Monsieur Denis

Je Remercie Madame

Semia Cherif

Je Remercie

Madame Raja

Judith Muller

Karine Laboux

Emmanuelle Guevara

J'adresse

Dalenda Khoufi

My parents; mom and dad, no words can describe the love, respect and gratitude for your endless support all along my studying journey. This support is the key of all achievements. I'm thankful for everything; from tiny details to big sacrifices. Amine, my husband and my mate

GENERAL INTRODUCTION

 2.5: The Means of differences and their corresponding p-values for metalworking industry modeling Table 2.6: Pearson's correlation coefficients and their corresponding p-values for the RSW modeling (P4) Table 2.7: Pearson's correlation coefficients and their corresponding p-values for the steel cutting job tasks (P1 and P6) Table 2.8: The Means of differences and their corresponding p-values for the steel cutting job tasks (P1 and P6) Table 2.9: Pearson's correlation coefficients and their corresponding p-values for the plastic injection job tasks (P15) Table 2.10: The Means of differences and their corresponding p-values for the plastic injection job tasks (P15) Table 2.11: Pearson's correlation coefficients and their corresponding p-values for welding job tasks (NF in P5 and P9) Table 2.12: The Means of differences and their corresponding p-values for the welding job tasks (NF in P5 and P9) Table 2.13: Pearson's and Spearman's correlation coefficients and their corresponding pvalues for welding job tasks (combined exposure in P16) Table 2.14: Pearson's and Spearman's correlation coefficients and their corresponding pvalues for of electroplating job tasks (NF in P10 and P13) Table 2.15: Pearson's and Spearman's correlation coefficients and their corresponding pvalues of electroplating job tasks (FF in P10, P12 and P14) Table 2.16: Pearson's and Spearman's correlation coefficients and their corresponding pix values for the packaging job tasks (FF for P3, P7 and P8)

I. Context and general objectives I.1. State of the art

Some trace metallic elements are considered as among the most threatening toxic elements for human health especially for exposed workers and residents nearby industrial units and polluted sites [1]. They were identified by ATSDR (Agency for Toxic Substances and Disease Registry) in a list that prioritizes substances based on their frequency, toxicity, and potential for human exposure. On the top of the list stands arsenic (As), then lead (Pb) in the second place and cadmium (Cd) in the seventh place [2]. Even in trace amounts, TMEs are considered as pollutants of great concern because of their toxicity, in particular due to their carcinogenic and mutagenic potencies [3][4][5] and their accumulation and non biodegradation in the living environments [6][7][8].

Acute exposure causes harmful effects such as respiratory irritations and lung inflammation [3,4], hepatic cell destruction, kidney and neurological damages and sometimes even death [9]. However, chronic exposure to TMEs is more of concern due to their cumulative property [10].

On the other hand, combined exposure to various chemical products is unavoidable in everyday life, and humans are exposed to mixtures simultaneously and /or consecutively [11].

It must be emphasized that the majority of polluted sites contain several TMEs at the same time [12][13][14][15].

Nonetheless, considering the complexity of studying chemical mixtures and the lack of toxicological studies, the greater part of chemical risk assessment studies of TMEs deal with exposures and associated effects to single substances [16][17][18]. Response prediction of biological organisms exposed to more than one toxic chemical is one of the hardest tasks in environmental toxicology and risk assessment [16]. Thus, it is necessary to study the combined effects of mixtures reflecting more realistically the real exposure [11]. That is why, in this study, we will assess the joint effects of exposure to trace metallic elements mixtures.

Among the reasons of the increasing interest about mixtures is that some unresolved questions arise [11]. For example, joint exposure to Cd and Pb showed synergistic effects on kidney, neurological and reproduction impairments [10].

I.2. Study site

Sfax is Tunisian's second largest city, and is considered the economic capital due to the presence of many industrial zones [19] and its significant role in exports. Figure 1 shows the location map of industrial areas and the main sources of pollution in the Sfax metropolis. In this region, residents living close to industrial zones for years are constantly exposed to harmful metallic pollutants [1]. In fact, Sfax is suffering from a serious ecological situation caused primarily by many industrial units [20]. It is important to note that effluents from some industrial activities exceed the trace metallic element levels fixed by the current standards [20]. These metals are generally derived from many anthropogenic sources like industrial sewage and fumes [1,6,14,[20][21][22].

II. Literature review II.1. Metallic pollution in Sfax:

Several studies conducted in the Sfax region have shown that industrial activities are generating multiple metallic pollutants affecting all three compartments of the environment (air, water and soil) [18][19][20][21] and pollutants are found in soils [15,23], surface and sub-surface sediments [12,14,24] as well as in sea water along the Sfax coastline [25]. Moreover, several living beings have been proven to be affected, including marine organisms (fish, mollusks, cuttlefish, tuna, etc.) living near the industrialized coastline [6][7][8]13,21,[26][27][28]and the fishing harbor of Sfax [14,22,[29][30][31]. Epidemiological studies among populations from Sfax metropolis have investigated the relationship between metallic pollution and certain illnesses [18,20,32,33]. [19].

These studies focused on coastal zone residents and the downtown population, and mention that occupational exposure to TMEs may be a probable factor in explaining the high risk of various cancers identified in the region. The metals most frequently found are Cd, followed by Zn, Pb, Ni, Cr, As, Cu, and Hg [6][7][8][12][13][14][15]18,[20][21][22][23][25][26][27][28][29][30][31][32][33][34][35][36][37].

In this context, assessment of occupational exposure to such metals calls for thorough investigation both for the scientific communities and decision makers. To our knowledge, there is no study that has assessed the occupational exposure to TMEs, qualitatively nor quantitatively in this region. And the combined effects of mixtures have never been explored.

Thus, in this study, we are interested in occupational exposure to trace metallic elements in the Sfax metropolis and its quantitative human health risk assessment.

II.2. Human health risk assessment

II.2.1. Presentation of the approach

Human Health Risk Assessment (HHRA) was introduced in 1983 by the National Research Council (NRC) in the United States [38][39][40]. This scientific approach strives to use the available knowledge in a systematic manner through a standardized, transparent and coherent methodology [39]. It encompasses four steps: (i) hazard identification, (ii) dose-response assessment, (iii) exposure assessment, and (iv) risk characterization.

II.2.2. Hazard identification

Hazard identification is also referred to as "Hazard characterization" [40]. In the hazard identification step, one should define the potential adverse effects on biological endpoints/ target organs due to exposure to the considered trace metallic elements, based on the available toxicological data [41]. Toxicological data are principally explored in the Toxicological Profiles documents from ATSDR [42], the IRIS (Integrated Risk Information System) from USEPA [43] and the Toxicological Sheets from INRS (French National Research and Safety

Institute for the Prevention of Occupational Accidents and Diseases

). There are also some newer Development Support Documents established by TCEQ (Texas Commission on Environmental Quality) [44].

Hazard identification was initially introduced as the first step in HHRA. Still, according to a newer framework of risk assessment presented by IPCS (the International Programme on Chemical Safety) and WHO (World Health Organization) [45], it could be achieved in parallel with its closely linked second step of dose-response assessment and exposure assessment [40].

Accordingly, in the first and the second parts of our study manuscript, we will first deal with the exposure assessment step, introduce our methodology and expose the results. Then, in the third part, we will tackle the choice of the reference values and the risk characterization.

II.2.2.1. Trace metallic elements

Trace metallic elements are special inorganic pollutants. They attracted a particular attention of USEPA so as to develop a framework for metals risk assessment [46]. Some TMEs such as iron, copper and zinc are essential elements to human body since they are consumed as

nutrients [47]. Furthermore, unlike other pollutants, some TMEs are naturally occurring elements in the environment and their concentrations may differ across geographic sites [40].

Additionally, in contrast to organic compounds, metals cannot be produced or deteriorated by either chemical or biological reactions. But these reactions could change the valence state of the metal, or turn inorganic forms into organic ones, consequently altering their toxicity [46]. II.2.2.2. Identification of potential hazards of the selected trace metallic elements Potential adverse effects of the selected metals on biological endpoints/ target organs for the inhalation exposure are explored according to the identified exposure route.

The selection of the metals is detailed in the following part.

a. Health effects of chronic exposure to Al by the inhalation route Workers exposed to Al dust showed respiratory effects [48]. Lung problems vary from coughing and occupational asthma to alteration in chest X-rays and pulmonary fibrosis.

Animal studies have also revealed adverse respiratory effects, such as the increases in alveolar macrophages and increase in lung weight [48].

TRVs were not constructed for Al because there are doubts on whether the respiratory effects are due to general dust or specifically to the Al in it. Moreover, exposure assessment in occupational studies did not allow calculating an inhalation TRV [48].

On the other hand, occupational studies about workers exposed to Al dusts and fumes reported neuro-toxic symptoms [49,50].

Actual data do not show evidence of carcinogenicity [48].

b. Health effects of chronic exposure to Cr by the inhalation route Occupational and especially animal studies have thoroughly assessed the chronic effects of inhalation of Cr and its compounds Cr(III) and Cr(VI). The respiratory system and the skin were confirmed to be the main affected organs [51].

In some specific industries like electroplating, Cr(VI) compounds are used. It was reported that chronic exposure to this particular form of Cr may increase death risk due to non-cancer respiratory illnesses [51]. In our study, we are not considering chromium speciation, but merely the total and/or metallic Cr.

GENERAL INTRODUCTION 6

Eye inflammation was also reported from occupational studies where workers were exposed to mists and aerosols of Cr compounds [51].

Metallic Cr and Cr III are not classified as a human carcinogen according to WHO [52].

However, Health Canada [53] has considered total Cr as a carcinogen and established a Tumorigenic Concentration 05 TC05 = 4.6 µg/m 3 . This value refers to a concentration in the air that is associated with a 5% increase in incidence or mortality due to tumors. It is not a carcinogenic TRV, but a Unit risk can be derived based on this value.

c. Health effects of chronic exposure to Ni by the inhalation route Soluble and insoluble nickel compounds showed adverse respiratory effects in rodents. Lung problems include active lung inflammation, alveolar proteinosis and fibrosis [54]. Several occupational and more animal studies confirm the damages in the respiratory tract [4].

Epidemiological studies demonstrated that Ni is a carcinogenic element [55,56]. In 2012, Ni was classified by the WHO as carcinogenic to humans (group 1) [52].

d. Health effects of chronic exposure to Cu by the inhalation route

Occupational studies have reported that Cu is a respiratory irritant [3] with, among other consequences, pulmonary fibrosis.

Other adverse effects were also reported: gastrointestinal, hematological, hepatic and neurological [3].

Cu is not classified as to human carcinogenicity [57] e. Health effects of chronic exposure to Pb by the inhalation route Many studies on Pb chronic exposure have reported several adverse effects such as hematological, digestive, neurological, renal, cardiovascular, bone and reproductive toxic effect [5,58].

Some studies has indicated probable increases in lung and stomach cancer [5,59].

In 2006, inorganic compounds of Pb were classified by WHO as Probably carcinogenic to humans (stomach cancer) (group 2A) [52].

GENERAL INTRODUCTION 7

Moreover, OEHHA [60] has established an Inhalation Unit Risk = 1.2×10 -5 (μg/m3) -1 for kidney tumors based on animal data.

f. Health effects of chronic exposure to Zn by the inhalation route Acute exposure studies have shown respiratory adverse effects. However, there is little information about chronic exposure to zinc dust or fumes and their effects [61]. No TRV was established because of lack of exposure quantification.

The US EPA declared that there was no adequate data to assess Zn's carcinogenicity [62] II.2.2.3. Mixture and interactions of metals Metals are frequently occurring as components of a mixture, especially in occupational settings. That is why it is necessary to take into account the interactions between metals in the hazard characterization analysis [46,47].

TMEs' joint effects could be distinct from simply additive; they can be synergistic, antagonistic or potentiating [40]. There is another kind of interaction between metals which is named "molecular or ionic mimicry". It is a competitive interaction when two metals/metalloids are chemically similar; in most cases, a toxic metal replaces an essential metal [40]. Moreover, many MHs form complexes with several proteins in the human body, which could affect their toxicity [40].

The ATSDR has focused on some trace metallic elements and established two interaction profiles about lead, manganese, zinc, and copper, and arsenic, cadmium, chromium and lead [10,63]. Interaction data reports and guidance are limited [40] and relatively old. Thus, we will also explore new published studies.

II.2.2.4. Toxicokinetics: Physiologically Based PharmacoKinetic models

At the beginning of the study design for this project, in order to explore internal doses in target organs/tissues, we intended to use a PBPK (Physiologically Based PharmacoKinetic) model [64]. Though PBPK models have been used extensively for mixtures such as organic solvents, this is not, to the best of our knowledge, the case for TME mixtures [65]. In spite of the critical need for such a model, particular complexities regarding TMEs prevented their implementation due to the extreme variability of biological half-lives of the main toxic TMEs GENERAL INTRODUCTION 8 [66], which range from days (for As) to decades (for Pb). Hence, PBPK models will not be elaborated upon hereafter.

II.2.3.

Exposure assessment

II.2.3.1. Occupational exposure to trace metallic elements

In the general population, the most likely exposure route of TMEs such as Pb, Cu, Zn and Al is the oral pathway. By contrast, in occupational settings, workers are predominantly exposed by inhalation [5]. Thus, we are especially interested in the inhalation pathway and the airborne amount of trace metallic elements in the workplace.

For the inhalation route, metals are generally in solid phase under regular environmental conditions of the atmosphere, so they normally exist in particle phase [40]. However, in occupational circumstances using combustion process (e.g. for melting), metals and metal compounds may also exist as vapors.

II.2.3.2. Mathematical modeling of occupational exposure to chemicals

Recent developments in modeling allow prediction of exposure to chemicals by using descriptive environmental characteristics and/or the human physiological factors. Since we are dealing with occupational exposures and according to the selection criteria for the study companies (detailed in the first part), inhalation is the main exposure route. We have chosen mathematical models to estimate occupational exposure to airborne pollutants [67,68]. In this framework, a variety of models have been used to predict indoor air pollutants' concentrations. The models differ in their hypotheses as to (i) pollutant transport mechanisms and (ii) uniformity of the air mixture in the workplace. In our study, these models were executed using the IHMOD "Industrial Hygiene Modeling" software [START_REF]Exposure Assessment Strategies Committee[END_REF][START_REF] Spa | Euromaster Spa Masterbatches and compounds[END_REF], which is a model compilation for the calculation of inhalation concentration. It is available from the American Industrial Hygiene Association (AIHA) website [START_REF]Exposure Assessment Strategies Committee[END_REF]. IHMOD currently offers 11 models. The three most commonly-used categories are: (i) the Well Mixed Box, (ii) the Near Field and Far Field model and (iii) the Eddy Diffusion Turbulent model [67].

These predictive models of indoor air concentrations are based on environmental working conditions as well as certain other specific information about the manufacturing process [67].

These models were initially developed for solvents and other volatile compounds, in relation to their physicochemical proprieties. With regards to metals, to the best of our knowledge, similar models were applied only to arc welding processes, in a study in which Boelter et al.

calculated field-derived emission rates of total particulate encompassing only iron and manganese [71]. Another newer study [START_REF] Arnold | Evaluation of the well mixed room and near-field far-field models in occupational settings[END_REF] have used the well mixed box and Near Field and Far Field model to predict cobalt exposure levels in two job tasks involving powder weighing and mixing. The authors also calculated the emission rates using source air sampling. In the preset study, we aim to broaden the application of these mathematical models to TMEs, as well as to several types of emissions in various production processes.

a. Well mixed box model

This model suggests a simplified representation of chemicals dispersion. It estimates the air concentration of a completely well mixed room. It means that the pollutant is equally distributed over the room [73].

The input main parameters are the emission or generation rate "G", ventilation rate "Q" and the volume of the air in the workplace "V".

The predicted indoor air concentrations in the WMB are computed using the IHMOD software [START_REF]Exposure Assessment Strategies Committee[END_REF][START_REF] Spa | Euromaster Spa Masterbatches and compounds[END_REF], using the following formula; Where; G: generation rate or emission rate, expressed in mg/min Q: ventilation rate, expressed in m 3 /min V: volume of the air in the workplace, expressed in m 3

Cin: pollutant concentration in supply air, expressed in mg/ m 3 (generally assumed to be zero) KL: loss value due to "non-ventilatory" losses (sorption or chemical degradation of the pollutant) [73], expressed in fraction/time. Values ranges from 0 (default: no loss except via ventilation) up to 1.

C0: pollutant concentration in the room at t0, expressed in mg/m 3 . It is used when there is already a known concentration of the pollutant before the beginning of the emission [73].

After a certain time, the pollutant concentration becomes almost constant as much as the parameters remain the same [73]. It is called the steady state where the predicted concentration reaches a maximum value, and the formula turns into, as below;

𝐶 𝑊𝑀𝐵,𝑆𝑆 = 𝐺 𝑄
The input parameters are going to be detailed later. Near Field (NF) box includes, by definition, the emission source of the pollutant and the breathing zone of the worker whose exposure is to be estimated [74]. It is conceptualized depending on the exposure situation as a parallelepiped or hemisphere on the floor, on the machine or on work surface. The Far field (FF) is the remaining volume of the workplace, where pollutant concentration is assumed to be lower, and homogeneous over the FF [74].

The NF-FF model is recognized in the scientific community as an occupational exposure assessment tool [75].

The indoor air concentrations of TME in the NF and FF box are computed using the following formulas [74]; Where;

𝐶
G and Q: the same as in WMB model.

VNF: the NF volume, expressed in (m 3 ).

β: the inter-box air flow rate between the NF and the FF, expressed by (m 3 /min).

λ1 and λ2: the air turnover rates in the FF and NF, respectively (expressed by min -1 ).

At the steady state, the formulas become as the following; Prior to Q determination, mass conservation of the quantity of matter in the air needs to be verified, so as to prove that there is no backpressure effect in the workplace. Confirmation of the basic assumption allowing calculation of the ventilation rate for the whole workplace is a necessary pre-step. This assumption considers air in the workplace room to be an ideal gas, and that the air flow rate entering the room is equal to that leaving it [76].

𝐶
Mass conservation is calculated following the basic formulas of the ideal gas law:

G CFF β β V Q Q CNF VNF 𝑃 𝑖𝑛/𝑜𝑢𝑡 × 𝑉 = 𝑛 𝑖𝑛/𝑜𝑢𝑡 × 𝑅 × 𝑇 𝑖𝑛/𝑜𝑢𝑡
Where:

Pin/out: the air pressure entering or leaving the workplace room in Pascal (Pa)

V: the air volume (m 3 ); n: the quantity of matter (mol); R: the ideal gas constant (unit J.K -1 .mol -1 );

T: the temperature inside or outside the workplace in Kelvin (K)

So, it is necessary to demonstrate that the quantity of matter entering and leaving the room is approximately the same [76].

𝑛 𝑖𝑛 ≈ 𝑛 𝑜𝑢𝑡

We therefore calculate that nin / nout should be approximately equal to 1.

Then, we assume air direction to be constant; therefore the volumetric air flow rate is conserved:

𝑄 𝑖𝑛 = 𝑄 𝑜𝑢𝑡
When the workplace is naturally ventilated by doors and windows, the Qin entering from the main door is calculated by measuring the average air face velocity "Vface" through the door over the time range of interest, and recording the dimensions of the doors. The average "Q" within the volume of interest is calculated using the following formula [76]:

𝑄 𝑎𝑣𝑒𝑟𝑎𝑔𝑒 = 𝑉 𝑓𝑎𝑐𝑒 𝑎𝑣𝑒𝑟𝑎𝑔𝑒 × 𝑆
Where:

Vfaceaverage: mean of air face velocity values (m.s -1 ) S: the cross-sectional surface of the main door or air inlet opening (m 2 ) Some authors also use the air changes per hour ACH as an indicator of the ventilation rate [67]. It is calculated using the effective air volumes in workplaces V and the air flow rates Q by the following formula:

𝐴𝐶𝐻 = 60 × 𝑄 𝑉 GENERAL INTRODUCTION 13
ii.

The air volume "V"

In order to calculate the air volume inside of the room, all volumes within the room have to be subtracted, so to have a more accurate value. These volumes include the machines, material stocks, etc.

iii.

Generation rate "G"

The generation rate or the emission rate, noted G, is the amount of pollutant released over a period of time. It is generally expressed per mg. min -1 .

The available studies uses sample concentration measurements to calculate G. This is majorly used with volatile organic compounds [START_REF] Arnold | Evaluation of the well mixed room and near-field far-field models in occupational settings[END_REF][77][78][79][80][81]. As for metal compounds, both identified studies [71,[START_REF] Arnold | Evaluation of the well mixed room and near-field far-field models in occupational settings[END_REF] have used prior sample concentration measurements conducted in the vicinity of the emission source to determine the G for Fe, Mn and Co.

In fact, to demonstrate the usefulness and validity of these models as an alternative and costeffective method of indoor air concentration determination [82], we looked for other alternative methods in literature in order to avoid relying on concentration measurements to determine G.

There are also other methods in literature, where the G could be calculated without using air sampling. However, the utility of these methods need to be evaluated.

The main two methods are: (i) mass balance and (ii) Emission Factor (EF).

1) The mass balance method

During the manufacturing process, masses of the materials are maintained. The quantity of pollutant emitted into the workplace can thus be calculated using the equation [START_REF] Keil | Chapter 3: Modeling Pollutant Generation Rates[END_REF]:

mass into process -mass incorporated into product -mass collected as waste

= mass released in room

One should take into account the division of the mass per time (production per year or per day for example). The result is an average G.

To use the mass balance method, all forms of metal transformation during the processes are evaluated: mass of end-products, mass collected as waste (often sold to other companies for GENERAL INTRODUCTION 14 other usages) and even dust deposited on the workplace floor. The difference between the sum of the latter and the raw metal quantity will be the suspended aerosol. Concentrations of the various TMEs within this aerosol will be assessed.

2) The emission factor method

An EF is calculated for a specific process, and sometimes for specific parameters and conditions. It relates the quantity of pollutants to a particular activity. It facilitates estimation of the generation rate, especially where there is a lack of information or difficulty in calculating it [START_REF] Keil | Chapter 3: Modeling Pollutant Generation Rates[END_REF]. US-EPA (US-Environmental Protection Agency) has used EFs extensively to assess air pollution related to industrial emissions, compiling this data in the AP-42

Compilation of Air Pollutant Emission Factors [84,85]. The common equation for emissions estimation is the following [86]:

𝐺 = 𝐸𝑚𝑖𝑠𝑠𝑖𝑜𝑛 = 𝐴 × 𝐸𝐹 × ( 1 -𝐸𝑅 100 )
Where:

G is expressed in mass per time; in our case (mg. min -1 ) A = activity rate; ER = overall emission reduction efficiency, in % Generally, the EFs in AP-42 are calculated from all acceptable quality studies. Identification of true emission factors at a specific plant is difficult. For this reason, AP-42 is recommended, which provides tools for the estimation of emission factors applicable to the situation of interest [84,85]. In this investigation, since we were unable to find EFs for each process, we attempted to retrieve the information from external studies. In order to cope with these uncertainties, a Monte Carlo simulation could be considered [START_REF] Keil | Chapter 3: Modeling Pollutant Generation Rates[END_REF].

 Emission factors for electric arc welding

US EPA specifically treated some specific job processes in AP 42: Compilation of Air Pollutant Emission Factors [85]. Among those we are interested in, we can find the Electric arc welding and electroplating processes.

In the section dedicated to electric arc welding [87], US EPA provides EFs for some trace metallic elements (Cr, Ni, Pb) depending on the welding process and the electrode type.

Furthermore, in the chapter dedicated to metallurgical Industry in AP 42 [85], the US EPA treated also the electroplating process [START_REF]Chapter 12: Metallurgical Industry, section 20: Electroplating[END_REF]. In this section, we can find emission factors for uncontrolled emissions or controlled emissions from electroplating baths.

The EFs proposed by AP-42 for the electrodes were only established for Cr and Ni. However, the electrodes can also contain other TMEs. So, to calculate the emission rate G for the rest of the existing TMEs, AP 42 provides also the EFs of the total fumes.

Furthermore, the SAN DIEGO Air Pollution Control District established a fume correction factors FCF for SMAW and GMAW processes [START_REF]SAN DIEGO Air Pollution Control District[END_REF] in order to calculate the emission rate G for the TMEs which are not established in AP 42. In fact, factor calculations were fully based on an earlier report established by the Department of the Navy Carderock Division [START_REF] Of | Characterizing Shipyard Welding Emissions and Associated Control Options[END_REF].

The new proposed formula is:

𝐺 = 𝐴 × 𝐸𝐹 × 𝐹𝐶𝐹 × 𝐶 𝑖 × ( 1 -𝐸𝑅 100 )
Where;

Ci :the concentration of the metal (i) in the welding wire FCF: fume correction factor per NASSCO -Richard bell (metal mass/fume mass) =0.5464 for GMAW =0.2865 for SMAW.

 Emission factors for electroplating

A special focus was also given to chromium electroplating and chromic Acid anodizing where many EFs were established depending on the control system devices. Several control systems were considered such as moisture extractors chemical fume suppressants and polypropylene balls. In addition, only particular cases of Ni, Cu and Cd emissions were developed. Finally, for other metals or if we have another control system, US EPA provides equations to calculated our own EFs using specific technical information about the electroplating baths.

To estimate an EF for a metal "m" from non-chromium the following formulas are used [START_REF]Chapter 12: Metallurgical Industry, section 20: Electroplating[END_REF]:

i. For uncontrolled electroplating tanks: The EF values are expressed either in grains/ A-hr or in grains/ dscf (dry standard cubic foot).

𝐸𝐹 𝑚 = 3.
Possible conversions are allowed from grains/ A-hr to grains/ dscf and from grains/ dscf to mg/ dscm (dry standard cubic meter) by multiplying with 0.01 and 2290, respectively [START_REF]Chapter 12: Metallurgical Industry, section 20: Electroplating[END_REF][START_REF]Emission Factor Documentation for AP-42 Section 12[END_REF].

Finally, the concentrations have to be converted into mg/m 3 . In fact, US EPA is considering the plating emissions on dry basis. Thus, we have to take into account moisture in the emitted gas using the following formula [START_REF]Emissions Monitoring Guidance Note (AG2)[END_REF]:

𝐶 𝐷𝑟𝑦 = 𝐶 𝑊𝑒𝑡 × 100 (100 -𝐻 2 𝑂%)
In chromic acid anodizing, the EFs are converted from grains/hr-ft 2 to mg/hr-m 2 by multiplying with 697 [START_REF]Chapter 12: Metallurgical Industry, section 20: Electroplating[END_REF]. So, in order to calculate the G, the surface of the bath is required. This method doesn't consider the percentage of the metal in the materials which is variable for each material. In addition, it doesn't allow us to calculate the G of other metals. That's why we looked for an alternative method to calculate the G. In fact, this method is available for industrial use and has not been documented in a research references.

So, we tried to find an alternative way. Therefore, research studies that are interested in calculating the fume emission rate released from RSW were explored. Then, the TMEs generation rates could be calculated using the percentage of each metal in the materials.

3) Other model-specific parameters:

We formerly mentioned the key essential parameters for all models. Since we actually have 11 subtypes of models in IH-Mod modeling software tool [START_REF]Exposure Assessment Strategies Committee[END_REF][START_REF] Armstrong | Mathematical Modeling to Estimate Exposures" User's Blog[END_REF], there are other model specific parameters. As examples, we mention the concentration of a pollutant in room air at time t0, noted C0 and the emission rate constant, noted α. These two parameters are usually used with volatile organic compounds like toluene and acetone [67].

