Jean-Marc Lacome

Thibauld Penelon

Guillaume Leroy

Benjamin Truchot

ADAPTATION OF THREE ATMOSPHERIC DISPERSION MODELS FOR JACK RABBIT II CASE STUDY AND COMPARISONS WITH OBSERVATIONS: LESSONS FOR PREDICTIVE MODELLING AROUND INDUSTRIAL SITES

Keywords: Jack Rabbit II, chlorine field experiment, dense gas models, model validation

Chlorine release trials were conducted at the Dugway Proving Ground in Utah, during the Jack Rabbit II trials. An interesting database of emissions and dispersion of pressurized liquefied chlorine was set up. This database allows to make comparisons between atmospheric dispersion modelling and largescale observations about toxic massive releases (up to 10 ton). In this work, three test cases (2 trials free of obstacles and an obstructed environment) were studied on purpose for a model inter-comparison study. INERIS modelled these cases with three atmospheric dispersion models: the open source SLAB code, the commonly used PHAST software (commercial licence) and the 3D code FDS (Fire Dynamic Simulator). This paper focuses on the adaptation of the models and comparisons with observations in the near-field for CFD (Computational Fluid Dynamics) modelling and large field (up to 10 km) for other models. Discussions about harmonization of practices between models and practices for toxic consequences assessment around industrial sites are also proposed. Indeed, a massive release of pressurized liquefied material could generate complex phenomena around the release location: jet under pressure, rain-out, cooling, etc. For each of these atmospheric dispersion models, the source term can be set up in various ways depending on its level of complexity. A similar discussion is proposed about the unsteady meteorological flow that cannot be assessed by the simplest models.

INTRODUCTION

During the Jack Rabbit (JR) experimental campaigns (JR I in 2010and JR II in 2015and 2016) series of chlorine releases have been performed at Dugway Proving Ground (DPG) in Utah. Further details of the trials have been already presented in various presentations and publications (e.g. [START_REF] Fox | Overview of the 2016 Jack Rabbit II Chlorine Release Field Trials, 21st Annual George Mason University Conference on Atmospheric Transport and Dispersion Modeling[END_REF]). An original dataset has been developed that is valuable for which had concern in the possible effects of pressurized liquid chlorine releases such as storage tanks and transportation vessels (e.g., see [START_REF] Hanna | Observed chlorine concentrations during Jack Rabbit I and Lyme Bay field experiments[END_REF]). An intercomparing modelling exercise was launched in March 2018, coordinated by Tom Mazzola (Engility Corporation), Steven Hanna (Hanna Consultants) and Joseph Chang (RAND Corporation). Three of the nine Jack Rabbit II experiments were selected for the initial phase of the inter-comparison exercise and harmonized inputs were delivered to participants. The mass of pressure-liquefied chlorine released in the trials 1, 6 and 7 was respectively 4.5 tonnes, 8.4 tonnes and 8.6 tonnes. For Trial 1, a grid of Conex shipping containers was set up around the release point to simulate an urban array of buildings. For Trial 6 and 7 the grid of Conex was removed so that the release was performed in a free field environment. In Trials 1 and 6, the released jet was directed vertically downwards onto a concrete pad from a height of 1 m. In Trial 7, the jet was angled 45-degree downwards from the horizontal. Arc concentration sensors were set up downwind from the release point at various distances up to 11 km. The main topic this paper is the evaluation of 3 atmospheric dispersion models (SLAB, PHAST, FDS), based on strongly different approaches, by comparison with experimental data. The different characteristics of those models also offer the opportunity to discuss about harmonization of the practices for atmospheric dispersion modelling for unobstructed environment.

MODELLING APPROACHES

The first model that was run is the widely-used dense gas dispersion model SLAB. This model allows to simulate a ground-level evaporating pool, an elevated horizontal jet, a stack or elevated vertical jet, and an instantaneous volume source dispersion. All these types of sources, except for evaporating pool, could handle releases with gaseous form or liquid form or a mixture of vapor and liquid droplets. The vapordroplet mixture is treated as a monophasic fluid where droplet rain out and soil deposition are neglected. SLAB is able to handle dense gas dispersion but is not able to handle both evaporating pool and jet. The atmospheric dispersion is then computed based on heavy gas cloud collapse and is then based on the resolution of simplified equation of fluid mechanics. PHAST software is a calculation tool dedicated to model the effects of accidental hazards related to industrial activities. The atmospheric dispersion model of this software is of the "integral" type. Such type of model used a simplified resolution of fluid mechanic equation in the near field, solved along the curvilinear abscises of the jet, then dispersion is dealt with a Gaussian approach while the gas density becomes close to the ambient one. The complexity of this type of model is intermediate between Gaussian dispersion calculations for passive gases and three-dimensional software. It allows taking into account the different dispersion regimes. Indeed, it includes several sub-models corresponding to many different issues, such as pressurized gas jets, heavy gas dispersion and a gaussian dispersion model in the far field. The 3D runs were achieved with FDS (Fire Dynamic simulator), a freely available CFD (Computational Fluid Dynamics) code provided by the NIST (National Institute for Standard and Technology) (McGrattan, 2005). FDS was initially dedicated to model low velocity flow with density gradient such as smoke dispersion modelling in case of fire. Turbulence model is based on the Large Eddy Simulations (LES) approach that consists in the segregation between large scales, explicitly resolved, and small ones, modelled. The key criteria for such an approach is both to ensure that resolved scales are small enough, typically in the inertial zone of the turbulence spectrum, and that energy transfer between productive large scale and dissipative small ones is well modelled. Since the atmospheric turbulence anisotropy is contained in the largest scale and considering small scales are isotropic, this consequently enables solving the whole characteristics of turbulence in the atmospheric boundary layer. Unlike SLAB and PHAST, this tool does not require any assumptions about the orientation with respect to the direction of the wind.

INPUT PROVIDED FOR MODELLING

The chosen trials reproduce realistic massive releases where numerous physical phenomena can occur. For these trials, the main physical phenomena identified are the following: discharge conditions, interaction between the jet and the pad, pool formation and evaporation, thermodynamics of the cloud, interaction between the cloud and the ground including dry deposition, atmospheric conditions. Discharge conditions, pool formation and evaporation [START_REF] Spicer | Quantifying the Mass Discharge Rate of Flashing Two Phase Releases through Simple Holes to the Atmosphere[END_REF], and atmospheric conditions were investigated. Harmonized inputs were provided before running the simulations, they are reproduced in Table 1.

SLAB MODEL SET UP

Since the source term is complex, SLAB cannot handle simultaneously with the whole different contribution of the source term, various configurations were studied to highlight the sensitivity of source term set up. Two approaches are presented below:

• SLAB_1: The baseline corresponds to a horizontal jet release; the discharge period is equal to the primary release modified for rain out discharge; several averaging times have been tested. • SLAB_2: all the mass, both liquid and vapour, indicated is considered as an instantaneous release. The liquid fraction is taken to be consistent with the liquid mass fraction deducted from Table 1. The mean velocity wind profile is reconstructed with power function using the Monin length and surface roughness as input. Table 1 : Source and meteorological conditions provided to participants of the Jack Rabbit II model inter-comparison exercise. Coloured values indicate the following: red = Used for SLAB, PHAST and FDS input; gray: used by FDS and SLAB; orange = used for FDS validation; Blue = used by PHAST; Green = calculated internally by SLAB, PHAST and FDS.

Weather conditions

Trial • PHAST_1 : input directly from specification given to the modelers (see Table 1) for TRIAL 1, 6 and TRIAL 7; (PHAST_1_E refers to Pasquill class E) • PHAST_2: (for TRIAL 6) impinging jet and rain out computed by the dedicated PHAST submodel of a line rupture with presence of a bund having a surface equal to the surface pad one. The mean velocity wind profile was reconstructed thanks to a power function using Pasquill stability class and roughness as input.

FDS MODEL CONFIGURATION

The numerical domain was 300 m long, 200 m width and 30 m height. Due to several steps of validation required for CFD modelling, trial 6 and 7 were studied in priority. The domain is lined up with the direction of the release. The computational grid is made of 31 million hexahedral elements. The smallest cell length is 0.25 m corresponding to cells located close to the ground. A sufficiently refined mesh near the ground is recommended for FDS to represent properly the wall function. After several tests this thickness has been deemed as the better compromise between accuracy and efficiency. The top of the domain was an open condition and periodic boundary conditions were applied at the edges and outlet of the domain. Regarding the meteorological flow, the key issue consists in prescribing relevant velocity profile in terms of instantaneous velocity. Several approaches were previously tested [START_REF] Leroy | Harmonization in CFD approaches to assess toxic consequences of ammonia releases 17th International Conference on Harmonisation within Atmospheric Dispersion Modelling for Regulatory Purposes 9-12[END_REF]:

• the wind velocity signal in time for FDS inlet boundary condition is obtained by performing a Fourier analysis in time on the experimental signal; • boundary periodic wind with profile reconstruction using Dyer similarity functions using the Monin length and roughness as input. The FDS results in the present work uses the last approach, already tested on LNG trials [START_REF] Luketa | Model Evaluation Report for LNG Dispersion on Fire Dynamics Simulator Version 6[END_REF]. To represent a relevant boundary layer, the wall function must match [START_REF] Blocken | CFD Evaluation of Wind Speed Conditions in Passages between Parallel Buildings-effect of Wall-Function Roughness Modifications for the Atmospheric Boundary Layer Flow[END_REF] to the velocity profile, set up by a log law in FDS with aerodynamic roughness), by adjusting the ground grain roughness. A preliminary simulation of the meteorological flow is carried out to obtain a meteorological fully developed flow before the release to be modelled. Several tests and adjustments have been performed to reach levels of turbulence close to observations done just before the release. The comparison of the modelled flow with observed at first experimental levels is satisfactory as far as the mean velocity is concerned. The comparison of some FDS results with observed turbulence is presented in Table 2 for TRIAL 6. There is quite good agreement for this first sets of simulation. Further tests highlight the importance in accounting for the sensible heat flux to simulate stable conditions. The evaporating rainout has been simulated by an emissive surface equivalent to the pad surface, with the gaseous mass flow rate presented in Table 1. The source term of the jet has been set up by a gaseous release. No deposition was considered.

