence-labelled polystyrene latex size range (0.1-10 µm) after sampling time of 10 and 15 min. The results showed that the value of retention efficiency after 15 min of sampling is lower than that after 10 min. The retention efficiency decreased with decreasing the particles diameter first and fell to almost 0.85[-] in submicron size range, then recover to nearly unity.

Figure 3

 3 Figure 3.1. Schematic diagram of the aerosol generation and test facility. *: sampling zone.

Figure 3 . 2 .

 32 Figure 3.2. Photo of the test facility

  BioTrak ® particle counter (model 9510-BD, TSI Inc, USA) with a flow rate of 28.3 L.min -1 , counts total and viable particles in real-time(Figure 3.3c). This counter uses laser diffraction to measure and count the number of particles in an air sample, in the diameter range from 0.5 to 25 μm with total particulate counting efficiency of 50% at 0.5 µm and 100% for particles > 0.75 µm, according toISO 21501-4 (ISO 21501-4, 2018). The size of particles detected by BioTrak ® is divided into six value intervals: 0.5-0.7 μm; 0.7-1 μm; 1-3 μm; 3-5 μm; 5-10 μm and 10-25 μm. In addition, there are two fluorescent channels and one sizing channel for detection of viable particles with a diameter between 1 and 25 micrometers. For particle viability detection, the BioTrak ® uses Laser-Induced Fluorescence (LIF) technology. In simple terms, when microorganisms (viable particles) are exposed to laser light at 405 nm, they absorb and reemit light at higher wavelengths; a process called fluorescence. Fluorescent cell metabolites most commonly associated with viability are NADH, riboflavin and tryptophan, which are targeted by the LIF technique(Laflamme et al, 2005; Buschke et al, 2012). The results are expressed in particle viable counts per cubic meter of air (vcnt.m -3 ).

  3. (a) Impingers: (left) AGI-4, (right) SKC BioSampler; (b) Glass fiber filter with two-piece 1900 plastic cassettes; (c) BioTrak ® particle counter.

Figure 3 . 4 .

 34 Figure 3.4. Size droplet distribution: (a) Normalized number/volume Fraction, (b) cumulative distribution function. The x-axis is a log axis. As shown in Figure 3.4a, the curves of the number and volume distribution of aerosols had a sharp peak at 0.6 µm. The cumulative Log-normal distribution with the NMD and VMD values of 0.66 and 0.92 µm,respectively, indicate that 90% of the total number and volume of the aerosols were related to the aerosols inferior to 1 and 1.4 µm, respectively. However, due to limited number of channels, the cumulative Log-1930 normal function did not fitted very well to the raw data. Therefore, by interpolation from the values of the column 7 of Table3.2, the 90% of the total volume could be estimated to be inferior to about 2.5 micron.

( 5 .

 5 𝑈 ≅ 0.09 𝑚. 𝑠 -1 ), certain images were taken by Particle image velocimetry technique (PIV) in wind tunnel using the smoke as a tracer of the air (Figure 3.5). The experimental set-up and image processing have been presented in Appendix. F. It is seen right away that depending the aspiration velocity, all streamlines were drawn toward the inlet of impingers, and no limiting streamline can be determined. Typical visualization of the general patterns of the vectors/airflow trajectories near: (a) AGI-4; (b) SKC BioSampler, at their nominal flow rate in calm air. The vector maps were attaint by Particle 1970 image velocimetry technique (PIV) experiments, (see Appendix. F). To better observe all the air velocity vectors, their scales have been changed.

  6. (a) Liquid evaporation/ re-aerosolization rate of impingers at different sampling times; (b) The influence of the temperature on the evaporation rate of the liquid in the impingers (sampling time: 15 min. Relative humidity: 10-15%).

  .7).

Figure 3 . 7 .

 37 Figure 3.7. Schematic diagram of the principle of DLS technique and the patented technology of VASCO with its DTC and solvent proof cell.

  room temperature. A Few droplets of the sample were directly dropped into the VASCO cell with a micropipette (see Figure 3.8). The Dual Thickness Controller system (DTCTM) was set in the up position (by default) and the measurements at least triplicated, were performed using nanoQ TM software with each correlogram acquisition processed by the Pade-Laplace algorithm. All the criteria for optimizing the correlator setting are explained in detail in nanoQ & Vasco Instruction guide 2013.

Figure 3

 3 Figure 3.8. Measurement of colloidal particles size of the ABF in the solvent by using a nanoparticle size analyzer.

Figure 3 .

 3 Figure 3.9 shows a size histogram expressed in intensity as a function of Hydrodynamic diameter with 2040 two peaks and 0.234 and 1.072 µm with 0.46 and 0.53 relative intensities. The water contained ABF particles characterized by the following parameters: 𝐷 𝑚𝑒𝑎𝑛 𝑛𝑢𝑚𝑏𝑒𝑟 =0.243 µm, 𝐷 𝑚𝑒𝑎𝑛 𝑖𝑛𝑡𝑒𝑛𝑠𝑖𝑡𝑦 =0.653 µm, 𝐷 𝑚𝑒𝑎𝑛 𝑣𝑜𝑙𝑢𝑚𝑒 =0.784 µm, in Pade Laplace analysis mode, and 𝐷 𝑣10 = 0.224 µm, 𝐷 𝑣50 and 𝐷 𝑣90 =1.024 µm.The statistics 𝐷 𝑣10 , 𝐷 𝑣50 , and 𝐷 𝑣90 are diameters that respectively represent the cumulative volume less

Figure 3 . 9 .

 39 Figure 3.9. The size of the colloids of ABF as a function of intensity (a. u).

Figure 3 . 10 .

 310 Figure 3.10. Schematic of the experimental setup used to test physical collection efficiency of SKC BioSampler/AGI-4.

  filters, and directly to three test tubes containing 20 mL of DW as a standard liquid. The filters were air 2080 dried and placed individually in the test tubes containing 20 mL of DW. The three test tubes with filters were shaked for 1 hour (M2) with laboratory Orbital shakers (Stuart ® SSL1, Cole-Parmer, UK) and the other three remain in the water for 1 hour without agitation (M1). The fluorescence intensity of each test tubes was then measured with the fluorescence spectrometer. Figure 3.11 presents the results of dye recovery from the filters compared with the standard mix. No significant difference between the two methods was observed (33 a.u.). Nevertheless, as the method 1, M1 gives the closest result to the standard mix and it was retained.

Figure 3 .

 3 Figure 3.11. Comparison of Fluorescence intensity readings for Samples of ABF Recovered from filters through two methods with intensity readings for standard mix.

  12. The schematic diagram of the experimental setup to characterize the effect of sampling time on the viability of bacteria collected by impingers; (b) Experimental setup of BioTrak ® in sampling chamber 2120

  13. (a) Physical collection efficiency of impingers at different sampling times, 𝜂 𝑝ℎ𝑦𝑠 ; (b) Sampling efficiency, 𝜂 𝑠𝑎𝑚𝑝𝑙𝑒 ; (c) Residual efficiency, 𝜂 𝑅 . Error bars are ±1 standard deviation about a mean (Temperature: 18.9-23.7 °C, Relative humidity: 10-15%).

Figure 3 .

 3 Figure 3.14. Effect of sampling time on culturability of. CBtotal represents the ratio of culturable E.coli recovered by impingers (CFU.m -3 ) to total aerosols detected by BioTrak® (#.m -3 ). Error bars are ±1 standard deviation about a mean.

Figure 3 .

 3 Figure 3.15. (a) Comparison of samplers collecting/detecting the airborne live Escherichia coli: Impingers and BioTrak. CBviable represents the ratio of the culturable E.coli recovered by impingers (CFU.m -3 ) to viable aerosols detected by BioTrak ® (vcnt.m -3 ). Error bars are ±1 standard deviation about a mean.

Figure 4 .

 4 Figure 4.1. Open Circuit Wind-Tunnel overview

2 .

 2 Photos of (a) Housing of electronic variable speed drive, (b) Heat reclaim ventilation unit, (c) Wire thermocouples connected to the analog input channel, and (d) Hygrometer (test 48H).

  3). The total water volume atomized in the working section of wind tunnel, the spray pressure was maintained at 200 kPa to provide a flow rate of 0.025 L.min-1 [𝑉 2 = 𝑉 1 5 ]. The accuracy of the pressure-Flow rate was controlled by a balance (KERNPKS, precision: 0.01 g, KERN & Sohn GmbH, Germany) situated underneath the nozzle tip weighting the captured droplets for at least 30 minutes. The specifications of the spray nozzle has been summarized in Table 4.4.

Figure 4 .

 4 Figure 4.3. Axial-flow hollow cone nozzle; dimension; spray pattern 2310

Figure 4 . 4 .

 44 Figure 4.4. Malvern Spraytec Particle Analyzer When measuring droplet characteristics, the nozzle was correctly positioned with the nozzle body whereby, the spray plume downwards into the laser beam. The pressure was set at 200 kPa by a pressure regulator. It was measured using a pressure transmitter (Keller, Switzerland) which has a range of 0-1000 kPa and an accuracy of ± 0.01%.

  Figure 4.6, was equipped by a Flowexplorer DPSS transmitter-compact with 532 nm (green) and 561 nm (yellow-green) laser wavelengths, which have been adjusted on 50 mW output power. Both lasers are first received in a Bragg cell where the initial beams are separated into two equal beams with a frequency difference of 80 MHz. A standard front lens with 1000 mm focus-length was used for the transmitter, forming a PDPA measurement volume of 0.5293 × 0.5291× 19.6 mm 3 , along the principal directions. The scattered light created from a droplet passing this measuring volume forms an interference fringe pattern.The frequency of this scattered light is proportional to the droplet velocity while the spatial frequency of the interference fringe pattern is inversely proportional to the drop diameter. The Fiber PDA receiver probe was set up in a forward refraction mode at an angle of 30° and focal length of the lens of 1000 mm to collect the scattered laser light. Briefly, the Fiber PDA consists of front lens (adjust the measurement distance), an optical mask system A (which makes it possible to differentiate and adapt the collection angle for each of the three detectors, U1, U2, U3), the spatial filters, coupling optics of the different flows collected on multimode fibers. These run in a single cable leading to the Fiber PDA detector unit which comprises three photomultipliers (PM) and components for calibration and for optical filtering. Three photomultiplier tubes convert the scattered light into electrical signals to be processed for velocity and size information by the Burst Spectrum Analyser (BSA) also from Dantec Dynamic. The BSA software gives

Figure 4 . 7 .

 47 Figure 4.7. Schematic of the spatial location of the PDPA set-up and placing the nozzle tip at different positions upstream of the measurement point of PDPA.

Figure 4 .

 4 Figure 4.8 shows the trend of 𝐷 𝑣10 , 𝐷 𝑣50 and 𝐷 𝑣90 variation along the diameter of the spray at three axial distances from the nozzle tip, according to sampling time. Each segment, one of which is enclosed in a green rectangle, represents a full passage of spray nozzle in front of the laser beam of Malvern Spraytec (Figure 4.8). The results show that all droplet size distributions were similar and quasi-parabolic,

  These results allowed us to interpret the crosswind distribution of airborne spray plume at various distances from the nozzle head in the Appendix. E. Trend of 𝐷 𝑣10 , 𝐷 𝑣50 , and 𝐷 𝑣90 variation along the diameter of spray plume at axial distances of, (a): 50 mm, (b):100 mm, and (c): 150 mm from the nozzle tip. Malvern Spraytec system generated an overall volume frequency and cumulative volume of droplet size distributions at three axial distances of 50, 100, and 150 mm from the nozzle tip, presented in Figure 4.9. 2470

9 .

 9 Droplet volume distribution at axial distance z= (a) 50 mm; (b) 100 mm and (c)150 mm from the tip of the nozzle.

Figure 4 .