In our case, we are interested in identifying the specific parameters of NF-FF model. In order to calculate the indoor air concentration of TME in the NF box, the formulas previously presented are used;

The first parameter is β (m 3 .min -1 ); the inter-box air flow rate. It is calculated using the following formula [74];

𝛽 = 1 2 ⁄ . 𝐹𝑆𝐴. 𝑆
Where:

FSA (m 2 ): is the free surface area of the NF box which means the value of the geometrical surface of the NF box where air is free to flow through.

S(m/min):

is the average random air speed close to the NF boundary.

The second ones are λ1 and λ2 (min -1 ); corresponding to the air turnover rates in the FF and NF, respectively. We don't have to determine them because they are directly calculated from the other parameters by means of the following equations [74]:

𝜆 1 = 0.

II.2.4. Risk characterization methods for chemical mixtures

Regarding mixture risk assessment, two main methods are proposed in the literature: (i) methods for "whole-mixture data" where the mixture is studied as a whole component, such as fuels, diesel, coke oven emissions, etc. [41,47]; and (ii) methods for component data where the components are considered as a particular combination of specific chemicals [41].

There are several methods to calculate the combined or cumulative effect of exposure to multiple chemicals. The majority of these methods are either dose-addition methods (e.g. the hazard index method-HI), relative potency factors methods (RPF)) or response-addition methods. They all ignore interactions between the mixture components by considering them as toxicologically similar or as "toxicologically independent", respectively [41]. Here are more details about these methods.

(i) Addition hazard index : US EPA developed the Hazard index method in the Guidelines for the Health Risk Assessment of Chemical Mixtures [START_REF] Us | Guidelines for the Health Risk Assessment of Chemical Mixtures[END_REF]. Ideally, HI is recommended for chemical mixtures whose components have the same mode of action (MOA); in a practical manner, a similar target organ is deemed sufficient to apply the method [41]. The HI should be considered for a specific route of exposure and exposure period [41].

HI is the sum of individual hazard quotients (HQ) of the mixture components. By definition, a HQ is dimensionless and calculated by dividing the exposure measures by an acceptable level [41]. The acceptable level is the threshold Toxicological Reference Value (TRV, termed as VTR in French, Valeur Toxicologique de Référence) which is mostly replaced by RfC (Reference concentration, for inhalation exposure) as the TRV illustrated by US EPA [43].

(ii) The Relative potency factors method: it is a widely used method nowadays. This method is rather a Dose-Response Assessment method [41]. It is used for mixtures of chemicals with a common mode of action. An index compound (usually the most studied chemical) is used as a reference. Then, RPFs are calculated for each component, relative to this index compound, and a total equivalent quantity TEQ is computed for the whole mixture. Finally, a ratio between TEQ and the TRV of the index compound is calculated [96]. A more particulate version of RPF is the Toxic Equivalency Factor TEF method, widely used for dioxins or Poly Hydrocarbon Aromatic compounds. It is considered as a more specific method when the components share the same mechanism of action [41].

RPF is an excellent method for specific groups of mixtures. However, it stills a dose additive method. Hence, when synergistic or antagonistic interactions are suspected or demonstrated in literature, alternative methods should be considered. Actually, the US EPA recommends a new modified version of HI that takes into account mixture interactions [41], the Interactionbased hazard index.

(iii) Interaction-based hazard index: In fact, for many years, studies relied on "no-interaction" concept by justifying the similarity or the independence of toxic effects in their mixtures.

However, more recent work confirmed that these methods cannot be generalized for chemical mixtures when there is not enough or comprehensive evidence [41]. That is why new approaches have to be implemented to consider toxicological interactions.

To address some of these issues, the dose-additive HI was modified taking into account binary interactions between the pollutants in a mixture.

III. Specific scientific objectives

In this risk assessment study of metal mixtures, we will develop3parts:

(i) Estimation of pollutants concentrations using mathematical models for assessing occupational exposure [67,68];

(

I. Introduction

Over the last decade, mathematical models for estimating indoor air concentrations of chemicals in occupational settings were developed for exposure assessment as a possible alternative to air monitoring. Occupational hygienists are interested in such models because monitoring is not always available, so they could be used as a quantitative and scientifically explicit method to support expert judgment [1].

Several studies have used such models for volatile organic compounds exposures [2][3][4] and compared them with actual exposure monitoring. Predicted concentrations were considered as "reasonable estimations" as they were within a factor of 0.5 to 2 to measurements [5].

Regarding metals' exposure, to the best of our knowledge, similar models were applied only to the arc welding process [6] where the authors have calculated models' parameters based on air monitoring measurements. Contrarily to the latter approach, we seek to calculate all models parameters independently of air monitoring data.

In our study, the trace metallic elements are present in the air either as particulates during job tasks where dusts are generated from metallic materials, or in the vapor form as aerosols coming from processes that incur heating of metallic materials or substances containing TMEs.

The objective of this part is to apply the mathematical models for estimating indoor air occupational exposure to trace metallic element TMEs in metalworking industries. Unlike other studies that used simulation designs for jobs and workplaces, this study aims also to explore the model's prediction ability in real and more complex exposure scenarios.

II. Materials and Methods

II.1. Population sampling

The study involves workers directly exposed to TMEs at their workplaces and manipulating some of the abovementioned chemicals in manufacturing processes. For purposes of comparison, we also included administrative staff as non occupationally-exposed employees.

PART I 34

II.1.1. Selection of metalworking industrial sectors and companies

In order to localize companies handling TMEs, and to define the main pollutants of interest, we conducted a thorough review of earlier studies addressing metallic pollution in the Sfax metropolis .

In addition, with the help of the chamber of commerce [32], and following investigation of the local industrial sectors and recommendations from the union of small and medium-scale industries ("Union des Petites et Moyennes Industries": UPMI), we identified industries and companies that are recognized to be likely sources of TMEs emissions according to literature [33]. A wide range of industrial activities was covered, including the steel and metal machining and processing, welding, electrical and wiring, electroplating, phosphate fertilizers, plastic, paint and glass industries. Lastly, we randomly selected one company from each industrial sector, and contacted company executives to request their cooperation. Where participation was declined, we selected another company in the same sector and sought cooperation. To solicit approval, we explained to the entrepreneurs, by a written letter (in appendix), the aim of the study regarding possible risks to workers in industries manipulating TMEs.

II.1.2. Inclusion and exclusion criteria

Inclusion criteria: The selected companies were industries manipulating TMEs in their production processes likely to incur occupational exposure to a mixture of such chemicals.

Metal manipulation had to be identified where small particles emission was found in the air as small particles aerosol (cold: dust; or hot: vapor).

Exclusion criteria: (i) Companies not manipulating TMEs; (ii) and employees exposed to a single metal.

II.2. Description of companies and of the study materials

Two questionnaires were established and used to identify the TMEs that were processed and to collect information for the modeling scenarios in view to choose the proper model types or to calculate their parameters (in appendices section).

The first questionnaire was an identification/descriptive questionnaire aimed at identifying and defining:

PART I

35

 The specific activity of each job task and the emission source,  All substances and materials manipulated in the processes, to ensure that our qualitative and quantitative inventory was as accurate as possible,  The exposed and indirectly-exposed employees, based on their activities and possible exposure to TMEs.

The second questionnaire was related to the job-task of the exposed workers:

 Description of the overall atmosphere of the workplaces and the nature and quality of ventilation; it was also used to register measurements of the relative humidity and the inside and outside temperature and pressure.

 Collection of the dimensions of the workplaces, machines, stocks, etc.

 Identification of workers' position in relation to the emission configuration, in space and time

II.3. Mathematical modeling of occupational exposure

II.3.1. Model's choice

The main criteria for selection of the appropriate model are (i) the worker's position and location in relation to the emission source, and (ii) the configuration of workplace ventilation [5]. Indeed, variability or steadiness of the job process in time and space is an important factor in choosing a model subtype, which is why a study of each job process is necessary to model selection. In order to calculate model parameters, it was necessary to fill a second questionnaire about job and workplace specifications, as well as to perform certain direct measurements. These are specified in the next section.

II.3.2. Parameters calculations

II.3.2.1. Ventilation rate "Q"
First of all, we need to verify mass conservation of the quantity of matter in the air [34].

Mass conservation was verified for every workplace following the basic formulas of the ideal gas law mentioned previously. Then, the ratio between the quantities of matters entering and leaving the workplace nin / nout should be approximately equal to 1.

PART I

To this end, direct measurements of pressure and temperature inside and outside each workplace should be performed prior to using the method described below to calculate Q.Q is also commonly called as the total room air flow rate [5].

In our case, open doors and windows are the only or major source of ventilation; air comes in and out of these two openings, generally located at opposite ends of the rooms [34]. We assume air direction to be constant; therefore the volumetric air flow rate is conserved:

𝑄 𝑖𝑛 = 𝑄 𝑜𝑢𝑡
To calculate Qin entering from the main door, we measured average "Vface", and recorded the dimensions of the doors. Air face velocity measurements were conducted using fixed anemometers, every 15 minutes along the four-hour shifts. This was performed through 3 days in the middle of the week; Tuesday, Wednesday and Thursday, to get an idea of the variability of Q in the workplace.

In some cases, the workplace is separated into two or three compartments that are connected with communicating doors (e.g. figure 1.1 and 26 of A1 and A8, respectively). So, ideally, every compartment should be considered as an independent workplace. However, based on the available information, only the total quantity used into process for all compartments is provided which means only a total generation rate (defined later) could be calculated.

Therefore, to rectify this, we decided to calculate the individual ventilation rates for every compartment, and then calculate a volume-weighted average of Q for the whole workplace.

The ACH [5] was also calculated The air volume "V" II. β is calculated using the previously mentioned formula [35].

S (m/min) is calculated as the mean of several direct measurements with anemometers that were performed next to the NF every 15 minutes for 4hour-shifs throughout 3 days in the middle of the week; Tuesday, Wednesday and Thursday.

Both FSA and Swill be presented in results.

II.3.3. Processes & Jobs presentation

Job tasks are categorized according to their work processes. In addition, they are further subcategorized into more specific tasks. Each type is modeled individually.

All workplaces were mapped in order to show the workplace volume, the ventilation configuration and the location of the machines or the emission source. These figures highlight also the position of the workers in their job tasks.

Each worker is on a press machine for a continuous 4 hour-shifts.

II.3.3.1. Metal sheet cutting a. Stamping

The first job task is stamping, which means metal cutting forming. The press operator is using a mechanical or hydraulic press machine to cut or form steel sheets into specific shapes by stamping.

b. Sheering & slitting

Other kinds of steel sheets cutting are sheering or slitting. The first one is cutting metal steel rolls into big rectangular sheets using a steel sheet cutting machine. The second one is cutting large steel rolls by slitting them into narrower ones using a roll slitting machine.

PART I 38

II.3.3.2. Welding

Welding is a quite widespread process in several industries [36]. The welding process consists in joining two metal parts to connect them by heating up to a particular temperature either with or without a consumable filler metal [36,37].

There are many different types of welding processes (over 80 welding types) [37]. We are particularly interested in 4 different types; resistance spot welding (RSW), induction welding, Gas Metal Arc Welding (GMAW) and Shielded Metal Arc Welding (SMAW).

a. Resistance spot welding Spot welding or also referred to as RSW is the most common kind of resistance welding [38,39]. This type of welding consumes neither filler materials nor gases. It is used to weld thin steel sheets that are tightened by two electrodes. Then, a high electric current introduced through the two sheets, produces heat and coalesces them because of the resistance [38,39].

RSW is considered as "clean technique" compared to other welding kinds because of its low mass emission rate during process [36,39].

b. Induction welding

The induction welding process is also called high-frequency induction welding. In our study, this method is used in longitudinal welding of steel tubes. During the process, a highfrequency current is supplied through an induction coil, which is placed nearby or around the workpiece to be welded [39]. Subsequently, the induced current heats the metal by the Joule effect [40]. Then, the workpiece is deformed and the two edges are welded together without any additional material. This process is considered to have a relatively high welding rate because of the speed of heating [39].

c. Electric arc welding

The electric arc welding process encompasses several types. Among them, we can find GMAW (or MAG) and SMAW that are considered from the most common welding processes [36,37]. Both processes use consumable electrodes as a filler metal.

i. Shielded Metal Arc Welding (SMAW)

SMAW is also called Manual Metal Arc welding (MMA). It is particularly considered as an old, simple and a multipurpose welding processes [36].

PART I 39 SMAW process produces an electric arc between the covered electrode and the basic metal.

This process consumes only the electrode as filler metal. In fact, the electrode consists in the core which is a solid metal stick and a flux coating which is required to release a shielding gas for the welding process. Therefore, SMAW is a better process to use outside workplaces and even in windy weather conditions [37].

ii.

Gas metal arc welding (GMAW): Metal Active Gas (MAG)

MAG welding is a subtype of GMAW process. In GMAW, an electric arc is generated between the basic metal and the metallic wire, which is the consumable electrode. Contrary to SMAW, the GMAW process consumes also a shielding gas that is supplied externally [37].

The MAG subtype is using active gas such as carbon dioxide CO2.

It is considered to be a faster process than the SMAW. In fact, it is a continuous process contrarily to SMAW that is consuming elemental electrodes [36].

II.3.3.3. Electroplating a. Metal electroplating

The electroplating process is also known as the electrodeposition of a metallic coat onto an

article. An electric current passes through an electrolyte and reduces the metal cations that are dissolved in order to form a metal coating. Therefore, the articles must have an electrically conductive surface. Thus, these articles are simply metal workpieces [41]. This electroplating type is referred to as the metal electroplating

In our study, we particularly dealt with zinc and nickel electroplating processes. Each process encompasses two plating lines. Every line can include several plating baths where the electroplating of other metals could occur. For example, in nickel-plating lines, there are nickel, copper, chromium and brass plating baths.

b. ABS plating

Even though, plastics do not have an electrically conductive surface, electroplating could be realized over a plastic article after a special treatment such as electroless plating [41]. In our study, we are especially interested in the decorative chromium electroplating of a thermoplastic polymer named Acrylonitrile butadiene styrene (ABS). In fact, ABS is the most common plastic used in chromium electroplating [41]. The two lines include also cleaning pretreatment baths such as with chromic acid. Metal emissions from both pretreatments and plating baths are considered in our study. This job task will be considered in three different workplaces, workers are collecting the final products and either wrap them with plastic films or join them with metallic strips. They also gather them in cardboard boxes or specific locations. Finally, they often have to displace the products using adequate devices. Therefore, workers are not in a steady position. They could move in the whole workplace and commonly work in a specific area.

b. Quality control Quality controllers are merely verifying the quality of the electrodeposition of decorative chromium onto ABS workpieces. They are using magnifying lens to check every workpiece.

c. Plastic injection

Plastic injection molding is not among metallurgical or metalworking industries; however, this process involves the use of color additives called color masterbatches. In reality, metallic compounds such as salts, oxides and complexes are the basic substances in inorganic pigments of masterbatches [42]. Thus, color additives can contain trace metallic elements like Zn, Ni, Cu, Pb, Cr, Al, Cd [43,44] . Nevertheless, it should be noted that Pb and Cd are generally replaced [44] and that masterbatches designed to food and medication packaging are highly controlled and should not contain TMEs [42]. Consequently, we integrated the plastic injection process in our study to assess the possible indoor air exposure to TMEs of the workers.

II.3.4. Results presentation

All modeled concentrations are calculated as time weighted averages, TWA. The IHMOD tool enables us to calculate TWAs as a time function in regards to the previous concentrations.

III. Results

III.1. Voluntary metalworking companies

We stress that participation in this study was voluntary, both at the level of industrial plants and for workers.

PART I 41

Based on the metals issued from metallic pollution in Sfax metropolis, we identified, from the literature [33], the industrial sectors most likely responsible for occupational exposure to these TMEs. Table 1.1 illustrates the identified industrial sectors and the corresponding TMEs.

Afterwards, we identified53companies in Sfax metropolis from the selected metals sectors; they are also listed in table 1.1 (4 of them belong to two different industrial sectors). Next, 24 randomly selected companies were contacted to seek approval. Some companies refused at the first contact; other ones took us up to 6 months of negotiations, appointments and field visits, and also eventually declined to participate. In the end, as we can see in the table, only 5

industrial plants agreed to be involved.

The corresponding industrial activities of the 5 companies are the steel cutting, welding, electroplating and plastic industries.

All company names are hidden for confidentiality reasons; hereafter, we present a translation of their names or their main activities.

Table 1.1: Industrial sectors responsible for Occupational exposure to TMEs and probable companies in Sfax metropolis [32,33] Industrial sectors [

III.2. Identified materials and metals in each company/ workplace

In order to respect the confidentiality agreement with the participating companies, we attributed an identifier number to each one. As illustrated in table 1.2, we also ordered the workplaces and the job tasks as Ai and Pi, respectively.

We identified the used materials in every workplace using the first questionnaire. Materials commercial names and references are illustrated in table 1.3.Afterwards, TMEs were identified using research studies that have used the same materials, technical or safety data sheets or the official websites of the used materials (see table 1.3). The identified TMEs are presented in table 1.2 for each workplace.

Inox sheet metal

Inox 304 [46,52,53] Aluminized steel sheet DX51 AS120 [54] Galvanized steel sheet DX51D+Z [46,54,55] Basic coated electrode OK 48 00 (also known as E7018) [56] Ordinary welding rod E6013 [57] Coppered welding wire ER70S-6 [58-61]

Masterbatches

Color masterbatches [33,42,46] III. The airflow rate nearby each worker is low. In this setting, we are using the near field/ far field model NF/FF with a constant emission rate. Hence, the workplace is going to be divided conceptually into two zones, the near field NF that includes a single emission source and the worker's breathing zone [3], and the far field FF being the rest of the workplace. Elsewhere, as presented in figure 1.1, the workplace A1 is separated in three compartments that are connected with large communicating openings. And, since only the total quantity of materials used in the process for the wholeA1 is provided, the volume-weighted average method is used to calculate the ventilation rate of A1, noted QA1.  In the second situation, the workplace is named A4 (figure 1.2). There are 4 steel cutting workers cutting steel both by sheering and by slitting metal sheets P6.

Every two of them are working continuously close to their machine for a four-hour shift. A4 is a relatively long workplace whose dimensions are 60*20 m. It is mainly ventilated by a front door. The air flow rate is remarkably lower in the vicinity of workers. Therefore, we are using the NF/FF model with a constant emission rate.

The values of the mass conservation ratios are 1.07 and 1.01, respectively, values close to 1 which means that mass conservation is confirmed.  In the first job task P1, the NF is estimated as a parallelepiped box of air in front of a press machine. A worker is in seated position, so the height of the box is equal to the vertical distance between the metal workpiece press position, which represents the emission source, and about 15 cm above the worker's head to cover up the entire breathing zone as cited by INRS [62] according to the European norm. The length is equal to the distance between the worker and the metal workpiece press position. And the width is equal to the working surface of the metal workpiece press position (figure 3). It should be noted that the NF volume includes a little part of the machine which is estimated and subtracted in order to include only air.

Then, as the worker is in front of the press machine, the inter-box air flow rate βP1 does not flow through the front and about the half of the NF box bottom surface. Thus, only the remaining surfaces of a parallelepiped box are calculated in the FSA, which is equal to 2.775 m 2 . The NF also includes the two workers who are moving inside during a four-hour shift.

PART I

They could be either in standing or in seated position in front of the machine. So, the height of the box is equal to the average height of a person with his entire breathing zone [62].

The length and the width are equal to the distance covering the emission source (figure 1.4). The volume of the machine inside the NF is subtracted in order to include only air.

The inter-box air flow rate βP6 flows through all surfaces except the box bottom and the one side covered by the cutting machine. Thus, the FSA is equal to 37.67m 2 . The average random air speed S is calculated for P1 and P4 and they are 0.565 and 0.023 m.s - 1 , respectively. β, Q and V are calculated as previously described for both situations. All model's parameters are in table 1.4.

The air changes per hour ACH were also calculated for both workplaces. ACH are equal to 0.85 and 4.77 for A1 and A4, respectively.

The exposure modeling is estimated along 4-hour shift. Then, machines are stopped for 1 hours-break.

In the context of steel cutting, the mass balance method is used to calculate the generation rate G in both workplaces. In fact, this approach doesn't consider the specific actions done in the job task. So, we are calculating G for the three types of cutting by stamping, sheering and slitting with the same method.

 In workplace A1, we have a single type of cutting. Whereas, in A4, we assume that sheering and slitting are emitting the same amount of metal dust and using the same quantities of metal sheets. This is an approximation, because we do not have enough information to calculate G for every different type of cutting.

z x y Table 1.4: NF-FF model input values: constant emission rate of TMEs during steel sheet cutting

Giving the available information, we can only calculate global emission rates G A1,i and G A4,i (i for each metal) for the whole workplaces. Therefore, to control over estimation of exposure, we use an average generation rate Gaverage for every operational machine.

In the workplace A1, since, each press operator is doing the same operation onto his press machine, and using the same materials, we assume that he is emitting an individual amount of metals. Thus, the global emission rate G A1 is divided by the number of workers/ press machines in A1 to get an individual average generation rate GaverageA1. Actually, this estimation cannot be considered for many workers working at the same big machine that is emitting a global generation rate that induces exposure of everyone.

 As for workplace A4, every two workers are working on a larger machine. So, the global emission rate G A4 is divided by the number of the machines in the room to get an average generation rate GaverageA4 for both workers. are able to calculate a QA2 value with a sufficient confidence [34]. The intake velocity was calculated and is equal to11.78 m.s -1 .As a result, the ACH is equal to 5.53.

In RSW task job, the generation rate GaverageA2, P4 is calculated based on the research studies that are interested in mass emission rate using RSW process. For non-coated steel sheets, G calculations are based on a study conducted by Pohlmann et al. [63]. The authors summarized mass emission rates for several welding processes. Among them, they considered the RSW process, and, they found that the mass emission rate for a non-coated steel sheet is between 0.0377-0.0378 mg/s. The authors worked in many conditions and with several base materials to give us a more accurate interval of emission rates. This interval showed little variability, which help us to calculate G values with higher precision.

Thus, viewing the chemical composition of the used steel, we can calculate the mass emission rate for every metal.

Q and V are calculated as previously described.

In addition, C0 is considered to be as null value because it is assumed that there are no TMEs in the workplace prior to the work shift. Because A2 is located at the top floor of A1 (the part in the far left) we can consider, as to Cin, that the air supply comprises an amount of TMEs.

We assume this amount as cumulative FF concentrations of 7 press machines (located in the far left part) generated from the job task P1.

All models parameters are summarized in table 1.6. The induction welding machine is placed all along the workplace A5 (mapped in figure 1.9).

There is a single induction welding machine operator, noted P9, who is controlling and supervising the induction welding process. He is positioned approximately in the middle of the room, in the vicinity of the welding area that is limited and tagged with a dashed line in figure 1.9.All other workers are far away from the welding area.

A5 is a quite long workplace (100*20 m) and mainly ventilated by a one side door and a one large lateral opening. They are situated in the room's two extremities (see figure 1.9). It was also observed that the airflow rate nearby P9 is lower. Therefore, we are using the NF/FF model with a constant emission rate. The NF includes the welding area and the induction welding machine operator.

The values of the mass conservation ratios in A5 is 1.03, confirming mass conservation.

In P9, the NF is estimated as a parallelepiped box on the top of the welding area. The worker is either in stand or seated position, so the height of the box is equal to the vertical distance between the induction welding setting, which represents the emission source, and about 15 cm above the worker's head to cover up the entire breathing zone in both positions. The length is equal to the distance between the worker and the induction welding setting. And the width is equal to the induction welding surface of the workpiece (figure 1.9). As the NF box is on the top of induction welding setting, the inter-box air flow rate β does not flow through about the half of the bottom surface and the back surface that is affixed to the machine. Thus, the remaining surfaces of a parallelepiped box are calculated in the FSA, which is equal to 7.63 m 2 .

The average random air speed S is calculated for P9 and is equal to 0.098 m.s -1 .

The air changes per hour ACH is also calculated and is to equal to 11.91 for A5.

To calculate the generation rate GP9 for induction welding, the mass balance method is used.

In fact, using questionnaires, all information about the materials' quantities were collected.

Indeed, galvanized steel sheets were used; their reference is DX51D+ Z. The chemical composition of the material was explored in literature [54,55].

β, Q and V are calculated as previously described. All models parameters summarized are in table 1.8. As showed in figure 1.11, the workplace is particularly full of products and old machine stocks. Even though A3 is jointly ventilated by two doors located against one another, there is neither a control system nor mechanical ventilation in our workroom. Hence a low airflow rate nearby the SMA welder. It was also observed that the welder is close to the emission source and the welding fumes are considerably concentrated in the surrounding area.

Therefore, we are using the NF/FF model with a constant emission rate where the welder is exposed to the NF concentrations of TMEs.

 The second subtype of electric arc welding is GMAW.

GMAW process is occurring in the workplace named A5 (figure 1.9). In this particular situation, the job (noted P16) involves 2 different tasks; system control of the tube welding machine and periodic Gas metal arc welding (GMAW). P16 implicates 2 workers.

The main task job is the system controller task; the 2 workers are monitoring the machine for a four-hour-shift. The first one is regulating and checking the unrolling of the metal steel rolls.

Next to him, the second one is controlling the quantity and the speed of the metal sheets that are introduced into the induction welding compartment. Hence, first of all, the GMAW task is modeled individually and noted later as P16GMAW.

Then, the concentrations of both emission sources will be summed to calculate the total predicted concentrations for P16 for each TME (i); Ci as below:

𝐶 𝑖 (𝑃16) = 𝐶 𝑁𝐹,𝑖 (𝑃16 GMAW ) + 𝐶 𝐹𝐹,𝑖 (𝑃9)
For GMAW exposure, the 2 operators are working next to each other and slightly moving in a restricted area. Indeed, it was observed that both workers are exposed to the GMAW fumes;

the welder and his colleague. Therefore, we are using the NF/FF model where the NF includes both workers' breathing zones and the sitting where the emission is taking place. Moreover, the model's emission rate is considered cyclical over the four-hour shift; a constant emission rate during 4 minutes repetitively every 16 minutes.

The values of the mass conservation ratios for the workplace A3 and A5 are 1.005 and 1.03, respectively; mass conservation is confirmed.

 In the first job task P5, the SMA welder is working in a seated position. He is welding a metallic workpiece positioned in front of him (the emission source) that is at the same level as the seat of his chair. The NF is estimated as a half of a hemisphere. As shown in figure 1.12, its radius (1.15m) lies between the welder and the metallic workpiece being welded.

Vertically, the radius actually covers the distance between the base seat of a chair up to 15 cm above the worker's head to cover up the entire breathing zone [62]. Besides, the inter-box flow rate βA5 flows through all surfaces of the NF box. Then, the FSAs are equal to 6.23 and 78.42m 2 , for the NFs of P5 and P16GMAW, respectively.

And, to calculate βP5 and βP16 GMAW, S was calculated and is equal to 0.04 and 0.023 m.s -1 , respectively.

ACH are equal to 13.93 and 11.91. Thus, both workplaces A3 and A5 are adequately ventilated.

To calculate the generation rate G for electric arc welding, the emission factor EF method is used.

In the SMAWelding process, information about the base metal materials was not available.

For electric arc welding, the greatest amount of the welding fume is originated by the electrodes compared to the basic metal material [64]. It was demonstrated that the welding fumes composition is the same as the consumed wire [36,64,65]. So, to calculate G, we decided to only consider the TMEs emitted from the consumable electrodes for both SMAW and GMAW.