RESULTS

The centerline concentrations obtained are presented in Figure 1 and in Figure 2. Regarding SLAB results, for TRIAL 6, the puff instantaneous release (SLAB_2) case gives the highest concentrations in the near field. Sensitivity with averaging time is observed in the near field. Comparisons as a whole for TRIAL 6 and 7 do not show an overestimation for the SLAB and PHAST results. Other source term configurations could be tested but for emergency situation it is worth bearing in mind this preliminary trend. Comparisons appear in a better concordance for TRIAL 1, not plotted here.

No conclusion regarding the physics can however be done about models' performance since obstacles were ignored. Regarding the FDS results, the decrease of concentration downwind differs from previous models due to the fact that the FDS approach enable taking into account the interaction between the jet and the ground. Indeed, in the near field the recording video (http://www.uvu.edu/esa/jackrabbit/) show the cloud dilution and coherent turbulent structures. It would be desirable to validate this issue for CFD modelling. FDS results seem very conservative, but this is consistent with the physics taken into account at this stage, where droplet interaction with the ground and deposition on the ground were ignored.

CONCLUSIONS

The methodology to model JR II trial cases with SLAB, PHAST and FDS was discussed. Several configurations to assess emissive source term were presented for SLAB and PHAST. Results were compared with observed maximum centreline concentrations. Comparisons as a whole for TRIAL 6 and 7 do not highlight an overestimation for the SLAB and PHAST results. FDS ones indicate a decrease of the centerline concentration that differs with others. In the FDS model, one of the key phenomena, i.e. the interaction between the momentum of the jet and the ground, is more precisely modelled. This capability of CFD codes to consider obstacle effects in the atmospheric dispersion process should be more deeply investigated.

Figure 1 .:

 1 Figure 1.: Maximum arc-wise concentrations obtained for TRIAL 6

Figure 2 .:

 2 Figure 2.: Maximum arc-wise concentrations obtained for TRIAL 7

Table 2 :

 2 Recommended u* and turbulence for modeling provided to participants of the Jack Rabbit II model inter- comparison exercise and results obtained by FDS at the location of the release source for TRIAL 6

		u*	σhor	σhor/u*	σw	σw/u*
	Observations	0.093	0.416	4.824	0.647	1.161
	FDS_I	0.141	0.311	2.210	0.133	0.941

ACKNOWLEDGEMENTS

Authors would like to thank the French Ministry of Environment that financially supports this project. Authors sincerely thank the organizations responsible for funding and managing the Jack Rabbit II trials and the Jack Rabbit II Modeler's Working Group coordinators and participants and the whole team for the fruitful discussions.


Pierre Hennebert

LITERATURE EVIDENCE ON THE BROMINATED FLAME RETARDANT CONTENT IN PLASTICS OF CONSTRUCTION, OF TEXTILES/FURNITURES AND OF NON-FOOD PACKAGING: SHOULD THEY BE SORTED BEFORE RECYCLING?

Keywords: PBDE, DecaBDE, HBCDD, Br, Sb

The objective of this study is to bring information to check if sorting of plastics of construction, of textiles and of non-food packaging is necessary before reuse in circular loops. Data from literature on these plastics has been gathered from 8 publications and reports plus one report quoting 6 other reports or publications from 2013 to 2018. Published data are heterogeneous, and are presented by sample, with synthesis table (n, min, media, mean, max, centile for 1000 mg/kg and 500 mg/kg). Data of total bromine and antimony are also presented. The global literature data collected here show that approximately 8%, 16% and 9% of the reported concentrations of plastics from building materials, textile products and nonfood packaging exceed the EU concentration limit of 1000 mg/kg of regulated BFRs. Samples from continental Europe (69 out of 85 for construction plastics and 36 out of 55 for plastics for textile and furniture equipment) have very low concentrations, excepted four samples from Switzerland, Norway and Sweden. These findings should be checked by characterization campaign to decide if sorting of these plastics is necessary before recycling.

INTRODUCTION

The presence of brominated flame retardants (BFR) in plastics of electrical and electronic equipment and vehicles, and waste thereof, is well established. However, despite availability of scientific papers and technical reports, the common awareness is not so clear for plastics of construction (plumber pipes and electric sheath, windows frame, profiles, insulation foam, floor and wall covering, connexion boxes, ventilation devices, joints, seals, air and water sealing films, glue…), for plastics and synthetic fabric from textile equipment, furniture and upholstery, and for non-food plastics of packaging. The use of plastics in Europe in these sectors is important: 19.8% of total use of plastics for building and construction, 16.7% for medical equipment, plastic furniture and furniture equipment, technical parts used for mechanical engineering or machine-building, and 4.1% for household, leisure and sport (PlasticsEurope 2018), totalling so about 40% of the plastics used in EU, as much as packaging (food and non-food) plastics. Are some of these plastics brominated with concentrations hampering their recycling?

MATERIAL AND METHODS

Data from literature on plastics of construction, of textiles and of non-food packaging has been gathered from 8 publications and reports plus one report quoting 6 other reports or publications dealing with the plastics studied here. In total 14 sources from 2003 to 2018 were used. Published data are heterogeneous. Some data are single concentration data, and other data are range of concentration, with the number (n), the minimum (min), the median, the mean (mean), and the maximum (max) of the range. The mean concentrations presented here are the mean of all the single concentrations and the minimum, mean and maximum concentrations of the ranges. Data reported as "< x mg/kg" (lower than the limit of detection or quantification of the laboratory) have been accounted as "x" mg/kg. Data of textiles are sometimes reported as quantity per item or per square meter, hence not comparable with the other data of this study and were not used. Only data for the most frequently measured BFRs (PBDES, including decaBDE, and HBCDD) are presented here.

RESULTS AND DISCUSSION

The distributions of concentrations gathered here depends on the geographic origins of the samples (national regulations for fire protection) and their number per country, the potential choice of the samples by the authors (voluntary choosen because suspected to contain BFR or not), the individual weight of the plastic items... These data nevertheless seem usefull for documenting the question of the sorting of these plastics. The distributions of BFR are heterogeneous and it is useful to detail the results by category of plastics, by sample and by country (Table 1 andTable 2). The samples are sorted for decreasing polybromodiphenylether (PBDE), decabromodiphenylether (DecaBDE) and hexobromocyclododecane (HBCDD) concentrations. A synthesis is presented in Table 3, with the number of data, the median, mean and maximum concentrations, and the centile of data < 1000 mg/kg for BFR (a typical regulatory concentration limit), < 2000 mg/kg for bromine (the recommended concentration limit in the standard CENELEC CLC/TS 50625-3-1 for sorting plastics of small household appliances and screens), and < 8354 mg/kg for antimony (the concentration limit rendering the plastic carcinogenic according to HP7 criteria in the EU legislation). Details on regulatory concentrations in EU can be found in [START_REF] Hennebert | WEEE plastic sorting for bromine essential to enforce EU regulation[END_REF]. For non-food packaging, 65 data (without data ranges) were collected and are summarized in Table 3. These plastics are exclusively EPS (expanded polystyrene) and XPS (extruded polystyrene) with HBCDD as BFR. Concentrations are low, but 31 samples have a HBCDD concentration > 10 mg/kg, the highest being polystyrene packaging laboratory equipments, appliances and printers [START_REF] Rani | Hexabromocyclododecane in polystyrene based consumer products: An evidence of unregulated use[END_REF], [START_REF] Abdallah | Hexabromocyclododecane in polystyrene packaging: A downside of recycling?[END_REF]). The maximum HBCDD content exceeds the concentration limit for hazardous waste according to EU legislation.

For BFR, the distributions of concentration are totally skewed by some large concentrations, as shown in the tables and by the difference between the medians and the means (Table 3). The medians are all < 0.1 mg/kg or lower, excepted HBCDD in non-food packaging (11 mg/kg, n=57). The mean concentrations are the concentration that would be measured if all the samples (having all the same mass) were mixed and analysed. It is determined by the last few centiles of samples with very high concentrations, illustrated here by the maximum concentration, and by the centile of data with a concentration > 1000 mg/kg (8% for construction plastics, 16% for textile and furniture, and 9% for non-food packaging). For bromine and antimony, data are less numerous and indicates that 22%, 29% and 31% for the data exceed 2000 mg Br/kg for the three category of plastic, and one data from four data of construction and of textile and furniture exceeds the antimony concentration rendering this plastic hazardous.

CONCLUSIONS

The European plastics regulation in the context of a circular economy stipulates that plastics containing regulated BFRs (i.e. BFR recognized as persistent organic pollutants by the Stockholm Convention) must be sorted and managed separately from the non-brominated fraction in order to avoid the uncontrolled dispersion of these substances in recycled raw materials.