 4 Figure4.10(a-l) shows the airborne droplet characteristics ( 𝐷 𝑣10 , 𝐷 𝑣30 , 𝐷 𝑣32 , 𝐷 𝑣50 , 𝐷 𝑣90 , 𝐷 𝑛10 , 𝐷 𝑛50 and 𝐷 𝑛90 ) as a function of air velocity, at locations 0.45 to 1.65 m downwind from the nozzle tip that was installed at heights of 0.1, 0.175 and 0.275 m above the working section floor. As mentioned earlier, considering the main purpose of these experiments, that was to characterize the droplet size distribution near the inlet of the impingers, the measurements had only been conducted along the centerline of the cross-section (Height = 0.175 m, width = 0.25 m).

Figure 4 . 10 .

 410 Effect of air velocity on characteristics of airborne droplet generated from a nozzle tip positioned at a height of (a-d) 0.1 m, (e-h) 0.175 m, and (e-h) 0.275 m above the working section floor, and at the distances of 0.45 to 1.65

Figure 4 .Figure 4 .

 44 Figure 4.11 and Figure 4.12 represent function, 𝑓(𝐷), and cumulative, 𝐹(𝐷), volume of downwind airborne droplet size distributions using Rosin-Rammler method as a function of the air velocities (1.2, 2.25, 3.3 and 5.0 m.s -1 ) at various distances (0.45, 0.85, 1.25, and 1.65 m) downwind from the nozzle positioned at a height of 0.175 and 0.275 m above the floor, respectively.

Figure 5 .

 5 Figure 5.1. Photo of 5022SD-U. NaanDanJain impact sprinkler irrigation, used in the present study

Figure 5 . 2 .

 52 Figure 5.2. Experimental setup used for evaluation of: (left) application rate, (right) droplet size distribution

of

  Bulk Richardson values, Golder (1975) developed a Nomogram (B-z) and can be found in article of Zoumakis and Kelessis (1991) (Figure 5.3). This nomogram has been extended within the range -0.04 2820 <B< 0.08, representing the most unstable and stable conditions, according to EPA (2000) [see Figure 5.3]. It should be mentioned that the value obtained in experiment N°4 (2.47, neutral conditions according to SRDT) is located outside the limit of the lowest stable of Nomogram [B-stability classesz]).

Figure 5 .

 5 Figure 5.3. Classification scheme for the bulk Richardson number, B, as a function of the height z, in terms of the Pasquill A-F stability classes (Zoumakis and Kelessis, 1991).

Figure 5 . 4 .

 54 Figure 5.4. Scheme of sampler and meteorological station downwind from the sprinkler

7 .

 7 Additionally, in order the evaluate the genericity of the models, we have applied them on another dataset acquired in different environmental conditions [field data of[START_REF] Tomas | Transport Modeling in Sprinkler Irrigation[END_REF], and at a nozzle pressure of 350 kPa].

Figure 5 .

 5 Figure 5.5a illustrates the water distribution curve (converted to application rate, in mm.h -1 ) as a function of distance from the sprinkler head along the radius of throw. The curve confirms that the capacity of this dual-nozzle sprinkler can provide distribution uniformity with adapted spacing. As explained in section 5.2.1.1, this curve was used for calculating the total volume of applied water presented in Table 5.2.

Figure 5 .

 5 Figure 5.5b represents the range of droplet sizes formed at different distances from the sprinkler.

Figure 5 . 5 .

 55 Figure 5.5.(a): Water application curve, (b): Cumulative distribution in volume [%]

Figure 5 .

 5 Figure 5.6a to 5.6c show the crosswind distribution of aerosol plume at the heights of 1 and 2 m above the ground and deposited on the surface according to the line direction in regard to average wind direction, for airborne transport (PVC strings) and sedimented transport (Petri dishes). These distributions correspond to a 120-degree arc located at a distance of 20 m from the sprinkler head. Exp.5 and Exp.6 were chosen because of their contrasting standard deviation of wind direction (σθ, of 32.5 and 18.70 degrees, respectively), under slightly different wind speeds (3.1 and 3.6 m.s -1 , respectively). Note that the other meteorological parameters were almost constant.Figure 5.6 clearly illustrates the effect of the standard deviation of wind direction on horizontal dispersion of the aerosol plume. The best-fit curves derived from observations (using CurveExpert 1.4 Basic software) tend to be Gaussian (𝑓(∆𝜃) = 2960 𝑎 × 𝑒𝑥𝑝[-0.5 × (∆𝜃-𝑏) 2 2𝑐 2 ], where a, b and c are the coefficients), which agrees with Pasquill (1961) who observed that the horizontal dispersion could be estimated from measurements of horizontal winddirection fluctuations (see Appendix. G Table G. 4 and Figure G. 3 for details).

Figure 5 . 6 .

 56 Figure 5.6. Crosswind distribution of aerosol plume at a distance of 20 m at two heights of (a):1 m, (b): 2 m above the ground, and (c): deposited on the ground/surface, for the Exp.5 and Exp.6

Figure 5 . 7 .

 57 Figure 5.7. Downwind airborne transport as a function of wind speed, at the height of 2 m above the ground

  Figure 5.9 compares the results of two different temperatures (T= 32.5 and 22.9°C) on the vertical profile of airborne transport and sedimented transport downwind from the sprinkler head. These selected temperatures belong to Exp.2 and Exp.9 as other meteorological parameters than temperature remained almost constant. The values were normalized to their maximum: 10.1 mL.m -2 .h -1 for PVC strings and 2.67 mL.m -2 .h -1 for Petri dishes.

Figure 5 .

 5 Figure 5.9b shows that at 20 m, the sedimented transport is almost constant for both temperatures.

Figure 5 .

 5 Figure 5.9.(a): Vertical profile of normalized airborne transport and (b): normalized sedimented transport as function of temperature at the different distances from the sprinkler head. (Black dots: 3030Exp.2: 32.5 °C; red dots: Exp.9: 22.9 °C)

Figure 5 .Figure 5 .

 55 Figure 5.10 shows the overall correlations between observed data and the simulated results of the two models calibrated from PVC string data. The correlation provides an indication of the accuracy of the models. Given R 2 , globally, both models fitted the observed data well. The linear adjustment of the points results in a slope that indicates a systematic underestimation of the airborne transport simulated

𝑅 2 =

 2 0.909; 𝑅𝑀𝑆𝐸: 0.178; 𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑣𝑎𝑙𝑖𝑑 𝑜𝑏𝑠𝑒𝑟𝑣𝑎𝑡𝑖𝑜𝑛𝑠: 279/280As shown in Figure5.12, the overall correlations between observed data from Petri dishes and simulated results of sedimented transport through the M1 and M2 are 0.92 and 0.93, respectively, which indicate that the data were scattered around the angle bisector (y = x) with the coefficient of determination (R 2 ) over 0.9.

Figure 5 .Figure 5 .

 55 Figure 5.12. Correlation between observed and simulated data from Petri dishes (sedimented transport), y=ax, (a): Power-crosswind Gaussian model, M1, (b): Exponential-crosswind Gaussian model, M2 Figure 5.13 shows two examples of sedimented transport values as a function of distance (Exp 3 and 5). The other figures are available in G. 3. 2, Figure G. 9 and G. 10. These experiments have been selected because of their almost similar meteorological parameters except relative humidity (Exp. 3 with U = 3.0 m.s -1 , Temp = 26.92 °C, RH = 27.22%, RS = 740.23 w.m -2 and σθ = 38.89 degrees, and Exp. 5 with U = 3.1 m.s -1 , Temp = 29.43 °C, RH = 41.64% and RS = 562.45 w.m -2 and σθ = 32.55 degrees).

  14. Genericity of the both models with experimental data from Rain Bird 5000+ under operating pressure of 350 kPa and at height of 1m above the ground: (a) on 25 September 2013 (Wind speed:1.19 m.s -1 , Temperature: 23.8 °C, Relative Humidity: 73 %); (b) on 09 October 2013 (Wind speed: 2.88 m.s -1 , Temperature: 20.1 °C, Relative Humidity: 65 %); and (c) on 30 October 2013 (Wind speed:3.85 m.s -1 , Temperature: 17 °C, Relative Humidity: 52 %)

  15. Downwind E.coli transport as a function of wind speed, at (a): height of 1.5 m above the ground, (b): deposited on the ground/surface at the Normal Temperature and Pressure (NTP), and the relative humidity of 40% Conclusion In this study, nine field experiments were conducted to quantify the airborne and sedimented transport of the smaller droplets released from a sprinkler used for irrigation under different meteorological conditions. An accurate characterization of the fraction of spray flow spread by a sprinkler (usually applied over various crops) was carried out thanks to our tried and tested experimental protocol. The quantification of the aerosolized component was analyzed over a horizontal plane (up to 40 m from the emission source) and vertically (up to 10 m above the ground) in a downwind direction, beyond the irrigated (i.e. wetted) area. The measurements conducted in lab conditions have shown that droplets smaller than 0.15 mm (subjected to aerosolization) represented 0.15% of the total irrigated volume.

  Such a work is of interest to healthcare farmers in close proximity of sprinkler irrigation with treated wastewater The objectives of the part II were: 1) To compare different active sampling methods of airborne particles in controlled conditions from sprinkler irrigation of TWW, 2) To quantify the physical collection efficiency in low wind velocity, using a fluorescent tracer, 3) To investigate the effect of sampling time on the collection efficiency and on the culturability of the Escherichia coli (E.coli) cells sampled by the impingers (Chapter 3), and 4) To characterize the effect of air velocity and nozzle height on the airborne droplet size distributions downwind from the source using Phase Doppler Particle Analysis (PDPA), in a small scale wind tunnel (Chapter 4).

Figure A. 1 .

 1 Figure A. 1. Illustration of the effect of Stokes number (Stk) on the transport of particles in the flow about a bluff body[START_REF] Vincent | Aerosol Sampling[END_REF].

Figure D. 2 .

 2 Figure D. 2. Air flow velocity pattern at a field measurement of 91× 91 mm 2 using PIV at air velocity of 1.2 m.s -1 . (a) Mean air velocity in the X direction, (b) Mean air velocity in the Y direction.

  25 m s -1 and (a) 0.95 (b) 1.8 m downwind from the inlet of the working section Figure D. 3. Air flow velocity pattern at a field measurement of 91× 91 mm2 using PIV at air velocity of 4660 2.25 m.s -1 . (a) Mean air velocity in the X direction, (b) Mean air velocity in the Y direction.

  Figure D. 4. Air flow velocity pattern at a field measurement of 91× 91 mm 2 using PIV at air velocity of 3.3 m.s -1 . (a) Mean air velocity in the X direction, (b) Mean air velocity in the Y direction.

Figure D. 5 .

 5 Figure D. 5. Air flow velocity pattern at a field measurement of 91× 91 mm 2 using PIV at air velocity of 1.2 m.s -1 . (a) Mean air velocity in the X direction, (b) Mean air velocity in the Y direction.

  of the collection reservoir of the sampler containing the DW was measured by means of the balance with an accuracy of ±0.01 g. It was then stabilized in the center of cross-sectional area of the working section (Height = 0.175 m, width = 0.25 m) at 1.65 m downwind from the spray nozzle. The spray nozzle was situated at a height of 0.275 m above the working section floor. The air velocities ranged from 1.2 to 5.0 m. s -1 (Figure E. 1).

  Figure E. 1. Schematic of placing the active sampler (AGI-4/SKC BioSampler) and spray nozzle in the working section

  Figure E. 2. Overall view of the experimental set-up of the PVC strings in the wind tunnel

  Figure E. 4. Airborne spray distribution at 0.85 m downwind from the spray nozzle under the air velocity of 5 m.s -1 . The legend unit is in meter of water.

  Figure E. 5. Mean curves through the experimental points of[START_REF] May | The Impaction of Aerosol Particles on Cylinders, Spheres, Ribbons and Discs[END_REF].