R PART I 67
In the section dedicated to electric arc welding in AP-42 by US EPA [66], SMAW and GMAW processes were both considered.

EFs were quantified for the electrodes used in P5and P16GMAW [37] and they are presented in table 1.10. The following emission factors are rated as excellent, above average and average factors, for E70S-6, E6013 and E7018, respectively. For the three of the electrodes, EFs were established only for Cr and Ni. The steady state concentrations are cited in table 1.12 for the identified metals.

Metal electroplating

In the workplace A6 mapped in figure 1.17, there are two kinds of metal electroplating processes; zinc and nickel electroplating. Habitually, both zinc and nickel electroplating lines are active. In our study, we are adopting the scenario when both lines are functioning.

In the zinc-plating lines, the electroplating machine is an automated system which means nobody needs to be next to the plating baths. All the worker locations are in front of the lines, close to each other (positions indicated in figure 1.17). Therefore, all their job tasks are considered as a unique task named zinc-plating lines operator and noted P10. There are 7

workers.

The nickel-plating lines system has a different modus operandi; a system controller and two operators have to move all along between the two lines during the whole shift. This task job is named nickel-plating lines system controller and noted P11 (positions indicated in blue in figure 1.14). There are also, lifting-hooks and quality control operators that are always working next to each other in the front of the lines and relatively away from the plating baths compared to P11. This task job is named nickel-plating lines lifting-hooks operator and noted P12. There are 4 workers.

The workplace is ventilated thought three doors; two side doors and a one at the back of the room; and some windows. Based on the second questionnaire, the room is also ventilated with The air flow rates measured nearby the baths (P10) were low. Therefore, we are using the NF/FF model with a constant emission rate. The workers are considered to be in the FF because they are not closely next to the plating baths, even though the distance is not immense. In addition, since nickel-lines are simultaneously functioning, we are considering that P10is jointly exposed to the FF emissions from both zinc and nickel electroplating.

As for P11that is positioned closely next to the plating baths, we are considering that this task is exposed only to NF emissions of the nickel-plating lines. This is especially due to the low airflow rate that was observed between the lines where the workers were constantly circulating. So, we are using the NF/FF model with a constant emission ratein spite of the large dimensions of the emission sources.

Regarding P12, based on analogous reasoning with P10, workers should be jointly exposed to the FF emissions from both nickel-plating lines and zinc-plating lines. However, as previously mentioned, the zinc plating lines are equipped with local exhaust ventilation systems over the baths. That is why we are considering that P12 is exposed only to FF emissions from both nickel-plating lines. The values of the mass conservation ratio for the workplace A6 is 1.03, so the volumetric flow rate into the room must be equal to the one out of it. Therefore, we can choose one of them to calculate QA6. In the workplace A6, we decided to calculate Q based on local exhaust ventilation and the extractor fans flow rates. Having this reliable information, we are able to calculate a QA6 value with a sufficient confidence [34]. As a result, the ACH is equal to 15.21.

 The two-nickel plating lines include 3 and 4 baths, respectively. Table 1.14 illustrates the different trace metallic element baths in A6.

In nickel-plating lines, we have two job tasks P11 and P12. The first one is considered inside the NF and the second one is conceived to be exposed to lower concentrations in the FF.

The NF volume is rather big because the emission surface is wide. The NF is designed as a large parallelepiped box of air placed over the plating baths and covering the whole surface.

The length of the NF is that of the nickel-lines and the width is that of both lines and the distance between them (figure 1.17). A worker is either in a standing position or walking, so the height of the box is equal to the vertical distance between the surface of the baths, which represents the emission source, and about 15 cm above the worker's head to cover up the entire breathing zone.

The inter-box air flow rate β flow through all surfaces except for the baths surfaces in the NF box bottom surface. So, FSA and S are calculated and are equal to 284.14 m 2 and0.015m.s -1 , respectively.  In the zinc plating, each line includes one bath in each line (table 1

.14).

As previously mentioned, P10 is considered to be jointly exposed to the FF zinc-plating lines emissions and FF emissions from the nickel-plating lines.

The NF volume is quite big because the emission surface is wide. The NF is designed as a large parallelepiped box of air placed on the floor encompassing the two lines, but not the workers. The length of the NF is that of the zinc-lines and also the distance between the lines and the laboratory where P10 is located (see figure 1.14). The width is that of both lines and the distance between them. The height of the box is assumed as an average length of a worker and about 15 cm above the worker's head. This distance exceeds the bath surfaces of about 10 cm. It should be noted that the lines volumes are subtracted from the NF volume. Both models parameters are calculated and illustrated in table 1.13. In the context of electroplating, the emission factor EF method is adopted to calculate the generation rates; GA6: P10 and GA6: P11 for zinc and nickel plating lines, respectively.

In the section dedicated to electroplating process in AP-42 [67], we are either using readyestablished EFs to calculate the generation rates or calculating our own EFs using field based information.

 In nickel plating lines, except for Cr, all EFs were calculated using formula for uncontrolled electroplating tanks. On the other hand, for hard chromium electroplating bath, an EF for uncontrolled emissions of 0.12 grains/ A-hr was adopted from AP-42 [67].

 In zinc plating lines, EFs for Zn were calculated using the formula for controlled electroplating tanks. The estimated concentration of Cr is significantly higher than for other metals by a factor at least of100. This is could be due to the different approaches used to determine the EFs in the first place. Contrarily to Cu, Ni and Zn, the EF of Cr in uncontrolled chromium plating is already established in AP-42 and is considerably higher than our calculated EFs illustrated in table 1.8. This was justified by the fact that the cathode efficiency of chromium plating is very low compared to the other metals. Thus, the mist emission is higher because it is inversely proportional to cathode efficiency [67].

All emission factors

The steady state concentrations are cited in table 1.15. In the electroplating lines of ABS, the process is an automated system which means workers do not have to be next to the plating baths, but only occasionally for controls. The locations of the system controllers and the lifting hooks operators are all at the left side of the lines (positions indicated in figure 1.20). Hence, all these job tasks are considered as a unique job task named ABS plating lines operator and noted P13. There are 6 workers.

The workplace is mostly ventilated thought two side doors and barely through a communicating door with a hall. It was also reported that the A7is also ventilated with 2 extractor fans having an individual average air flow rate value of 11,950 m 3 .h -1 . Moreover, the air is also leaving the room through local exhaust ventilation systems over the plating baths with an average air flow rate value of 11,088 m 3 .h -1 .

The air flow rates measured nearby the workers (P13) were low. Therefore, we are using the NF/FF model with a constant emission rate. P13 are considered to be in the NF. Even though the NF volume is quite large, we estimated that P13 are more exposed because they are closer to the plating lines than the quality controllers (noted P14).

The values of the mass conservation ratios for the workplace A7 is 1.003. So, similarly to A6, we are calculating QA7 based on local exhaust ventilation and the extractor fans flow rates.

This reliable information can give us a ventilation rate result with a sufficient confidence [34].

Thereafter, the ACH is equal to 12.58.

The ABS plating lines include 7 baths. The table 1.11 illustrates the different trace metallic element baths in A7.

In ABS plating workplace, the NF volume is relatively big because of the emission surface and the consideration of the location of ABS lines operators. The NF is designed as a large parallelepiped box of air placed over the plating baths and the workers' location (illustrated in figure 1.17). The length of the NF is that of the ABS plating lines and the width is that of both lines, the distance between them and the distance between the workers and the lines (figure 1.17 The model parameters are calculated and illustrated in table 1.16. In ABS plating lines, the generation rates GA7: P13 is also calculated using the EF method;

based on EFs calculation equations and established EFs from AP-42 [67]. For chromic acid and decorative chromium electroplating baths, EFs for controlled emissions using moisture extractors are not illustrated in AP-42 [67]. In fact, the Cr is considered as the reference element for electroplating in the EF approach. Therefore, we cannot calculate the EFs for Cr neither for decorative electroplating, nor chromic acid anodizing. That is why, as PART I 83 an approximation, we decided to use the most similar EF values using another control mean.

So, based on the EFs for hard chromium electroplating, among all the control means, the EF value of controlled emission with fume suppressant is the closest value (0.00016 grains/dscf) to the EF value with moisture extractor (0.00014 grains/dscf) and having the same order of magnitude.

For EFs for nickel and copper, the EF for decorative chromium electroplating was adapted as a reference value. All adapted emission factors EFs and data used for calculations are in table 1.17.

Then, the predicted concentrations of Cr, Ni and Cu were computed using the IHMOD tool.

The model outputs are presented in figures 1.21 for the job task P13. The steady state concentrations are cited in table 1.18. The first indirect TME handling job task is product packaging and displacement. This job task is modeled in 3 different workplaces A1, A4 and A5.

Workers are moving around the room or working for a certain time in specific areas.

However, it was observed that they are not close to the workers who are working on trace metallic element handling jobs; specifically, not inside or close to the NF where TMEs are emitted. Hence, these workers are considered to be in the FF of the workplaces.

Yet, it has to be noted that the FF of the room cumulates all FF concentrations CFF from all machines inside the workplace. Only steel cutting workplaces (A1 and A7) include multiple ii. In the second situation, the packaging job task is noted as P7 in the workplace A4

(figure 1.2). There are 3 workers. The steady state concentrations are cited in table 1.19.

Quality control

The second indirect TME handling job task is quality controlling noted as P14. There are 8 workers who are controlling the quality of the decorative chromium electroplating of ABS objects. As indicated in figure 1.20 of the workplace A7, P14 workers are relatively further away than P13, so they are considered to be in the FF of the room.

Plastic injection

The last job task is plastic injection operator noted P15. While in this job task, workers are not directly handling metallic materials or substances, the color masterbatches used in the process is suspected to contain trace amounts of TMEs [42]. Therefore, we included the plastic injection work job in our study even though TMEs are considered to be present in trace amounts. The room is majorly ventilated through two main doors and windows just in front of them.

Elsewhere, it was observed that there is a low airflow rate nearby each worker since they usually work behind the injection molding machines (figure 1.26). Thus, the near field/ far field model NF/FF with a constant emission rate is used.

Elsewhere, as we can notice in figure 1.26, A8 is separated in two compartments that are connected. Since only the total quantity used into process for both compartments is provided, a volume-weighted average of the ventilation rate is going to be calculated for A8.

The values of the mass conservation ratios for workplace A8 for both compartments are 0.98 and 0.99.

The NF includes a single emission source and the worker's breathing zone. The NF is estimated as a parallelepiped box of air affixed to the machine's window. It is presented in figure 1.23. The breathing zone is at the same level as the window, so the height and the width of the box are slightly greater to the height and the width of the window in order to cover the entire breathing zone. And the length is equal to the distance between the worker and the injection machine (figure 1.27).

The inter-box air flow rate β flows through all sides except for the front surface. Thus, the free surfaces of the parallelepiped box are calculated in the FSA, which is equal to 5.11 m 2 .

Moreover, the average random air speed S is calculated and it is 0.030m.s -1 .

To calculate the GA8: P15, the mass balance method is used. Using questionnaires, only labels of the used products ware collected; no technical sheets were available to check their compositions. The official websites of the manufacturing companies do not mention the existence of TMEs [68,[START_REF]Exposure Assessment Strategies Committee[END_REF]; they affirm that their "heavy metal free products" satisfy standard certification requirements [START_REF] Spa | Euromaster Spa Masterbatches and compounds[END_REF]. In the other hand, the six TMEs are going to be considered because they were all mentioned in literature to be used as plastic pigments [44]. Still, the Pb is strictly controlled or substituted [44]. So, the percentage of Pb is assumed to be the lowest. Whereas, some zincadditive compounds have been considered as non-toxic (need more reference) [44]. Thus, we assume that Zn is used in larger quantities than the other metals. For the rest of the metals, quantities are considered as equal amounts because of lack of information about the masterbatches color used into process.

Consequently, the percentage for Zn is considered as the highest, so an average generation rate is calculated based on emissions from 1 to 100 ppm. Then, for Cu, Cr, Ni and Al an average generation rate is calculated based on emissions from 10 to 100 ppm. Finally, the lowest generation rate of Pb is calculated based on emissions of 1 ppm. GA8: P15, βP15, QA8 and V A8 are calculated as previously described and are presented in table

1.21.
The air change per hour ACH was also calculated for A8 and is equal to 4.59.

IV. Discussion

IV.1.

Contribution to modeling and comparisons

IV.1.1. WMB modeling: Several & identical emission sources

WMB model was only used in A2 for RSWelding process (P4). As defined, this model type is simpler than the NF-FF model. However, it assumes that air is perfectly mixed in the whole workplace, which is rare to find in real conditions, based on our observations, especially when the volume is quite large. So, we found that WMB model will be adequately used only if there is a mixing device, which is the case of A2 where a quite large mixing fan (1m× 1m) was used.

In order to verify the relevance of our choice, we wanted to compare the WMB results with those, if we had adopted a NF-FF model. So, we calculated the inter-box air flow rate βP4 near the NF box that includes one worker and his corresponding emission source. It was equal to 325.5 m 3 .min -1 . Thus, if we compare it with the ventilation rate (QA2 = 31.81 m 3 .min -1 ), we can clearly see that βP4>> QA2, which means that CNF~ CFF [35]. Consequently, there is no difference in TME concentrations near or far from the emission source, which backs the relevance of the WMB use.

On the other hand, in RSW task job, the generation rate GaverageA2 cannot be calculated by mass balance methods because the mass of product introduced into process is the same as the mass incorporated into the products and there is no wasted mass. In fact, it is a very small PART I 94 amount that cannot be estimated by mass loss. On the other hand, US EPA did not consider this type of welding in AP-42 [66,71]. AP-42 illustrates emissions factors or methods to calculate the EFs only for Electric arc welding process. It considers neither the RSW type nor the induction welding process. The two former processes are different from the Electric arc welding in that they do not use any filler metal during the welding process. However, the choice of the emission factor for Electric arc welding is based on the use of the welding electrode type. Moreover, the EF for RSW suggested by the "ChemTRAC" method [START_REF] Arnold | Evaluation of the well mixed room and near-field far-field models in occupational settings[END_REF] does not consider the percentage of the metal in the materials, which is variable for each material even for Cr and Ni. So, we were constrained to use results of research studies. Our calculations were based on the study conducted by Pohlmann et al. where they used several types of materials and with many conditions to calculate mass emission ranges for several welding processes, including RSW [63].

In order to calculate the total emission from several RSW machines, we multiplied the personal G for each metal by the number of functioning machines. Then, we used this global generation rate Gglobal in the modeling calculations. In our case, only two machines were functioning in the monitoring week, so, Gglobal was the result of G multiplied by 2.

IV.1.2. NF-FF modeling

NF-FF models were applied for a number of exposure scenarios mainly for volatile organic compounds like we previously mentioned [2,3,[73][74][75][76], and for organobromine compound [77]. Besides organic compounds, a newer study has also considered respirable dust and silica in some exposure scenarios [78]. Two studies had considered metal emissions [6,78] from electric arc welding and weighing and mixing of a cobalt containing powder. However, the common factor between all of these modeling scenarios is that they used former air sampling data to calculate the model's parameters. Moreover, they used a single emission source of a pollutant in their simulation designs, except for the most recent one [78] where a mean generation rate was calculated based on monitoring from multiple emission sources in silica exposure scenario.

Contrarily to the former studies, we are conducting modeling of TME emissions based only on field related information, literature and simple measurement like air velocity and temperature, in real workplaces' conditions. Hence, in real life situations, emission sources either be only unique or multiple and/or only similar or distinct. In fact, in some metalworking industries like steel cutting, it is common to find several machines in one [6]. They calculated the generation rate G and the inter-box air flow rate β using the monitoring results. As an alternative cost effective and practical method, we calculated G using EFs from USEPA [67] for Ni and Cr and the corresponding fume correction factors FCF established by the SAN DIEGO Air Pollution Control District [65] for Al, Cu and Pb. Then, we will explore their predictive effectiveness.

In response to the study of boelter et al., Nicas has proposed a three zone model [79] because he believes that the total amount of the fume plume of welding is not totally well-mixed inside the NF. He elucidates that a fraction is leaving the NF to a third zone named the upper zone, other than the FF. He proposes three modified equations to calculate the concentrations in the 3 zones. In our study setting A3, there was only one worker who was performing SMAWelding (P5). That is why; we were only considering the NF zone. Nicas added a fraction f1× G that is mixing in the NF. At the steady state, the equation of the NF concentration is as below:

𝐶 𝑁𝐹 ,𝑆𝑆 (𝑡) = 𝑓 1 × 𝐺 𝑄 + 𝐺 𝛽
Obliviously, Boelter et al. have considered f1=1. After verifications, Nicas found that f1 was indeed approximately equal to 1 for total fumes [79]. And, since our calculations are based on total the EFs for total fumes, we estimated that we do not need to take into account this fraction in our calculations and assumed it as 1. Moreover, f1 calculations were based on the monitoring concentrations, which could only be verified after monitoring. So, calculations of the CNF were established using the basic equation. In the workplace A5, there was a single induction welding emission source. This process is taking place with a quite large industrial machine. So, as indicated in Workplaces figure

(figure 1.9). The operator noted P9 and the packaging job task noted as P8 are considered to be exclusively exposed to the TME concentrations from induction welding in the NF and FF, respectively. We acknowledge that the existence of another emission source, notably the GMAW at the back of the room, could affect the exposure intensity. However, there were great distances between them and the GMAW location especially for P8.Moreover, we assume that the side door next to GMAW location (figure 1.9) contributes to the non-spread of the TME emissions from the latter process. Still, our assumption's adequacy was explored by comparing the predicted concentrations with the air sampling concentrations.

As for G calculation, the mass balance method was adopted because there are no EFs available for the induction welding. Moreover, unlike the RSW, this process has known waste masses. So, the mass balance method was indeed the most suitable one for this process.

c. ABS plating

In A7, there was also a single emission source from ABS plating lines. There were seven baths but we considered them as a unique emission source. It is worth to mention that, even though there are 4 Ni baths (noted B3, B4, B5 and B6), only one of them is functioning during every shift. So, since we could not have information related to their functioning sequence, we calculated an average G for all Ni baths.

In A7 and also in A6, we are aware that the NF volumes are relatively big. This could minimize the difference of the exposure intensity between the NF and FF, which could be closer to the WMB approach. However, this cannot be adjusted because of the emission source extended surface. Moreover, we are convinced about our choice because βP13 (194.06 m 3 .min -1 ) is three times lower than QA7 (583.20 m 3 .min -1 ).Consequently, this modeling scenario allows us to check the modeling performance of the NF-FF model in such circumstances.

IV.1.2.2. Several & similar emission sources a. Steel cutting

In the steel cutting job task, we had two different workplaces. In A1, we have 23 personal stamping machines (P1) whereas, in A4, there are 3 sheering and slitting machine (P6). Each PART I 97 worker was assumed to be exposed to its NF concentrations regardless to the other emission sources.

Packaging workers (P3 and P7in A1 and A6, respectively) are either moving in the FF of the workplace or mostly working in confined space where TME concentrations are assumed to be accumulating. Exposure of the packaging workers was calculated as the cumulative sum of all individual FF concentrations because of the poor ventilation of A1 and P7; their ACH is 0.85 and 4.77 (discussed later).

We combined the predicted concentrations in the NF and FF on the same figure. In figure 1.29 corresponding to A1, we can clearly notice that the CFF concentrations increases slowly compared to the CNF. But they surpass the CNF after 120 min and do not reach a steady state but continue their increase until the end of the emission. This violates the definition of the NF-FF model. Therefore, we will wait for air monitoring to compare them with our approach results. As for figure 1.30, related to A7, the CNF increases very slowly but they reach a steady state after about 60 min and the CFF is always largely less than CNF. Lastly, we can notice that the speed of increase of the CFF accumulation is commensurate with the emission sources.

b. Plastic injection

In A8, there were 5 to 6 plastic injection machines (P15). We only considered the NF because of the workers' locations.

As we previously mentioned, this plastic injection industry does not pertain to the metalworking field, however, it is suspected to elicit TME emissions. All the materials' identification was based on literature [33,44]. And the G determinations were based on estimations [44]. Thus, air monitoring is necessary to confirm the presence of TMEs emissions and also check the order of magnitude of TME concentrations with the predicted results.

IV.1.2.3. Two distinct emission sources a. Job task noted P16

In A5, we encounter a quite complicated job post (P16) that includes two different tasks.

PART I 99

The exposure scenario of P16 was divided into NF emission from cyclic task P16 GMAW and FF emissions from the induction welding machine (P9). Each emission amount was calculated separately, and then summed.

b. Metal electroplating

In A6, there were two different electroplating lines. Zinc-plating lines operator noted P10 who were assumed to be exposed to the FF zinc plating and FF from nickel plating P11. As we can see in figure 1.17, workers are next to the NF. However, we cannot consider them in it because of the low air velocity nearby. The same holds true about nickel-plating lines liftinghooks operators (P12), assumed to be exposed to the FF from nickel plating. The FF zinc plating emissions were denied because zinc plating lines are equipped with local exhaust ventilation systems over the baths. We acknowledge that P12 is also close to nickel-plating lines but we cannot consider it is a same job task as P11 because the latter is moving between the lines and is working in the vicinity of plating baths. Still, we are eager to know if our modeling approach is comparable with the air monitoring results.

Another potential concern is the use of ready to use EFs of Cr. It was observed that these EFs have higher of magnitude compared to the calculated EFs.

IV.1.3. Adaptation of the mass balance equation

The original mass balance equation [80] does not consider the quantity of deposited metallic dust, which could yield overestimation of exposure, especially when considerable quantities of deposited dust are observed (particularly in steel cutting job tasks).

Aerosol deposition was considered by Schneider et al. [81] in a NF-FF model. He described dust deposition as an equivalent air exchange rate that leads to mass loss of particulate matter from air to the floor or other surfaces. This notion was also discussed by Reinke et al. in

Mathematical Models for Estimating Occupational Exposure to Chemicals [82] and was named a "sink" or non-ventilatory loss of mass. They considered it as a proportional factor to the estimated pollutant concentration. However, they highlighted that it is usually disregarded because it is extremely difficult to estimate.

Thus, in order to remedy overestimation and to quantify the mass loss of dust, we consider this mass loss as a fixed amount when computing the generation rate G. Thus, we collected the dust in the two companies where the mass balance method is adopted (workplace 1 and 5),

PART I 100 at the end of the week shift, and weighed it for integration in the mass balance equation. As an approximation, we neglected the contribution of outdoor airborne particle sources (due to the small air exchanges in the workplaces) and we assumed that the collected dust contains only metallic dust; this yields overestimation of this dust-associated metallic sink.

We acknowledge the limitations of this approach, although we are suggesting a simpler and more accurate approach to assess the impact of deposited dust when estimating workplace air concentrations of metals.

IV.1.4. Ventilation rate method

In our study, we predominantly used the anemometer technique which is considered one of the main methods to determine Q [34]. It was chosen because we have previously assumed that the open doors and windows are the only or major source of ventilation; air comes in and out of these two openings, generally located at opposite ends of the rooms [34]. So, after verification of the mass conservation, we can calculate the Qin that is equal to Qout. In addition, this technique is cost effective compared to other methods.

There is another effective method to calculate Q which is the tracer gas technique. This technique requires expensive technical instruments. Boelter et al. used both techniques to calculate the ventilation rate in a workplace in order to use it in a NF-FF model [6].

Comparing the two values, they found that the anemometer technique moderately overestimated Q. In order to correct this and to consider the variability during and between the days, we adopted the measurement approach previously exposed over 3 days a week and then we calculated average values.

In addition, we also used another method to calculate Q based on local exhaust ventilation systems. It is considered a more accurate method because we have a precise value of the exhaust rate. Keil confirms that if the exhaust rate of local exhaust ventilation is available, we are able to calculate a ventilation value Q with a sufficient confidence [34]. Sometimes, the anemometer method is quite difficult to establish. For example, the workplace A2 (where RSW was taking place) is located at the top floor and is already inside another room and is ventilated through wide surfaces. Also, in the workplaces A6 and A7 (where electroplating of metal and ABS was respectively taking place), the natural ventilation was supplied by 3 doors from different directions and many other little openings. So, calculations based on the anemometer method is more sophisticated and with a higher margin of error. Thus, since we PART I 101 have an alternative method using the local exhaust ventilation rates; we chose to calculate Qs with it, as long as the mass conservation is confirmed.

In our study, air changes per hour ACH were calculated for the workplaces. They ranged from 0.85 in A1 (steel cutting workplace) to 15.21 in A6 (metal electroplating). If we compare our values with those found by Bolter et al. (20.4) [6]; in all our workplaces, ACH were smaller than it. In fact, the common general ventilation is typically between 1.2 -12 ACH [START_REF] Keil | Chapter 3: Modeling Pollutant Generation Rates[END_REF]. Still, ventilation rate can even reach 0.3 ACH for a closed window dwelling [34].

For instance, ACH is under 5 in A1, A6 and A8; their values are 0.85, 4.77 and 4.59,

respectively. These values are relatively small particularly for A1. The latter is poorly ventilated; its ACH is even smaller than the common general ventilation, which is probably due to the large volume of the workplace (38747.89 m 3 ).

When it comes to the other workplaces, in 3 posts (P5, P6 and P7) in 3 different workplaces, the ACH exceed the maximum value for the common general ventilation (13.93, 15.21 and 12.58, respectively).

IV.2. Difficulties and challenges

IV.2.1. Companies contact and participation

The first difficulty was in seeking the approval of the companies to participate. Occupational exposure assessment is not mandatory in Tunisia. So, the majority of the companies' managers do not feel concerned. Some companies denied participation categorically at the first contact and refused to have an appointment. They just expressed that they were not interested.

Others had expressed their concern about the monitoring results and were anxious either about publications of the results or potential problems with the corresponding authority structure, even though, we had confirmed that all identities and names are were to be confidential.

For the rest, they were worried about the reactions of their employees towards the study. They claimed that workers would probably think their job tasks are extremely harmful.

Furthermore, few companies have given us their principle agreement; we had several appointments and did some field visits to the workplaces. However, after up to 6 months, they finally pulled out from the study.

PART I 102

IV.2.2. Information availability

The second challenge was the information collection using the questionnaires in order to establish the models' parameters. All companies did not have complete information about the names and the mass amounts of the used materials. So, we took several appointments to have all the required information. Some of them did not have the exact references and sometimes products had only local references. So we had to do supplementary research to gather all the exact references in order to look for their metal compositions. Moreover, data and safety sheets were not provided by the companies, we explored all chemical compositions like previously mentioned.

In one case, it took up to 24 months to obtain the required information.

IV.3. Limitations and bias

IV.3.1. Uncertainty analyses

Modeling can involve uncertainty in exposure estimation. Input data are frequently not precise. Uncertainty can be due to natural variability or lack of knowledge [84].

Natural variability is commonly referred to as "type 1" in risk assessment [84]. For example, it is due to variability in hourly /daily air velocity in calculations of ventilation rates. As for lack of knowledge, it is commonly called as "type 2" [84]. It represents the uncertainty in measurement devices and shortage of information.

Therefore, in order to quantify the uncertainty in parameters estimate, a Monte Carlo simulation was conducted [85,86]. This approach was used in some studies to estimate volatile organic compounds concentrations [2][3][4]. Moreover, this approach will offer a confidence interval range of the output and a distribution, not only one value.

Therefore, as a perspective, an uncertainty analysis will be conducted using the Monte Carlo simulation in order to publish our findings.

IV.3.2. Margin of error due to global information

We noticed that the collected information about the materials' types could present a margin of error. The available information could sometimes bear on annual consumption. So, the proportion of a specific material usage in the monitoring week may be different than the proportion during the whole year. Consequently, the amount of a particular metal can vary.