The global literature data collected here show that approximately, in the selected publications, 8%, 16% and 9% of the reported concentrations of plastics from building materials, textile products and nonfood packaging exceed the EU concentration limit of 1000 mg/kg of regulated BFRs. Samples from continental Europe (69 out of 85 for construction plastics and 36 out of 55 for plastics for textile and furniture equipment) have very low concentrations, excepted seven samples from Ireland, Norway, Sweden and Switzerland,.

Major waste fractions including plastics from construction and demolition waste must be sorted in the EU to facilitate re-use and recycling [START_REF] Eu | /851 of the European Parliament and of the Council of 30 May 2018 amending Directive 2008/98/EC on waste[END_REF]. Mandatory separate collection of textiles is planned in the EU by 2025 at the latest. Characterization campaigns should be considered to confirm if their RFB content involve a specific sorting before recycling for some category of waste among them.

Table 1 :

 1 Literature-reported concentrations of some brominated flame retardants in plastics of construction

	Sample / Concentration (mg/kg)	PBDE	DecaBDE HBCDD Country	Category
	Epoxy adhesive	300000 300000		Norway
	Electrical insulation	200000 200000		Sweden
	PUR foam 1	140000			USA
	PUR foam 2 construction 2	120000 4799			Not known Global literature	With PBDE
	Damp-proof membrane/film	1000	1000		Switzerland
	pipe insulation	63	626	0	Czechia
	recycled plastic materials	2	18	1	Czechia
	blue sealant foam	0	0	5400	Czechia
	EPS D	0	0	3050	Ireland
	EPS 5			1609	Korea
	EPS 4			1519	Korea
	EPS 8			1335	Korea
	EPS 7			1098	Korea
	pale mounting foam	0	0	832	Czechia
	air conditioning -alu foil	1	6	545	Czechia
	polystyrene	0	0	469	Czechia
	building polystyrene	0	0	469	Czechia
	air conditioning -inside foil	0	0	412	Czechia	With HBCDD
	fibre net	0	0	250	Czechia
	EPS 6			194	Korea
	polystyrene board	0	0	127	Czechia
	XPS D	0	0	35	Ireland
	air conditioning -cellophane foil	0	0	16	Czechia
	drywall	0	0	9	Czechia
	insulation HARDSIL NT	0	0	8	Czechia
	air conditioning -glass fibre foam	0	0	8	Czechia
	yellow mounting foam	0	0	3	Czechia
	green mounting foam	0	0	2	Czechia
	phenolic foam Insulation	0	4	0	Czechia
	foam insulation 2	0	2	0	Czechia
	foam insulation 3	0	1	0	Czechia
	wood fibre insulation 3	0	1	0	Czechia
	heat exchanger	0	0	0	Czechia
	paper insulation	0	0	0	Czechia
	rubber	0	0	0	Czechia
	construction 1	0			Global literature
	outdoor paint	0	0	0	Czechia
	insulation panel	0	0	0	Czechia
	window finishing bead 1	0	0	0	Czechia
	foam insulation 1	0	0	0	Czechia
	recycled beverage cartons paper insulation 0	0	0	Czechia
	insulation hemp rope	0	0	0	Czechia
	insulation aluminium foil	0	0	0	Czechia
	waste pipe	0	0	0	Czechia
	resilient linoleum 2	0	0	0	Czechia
	linoleum 2	0	0	0	Czechia
	water-resisting paint	0	0	0	Czechia	Without BFR
	linoleum 1	0	0	0	Czechia
	paper sound insulation	0	0	0	Czechia
	polyacrylate material	0	0	1	Czechia
	laminated plastic floor covering	0	0	1	Czechia
	oriented strand board 2	0	0	0	Czechia
	window corner bead	0	0	0	Czechia
	plaster	0	0	0	Czechia
	asphalt insulation	0	0	0	Czechia
	formica	0	0	0	Czechia
	oriented strand board 1	0	0	0	Czechia
	chipboard 3	0	0	0	Czechia
	wood fibre insulation 1	0	0	0	Czechia
	green sealant foam	0	0	0	Czechia
	cotton insulation	0	0	0	Czechia
	sealant 1	0	0	0	Czechia
	oriented strand board 5	0	0	0	Czechia
	wooden board 2006	0	0	0	Czechia
	sealant 2	0	0	0	Czechia

Table 2 :

 2 Literature-reported concentrations of some brominated flame retardants in plastics of textiles and furniture

	Sample / Concentration (mg/kg)	PBDE	DecaBDE HBCDD Country	Category
	Polyester textile C	130000 120000	3	Japan
	c-decaBDE treated polyester upholstery textiles used in curtain
	manufacture	120000 120000		Japan
	PUR foam for upholstered furniture	41040			USA
	adhesive layer of reflective tapes	30000	30000		Asia to Norway
	PUR foam for mattresses	25000			USA	With PBDE
	textile 2	20003			Global literature
	textile 1	11843			Global literature
	upholstery foam	7023	7023		India
	window blind	4799	4799		India
	carpet	85	85		Turkey
	polyester Textile A	0	1	43000	Japan
	polyester textile B	2	10	42000	Japan
	curtain textile A polyester blue			26000	Japan
	curtain textile B polyester black			24000	Japan	With HBCDD and
	furniture fabrics	9999	19953	15050	Ireland	with or without
	furniture foam filling	1060	2119	2275	Ireland	PBDE
	curtain D	7	14	15	Ireland
	carpets	907	1810	7	Ireland
	mattresses	115	230	3	Ireland
	Insulation/carpet padding	1	2		The Netherlands
	Textile treated with DecaBDE	0			Japan
	persian carpet	0	0	1	Czechia
	curtain 2006	0	0	1	Czechia
	grey carpet 1	0	0	0	Czechia
	green carpet 1981	0	0	0	Czechia
	table cloth 1996	0	0	0	Czechia
	foam 1	0	0	0	Czechia
	red carpet	0	0	0	Czechia
	blanket	0	0	0	Czechia
	textile material 1	0	0	0	Czechia
	curtain 2005 textile material 2	0 0	0 0	0 0	Czechia Czechia	Without BFR
	blue carpet	0	0	0	Czechia
	grey carpet 2	0	0	0	Czechia
	brown carpet	0	0	0	Czechia
	green carpet 2006	0	0	0	Czechia
	upholstery material 1	0	0	0	Czechia
	foam 2	0	0	0	Czechia
	coir (coconut fibre)	0	0	0	Czechia
	cover textile	0	0	0	Czechia
	upholstery material 3	0	0	0	Czechia
	pillow fill	0	0	0	Czechia
	textile material 3	0	0	0	Czechia

Table 3 :

 3 Summary of literature-reported concentrations of some brominated flame retardants, total bromine and antimony in plastics of construction, of textiles and furniture, and of non-food packaging

	Category	BFR, Br, Sb	Concentration (mg/kg)			data < 1 000 mg/kg	data < 500 mg/kg
			n	Min	Median	Mean	Max
	Construction	PBDE	716	0	0	9819	300000	99%	99%
		Of which DecaBDE 81	0	0	6792	300000	95%	94%
		HBCDD	84	0	0	223	5400	92%	89%
		TBBPA	6	0	0	0	0	100%	100%
		Br	9	0	45	2122	9410	% < 2000 mg/kg : 78%
		Sb	4	103	984	3768	13000	% < 8354 mg/kg : 75%
	Textile and	PBDE	437	0	0	7583	130000	95%	94%
	furniture	Of which DecaBDE 75	0	0	6376	120000	84%	81%
		HBCDD	59	0	0	3463	43000	86%	86%
		TBBPA	-	-	-	-	-	-	-
		Br	70	0	99	7175	128300	% < 2000 mg/kg : 71%
		Sb	4	90	944	2975	9922	% < 8354 mg/kg : 75%
	Non-food	PBDE	-	-	-	-	-	-	-
	packaging	Of which DecaBDE -	-	-	-	-	-	-
		HBCDD	57	0	11	232	5897	91%	88%
		TBBPA	8	0	0	0	1	-	-
		Br	6	0	10	1153	5600	% < 2000 mg/kg : 69%
		Sb	-	-	-	-	-	-


Anitha Muralidhara

Gert-Jan Gruter

Christophe Len

Guy Marlair

EXPLORATORY ANALYSIS OF FIRE RISKS PERTAINING TO FURANIC PLARFORM CHEMICALS

Bio-based feedstock are the current interest and used extensively in producing chemical intermediates, platform molecules and final materials. Key furanic compounds such as 5hydroxymethylfurfural (HMF), methoxymethylfurfural (MMF) and furfural (FF) are produced from the dehydration of hexoses (C6) and pentose (C5) sugars present in the lignocellulosic biomass. With their structural and functional diversity, these compounds have found plenty of useful application pathways. However, in support of sustainably assessment of relating value-chains, safety oriented studies concerning furanic compounds are so far scare while many if not all of these compounds are combustible in nature. The current study therefore targets a complimentary approach generating safety related information on a wide range of furanic compounds with a scenario-based assessment. The results can bring out a preliminary insight on the risk profiles of furanics, helping the users to make suitable choices of compounds for a given application.