  [START_REF] Fritz | Development of a System for Determining Collection Efficiency of Spray Samplers[END_REF] found a sigmoid function to fit the[START_REF] May | The Impaction of Aerosol Particles on Cylinders, Spheres, Ribbons and Discs[END_REF] data for the collection efficiency (𝜂 𝑐 𝑖 ) of a cylinder versus Stokes number, as follows,

  of a spheres versus Stokes number, is given by,

Figure

  Figure E. 6a to 6c show the vertical profile of airborne spray volume (Eq. E-9) obtained from PVC strings under various air velocities (1.2, 2.25, 3.3 and 5.0 m.s -1 ) at distances of 0.85 and 1.65 m downwind from the spray nozzle tip positioned at heights of 0.175 and 0.275 m above the floor. All the values were normalized to a maximum value of 0.045 mL (Figure E. 6c, for U= 5.0 m.s -1 , and measurement point of 0.125 above the floor).

  centerline. When the air velocity is relatively strong, the spray droplets can act as a porous resistive body over a certain distance below the nozzle, which deflects the flow lines. Besides, for a strong air velocity (𝑈 𝑎 /𝑈 𝑗 ≥ 0.3), the 50 µm droplets immediately escape from the spray jet into the air flow and this detrainment is enhanced with narrower spray angles. Figure E. 6. Distribution of airborne spray volume in the vertical direction under various air velocities using PVC strings at 0.85 and 1.65 m downwind from the nozzle height positioned at a height of (a-b) 0.175 m, (c-d) 0.275 m above the floor.

Figure E. 7

 7 Figure E. 7 shows the effect of air velocity and nozzle height on distribution of airborne spray volume at two distances (0.85 and 1.65 m) downwind from the nozzle tip. All the values were normalized to a maximum value of 2.49E-03 mL, which correspond to the measurement point : Y = 0.125 m; Z = 0.25 m, Nozzle height = 0.275 m above the floor, and under the air velocity of 5 m.s -1 (see Figure E. 7o).

Figure E. 7

 7 Figure E. 7 shows that regardless of the magnitude of air velocity and distance from the nozzle, the highest airborne spray volume occurred in downwind direction along spray plume centerline. This is in accordance with the results obtained from the measurements of initial droplet distribution across the plume in section 4.3.1, which were expressed that the range of the droplets size at the center of the spray was higher than towards the edge of the spray.

Figure E. 7

 7 Figure E. 7 also shows that the effect of nozzle height, downwind distance and air velocity on distribution of the airborne spray volume in vertical direction is similar to the results obtained using PVC lines. Therefore, both passive samplers are able to correctly characterize the dispersion of airborne droplet/ spray plume. The total airborne spray transport potential values obtained using both samplers for each configuration have been analyzed and compared in the next section (TableE. 2).

G. 1 . 2 .

 12 Figure G. 1. Variation of wind speed, wind direction and standard deviation of wind direction from the height of 2 to 10 m

  showing that fluctuations in wind direction and speed from their respective mean values were uncorrelated with one another, the intermediate variable, ϵ is computed as: (-𝑛 -1 ∑ 𝑈 𝑖 𝑠𝑖𝑛𝜃 𝑖 ) 2 + (-𝑛 -1 ∑ 𝑈 𝑖 𝑐𝑜𝑠𝜃 𝑖 ) 2 ] 1 2 ⁄

  Figure G. 3. Wind Rose (by WRPLOT View Freeware 8.0.2 software) of: Exp.5 (left); Exp.6 (right)

  6. Simulation (lines/dashed lines) of downwind airborne transport and experimental data (black dots) from PVC strings at two heights of 1 and 6 m above the ground which related to: (a) Exp.2, (b) Exp.3, (c) Exp.5, (d) Exp.9 (see table. 1)G. 3. 1. 2. Simulation of vertical dispersion of aerosol plume

Figure

  Figure G. 7a to 7l show simulated airborne transport as a function of height, for three distances.These curves indicate that both models globally predict the trend of vertical dispersion well.

Figure G. 9

 9 Figure G. 9 shows the sedimented transport as function of distance. Contrary to the airborne transport, except for the first two distances (16 and 18 m), there is no significant difference between Exp.3 and 5. Therefore, this result confirms that the relative humidity has low or no-impact on the sedimented transport. As shown in FigS10d, experiments with a relative low temperature, sedimented transport at distances close to the sprinkler (up to 22 m) are modelled with less accuracy than ones at longer distances, considering relative differences between measured and modelled sediments. It is evident that under similar meteorological conditions, as temperature decreases, more downwind sedimented transports are measured near the wetted area. It might be because fewer number of experiment realized under relative low temperature so that the models less accurately fit these measuring points.

  10. Simulation (line/dashed line) and experimental data (dots) of the crosswind sedimented transport of the fine droplets for: (a) Exp.5 and 6, (b) Exp. 8 and 9, and (c) Exp.1 and 7

Table 5 .

 5 1 Average micrometeorological parameters during the experiment ............................................131 Table 5.2 Synthesis of droplet size distribution within a wetted area in laboratory conditions ...............136 𝜂 𝑜𝑣𝑒𝑟𝑎𝑙𝑙 , 𝜂 𝑃ℎ𝑦𝑠 , 𝜂 𝑇 , 𝜂 𝑐 , 𝜂 𝑅𝐸𝑇 , 𝜂 𝑅𝑒𝑐 , and 𝜂 𝐵𝑖𝑜

	Nomenclature
	Greek alphabet
	α	Thermal diffusivity

Table 1 .

 1 1. Ranking of the top 22 countries reusing treated municipal wastewater for irrigation purposes [10 9 m 3 . Year -1 ] and two important countries in agriculture as well as our destination France 690

	Rank	Country	Cultivated	% of	Area	Area	Area	% of area	% of area	% of area	TMWW	Direct	Direct	Not
			Area	cultivate	equipped	equipped	equipped	equipped	equipped	equipped	[10 9 m 3	use of	use of	TMWW
			[1000 ha]	d land	for	for	for	for	for	for	Year -1 ]	TMWW	TMWW	Used for
				irrigated	Surface	Sprinkler	Localized	irrigation	irrigation	irrigation		[10 9 m 3	for	Irrigation
				[%]	Irrigation	Irrigation	Irrigation	actually	by direct	by direct		Year -1 ]	Irrigation	[10 9 m 3
					[1000 ha]	[1000 ha]	[1000 ha]	Irrigated	use of	use of not			[10 9 m 3	Year -1 ]
								[%]	TMWW	TMWW			Year -1 ]
									[%]	[%]
		Mexico	22607	28.0	5 841	362.8	167	83.1	1.325	5.31	4.46	1.55	1.38	4.33
		China	135676	70.4	65866	3154	843.1	83.66	5.2	1.86	49.31	3.86	1.26	-
		UAE	81.7	68.6	6.446	3.42	44.7	139.3	29.23	-	0.74	0.57	0.55	-
		Albania	696	33.4	397	0.026	-	58.54	-	-	-	-	0.54	-
		Saudi	3595	26.5	1429	1449	400.8	29.31	1.58	0.09	1.66	0.3	0.54	-
		Arabia
		Syrian	5733	23.3	1043	187.1	110.9	90.23	0.67	2.98	0.55	0.55	0.37	0.42
		Iran	17438	49.3	7 951	299.6	449.3	73.83	-	2.76	0.89	-	0.33	0.24
		Egypt	3836	165.1	2 891	434.2	497.7	89.51	0.93	-	4.28	1.2	0.29	-
		Israel	483	33.5	-	60.00	168.8	80.67	17.78	-	0.45	0.55	0.28	-
		Kuwait	14	106	10.75	2.14	4.094	68.9	53	-	0.29	0.08	0.28	-
		Australia	31306	7.3	1 653	473.0	172.4	100	-	-	2	0.14	0.14	-
		Chile	1 680	58.7	801.3	57.4	249.8	98.67	11.7	-	0.77	-	0.14	-
		Peru	5899	43.7	2 362	86.87	130.9	70.08	0.052	0.36	0.28	0.03	0.11	-
		Jordan	280	29.5	15.19	1.69	73.73	82.78	4.62	0.83	0.18	0.16	0.10	-
		Italy	9160	29.1	1 543	1591	990	61.9	0.097	-	3.90	0.05	0.09	-
		Qatar	17	77.7	9.908	1.85	1.444	100	6.50	-	0.28	0.13	0.07	-
		Greece	3222	39.7	-	-	-	76.8	0.31	-	0.57	0.1	0.07	-
		Kazakhstan	29880	3.6	1 264	122.0	93.64	79.87	1.86	-	0.58	0.05	0.05	-
		Turkey	23180	18.1	4 690	500	150	93.58	0.17	0.17	4.24	0.05	0.05	-
		Libya	2050	19.8	-	-	-	83.75	0.73	-	0.04	-	0.04	-
		Oman	109	52.8	51.4	33.40	24.1	52.8	-	-	0.09	0.06	0.024	-
		Cyprus	130	23.6	1.907	1.907	34.33	61.6	99.14	-	0.03	0.02	0.02	-
	-	India	169317	54.7	68 172	1592	636.4	93.9	0.0019	0.01	4.42	-	-	1.23
	-	USA	160437	14.2	12 078	12780	1850	84.76	0.05	-	45.35	2.77	-	-
	-	France	19132	7.2	115.88	2438.4	108.3	48.98	0.149	-	3.77	0.41	-	-

*TMWW :Treated Municipal wastewater. Source of data, year :2019 : https://www.fao.org/aquastat/statistics/query/results.html

  ).

	Wheel -Move	Linear-Moving Lateral	Travelling Gun
	Hand Move System	Solid Set	System Wheel Chassis Booms

Figure 1.6. Examples of the types of sprinkler heads available. *LEPA: Low Energy Precision Application (Sources: Big Gun-https://www.kometirrigation.com/; Rotator ®https://nelsonirrigation.com/; Bubbler-LEPA-https://www.senninger.com; Impact sprinkler and Rotorhttps://www.rainbird.com/; Micros prinkler-https://www.antelco.com/; Fogger-740 https://www.rivulis.com/)

Table 1 .

 1 2. Summary of the field studies examining the factors affecting microorganism concentrations in aerosol from sprinkler irrigation of (treated or untreated)wastewater

	Authors	Type of sprinkler systems	(Not/Partially) treated	Tracer/ microorganisms in	Downwind
	(year)		Wastewater quality	reservoir	of source*
					[m]

Irrigation at night and wind > 7 m.s -1 → observed FC and FS about 4cfu/m 3 at 350 to 409 m from pipeline wastewater ✓ Effect of storage of WW in reservoirs → 100 times reduction in downwind air densities of indicator organisms ✓ Mean Enterovirus level in the air in the summer > in the spring

			Treated WW: Lagoon	-Standard plate count	20-200	✓ Bacterial aerosol levels at: 21 to 30 m downwind averaged 485
			(depth: 1.5 m for 90	(8.0E+03 to 6.6E+04		CFU/m 3 > at 9.1 m AGL and at 200 m downwind by one order
			days)→a chlorine contact	CFU/mL)		magnitude
			chamber (1 hour contact	-Total coliforms (avg.170		✓ 75% of the bacteria-bearing aerosol at 21 to 30 m downwind
			time)→a holding basin (4	CFU/ mL)		were within the range of pulmonary deposition (1-5µm)
			to 6 days)	-Fecal coliforms (0.55		✓ Mean aerodynamic bacteria aerosol size → 2.5 to 2.8 µm
				CFU/mL)		✓ Particle size of bacterial aerosols recovered 200 m downwind
				-Fecal Streptococcus		suggest that they are of wastewater origin
				-Enterobacteriaceae		✓ A mathematical dispersion model for predicting aerosol
				-Klebsiella		strength during each run at ground level→0.56% of nonvolatile
						materials in the wastewater escaped the spray zone as an
				-Tracer: Uranine		aerosol, for bacterial aerosols→smaller by 52% at 21 to 30 m
				(Fluorescein disodium)		and by 77% at 200 m
	Camann et	Center Pivots	Irrigation from pipeline	-Fecal coliforms, FC	Sampling	✓ About 38 to 44% of viable particles smaller than 4.7 µm over the
	al. (1988)		wastewater: Trickling	(In pipelines :10 6 FC/100mL,	20-409	limited range of downwind distances
	filter effluent (virus levels 100-1000 PFU/L) Irrigation from WW reservoir (virus Levels <10 PFU/L) ✓ Shuval et al. -In reservoir: 10 3 -10 4 FC/100 mL) -Fecal streptococci, FS -Mycobacteria, MY -Clostridium perfringens -Coliphage -Enterovirus Sprinklers (type was WW drawn from algal -Total bacteria count 30, 230, ✓
	(1989)	not specified)	laden oxidation ponds	-Total coliforms, TC	430 and
				-Fecal coliforms, FC	730
				-Fecal streptococci, FS
				-Enteric viuses
				-Nanochloris