PART I 103 Thus, we believe that more precise information within a specific period of time could lead to more exact predicted concentrations with a better range of confidence.

IV.3.3. Emission factors' ratings

In electric arc electroplating, the used emission factors EFs are rated as "excellent", "above average" and "average" factors, for E70S-6, E6013 and E7018, respectively. AP-42, emission factors are tested and divided into 5 ratings according to the quality of the studies and methodologies and according to the representativeness of the EFs for a number of facilities [87].

The EFs that were used for metal electroplating were rated as "above average", which means that these factors were established from a "reasonably number" of research occupational studies about electroplating facilities [87]. This rating is based on shortage of information about random collection of these facilities. But we found no particular preference.

As for EFs used for ABS modeling, both factors are rated as "below average". According to AP-42, this rating means that they were established from a limited number of research occupational studies. So, these facilities are probably not randomly collected, and then they could encompass variability in calculations.

IV.3.4. Average Generation rate calculation

The generation rate G was always considered as an average value calculated from maximum and minimum values. These ranges are due to two main factors. According to the collected information from the companies' managers, the mass amounts were usually provided as intervals. In addition, steel chemical compositions are not presented as point values in data sheets but often as requirements of specific metals in an interval range. And sometimes, these ranges are slightly different in research studies that have actually analyzed the material. So, we decided to work with the mean value of the generation rate, noted as Gaverage. We acknowledge that this will increase the confidence range, but this is one of our challenges because companies' managers could not provide us with more accurate information and steel chemical compositions are variable. In a perspective, and in order to remedy that, this interval will be considered to estimate the uncertainty of our modeling approach using the Monte Carlo approach.

PART I 104

IV.3.5. Exhaustive Trace metallic elements identification

We identified a potential deficiency in occupational exposure modeling. Unlike indoor air monitoring that is using analytical chemical instruments to identify TME presence; occasionally models cannot predict the presence of all trace metallic elements despite of their existence, notably for ultra small amounts. And this is in effect of the non-exhaustive available information in data sheets. This could be considered as a shortcoming but it was not frequently encountered.

V. Conclusion of part 1

In this first part, our main objective was to use, in lieu of systematic measurements, an alternative and reliable method to predict indoor air TME concentrations without costly measurements and using available field-related information and literature. We used mathematical models for estimating occupational exposure to TMEs in several metalworking industries. Moreover, we strive to assess the models' effectiveness not in simulation designs but in real-life exposure scenarios where there are many workers, notably with more than one job task and one emission source at the same workplace. Yet, we faced some challenges and difficulties and we observed some limitations and will discuss future perspectives. Finally, we believe that workplace air monitoring is a crucial and determinant step to validate the predicted concentrations and to check the effectiveness of our modeling approaches.

III.3. Correlation analysis between

I. Introduction

As stated in part 1, these mathematical models were initially developed and applied to solvents and other volatile compounds and. several studies demonstrated that their predicted concentrations are "reasonably" comparable to air monitoring measurements within a factor of 0.5 to 2 folds [1].

With regards to metals, only two studies have used this kind of models for metal contaminants. The first one was dealing with arc welding process, where the author considered only iron and manganese [2]. The second one was dealing with two job tasks involving weighing and mixing of a cobalt containing powder [3]. In contrast to both studies, our modeling approach does not use air monitoring measurements to calculate any models' parameters. Therefore, in our study, a validation step is necessary to evaluate our models estimations.

For purposes of comparison between the modeled indoor air concentrations and the measured ones, we are using statistical tools that are bringing to light the relationships and discrepancies between them.

On the other hand, a biomonitoring study is conducted to explore the relationships between the internal and external doses. With the aim of investigating the contribution of the occupational pathway to the internal urinary metallic concentrations, we included administration workers in the biomonitoring survey from the same companies.

II. Materials and methods

II.1. Air monitoring

II.1.1. Equipment and material acquisition

In order to conduct the indoor air measurements, a complete material package for active monitoring was looked for. We used the database "MétroPol" illustrated by INRS [4] to choose the suitable sampling means (Quartz Filters). In addition, we used the official site of the equipment supplier [5] to properly choose the adequate pumps and the complementary equipment and accessories.

Air monitoring methodology

Indoor air TMEs concentrations were measured using (i) personal samplers set up near the worker's breathing zone, or (ii) fixed samplers placed in the workplace at average height corresponding to the breathing zone of the worker.

For statistical considerations and in order to minimize the natural variability of concentration measures, we conducted six replicate measurements, as recommended in Mathematical models for estimating occupational exposure to chemicals [1].

Air monitoring was conducted over 10 weeks. For each job task, the six replicates were carried out through 3 days in the middle of the week; Tuesday, Wednesday and Thursday, in order to get an idea of the exposure variability between days.

The air flow rate of the personal sampling pump Pump SKC ® was regulated to 2L/min using the method provided by INRS [9][10][11]. Air monitoring covered a four-hour shift [7,12].

Afterward, statistical analyses of the air monitoring data was carried out to evaluate distribution normality (or log-normality) of the six replicate measurements. Hence, if the distribution of the six replicates is normal, the mean value is adopted, otherwise, the median is selected. Either way, the selected values were noted as the "location measures" (LM) of the measured TME concentrations. Statistical analyses were realized using IBM SPSS Statistics, version 20.

II.1.3. Indoor air concentration calculations

Since the collection of trace metallic elements was performed onto quartz fiber filters, we had to take into account the matrix effect of our collecting substrate. Indeed, they contain a nonnegligible amount of the considered metals. Thus, the mean filter blanks 𝐶 ̅ 𝐹𝑖𝑙𝑡𝑒𝑟 𝐵𝑙𝑎𝑛𝑘 were calculated for every metal in both lots. Typically, their values should exceed the quantification limits of our analytical method in order to be able de subtract them from the sample results [13].

The indoor air concentrations Cin air were then calculated using the average sample flow rate and sampling time for each workstation. The average sample flow rate is the mean between the initial Qi and final Qf flow rates. These measurements were conducted before and after each use of pumps to make sure of their stability over time [11] using a Mini-Buck flow calibrator M5 which is a soap bubble flow meter.

PART II 120

The final formula is the following [11,13]:

𝐶 𝑖𝑛 𝑎𝑖𝑟 = (𝐶 𝑥 -

Biomonitoring: Urinary sampling

In order to assess TMEs excretion in the urine of workers and to check the influence of occupational exposure, urinary sampling was chosen for biomonitoring purposes. Urinary metals can be viewed as an indicator for the airborne exposure. Moreover, it is easier to convince workers to have a urinary sampling than a blood one. However, this is not always valid. For example, urinary aluminum was showed to be a better indicator than the serum aluminum of inhalation occupational exposure [14,15]. It is also considered to be a good indicator of occupational exposure for Ni and Zn [16,17]. However, urinary analyses of Pb are considered as weak exposure indicator of the internal dose of lead because urinary sampling for Pb could be biased by external contamination [18].

Spot urinary sampling was conducted at the end of the shift and the end of the week (Friday noon or afternoon, depending on the work schedule of the company).This timing was chosen based on the data base Biotox illustrated by INRS [19]. In the Biotox documents for all the six selected metals Cr [20], Ni [17], Cu [21], Zn [16] and Pb [18], the privileged time stated for occupational exposure is end of the shift, end of the day. Whereas, for Al, this timing could more represent the recent exposure [14].

Workers from the five companies were solicited to participate; among them 61 volunteer workers accepted to provide urinary sampling.

Urines were collected in polypropylene bottles (50 ml), immediately transported to the laboratory (time elapsed between 10 and 25 minutes) and kept at -80°C until analysis.

PART II 121

II.3. Sample analysis

TME quantification was conducted by ICP-MS (Agilent Technologies 7700 Series ICP-MS).

The purity of the plasma torch argon was around 99.999%.

An external multi-element standard was used for direct calibration (Environmental calibration Standard), from Agilent Technologies (USA). And, purified water was used by means of Milli-Q Direct 8 system (Millipore, SAS, Molsheim, France).We chose the most predominant isotopes; Al 27, Cr 53, Ni 60, Cu 63, Zn 66 and Pb 208.

We followed the analytical procedures provided by INRS [7] to determine TMEs airborne concentrations. As for, urine sample analyses were conducted using multi-elementary ICP-MS method by Goullé et al. [22]; aliquots of urine (0.4 ml) were diluted in 3.6 ml of HNO3(0.65%, SIGMA-ALDRICH, Germany).

II.4. Comparison of exposure measurements to modeled results for trace metallic elements in metalworking industry

We compared the values of the modeled TME concentrations with the location measures of the measured ones. The location measures values are defined as mentioned above.

Additionally, to avoid underestimation or overestimation of some situations where exposure is significantly higher or lower due to the natural variability, we decided to validate a second scenario where we compare the modeled values with the arithmetic mean of the measured values. To this intent, statistical analyses of all data are evaluated to check the normality of distribution for both validation scenarios.

To evaluate the match between measured and predicted TME exposure levels, different methods were used in literature. They vary from value comparisons [2,23,24] to statistical tests [25,26]. Though, the correlation approach is judged as a statistically more informative method for such models [1,25], thus, we adopted it.

In all our statistical testing, we are considering the significance level at α = 0.05. Thus, if pvalue is lower than 0.05, the null hypothesis is rejected. All statistical analyses were achieved using IBM SPSS statistics, version 20.

Normality tests are also conducted for all the samples when the correlations are explored. The null hypothesis of the Shapiro-Wilk test is that distribution is normal. If both samples are not, a log-transformation is conducted and the normality is checked again.

PART II 122

Pearson correlations are used when one of the samples is normally distributed. Otherwise, the non parametric Spearman is used.

Then, the calculations of the statistical significance of the correlations are automatically achieved by the SPSS statistics software. The null hypothesis of the performed test is that no significant correlation is observed. In addition, we also adopted other methods used for comparison by testing the difference between the paired values (the modeled TMEs concentrations and the mean/median of measured ones), with dependent Student's t-test or nonparametric Wilcoxon signed-rank tests, as appropriate.

The dependent Student's t-test is testing the mean difference between the predicted and the measured concentrations samples. The null hypothesis of this test is that the mean difference could be zero. So, the predicted and measured values could be equal. Thus, when the significance p-values are higher than 0.05, the null hypothesis is accepted.

First of all, we decided to compare all the predicted concentrations (all metals in all task jobs) with the measured ones for the metalworking industries, in order to check the models global predictive ability. Then, we compared the predicted concentrations depending on the process and the modeling scenario because every type is based on different calculations. Similar task jobs are gathered together.

We will compare the following data;

II.5. Correlation analysis

Urinary metals can be related to occupational exposure via inhalation; for instance, for Al, 85% of inhaled amount is excreted in urines [14]. For zinc also, a correlation has been demonstrated between airborne concentration and urinary metals, in a study where workers PART II 123 were exposed to cutting fluids [16]. Soluble compounds of Ni, showed also good correlations [17].

Therefore, we explored if the correlation between the airborne measured concentrations and the urinary metals.

III. Results

III.1. Indoor air monitoring results

In table 2.1, statistical analyses of the air monitoring data are shown for the six replicate measurements for every metal and every task job P.

If we consider the LM values, we can notice that the highest Al concentrations are from P5 and P16; corresponding to SMAW process and the combinations of GMAW and induction welding, respectively.

Then, the highest Cr LM concentrations are also P5, P6 and P10 corresponding to the SMAW, steel shearing/ slitting and the combination of GMAW and zinc plating.

As for Ni, the majority of the task jobs have the same level; around 0.5 µg/m 3 , except for P1, P4, P7, P8 and P15 that are corresponding to stamping, RSW, packaging in the stamping and induction welding workplaces and plastic injection.

For Cu, the LM concentration in P5 is considerably higher than all job tasks. Also, for Pb, P5 has the highest concentration, and then comes P6 and P3 that have almost a half lower concentrations than P5.

Lastly for Zn, P6 has highest concentration, and then all job tasks have almost the same level except for P4 and P13; corresponding to RSW and quality control in ABS plating workplace.

So, we observe that highest concentrations are generally in P5, P6, P16 and P10. Therefore according to air monitoring these are the most exposing job tasks. The three box plots show the presence of many outliers. Figure 2.2 shows that these points were considerably far away from the majority of the values, so, the boxplots were reduced to lines. The boxplots showed the presence of the same single outlier point (point 59 in figure 2.4) that is relatively far away from the lower whisker which means that this is highly affecting the distribution. This point corresponds to the concentration of Cr in the Plastic injection job task.

The prediction of the latter was absolutely based on literature among other metals, which means that its removal would not affect the correlation verification. Consequently, we decided to remove it. Moreover, figure 2.5 shows the new distributions of the transformed data and their improvements compared to the distributions of initial data in figure 2.5 (boxplots). Boxplots still encompass outliers, but we can ignore them since the distributions are normal for Ln(LM) and Ln(Predicted). This will allow using parametric methods: Pearson's correlation for correlation calculations and Student's t-test for paired difference tests, using the logtransformed data. Student's t-test will also provide confidence intervals of the mean PART II 131 differences, which is an additional tool to evaluate the deviation between the predicted and the measured concentrations. These boxplots can also be used for initial comparison of the distributions of the three variables: Ln(LM), Ln(Mean) and Ln(Predicted), in metalworking industry.

We can notice that Ln(Predicted) distribution is larger than both distributions of Ln(measured). However, the bottom whisker of the Ln(Predicted) boxplot shows that in some cases our modeling approaches underestimate the exposure level of trace metallic elements.

Therefore, more modeling-specific comparisons will show which models are better in predicting exposure.

III.2.2.2. Correlation

First, scatter dots were plotted as showed in figure 2.6 and 2.7 in order to check the degree of association between Ln(LM) and Ln(Predicted), and Ln(Mean) and Ln(Predicted), respectively. We can clearly notice that the concentration values are highly dispersed and do not follow the linear function in both figures. The p-values of Pearson's correlation coefficients(r) (table 2.4) are lower than 0.05 for Ln(measured LM) vs Ln(Predicted) and for Ln(measured Mean) vs Ln(Predicted). This means that the correlations are significant between the Predicted concentrations and both measured LM and measured Mean concentrations. Further, the correlation's significance is stronger between the Predicted concentrations and the measured LM concentrations (p-value <0.01).

However, Pearson's coefficients (r) are relatively low. So, we believe that global correlation is not enough for comparison, which is also confirmed visually by the scatter dots (figure 2.6 and 2.7). This is probably due to the non-homogeneity of the global sample. Ln(Predicted), and Ln(Mean), Ln(Predicted). Table 2.5 shows the mean values of the calculated difference (Ln(Predicted) minus Ln(measured LM or Mean)), their 95% confidence intervals and the p-values of the difference. The difference significance p-values are higher than 0.05 for both Ln(measured LM) vs Ln(Predicted) and for Ln(measured Mean) vs Ln(Predicted)suggesting these values can be considered as statistically close. The mean differences are presented with their 95% confidence interval. The mean difference of Ln(measured Mean) minus Ln(Predicted) is about ten times lower than the mean difference between Ln(measured LM) minus Ln(Predicted). However, the 95% confidence intervals have almost the same size.

III.2.3. Comparing measured versus WMB predicted concentrations

III.2.3.1. Distributions

Since the three p-values of the Shapiro-Wilk they are higher than 0.05, the three distributions are normal and the Pearson's correlation method will be used along with the Student's t-test.

We present an initial comparison using boxplots of the distributions of the three variables:

LM measured, Mean measured and Predicted concentrations, for RSW process. The Predicted concentrations distribution is smaller than both distributions of measured ones. On the other hand, Mean measured distribution is larger than the LM measured distribution, which shows the high variability in exposure levels in this particular job task. the predicted and measured Ln values are close and could be similar in some cases. and Predicted values, respectively), we conclude that the three variable distributions were not normal for the welding task jobs P16.

After log-transformation, the three p-values become 0.81, 0.12 and 0.30 for Ln(LM), Ln(Mean) and Ln(Predicted), respectively, showing normality is acceptable. Thus, the Pearson's correlation method and the Student's t-test will be used.

A distribution comparison is presented using boxplots of the three variables: the

Correlation between urinary metals and models estimations

Correlations were also investigated between urinary metals and the predicted air concentrations (normality tests and correlation calculations are presented in table 2.19).

None of the TMEs showed significant correlations between urinary metals and the predicted air concentrations.

IV. Discussion

IV.1. Methodological choices

Although six replicates are recommended to characterize concentrations of workplace pollutants [1], they cannot fully determine the distribution because concentrations may vary depending on many factors such as the ventilation rate variability between days according to meteorology, the workers activity profile (e.g. number of tasks per day, which may also vary between workers and from day to day), etc. As illustrated in table 2.1, in many cases, the mean values were significantly higher than the LM values. For this reason, we considered a second comparison scenario with the mean values.

PART II 161

The log-transformation of air concentration samples was achieved to seek normal distribution.

This operation decreases the variation range and brings the extreme values closer to the most common range.

It is well known that air pollutant measurements are frequently log-normally distributed [27][28][29]. So, extreme values are rather exceptional cases. Without this operation, extreme values have an overly high influence on the correlation calculations, which may decrease the correlation coefficients, leading to biases results and mislead conclusions. Consequently, we believe that the log-transformation does not only serve to adjust the samples into normal distribution, but also contributes to more accurate results.

The choice of two scenarios for correlations with the predicted concentrations was established when one or two extreme values were observed. Due to the small sizes of our samples ( 6values), we decided to accommodate a statistically rigorous method by choosing the median of the distribution when normality was not met. On the other hand, it was worth to think about the effect of the extremes values by choosing a second scenario with the mean values.

According to our results, the global correlation for the modeling approach showed to be more significant (p-value <0.01) between the predicted concentrations vs measured Mean concentrations than vs the measured LM. This means that it is worth to establish comparisons with means of the measured concentrations because in some task jobs, the difference between the mean and the median is high.

However, in NF prediction for both steel cutting and welding, the significance was the same for the location measures and the Mean. This could be due to the small sizes of our samples, so that the extremes values did not have much influence. Thus, more replicates would be preferable, depending on the available resources.

On the other hand, the Pearson's coefficients (r) of the global correlation were relatively low, a finding that backs our methodology to stratify the global samples and check correlations for specific modeling methods.

IV.2. Air monitoring

As a general observation, we already noticed that the highest TME concentrations of indoor air are generally found in the P5, P6, P16 and P10 job tasks. This is quite similar to our modeling estimations, if we disregard the plating task jobs that are suspected to be PART II 162 overestimating the exposure due to Cr EFs. The job tasks with the highest exposure levels are also P5, P6 and P16.

Moreover, some other similarities were identified, such as for steel cutting job tasks and the packaging tasks related to them. Air monitoring results are in agreement with the magnitude of predicted exposure using the NF-FF models based on our cumulative approach.

The packaging workers P3 in workplace A1 are exposed to significantly higher concentrations for all TMEs (up to four folds, in figure 2 This is likely due to the number of machines in the workplace. The more emission sources, the higher the TME concentrations. Moreover, this could also be due to differences in ventilation rates between the workplaces. Although A1 is a large workplace, it is poorly ventilated according to our Q calculations (ACH = 0.85).

IV.3. Predicted versus measured concentrations

Global correlation for our modeling approach showed to be significant to strongly significant between the predicted concentrations vs the measured LM, and the measured Mean concentrations, respectively. Although the Pearson's coefficient correlation is relatively low, this result is satisfying and shows that mathematical models for estimating occupational exposure can be used to predict indoor TMEs concentrations in metal working industry. Yet, some processes showed stronger correlations than others. were found between predicted and measured TMEs concentrations. This demonstrates the pertinence of our approach of the NF predictions for steel cutting processes.

The two basic inputs of indoor air monitoring are the generation rate G and the ventilation rate Q [30]. Their determination is considered to be decisive to the correctness and accuracy of the models' outputs [3]. Our findings suggest a good accuracy of our input characterization approach, notably of the G and the Q. We used the mass balance method to calculate the generation rates in the steel cutting processes via our modified formula (presented in part 1). Our results support that this approach is sufficiently effective to be used to similar processes where the chemical composition of the materials are available.

IV.3.1.2. NF predictions in welding processes

High correlations were also obtained for NF predictions for welding processes: Shielded Metal Arc Welding (SMAW) (P5) and Induction welding (P9). Two distinct methods were used to calculate their generation rates: the emission factor EF and the mass balance methods, respectively.

The EFs developed by US EPA for the SMAW process [31] conjointly used with a processspecific fume correction factor FCF developed by the SAN DIEGO Air Pollution Control District [32] showed efficient in emission predictions.

Contrarily to our approach, the unique study that used the NF-FF model in similar process have determined the G using prior air monitoring [2]. The authors have monitored exposure levels inside and outside the helmet, whereas in our case, the welder is not actually wearing such a PPE (personal protective equipment), but only holding it with his hand relatively away from his face (figure 1.12 in part I). This could explain the high exposure levels observed in P5.

PART II 164

We suspect that effective results can also be obtained for the Gas Metal Arc Welding (GMAW) process, because the EFs and the correction factor are constructed using the same methods. Unfortunately, we could not verify this in our study because the workers were simultaneously exposed to two kinds of emission source (P16) and, in this case, the resulting exposure levels were not correlated with the predicted ones.

Elsewhere, the Induction welding process is quite different from the SMAW, mainly because of the absence of consumable filler metal. Results showed good effectiveness of the mass balance method with such process, as long as all quantities and chemical compositions are known.

IV.3.1.3. NF predictions in plastic injection

As previously indicated, the plastic injection process does not involve metallic materials and TMEs are not considered as pollutants in such industry. However, air monitoring results showed TME concentrations in the same order of magnitude as in other job tasks for Al, Zn and Pb. Still, they showed the lowest concentrations for Cr and Ni, and Cu was under the limit of detection. Surprisingly, our modeling predictions for plastic injection process showed effective results.

One element was disregarded because it represented an outlier for the whole sample. Yet, this modeling method presented a significant correlation, with the highest coefficient (r=0.92). In this particular case, because we did not have an idea about the composition of the applied masterbatches, our calculations were based on literature and assumptions. These results confirmed that the mass balance method used with our approach of aggregate exposure from several emission sources is also effective in such processes.

Moreover, for both steel cutting and plastic injection, the volume-weighted averages of Q were calculated for the whole workplace, even though it was separated into two or three compartments (figure 1 and 26 of A1 and A8, respectively). This method was adopted because of lack of information about the quantity of material used in each compartment. Hence, our results showed that our method is sufficiently effective in both cases.

Finally, the determined G values for the 3 latter processes can also be re-used in the same workplaces under different conditions: after a deviation in the number of active machines, in other seasons when the Q are different, or when new material or equipment are introduced or PART II 165 removed that may change the air volume (machines, stock, etc.), as long as the same amount and kind of material is used.

Nevertheless, it is worth to note that the 95% confidence intervals of the mean differences are rather wide. In similar findings for benzene vapor predictions [25], Nicas et al. stated that this was due to small size samples, with only 10 data pairs. In our study, we had 5, 9 and 10 data pairs for NF concentrations in plastic injection, steel cutting and welding. So, the increase of the size sample is likely to make the intervals shrink.

IV.3.2. Insufficient predict effectiveness of the modeling approach

Absence of correlation in the other job tasks could be due to many reasons we discuss hereafter.

IV.3.2.1. WMB predictions for RSW job task

No significant correlations were found for the Resistance Spot Welding (RSW) process. We believe that this is probably due to the G determination method. Both the emission factor and mass balance were not suitable for the RSW process (discussed in Part I). So we relied on a published study [33] that established an average emission rate calculated from several air monitoring trials in different circumstances. We suggest that the establishment of EFs for the RSW process would be useful.

WMB model was solely used for the RSW process. We believe that our model choice is not responsible for the discrepant results because it had been previously verified by calculating the inter-box air flow rate β near an emission source in case of the NF-FF model and the results showed (discussed in Part I) that the air is perfectly mixed.

IV.3.2.2. FF predictions in packaging job tasks

In the case of packaging job tasks in steel cutting workplaces, while our modeling approach of aggregate exposure from several emission sources effectively predicted the NF emissions, this was not the case for FF emissions. This could be probably due to the fact that air in both fields has to be homogeneously mixed. Now, this is probably not true, because the FF volume is considerably bigger than the NF in the workplaces.

In particular for the predictions in A1, the FF concentrations were higher than the NF concentrations, which violated one of the basics of the NF-FF model [1]. Still, our method PART II 166 was able to detect the order of magnitude of the FF concentrations compared to the NF concentrations.

IV.3.2.3. Prediction for electroplating job tasks

Only few concentrations were correctly predicted and no correlations were found either for the NF or the FF concentrations.

Contrarily to the EFs used for SMAW process, the EFs were not effective in the generation rate determination especially for Cr. The ready-to-use EFs for Cr unreasonably overestimated the exposure levels in both workplaces with a single or multiple emission sources. This could be explained by the fact that these EFs depend basically on the kind of control system devices.

This could be improved because control devices are more efficient nowadays compared to the ones used in the studies from which the EFs were derived decades ago [34].

For the other metals, the calculated EFs presented better results but good predictions were rare. Even when discarding the Cr predictions, no correlation was found. Despite that the calculated EFs are derived from field based information; the Cr EF is incorporated as the reference value, which can be the cause of the non-correlation. Therefore, establishment of newer EFs that depend on specific field information is needed.

IV.4. Biomonitoring analysis

Biomonitoring results show higher Al, Ni and Cu values in the median urinary concentrations among the metalworking workers (all job tasks) than in non-occupationally exposed workers.

In contrast, Cr and Pb concentrations are approximately similar while for Zn, a slightly higher value was observed.

These results for Al are in agreement with the literature. Al elimination depends on the exposure pathway and the metal chemical form. It was reported that contrarily to the oral route, 83% of the inhaled Al is eliminated through urines [14,35]. Since inhalation route is the main pathway in our study and in occupational exposure to Al in general [14,36], the elevated levels in the urines of workers are likely to be due to occupational exposure, in accord with other findings in the literature [36]. Moreover, urinary Al is considered to be a better indicator of occupational exposure than plasma [15,35].

Al accumulates in the human body, notably in the skeleton and the lung [14,36]. The half-life of Al urinary elimination increases with exposure duration and the redistribution of Al in PART II 167 storage tissues. It is a three-phase process, starting with several hours (up to 40 hours), then some weeks, and a half-life of several months and even years [14].

In a recent study, a biological reference value for urinary Al in the general population was proposed, with a 90 th percentile concentration lower than 11.5 µg/L [37].

Our results about Ni also agree with the published studies [17,38]. Urinary Ni does not depend on the exposure pathway and is considered as an indicator for occupational exposure [17].

The European Union Scientific Committee on Occupational Exposure Limits has established a Biological Guidance Values (BVG) for urinary Ni in the general population as 3 µg/L (last update in 2011) [17]. All our study workers, even the non-occupationally exposed ones, have higher urinary levels by 56 and 35 folds, respectively.

The half-life of Ni urinary elimination depends on the solubility of the compounds. For insoluble compounds, it is estimated as several months. For soluble compounds, it is a two phase-process beginning with 1 to 2 days, then it takes 1 to several months [17].

As for the urinary Cu, it is not considered as a good indicator for occupational exposure. This is mainly due to the fact that urinary excretion is low and because of high variations in urinary Cu in the general population [21]. A Belgian study established a biological reference value for urinary Cu in the general population, with the 90 th percentile concentration lower than 20 µg/L [39]. In our study, both occupationally and non-occupationally exposed workers exceed this reference value, with the exposed workers presenting 41% higher urinary concentrations, a finding that this excess in urinary Cu could be due to occupational exposure. We do not have firm evidence since no correlation was demonstrated in the literature nor in our study between air monitoring levels and urinary concentrations [21].