INTRODUCTION

Climate change is globally driving humankind towards finding fossil free energy vectors, materials and chemicals alternatives. The demand for energy and materials is growing at an elevated pace. In fulfilling this quest, biomass currently stands as a unique alternative to produce fuels, chemicals and materials due to its abundant availability and economically viable options. In the search of adequate bio-based feedstock in this context, lignocellulosic biomass has gained significant attention in the production of alternative and unrivaled bio-based building blocks. [START_REF] Fitzpatrick | A Biorefinery Processing Perspective: Treatment of Lignocellulosic Materials for the Production of Value-Added Products[END_REF] Acid catalyzed Dehydration (ACD) of C6 and C5 sugars from lignocellulosic biomass can produce some of the key furanics intermediates such as HMF and furfural further leading to plenty of final molecules with useful applications (Figure 1). [START_REF] Soetedjo | Biobased Furanics: Kinetic Studies on the Acid Catalyzed Decomposition of 2-Hydroxyacetyl Furan in Water Using Brönsted Acid Catalysts[END_REF] Furanics are a large family of chemicals, with diverse structural and functional attributes, [START_REF] Kamm | Biorefineries-Industrial Processes and Products: Status Quo and Future Directions[END_REF] leading them into various industrial applications such as solvents, pharmaceuticals, polymers, catalysis etc. [START_REF] Caes | Biomass to Furanics: Renewable Routes to Chemicals and Fuels[END_REF] Recently, the US department of energy has recognized some bio-derived furanic compounds as key building block chemicals owing to their wide application capabilities among which furfural is a key product. [START_REF] Werpy | Top Value Added Chemicals from Biomass Volume I -Results of Screening for Potential Candidates from Sugars and Synthesis Gas[END_REF][START_REF] Delbecq | Hydrolysis of Hemicellulose and Derivatives-A Review of Recent Advances in the Production of Furfural[END_REF] The first bio-based industrial production of furfural was by Quaker oats in early 19th century. HMF is another key furanic intermediate results in the production of 2,5-furandicarboxylic acid (FDCA) to produce bio-based polyethylene furanoate (PEF) plastic. [START_REF] Van Putten | Hydroxymethylfurfural, a Versatile Platform Chemical Made from Renewable Resources[END_REF] Hydrogenation of HMF results in 2,5dimethylfuran often used as a fuel additive. HMF when reacts with a simple alcohol such as methanol or ethanol that leads to the production of 5-(methoxymethyl)furfural (MMF) and 5-(ethoxymethyl)furfural (EMF) generally named as alkoxymethylfurfurals (AMF or RMF). [START_REF] Alipour | A Review on Synthesis of Alkoxymethyl Furfural, a Biofuel Candidate[END_REF] The energy density of EMF (8.7 kWh/L) is nearly equal to that of a regular gasoline (8.8 kWh/L) and comparable to diesel fuel (9.7 kWh/L) and much higher than ethanol (6.1 kWh/L). [START_REF] Jong | Furanics: A Novel Diesel Fuel with Superior Characteristics[END_REF] AMFs derived by HMF are able to produce higher yields, using cheaper raw materials containing lignocellulosic biomass, and are liquids at room temperatures making them effective candidates for fuel additives. [START_REF] Alipour | A Review on Synthesis of Alkoxymethyl Furfural, a Biofuel Candidate[END_REF] 1. Schematic representation of key furanic compounds produced from lignocellulosic biorefinery (adapted from Aristizábal M. et al. 2015) [START_REF] Aristizábal | Biorefineries Based on Coffee Cut-Stems and Sugarcane Bagasse: Furan-Based Compounds and Alkanes as Interesting Products[END_REF] By understanding the importance of these bio-based furanic platform chemicals, significant dedicated efforts were invested in the recent years to commercialize the relating ACD process; with advanced and efficient process technologies to cope with the large diversity of available feedstocks, and target molecules. [START_REF] Kamm | Biorefineries-Industrial Processes and Products: Status Quo and Future Directions[END_REF] By contrast, and despite their obvious combustibility property, much less efforts were devoted to the generation of the physico-chemical safety data in terms of ignitability, flammability, oxidizing potential, or thermal stability of furanic compounds. Access to safety data is very important at early stages of furanics end-use development to learn on physico-chemical risk trends according to their structures, since a wide variety of risk profiles can be anticipated from a diverse group of chemicals in a single family.

Bio-based origin of furanic compounds do not make them completely risk free. Moreover, not all compounds of the furanic family have received harmonized classification under the official hazardous material classification systems such as CLP, [START_REF] Echa | Guidance on the Application of the CLP Criteria, Guidance to Regulation (EC) No 1272/2008 on classification, labelling and packaging (CLP) of substances and mixtures[END_REF] GHS, [START_REF]Globally Harmonized System of Classification and Labelling of Chemicals (GHS)[END_REF] and TDG. [START_REF]UN Recommendations on the Transport of Dangerous Goods -Model Regulations, 20[END_REF] Information on some important thermo-chemical properties such as flash points, upper and lower flammability limits, auto ignition temperature are not easily accessible for many important compounds. These are the key information a user needs to refer for the safe handling, storage and transportation of reactive chemicals.

Many furanics are flammable in nature and the materials derived from them may end up in various built environments, including public access buildings or collective dwellings where fire risk is a key concern. On a more general way, safe transportation, storage and handling of such chemicals and materials shall be ensured in adequate manner. It is therefore important to evaluate the reaction to fire properties of these chemicals or materials in their early stage of development.

Safety still today is often considered as an underscored aspect of sustainability and has been receiving a low priority in many instances. Howwever tackling safety issues right from the beginning may provide competitive advantage in many ways. Safety studies shall be considered as part of multi-criteria sustainability assessment as they can help selecting the most appropriate options in terms of processes or final materials involving furanics. Although not on focus in our review work, health and environment related issues pertaining to furanics compounds are also a subject for further research. All the more these aspects are still a matter of debate and conflicting views in some cases, [START_REF] Ventura | Evaluating the Toxicity of Biomass Derived Platform Chemicals[END_REF][START_REF] Muralidhara | Humins in the Environment: Early Stage Insights on Ecotoxicological Aspects. Biofuels, Bioprod[END_REF] our literature review shows that no significant research has been dedicated so far to safety profiling of furanics with regard to their potential of risks of physico-chemical nature (thermal reactivity/flammability, oxidising properties, corrosivity, etc.,). Fire hazard may of course be significant since some compounds have been considered due to their high energy density as bio-based alternative fuels components. [START_REF] Jong | Furanics: A Novel Diesel Fuel with Superior Characteristics[END_REF][START_REF] Conturso | Effect of Furanic Biofuels on Particles Formation in Premixed Ethylene-air Flames: An Experimental Study[END_REF][START_REF] Christensen | Renewable Oxygenate Blending Effects on Gasoline Properties[END_REF] This is a dedicated study focusing on generating physico-chemical safety related information including information on ignition, combustion and fire propagation potential of a few selected furanics of commercial interest. Reaction to fire performances of these compounds will be learned via the experimental investigation using the Fire Propagation Apparatus (FPA). The results would give an idea on the heat impact leading to thermal threats and the chemical impact leading to toxic species release from furanic fires. In addition, the study also focuses on understanding the fire load of several furanic compounds by estimating heat of combustion data. The study clearly exemplifies the varying risk profiles originating from the same family of chemicals with a key message to avoid generalizing the hazard statements of a specific compound to the whole family of chemicals.

MATERIALS AND METHODS

Test samples

Key furanic compounds of commercial interest were selected from various sources illustrating wide varieties in structural and functional attributes (such as variation in alkyl chain lengths, molecular masses, and different functional groups) as shown in Table 1.

A few examples of furanic compounds used in the exploratory work

Estimation of heat of combustion values

Heat of combustion (HC) of any given product, as relating to fire lead, stands as a basic information in any fire risk assessment. The theoretical heat of combustion is the maximum amount of heat a product might liberate in case of complete combustion in a fire event. HC values can be represented as Low heating value (LHV) when the water vapor produced by complete combustion is released in gas state and high heating value (HHV) when the water is accounted for in the energy balance in the liquid form. The most preferred and observed value in the literature is the LHV, obtained from the oxygen bomb calorimeter. The net heat of combustion values of organics generally fall in the range of 20 MJ kg -1 to 45 MJ kg -1 . [START_REF] Marlair | An Analysis of Some Practical Methods for Estimating Heats of Combustion in Fire Safety Studies[END_REF] When direct measurement is not feasible, the HC values can be calculated using the elemental compositions of desired compounds through a series of empirical correlations developed mainly for fossil fuels.

Among many empirical correlations available, the one used in our calculations is the Boie correlation.

HHV (MJ kg -1 ) = 35.160 C + 116.225 H -11.090 O + 6.280 N + 10.465 S [1] where C, H, O, N and S are the mass fractions of carbon, hydrogen, oxygen, nitrogen and sulphur in the burning fuel.