All night samples at 730 m were positive for bacterial indicator organisms ✓ Resistance of aerosolized enteric viruses to hostile

	Continued
	Authors	Type of sprinkler	(Not/Partially)	Tracer/ microorganisms in	Downwind of	Comments
	(year)	systems	treated		reservoir	source*	(Factors
			Wastewater			[m]
			quality
							environment
							factors > bacterial indicator organisms
							✓ Nanochloris (1-2µm, predominant algal species) of wastewater
							origin and was detected up to 730 m from sprinkler irrigation
	Devaux et	Sprinklers (1996-1997:	Tertiary treated with 3	-Faecal coliforms, FC	10-150	✓ An epidemiological and environmental approach used to
	al. (2001)	irrigated area 50 ha,	ponds (1996-1997)		-Enterococci, SF		evaluate the security for the exposed populations (farmers and
		since 1998 : 580 ha)	Secondary treated with 8	-Salmonellae		surrounding population)
			ponds (since 1998)				✓ No Faecal bacteria were observed in aerosols with water
							concentrations equal to 10 3 CFU/100 mL Faecal coliforms or
							Streptococci and an exposure time of 20 min

affecting the fate and viability of bioaerosols)

	Donnison et	High-pressure spray,	Controlled water	-Bacillus subtilis spores, Bs	50-125	✓ Linear regression between Se inactivation (y) and RH at 100 m
	al. (2004)	HPS (8 nozzles, P =	by adding micro-	(HPS = 7.7×10 5 /mL, LPS =		downwind from sprinkler:
		1400 kPa, Q = 770 L.h -1 ,	organisms	6.6×10 5 /mL)		High-pressure sprayer: y = -2.58 + 0.026%RH, and
		droplet size < 200 µm)		-Serratia entomophila, Se		Low pressure sprayer: y =-1.20 +0.012%RH→Low-pressure
		Low-pressure spray, LPS		"Physiologically similar to		sprayer produced fewer aerosols of proportionally larger size that
		(Head rotary sprinkler, P		E.coli" (HPS = 3.5×10 5 /mL,		do remain suspend in air and evaporate to form small droplets→
		= 300 kPa, Q = 2200 L.h -		LPS = 7.6×10 5 /mL)		contain several microorganisms→a greater chance of survival
		1 , Nozzle size = 3.5 and				✓ Viable B.s
		5.5 mm)

and Se spores were recovered up to 100 m from the LPS and up to 200 m from HPS ✓ Nozzle size↓ (from

						5.5 to 3.5 mm)→ secondary stress→the
						number of viable B. s↓ (Wind speed < 0.6 m.s -1 )
	Rojas-	Sprinklers (type was not	TWW stored in a	-Fecal coliforms, FC	Near sprinkler,	✓
	Valencia and	specified)	central cistern	(122-6.0E+05 CFU/100mL)	and distances
	Mier, (2013)			-Total coliforms	based on the
				(1.40E+05-6.0E+06 CFU/	wind effect and
				100mL)	people passing
				-Heterotrophic
				-Bacteria (4.90E+06-7.5E+07
				CFU/100mL)
				-Vibrio sp. (N.D)

Height of measurement (ground, 0.94 and 1.15 m): Most of the microbial load was found near the ground or at a height of 0.94 m because

	of sedimentation
	✓ The air at the time of the irrigation was slightly contaminated
	(200 -800 CFU.m -3 air). However, the presence of fecal coliforms
	and 12 species of bacteria, which 8

of them reported as potentially pathogenous bacteria identified

					in the environment
					of the irrigation zone.
	Courault et	Canon and Center-pivot	Tertiary	-Norovirus, NoV GI, GII	A few meters
	al. (2017)	(Height of sprinklers=2.5	treatment+ passes	(1.0E+3 GC/L)	downwind from
		m AGL; volume of the	through 8	-Rotavirus, RV	the sprinkler
		water used for irrigation	different lagoons	(1.0E+03-1.0E+04 GC/L)
		=up to 2×10 4 m 3 of water	for >13 days	-Enterovirus, EV (N.D)
		daily or 6×10 5 to 1×10 6		-Hepatitis E virus, HEV
		m 3 per year)		(7.4E+02 GC/L)
				-Hepatitis A virus, HAV
				(N.D)

Effect of type of pathogens: ✓ Noroviruses were the most abundant in raw water

	(N1.0E+04
	GC/L and ~1.0E+03 GC/L in );

low quantities of airborne viruses were

	detected (1.0E+03 GC/m 3 )
	✓ Hepatitis E virus was detected in all sampled compartments (in
	WWTP, in pond water, in air above crop)→ resistant to water
	treatment.

Table 1 .

 1 3. Guidelines for the safe use of spray irrigation with treated wastewater

		Treated wastewater quality
	Country
	or organisms	Category of use
	(year)

) E.coli: ≤ 100 CFU/100mL Legionella sp.: <1000

	Table 1.3. Continued
				Treated wastewater quality		Buffer zones (setback
	Country or organisms (year)	Category of use	Bacterial indicators	Helminth/ Nematode eggs	Viral indicators	Other indicator microorganisms	distances)
				[no./L]
	Greece	Unrestricted irrigation-All crops such as fruit trees,	E.coli : ≤5 CFU/100 mL for 80% of
	(2011)	vines, vegetables, or crops whose products eaten raw,	samples and ≤50 CFU/100mL for	_	_	_	_
	[3]	greenhouses)	95% of samples
	Israel	Unrestricted Irrigation in accordance with soil	Faecal coliform ≤ 10 CFU/100mL
	(2005)	sensitivity and without risk to human health, flora, soil,		_	_	_	_
	[4]	and water sources
	Jordan	Only golf courses when irrigated during night and	E.coli : ≤ 1000 MPN/100 mL	≤1			50 to 100 m from houses and
	(2006) [5]	sprinklers must be movable and not accessible for day.					roads
	Kuwait	_	Total Coliform ≤400 MPN/100mL	_	_	Egg Parasites: <1 eggs/L	_
	(2001)[6]		Fecal Coliform ≤ 20 MPN/100 mL			Worm Parasites: absence
		cooked food crops, forage crops, vineyards, orchards	Faecal coliform ≤ 1000 CFU/		_	_	Single house (S): 60 and
	Portugal		100mL				Group of houses (G): 80
	NP4434	Cereals (except rice), Non-food crops with industrial	Faecal coliform ≤ 10000 CFU/	≤1	_	_	S: 70 and G:100
	(2005)	processing, forest and lawns	100mL
	[7]	Golf course, Public parks and gardens (Irrigation	Faecal coliform≤ 200 CFU/ 100mL		_	_	S: 30 and G:50
		must avoid contact with people)
	Italy	_	E.coli : ≤100 CFU/100 mL and ≤10	0
	(2003) [8]		CFU/100 mL (80% of the samples)		_	_	_
			Salmonella sp.: absence
		Food crops without industrial processing	E.coli: 100 CFU/100 mL			Sampling to confirm the absence
			Legionella sp.: 1000 CFU/L		_	of pathogens (if for 3 sample;	_
			Salmonella sp.: absence			M>1000)
		Food crops with industrial processing	E
	Spain (2007) RD 1620		CFU/L	≤0.1	_	_
	[9]
		Industrial, energy and seeded crops (class D)	E.coli: ≤ 10000 CFU/100mL Legionella sp.: <1000 CFU/L		_	_
	California	food crops (adequately disinfected, oxidized,	Total coliform: 2.2 CFU/100mL (7-				From Title 22 California Code
	(2001)	coagulated, clarified, filtered wastewater)	day med)-23 CFU/100mL (not more				of Regulations: No spray
	[1]		than one sample exceeds this value in 30 d)-240/100mL (max)	_	_	_	irrigation take place within 34.5 m (100 feet) of a residence or a
							public places,	other than
							disinfected tertiary recycled
	Cyprus [2]	Cooked vegetables	Faecal coliform ≤100 MPN*/100 mL	ND**	_	_	_

.coli ≤ 1000 CFU/100 mL Legionella sp.: a CFU/L Salmonella sp.: absence _

	Table 1.3. Continued
	Country or organisms (year)	Category of use	Bacterial indicators	Treated wastewater quality [no./L] Helminth/ Nematode eggs Viral indicators	Other indicator microorganisms	Buffer zones (setback distances
		Food crops without industrial processing	E.coli: <250 CFU/100 mL			F-specific RNA phages (F-	Depending on the type of
						sRNA), Faecal enterococci (Fe),	sprinkler and for low pressure,
				_	_	Spore-reducing anaerobic	distance from sprinkler: 5-10
	France (2014) [10]	Food crops with industrial processing, fresh fodder and Pasture**,Fruit trees (transformed), and Flowers sold	E.coli ≤ 250-10000 CFU/100 mL	_	_	bacteria spore (S-rb) (Log.reduc.): ≥4 F-sRNA, Fe and S-rb (Log.reduc.): ≥3	20-50 20-200 Sinon, twice the range of the
		cut*					sprinkler
		Other cereal and fodder crops*, Nurseries and Shrubs and Other Floral Crops*	E.coli ≤ 10000-100000 CFU/100 mL	_	_	(Log.reduc.): ≥2 F-sRNA, Fe and S-rb	20-300
	*MPN =Most Probable Number;** ND = not detected;
	References: [1] Fattahi (2010); [2] Paranychianakis et al. (2015); [3] Ilias et al. (2014); [4] Inbar (2007); [5] Breulmann et al. (2020) ; [6] Abusam and Shahalam
	(2013) ; [7] do Monte (2007) ; [10] NOR-AFSP1410752A (2014)
	[8] http://extranet.regione.piemonte.it/ambiente/bga/archivio_documenti/2003_sem_02_30/30_atti_stato/dm_185_12_06_2003.pdf
	[9] https://www.iwa-network.org/filemanager-uploads/WQ_Compendium/Database/Selected_guidelines/042_2.pdf
						Taenia saginata and Taenia
						solium: 1 egg/L)
						Sampling to confirm the absence	_
						of pathogens (if for 3 sample;
						M>10000)
		Non-food crops	E.coli ≤ 10000 CFU/100 mL
			Legionella sp.: 100 CFU/L		_	_	_
			Salmonella sp.: absence
		Golf course	E.coli ≤ 200 CFU/ 100 mL and
			≤10000 CFU/100 mL (For micro-sprinkler)		_	_	_
			Legionella spp.: 100 CFU/L
	890

Table 1 .

 1

	4. Key to Solar Radiation Delta-T (SRDT) Method for Estimating Pasquill-Gifford (P-G) Stability
	Categories (EPA, 2000)
			DAYTIME
	Wind speed		Solar radiation (W.m -2 )
	(m.s -1 )	≥ 925	925-675	675-175	< 175
	<2	A	A	B	D
	2-3	A	B	C	D
	3-5	B	B	C	D
	5-6	C	C	D	D
	≥ 6	C	D	D	D
			NIGHTTIME
	Wind speed		Vertical temperature gradient
	(m.s -1 )	<0		≥ 0
	<2.0	E		F
	2.0-2.5	D		E
	≥ 2.5	D		D
	A, extremely unstable conditions; B, moderately unstable conditions; C, slightly unstable conditions; D,
	neutral conditions applicable to heavy overcast, day or night; E, slightly stable conditions; F, moderately
	stable conditions.

Table 2 .