The absorption of Cr through inhalation in occupational studies was reported to be more important for Cr(VI) compounds (may be found only in the plating processes in our study).

However, Cr(VI) quantification in biological matrices is impossible, because of its rapid reduction to Cr(III). So, urinary Cr is considered as total chromium [20]. Total urinary Cr is considered as a good indicator for occupational exposure. However, it strongly depends on the solubility and the speciation of the metal. It is generally used for specific job tasks, notably electroplating and welding. Among all the six study metals, only Cr had biological limit values specifically established for the electroplating processes : the French agency ANSES PART II 168 (Agence nationale de sécurité sanitaire de l'alimentation, de l'environnement et du travail) has recently constructed a VLB (Valeur Limite Biologique) equal to 2.5 µg/L [20].

Cr excretion is mainly urinary and its half-life in urines has 3 phases; it begins with few hours (up to 7 hours), then few days (up to 30 days) and until 4 years.

In our study, median concentrations of urinary Cr, among exposed workers, is similar to that in non-occupationally exposed subjects. And, the concentrations of all job tasks are surprisingly close. This homogeneity suggests that Cr(VI) compounds are almost inexistent in our job tasks and that Cr(III) and metallic Cr are predominant. Urinary Pb results show slightly higher values among exposed workers. Urinary Pb is not the preferable method to assess occupational exposure because external contamination could alter the results [18]. However, half of the total inhaled dose of inorganic Pb is excreted through urines [40]. An increase in urinary Pb is observed for big particle sizes, notably with metallic dust [40]. For the general population, a recent French study has set a biological reference value for urinary Pb, (3.76 µg/L) corresponding to the concentration of the 90th percentile in the population [37]. Both occupationally and non-occupationally exposed workers exceed this value by 15 and 11 folds, in our study population, respectively. Finally, urinary Zn is preferred to plasmatic dosage in occupational biomonitring [16].

However, urinary excretion of Zn is partial, about 20%. There is little data about urinary Zn after inhalation in occupational settings but, a study demonstrated a correlation between airborne concentrations and urinary Zn [16].

Nisse et al. have also established a biological reference value for urinary Zn, with the 90 th percentile concentration lower than 1.04 mg/L [37]. All our workers urinary Zn levels exceed this concentration by 5 to 22 folds.

In accord with other studies on Al [14], our results show medium correlations between urinary metals and indoor air measured concentrations at the workplace. In a study conducted on Al welders during 5 years [41], a similar average correlation coefficient of 0.367 was found in relationship between the total dust concentration and urinary Al.

As for Cu, it was reported that correlation studies between urinary metals and indoor air concentrations were not strong due to a high variability in urinary Cu in the general population because of many factors such as the pregnancy, estrogen treatments, oral contraceptives and some pathologies [21]. Still, in our study, we found a significant PART II 169 correlation with a medium correlation coefficient (0.34). Because the majority of the variability factors are related to women, our correlation significance could be explained by the relatively small proportion of women in our sample (~ 23 %). Likewise, strong and significant correlations were reported in a Taiwanese study between Cu aerosols and urinary concentrations solely for the stamping process [42].

Still, the correlations significance were not strong (p-value = 0.04) which is probably due to lack of statistical power in relation with the limited sample size or with interference of the factors mentioned above.

Eventually, correlation results of Al and Cu support that a proportion of TMEs is coming from occupational exposure.

As for Zn, the Biotox report by INRS cited that a significant correlation was only found in the same Taiwanese study as for copper [16,42]. This was not the case in our study. This could be due to the fact that the correlation concerns only the stamping process and similar activities.

Besides, it was reported that urinary Zn undergoes important daily variations [16]. So, it might be preferable to conduct several samples during a workweek as it was done in the Taiwanese study [42].

As for Ni and Pb, we found no significant correlation. Only soluble compounds of Ni and organic compounds of Pb were reported to induce correlations between urinary metals and airborne concentrations [17,18]. Thus, we believe that, the chemical form differences were the major reason for our findings. PART II 170 Unfortunately, we did not measure metal particles in the office areas, mainly for time constraints reasons. This impaired our ability to include in our analysis data that are more spread, yielding limitations in statistical power. Moreover, it would be interesting to select other non-occupationally exposed persons outside the industrial field, such as students, in order to more firmly test our assumption.

Further, we cannot rule out other scenarios such as environmental pollutant sources or other specific sources in relation to population life-styles. For instance, the use of aluminum cooking vessels is very common in Tunisia. So, this could be a possible source of oral exposure. Finally, little is known about metals in food products in the study region, which could be another source of exposure variability. Inclusion of alternative non-occupationally exposed persons could help explore these confounding factors in future studies.

V. Conclusion of part 2

In this second part, our main objective was to compare airborne measured concentrations of TMEs with a number of modeling approaches in different task jobs in order to "validate" their usage.

We used correlation methods to assess the association between them and Student's t-test to have an exact idea about the mean differences and their confidence intervals. This step showed a significant to strongly significant global correlation. After stratification of the concentrations according to their job tasks and modeling method, NF concentrations predictions showed to be significant for steel cutting, welding and plastic injection. Moreover, their correlation coefficients are relatively high.

In a second step, we explored the correlations between urinary metals and indoor air measured concentrations. There were significant correlations only for Al and Cu, suggesting that they are due to occupational exposure. Still, we think that enlarging the size of our population and taking multiple samples for each worker might improve correlations.

Higher Al, Ni and Cu urinary levels were observed among metal working subjects, confirming the correlation findings for Al and Cu and also suggesting that they are due to occupational exposure. But, even non-occupationally exposed workers have high urinary metals for the six study metals. We believe these subjects were indirectly occupationally exposed through ventilation sources.

I. Introduction

In a Human Health Risk Assessment HHRA framework context, we have identified the used TMEs and quantified them in indoor air. Potential adverse effects on biological endpoints/ target organs were previously explored according to the inhalation exposure route. Then, in the dose-response step, reference values will be investigated. Finally, in the risk characterization final step, all information from the three previous steps will be gathered to associate and interpret the potential risk(s) for the exposed population.

The main objective of this part is to characterize the risk of the TME mixture in the metalworking industries. To do so, we selected threshold and non-threshold reference values.

Afterwards, we used a method that integrates the interactions between the mixtures' components for risk characterization by using the weight of evidence derived from the literature. In addition, a carcinogenic risk of the mixture was quantified.

II. Materials and methods

II.1. Hazard identification

Toxicological data were explored in the Toxicological Profiles documents from ATSDR [1][2][3][4][5][6][7], the IRIS (Integrated Risk Information System) from USEPA [8][9][10][11][12][13][14][15][16] and in the Toxicological Sheets from INRS [17][18][19][20][21][22][23].

II.2. Reference values selection

II.2.1. Toxicological reference values vs occupational exposure limit values

The toxicological reference values (TRV) generally correspond to quantitative theoretical estimations on which a population could be exposed to a particular substance without an observed adverse effect [24]. They are indicators of the dose-effect relationship [25]. TRVs are developed based on epidemiological and most commonly on toxicological available studies. These values are specific to the effect/ target organ, to the exposure pathway (inhalation, oral and dermal) and to exposure durations (acute, sub-chronic and chronic) [26].

There are two kinds of TRVs; (i) for threshold effects and (ii) for non-threshold (most commonly, carcinogenic) effects.

PART III 178

Whereas the occupational exposure limit values, called in French Valeurs Limites d'Exposition Professionnelle (VLEP), established for prevention purposes in order to protect the exposed workers from chemical substances that could induce occupational illnesses. They are currently constructed only for the inhalation route and only for threshold endpoints. They are also developed based on toxicological data, but preferably on occupational exposure studies [25]. Moreover, extrapolation method is different than for TRVs and less protective because it only considers occupationally-exposed populations and often stem from negotiations between the different stakeholders [26].

II.2.2. Selection arguments

In a research context dealing with environmental health studies, TRVs are used or developed for the general population. As to occupational health studies, researchers can choose either TRVs or VLEPs according to their needs and objectives [24,25]. For the same pollutant, the VLEP threshold concentrations value is significantly higher than the TRV, due to less protective conversion and security factor [25]. VLEPs are established to prevent well established occupational illnesses, however, they do not necessarily protect workers from "non-severe" and reversible effects, such as irritations [26]; one may also question to what extent some effectively protect against known effects, given the magnitude of the difference with the corresponding TRVs.

In our study, in a context of HHRA, we decided to use the TRVs of the selected TMEs for the inhalation route and for chronic exposure, in order to reduce health risks.

TRVs of Al, Cr, Ni, Cu, Pb and Zn were explored within the World Health Organization data sets and browsing reports from numerous national and regional agencies that are constructing TRVs. We will highlight hereafter the most known and well-recognized Agencies from France, Netherlands, United States of America and Canada.

II.2.3. Choice of critical toxic effects

In order to establish risk characterization of TMEs mixture, critical toxic effects are explored.

Then, common toxic effects, by target organ, of TMEs' combination are identified.

PART III

II.3. Methods of health risk assessment for chemical mixtures

II.3.1. Addition hazard index

The hazard Index HI is simply calculated as below;

𝐻𝐼 = ∑ 𝐻𝑄 𝑖 𝑛 𝑖=1 = ∑ 𝐸𝑥𝑝𝑜𝑠𝑢𝑟𝑒 𝑖 𝑇𝑅𝑉 𝑖 𝑛 𝑖=1
The main advantages of the HI method are: transparency, easiness to understand and direct relationship to TRVs [27,28].

However, it has many shortcomings such as the summation of individual uncertainty factors [28] and the total neglect of possible interactions between the components [29], which can lead to either over or under-estimation of the risk. It is recommended to be quite careful with the interpretation of HI results values [27].

II.3.2. Interaction-based hazard index

The interaction-based Hazard Index HIint is a modified Hazard Index that takes into account binary interactions data between chemicals. It was initially introduced by the US-EPA [29] to improve the dose-additive hazard index, which underestimates cumulative risk.

The final following formula was introduced by US EPA [29]:

HI INT = ∑ HQ j n j=1 . (∑ f jk n k≠j . (M jk ) B jk .g jk )
Where;

Mjk: the magnitude of the interaction between the component j and k;

Bjk: the WOE (weight of evidence) score for the interaction of chemical j affecting toxicity of chemical k. These scores are fixed depending on a classification [29,30]; f: the exposure factor: it is the toxic hazard of the k th chemical relative to the total hazard from all components possibly interacting with chemical j (so k cannot equal j), and is expressed by the following formula; f jk = HQ k HI-HQ J g: weighting factor for relative proportions of the components j and k and is expressed by the following formula; 𝑔 𝑗𝑘 = √ 𝐻𝑄 𝑗 𝐻𝑄 𝑘 (𝐻𝑄 𝑗 +𝐻𝑄 𝑘 ) 2 ⁄

The WOE scoring method was originally established by Mumtaz and Durkin [30,31]. Then, US EPA slightly modified the scores to derive a simpler classification [29].Score attribution depends on the available evidence of toxicological interactions in the literature. The B score is a number that varies from -1 to +1. We decided to use the original classifications and scores, because they are more detailed [30,31]. Moreover, they were also adopted in HIint calculations for other combinations of metal mixtures [32,33].

Binary WOE scores are calculated as following:

𝐵 𝑊𝑂𝐸 = 𝐷𝑖𝑟𝑒𝑐𝑡𝑖𝑜𝑛 𝑓𝑎𝑐𝑡𝑜𝑟 × 𝑤𝑒𝑖𝑔ℎ𝑡𝑖𝑛𝑔 𝑓𝑎𝑐𝑡𝑜𝑟 𝑤𝑒𝑖𝑔ℎ𝑡𝑖𝑛𝑔 𝑓𝑎𝑐𝑡𝑜𝑟 = ∏ 𝑤𝑒𝑖𝑔ℎ𝑡𝑖𝑛𝑔 𝑠𝑐𝑜𝑟𝑒𝑠
The used factors and scores are summarized in the tables 3.1 [30].

The interaction magnitude M is defined as a proportionality factor of additivity deviation that varies from 0.2 to 5 according to US EPA. Moreover, it could be derived by dividing the observed EDx by the predicted EDx for dose addition [34]. EDx is the Effective Dose of x % response rate in test animals [29]. In practice, studies frequently describe the magnitude of interaction qualitatively. Hence, a default value of 5 is recommended in case of lack of data [29]. Thus, depending on the available data, we are trying to determine M from studies conducted on animals. Otherwise, the default value of 5 is used.

HIint can be calculated using RfCs for different effect/ target organ of interest. However, to be more specific and consistent, target organ specific HIint is preferred. Risk estimate based on VTRs for a different effect can lead to an overestimation [27]. Indeed, Teuschler [35] recommends that the basic level of similarity between the mixture components required for chemical mixture risk assessment is a common toxic effect /target organ.

Thus, HIint for a common toxic effect(s)/ target organ (s) will be calculated for the metalworking industry by selecting the 90 th percentile concentrations of all measured data for each selected TME.

Toxicological Significance

A

The toxicological significance of the interaction has been directly demonstrated.

B

The toxicological significance of the interaction can be inferred or has been demonstrated for related chemicals.

C

The toxicological significance of the interaction is unclear.

Total cancer risk

It was previously named as "excess or incremental risk" by the US EPA [36]. The total cancer risk is a response-addition method for carcinogenic pollutants [30]. It assumes that PART III 182 carcinogens could have independent actions. It can also be calculated for different types of cancers [37].

It is calculated by summing the individual cancer risks [30]:

𝑇𝑜𝑡𝑎𝑙 𝑐𝑎𝑛𝑐𝑒𝑟 𝑟𝑖𝑠𝑘 = ∑ 𝑐𝑎𝑛𝑐𝑒𝑟 𝑟𝑖𝑠𝑘 𝑖 𝑖 ∑ 𝑈𝑅 𝑖 × 𝐶 𝑒𝑥𝑝,𝑖

𝑖

Where:

URi: is the slope factor (carcinogenic potency) for carcinogenic i Cexp, i: is the exposure concentration of carcinogenic i.

III. Results

III.1.

Occupational exposure limit values Table 3.2 exhibits occupational exposure limit values constructed by official institutions from France, Germany and the United States of America.

The indoor air concentrations of the majority of the TMEs in our study plants are under these regulatory values.

However, concentrations exceed these values for the job posts P10 and P11 where Cr(VI) compounds are used. When compared to the most protective limit value -the PEL constructed by NIOSH [38] -the Cr airborne concentrations exceed the PEL for P11 (0.000247 mg Cr/ m 3 ), and by twofold for P10 (0.000432 mg Cr/ m 3 ).

PART III

Effects with thresholds

The respiratory tract is the common target organ of chronic exposure to Cu, Cr, Ni (table 3.5).

Both the upper and the lower respiratory tract are involved. Several adverse effects were identified. They include active lung inflammation (Ni [3] and Cr [7]), up to pathological changes in lungs (Ni [49] and Cr [53]) and morphological and functional changes in alveolar cells (Ni [41,56]) such as septal cell hyperplasia (Cr [7]).

III.3.2. No-threshold effects

Lung cancer is confirmed to be the non-threshold critical effect of chronic inhalation exposure to Ni. As to Pb, the non-Threshold effect is kidney cancer. A study showed that CrIII has a synergetic effect on Ni by enhancing severe respiratory effects. It was conducted on rabbits exposed by the inhalation route over 4 months [3].

Two in vitro studies [57,58] showed an antagonistic effect of non-toxic concentrations of Ni on Cr cytotoxicity. However, cytotoxic concentrations of Ni showed a synergetic effect on Cr [59]. Other synergistic effects of Ni on Cr were demonstrated in DNA damage and oxidative stress [57]. Since we are adopting a protective scenario, the synergetic effect is adopted in the WOE scoring.

A study on rats exposed by ingestion observed an increase in copper deficiency when Ni was added to the diet [60]. The mechanism of interaction between Ni and Cu was not well characterized.

In vitro study showed a strong synergetic effect of small concentrations of Cu on the cytotoxicity of Ni towards alveolar macrophages extracted from rabbits [61]. However, when PART III 189 the Cu concentrations were increased, the interaction was hardly noticeable. Since we are adopting a protective scenario, an indeterminate effect is adopted in WOE scoring.

An in vitro study assessed and showed and antagonistic effect of Cu on the mutagenic health effects of Cr [7]. We began with individual hazard quotient HQ calculations of the 90 th percentile concentrations of the metalworking industry for the three metals that yield respiratory effects, Cr, Ni and Cu. Then, the addition HI was calculated in order to compare it with the interaction-based HIint for respiratory adverse effects (Table 3.8).

We can clearly note that even individual HQs exceed one, up to 67.9 for Ni.

Then, after the combination of all WOE data that span from antagonistic to synergetic effects, the value of HIint showed a value greater than the addition HIadd, suggesting that a global interaction between Cr, Ni and Cu leads to more than additive effect.

III.4.2. Non-threshold risk characterization

The total cancer risk is calculated using the 90 th percentile concentrations for Ni and Pb for two different kinds of cancers. The resulting value in table 3.9 means that more than 3 additional cases of cancer are expected to occur during a lifetime in a population of 10000 persons. [63].

However these methods were established and used for single chemical substances and not for complex mixtures, mainly because they use physico-chemical properties of the liquid.

IV.1.2. Weight of evidence of binary toxic interactions

Toxicological interactions between TMEs are mainly accommodated by ATSDR in their two interaction profiles [46,64]. However, binary combinations for Cr, Ni and Cu have not been investigated in these profiles. Elemental toxicological profiles of the study metals were explored for interaction data. Two by two interactions were mostly characterized according to in vitro and in vivo published data. Yet, the interaction effect of Cr on the toxicity Cu could not be determined because of lack of data, according to the best of our knowledge. Additional toxicological and mechanistic studies on mores TMEs combinations would be of a great use to implement the WOE method.

IV.2. Toxicological Reference Values

IV.2.1. Choice of TRVs for risk characterization

As previously mentioned, in the HHRA research context of occupational exposure, one may not only VLEPs (the French version), but also TRVs. Hence, in our study, the TRVs were chose over the occupational exposure limit values VLEP for the reasons we comment hereafter.

VLEP are especially developed to manage risks to occupational populations. They incorporate small uncertainty factors, compared to TRVs, because the occupational settings are considered to be principally controlled by the implementation of collective protective equipment [26]. Indeed, in occupational settings, compliance with these values also encompasses usage of these protective tools [25]. However, the presence, and effectiveness of such equipment is variable from a company to another, especially in Tunisia where industrial surveillance of exposure levels is not obligatory.

Moreover, even in countries where the collective protective equipments are more effective than in Tunisia, compliance with VLEP is not always a guarantee to prevent adverse effects on human health. In many cases, VLEP values are quite high because they are negotiated values. Indeed, throughout the construction of VLEPs, other inputs than scientific data are PART III 193 used. Stakeholders such as governmental departments, workers unions, industrial corporations, etc. contribute also in the establishment of the VLEPs by considering their applicability and economic costs [26].

On the other hand, TRVs are strictly constructed based on a scientific basis and evidence from toxicological and epidemiological studies throughout rigorous construction methods. In our case, based on animal exposure studies and uncompromising quality construction approaches, TRVs derived from BMD approach "Benchmark Dose" (for Cr [53] and Ni [49]) or from the NOAEL approach "No-Observed-Adverse-Effect Level" (for Cu [44]) are considered to provide a high level of protection against respiratory adverse effects, when not exceeded, whereas one cannot guaranty a high protection level of VLEPs.

Elsewhere, when it comes to exposure to mixtures, research studies are increasingly demonstrating that the exposure limit values, even in some cases including the most protective ones such as TRVs, are not absolutely protective [65] because these values were established based on single exposure studies where the pollutant is considered individually, not taking account of interactions.

Therefore, we believe that the use of TRVs is more prudent to study health impacts, especially when dealing with mixtures. Unlike VLEPs, TRVs also allow to explore the interaction implications on the risk level.

Of relevance for the purpose of our study, there is a decision support tool named "Mixie" that can be used for risk characterization using the VLEP. It calculates an "Indice d'exposition des effets Additionnels " noted IAE (or Rm in the Canadian version) that is similar to the addition the HI we used, but applied to VLEPs data instead of TRVs. Mixie was developed by Montreal University and the "Institut de recherche Robert-Sauvé en santé et sécurité au travail" [66], and revised by the INRS in the French version [67]. This tool is designed to help assess multi-exposure to chemicals in occupational settings. However, it integrates all VLEPs, irrespective of specific critical effects or target organs. Now, in order to aggregate the effects of a mixture, it is considered to be more rigorous to use TRVs that are established for the same endpoint [35].

PART III 194 Moreover, analogously to the addition HI, this tool does not take into account interactions between the components. Thus, we believe that Mixie could be an alternative in cases when no TRV is available but should be used with caution in such circumstances.

Finally, we believe that our findings support the relevance of our choice because even though the VLEPs were exceeded only in two job posts, the HIint calculated for the 90 th percentile concentrations in our study population tremendously exceeded unity (93.6).

IV.2.2. TRVs selection

The most protective TRVs were chosen. However, we are aware that they may not always be the most well grounded ones. That is why; we only relied on the best known agencies that are known to conduct high quality evaluations and reports. Moreover, we checked the critical studies, the critical effect/ target organ, the duration and route of exposure in the critical study, the tested species or population, the derivation methodology and the uncertainty factors that had been used.

Since all the critical effects were about the respiratory tract for threshold TRVs and lung cancer for Ni, we considered them as comparable values and decided to choose the most protective ones.

IV.3. Strengths and weaknesses of interaction-based hazard index

As to exposure to TME mixtures, to the best of our knowledge, the HIint method based on WOE approach was used for very few studies. One assessed risks associated with ingestion of a mixture of As, Cu and Zn from milkfish [32]. Lin et al. used the classic dose-additive HI, HIint for different critical effects for the whole mixture and then target-organ-specific interaction-based hazard indexes for gastrointestinal and hematological effects. They found a lower risk when they considered binary interactions using HIint. The same approach was conducted to assess As, Mn and Cd in drinking water [33]. The older version of HIint [29,31] was used to evaluate Cr, Cu, Zn, Cd, Hg and Pb in Food Crops [68].

All these studies deal with the oral exposure to TME mixtures. Inhalation exposure of TME mixtures has not been explored using this approach to date. population of 10 5 . So, a total cancer risk equal to 3.7 10 -4 is considered to represent a high risk in the metalworking workers population.

IV.3.3. Other possible interactions

Due to the absence of TRVs for all the six considered TMEs, other possible interactions could not be considered in the HIint calculations. For instance, in the ATSDR interaction profile, antagonistic interactions between Cu-Zn and Zn-Cu were demonstrated [64]. Incorporation of such interaction data could change the HIint value. Therefore, we believe that the establishment of TRVs is necessary to help consider more binary interactions in larger TME mixtures.

IV.3.4. Methodological choice

The global risk characterization calculations were based on the 90 th percentile of the exposure distribution of all job tasks. Thus, the HIint and total cancer risk results involve 10% of the most highly exposed job posts and notably from the most polluted job tasks (P5, P6, P10 and P16) that were identified in Part 2. Indeed, some indoor air concentrations of the considered metals have exceeded the 90 th percentile concentrations in P5 and P6.

It is also worth to note that even smaller job-specific HIint can present concerns about respiratory toxic effects when substantially higher than 1, say above 5 given the security factors often included in derivation of the TRVs. According to our indoor air monitoring result, this is the case of the majority of the job tasks because all mean and LM measured concentrations of Ni and more than the half of Cr concentrations are above5 folds of their corresponding TRVs.

V. Conclusion of part 3

In the third part, our main objective was to conduct a risk characterization of the occupational inhalation exposure to mixture of TMEs in the metalworking industry. To do so, we used the HIint approach based on the relevant TRVs, binary interaction scores and the level for the 90 th percentile of the exposure distribution we observed after our workplace measurements. Metalspecific HQs of Cr, Ni and Cu are exceeding one, which indicates possible toxic risks. Then, HIint showed higher risks than the HI, suggesting more than additive respiratory effects from inhalation exposure to the study metals.

PART III

197

We discussed some limitations of our results that are essentially due to lack of information in the literature, such as the non-availability of all the VTRs for chronic inhalation of Al, Pb and Zn. We believe that despite all these limitations, our study shows that there are real concerns about potential risks to workers exposed to TME mixtures by inhalation in the metal working industry, especially in relation to the synergetic effect between Cr, Ni and Cu.

PART III

The health risk assessment process in relation to occupational exposure to chemicals is dedicated to inform risk prevention and industrial hygiene, like its counterpart for environmental hazards [1]. Companies' managers should be more aware of such procedures in order to improve the working environment and to ensure control of risks in the workplaces.

In the Tunisian regulatory framework, there are two sections about the prevention of occupational risks in the chapter VI of law number 94-28 of February 21 th , 1994 [2] and in chapter V of the law number 95-56 of June 28 th , 1995 [3] for the private and public sectors, respectively. These two texts require from the managers to implement a prevention strategy to avoid work accidents and occupational illnesses by taking all appropriate preventive actions relative to their activity. Yet, they are general and vague; no specific rules and actions are required. For instance, no indoor air quality standards and regulations are available. Thus, the managers do not feel obliged to conduct exposure assessment if there is no accident or a problematic situation. However, we believe that it is necessary to conduct exposure assessment and surveillance for preventive reasons in order to recognize the appropriate measures and invest in equipment that regulates the pollutants' levels. Depending on the airborne concentrations, the quality, quantity and the strength of the prevention systems can vary substantially. So, in order to effectively comply with these laws; a new culture of periodic risk assessment has to be set up.

In the particular setting of our study, the implementation of such a culture will reduce the high risk of respiratory conditions in the metalworking industry, which would in turn result in better health and productivity of workers and less medical leaves. Such measures might also contribute to higher confidence from workers and professional involvement.

Still, we acknowledge that air sampling and the corresponding analyses are not always accessible because of their high price. Therefore, mathematical models for estimating occupational exposure to chemical can be useful and represent an alternative tool that is both time-saving and cost-effective in comparison with direct air monitoring approaches.

Moreover, occupational hygienists are also interested in such models because one cannot monitor everything, so they could be used as a quantitative and scientifically explicit method to support expert judgment [4]. Such models can be used in exposure assessment purposes in workplaces or even in the planning step before the implementation of a new workplace. Thus, in our study we assessed the models prediction abilities for TMEs in several job tasks and demonstrated the effectiveness of the NF-FF model to predict indoor air concentrations in different types of steel cutting and welding and plastic injection.

We seek to cooperate with governmental institutions and bodies such as the Tunisian Occupational Health and Safety Institute ISST (Institut de Santé et de Sécurité au Travail) in order to take advantage of this modeling approach at a national scale and apply these models in exposure assessment in other industrial settings. In this intention, and in order to test this approach to more diverse settings and other industrial branches, we solicit their support, especially to establish contacts and recruitment of companies.

As we previously mentioned, the exactitude of technical information to establish the models parameters yields better results. So, it is important that the companies' managers are convinced of all the benefits of the risk assessment procedure. In this respect, the dialogue between all stakeholders is necessary and beneficial for everyone.

We also address the Ministry of Vocational Training and Employment and highlight the high risk encountered in the metalworking industry; an awareness program geared to the managers and the workers would be useful to expose the multiple advantages of the exposure assessment approach and to introduce this tool, which is new in the Tunisian context.