Database establishment

More than 30 furanic compounds depicting wide range of molecular structures containing one or more furan rings were listed and categorized based on the alkyl chain lengths and associated functional groups. MSDS, websites of various chemical traders, producers and literature review were some of the main sources of the available data. Table 1 is an example of the exploratory work carried out concerning furanics. As we can observe, the selected compounds clearly show diverse structural and functional attributes. Two important factors considered for the assessment is the flash point data and the heat of combustion values of furanics. As we can observe, the flash point data listed in the table ranges from -36 to 207 °C and the heat of combustion values calculated using the empirical Boie correlation ranges from 13 to 45 MJ kg -1 . The wide range of values observed from the exploration of those two key indicators of the fire hazard clearly supports the assumption that the fire behaviour and related fire risk of furanics may highly vary in practice and shall be carefully assessed in due course, considering both their synthesis, transformation and final end-use. This gives also a clear message that their reaction to fire performances of furanics shall not be generalized from limited measurements pertaining to one or another furanic compound, as it was erroneously done for ionic liquids for example. [START_REF] Diallo | An Innovative Experimental Approach Aiming to Understand and Quantify the Actual Fire Hazards of Ionic Liquids[END_REF]

Fire calorimetry

A few furanic compounds were selected to test their reaction to fire performance. The tests were carried out using the fire propagation apparatus (FPA) commissioned at INERIS. The FPA is a multipurpose, polyvalent testing apparatus covered by various national and international standards such as ASTME2058, [START_REF] Marlair | Evaluation of the Performance of Three ASTM E 2058 and NFPA 287 Fire Propagation Apparatuses[END_REF] and NFPA 287, 21 ISO12136, [START_REF]Reaction to Fire Tests-Measurement of Material Properties Using a Fire Propagation Apparatus[END_REF] capable of measuring key parameters addressing the full life cycle of a fire event from ignition, combustion, fire propagation and extinguishment data.

In the current investigation, experiments were conducted to understand the reaction to fire behaviour of key furanic intermediates as well as their mixtures. Samples of about 20 g were used and the tests were performed at well-ventilated fire conditions under a controlled environment with an inlet airflow rate of 200 Nl h -1 . The tests were performed under calibrated external heat flux 25 kW m -2 provided by the four infrared heaters under piloted ignition conditions (using either an electric spark or a pilot flame).

Key measurements from the FPA such as heat release rates, mass loss rates were calculated by the application of fire calorimetry laws in redundancy, based on the principles of Oxygen Consumption (OC) and Carbon dioxide Generation (CDG). [START_REF] Brohez | Soot Generation in Fires: An Important Parameter for Accurate Calculation of Heat Release[END_REF] Concentration of the fire effluents were obtained by use of the online Fourier transform infrared spectrophotometer (FTIR) coupled to the FPA. By controlling the inlet airflow rate, it is possible to simulate fuel rich (under-ventilated) or fuel lean (well-ventilated) environments in the tests, thereby allowing the possibility to explore the full spectrum of ventilation conditions that may apply in real fire scenarios. Ventilation conditions set in practice are controlled through the determination of real time equivalence ratio (Phi factor) is the [(fuel/air)/ (fuel/air)stoichiometric]. [START_REF] Brohez | Fire Calorimetry Relying on the Use of the Fire Propagation Apparatus. Part I: Early Learning from Use in Europe[END_REF] 2. In operation and schematic of the fire propagation apparatus at INERIS adapted from Muralidhara et al., 2018 25

RESULTS

Exploratory testing on flammability and fire behaviour of some furanic compounds mentioned in table 1 have been performed. The current work focuses on presenting the fire behaviour of HMF, MMF and their mixtures (alkoxymethylfurfurals) in various proportions presenting the industrial interest of key furanic building blocks.

Table 2 presents the various products tested and main characteristics resulting from the fire tests in terms of mass loss and mass loss rates, combustion duration, resistance to ignition and carbon mass balance (from carbon in the test samples and all carbonated measured emissions or solid residues). However, the combustible nature and energy content of the test mixture may clearly achieve a selfsustaining flaming combustion process in the current test conditions, irrespective of the given official CLP classification of the products and their test mixtures. The ratio of MMF/HMF seems to play some role in ignition delay characteristics of the mixtures, although differences in flash points of the two neat chemicals do not support this observation. Higher concentration of MMF resulted in shorter ignition delays i.e. -76s, 70s, 69s, and 61s for 20%, 40%, 60% and 80% MMF respectively.

Both thermal and chemical impact originating from the furanic fires will be further assessed in detail by making use of other measured parameters, including the influence of ventilation conditions that may significantly affect CO driven fire induced toxicity. This work has a similar approach as the exploratory work conducted earlier by the same group on the fire behavior of humins; a biomass side stream residue generated during the ACD process [START_REF] Muralidhara | Insights on Thermal and Fire Hazards of Humins in Support of Their Sustainable Use in Advanced Biorefineries[END_REF] .

Future work

The primary focus is on generating and further exploring the safety-oriented data for some of the key furanics of interest. From the database, specific patterns in terms of flash point and heat of combustion data in relation to the structural and functional aspects of furanics will be identified and related trends will be explored trying to identify some safety property-structure relationships. This information gives an initial understanding on the heat impact anticipated from a specific material prior to the actual experiments. Such data can be very useful in case of testing very expensive chemicals or for compounds that are newly being synthesized. The results can be useful in estimating the suitability of compounds in a specific application based on their anticipated risk profiles.

CONCLUSIONS

Bio-based value chains are becoming increasingly important in producing chemicals and materials for the future needs, thereby improving bio-based economy. Plenty of chemical (furanic) intermediates, platform molecules and final materials containing furanic compounds are expected to end up in soon future in varieties of applications, beyond those already on the market. For such a large group of chemicals, with structural and functional diversity, varying risk profiles can be anticipated. Therefore, early appraisal of safety profiles of new compounds of interest of the furnanic family may can be very important to make the best choice at the right time in innovation processes. This can be achieved by conducting dedicated safety-oriented studies combining testing and modelling approaches. Generating safety oriented patterns and defining some trends from the current work can help in filling the existing data gaps. Such information may lead to better suitability of compounds in desired applications. In addition, a dedicated fire risk assessment can help the users to get better insights on the thermal and chemicals risks associated with furanic fires and thus help in taking necessary precautions while handling, transportation and storage of such chemicals/materials. This can also be beneficial to legal obligations in the field of registration, classification and labeling of new chemicals.

2 .

 2 Burning behaviour of HMF, MMF and their mixtures in the FPA under well-ventilated fire conditions HMF-MMF mixtures (

ABBREVIATIONS

  ABBREVIATIONS

  Experimental heat release rate and cumulative energy release profiles of HMF, MMF and their mixtures under well-ventilated fire conditions Figure3illustrates the variation of the heat release rate (HHR) profiles as a function of time and according to proportions of the two studied compounds in the mixture. The results from the FPA combustion tests are summarized in table 2. The data obtained and the visual observations made during the combustion tests clearly indicate that the tested compounds offer some initial resistance to ignition.

		19.4	19.2	19	19.5	19.5	21.1
	Mass loss (%)	94.8	96.2	96.7	97.7	98.1	100
	Ignition time (s)	157	76	70	69	61	88
	Duration of combustion (s)	229	232	233	224	217	224
	Average mass loss rate (g. m -2 s -1 )	24.1	23.5	23.2	24.8	25.6	26.8
	Max mass loss rate (g. m -2 s -1 )	66.1	38	43.3	48.7	53.6	49.9
	Phi factor max 26	0.22	0.21	0.24	0.28	0.32	0.28
	Peak HRR (kW m -2 )	931	805	829	970	1167	865
	Carbon mass balance (%)	96.2	98.7	98.9	93.3	94.6	97.5
	3.


Alicia Gressent
email: alicia.gressent@ineris.fr

Laure Malherbe
email: laure.malherbe@ineris.fr

Augustin Colette
email: augustin.colette@ineris.fr

Keywords: Air Quality Mapping, Microsensor, Data Fusion, PM 10

à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Data fusion for air quality mapping using sensor data : feasibility and added-value through an application in Nantes

Alicia Gressent, Laure Malherbe, Augustin Colette

To cite this version:

Alicia Gressent, Laure Malherbe, Augustin Colette. Data fusion for air quality mapping using sensor data : feasibility and added-value through an application in Nantes. European regional conference of The International Environmetrics Society (GRASPA 2019), Jul 2019, Pescara, Italy. pp.51. ineris-

Data fusion for air quality mapping using sensor data: feasibility and addedvalue through an application in Nantes 1 Introduction

Air quality monitoring is conventionally based on a network of stations which allows a continuous report of pollutant concentrations. The related measurement uncertainty is constrained by the European existing legislation [1, 2] ensuring observation accuracy. Nevertheless, the installation and maintenance of such a network are expensive and so the number of stations in each region is limited. The use of numerical modelling on various scales (regional, urban, local) has thus increased during the last 15 years to supplement station observations and support air quality assessment. In parallel, the technological progress allowed the development of miniaturized and low-cost instruments to measure pollutant concentrations [START_REF] Kumar | The rise of low-cost sensing for managing air pollution cities[END_REF]. Many projects of crowdsourcing and citizen science are emerging. In addition, field measurement campaigns are conducted to assess the potential of these low-cost devices by deploying fixed sensors (on top of buildings, street lights, reference stations) and/or mobile sensors (on top of cars, bikes, or carried by citizens) offering higher spatial coverage than reference stations. Because microsensors suffer from metrological weaknesses, a calibration is generally applied to the raw data [START_REF] Spinelle | Field calibration of a cluster of low-cost available sensors for air quality monitoring. Part A: Ozone and nitrogen dioxide[END_REF][START_REF] Maag | A survey on sensor calibration in air pollution monitoring deployments[END_REF]. The large amount of collected information offers new opportunities of development in air quality modelling and mapping at urban scale that are the scope of recent studies. Statistical methodologies are broadly used to derive air quality maps from sensor data, in particular the Land Use Regression models (LUR), but they generally do not take spatial dependence into account. Geostatistical approaches have been less frequently applied to such type of data but provide significant advantages to combine sensor measurements and auxiliary information such as dispersion model outputs [START_REF] Schneider | Mapping urban air quality in near real-time using observations from low cost sensors and model information[END_REF].