 2 1. Some of empirical equations of collection efficiencies of airborne droplets on various types of passive samplers

	Authors			Equations	Validity limit		Comments
	Aylor (1982)	𝜂 𝑐 =	1 + 0.442(𝑆𝑡𝑘 𝑟𝑒𝑓 ) -1.967 0.86	_		_
	Walklate (1992)	𝜂 𝑐 = 1, 𝜂 𝑐 = 0.5 + 0.225 ln (𝑆𝑡𝑘 𝑟𝑒𝑓 ) 𝜂 𝑐 = 0.135	𝑆𝑡𝑘 𝑟𝑒𝑓 > 6.76, 0.135< 𝑆𝑡𝑘 𝑟𝑒𝑓 < 6.76, 𝑆𝑡𝑘 𝑟𝑒𝑓 < 0.135		Based on the May and Clifford (1967) data
	Craig et al.	𝜂 𝑐 =	𝜑	and 53, respectively,	84	Equation fitted to the May and Clifford (1967) data
	(2000)				-Cylinders are 136, 1, 0.9,	for ribbon and cylinder
					0.84 and 43, respectively
	Parkin
	and
	Young
	(2000)

𝛼 + 𝑒 -𝛽-𝜙ln (𝑆𝑡𝑘 𝑟𝑒𝑓 ) -𝛾 𝜑, 𝛼, 𝛽, 𝜙, and 𝛾 for: -Ribbons are 152, 1, 0.

Table 2 .

 2 2. Aspiration efficiency equations for isoaxial sampling

	Reference	Aspiration efficiency, η asp		Validity limit
	Belyaev and Levin (1974)	1 + [ U 0 𝑈 𝑠 where, k = 2 + 0.617 [ -1] [1 -1 + kStk 𝑟𝑒𝑓 1 U 0 ] U 𝑠	]	0.18≤Stk 𝑟𝑒𝑓 ≤2.03 and 0.17≤ U 0 U 𝑠 ≤5.6

Table 3 .

 3 1. Aspiration characteristics of the studied impingers at NTP

	Type	Inlet	Outlet	Wall	Nominal	Sampling air
		diameter, 𝐷 𝑖𝑛 [mm]	diameter, 𝐷 𝑜𝑢𝑡 [mm]	thickness [mm]	flow rate [L.min -1 ]	velocity, 𝑈 𝑠 [m.s -1 ]
	AGI-4	8.7	11.0	1.15	6	1.68
	SKC BioSampler	9.5	12.8	1.65	12.5	2.94

Table 3 .

 3 2. Calculations for normalization of number and volume Fraction :

	𝒅𝑵 𝑵 𝒕𝒐𝒕 .𝒅 𝐥𝐨𝐠(𝐷)	or	𝒅𝑽 𝑽 𝒕𝒐𝒕 .𝒅 𝐥𝐨𝐠(𝐷)

Table 3 .

 3 3 presents the Stokes number and aspiration efficiency of the AGI-4 and SKC BioSampler in calm air for droplets with diameters corresponding the midpoint of size intervals of BioTrak ® .

Table 3 .

 3 3. Calculations of Stokes number and aspiration efficiency of AGI-4 and SKC BioSampler in calm air 1960

	Droplet size	𝑉𝑡𝑠		𝑆𝑡𝑘 𝑖					𝜂 𝑎𝑠𝑝(𝑐𝑎𝑙𝑚)
	[µm]	[m.s -1 ]		[-]					[-]
			AGI-4		SKC		AGI-4			SKC
			[6 L.min	-1 ]	[12.5 L.min	-1 ]	[6 L.min	-1	]	[12.5 L.min	-1 ]
	0.6	1.38E-05	2.73E-04	4.37E-04		0.999			0.998
	0.85	2.60E-05	5.13E-04	8.20E-04		0.998			0.997
	2	1.30E-04	2.36E-03	3.78E-03		0.991			0.986
	4	5.02E-04	9.44E-03	1.51E-02		0.966			0.946
	7.5	1.51E-03	3.32E-02	5.31E-02		0.894			0.836
	10	3.06E-03	5.90E-02	9.44E-02		0.829			0.745
	17.5	8.7E-03	1.81E-01	2.89E-01		0.626			0.505

Table 3 .

 3 [START_REF] Brusselman | Wind tunnel evaluation of several tracer and collection techniques for the measurement of spray drift[END_REF]. Measurement errors for the investigated impingers, using ABF as a tracer

		1 -𝜂 𝑝ℎ𝑦𝑠		1 -𝜂 𝑐	1 -𝜂 𝑅
	Sampling time [min]	[%]			[%]	[%]
		AGI-4 SKC BioSampler AGI-4 SKC BioSampler AGI-4 SKC BioSampler
	5	8.2	22.9	3.4	27.9	-0.48	3.5
	15	6.8	23.9	14.6	26	7.7	1
	30	7.4	24.4	13	29.4	5.6	3.9
	60	7.3	32.7	18.3	36.6	11	3.9
	Average	7.45	23.73	13.8	27.76	6.65	3.76
	Standard deviation	0.5	0.76	6.35	4.63	1.48	0.23

Table 4 .

 4 1. Mean air velocities at the locations of the samplers (active\passive) or optical systems 𝑢 ̅ and 𝑣̅ are the mean air velocity in the X and Y direction, respectively; |𝑢 ̅| is the modulus of the mean air velocity, |𝑢 ̅| = (𝑢 ̅ 2 + 𝑣̅ 2 ) 1/2 . Indexes 'tot' and 'c' represent mean air velocity across the whole of the tunnel's cross-sectional area, and in the center plane of the measurements, respectively. 2280

	X [m]		0.95		1.40			1.80
	Measured points	Grid	Z=0.25 m		Z=0.25 m		Grid	Z=0.25 m
		Z=0.065-0.4 m	Y=0.135-0.225 m	Y=0.135-0.226 m	Z=0.065-0.4 m	Y=0.135-0.225 m
		Y=0.01-0.335 m		X=1.355-1.446 m	Y=0.01-0.335 m
	Mean air velocity [m.s -1 ]	𝒖 ̅ 𝒕𝒐𝒕	𝒖 ̅ 𝒄	𝒖 ̅ 𝒄	𝒗 ̅ 𝒄	|𝒖 ̅ 𝒄 |	𝒖 ̅ 𝒕𝒐𝒕	𝒖 ̅ 𝒄
	Fr=7.3 Hz	1.2	1.18	1.187 1.4E-2 1.19	1.2	1.21
	Fr=12.4 Hz	2.1	2.16	2.246 2.1E-2 2.25	2.1	2.25
	Fr=17.5 Hz	3.05	3.21	3.328 2.9E-2 3.33	3.2	3.34
	Fr=25.4 Hz	4.9	5.00	5.023 2.8E-2 5.02	5.0	5.04
	Located optical	Passive samplers		PIV+ Active		PDPA, Passive and active
	system\Sampler				samplers		samplers
	Note:

Table 4 .

 4 2 represent the Reynolds number under the different air velocities obtained from Eq. A-2.

Table 4 .

 4

	Air velocity	𝑹 𝒆
	1.2	3.23×10 4
	2.25	6.11×10 4
	3.3	9.05×10 4
	5.0	1.36×10 5

2. Reynolds number under the different air velocities

Table 4 .

 4 3. Some of properties of air at 20 °C and pressure of 101.3 kPa [1atm]

	Property	Value	Units
	Dynamic air viscosity (μ)	18.203×10 -6	Pa.s
	Kinematic air viscosity (ν) 15.106×10 -6	m 2 .s
	Density (ρ)	1.205	Kg.m 3
	Thermal diffusivity (α)	20.74×10 -6	m 2 .s
	Mean free path (γ)	0.066	μm
	Molecular weight of air	28.9	g.mole -1
	Gravity (g)	9.81	m.s -2

Table 4 .

 4 4. Spray nozzle specification

	Spray angle	Orifice diameter	Mesh size	Flow rate at	Initial projection	Spray diameter at
	[degrees]	[mm]	[mm]	p=200 kPa	velocity at P=200 kPa	H** =100 mm
						[L.min -1 ]	[m.s -1 ]*	[mm]
	80		0.25	0.1	0.025	8.49	140
	*𝑈 𝑗 = (	𝑞 𝑎	) Where, 𝑞 is Flow rate, a is nozzle cross-sectional area;** H is the axial distance from nozzle top
	Initial droplet size distribution from sprayer in laboratory conditions
	Prior to the experiments in the wind tunnel, a laser-diffraction-based drop size analyzer, Malvern
	Spraytec (Malvern Panalytical, Malvern, UK), was used to characterize the initial droplet size distribution
	of the spray nozzle in no wind conditions. The experimentation was carried out at ReducPol testing
	laboratory, located in the UMR ITAP INRAE Montpellier. As illustrated in Figure 4.4, this instrument
	consists of:

Table 4 . 5

 45

	Property	Optical PDA	Optical PDA
		System-U1	System-U2
	Wavelength [nm]	561	532
	Focal length [mm]	1000	1000
	Beam diameter [mm]	1.35	1.35
	Beam expander ratio	1	1
	Beam spacing [mm]	54	54
	Frequency shift [Hz]	8E+007	8E+007
	Laser model	Laser A	Laser A
	Laser Power [W]	0.05	0.05
	Number of fringes	50	50
	Fringe spacing [µm]	10.39	9.855
	Beam half-angle [deg]	1.547	1.547
	Probe volume δX [mm]	0.5293	0.5019
	Probe volume δY [mm]	0.5291	0.5018
	Probe volume δZ [mm]	19.6	18.59
	Receiver type	Fiber PDA	Fiber PDA

. Configuration of PDPA-system for measurements in wind-Tunnel

Table 4 .

 4

	Title\Height	50 mm	100 mm	150 mm
	Dv10 [µm]	44.39	57.91	63.2
	D32 [µm]	73.13	85.8	90.39
	Dv50 [µm]	82.5	91.14	94.88
	Dv90 [µm]	150.7	142.4	142.1
	RSF [-]	1.29	0.93	0.83
	CV [PPM]*	28.04	35.74	29.89
	Volume <10 µm [%]	0.016	0	0
	Volume <50 µm [%]	15.36	3.96	1.55
	Volume <100 µm [%]	64.8	59.76	56.07
	Volume <150 µm [%]	89.51	92.19	92.41
	*CV: the volume concentration, which is measured in parts per million (PPM)

6. Synthesis of droplet size distribution of the spray nozzle under a pressure of 200 kPa in laboratory conditions.

Table 4 . 7 .

 47 P-Value (Pearson) between variables and 𝐷𝑣 50 and 𝐷𝑣 90

	Variables	𝐷𝑣 50	Variables	𝐷𝑣 90
	𝐷𝑣 50	0	𝐷𝑣 90	0
	𝑈	0.003	𝑈	0.001
	𝐷𝑖𝑠𝑡	0.086	𝐷𝑖𝑠𝑡	0.037
	Nozzle height	<0.0001	Nozzle height	<0.0001
	Values in bold are significant at alpha = 0.05 level

Table 5 .

 5 1 Average micrometeorological parameters during the experiment

	Exp.	Start time [UTC]	Wind speed [m.s -1 ]	Wind direction [degrees]	Temperature [°C]	Relative Humidity [%]		VPD [kPa]	Solar [w.m-2] radiation	σƟ [degrees]	Stability [SRDT] classes	B [-]
			Hgt:	Hgt :	Hgt:	Hgt:	Hgt:	Hgt:	Hgt:	Hgt:	Hgt:	Hgt:	Hgt:	Hgt:	Hgt:	Hgt:
			2m	10m	2m	10m	2m	10m	2m	10m	2m	10m	1m	2m	10m	10m
	1	17:42	2.1	2.3 * 174	165	26.1	25.9	72.5	72.0	0.8	0.9	549.1	30.2	30.1	C	-0.0046
	2	18:48	2.1	2.7	223	217	32.9	32.5	43.3	46.1	2.8	2.7	279.1	37.1	35.7	C	-0.0049
	3	10:48	2.1	3.0	33	43	27.4	26.9	29.2	27.2	2.6	2.6	740.2	31.0	38.9	B	-0.0064
	4	7:09	0.14	0.16	-	-	18.1	18.9	100	96.5	0.0	0.08	60.5	-	-	D	2.47
	5	15:02	2.3	3.1	139	132	29.9	29.4	43.6	41.6	2.3	2.4	562.5	35.2	32.6	C	-0.0052
	6	14:48	2.3	3.6	249	236	29.3	29.2	43.5	40.2	2.3	2.4	365.0	29.7	18.7	C	-0.00077
	7	15:39	2.7	3.4	109	105	27.3	27.2	78.6	77.8	0.8	0.8	245.0	33.8	26.3	C	-0.0005
	8	13:09	1.0	1.1	117	94	23.7	23.4	67.2	66.8	1	0.9	694.0	56.9	52.3	A	-0.030
	9	15:50	1.9	2.4	135	134	23	22.9	54.1	51.6	1.3	1.3	326.9	26.5	23.2	C	-0.0019

*

Measured at height of 4 m above the ground 2830

Table 5 .