It is worth to note that this work was conducted thanks to reasonableness, awareness and the good will of the managers of the five participating companies. As previously mentioned, many others have refused to be involved. The participating volunteers are, obviously, conscious of the issues and are likely to be more careful about occupational risks in their workplaces, which is probably not the case of all metalworking companies in Sfax. That is why we call for caution about the general situation in Sfax. Hence, risk levels described in this study are definitely underestimated regarding the whole metalworking industry in the Sfax Metropolis.

At the end of this study, we brought to light, via the human health risk assessment -HHRAapproach, that workers in the metalworking industry in Sfax Metropolis are exposed to trace metallic elements mixtures through inhalation of dust and aerosols at levels that may elicit respiratory illnesses; the risk levels may even be enhanced by synergetic effects. Now the measured airborne concentrations do not exceed the VLEPs published in the literature or in effect in other countries, except for two job posts in metal electroplating.

During the HHRA, for exposure assessment purposes, we used mathematical models for estimating occupational exposure to TMEs in several metalworking processes and real-life exposure scenarios using available field-related information, literature and inexpensive measurements (temperature, air velocity, etc.). The models efficiencies were assessed by comparing direct measured airborne concentrations of TMEs with the predicted concentrations in order to "validate" their usage. Associations were checked using correlation methods together with Student's t-test to have an exact idea about the mean differences.

Results showed a strongly significant global correlation. When we further conducted comparisons, depending on the process and the modeling methods, the NF predictions of TME concentrations showed to be applicable for three kinds of steel cutting, two kinds of welding and in the plastic injection process.

We also tried to investigate the correlations between urinary metals and the measured airborne concentrations; however, no significant correlation for all the considered TMEs was determined. We assume that this is mainly due to lack of statistical power, due to the rather small size of our population but we cannot rule out interference with uncontrolled confounders.

Urinary Al, Ni and Cu were higher among the exposed workers, which suggest that they are due to occupational exposure. Now, non-occupationally exposed workers have also high urinary metals for the six study metals, compared to the biological reference value in the general population. We explain this by the fact that these workers were indirectly occupationally exposed through ventilation sources.

Finally, for risk characterization purposes, we used the interaction-based Hazard Index approach by means of the WOE approach and the corresponding Threshold Reference Values to calculate a global HIint hazard index based on the 90 th percentile of the exposure GENERAL CONCLUSION & PERSPECTIVES 209 distribution of all job tasks. Results are of great concerns about non-carcinogenic respiratory toxic effects, especially for the Shielded metal arc welding and the slitting and metal shearing and slitting task jobs. This risk is also likely to be enhanced because of the more than additive interaction resulting from the combination of the Cr, Ni and Cu.

For the non-threshold effects, a total cancer risk was calculated that recognized high risks of lung and kidney cancers (the predicted life-long total cancer risk for exposed workers is 3.7×10 -4 ).

The results derived from the HHRA procedure, either for predictive models for risk characterization of mixtures, are interesting and useful for the different stakeholders in the occupational field such as companies' managers, workers' union and public institutions responsible for risk control and industrial hygiene.

Along this doctoral thesis, we also identified limitations in applying modeling approaches to predict exposures and risks in face of multiple chemical exposures. This represents serious challenges for research. We have suggested some topics for a research agenda, such as;

 uncertainty analysis by Monte Carlo in order to determine the output distribution and interval,  air monitoring in office areas to verify the exposure levels,  urinary sampling for other non-occupationally exposed persons outside the industrial field to have background levels and confirmation about the occupational proportion of TMEs in urines  Specific monitoring of nickel sulfate and chromic acid for the electroplating process so as to provide more accurate information about the risk, because their TRVs are considerably lower than for the elemental metals.  Construction of a Unit Risk for total Cr based on the Tumorigenic Concentration 05 established by Health Canadian order to improve the total cancer risk estimation, especially because the total Cr is related to lung cancer like the Ni.  Derivation of generation rates from our air monitoring results (in the breathing zones) for the job processes where our modeling approach was not applicable, and assessment of their prediction ability.  Establishment of HIint for every job task in order to allow comparisons between specific risks depending on the process.

We think these topics offer new opportunities for national and international collaboration, with the support of the ministry of higher education and scientific research.

2.

3.

4.

5.

6.

7.

8.

 Les salariés utilisateurs des substances contenant des métaux lourds portentils :  des vêtements de travail différents de leurs vêtements de ville ? Oui 1 Non 2

 Si Oui, précisez le % de salariés

2.

3.

4.

5.

6.

7.

8.

AUTRES PRODUITS

Nom

Entreprise productrice Quantité utilisée/an Nbre de Salariés directement exposés 1.

2.

3.

4.

5.

6.

7.

8.

Quels

2.

3.

4.

5.

6.

7.

8.

AUTRES PRODUITS

Nom

Entreprise productrice Quantité utilisée/an Nbre de Salariés directement exposés 1.

2.

3.

4.

5.

6.

7.

8.

Quels

2.

3.

4.

5.

6.

7.

8.

AUTRES PRODUITS

Nom

Entreprise productrice Quantité utilisée/an Nbre de Salariés directement exposés 1.

2.

3.

4.

5.

6.

7.

8.

Vérifiez que vous avez bien répondu à toutes les questions. Pour toute question concernant le questionnaire, vous pouvez contacter

Fatma OMRANE au 21552700 ou omrannefatma@gmail.com

Enquête Environnement et Santé

Les matériaux :

 Masses des matières premières utilisées dans le processus : ………………………/ an, sem., J ………………………/ an, sem., J ………………………/ an, sem., J ………………………/ an, sem., J ………………………/ an, sem., J ………………………/ an, sem., J ………………………/ an, sem., J ………………………/ an, sem., J  Masse des produits : ………………………/ an, sem., J  Masse des déchets recueillis du processus …………………/ an, sem., J Trace metallic elements (TMEs) are pollutants of great concern even in trace amounts because of their toxicity and cumulative property. Some of them can be carcinogenic. The Sfax metropolis, located in the southern region of Tunisia, has been affected by releases of TMEs for decades. Several studies confirmed that this pollution is predominantly originated from anthropogenic sources, mainly from industrial activities. It represents a threat to the health of residents, particularly for those also exposed during occupational activities in industrial processes. The present study aims to assess health risks associated with occupational exposure in industries handling TMEs in their production processes, following the human health risk assessment approach.

To this end, five companies using raw material containing TMEs to produce a variety of metallic products accepted to participate to the study. The metals that were investigated are Al, Cr, Ni, Cu, Zn and Pb. Mathematical models for estimating occupational exposure to chemicals were used to predict indoor air TME exposure levels in 15 different job tasks. Air monitoring was conducted in order to compare the predicted workplace air concentrations versus the direct measured ones, using both workplace-fixed monitors and personal samplers. And finally, urine samples were collected from 61 workers to assess whether TMEs excretion correlate with job exposure levels. Globally, the predicted air estimates relate well with measured concentrations over the whole set of job tasks. Better predictions were found for certain activities, in particular for steel cutting and welding processes.

The values that correspond to the 90 th percentile of the exposure distribution were then used in the interaction-based hazard index HIint to assess health risks associated with the mixtures of TMEs. Total cancer risk was also investigated.

Results showed high exposures for metals that may elicit respiratory conditions, with a HIint reaching 93.6, the highest levels being for the shielded metal arc welding and metal shearing and slitting tasks. The risk is enhanced by a synergetic effect between Cr, Ni and Cu. High risks of lung and kidney cancers were demonstrated (the predicted life-long total cancer risk for exposed workers is 3.7×10 -4 ). This work shows that mathematical models can be accurate in predicting TME airborne exposure levels for several processes in the metallurgic industry, a result that is of interest to help the different stakeholders to monitor efficiently exposure surveillance and abatement. Progress in industrial hygiene is needed in this industrial sector to reduce the high level of health risks currently experienced by the metalworking workers. Key words: Occupational exposure, trace metallic elements, human health risk assessment, mathematical models, air monitoring, biomonitoring.

Figure i : 1 :Figure 1 . 1 :

 i111 Figure i: Location map showing industrial areas and the main sources of pollution in the Sfax metropolis Figure ii: Well mixed box model presentation; G: generation rate, Q: ventilation rate, V: air volume, CWMB: predicted concentration of the pollutant Figure iii: Near-Field, Far-Field model presentation; G: generation rate, Q: ventilation rate, β: inter-box air flow rate, V: air volume, VNF: NF air volume, CNF/FF: predicted concentration of the pollutant in the NF and FF, respectively

Figure 1 . 2 :Figure 1 . 3 :Figure 1 . 4 :Figure 1 . 5 :Figure 1 . 6 :Figure 1 . 7 :Figure 1 . 8 :Figure 1 . 9 :Figure 1 . 10 :Figure 1 . 11 :Figure 1 . 12 :Figure 1 . 13 :Figure 1 . 14 :Figure 1 . 17 :Figure 1 . 18 :Figure 1 . 19 :Figure 1 . 20 :Figure 1 . 21 :Figure 1 . 22 :Figure 1 . 23 :Figure 1 . 24 :Figure 1 . 25 :Figure 1 . 26 :Figure 1 . 27 :Figure 1 . 28 :Figure 1 . 29 :Figure 1 . 30 :

 1213141516171819110111112113114117118119120121122123124125126127128129130 Figure 1.2: Scheme of workplace A4 (steel sheering): x = 6.25 m, y = 1.6 m, z = 1.9 m Figure 1.3: NF dimensions of P1 (steel sheets stamping): x = 0.8 m, y = 0.5 m, z = 1 m Figure 1.4: NF dimensions of P6 Figure 1.5: Modeled indoor air concentrations of Al, Cu, Ni and Cr in the NF of Steel sheet stamping (P1) during four-hour shift Figure 1.6: Modeled indoor air concentrations of Al, Cu, Ni, Cr and Zn in the NF of sheering and slitting (P6) during four-hour shift Figure 1.7: Scheme of workplace A2; P4: RSW Figure 1.8: Modeled indoor air concentrations of Al, Cu, Ni and Cr in the whole workplace A2 for RSW (P4) during four-hour shift Figure 1.9: Scheme of workplace A5 Figure 1.10: Modeled indoor air concentrations of Al, Cu, Ni, Cr and Zn in the NF of induction welding (P9) during four-hour shift Figure 1.11: Scheme of workplace A3: SMAW; P5: SMAW Figure 1.12: NF dimensions of P5 (SMAW): R = 1.15 m Figure 1.13: NF dimensions of P16GMAW (GMAW): x = 6 m, y = 3.7 m, z = 1.9 m Figure 1.14: Modeled indoor air concentrations of Al, Cu, Ni, Cr and Pb in the NF of SMAW (P5) during four-hour shift Figure 1.15: Modeled indoor air concentrations of Al, Cu, Ni, Cr and Pb in the NF of GMAW (P16GMAW) during four-hour shift Figure 1.16: Modeled indoor air concentrations of Al, Cu, Ni, Cr, Zn and Pb for P16

Figure 2 . 1 :Figure 2 . 2 :

 2122 Figure 2.1: Equipment and materials for HM indoor air monitoring; a: Cassettes and quartz filters, b: sampling pump, c: calibrator

Figure 2 . 3 :Figure 2 . 4 :Figure 2 . 5 :Figure 2 . 6 :Figure 2 . 7 :Figure 2 . 8 :Figure 2 . 9 :Figure 2 . 10 :Figure 2 . 11 :Figure 2 . 12 :Figure 2 . 13 :Figure 2 . 14 :Figure 2 . 15 :Figure 2 . 16 :Figure 2 . 17 :Figure 2 . 18 :Figure 2 . 19 :Figure 2 . 20 :Figure 2 . 21 :Figure 2 . 22 :Figure 2 . 23 :Figure 2 . 24 :Figure 2 . 25 :Figure 2 . 26 :Figure 2 . 27 :Figure 2 . 28 :Figure 2 . 29 :Figure 2 . 30 :Figure 2 . 31 :

 23242526272829210211212213214215216217218219220221222223224225226227228229230231 Figure 2.3: Normal Q-Q plots for the measured location measures values; (a) real data before transformation, (b) data after log transformation Figure 2.4: Boxplots for the log transformed location measures values and log transformed Mean values of TMEs concentrations Figure 2.5: Boxplots for the Ln(location measures), Ln(Mean) of the measured TMEs concentrations, and the Ln(Predicted) TMEs concentrations

  and general objectives ..............................................................

Figure i .

 i Figure i. Location map showing industrial areas and the main sources of pollution in the Sfax metropolis

Figure ii :

 ii Figure ii: Well mixed box model presentation; G: generation rate, Q: ventilation rate, V: air volume, CWMB: predicted concentration of the pollutant

  Figure iii: Near-Field, Far-Field model presentation; G: generation rate, Q: ventilation rate, β: inter-box air flow rate, V: air volume, VNF: NF air volume, CNF/FF: predicted concentration of the pollutant in the NF and FF, respectively

PART I 40 II. 3 . 3 . 4 .

 40334 Indirect trace metallic element handling jobs a. Product packaging and displacement

Figure 1 . 1 :

 11 Figure 1.1: Scheme of workplace A1; P1: Press operator and P3: Packaging *: fixed locations of workers, *: Mobile positions, movement areas of mobile workers

Figure 1 . 2 : 51 Figure 1 . 3 :

 125113 Figure 1.2: Scheme of workplace A4 *: fixed locations of workers, *: Mobile positions, : movement area of mobile workers

52 Figure 1 . 4 :

 5214 Figure 1.4: NF dimensions of P6 (steel sheering): x = 6.25 m, y = 1.6 m, z = 1.9 m

Figure 1 . 5 :Figure 1 . 6 :

 1516 Figure 1.5: Modeled indoor air concentrations of Al, Cu, Ni and Cr in the NF of Steel sheet stamping (P1) during four-hour shift

GFigure 1 . 8 :

 18 Figure 1.8: Modeled indoor air concentrations of Al, Cu, Ni and Cr in the whole workplace A2 for RSW (P4) during four-hour shift

Figure 1 . 9 :

 19 Figure 1.9: Scheme of workplace A5 *: fixed locations of workers, *: Mobile positions, : movement areas of mobile workers

Figure 1 . 10 :

 110 Figure 1.10: Modeled indoor air concentrations of Al, Cu, Ni, Cr and Zn in the NF of induction welding (P9) during four-hour shift

Figure 1 . 11 :

 111 Figure 1.11: Scheme of workplace A3; P5: SMAW



  In the second job task P16GMAW, the NF is estimated as a parallelepiped box that contains the GMAWelding setting. The length and the width are covering the space where both workers are moving and the emission source. The workers are in standing position, so, the height of the box is equal to the vertical distance between the welding setting level and about 15 cm above the worker's head to cover up the entire breathing zone. The inter-box air PART I 66 flow rate βA16 flows through all surfaces except the little part of bottom surface overlapping with the welding setting on the machine.

Figure 1 . 12 :

 112 Figure 1.12: NF dimensions of P5 (SMAW): R = 1.15 m

Figure 1 .

 1 Figure 1.13: NF dimensions of P16GMAW (GMAW): x = 6 m, y = 3.7 m, z = 1.9 m

Figure 1 . 14 :Figure 1 . 15 :Figure 1 . 70 Figure 1 .

 1141151701 Figure 1.14: Modeled indoor air concentrations of Al, Cu, Ni, Cr and Pb in the NF of SMAW (P5) during four-hour shift

Figure 1 .

 1 Figure 1.16 shows the sum of trace metallic element concentrations for P16, where the NF concentrations of GMA welding and the FF of the induction welding are taking place.

Figure 1 . 16 :

 116 Figure 1.16: Modeled indoor air concentrations of Al, Cu, Ni, Cr, Zn and Pb for P16 (GMAW and system control of the tube welding machine) during four-hour shift

PART I 73 3

 73 extractor fans having an individual average air flow rate value of 11 950m 3 .h -1 . Air is also leaving the room through two local exhaust ventilation systems over the zinc-plating baths with an average air flow rate value of 17784 m 3 .h -1 .

Figure 1 . 17 :

 117 Figure 1.17: Scheme of workplace A6, *: fixed locations of workers, *: Mobile positions, : Movement area of mobile workers : NF lengths and widths

  box air flow rate β flow through the top surface and also through the two side and back surfaces except for the bath dimensions. So, FSA and S are calculated and are equal to 192.73 m 2 and 0.022 m.s -1 , respectively.

Table 1 . 13 :NF

 113 NF-FF models input values: constant emission rate of TMEs electroplating

  using questionnaires b :[41] c : Converted to mg/m 3 (developed by US EPA, AP 42-emission factors)[67] d : Calculated *: as CuSO4, **: as NiCl2, ***: as NiSO4 PART I 78 Then, the predicted concentrations of Cu, Ni, Cr and Zn were computed using the IHMOD software tool. The models outputs are presented in figures 1.18 (for the job tasks A11and A12) and 16 (for A10).

Figure 1 .Figure 1 . 18 :Figure 1 . 19 :

 1118119 Figure 1.18 shows the NF model predictions of trace metallic element concentrations in nickel-lines electroplating for electrodeposition onto metals. The NF concentration values quickly rises in the first 20 minutes. Then, for Cu and Zn after nearly 30 min, concentrations almost reach the CSS. As to Ni and Cr, it took them 70 min to reach the CSS.

81 Figure 1 . 20 :

 81120 Figure 1.20: Scheme of workplace A7; P13: ABS plating line operator, P14: Quality controller *: fixed locations of workers, *: Mobile positions, : Movement area of mobile workers : NF length and width of P13 modeling

Figure 1 . 21 :Figure 1 .

 1211 Figure 1.21: Modeled indoor air concentrations of Cr, Ni and Cu in the NF of decorative chromium electroplating of ABS (P13) during four-hour shift

  emission sources. So, exposure levels of the packaging workers ware calculated as the cumulative sum of all individual FF concentrations: CFF, packaging = CFF, steel cutting × Number emission source i. In a first situation, the packaging job task is noted as P3 in the workplace A1. There are 4 workers who are all moving around the workplace. They are sometimes remaining in a specific area in the far left of the workplace A1 (figure 1.1).

Figure 1 . 22 :Figure 1 . 23 :Figure 1 . 24 :

 122123124 Figure 1.22: Modeled indoor air cumulative concentrations of Al, Cu, Ni and Zn in FF for packaging & displacement (P3) during four-hour shift in the workplace A1

Figure 1 .Figure 1 . 25 :

 1125 Figure 1.25 presents the predicted concentrations of Cr, Ni and Cu that are computed using the IHMOD tool.

PART I 89 Plastic

 89 injection operators are controlling the injection molding machines in the workplace named A8 and mapped in figure1.26. There are 5 to 6 workers. In P15, each worker is seated in front of a machine window in which the injection molding is taking place. The operator commands the thermoplastic injection while the window is closed, wait for the injection for a few seconds, and then open it to extract the plastic workpiece. He also checks it and adjusts it if there is an extra bit of plastic. In this modeling situation, we are considering the open window surface as the emission source.

PART I 90 Figure 1 . 26 :* 91 Figure 1 . 27 :

 9012691127 Figure 1.26: Scheme of workplace A8; P15: plastic injection operator *: fixed locations of workers

Figure 1 .Figure 1 . 28 : 93 Figure 1 .

 1128931 Figure 1.28 presents the predicted concentrations of Al, Cu, Ni, Cr, Zn and Pb that are computed using the IHMOD tool.

Figure 1 . 29 :Figure 1 . 30 :

 129130 Figure 1.29: Modeled indoor air concentrations of Al, Cu, Ni and Cr in the NF of (stamping P1) and the FF (packaging P3) during four-hour shift

III. 3 . 1 .III. 3 . 2 .III. 3 . 1 .IV. 1 .IV. 2 .IV. 3 . 2 .IV. 4 .

 31323112324 the internal and external doses ................................ Biomonitoring results ...................................................................................... Correlation between urinary metals and indoor air measured concentrations 158 Correlation between urinary metals and models estimations......................... IV. Discussion ........................................................................................ Methodological choices ............................................................................................ Air monitoring ......................................................................................................... IV.3. Predicted versus measured concentrations ............................................................. IV.3.1. Prediction effectiveness of the modeling approach ......................................... IV.3.1.1. NF predictions in steel cutting processes ....................................................... IV.3.1.2. NF predictions in welding processes ............................................................. IV.3.1.3. NF predictions in plastic injection ................................................................. Insufficient predict effectiveness of the modeling approach ........................... IV.3.2.1. WMB predictions for RSW job task .............................................................. IV.3.2.2. FF predictions in packaging job tasks ............................................................ IV.3.2.3. Prediction for electroplating job tasks ........................................................... Biomonitoring analysis............................................................................................. V. Conclusion of part 2 ........................................................................... VI. Bibliography .................................................................................... Ma thema tical Models f or Es timat ing Occu patio nal Expos ure to Trace Metallic Ele ments in Me talworkin g I ndus tries (I ndo or Air Monitori ng) & Biomon itoring

117  2 Figure 2 . 1 :

 117221 Figure 2.1: Equipment and materials for TME indoor air monitoring; a: Cassettes and quartz filters, b: sampling pump, c: calibrator

  NF predictions in the steel cutting industry (iii) NF modeled concentration for plastic injection (iv) Modeled concentration for welding industry: NF predictions in SMAW and induction welding process, and combination predictions for P16 (v) Modeled concentration for electroplating industry: NF predictions for Ni and ABS plating, and combination predictions for metal electroplating

Figure 2 . 2 :Figure 2 . 3 :

 2223 Figure 2.2: Boxplots for the location measures and Mean of the measured TMEs concentrations and the predicted TMEs concentrations

Figure 2 . 4 :

 24 Figure 2.4: Boxplots for the log transformed location measures values and log transformed Mean values

Figure 2 . 5 :

 25 Figure 2.5: Boxplots for the Ln(location measures), Ln(Mean) of the measured TMEs concentrations, and the Ln(Predicted) TMEs concentrations

Figure 2 . 6 :Figure 2 . 7 :

 2627 Figure 2.6: Linear regression of the Ln(Predicted TMEs concentrations) against the Ln(location measures of the measured of the TMEs concentrations) across the fifteen job tasks (n=66)

Figure 2 . 8 :

 28 Figure 2.8: Distribution comparison of LM measured concentrations of TMEs, Mean measured concentrations and modeled concentrations for of the RSW modeling (P4)

  Ln(Predicted) distribution is lot wider than Ln(Mean measured) and Ln(LC measured) distributions. Moreover, its box of Ln(Predicted) is quite large and exceeds the bottom whiskers of the Ln(Mean measured) and Ln(LC measured) boxplots, suggesting the model is often underestimating exposure.

Figure 2 . 18 :Figure 2 . 20 :III. 2 . 7 .

 21822027 Figure 2.18: Distribution comparison of Ln(LM measured concentrations of the TMEs), Ln(Mean measured concentrations of the TMEs) and Ln(modeled TMEs concentrations) of welding job tasks (combined exposure in P16)

Figure 2 . 21 :

 221 Figure 2.21: Distribution comparison of Ln(LM measured concentrations of the TMEs), Ln(Mean measured concentrations of the TMEs) and Ln(modeled TMEs concentrations) of electroplating job tasks (NF in P10 and P13)

Figure 2 . 29 :Figure 2 . 30 :

 229230 Figure 2.29: Linear regression of the Ln(urinary Al) against the Ln(Al air concentration) of 39 exposed workers

  .31.a). On the other hand, the packaging workers P6 in workplace A4 are exposed to lower concentrations for all TMEs (figure 2.31.b).

Figure 2 . 31 :

 231 Figure 2.31: Indoor air monitoring concentrations (location measure values) of TMEs in steel cutting workplaces A1 (figure a: P1: stamping, P3: Packaging) and A4 (figure b: P6: slitting &shearing, P7: Packaging)

PART II 163 IV. 3 . 1 .

 16331 Prediction effectiveness of the modeling approachStrongly significant correlations were demonstrated for NF predictions in the steel cutting and welding jobs, and to a milder extent for plastic injection. Correlation with mean measured values showed to be approximately the same as LM values. Student's T test for paired samples showed also that the mean difference could reach zero. IV.3.1.1. NF predictions in steel cutting processes Besides the strong statistical significance, relatively high correlation coefficients (r ~0.85)

  Urinary concentrations of Al, Ni, Cu and to a lesser extent for Pb, values are greater among the exposed workers, suggesting an occupational origin for Ni and Pb and confirming that for Al and Cu.Urinary levels of all occupationally or non-occupationally exposed workers exceed the available biological reference values. Now, the differences between exposure pathways (occupational and non-occupational) are less sharp then expected since the administration offices of the 5 companieswhere non-exposed subjects were recruited -are either communicating with the metal workplaces through doors, or have doors or windows directly next to those of the metal working areas. This gives place to indirect exposure via ventilation and air flow.

III. 4 . 1 . 1 .

 411 Weight of evidence for binary toxic interactionsBinary toxic interactions were explored for the 3 metals Cr, Ni and Cu.

  ii (-0.493)[7] ? II A 2 b ii (0)[61] NB: In reference to table 3.1, the case for the effect of Cr on the toxicity of Ni should be read as>II A 1 a i (+0.71); >(corresponding to +1): Direction of Interaction is Greater than additive, II(score = 0.71): Mechanistic data have not been well characterized for the chemicals of concern, A(score = 1): The toxicological significance of the interaction has been directly demonstrated, 1(score = 1): Anticipated exposure duration and sequence, a (score =1): In vivo data, i(score =1): Anticipated route of exposure. III.4.1.2. Health risk of TMEs mixture in metalworking industries

Nombre de salariés utilisateurs

  ayant une durée cumulée d'exposition aux ML au sein de l'entreprise (que cette exposition persiste ou non actuellement), de : Quels sont les métaux lourds utilisés pour ces activités ?Précisez le nom des substances (commercial et/ou d'usage courant)

 2 I_I_I_I_I_

 2 Quelle est la superficie de l'atelier ? I_I_I_I_I_I m Quelle est l'architecture de l'atelier ?  Nombre de pièces I_I_I I_I_I  Faites un schéma de l'atelier en précisant :  Faites un descriptif de l'ambiance générale de l'atelier : OUI1 NON2  le bâtiment est une nouvelle construction   I_I  les locaux sont bien entretenus   I_I  il existe des vestiaires et une cantine   I_I  il est situé dans des locaux d'habitation   I_I Comment est assurée la ventilation dans l'atelier ?  Aucune aération (espace confiné) 1 I_I  Aération naturelle (portes et fenêtres) 2  Ventilation mécanique générale (extraction d'air) : 3  Extraction à la source d'utilisation des ML 4  Autre 5 Si autre, veuillez préciser : ………………………………...…… Longueur = …… m, largeur = ….. m, Hauteur = ….. m Quels sont les métaux lourds utilisés pour ces activités dans l'atelier 1 ? Précisez le nom des substances (commercial et/ou d'usage courant) et l

  sont les métaux lourds utilisés pour ces activités dans l'atelier 2 ? Précisez le nom des substances (commercial et/ou d'usage courant) et l

  sont les métaux lourds utilisés pour ces activités dans l'atelier 3 ? Précisez le nom des substances (commercial et/ou d'usage courant) et l

Table 1 .

 1 1: Industrial sectors responsible for Occupational exposure to TMEs and probable companies in Sfax metropolis

Table 1 .

 1 2: Presentation of TMEs contained in the used materials in every job task

Table 1 .

 1 3: Materials' commercial names and references

Table 1 .

 1 4: NF-FF model input values: constant emission rate of TMEs during steel sheet cutting

Table 1 .

 1 5: NF steady state concentrations CNF, SS during steel sheet cutting

Table 1 .

 1 6: WMB model input values: constant emission rate of TMEs during RSW welding

Table 1 .

 1 7: Steady state concentrations in the WMBCWMB, SS during RSW

Table 1 .