In this paper, data collected from fixed and mobile micro-sensors are used together with urban-scale modelling data to map PM 10 in the city of Nantes (France).

PM 10 data

PM 10 sensor data were provided by AtmoTrack (https://atmotrack.fr), a French company created in 2015 in Nantes. PM 10 data measured at reference monitoring stations (quarter-hourly mean concentrations) and simulation data (ADMS-Urban model) on the city of Nantes were provided by the French air quality monitoring association Air Pays de la Loire (http://www.airpl.org/).

Sampling routes and frequency of measurements

In November 2018, PM 10 sensor measurements were collected in Nantes. During this sampling period, the company deployed 16 fixed sensors including 3 sensors at the Victor Hugo station (reference station for traffic typology) and 3 other sensors at the Bouteillerie station (reference station for urban background typology). In addition, 19 mobile sensors were installed on-board of driving school cars to measure PM 10 concentrations over numerous routes each day of the sampling period. The vehicles routes ensure a satisfactory spatial coverage over the entire urban area even if they are totally dependent on the driving school car itineraries and on the lesson time (only daytime).

Measurement accuracy

Considering the measurement uncertainty, the three available datasets (data from the reference stations, the fixed sensors and the mobile sensors) can be related to three monitoring networks of respectively low (up to 25%), medium (up to 50%) and high (up to 125%) uncertainty (Figure 1). Microsensors offer a unique spatial and temporal coverage of pollutant concentrations. However, the accuracy of the measurements and their meaning, in case of mobile sensors, are real challenges to include them in air quality maps. In the following sections, a methodology of data fusion is detailed and a first test using fixed and mobile sensor data in Nantes (France) is presented.

Data fusion

Kriging [START_REF] Chilès | Geostatistics: Modeling Spatial Uncertainty[END_REF] involves deriving linear combination of the data which ensures minimal estimation variance under a non-bias condition. Its strength is to give an information about the uncertainty of the estimated map. Among the kriging methods, the universal or external drift kriging makes it possible to consider auxiliary information to increase the estimation accuracy. The main hypothesis is that the global mean is not constant through the domain and relies on explanatory variables, entailing an additional condition on the kriging weights. This approach has long been applied to air quality mapping [START_REF] Wackernagel | Geostatistical risk mapping with chemical transport model output and ozone station data[END_REF][START_REF] Malherbe | Formulation and quantification of uncertainties in air quality mapping[END_REF][START_REF] Lahoz | Data assimilation: making sense of earth observation[END_REF][START_REF] Beauchamp | Dealing with non-stationarity through explanatory variables in kriging-based air quality maps[END_REF] and was used in this work to perform data fusion between: -the hourly average concentrations measured by the fixed and mobile microsensors (after bias correction) as main variable; -the 2016 annual average concentrations of the pollutant simulated by the ADMS-Urban dispersion model (https://www.cerc.co.uk/environmental-software/ADMS-Urban-model.html) as drift of the mean.

In addition, the measurement uncertainty of the sensors was taken into account by defining the variance of measurement errors (hereafter VME) as an input of the calculation.

Results

Estimation of PM 10 concentration fields

Data fusion was performed for 27/11/2018, the day for which the amount of data was the largest. At every measurement position, the hourly mean of the observations is calculated, and external drift kriging is applied. The mobile and fixed sensor observations at 5pm and the annual modelled concentration field are presented for PM 10 in figure 2a). Figure 2b) presents the VME for the same sampling routes. In this case, the measurement uncertainty is set to 25%, i.e. to the maximum uncertainty of the reference station observations. The uncertainty definition is totally arbitrary here and could be considered between 25% to 125%. Note that the fixed station measurements are not included in this estimation because they were used to correct and prepare the sensor data before kriging. As shown by the fused maps (Figure 2c and 2d), the modelled annual average allows to define the general patterns of the pollutant fields. Then the sensor observations which are associated with higher concentrations (by a factor of two) increase the concentration levels in the estimation domain, with some PM 10 hotspots where data were collected (Figure 2c). When data fusion is performed with higher VME (75%, figure 2d), the hotspots are not represented anymore and the local effects of the sensor data is minimized.

The recent technological developments for miniaturizing the instruments that measure outdoor ambient air offer new possibilities for air quality modelling and mapping. The new portable and low-cost devices could provide observations of pollutants with higher spatial coverage than the reference monitoring networks. As long as several challenges can be dealt with (measurement uncertainty, representativeness of the sampling…), they could help to produce more accurate pollution maps. In this work, we investigate the potential added value of these data for air quality mapping by applying a data fusion technique. The dataset refers to PM pollution in the French city of Nantes. Hourly averaged sensor data and the annual mean concentration field simulated by ADMS-Urban model are combined by external drift kriging to estimate hourly PM 10 concentrations, taking the variance of the measurement error into account. Those calculations were performed for one day but the next step of this work will be to consider each hour of the whole sampling period (November 2018). Further investigations will be carried out to estimate the influence of the amount of data, their position and their related uncertainties on the interpolation results. In addition, several ways of improvement have been identified such as the consideration of the spatial anisotropy in kriging and the application of spatiotemporal kriging. Besides geostatistical methods, machine learning techniques will be tested allowing to learn about historical data to improve the current estimate.

Figure 1 :

 1 Figure 1: Comparison of the three networks (fixed sensors in blue, mobile sensors in orange and reference station in black) at Victor Hugo station for November 2018. Example of PM 10 .

Figure 2 :

 2 Figure 2: Data fusion of the sensor data on 27/11/2018 at 5pm: the 2016 annual average concentrations simulated by ADMS-Urban and the hourly-averaged sensor data (a), the variance of the measurement errors (b), the fused map with 25% uncertainty on measurements (c), and the fused map with 75% uncertainty on measurements (d).

Acknowledgments

We thank AtmoTrack company for providing their sensor data and Air Pays de La Loire for providing the reference station observations and the results from their simulations with the ADMS-Urban model.


Guillaume Lecocq
email: guillaume.lecocq@ineris.fr

Antoine Dutertre

Emmanuel Leprette

Impact of photovoltaic power plants on far-field effects of UVCEs

Keywords: photovoltaic power plants, premixed gaseous flame propagation, overpressure effects distances, CFD

Photovoltaic power stations are developing worldwide as they emit no greenhouse gases while producing electricity. In France, industries such as Total plan to install such stations in fields surrounding their chemical and refining plants. The risk analyses performed for these plants often highlighted Unconfined Vapor Cloud Explosions scenarios, related to heavy gases releases, for which characteristic overpressure effect distances were computed The present work aims at quantifying the impact of the presence of a photovoltaic power station in a potential flammable cloud. It is nevertheless limited to a station geometry provided by Total.

The problem is not straightforward as two main physical trends appear: flame acceleration due to panels obstruction and flame extinction when the flame reaches the top of the flammable cloud. It has first to be determined if explosion runaway is possible, this one due to the obstacles repetition on the flame path. Then, characteristic overpressure effects distances can be computed for photovoltaic power plants of any size.

To this goal, a CFD-based method is proposed and detailed. The open-source CFD code OpenFoam is used as well as phenomenological considerations for computing characteristic overpressure effects distances.

Introduction

Some industries such as Total own large lands surrounding their production plants such as refineries. They are often unused and free of any building and therefore perfect candidates for installing photovoltaic power plants. For some of these, scenarios of Unconfined Vapor Cloud Explosions (UVCE) were identified during risk analyses. This kind of phenomenon is related to the potential release of flammable products followed by an ignition of the generated flammable cloud. In the French regulation, the UVCE overpressure effects are quantified by the distances at which the characteristic thresholds of 200, 140, 50 mbar are reached. These distances give regulatory perimeters which constrain land-use planning. Installing photovoltaic power plants requires to check if the preexisting overpressure effects distances would be significantly modified by the presence of rows of panels in the flammable cloud.

A study co-funded by Total and INERIS was carried out in order to develop a method for quantifying overpressure effects related to the explosion of a flammable cloud covering photovoltaic panels representative of the ones that could be installed by Total around its production plants. This partnership enabled INERIS to gain expertise for modelling dangerous phenomena appearing with the development of photovoltaic power plants and Total to develop a philosophy for their safe positioning. The paper details some elements of the study.

Scenario and problem description

Scenarios of massive releases of heavy hydrocarbons such as propane or equivalent medium reactivity gases could be identified for industrial plants. Due to buoyancy effects, such products when mixed with air generate "flat" flammable clouds ie with a maximum height about 3-4 m and a length in order of hundreds of meters. The flammable cloud size is limited by the iso-surface with a concentration that equals the lower flammability limit.

Current projects plan the installation of photovoltaic power plants around some industrial plants. A design example was supplied by Total and is shown in the Figure below. In this case, a rotation of the panels is possible. For this work, two bounding cases were considered: parallel to the ground or inclined with an angle of 45° as in Fig. 1. The panels width and the distance between the rotation point and the ground are about a few meters. A few meters also separate the panels rows.

Fig. 1. Cross-section of a photovoltaic power plant designed by Total

The explosion scenario deals with a flammable cloud that can theoretically cover dozens of rows of panels at full height. Before any modelling, the regarded phenomenology should be analysed.