 5 2 Synthesis of droplet size distribution within a wetted area in laboratory conditions

			Maximum radius [%]			Total
	Parameters	30	40	60	70	80	90	Measured Fitte
									d
	DN50 [mm]	0.61	0.75	1.02 1.14 1.34 1.72
	DV50 [mm]	0.84	1.00	1.22 1.41 1.49 1.86	1.15	1.15
	D32 [mm]	0.79	1.02	1.21 1.32 1.45 1.87	1.52	1.5
	Total discharge [L.h -1 ]	181.4	172.6 150.	68.3 92.8 86.0	752	752
				9
	Percent of total discharge [%]	24.12	22.95 20.1	9.1	12.4 11.4	100	100
	Percent of volume in droplets <1.00 mm [%]	74.26	50.22 22.9 11.4 6.40 1.49	36.03	36.27
	Percent of volume in droplets <0.50 mm [%]

  Models 1 and 2 (M1 and M2) are calibrated by the combination of spatial parameters (distance, height and angle of the sample from average wind direction), wind speed, and standard deviation of wind direction (which explained most of the variability of observations from PVC strings) while keeping a large number of observations (See Table G. 5: N° of obs.: 265/288=92%, 284/288=98.6 %, R 2 =0.783 and 0.88 for model M1.1 and M2.1, respectively). Temperature and relative humidity explain residual variability (see Table G. 5, M1or2.3, M1or 2.5 and M1or 2.11). The effect of the air vapor pressure deficit on the transport is low because the VPD is taken into consideration based on its components (Relative humidity and temperature) and wind speed (M1 or 2.7 in Table G. 5).

	Therefore, based on R² and RMSE, the best airborne transport models considering the independent
	variables of wind speed (𝑈), standard deviation of wind direction (σɵ), temperature (𝑇𝑒𝑚𝑝), relative
	humidity (𝑅𝐻) and spatial parameters are:
	𝑀 1 . 𝑇 𝑎 = 𝐷𝑖𝑠𝑡 -3.03 × 𝐻 -0.82 × 𝑈 3.35 × 𝑇𝑒𝑚𝑝 1.51 × 𝑅𝐻 0.74 × 𝑒	{-0.5×( ∆𝜃 𝜎 𝜃	2 )	}	(Eq. 5-5)
	𝑅 2 = 0.902; 𝑅𝑀𝑆𝐸: 1.13 𝑚𝐿. 𝑚 -2 . ℎ -1 ; 𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑣𝑎𝑙𝑖𝑑 𝑜𝑏𝑠𝑒𝑟𝑣𝑎𝑡𝑖𝑜𝑛𝑠: 288 288 ⁄
	𝑀 2 . 𝑇 𝑎 = 𝑒	[(-0.12×𝐷𝑖𝑠𝑡)+(-0.37×𝐻)+(1.08×𝑈)+(0.05×𝑇𝑒𝑚𝑝)+(0.01×𝑅𝐻)-{0.5×( ∆𝜃 𝜎 𝜃	) 2	}]	(Eq. 5-6)
	𝑅 2 = 0.915; 𝑅𝑀𝑆𝐸: 0.943 𝑚𝐿. 𝑚 -2 . ℎ -1 ; 𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑣𝑎𝑙𝑖𝑑 𝑜𝑏𝑠𝑒𝑟𝑣𝑎𝑡𝑖𝑜𝑛𝑠: 287/288

  2.89 × 𝑇𝑒𝑚𝑝 1.83 × 𝑒

		{-0.5×( ∆𝜃 𝜎 𝜃	) 2	}	(Eq. 5-7)
	𝑅 2 = 0.901; 𝑅𝑀𝑆𝐸: 0.186; 𝑁𝑢𝑚𝑏𝑒𝑟 𝑜 𝑣𝑎𝑙𝑖𝑑 𝑜𝑏𝑠𝑒𝑟𝑣𝑎𝑡𝑖𝑜𝑛𝑠: 279/280
	𝑀 2 . 𝑇 𝑠 = 𝑒	[(-0.15×𝐷𝑖𝑠𝑡)+(0.87×𝑈)+(0.02×𝑇𝑒𝑚𝑝)-{0.5×( ∆𝜃 𝜎 𝜃	2 )	}]

  is,

	𝐶 𝐷 =	𝑅𝑒 𝑝 24			𝑅𝑒 𝑝 ≪ 1
	𝐶 𝐷 =	24 𝑅𝑒 𝑝	(1 + 0.15𝑅𝑒 𝑝	0.687 )	𝑅𝑒 𝑝 < 1000	Eq. A-7
	𝐶 𝐷 = 0.44		𝑅𝑒 𝑝 > 1000

  In the Stokes regime (the limits of low Reynolds numbers), the factor of 𝐶 𝐷 𝑅𝑒 𝑝 24 is unit. Additionally, considering 𝐶 𝑐 for the particles inferior to 10 µm, this equation yields:Even though the above relation for calculation of relaxation time is valid for all velocities as long as the Reynolds number is low (Stokes regime) and the ratio changes littles for higher Reynolds numbers. 𝐶 𝐷 is almost independent of velocity (Eq. A-7) and relaxation time approximately depends on velocity. A similarity expression ( 𝜏 𝑠 = 𝑈𝑡𝑠 𝑔 ) is constructed, combining Eq. A-16 with Eq. A-11. However, this expression only applies to the terminal settling velocity.

		In highly turbulent flows,
						𝐹 𝑑𝑟𝑎𝑔 𝑈 𝑝	is known as the friction coefficient.
		Combining Eq. A-3 (𝑅𝑒 𝑝 ) and the right-hand side of the equation above and dividing through the particle
		mass gives,
		𝑑𝑈 𝑝 𝑑𝑡	=	18 µ 𝑎 𝜌 𝑝 𝐷 𝑝 2	𝐶 𝐷 𝑅𝑒 𝑝 𝑈 ∞ 24	Eq. A-13
		𝜏 𝑝,𝑠𝑡𝑘 =	𝜌 𝑃 𝐷 𝑃 2 𝐶 𝑐 18𝜇 𝑎	Eq. A-14
		As observed in the above equation, the relaxation time is independent of the particle/droplet velocity
	4540	and is a function of the particle/droplet size and density, and dynamic air viscosity. Using Eq. A-9, a
		relation between terminal settling velocity from Stokes regime and the relaxation time, 𝜏 𝑠,𝑠𝑡𝑘 , can be
		produced,
		𝜏 𝑠,𝑠𝑡𝑘 =	𝑈𝑡𝑠 𝑔	Eq. A-15
		Therefore, for non-Stokes regime, the Eq. A-12 is divided by particle mass and the relaxation time, 𝜏 𝑣
		can be rewritten as,
		𝜏 𝑝 =	4𝜌 𝑝 𝐷 𝑝 3𝜌 𝑎 𝑈 ∞ 𝐶 𝐷	Eq. A-16

  . The Brownian diffusivity 𝐷 𝐵 (or diffusion coefficient), which relates the air properties and the particles through viscous drag, can be evaluated by the Stokes-

		Einstein expression:
		𝐷 𝐵 =	𝐾 𝐵 𝑇 𝑎𝑏𝑠 𝐶 𝑐 3𝜋𝜇 𝑎 𝐷 𝑝	= 𝑘 𝐵 𝑇 𝑎𝑏𝑠 𝐵 𝐵	Eq. A-23
	4600	where 𝐾 𝐵 is Boltzmann's constant (1.38 × 10 -23 𝑁. 𝑚. 𝑘 -1 ) and 𝑇 𝑎𝑏𝑠 is absolute temperature. The term
		𝐶 𝑐 3𝜋𝜇 𝑎 𝐷 𝑝 (

Table D .

 D 2. Velocity distribution mapping at 2.

	X = 0.95 m Fr= 7.3 Hz				Z [m]
	Y [m]	0.065	0.1	0.15	0.2	0.25	0.3	0.35	0.4
	0.335	1.2	1.1	1.0	1.0	1.1	0.9	0.9	1.0
	0.305	1.1	1.1	1.1	1.1	1.1	1.2	1.0	1.1
	0.255	1.2	1.2	1.2	1.2	1.2	1.2	1.2	1.3
	0.205	1.3	1.2	1.2	1.2	1.2	1.1	1.2	1.2
	0.155	1.2	1.2	1.2	1.2	1.2	1.2	1.3	1.3
	0.105	1.3	1.3	1.3	1.3	1.3	1.3	1.3	1.3
	0.055	1.4	1.3	1.3	1.3	1.3	1.3	1.3	1.2
	0.01	1.1	1.1	1.1	1.2	1.2	1.2	1.1	1.1
	ULB [m]	Min	Max Median Mean Standard deviation
	0.285	0.9	1.4	1.2	1.2		0.08
	X = 1.8 m Fr= 7.3 Hz				Z [m]
	Y [m]	0.065	0.1	0.15	0.2	0.25	0.3	0.35	0.4
	0.335	1.2	1.1	1.1	1.0	1.0	0.8	1.0	1.1
	0.305	1.2	1.2	1.1	1.0	1.0	0.8	0.9	1.0
	0.255	1.3	1.2	1.2	1.2	1.2	1.1	1.1	1.1
	0.205	1.3	1.3	1.3	1.2	1.2	1.1	1.1	1.2
	0.155	1.3	1.3	1.3	1.2	1.3	1.2	1.2	1.2
	0.105	1.3	1.3	1.3	1.3	1.3	1.3	1.3	1.3
	0.055	1.3	1.2	1.3	1.3	1.3	1.3	1.3	1.3
	0.01	1.1	1.1	1.1	1.2	1.2	1.2	1.2	1.1
	ULB [m]	Min	Max Median Mean Standard deviation
	0.27	0.8	1.3	1.2	1.2		0.10

  5.1.1). Airborne spray transport (transported volume) (𝑇 𝑎-𝐼𝑚𝑝 ) based on the impingers, expressed in mL.m -3 𝑡 𝑠 is sampling time in hours, and 𝑉 𝐶 represent the airborne spray volume collected by imingers in mL, according to following expression:

			air.h -1 , was calculated as follows:
	𝑇 𝑎-𝐼𝑚𝑝. =	𝑉 𝐶 𝑄 𝑠 . 𝑡 𝑠	Eq. E-1
	where 𝑄 𝑠 is the(nominal) sampling air flow in m -3 air.h 𝐶 𝑠 . 𝑉 𝑓𝑖𝑛𝑎𝑙 -1 , 𝑉 𝐶 = 𝐶 0

Table E .

 E 1. Comparison of performance of the SKC BioSampler and AGI-4 in moving air

	Distance	U	𝜂 𝑎		𝑇 𝑎-𝐼𝑚𝑝. [mL.m -3 air.h	-1 ]	Volume of deposited droplets on the inner surface
	[m]	[m.s -1 ]						[mL.m -3	air.h	-1 ]
			AGI-4	SKC	AGI-4	SKC	AGI-4	SKC
	0.45	2.25	124.3	77.8	25.0		36.1	0.63	0.76
	0.85	3.3	175.4	104.8	27.1		37.7	1.26	0.87
	1.25	5.0	270.3	157.9	28.0		44.3	3.85	1.61
		1.2	85.3	67.3	0.18		0.15	0.026	0.008
	1.65	2.25 3.3	121.3 170.0	80.8 104.4	9.6 18.9		10.7 21.9	0.9 1.85	0.53 0.97
		5.0	266.3	156.5	23.4		37.3	3.1	1.50
	E. 3. 2.

Distribution of airborne spray volume in the vertical direction using PVC strings

Table E .