 1 8: NF-FF model input values: constant emission rate of TMEs during induction welding

Table 1 .

 1 9: NF steady state concentrations CNF, SS during induction welding

Table 1 .

 1 10: AP 42-emission factors of total fume, Cr and Ni for electric arc welding processes

Table 1 .

 1 11: Table 1.11: NF-FF model input values: constant emission rate of TMEs during SMAW: P5 (240 min) and GMAW: P16GMAW (4 min over 20 min)

Table 1 .

 1 12: NF steady state concentrations CNF,SS SMA welding process

Table 1 .

 1 13: NF-FF models input values: constant emission rate of TMEs electroplating

Table 1 .

 1 14: Trace metallic element baths in every line for both zinc and nickel electroplating in A6

Table 1 .

 1 15: NF and FF steady state concentrations and the final predicted concentrations for each job task during metal electroplating

Table 1 .

 1 16: NF-FF models input values: constant emission rate of ABS plating

Table 1 .

 1 17: Trace metallic element baths in ABS plating lines in A7

Table 1 .

 1 18: NF steady state concentrations and the predicted concentrations CNF,SS during ABS plating

Table 1 .

 1 19: Cumulative FF steady state concentrations CFF,SS for P7 and P8, and

	viii

Table 1 .

 1 20: FF steady state concentrations CFF,SS during ABS electroplating

Table 1 .

 1 21: NF-FF model input values: constant emission rate of TMEs during plastic injection

Table 1 .

 1 22: NF steady state concentrations CNF,SS during plastic injection molding

	PART 2:

Table 2 .

 2 1: Statistical analysis of indoor air measured concentrations of the selected TMEs for each task job

	124,

Table 2 .

 2 2: Indoor air measured and predicted concentrations of the selected TMEs for each task job

Table 2 .

 2 3: The Shapiro-Wilk p-values of the normality tests for original and logtransformed variables

Table 2 .

 2 4: Pearson's correlation coefficients and their corresponding p-values for metalworking industry modelingTable

Table 2 .

 2 17: Statistical analysis of urinary metal concentrations of the selected TME s for each task job

	156,

Table 2 .

 2 18: Normality tests and correlation coefficients of the urinary TME concentrations vs air measured concentrations

Table 2 .

 2 19: Normality tests and correlation coefficients of the median urinary TME concentrations vs predicted air concentrations

	PART 3:

Table 3 .

 3 1: (a) Classification and (b) scores of the WOE method

Table 3 .

 3 2: Occupational exposure limit values from different institutions to MHs of concern

Table 3 .

 3 3: Threshold TRVs from different Agencies of chronic inhalation exposure to MHs of concern

Table 3 .

 3 4: Non-threshold unit risks from different Agencies of chronic inhalation exposure to MHs of concern

Table 3 .

 3 5: Non-carcinogenic critical effects /target organs and most protective TRVs for chronic inhalation exposure to MHs of concern

Table 3 .

 3 6: Carcinogenic critical effects and most protective TRVs for chronic inhalation exposure to MHs of concern

Table 3 .

 3 7: Binary WOE scoring

Table 3 .

 3 8: HQs of Cr, Ni and Cu and addition HI and interaction-based HIint for respiratory / lung effects

Table 3 .

 3 9: Lifetime cancer risks of Ni and Pb and total cancer risk



  Emission factors for Resistance sport weldingElsewhere, the city of Toronto has illustrated a method to calculate the emission rate in the

	EF: is estimated as 0.000033 kg/m per weld length
					𝐿 𝑤 = 𝑑 𝑤 × 𝑁 𝑝𝑖𝑒𝑐𝑒 × 𝑁 𝑤 𝑝𝑖𝑒𝑐𝑒
	Lw: weld length; expressed by m
	dw: weld diameter; expressed by m
	Npiece: number of workpieces (or metal sheets)
	N w piece : number of welds per workpiece(or per metal sheet)
	context	of	a	Public	Health	program	named	Chemical	Tracking	in
	Industry "ChemTRAC" [93]. An emission factor is specially established for RSW and
	presented only Ni and Cr.
	The used formula is the following:
					Metal Released Ni/Cr = L W × EF
	Where;
	Metal released: is equivalent to G and expressed by kg/ time

1 st PART: Indoor Air Concentrations Modeling of Occupational Exposure to Trace Metallic Elements in Metalworking Industries PART I 1 st PART: Indoor Air Concentrations Modeling of Occupational Exposure to Trace Metallic Elements in Metalworking Industries I. Introduction .......................................................................................... 33 II. Materials and Methods ..................................................................... 33 II.1. Population sampling...................................................................................................33 II.1.1. Selection of metalworking industrial sectors and companies ...........................34 II.1.2. Inclusion and exclusion criteria .........................................................................34 II.2. Description of companies and of the study materials ................................................34 II.3. Mathematical modeling of occupational exposure ....................................................35 II.3.1. Model's choice ....................................................................................................35 II.3.2. Parameters calculations .....................................................................................35 II

  .3.2.1. Ventilation rate "Q" .........................................................................................35

	96. Fournier K, Glorennec P, Bonvallot N. Derivation of toxicological reference values for
	taking mixtures into account in health risk assessment: Existing methods and recent
	applications. Environ Risques Santé. 2014;203-221.
	97. Mumtaz MM, Durkin PR. A weight-of-evidence approach for assessing interactions in
	chemical mixtures. Toxicol Ind Health. 1992;8:377-406.
	98. INRS. Métaux et métalloïdes M-122 -MétroPol [Internet]. 2016 [cited 2016 Jun 13].
	Available	from:
	http://www.inrs.fr/publications/bdd/metropol/fiche.html?refINRS=METROPOL_122
	ii)	Performance of direct measurements of indoor air TMEs concentrations [98],
		comparing them with the theoretical results derived from models so as to
		"validate" the models, and biological monitoring of TMEs in the urine of workers
		volunteering to participate, to evaluate how well they correlate with model
		estimations.
	(iii)	Finally, the risk characterization step of the trace metallic element mixtures in the
		metalworking industry, using a decision support' tool to investigate effects of
		interactions, based the hazard index. II.3.2.2. The air volume "V" .........................................................................................36
		II.3.2.3. Generation rate "G" .........................................................................................36
		II.3.2.4. The inter-box air flow rate "β" .........................................................................37

II.3.3. Processes & Jobs presentation...........................................................................37 II

  .3.3.1. Metal sheet cutting ..........................................................................................37a. Stamping .........................................................................................................................37 b. Sheering & slitting ...........................................................................................................37 II.3.3.2. Welding...........................................................................................................38 Electroplating ..................................................................................................39 Metal electroplating .........................................................................................................39 b. ABS plating .....................................................................................................................39 II.3.3.4. Indirect trace metallic element handling jobs....................................................40

	a. b. c. a. a. b. II.3.3.3. PART I Resistance spot welding ...................................................................................................38 Induction welding ............................................................................................................38 Electric arc welding .........................................................................................................38 i. Shielded Metal Arc Welding (SMAW) ..........................................................................38 ii. Gas metal arc welding (GMAW): Metal Active Gas (MAG) ..........................................39 Product packaging and displacement ................................................................................40 Quality control.................................................................................................................40 c. Plastic injection ...............................................................................................................40
	31

II.3.4. Results presentation ...........................................................................................40 III. Results................................................................................................ 40 III.1. Voluntary metalworking companies ..........................................................................40 III.2. Identified materials and metals in each company/ workplace ..................................45 III.3. Occupational exposure modeling ...............................................................................47 III.3.1. Metal sheet cutting modeling .............................................................................47 III.3.2. Welding modeling ..............................................................................................55 III

  .3.2.1. Resistance spot welding ..................................................................................55

	III.3.2.2. Induction welding ...........................................................................................59
	III.3.2.3. Electric arc welding ........................................................................................62

III.3.3. Electroplating modeling.....................................................................................72 III

  .3.3.1. Metal electroplating ........................................................................................72 III.3.3.2. ABS plating: ...................................................................................................

79 III.3.4. Modeling of indirect trace metallic element handling jobs in the same workplaces ..........................................................................................................................84

	III.3.4.1. Product packaging and displacement ...............................................................84
	III.3.4.2. Quality control ................................................................................................87
	III.3.4.3. Plastic injection ...............................................................................................88

IV. Discussion .......................................................................................... 93 IV.1. Contribution to modeling and comparisons ..............................................................93 IV.1.1. WMB modeling: Several & identical emission sources ....................................93 PART I 32 IV.1.2. NF-FF modeling .................................................................................................94 IV

  .1.2.1. Unique emission source ..................................................................................95 a. Shielded Metal Arc Welding (SMAW) .............................................................................95 Steel cutting.....................................................................................................................96 b. Plastic injection ...............................................................................................................98 IV.1.2.3. Two distinct emission sources .........................................................................98 a. Job task noted P16 ...........................................................................................................98 b. Metal electroplating .........................................................................................................

	b.	Induction welding ............................................................................................................96
	c.	ABS plating .....................................................................................................................96
	IV.1.2.2. Several & similar emission sources .................................................................96
	a.

99 IV.1.3. Adaptation of the mass balance equation..........................................................99 IV.1.4. Ventilation rate method ................................................................................... 100 IV.2. Difficulties and challenges........................................................................................ 101 IV.2.1. Companies contact and participation.............................................................. 101 IV.2.2. Information availability ................................................................................... 102 IV.3. Limitations and bias ................................................................................................. 102 IV.3.1. Uncertainty analyses ........................................................................................ 102 IV.3.2. Margin of error due to global information ..................................................... 102 IV.3.3. Emission factors' ratings ................................................................................. 103 IV.3.4. Average Generation rate calculation ............................................................... 103 IV.3.5. Exhaustive Trace metallic elements identification .......................................... 104 V. Conclusion of part 1 ........................................................................... 104 VI. Bibliography .................................................................................... 105 1st PART : Ind oor PART I 33 1st PART : Ind oor Air C once ntr ati ons Mod eli ng o f Occup ati ona l Ex pos ure to T race Me tall ic Elem e nts in M eta lwor ki ng I nd ustri es

Table 1 .2: Presentation of TMEs contained in the used materials in every job task

 1

	Company	Workplace	Job task ID	References of materials	Metals
	ID	N°
	I	A1	P1: Steel stamping	S355MC	Al, Cu, Ni, Cr
				S235JR
			P3: Packaging	Inox 304
				DX51 AS120
		A2	P4: Resistance spot welding	S355MC	Al, Cu, Ni, Cr
				S235JR
				DX51 AS120
	II	A3	P5: Shielded Metal Arc Welding	OK 48 00 (also known as E7018)	Al, Cu, Ni, Cr, Pb
				E6013
	III	A4	P6: Steel cutting & shearing	S235JR	Al, Cu, Ni, Cr, Zn
			P7: Packaging &displacement	DX51D+Z

Table 1 .3: Materials' commercial names and references Materials' commercial names References of materials Sources

 1

	Hot rolled steel sheet	S355MC	[45-51]
		S235JR

Table 1 .5: NF steady state concentrations CNF, SS during steel sheet cutting NF steady state concentrations

 1 nearly 90 min, concentrations almost reach steady state concentrations. Figure 1.6 shows corresponding data for steel sheet sheering and slitting workplace. The NF concentration values also quickly rise in the first 20 minutes. Then, after nearly 60 min, concentrations almost reach steady state concentrations. These steady state concentrations are exhibited in table 1.5.

			Situation 1:	Situation 2:
		(µg/m 3 ),	Stamping	Sheering and slitting
		CNF,SS (240-min)	A1: P1	A4: P6
		Al	0.140	1.87
		Cu	0.019	3.10
		Ni	0.169	1.49
		Cr	0.334	3.40

PART I 55 Then, after  Zn -4.14 III.3.2. Welding modeling III.3.2.1. Resistance spot welding The Resistance spot welding RSW job task is noted as P4. As mapped in figure 1.7, there are between 3 to 5 RSW operators in the workplace A2. Every worker is welding workpieces using an individual RSW machine (see figure 1.7).

The room is ventilated thought the main door and a side opening. However, because A2 is located in the top floor of another room, the ventilation is poor. Another source of ventilation is the local exhaust ventilation that is integrated in each RSW machine. They are located just above the welding spot device and it was observed that it directly vacuums a great amount of welding fumes.

PART I 56

Figure 1.

7: Scheme of workplace A2; P4: RSW *: locations of workers

  Nevertheless, air is evenly distributed in the A2 due to the setting up of a large electric fan (1m*1m) (figure 1.7). Indeed, the measured air velocity values were almost equal over the room. Therefore, we are assuming that the air is perfectly mixed. Consequently, we are using the WMB model.The values of the mass conservation ratios for A2 and the value is 1.05. It is close to 1, so mass conservation is confirmed and the volumetric flow rate into the room must be equal to the one out of it. Subsequently, we can choose one of them to calculate QA2. So, we decided to calculate Q based on local exhaust ventilation in A2. Having this reliable information, we

					PART I
	Stock
		Side opening
	Electric	Stock		door Front	Stairs
	fan
		*	*	*
				Dressing
		P4	room
					57

Table 1 .6: WMB model input values: constant emission rate of TMEs during RSW welding

 1

	WMB Parameters	Situation: RSW welding
			A2: P4
	V A2 (m 3 )	345.29
	C0(mg/ m 3 )	0
	Cin (mg/ m3)	0
		Al	0.0000574
		Cu	0.0000077
		Ni	0.0000693
		Cr	0.000137
	QA2 (m 3 .min -1 )	31.81

1.8 shows the WMB model prediction of trace metallic element concentrations in RSW workplace. The concentration values rise in the first 35 minutes. Then, after nearly 90 min, concentrations reach the steady state concentrations cited for every metal in table 1.7. PART I 58

Table 1 .7: Steady state concentrations in the WMBCWMB, SS during RSW WMB steady state concentrations (µg/m 3 ),

 1

		Situation:
		RSW welding
	CWMB,SS (240min)	A2: P4
	Al	0.305
	Cu	0.103
	Ni	0.067
	Cr	0.133
	III.3.2.2. Induction welding

Table 1 .8: NF-FF model input values: constant emission rate of TMEs during induction welding

 1

	NF-FF Parameters	Situation: induction welding
			A5: P9
	V A5 (m 3 )	12812.25
	VNF, P9 (m 3 )	1.88
	βP9 (m 3 .min -1 )	22.42
	QA5 (m 3 .min -1 )	2543.42
	G A5: P9,average (mg. min -1 )
			0.0199
			0.0050
			0.0066
			0.0028
		Zn	0.0565
	Figure 1.10 shows NF model prediction of trace metallic element concentrations in induction
	welding workplace. The NF concentration values quickly rises in the first 20 minutes. Then,

 Al  Cu  Ni  Cr after nearly 90 min, concentrations almost reach the steady state concentrations cited for every metal in table 1.9.

Table 1 .9: NF steady state concentrations CNF, SS during induction welding NF steady state concentrations (µg/m 3 ), CNF,SS (240 min) Situation: induction welding A5: P9

 1

		Al	0.884
		Cu	0.222
		Ni	0.293
		Cr	0.124
		Zn	2.510
	III.3.2.3. Electric arc welding
	 The first considered job task is Shielded Metal Arc Welding (SMAW) that is noted
	P5.

SMAW is taking place in the workplace A3 (figure

1.11)

. There is only one worker who is assumed to be continuously working for a four hour-shift; he takes less than a couple of minutes to position a new electrode when the prior one is totally consumed.

PART I 63

Table 1 .10: AP 42-emission factors of total fume, Cr and Ni for electric arc welding processes

 1

	[37]
	Welding process Electrode type	Emission factor (g/kg of electrode consumed)
			Total fume	Cr	Ni
	SMAW	E6013	19.7	0.004	0.002
		E7018	18.4	0.006	0.002
	GMAW	E70S-6	5.2	0.001	0.001

The models parameters are presented in table

1.11.

Table 1 .11: NF-FF model input values: constant emission rate of TMEs during SMAW: P5 (240 min) and GMAW: P16GMAW (4 min over 20 min)

 1

	NF-FF Parameters	Situation 1: SMAW	Situation 2: GMAW
			A3: P5	A5: P16GMAW
	V (m 3 )		593.57	12812.25
	VNF (m 3 )	1.59	42.18
	β (m 3 .min -1 )	7.42	54.11
	Q (m 3 .min -1 )	137.85	2543.42
	G average (mg. min -1 )
			0.0742	0.1006
			0.0140	1.8104
			0.0064	0.0007
			0.0180	0.0007
		Pb	0.0280	0.2514

 Al  Cu  Ni  Cr

Table 1 .12: NF steady state concentrations CNF,SS SMA welding process NF steady state concentrations (µg/m 3 ), CNF,SS (240min) Situation 1: SMAW A3: P5 Situation 3:A5: P16 NF GMAW + FF P9

 1

		Al	10.401	0.897
		Cu	1.962	17.007
		Ni	0.897	0.009
		Cr	2.52	0.007
		Zn	-	0.022
		Pb	3.924	2.222
		III.3.3.	Electroplating modeling
		III.3.3.1.

Table 1 .14: Trace metallic element baths in every line for both zinc and nickel electroplating in A6

 1

	Plating type	Zinc-plating lines

EFs and data used for EFs and generation rate calculations are in table 1.14. PART I 77

Table 1 .15: NF and FF steady state concentrations and the final predicted concentrations for each job task during metal electroplating NF and FF steady state concentrations (µg/m 3 ), CNF/ FF,SS (240-min)

 1

			Situation 1:	Situation 2:	Situation 3:
			Nickel-plating	Nickel-plating	Zinc-plating lines:
			lines: NF	lines: FF	FF
		Cr	1112	137	-
		Ni	10.7	1.32	-
		Cu	2.45	0.30	-
		Zn	0.86	0.11	2.22

Steady state concentrations (µg/m 3 ) Nickel-plating system controller A6: P11 Nickel-plating lifting-hooks operator A6: P12 Zinc-plating operator A6: P10

			1112	137	137
			10.7	1.32	1.32
			2.45	0.30	0.30
		Zn	0.86	0.11	0.11+ 2.22 (=2.33)
		III.3.3.2.	ABS plating:
	The workplace A7 is mapped in the figure 1.20, where the decorative chromium

 Cr  Ni  Cu electroplating of ABS is taking place. The plating lines include numerous baths as indicated in table 1.17. PART I 80

Table 1 .16: NF-FF models input values: constant emission rate of ABS plating NF-FF Parameters Situation 1: ABS plating line operator

 1

	A7: P13

 Cr  Ni

Table 1 .17: Trace metallic element baths in ABS plating lines in A7 Plating type ABS plating lines

 1

	Bath	B1	B2	B3	B4	B5	B6	B7
	number
	Metal in	CrH2O4	Cu	Ni	Ni	Ni	Ni	Cr
	the bath a
	Cm a	-	[170-	[40-65]**	[60-80]** +	[30-40]** +	[200-	-
	(g/L)		200]*	+ [220-	[260-	[420-470]	260]
				280]***	300]***	***	**
	EFaverage	44.61 b	2.88	4.63 10 -4c	5.3810 -4c	7.21 10 -4c	4.07	10.43
			10 -4c				10 -4c	10 -4d
	a : Collected using questionnaires
			m 3
	d : Converted to mg/m 3 (developed by US EPA AP 42-emission factors) [67]
	*: as CuSO4, **: as NiCl2, ***: as NiSO4

b : Converted to mg/hr-m 2 (developed by US EPAAP 42-emission factors)

[67]

c : Calculated and expressed in mg/

Table 1 .18: NF steady state concentrations and the predicted concentrations CNF,SS during ABS plating NF steady state concentrations (µg/m 3 ), CNF, SS (240 min)

 1

	Situation:
	ABS plating lines:
	P13: A7

Modeling of indirect trace metallic element handling jobs in the same workplaces

III.3.4.1. Product packaging and displacement

Table 1 .19: Cumulative FF steady state concentrations CFF,SS for P7 and P8, and cumulative FF concentrations at t = 240min CFF(240) for P3 during packaging & displacement

 1

	Cumulative FF	Situation 1:	Situation 2:	Situation 3:
	concentrations (µg/m 3 ),	packaging &	packaging &	packaging &
	(240min)	displacement	displacement	displacement
			A1: P3 (CFF(240))	A4: P7 (CFF,SS)	A5: P8 (CFF,SS)
		Al	0.189	0.225	0.0076
		Cu	0.025	0.373	0.0019
		Ni	0.225	0.179	0.0025
		Cr	0.446	0.409	0.0011
		Zn	-	0.498	0.0216
		III.3.4.2.

Table 1 .20: FF steady state concentrations CFF,SS during ABS electroplating FF steady state concentrations (µg/m 3 ), CFF,SS (240-min) Situation : Quality controller A7: P14

 1

		Cr

Table 1 .22: NF steady state concentrations CNF,SS during plastic injection molding NF steady state concentrations

 1

			Situation:
	(µg/m 3 ), 240-min	Plastic injection operator:
		CNF,SS	A8: P15
		Al, Cu, Ni and Cr	0.090
		Zn	0.817



Pb

0.016

4. Comparing measured versus to NF predicted concentrations in steel cutting industry 136 III

  .2.4.1. Distributions ................................................................................................. III.2.4.2. Correlation .................................................................................................... III.2.4.3. Paired difference tests ...................................................................................

	III.2.5.

PART I

III.2.

Comparing measured versus to NF predicted concentrations in plastic injection 140 III

  .2.5.1. Distributions ................................................................................................. III.2.5.2. Correlation .................................................................................................... III.2.5.3. Paired difference tests ...................................................................................

III.2.6. Comparing measured versus to NF-FF model predicted concentrations in welding industry ............................................................................................................... III.2.6.1. Measured vs NF predicted concentrations in welding .................................... a. Distributions .................................................................................................................. b. Correlation .................................................................................................................... c. Paired difference tests .................................................................................................... III.2.6.2. Measured vs predicted concentrations combination in welding ...................... a. Distributions .................................................................................................................. b. Correlation ....................................................................................................................

III.

2.7. Comparing measured versus to NF-FF predicted concentrations in electroplating industry

  . Correlation .................................................................................................................... III.2.7.2. Measured vs FF predicted concentrations in electroplating ............................ . Distributions .................................................................................................................. b. Correlation .................................................................................................................... .2.8.2. Correlation ....................................................................................................

..................................................................................................... III.2.7.1. Measured vs NF predicted concentrations in electroplating ............................ a. Distributions .................................................................................................................. baIII.2.8. Comparing measured versus to FF predicted concentrations in the packaging job tasks 153 III.2.8.1. Distributions ................................................................................................. III

Table 2 .1: Statistical analysis of indoor air measured concentrations of the selected TMEs for each task job Task jobs

 2

	Al

Note: LM: Location measure value: mean if normal distribution/ median if not, M: arithmetic Mean, SD: Standard deviation, *: Normal distribution (p-value of the Shapiro-Wilk test is >0.05)

and measured concentrations of airborne trace metallic elements in metalworking industryTable 2 .

 2 2 shows the predicted indoor air concentrations of TMEs versus the mean and LM of the measured concentrations.

Table 2 .2: Indoor air measured and predicted concentrations of the selected TMEs for each task job

 2 We checked normality of the distribution of all HMs concentrations of the predicted values, the location measures LM of the measured values and the Mean of the measured values before all the correlation analyses. The following table 2.3 shows the Shapiro-Wilk p-values of the normality tests for all concentration samples. If none of the three variable distributions was normal, a log-transformation was conducted to seek normality. The new log-transformed variables will be noted as Ln(LM), Ln(Mean) and Ln(Predicted) for the log-transformation of the measured location measures values, measured Mean values and the predicted values, respectively. Thus, the new p-values of the Shapiro-Wilk test of the log-transformed samples are also presented.

			Al			Cu			Cr			Ni			Pb			Zn
	Tas k jobs	LM(µ g /m 3 )	M (µg/m 3 )	P (µg/m 3 )	LM(µg/ m 3 )	M (µg/m 3 )	P (µg/m 3 )	LM(µg/ m 3 )	M (µg/m 3 )	Pr (µg/ m 3 )	LM(µg /m 3 )	M (µg/m 3 )	P (µg/m 3 )	LM(µg/ m 3 )	M (µg/m 3 )	P (µg/ m 3 )	LM(µg/ m 3 )	M (µg/m 3 )	P (µg/m 3 )
	P1	0.078 0.154 0.140	0.008	0.015 0.019	0.017	0.334	0.151	0.169	0.039	0.286	-	0.774	0.711	-
	P3	0.818	0.189	0.178	0.927 0.025 0.034 0.124 0.446	0.383	0.225	0.163	-	1.485	-
	P4	0.391	0.301	0.170	0.619 0.103	0.009	0.133 0.125 0.220 0.067 0.045	0.086	-	0.240	0.516	-
	P5	4.325 6.233 10.401	1.935	1.962 0.467 0.545 2.52	0.649	0.897	0.365	3.924 1.310	2.056	-
	P6	1.731 2.206	1.87	0.191	0.601	3.10	1.277 4.193 3.40	0.523	2.059 1.49	0.137	4.14	3.819	-
	P7	1.376	0.225	0.021	0.373 0.049 0.098 0.409 0.100	0.134 0.179	0.011	-	1.371	0.498
	P8	0.775	0.008	0.022	0.002	0.091	0.001	0.183	0.003	0.034	-	0.804	0.022
	P9	0.751	0.884	0.258	0.222	0.075	0.124	0.489	0.293	0.004	-	1.796	2.510
	P10	0.455	-	0.757	0.30	0.432	137	0.444	1.32	0.067	-	2.235	2.33
	P11	0.687	-	0.437	2.45	0.247	1112	0.700	10.7	0.002	-	0.626	0.86
	P12	0.124	-	0.279	0.499	0.30	0.130 0.691 137	0.585	1.32	0.009	-	1.612	0.11
	P13	0.362	-	0.315	0.378	0.188	6.7	0.595	0.696	~ 0		-	0.206	0.349
	P14	0.375		0.245	0.289	0.093	0.181	1.66	0.585	0.172	0.007	-	0.432	0.846	-
	P15	0.267	0.09	~ 0		0.09	0.002	0.09	0.088	0.09	0.073	0.016	0.865	0.817
	P16	3.423	0.897	0.341	17.00 7	0.190	0.00 7	0.311	0.009	0.042	0.32 9	2.222	1.725	0.02 2
	P: Predicted concentrations

Table 2 .3: The Shapiro-Wilk p-values of the normality tests for original and log-transformed variables Modeling Normality test of the original data Normality test of log-transformed data LM measured (µg/m 3 )

 2

		Mean measured (µg/m 3 )	Predicted (µg/m 3 )	LM measured (µg/m 3 )	Mean measured (µg/m 3 )	Predicted (µg/m 3 )
	All selected companies	0.00	0.00	0.00	0.02	0.00	0.20
	All selected
	companies(without	-	-	-	0.11*	0.04	0.21*
	outliers)
	WMB for RSW	0.72*	0.10*	0.23*	-	-	-
	NF for steel cutting	0.00	0.04	0.01	0.81*	0.14*	0.19*
	NF for plastic injection	0.33*		0.00	-	-	-
	NF for welding	0.00	0.00	0.00	0.92*	0.93*	0.81*
	Combination in welding	0.03	0.01	0.00	0.81*	0.12*	0.30*
	NF for electroplating	0.50*		0.00	-	-	-
	FF for electroplating	0.00	0.00	0.00	0.85*	0.68*	0.03
	FF for packaging	0.01	0.02	0.03	0.19*	0.11*	0.02

* p-value of the Shapiro-Wilk is >0.05: normal distribution PART II 128 III.2.