According to experimental works, flat flammable clouds generate slow flames (ie the flame speed is about a few m/s) when there is no obstruction in the flammable zone [START_REF] Zeeuwen | Experimental investigation into the blast effect produced by unconfined valpour cloud explosions[END_REF]. Furthermore, according to theoretical works (Daubech, 2016), two explosion phases should be distinguished in the case of an ignition source in the flammable zone: a spherical or hemispherical phase during which the burnt gases expansion contributes to the flame propagation followed by an azimuthal phase during which this effect is lost. Two theoretical expressions were derived from the work of Daubech et al. for quantifying the far-field pressure generated by each phase. In the case of flames with a constant speed, they write:

Eq. (1) ∆ , ≈ -1 Eq. (2) ∆ , ≈ -1
is the atmospheric volume mass, H is the flammable cloud height, is the burnt gases expansion rate and is the constant flame speed. This set of equations highlights that far from the flame the azimuthal phase pressure is about 2 times lower than the hemispherical phase pressure.

If there are repeated obstacles in the flat cloud, it can be questioned if the relative importance of each phase in the pressure generation process is kept. In case of ignition in the power plant covered with a flammable cloud, the flame is first spherical, then interacts with the closest panel or panels leading to a distortion which is prone to increase the burned gases production rate. The flame at a given moment reaches the lower flammable concentration at the top of the cloud and a part of the flame front quenches. This latter phenomenon induces a progressive venting of the burnt gases which contributes to slow down the flame whereas in the same time, the flame keeps on propagating in the direction parallel to the ground. During the propagation across the rows of panels, there is a permanent competition between an increase in pressure generation due to flame distortion and venting due to a a flame reaching the flammable cloud limits.

Previous experiments showed the separation distance between obstacles could be a key parameter for pressure effects [START_REF] Van Der Berg | Research to improve guidance on separation distance for the multienergy method (RIGOS)[END_REF]. If the obstacles are too close, there can be a continuous acceleration from an obstacle to another leading to a runaway phenomenon. The maximum overpressure in the flammable cloud is then limited by the acceleration length for the flame ie the minimum length between the flammable cloud length and the photovoltaic power plant length. Both of them being about hundreds of meters, an explosion runaway cannot be excluded theoretically on this basis.

A practical consequence of the occurrence of such a phenomenon when quantifying the regulation overpressure effects distances is to account for all the flammable mass of the cloud and to assume maximum overpressure in the cloud about several bars. Such assumptions could lead to large overpressure effect distances and restrict the installation of photovoltaic plants.

A method has to be proposed to study the runaway risk and to quantify the regulation effects distances. In the current paper, a modelling approach was chosen.

A method mixing CFD and phenomenology

Modelling tools such as the CFD software FLACS [START_REF] Hansen | Improved far-field blast predictions from fast deflagrations, DDTs and detonations of vapour clouds using FLACS CFD[END_REF] were developed for addressing complex set-ups at the industrial scale. In short, it relies on a URANS approach for modelling turbulence and obstacles are partly resolved by the mesh. A sub-grid porosity accounts for unresolved obstacles. This porosity as well as other physical parameters were calibrated against a large number of explosion trials involving obstacles at large-scale.

This tool was first employed to get a first guess of the pressure effects related to a UVCE developing in a photovoltaic power plant. The related computations did not highlight a global flame acceleration phenomenon. The computed overpressure levels were not significant as they were lower than 20 mbar, except in a few points of the geometry. These punctual peaks dissipated in a few meters and had no effect on the global pressure field.

In order to confirm these results, another approach based on CFD is proposed for modelling a potential explosion in the photovoltaic power plant. For studying the risk of explosion runaway, there must be a few rows of photovoltaic panels in the computational domain whose topology should ideally be 3D. The computational domain has to be a few dozens of meters long in each direction. This first aspect if of importance as computation resources are limited. They will limit the minimum size of the cells of the mesh.

At the moment, it may appear tricky to choose a relevant set of physical sub-models when using CFD to account for large-scale explosions mechanisms. Even an explosion in a flat flame is complex as the following physical effects may appear: interaction of the flame with the wind, self-acceleration due to instabilities more or less pronounced with the fuel and local equivalence ratio and partly explained by non-unitary Lewis number and Markstein length effects. The latter physics requires a mesh fine enough to get a realistic flame thickness and a description of differential diffusion of species and temperature.

Adding obstacles in the cloud, other physical phenomena appear and have to be accounted for, especially if the solar panels rows are inclined: a Karman vortex street in the wake of obstacles, with a characteristic Reynolds number increasing as the flame approaches obstacles, an acceleration of the flame and the fresh gases in front of it between the ground and the bottom of the panels.

Concerning turbulence modelling, Large-Eddy Simulations are attractive as they tend to become the state-of-the-art for engineering problems and get more and more precise as the mesh is refined, limiting the modelled part. Nevertheless, the validity of sub-grid scale models for turbulence and flame/turbulence could be questioned for cells with a characteristic width of a dozens of centimeters. In engineering computations, the characteristic cell width is lower than a millimeter. Furthermore, even well-resolved middle-scale explosions showed a pressure field depending on the chosen turbulent/flame SGS model [START_REF] Sarli | Sub-grid scale combustion models for large eddy simulation of unsteady premixed flame propagation around obstacles[END_REF][START_REF] Quillatre | Simulation aux grandes échelles d'explosions en domaine semi-confiné[END_REF]. URANS modelling works were proposed for modelling a methane flame acceleration [START_REF] Lecocq | Further insight into the gas flame acceleration mechanisms in pipes. Part II: numerical work[END_REF] in a 24-m long tube but there is no guarantee all the chosen parameters could be re-used at larger scale with a global 3D topology flame.

A modelling method grounded on CFD and phenomenological considerations is proposed and tested in what follows.

a. Modeling method

The current work does not deal with the flammable product dispersion and the flammable cloud generation. It focuses on the explosion stage only.

The 3D flame propagation is modelled with a CFD approach. The transport equations of pressure, momentum, energy, chemical species and progress variable are solved numerically by the OpenFoam code [START_REF] Weller | A tensorial approach to computational continuum mechanics using object-oriented techniques[END_REF], version 5. The solver is compressible. The chemistry is addressed with a onestep reaction and only propane, oxygen, nitrogen, carbon dioxide and water mass fractions are transported. There is no turbulence model. The modelling mainly aims at obtaining a conservative wrinkling factor for the flame during its propagation.

A turbulent premixed flame can be modelled with a flame surface-density (Σ ) approach. This quantity is approximated as Ξ|∇ |, where Ξ is the wrinkling factor of the flame related to an increase of the un-resolved part of the flame surface and is the progress variable. The chemical source terms appearing in the transport equations of the progress variable and chemical species mass fractions can be closed as [START_REF] Lecocq | Hybrid presumed pdf and flame surface density approaches for Large-Eddy Simulation of premixed turbulent combustion Part 1: Formalism and simulation of a quasi-steady burner[END_REF]:

Eq. (3) = ! " # Ξ|∇ |
Eq. ( 4)

$ % & = ' ( ) -' ( !
Where ! is the volume mass of the fresh gases, " # is the laminar flame speed, ' ( ) (resp. ' ( ! ) is the burnt (resp. fresh) gases mass fraction of the i th chemical species.

This model does not include differential diffusion between temperature and chemical species. If Ξ is set to 1, in case of ignition in a flammable medium at rest with an homogenous concentration, the model accounts for a spherical flame that propagates at a speed: burnt gases expansion rate times the laminar flame speed. Some un-resolved phenomena at grid scale can be included through modifications of the wrinkling factor.

According to some works (Daubech, 2016), a spherical flame propagation without obstacles can be analytically described. The flame speed *(+ can write as: Eq. ( 5)

*(+ = = " # Ξ ,-./0
Where is the burnt gases expansion rate and Ξ ,-./0 is the wrinkling related to the wind and intrinsic flame instabilities. This latter wrinkling factor was written as: Ξ ,-./0 = Ξ 1 Ξ ,2 with Ξ 1 the wrinkling related to instabilities solely and Ξ ,2 the wrinkling related to the turbulence of the wind solely. This choice is cautious as recent works have shown at lab scale that depending of the flame position in the theoretical combustion diagram an effect was dominant on the other but there was no combination of the effects [START_REF] Yang | Role of Darrieus-Landau instability in propagation of expanding turbulent flames[END_REF]. For a propane flame, the asymptotic value of Ξ 1 and predicted by theory, depends only on the burnt gases expansion rate and is about 3.5. The wind effect is computed with the Gülder correlation [START_REF] Gülder | Inner cut-of scale of flame surface wrinkling in turbulent premixed flames[END_REF] and an integral length scale for the atmospheric turbulence about 1 m and a fluctuating speed about 0.5 m leading to a value of 8 for Ξ ,2 . Adding all these contributions, mathematically speaking by multiplying the wrinkling factors, the theoretical flame speed during the hemispherical phase of flame propagation is about 90 m/s.