 E 2. Observed total airborne spray transport potential using the PVC strings and spheres

						𝑇𝑃 𝑎 𝑡𝑜𝑡𝑎𝑙 [%]
	Nozzle	Distance	U=1.2	U=2.25	U=3.3	U=5.0
	height	[m]	[m.s -1 ]	[m.s -1 ]	[m.s -1 ]	[m.s -1 ]
	[m]		PVC	Spheres	PVC	Spheres PVC	Spheres PVC	Spheres
			strings		strings		strings		strings
	0.175	0.85 1.65	4.15 1.26	2.89 0.91	16.54 8.87	10.81 5.70	22.20 14.68	17.21 11.57	34.84 21.16	30.98 20.13
	0.275	0.85 1.65	20.30 6.41	15.35 4.1	51.27 27.63	41.45 18.98	67.85 47.41	61.1 38.3	84.45 63.81	82.9 55.20

Table E .

 E 3. Estimated collection efficiency of the PVC strings and spheres through the curves fitted to[START_REF] May | The Impaction of Aerosol Particles on Cylinders, Spheres, Ribbons and Discs[END_REF] data 𝜂 𝑐[%]

	Nozzle	Distance	U=1.2	U=2.25	U=3.3	U=5.0
	height	[m]	[m.s -1 ]	[m.s -1 ]	[m.s -1 ]	[m.s -1 ]
	[m]		PVC	Spheres	PVC	Spheres PVC	Spheres PVC	Spheres
			strings		strings		strings		strings
	0.275	0.85 1.65	80.02 68.21	71.39 51.26	88.54 85.84	78.99 74.00	95.19 90.04	89.42 81.0	97.21 93.71	94.20 87.59

Table E .

 E [START_REF] Brusselman | Wind tunnel evaluation of several tracer and collection techniques for the measurement of spray drift[END_REF]. Observed airborne spray transport potential at a height of 0.175 m above the floor (collector line 4) using the PVC strings and spheres

						𝑇𝑃 𝑎 4(0.175 𝑚) [%]
	Nozzle	Distance		U=1.2	U=2.25	U=3.3	U=5.0
	height	[m]	[m.s -1 ]	[m.s -1 ]	[m.s -1 ]	[m.s -1 ]
	[m]		PVC	Spheres	PVC	Spheres PVC	Spheres PVC	Spheres
			strings		strings		strings		strings
	0.275	0.85 1.65	1.29 0.42	0.77 0.21	6.46 2.02	3.78 1.03	12.92 6.80	9.8 4.1	19.19 11.37	15.33 8.9

Table G .

 G 4. Regression models of distribution of aerosol plume in the crosswind direction for observation data from E5 and E6

	Exp.	Height	Regression	R 2
		ground	= 1.20 × exp[-0.5 ×	(∆𝜃 -4.39) 2 (34.46) 2 ]	0.95
	5	1	= 14.88 × exp[-0.5 ×	(∆𝜃 -7.56) 2 (33.33) 2 ]	0.95

Table G .

 G 5. Coefficients obtained for all combinations of parameters in the basic empirical models using PVC strings data (288 data). 𝑎 ~𝐷𝑖𝑠𝑡 𝑑𝑖𝑠𝑡 × 𝐻 ℎ × 𝑈 𝑢 × 𝑇𝑒𝑚𝑝 𝑡𝑒𝑚𝑝 × 𝑅𝐻 𝑟ℎ × 𝑉𝑃𝐷 𝑣𝑝𝑑 × 𝑅𝑠 𝑟𝑠 × 𝑎𝑏𝑠(𝐵) 𝑏 × 𝑒

	Empirical model	M1:𝑇 {-0.5×(	∆𝜃 𝜎 𝜃	)	2	}
	Parameter	1		2	3	4	5	6				7	8	9	10
	dist	-2.04 ***	-1.46 ***	-2.08 ***	-1.76 ***	-3.08 ***	-3.05 ***			-1.93 ***	-1.74 ***	-2.44 ***	-2.61 ***
	h	-0.84 ***	-0.63 ***	-0.86 ***	-0.92 ***	-0.91 ***	-0.73 ***			-0.73 ***	-0.71 ***	-0.88 ***	-0.73 ***
	rh				0.98 ***	-0.53 ***
	temp						2.17 ***	2.46 ***
	vpd											-0.37 ***	0.30 ***
	rs												0.33 ***	0.9 ***
	u	7.21 ***		4.39 ***		3.99 ***					7.15 ***	6.26 ***
	b			-0.99 ***		0.99 ***		-0.54 ***				-1.11 ***	-0.79 ***
	N° of obs.	265		269	284	266	283	281				278	271	264	279
	R 2	0.783	0.787	0.89	0.812	0.845	0.89				0.846	0.834	0.813	0.855
	RMSE	0.912	0.793	1.12	0.953	1.12	0.99				1.14	1.15	0.848	1.13
	Table G. 5. Continued M1
	Parameter	11	12	13	14	15	16	17				18	19	20	21
	dist	-3.03 ***	-3.21 ***	-2.94 ***	-3.23 ***	-2.59 ***	-2.74 ***	-3.07 ***	-3.03 ***	-2.70 ***	-2.97 ***	-2.66 ***
	h	-0.82 ***	-0.75 ns	-0.86 ***	-0.72 ***	-0.84 ***	-0.72 ***	-0.82 ***	-0.82 ***	-0.81 ***	-0.77 ***	-0.80 ***
	rh	0.74 ***	-0.095 ***					0.78 ***	0.72 ***	0.95 ***
	temp	1.51 ***	2.59 ***	2.46 ***	2.22 ***			1.42 ***	1.54 *	2.08 ***
	vpd					-0.67 ***	-0.13 ***						-0.49 ***
	rs			-0.26 ***	0.24 ***	0.64 ***	0.98 ***	0.05 ns	0.49 ***	0.14 *	0.88 ***
	u	3.35 ***		4.11 ***		5.97 ***		3.33 ***	3.26 ***	3.17 ***	1.18 ***	2.90 ***
	b		-0.5 ***		-0.54 ***		-0.78 ***					-0.013 ns	-0.09 ns	0.37 ***	-0.36 ***
	N° of obs.	288	280	285	284	285	279	288			288	288	284	284
	R 2	0.902	0.894	0.842	0.872	0.873	0.858	0.902	0.902	0.89	0.87	0.88
	RMSE	1.13	0.971	1.16	1.09	1.28	1.11	1.13		1.14	1.21	1.06	1.21
	***p

Table G .

 G 7. Coefficients obtained for all combinations of parameters in the basic empirical models using Petri dishes data (280 data). 𝑇 𝑠 ~𝐷𝑖𝑠𝑡 𝑑𝑖𝑠𝑡 × 𝑈 𝑢 × 𝑇𝑒𝑚𝑝 𝑡𝑒𝑚𝑝 × 𝑅𝐻 𝑟ℎ × 𝑉𝑃𝐷 𝑣𝑝𝑑 × 𝑅𝑠 𝑟𝑠 × 𝑎𝑏𝑠(𝐵) 𝑏 × 𝑒

	Empirical model	M1: {-0.5×(	∆𝜃 𝜎 𝜃	)	2	}
	Coefficient	1	2	3	4	5	6					7	8	9	10
	dist	-2.24 ***	-1.36 ***	-2.88 ***	-1.71 ***	-3.06 ***	-2.88 ***				-2.26 ***	-1.75 ***	-2.73 ***	-3.06 ***
	rh			1.36 ***	0.47 ***
	temp					1.83 ***	2.65 ***
	vpd											0.106 ns	0.64 ***
	rs												0.38 ***	0.93 ***
	u	5.96 ***		3.39 ***		2.89 ***						5.93 ***	5.27 ***
	b		-0.61 ***		-0.49 ***		-0.56 ***					-0.78 ***	-0.62 ***
	N° of obs.	271	235	268	233	279	274					271	268	274	275
	R 2	0.834	0.733	0.904	0.728	0.901	0.857					0.835	0.834	0.86	0.87
	RMSE	0.223	0.144	0.167	0.134	0.186	0.19					0.22	0.226	0.212	0.203
	Table G. 6. Continued M1
	Coefficient	11	12	13	14	15	16	17				18	19	20	21
	dist	-3.04 ***	-2.96 ***	-2.98 ***	-3.22 ***	-2.80 ***	-2.82 ***	-3.01 ***	-3.15 ***	-2.94 ***	-3.14 ***	-2.86 ***
	rh	0.29 ***	-0.36 ***					0.21 **	-0.34 ***	0.37 ***
	temp	1.56 ***	2.38 ***	2.03 ***	1.74 ***			1.76 ***	1.97 ***	1.61 ***
	vpd					-0.22 ***	0.37 ***						0.08 ***
	rs			-0.16 ***	0.26 **	0.46 ***	0.80 ***	-0.09 *	0.29 ***	0.68 ***	0.25 ***	0.79 ***
	u	2.72 ***		2.97 ***		5.22 ***		2.80 ***	1.57 ***	0.97 ***	1.01 ***
	b		-0.41 ***		-0.41 ***		-0.59 ***						-0.43 ***	-0.30 ***	-0.28 ***	-0.47 ***
	N° of obs.	278	278	278	278	274	278	278				279	278	279	276
	R 2	0.907	0.91	0.906	0.912	0.865	0.872	0.908		0.918	0.891	0.925	0.895
	RMSE	0.175	0.172	0.176	0.17	0.209	0.21	0.174		0.169	0.194	0.162	0.187
	***p

}](Eq.

when demineralized water is used as a liquid collection. Otherwise, since the SKC BioSampler was developed for using the non-evaporating liquid (mineral oil), its performance will be certainly higher than AGI-4, especially since its residual loss is lower.

Increasing sampling time from 5 min to 15 min, reduced the culturability of E.coli in both AGI-4 and 2220 SKC by a factor of 37% and 14%, respectively. Also, it was showed that the BioTrak ® can measure in real-time the total number of particles in the air, which can be useful as a rapid technique, especially, for quantifying the potential presence of contaminants, but its accuracy in measuring the viable particles was lower than accuracy obtained with the impingers, particularly in low relative humidity conditions. In addition, it physically detects the presence of viable bacteria but does not identify its type.

Experimental site

In-situ transport measurements were performed on an INRAE experimental field (Lavalette site, INRAE Montpellier, 43°38'49.5" N; 3°52'21.64" E; Z= 50 m above mean sea level), and located in the South-East of France. Water was pressurized from a pump (Jet DAB 102M, China) attached to a 1m 3 water tank covered with a thermal insulating sheet. An electric sprinkler pump (FPSP800, Castorama France) was used to keep the fluorescent tracer inside the tank in suspension, and ensure the uniformity of spray concentration throughout each test.

Spray solution

The spray solution was a mixture of water and ABF (Acid Brilliant Flavine, WALDECK, Münster), at a 0.4 g.L -1 concentration level. The specification and the size of colloidal particles of the ABF in the solvent (demineralized water) have already been presented (see section 3.5.1.1).

Atmospheric Conditions Measurements

Before experimentations

To evaluate temporal and spatial heterogeneities in prevailing wind speed and direction due to obstacles in the vicinity of the site (hedges and buildings), before carrying out the experiments, for two consecutive weeks, wind speed and their two horizontal components (U-V) were measured simultaneously at four different points within the test site, and at the height of 2 m above the ground.

Furthermore, to verify all the sampler position possibilities, the measurement points were varied (at the end of the first week), while keeping a point as reference. The measurements were performed using four three-cup anemometers (A100R and A100L2 models, Campbell Scientific, accuracy: 1% ±0.1 ms -1 ) and four wind vanes (W200P, Campbell Scientific Ltd, accuracy: ±2°) connected to two data loggers (CR1000X and CR3000, Campbell Scientific Ltd, Great Britain, measurement precision: 0.01units) using a 5-minute interval sampling rate. In addition, the prevailing wind direction, based on 10 years of hourly wind data of the weather station located in the agricultural research site (Lavalette, station N° 34172005), was taken into consideration to control to validate the short term meteorological parameter measurements (Appendix. G, Table G. 2). The results of our analyses were used to determine the location of the experimental set-up so that obstacle impact on wind direction would be minimized.