2. Comparing measured versus predicted concentrations in all selected companies

III.2.2.1. Distributions

Table 2 .4: Pearson's correlation coefficients and their corresponding p-values for metalworking industry modeling Ln(measured LM) vs Ln(Predicted) Ln(measured Mean)

 2

	vs
	Ln(Predicted)

* Correlation is significant at the 0.05 level ** Correlation is significant at the 0.01 level III.2.2.3. Paired difference tests In a second step, we established the dependant samples Student's Test for Ln(LM) and

Table 2 .5: The Means of differences and their corresponding p-values for metalworking industry modeling

 2

		Ln(measured LM)	Ln(measured Mean)
		vs	vs
		Ln(Predicted)	Ln(Predicted)
	Mean difference
		0.268	0.026
	Confidence interval
		[-0.353 to 0.889]	[-0.572 to 0.624]
	Difference significance: P-value	0.39	0.93

Table 2 .11: Pearson's correlation coefficients and their corresponding p-values for welding job tasks (NF in P5 and P9)

 2

		Ln(measured LM)	Ln(measured Mean)
		vs	vs
		Ln(Predicted)	Ln(Predicted)
	Pearson's (r)
		0.78	0.80
	Correlation significance: P-value	0.008**	0.006**

** Correlation is significant at the 0.01 level c. Paired difference tests The Student's Test for the mean difference (Ln(Predicted) minus Ln(LM measured) or Ln(Mean)), (table 2.12) show high p-values, confirming the results of the regression method:

Table 2 .12: The Means of differences and their corresponding p-values for the welding job tasks (NF in P5 and P9) Ln(measured LM) vs Ln(Predicted) Ln(measured Mean) vs Ln(Predicted)

 2 p-values of the Shapiro-Wilk test (0.03, 0.01 and 0.000for LM values, Mean values

	Mean difference	0.562	0.510
	Confidence interval	[-0.064 to 1.187]	[-0.095 to 1.114]
	Difference significance: P-value	0.07	0.09
	III.2.6.2. Measured vs predicted concentrations combination in welding
	a. Distributions

This particular job task P16 is the combination of the exposure to the CNF from the GMAW and the CFF from induction welding P9. PART II 145 With low

Table 2 .17: Statistical analysis of urinary metal concentrations of the selected TMEs for each task job

 2

	Task jobs	Al			Cr			Ni
		M (mg/L)	SD	Range	M (mg/L)	SD	Range	M (mg/L)	SD	Range
	P1	9.232	0.430	8.617 to 9.847	0.300	0.0311	0.239 to 0.327	0.107	0.0233	0.056 to 0.124
	P3	8.229	0.885	8.045 to 9.661	0.266	0.031	0.258 to 0.315	0.928	0.007	0.092 to 0.105
	P4	8.997	0.782	8.44 to 9.550	0.290	0.015	0.279 to 0.301	0.101	0.005	0.098 to 0.105
	P5	28.6811	-	-	0.2698	-	-	.1951	-	-
	P6	9.524	10.357	9.203 to 32.077	0.293	0.015	0.279 to 0.319	0.107	0.0568	0.093 to 0.217
	P7	17.565	12.165	8.964 to 26.167	0.312	0.0327	0.289 to 0.335	0.157	0.083	0.098 to 0.216
	P8	19.415	14.841	8.922 to 29.910	0.322	0.075	0.269 to 0.375	0.179	0.118	0.096 to 0.262
	P9	8.5817	-	-	0.2793	-	-	0.1008	-	-
	P10 & P12	27.946	4.343	24.875 to 31.017	0.294	0.002	0.292 to 0.295	0.219	0.017	0.207 to 0.231
	P11	30.216	-	-	0.2750	-	-	0.2105	-	-
	P13	8.771	0.117	8.688 to 8.853	0.295	0.003	0.293 to 0.298	0.101	0.002	0.099 to 0.102
	P14	26.668	8.458	8.598 to 29.197	0.329	0.040	0.272 to 0.364	0.215	0.059	0.101 to 0.258
	P15	8.897	12.317	8.783 to 30.1740	0.284	0.049	0.282 to 0.368	0.098	0.096	0.095 to 0.263
	P16	24.960	9.592	8.515 to 25.293	0.304	0.021	0.275 to 0.31	0.205	0.065	0.094 to 0.207
	OE	13.54	9.010	8.229 to 30.216	0.293	0.019	0.266 to 0.329	0.168	0.213	0.098 to 0.928
	NOE	8.589	5.764	8.368 to 28.726	0.287	0.222	0.267 to 0.340	0.107	0.035	0.095 to 0.222

Table 2 .19: Normality tests and correlation coefficients of the median urinary TME concentrations vs predicted air concentrations

 2

	Metal	Normality test of the original data M U Predicted (mg/L) (µg/m 3 )	Normality test of log-transformed data Ln(M U) Ln(predicted) (mg/L) (µg/m 3 )	Correlation coefficient (r)	Correlation significance: P-value
	Al	0.009	0.000	0.014	0.897*	0.222 a 0.358 b	0.538 0.310
	Cr	0.881*	0.000	-	-	-0.290 a -0.275 b	0.314 0.342
	Ni	0.000	0.000	0.002	0.686*	0.015 a 0.216 b	0.960 0.459
	Cu	0.030	0.000	0.107*	0.970*	0.129 a 0.121 b	0.660 0.681
	Zn	0.374*	0.026	-	-	0.711 a	0.048
	Pb	0.110*	0.858*	-	-	-0.017 a 0.500 b	0.989 0.667

MU: Metal in Urine a : Pearson's coefficient, b : Spearman's coefficient * p-value of the Shapiro-Wilk is >0.05: normal distribution

Table 3 .1: (a) Classification and (b) scores of the WOE method:

 3

	PART III

Table 3 .3: Threshold TRVs from different Agencies of chronic inhalation exposure to MHs of concern DATA BASE

 3

			Cr	Ni
		Al	metal & CrIII	Metal & dust	Cu	Pb	Zn
			insoluble
			compounds
	ATSDR	NM [5]	MRL a = 5 µg.m -3	NM [3]	NM [1]	NM [4]	NM [2]
			[7]
	USEPA	NM	NM [15]	NM [8-10]	NM [11]	NM [13]	NM [14,47]

Table 3 .4:Non-threshold unit risks from different Agencies of chronic inhalation exposure to MHs of concern

 3 : inhalation Unit Risk or Unit Risk Factor: increased cancer risk from inhalation exposure to a of 1 µg.m -3 for a lifetime The Tumorigenic Concentration 05: concentration in air associated with a 5% increase in incidence or mortality due to tumors (is used to calculate a unit risk)

	DATA BASE		Cr	Ni
		Al	metal & CrIII	Metal& dust	Cu	Pb	Zn
			insoluble compounds
	ATSDR	NM [5]	NM [7]	NM [3]	NM [1]	NM [4]	NM [2]
	USEPA	NM	NM	UR e = 1.8 .10 -4 per µg. m -3	NM	NM	NM
	ANSES	NM	NM	NM	NM	NM	NM
	Health Canada [41]	NM	NM	NM	NM	NM	NM
	OEHHA	NM	NM	NM	NM	UR e = 1.2 10 -5 per μg. m -3	NM
	WHO [52]	NM	NM	UR e = 3.8.10 -4 per μg. m -3	NM	NM	NM
	TCEQ [43]	NM	NM [53]	URF e =1.7 10 -4 per µg. m -3 [54] NM	NM	NM
	RIVM [44]	NM	NM	NM	NM	NM	NM

NM: Not Mentioned, a : Minimal Risk Levels b : Tolerable Concentration in Air c : Chronic Reference Exposure d :Level Reference Value e f :

Table 3 .

 3 5 and 3.6 show the selected, most protective, TRVs for threshold and carcinogenic critical effects /target organs.

Table 3 .5: Non-carcinogenic critical effects /target organs and most protective TRVs for chronic inhalation exposure to MHs of concern

 3

	Metal	Target organ	Most protective TRVs	Agency
	 Al	-	-	-
	Cr: Metal	lung and	Chronic ReV = 0.14 μg. m -3	TCEQ [53]
		respiratory
		tract effects
	 Ni: metal &	Lung damage	REL = 0.014 Ni μg. m -3	OEHHA [49]
	dust	&
		inflammation
	 Cu	Respiratory	TCA = 1 µg.m -3	RIVM [44]
		tract effects
	 Pb	-	-	-
	 Zn	-	-	-

Table 3 .6: Carcinogenic critical effects and most protective TRVs for chronic inhalation exposure to MHs of concern Metal Carcinogenic critical effect Most protective TRVs Agency

 3

	 Al	-	-	-
	 Cr	-	-	-
	 Ni: metal &	Lung cancer	UR = 3.8.10 -4 per μg.	WHO [52]
	dust		m -3
	 Cu	-	-	-
	 Pb	Kidney cancer	UR= 1.2 10 -5 per μg.	OEHHA [55]
			m -3
	 Zn	-	-	-

Table 3 .7: Binary WOE scoring

 3 (see table3.1 for the scores of the WOE method)

	Effect of On the toxicity of
	Cr	Ni	Cu
	Cr	>II A 1 a i (+0.71) [3]	?

Table 3 .8: HQs of Cr, Ni and Cu and addition HI and interaction-based HIint for respiratory / lung effects

 3

	PART III

Table 3 .9: Lifetime cancer risks of Ni and Pb and total cancer risk

 3

	Ni	Pb

Limitations in the literature data IV.1.1. Toxicological reference values

	PART III
	lack of quantitative exposure data to establish an appropriate dose-response relationship.
	ATSDR indicates that additional toxicological studies are needed to establish chronic-
	duration TRVs [1,2,62].
	Yet, it is worth to note that other forms of Ni and Cr did have threshold and non-threshold
	TRVs. These chemical compounds are nickel sulfate NiSO4andthe chromic acid CrH2O4 that
	we can usually be used in electroplating processes.
	The respiratory tract is also the target organ of chronic exposure to CrH2O4 and NiSO4. Both
	the upper and the lower respiratory tract are involved. The identified adverse effects vary
	from nasal irritations (CrH2O4 [7]), active lung inflammation (NiSO4 [41,56], and
	hypergenesis of dust cells (NiSO4 [41,56]).Thus, threshold TRVs were established for Ni
	sulfate by ATSDR [3] and Health Canada [41]; MRL = 0.09 µg Ni/m 3 and TC = 0.0035 µg
	Ni/m 3 respectively. As for chromic acid, only OEHHA has established a REL (reference
	exposure level) equal to 0.002 µg Cr/m 3 that could be used with total Cr concentrations [48].
	Moreover, CrH2O4is considered to be a Cr(VI) compound. Hence, both CrH2O4 andNiSO4 are
	also considered to induce lung cancer. A unit risk was established by USEPA [15] UR= 1.2
	.10 -2 per µg.m -3 . And Health Canada [41], has constructed a Tumorigenic Concentration
	05TC05= 0.07 mg. m -3 .As previously mentioned, it is not a carcinogenic TRV, but a unit risk
	can be derived from this value.
	Threshold TRVs of NiSO4 and CrH2O4are considered for total metal exposure. If we compare
	them with the formerly adopted threshold TRVs of elemental metal, we can notice that the
	most protective threshold TRV of NiSO4 proposed by Health Canada [41] is 4 times lower
	than the TRV adopted for Ni. And the threshold TRV of CrH2O4 is 70 times lower than the
	TRV adopted for Cr. If we had the possibility to use exposure data on NiSO4 and CrH2O4 to
	compute specific HIint for electroplating process, the result would most probably show a high
	value. Unfortunately, this was not possible because both Ni and Cr are released within
	complex mixtures where other substances are present in the plating baths besides NiSO4 and
	CrH2O4, and we cannot know how much Ni or Cr are released asNiSO4 and CrH2O4,
	respectively.
	191

TRVs of chronic exposure by inhalation have been explored by several agencies. However, among the six selected TMEs, Al, Zn and Pb do not currently have published TRVs for threshold effects, nor Al, Cr, Cu and Zn for non-threshold effects. This is principally due to As a perspective, for future analyses or studies, an electroplating specific HIint can be calculated by NiSO4 and CrH2O4 quantifications, either by monitoring or by modeling if PART III 192 possible. There are methods to calculate the evaporation amount from open surfaces

EVALUATION DES IMPACTS SANITAIRES DES EXPOSITIONS PROFESSIONNELLES A DES MELANGES DE METAUX LOURDS DANS LA METROPOLE DE SFAX N° de saisie  FICHE DESCRIPTIVE DES TACHES (ETUDE DE POSTE)

  Date : …../……./ 201......  de l'atelier : ……………..hPa ou mmHg ou Bar  à l'extérieur de l'atelier : ……………..hPa ou mmHg ou Bar  Humidité : ………………………………..%

	Mode de la génération des contaminants :
	 Continu	
	 Discontinu	
	 Atelier : (Préciser les durées) ……………………………….…………………
	Ventilation avec extraction Atmosphère confinée  Cyclique Ventilation naturelle (Préciser les durées) ……………………………….………………… Non 0 Oui 1 Non 0 Oui 1  Non 0 Oui 1	  
	Climatisation centrale Ventilateur (au plafond,…)  Croissant (Préciser les durées/ les débits (V/t)) ……………………………….……… Non 0 Oui 1 Non 0 Oui 1 	 
	………………………………
	Indiquer l'emplacement
	 du(s) travailleur(s) (la(s) poste(s))  Décroissant   des machines (Préciser les durées/ les débits (V/t)) ……………………………….………  des source(s) d'émission des métaux  des sources des ventilations ………………………………
	Longueur = …… m, largeur = ….. m, Hauteur = ….. m Source d'émission des métaux :
			 
	Nombre d'agents :	 	 
	Hommes	 	 
	Femmes	 	 
	Produit(s) manipulé(s) :	nombre  	 
	 ……………………………………………….
	 ……………………………………………….
	 ……………………………………………….
	 ……………………………………………….
	 ……………………………………………….
	 ……………………………………………….
	 ……………………………………………….
	 ……………………………………………….
	 ……………………………………………….
	 ……………………………………………….
	 ……………………………………………….
	 ……………………………………………….
	Métaux manipulés / étudiés Volume / superficie ZINC  de l'atelier : ………… CHROME  des contenus de la pièce   CUIVRE  ……………………………………… NICKEL PLOMB CADMIUM ……………………………………    ALUMINIUM  FER ……………………………………… ……………………………………  ……………………………………… ……………………………………	
	………………………………………	……………………………………
	……………………………………… Conditions de travail : ……………………………………… Si non, % temps à l'extérieur …………………………………… Oui 1 ……………………………………   Surfaces des points de ventilations : ……………………………………… ……………………………………	 
	………………………………………	……………………………………
	………………………………………	……………………………………
	………………………………………	……………………………………

Codage Entreprise : « …………………………………………………. »   Dénomination de l'atelier : « ………………….………………...…………….……. »   Dénomination du(s) poste(s) : « ………………….………………...………….……. ………………….………………...………….…….»  Travail à l'intérieur (les 3 derniers jours): Non 0  Température :  de l'atelier : ……………...°C  à l'extérieur de l'atelier : ……………...°C  Pression Atmosphérique :  Ponctuelle   Une zone : limitée  : Étendue  (Préciser la superficie : ………………………………)

  Mesures des vitesses de l'air : (dans chaque point de ventilation)+ (à proximité de la source d'emission)

	Abstract:
	Enterprise : ……………. : Semaine ……..
	Enterprise : ……………. : Semaine …….. Atelier : ………………
	Enterprise : ……………. : Semaine …….. Atelier : ……………… Jour 3 : …/…/17
	Atelier : ……………… Jour 2 : …/…/17
	Jour 1 : …/…/17 Time 8h	8h15	8h30	8h45	9h	9h15	9h30	9h45	10h	10h15 10h30 10h45 11h	11h15 11h30 11h45 12h
	Time (15	8h	8h15	8h30	8h45	9h	9h15	9h30	9h45	10h	10h15 10h30 10h45 11h	11h15 11h30 11h45 12h
	Time (15 min)	8h	8h15	8h30	8h45	9h	9h15	9h30	9h45	10h	10h15 10h30 10h45 11h	11h15 11h30 11h45 12h
	(15 min)
	min) Temp
	Temp (°C)
	Temp (°C)
	(°C)
	Air
	Air velocity
	Air velocity (1min
	velocity (1min average)
	(1min average) m/s
	average) m/s
	m/s Time	13h	13h15 13h30 13h45 14h	14h15 14h30 14h45 15h	15h15 15h30 15h45 16h	16h15 16h30 16h45 17h
	Time (15	13h	13h15 13h30 13h45 14h	14h15 14h30 14h45 15h	15h15 15h30 15h45 16h	16h15 16h30 16h45 17h
	Time (15 min)	13h	13h15 13h30 13h45 14h	14h15 14h30 14h45 15h	15h15 15h30 15h45 16h	16h15 16h30 16h45 17h
	(15 min)
	min) Temp
	Temp (°C)
	Temp (°C)
	(°C)
	Air
	Air velocity
	Air velocity (1min
	velocity (1min average)
	(1min average) m/s
	average) m/s
	m/s

3rd PART : Risk C harac teriza ti on o f Occup ati on al Ex pos ure t o T race Me tall ic El em ent Mix tur es in Met alw orki ng I nd ustr ial I nd oor Air

Remerciements Résumé:

Les éléments trace métalliques (ETM) sont des polluants qui sont sources de préoccupations majeures à cause de leurs toxicités et de leurs propriétés cumulatives. Certains d'eux peuvent être cancérogènes. La métropole de Sfax, située au sud de la Tunisie, a été touchée par des rejets et émissions d'ETM depuis des décennies. Plusieurs études ont confirmé que la pollution métallique est principalement d'origine anthropique, liée en particulier aux activités industrielles. Cela présente un risque sur la santé des habitants, particulièrement pour ceux qui sont également exposés par leur métier dans des procédés industriels. L'objectif de cette étude est d'évaluer les risques sanitaires associés à l'exposition professionnelle dans les industries qui manipulent des ETM dans leurs processus de production, en suivant l'approche de l'évaluation des risques sanitaires. Dans ce but, cinq entreprises qui utilisent des métaux comme matière première pour produire une variété de produits métalliques, ont accepté d'adhérer à notre étude. Les métaux qui étaient explorés sont Al, Cr, Ni, Cu, Zn and Pb. Des modèles mathématiques de prédiction des expositions professionnelles aux agents chimiques ont été utilisés pour estimer les concentrations des ETM dans l'air intérieur pour 15 postes différents. Des prélèvements atmosphériques ont été effectués afin de comparer les concentrations prédites à celles mesurées, en utilisant des prélèvements individuels ou sur postes fixes. Finalement, des prélèvements urinaires ont été collectés chez 61 travailleurs afin d'évaluer le lien entre l'excrétion des ETM et les niveaux atmosphériques. Globalement, les estimations des concentrations atmosphériques avaient une bonne concordance avec les valeurs mesurées sur l'ensemble des postes de travail. Des meilleures prédictions ont été trouvées pour certaines activités, en particulier pour des processus de découpage des tôles et de soudures. Les valeurs qui correspondent au 90 ème percentile de la distribution de l'exposition ont été utilisées pour le calcul du « interaction-based hazard index HIint » pour évaluer les risques associés aux mélanges d'ETM. Un excès de risque total de cancer a été aussi calculé. Les résultats ont montré des expositions élevées qui peuvent provoquer des pathologies respiratoires, avec un HIint allant jusqu'à 93,6. Les niveaux les plus élevés sont attribués à la soudure à l'arc à l'électrode enrobée et au débitage et cisaillage des tôles. Ces risques augmentent à cause de l'effet synergique qui existe entre Cr, Ni et Cu. Des risques élevés de cancer du poumon et du rein ont été encore démontrés (risque total vie entière de cancer pour les ouvriers exposés : 3.7×10 -4 ). Ce travail montre que les modèles mathématiques peuvent prédire correctement les niveaux d'exposition des ETM dans l'air intérieur pour plusieurs processus de la métallurgie. Ce résultat est intéressant pour aider les différents acteurs pour piloter de manière efficiente les systèmes de surveillance et la réduction des expositions dans ce secteur économique. Des progrès en matière d'hygiène industrielle sont nécessaires dans ce secteur industriel pour minimiser le risque sanitaire élevé auquel sont actuellement exposés les travailleurs concernés. Mots clés: exposition professionnelle, éléments trace métalliques, évaluation des risques sanitaires, modèles mathématiques, prélèvements atmosphériques, biométrologie.

heavy metal HQ: hazard quotients IAE (or Rm): Indice d'exposition des effets Additionnels National Institute for Occupational Safety and Health NM: Not Mentioned, NRC: National Research Council OEHHA: California Office of Environmental Health Hazard Assessment OSHA: Occupational Safety and Health Administration Reference concentration RIVM: Netherlands National Institute for Public Health and the Environment RPF: relative potency factors methods RSW: resistance spot welding SMAW: Shielded Metal Arc Welding SS: steady state TC or TCA: Tolerable Concentration in Air TCEQ: Texas Commission on Environmental Quality TEF: Toxic Equivalency Factor TEQ: total equivalent quantity TLV: Threshold Limit Value TRV: Toxicological Reference Value TTC: target-organ toxicity concentration TTD: target-organ toxicity dose TWA: time -weighted average UPMI: Union des Petites et Moyennes Industries URF or UR: inhalation Unit Risk or Unit Risk Factor: increased cancer risk from inhalation exposure to a of 1 µg.m-3 for a lifetime US EPA: US Environmental Protection Agency V: air volume VLEP: Valeurs Limites d'Expositions Professionnelles VTR: Valeur Toxicologique de Référence

Scatter dots were plotted as showed in figure 2.9 and 2.10 in order to check the degree of association between LM and Predicted values, and Mean and Predicted values, respectively.

Pearson's correlation coefficients (r) and the p-values for their significance are shown in table 2.6.Both p-values are higher than 0.05. So, even though the Pearson's (r) is high for Ln(measured LM) vs Ln(Predicted), the correlation is not significant.

This could be due to the small size of this sample; so we decided to conduct a non parametric method to evaluate the correlation. Spearman's correlation coefficients (r) and the p-values for their significance are shown also in table 2.4 Both p-values are higher than 0.05, showing no significant correlation between the Predicted concentrations and Ln(measured LM/Mean).

Therefore, it is useless to conduct the Student's Test. The log-transformed variables showed normality of the distributions. Consequently, the Pearson's correlation method will be used, and the Student's t-test.

III.2.4.2. Correlation

Scatter dots were plotted as showed in figures 2.12 and 2.13 in order to check the degree of association between Ln(LM) and Ln(Predicted), and Ln(Mean) and Ln(Predicted), respectively.

Pearson's correlation coefficients (r) and their significance levels are shown is table PART II 139

In addition, both Pearson's coefficients (r) are relatively high and close to each other (~0.85).

We consider that the NF Predicted TMEs concentrations are strongly and significantly correlated with both LM and Mean of the measured of the TMEs concentrations released in steel cutting job tasks (P1 and P6). The NF predicted concentrations in welding includes the SMAW (P5) and induction welding (P9) processes. Pearson's correlation coefficients (r) and the p-values for their significance are shown is table 2.12.

Pearson's (r) are very low (~0.11) and both p-values are higher than 0.05. So, there is no correlation between Ln(LM) and Ln(Predicted), and Ln(Mean) and Ln(Predicted). So, it useless to conduct the Student's Test.

We tried the non-parametric correlation method. Spearman's correlation coefficients (r) and the p-values for their significance is also shown is table 2.13. The p-values are high, confirming that no correlation between the Predicted values and measured LM and Mean. Therefore, it is useless to conduct the Student's Test.

However, we tried the Spearman's correlation non-parametric method (Table 2.14); its pvalue is also high confirming absence of correlation. The FF predicted concentrations in packaging job tasks is considered for P3 and P7 in steel cutting workplaces having several sources and P8 in the induction welding workplace.

After log-transformation, the normality tests showed that LM measured concentrations and Mean measured concentrations are log-normally distributed, not Ln(Predicted).

So, it is allowed to use parametric methods; the Pearson's correlation method and the Student's t-test.

We present an initial comparison using boxplots of the distributions of the three variables; Ln(LM), Ln(Mean) and Ln(Predicted), for packaging job tasks. Ln(Predicted) distribution is larger than both distributions of Ln(measured). Moreover, the top whisker of the Ln(Predicted) boxplot does not reach the same level as the top whiskers of the Ln(LM), Ln(Mean) measured boxplots. Non-parametric methods did not alter this conclusion.

PEL e by Cal/ OSHA g (USA) [

Data availability

TRVs of the selected TMEs (Al, Cr, Ni, Cu, Pb and Zn) were explored. We gave precedence to the most known and well-recognized Agencies: Agency for Toxic Substances and Disease Registry (ATSDR) [6], IRIS (Integrated Risk Information System) by US EPA [39], the French Agence nationale de sécurité sanitaire de l'alimentation, de l'environnement et du travail (ANSES) [40], Health Canada [41], California Office of Environmental Health Hazard Assessment (OEHHA) [42], Texas Commission on Environmental Quality (TCEQ) [43] and

Netherlands National Institute for Public Health and the Environment (RIVM) [44] (in Table 3. 3 and 3.4).

A target-organ toxicity dose TTD or TTC ('concentration', for inhalation [29]) can be used as a TRV if the latter is constructed for a different target organ [29,45]. The ATSDR endorsed the TTD concept [30] and derived target-organ toxicity values for some TMEs but only for oral exposure in the interaction profiles intended for metals [30,46].

Sometimes, we have more than one VRT for the same substance. We decided to choose the most protective one. TRVs for Cu, Cr, chromic acid, Ni, NiSO4and Pb are listed in the two tables below. Table 3.3 contains threshold TRVs from different agencies for chronic inhalation exposure, and the second one, 3.4, includes non-threshold unit risks.

IV.3.1. Interaction magnitude

Besides the WOE scores, the interaction magnitude Mij could also have an impact in the HIint calculations. Ryker &Small have showed that even if a maximum value of Mij is considered, when the Bij score is too low, the HIint is close to the addition HI. But when Bij is close to 1, the Mij has a strong impact on the results [33].

The default value recommended by USEPA for the interaction magnitude Mij is 5 [29].

However, it has been showed that Mij could even reach 10 [33]. Thus, a Mij is recommended if data are available.

IV.3.2. Health risks from inhalation of TMEs mixture

Table 3.8 shows that the HQ90Pfor Cr, Ni and Cu, respectively are 4.1, 67.9 and 1.8, all above 1. Thus, even exposure to only one of these metals is associated with a possible risk, and adverse non-cancer respiratory effects cannot be ruled out. Now, as it is well established, HQ cannot be considered as scaling a probability, but it indicates the magnitude of exceedance of the highest exposures considered as harmless.

HQs of different chemicals are not always comparable, but since they affect the same target organ, the calculation of HI and especially HIint makes sense.

Given all the Bij scores for each binary combination, the HIint is 93.6for chronic-duration respiratory and lung effects. It showed a 26% increase compared to the addition HI. So, the resulting global effect between these 3 metals showed synergetic.

HIint is not proportional to the risk but there is a higher concern of respiratory toxic effects when workers are exposed to such levels of Cr, Ni and Cu.

As to total cancer risk, we only had two unit risk values for different types of cancers from lifetime exposure to Ni and Pb. So, we could not calculate a tumor-specific cancer risk. Thus, we aggregated the cancer effect, which is also allowed [30,37]. We found that over a lifetime inhalation exposure, more than 3 extra workers might experience a lung or kidney cancer in a population of 10000 workers.

We do not have an exact idea of the size of the occupationally exposed population in the metalworking industry in Tunisia. However, lifetime cancer risk is generally compared to a