Recent experimental works [START_REF] Bauwens | Experimental study of spherical-flame acceleration mechanisms in largescale propane-air flames[END_REF] gave for a stoichiometric spherical propane/air flame propagating on a length about 1 m, in a medium initially at rest, an increase of flame surface about 1.5 explained by Darrieus-Landau instabilities. In the target computation, the flame is interacting with photovoltaic panels roughly one meter after ignition. In order not to be too much conservative and avoid artificial explosion runaway, in the following, Ξ 1 is set to 1.5. The reference propane/air flame propagates at speed of 45 m/s when undergoing burnt gases expansion effects and 6 m/s when it is no longer the case. A case with propylene gives a minimum flame speed of 55 m/s and 7 m/s. The flame speeds for the spherical phases have the same order of magnitude as encountered with the propagation of a spherical stoichiometric hydrogen/air flame [START_REF] Schneider | Fraunhofer-ICT Internal Report: PNPSichcrheitssofortprogramm, Prozebgasfreisetzung-Explosion in der Gasfabrik und Auswirkungen von Druckwellen auf das Containment[END_REF]) without wind effects. They are one order of magnitude greater than the ones measured in a propane/air explosion free of obstacles. Both observations mean the computed flame speeds remain conservative.

b. First tests

Free fields tests were performed with CFD in a 3D computational domain with 15 cm wide cells in the zone of interest (measured flame and pressure wave propagation). A flammable cloud at rest with a homogenous propane/air concentration was initialized, the height of the cloud being 4 m, the halflength of the cloud being 20 m. The wrinkling factor was tuned to get a minimum flame speed of 72 m/s during hemispherical phase. The computation was redone with another mesh made of 10 cm wide cells in the interest zone.

The results in terms of overpressure at ignition point and 30 m away, out of the flammable cloud are shown in Fig. 2. It can be seen that the same hemispherical phase is predicted at ignition point by both computations. Out of the flammable cloud, the same maximum overpressure is recovered. It means the wave propagation is weakly impacted by the mesh resolution. The azimuthal phase is nevertheless different in both cases: the maximum overpressure at ignition point is about 65 % higher with the fine mesh. Out of the flammable cloud, the same gap is retrieved. This difference between the two meshes is explained by a higher resolved flame wrinkling during the transition between the spherical and the azimuthal phase (Fig. 2b). It is explained by a limit of the proposed CFD model: the added wrinkling factor does not depend on the mesh size whereas if the mesh is refined, more wrinkling appears at the resolved level. The overpressure values can be compared with the theory. According to [START_REF] Schneider | Fraunhofer-ICT Internal Report: PNPSichcrheitssofortprogramm, Prozebgasfreisetzung-Explosion in der Gasfabrik und Auswirkungen von Druckwellen auf das Containment[END_REF], for a constant flame speed flame with a flame speed lower than 120 m/s, overpressure in the burnt gases writes:

Eq. ( 6 For a flame propagating at 72 m/s, Eq. ( 6) gives 78 mbar whereas a value of 80 mbar was obtained with CFD. Eqs. ( 1) and ( 2) can be used to predict the overpressure field out of the flammable cloud. For a constant flame speed of 72 m/s (resp 9 m/s) during the hemispherical (azimuthal) flame propagation, 30 m away from ignition point, the maximum overpressure is 14 mbar (resp. 1 mbar).

Both CFD computations predict larger values: the overestimation is about 100 % for the hemispherical phase and one order of magnitude for the azimuthal phase. The computation performed with a reference mesh being conservative when compared with theory, it is kept for the computations with a photovoltaic power plant.

CFD computations with a photovoltaic power plant a. Parametric tests

The reference situation is the one of a stoichiometric propane/air cloud at rest covering the photovoltaic panels. Several parameters were of interest: the orientation of the photovoltaic panels (parallel to the ground or inclined), the separation distance between two rows, the height of the cloud and the reactivity of the cloud (propane/air or propylene/air mixture). The computations to be performed to investigate some trends are numerous. It was chosen to perform these computations with an assumption of a 2D-symmetry for a flame propagating perpendicularly to the rows. It implicitly means the flame is cylindrical and the width of the panels is infinite.

The cell width in these computations is 3 cm. When comparing the burnt gases pressure for a given flame speed with theory, the overestimation of the 2D mesh is about 100 %. The computations that follow give overpressures exceeding real ones but enable to identify the main trends, changing a parameter at a time. The criterion between two computations is the overpressure out of the flame. The Fig. 3 details through a zoom on the computational domain the location of main pressure probes relative to the flammable cloud and the photovoltaic panels. In what follows, a 45 m/s (resp. 55 m/s) flame is a flame propagating with a speed of 45 m/s (resp. 55 m/s) during the hemispherical phase. Computations for a 55 m/s flame propagating in a power plant with either flat or inclined panels show that the far-field pressure effects are higher in the second case (Fig. 4). It tends to confirm that the obstruction and related flame acceleration have a stronger effect on the pressure field than the venting effect between two rows of panels. This result is coherent with the experimental data of [START_REF] Zeeuwen | Experimental investigation into the blast effect produced by unconfined valpour cloud explosions[END_REF]. A propane/air flame was propagating in a 25 m x 25 m array of regularly spaced vertical tubes with a diameter of 1 m below a full roof. In the array, despite the lack of venting at the roof a maximum pressure of 20 mbar was measured.

Another computation with inclined rows of panels highlights the stronger acceleration (Fig. 5) on a side of the power plant (towards the left in Fig. 3), giving an increase in overpressure about 50 % when compared with the other side.

A 55 m/s flame gives overpressure effects of the same order than a 45 m/s flame when it propagates in inclined rows of panels (Fig. 6). The increase in overpressure is nevertheless about 50 %.

The height of the cloud also plays a role in pressure effects. For half a cloud normally covering the power plant, the far-field pressure peak is about half the pressure peak obtained with a full height cloud (Fig. 7). The venting occurs earlier enabling to reduce pressure effects for the smallest cloud. Finally, the highest far-field effects are obtained in the case of a flammable cloud fully covering the power plant, with inclined panels and a quick flame propagating to the left (see Fig. 3).

b. 3D tests and overpressure effects distances

3D CFD computations were finally performed for inclined panels fully covered by a stoichiometric flammable cloud at rest. As mentioned in the beginning of the paper, a first goal was to check if an explosion runaway can occur in the power plant. To do so, the overpressure is computed at a given distance from the side of the flammable cloud. The number of panels is increased from 4 to 6 as well as the size of the flammable cloud to keep all of them covered. The pressure signal computed far from the flammable cloud for the case of the 45 m/s flame are shown in Fig. 10. In all cases, the first pressure peak is related to an interaction of the flame with the two first panels. This first peak is followed by N other pressure peaks where N equals the number of rows minus 2. The final pressure peak which is all the time the highest seems to reach a plateau in terms of magnitude with an increasing number of rows. An explosion runaway can be discarded regarding these aspects.

Almost the same set of characteristic curves is obtained with a 55 m/s flame (Fig. 11). The conclusions just above remain valid.

For both flames, the threshold of 50 mbar is reached about 20 m from the border of the cloud. This distance effect is higher than the one obtained without obstacle. Installing a photovoltaic power plant may have an impact on the regulatory overpressure effects distances but it remains moderate.

Conclusions

Total and INERIS worked on the topic of the potentially aggravating effect related to the presence of a photovoltaic power plant in flammable cloud in case of an UVCE. The study was limited to a geometry of a power plant supplied by Total and a flammable cloud formed with hydrocarbons such as propane and propylene.

First FLACS computations showed the pressure effects in the flammable cloud remain below 20 mbar. In order to confirm these results, a method based on phenomenological considerations and CFD was proposed. It appeared with this latter that no explosion runaway occurred for the regarded cases and the maximum distance at which an overpressure of 50 mbar was reached was 20 m from the side of the cloud. Installing a power plant, with the restrictions mentioned in the first paragraph, has an impact when compared to a situation with no obstacle but it remains moderate. These conclusions are globally in line with FLACS simulations, although a bit more restrictive.

  Fig. 2. a) Computed pressure signals at ignition point (line) and 30 m from ignition point (dash line). Blue: reference mesh. Red: fine mesh. b) Propane mass fraction contours 300 ms after ignition. Top: reference mesh, bottom: fine mesh.

Fig. 3 .

 3 Fig. 3. Location of the pressure probes relative to the flammable cloud and the power plant.

Fig. 4 .Fig. 5 .

 45 Fig. 4. Overpressure effects 45 m on the left of ignition point. Case of a 55 m/s flame and inclined panels. Blue line: flat panels. Red line: inclined panels.

Fig. 6 .

 6 Fig. 6. Overpressure effects 45 m on the left of ignition point. Case of 45 m/s and 55 m/s flames and inclined panels. Blue line: "quick" flame. Red line: "slow" flame.

Fig. 7 .

 7 Fig. 7. Overpressure effects 45 m on the left of ignition point. Case of a 55 m/s flame. Blue line: flammable cloud covering the full height of the power plant. Blue line: cloud covering half this height.

Fig. 8 .

 8 Fig. 8. Principle of the 3D CFD computations. The number of panels the flame interacts with increases from 4 to 6.The cell width in the zone of interest is 15 cm. A 45 m/s flame representative of propane/air mixture and a 55 m/s flame representative of propylene/air mixture are tested (Fig.9).

Fig. 9 .

 9 Fig. 9. Example of a CFD result related to flame/solar panels interaction. Shown: pressure fields, flame front (orange), solar panels (dark blue).

Fig. 10 .

 10 Fig. 10. Computed overpressure 10 m from the flammable cloud border for the cases of no obstacle, 4, 5 and 6 rows of panels in the flammable cloud. Case of the "slow" flame.

Fig. 11 .

 11 Fig. 11. Computed overpressure 10 m from the flammable cloud border for the cases of no obstacle, 4, 5 and 6 rows of panels in the flammable cloud. Case of the "quick" flame.