Experimental work

Nine experiments were carried out from July to October 2020 under daylight conditions characterized by climatic variability (Table 5.1). Meteorological measurements were recorded using a portable meteorological station installed in the immediate vicinity of the experimental set-up, at a lateral location 25 m distance from the sprinkler head. These measurements were comprised of average wind speed (A100R and A100L2 models, Campbell Scientific) and direction (W200P, Campbell Scientific Ltd), air temperature and relative humidity (CS215, Campbell Scientific Ltd, accuracy: ±0.3 °C at 25 °C), and exploration of a larger range of meteorological values, which might be more difficult to encounter in situ.

Figure 5.13. Examples of sedimented transport simulation (line/dashed line) downwind from the sprinkler head and experimental data (dots) using Petri dishes from Exp.3 (black) and Exp.5 (blue), see Table 5.1.

Study of model genericity

The genericity of both our models was evaluated using other dataset including obtained field data 3130 and the empirical models developed by [START_REF] Tomas | Transport Modeling in Sprinkler Irrigation[END_REF]. The observation data was from three experiments, which were performed with another impact sprinkler model under an operating pressure of 350 kPa and contrasting wind speeds (1.19, 2.88, and 3.85 m.s -1 ). As shown in Figure 5.14, the agreement between the model 1 and the field data was excellent. As commented earlier, model 2 does not perform very well for distances higher than 30 m. Therefore, it can be concluded that model 1 could be successfully used to simulate airborne transport from different type of impact sprinklers. 𝑈 𝑠 < 1) , by decreasing the Stokes number, the aspiration efficiency becomes near unity. Given that the aspiration velocity and inlet diameter of SKC BioSampler was higher than AGI-4 (see Table 3.1), its aspiration efficiency was lower than the one of AGI-4 in all the configurations.

The results of the experiments indicate that except for the air velocity of 1.2 m.s -1 , the airborne spray collected in SKC BioSampler was higher than the one collected in AGI-4 (Table E. 1). These results were in contrast with what was stated in calm air conditions when using the ABF as a tracer. Apart from the air flow conditions, it could be due to the use of airborne spray plume in a wide rang of sizes (𝐷 𝑣50 ≃ 74 µm, 𝐷 𝑣90 = 104 µm), instead of droplet nuclei with a size of < 2.5 µm (90 % of the total volume), in calm air conditions. Besides, as shown in, Table E. 1, the droplets tend to deposit on the inner surfaces of the AGI-4 and the BioSampler, including the inside walls of the nozzles and curved inlet section, and the most significant deposition occurred at air velocity of 5.0 m.s -1 on the curved inlet section of AGI-4. [START_REF] Guo | An experimental method for efficiently evaluating the size-resolved sampling efficiency of liquid-absorption aerosol samplers[END_REF] also found that a large proportion of particles larger than 4 μm are present on the sampler inner wall, while particles smaller than 4 μm are rare .

Our results also showed that the mass of the ABF recovered from the glass fiber filters situated downstream the impinger's suction line, which quantify the amount of re-aerosolization/evaporation of the collected airborne droplets containing the ABF for both impingers was negligible (6.86E-03-2.69E-02 and 7.24E-03-8.42E-02 mL.m -3 air.h -1 for SKC Biosampler and AGI-4, respectively).

Despite the wall-deposited droplets in AGI-4, considering the predicted aspiration efficiency values, it was expect that the transported volume sampled by AGI-4 would be more than SKC BioSampler, but these results may be thanks to the following reasons: i) the deposition of more droplets on the wall of the entrance of the SKC BioSampler and re-aspiration of them into the SKC BioSampler during sampling compared to AGI-4 ( 𝐷 𝑂𝑢𝑡 𝐷 𝑖𝑛 ⁄ for SKC Biosampler and AGI-4 is 1.35 and 1.26); ii) the empirical equation was developed for laminar flow conditions, but at the air velocities of 1.2 to 5.0 m.s -1 , the turbulent flow (Re > 4000) occurred in the wind tunnel.

Even with the presence of numerous confounding factors, our results indicate that at the optimal configurations which the airborne droplet size distribution and the volume of airborne spray were almost equal (2.25 m.s -1 at 0.45 m and 3.3 m.s -1 at 0.85 m from the nozzle head), the transported volume sampled by both impingers were almost equal. It can be therefore concluded that the measurement and the analysis data were well done. Note that air velocity of 5.0 m.s -1 at 1.25 m from the nozzle has been subjected to the same droplet size distribution, but the volume of airborne spray was not equal to the others. Therefore, it seems that the aspiration efficiency of both impingers when using airborne spray plume with such density would not be much affected by (sub/super)isokinetic conditions. (a

E. 3. 4. Comparison of collection efficiency of the passive samplers in moving air

A comparison was made between the results of total airborne spray transport potential (% of spray volume that has been transported) using PVC strings and spheres in order to evaluate their efficiency for collecting the airborne spray plume under various air velocities and spatial configurations, as shown in Table E. 2.

The results showed that regardless of airborne droplet size distribution corresponding to each configuration (nozzle height, distance and air velocity), the collection efficiency of 2 mm diameter PVC strings was higher than the one of 5 mm diameter spheres. There is a general agreement between our experiment and the results of [START_REF] May | The Impaction of Aerosol Particles on Cylinders, Spheres, Ribbons and Discs[END_REF] (Table E. 3).

E. 4. Conclusion

This chapter reported a methodology for evaluating the performance of two passive samplers (PVC strings and spheres) and two active samplers (AGI-4 and SKC BioSampler) in collecting airborne spray droplets in the size range of sub microns to 200 µm (𝐷 𝑣90𝑚𝑎𝑥 =138 µm) in a small-scale wind tunnel. The experimental data were then compared to the predicted ones from the empirical equations.

The results showed that except for the air velocity of 1.2 m.s -1 , the airborne spray transport collected in SKC BioSampler was higher than the one collected in AGI-4. At a distance of 1.65 m from the nozzle head, from 2.25 to 5.0 m.s -1 , transported volume sampled by SKC Biosampler and AGI-4 rose by 71.3, 59 % respectively.

Given that at the same airborne droplet size distribution and the volume of airborne spray (2.25 m.s -1 at 0.45 m and 3.3 m.s -1 at 0.85 m from the nozzle head) the transported volume sampled by both impingers were almost the same, it seems that the aspiration efficiency of both impingers when using airborne spray plume with such density would not be much affected by (sub/super)isokinetic conditions. However, it would be necessary to perform supplementary tests in more controlled conditions (e.g. the size of droplets).

To observe vertical profiles and distributions of airborne spray volume obtained from PVC strings and spheres can be understand the details on the spatial behaviors of spray plume under the effect of air velocity and nozzle height from the sol/target.

The results also showed that regardless of the air velocity, collection efficiency of 2 mm diameter PVC stings was higher than 5 mm diameter spheres. Therefore, the PVC strings are a good choice for airborne spray transport measurement in field-study.

Appendix. F. Particle Image Velocimetry (PIV) set-up

Particle Image Velocimetry (PIV) is an optical measurement technique where the velocity field of an entire region within the flow is measured simultaneously. The technique yields quantitative visualizations of the instantaneous flow patterns, which are typically used to support the development of phenomenological models for complex flows or for validation of numerical simulations [START_REF] Sciacchitano | Uncertainty quantification in particle image velocimetry[END_REF]. A preliminary study has been conducted to determine the length of disturbance of air stream upstream the sampler inlet under non-isokinetic conditions using this technique, but a summary of experimental set-up has only been presented in this thesis and the results could be published later.

F. 1. Smoke liquid as a tracer

Air seeding provides an assessment of the airflow around the impingers by means of PIV. For the experiments in open circuit wind-tunnel, the supply of tracers is very often critical for the feasibility of the PIV measurements and it is important that seeding particles be non-toxic, non-corrosive, non-abrasive, non-volatile and chemically inert [START_REF] Melling | Tracer particles and seeding for particle image velocimetry[END_REF]. Considering these requirements, the smoke is an ideal tracer, which have been often used for gas flow studies [START_REF] Hamdi | Comparison of different tracers for PIV measurements in EHD airflow[END_REF][START_REF] Leger | Influence of a DC corona discharge on the airflow along an inclined flat plate[END_REF].

In this study, the pro high-density smoke liquid (HQ Power VDLSLHT5, Belgium) was used to generate the smoke seeding particles. The smoke particles were generated by heating to vaporization using an DMXcontrolled smoke machine (EHQ POWERTM. The machine was positioned at a distance of about 0.5 m in front of wind tunnel entrance, so that the smoke stream was conducted uniformity into the working section (Figure

F. 2. Components of PIV

The laser system consisted of a 200 mJ laser beam with a wavelength of 532 nm emitted by a double-cavity Nd:YAG laser at a rate of 15 Hz (Dantec Dynamic, Denmark), an integrated power supply containing control, power and cooling units, and a manual remote control box.

The laser beam coming out from the laser head was adjusted through a base and then directed to a groove

F. 4. Acquisition images

The flow velocity (𝑢 ⃗⃗) was based on determining the displacement of the smoke seeding particles recorded on two successive frames (∆𝑥 ⃗) that were separated by the time delay between two laser pulses (∆t), as follow:

where ∆𝑋 ⃗ is the tracer particle displacement and 𝑀 is the optical magnification (𝑀 = 1 𝑆 𝑓 ⁄ ). Each PIV configuration (air samplings and various upstream airflow velocities) led to different time delay in order to minimize the out-of-plane displacement of particle images between the paired captures. The recommended inplane displacement is limited to 1 4 ⁄ of the size of the interrogation window (IA), whose size defines the spatial resolution of the measurement. However, the amplitude of the displacement correlation peak is negligible when the in-plane displacement is less than 1 4 ⁄ of the size of the IA (ITTC -Recommended and Procedures and Guidelines, 2014).

Appendix. G. Supplementary information for chapter 5 The results revealed that relative humidity and solar radiation are correlated negatively (-0.62). However, for the eight experiments realized for this study (in summer 2020), the correlation coefficient between these two parameters is -0.38. 2𝑐 2 ]). The values of the coefficient c for the regression models were highly correlated with the ones of σθ for these two experiments. It should be noted that the coefficient 'a' is a function of the combination of spatial and meteorological parameters, particularly, wind speed. The distribution of aerosol plumes was not perfectly symmetric with respect to the average wind direction (b≠0). In other word, the peak of transport was not exactly in coincidence with average wind direction. However, to simplify the generalization of the empirical models presented later, this coefficient was assumed to be zero.

G. 3. Empirical modeling G. 3. 1.Airborne transport

It should be stated that the models containing the Bulk Richardson number fitted well with the observation data. However, they were not considered as the final models because this parameter is derived from wind speed and fluctuation of temperature.

Generally, the RMSE values of model 2 were lower than model 1. In fact, the good RMSE depend on the range of the dataset and the maximum and minimum values. Since, the difference between maximum and minimum values are considerable, an attempt was made to compare the residual of each observed value, which is the difference between the observed and predicted value by both models, at the same condition (the number of valid observations). The results indicated that, in most cases, when using the model 2, the residuals obtained from the large values, which belong to the measuring points near to the sprinkler head, were less than model 1.

These results are observable through the curve-fittings presented in the next figures. Besides, in some cases, the increase in the number of valid observation data and R 2 of a model has been due to validation of more observation data with larger values, which consequently have resulted in an increase of the RMSE values. At the height of 6 m, this ratio decreased to 0.64-1.05. According to these results, lower standard deviation of wind direction and higher relative humidity, albeit at lower altitudes, tend to increase downwind airborne transport (see also Figure G. 7). Indeed, this unexpected effect of relative humidity on the airborne transport might be due to the inter-correlation of relative humidity and solar radiation. Consequently, the positive sign of relative humidity coefficient in the models 1 and 2 may also have been the result of its combination with parameter of height. However, for explaining the role of relative humidity, it would be necessary to perform more experiments.

G. 3. 1. 1.Simulation of downwind airborne transport

Equipe d'encadrement