Pierre Hennebert
email: pierre.hennebert@ineris.fr

Arnaud Papin

An analytical protocol for the knowledge of waste by substances

Keywords: Hazardousness, Substances, Mass balance, Waste classification

The hazardousness of waste could be soon assessed in Europe by the hazard properties of its constituents. A quasi exhaustive knowledge of its constituents will therefore be necessary. A conceptual scheme of waste composition is proposed for analysis purpose, including unresolved pools of probably higher molecular weight organic substances supposed to be less bioactive and less hazardous ("non extractible organic compounds", "unidentified volatile compounds" and "unidentified semi-volatile compounds"). Screening ICP methods are used for major elements, and screening GC MS methods are used for volatile and semi-volatile organics. 32 laboratory samples of different industrial wastes have been tested following (with differences) this protocol by two (routine) service laboratories, with about 7 000 parameter results. A satisfactory analytical balance of 90 % is reached for 20 samples (63 % of the samples) during this first run, with identified reasons for most of the unsatisfying results. A first exploratory classification of the wastes for their hazardousness according to the Seveso legislation was performed based on data from the (chemical) CLP regulation. Using the CLP data, out of 32 samples, 27 (84 % of the samples) were classified identically by the two laboratories (23 not hazardous and 4 hazardous). Using additional EC 50 data, out of 32 samples, 27 (84 % of the samples) were classified identically by the two laboratories (13 not hazardous and 14 hazardous).

Introduction

The hazard of the wastes must be assessed for regulatory compliance. The on-going work of the European member states for the update of the Waste Directive defines 14 or 15 hazard criteria, mainly based on the properties of substances and the classification, labeling and packaging of substances and mixtures regulation (CLP, EC 1272/2008). Wastes will be considered as mixtures of substances, and their properties assessed by calculation from the properties of the substances, or from tests. Since tests are not available for all criteria, wastes should be known by total substances content. A protocol is proposed for this. It has been applied to 15 solid and 17 liquid wastes. Fully detailed protocol and results (40 p) will be submitted to a scientific journal for publication.

Materials

The professional associations of hazardous waste handling (SYVED, SYPRED) proposed, in consultation with the French Ministry of Ecology (MEDDTL), a list of wastes to be studied. They sent laboratory samples to INERIS. The professional association of hydraulic binders (ATILH) added two samples of alternative fuels in cement kiln. These samples were supposed to be representative of the 'pool' of waste collected (constitution of primary samples, mixing -homogenization, quartering, and so on, to produce a laboratory sample of about 5 kg or 5 liters). Among these samples, three shredded packaging and contaminated materials have been received, however, in 200 liter drums. A large panel of 15 solid and 17 liquid wastes covering most types of industrial wastes (listed in Table 1) has been studied.

Methods

Analytical protocol

The protocole should fulfill the following constraints: (i) the sum of measured concentrations (including indices and groups) should reach 90 et 110 % w/w ; (ii) the fractions, indices and groups should be minimized ; (iii) the measurements of metals and anions should allow, with other information sources, to build a possible mineralogical composition ; (iv) the analytical effort must be adapted to the final output, frequently binary (hazardous/not hazardous) ; (v) the results must allow the classification of the waste according to different regulations (Waste directive, Seveso directive, …). A protocole (4 pages) mainly using CEN standardized methods is proposed (Hennebert, 2011a). In brief, the water content, the sum of total major elements and metals content, the ash content less sum of metals, the sum of the volatile and semi-volatile compounds and the unidentified organic compounds (see below) are added and should be close to 100% (Figure 1). Anions (total halogens, free and bound cyanides, chromium VI) are measured to compute possible mineralogical phases of the elements. New analytical parameters are proposed: for solid wastes, "non extractible organic compounds" is the mass lost by calcination of the dried solid residue remaining after the extraction of semi-volatile substances.

The aim is to globally quantify cellulose, lignin and polymers and organic compounds with high molecular weight, which are assumed not being hazardous. For liquid wastes, the parameters "unidentified volatile compounds" and "unidentified semi-volatile compounds" are calculated from the unresolved chromatographic areas of the corresponding chromatograms.

During the analytical campaign of 32 wastes, some amendments were brought to the protocol. They were not always applied by the laboratories, in particular the quantitative methods for individual chlorinated compounds and the use of 3 response factors for chromatographic calibration. One amendment was added after the campaign.

As the protocol is intended to be used routinely, the analyses were intentionally performed by two commercial service laboratories. Some methods are left open in the protocol, and some methods of the protocol were not followed. Others were more precisely defined during the analysis campaign. There were some deviations to the protocol , in particular the parameters "unidentified volatile compounds" and "unidentified semi-volatile compounds" were replaced by Lab1 by "volatile total petroleum hydrocarbon C5-C9" and "total petroleum hydrocarbon C10-C40", and the "non-extractable organic substances" was biased for Lab1 since the aliquot for the extraction of semi-volatile substances has suffered water extraction prior to extraction, drying of residual solvent and water, and calcination (at 500 °C rather than 550 °C), resulting in a loss of mass and hence an over-estimation of the "non extractable organic substances".

Laboratory Sample 100%

Loss on ignition 550 °C (including carbonates and cyanides)

Elements and metals

Detailed results are available at (Hennebert, 2011b).The measure of major and minor elements by ICP is semi-quantitative in the applied protocol (except the 12 "heavy metals" and Si). The output of the analyses is then a given range of concentration, different for the two laboratories. The center of the range is taken as result for the parameter. The concentrations are summed per sample in Figure 2. The correlation between laboratories is obvious but should for some samples and elements be improved.

Limits of quantification from 0.1 to 10 mg kg -1 (depending on samples) for mercury and 0.1 and 5 mg kg -1 for cadmium have been reported, and therefore used. Given the potential impact of the concentration of cadmium and mercury on the classification of waste hazardousness, it is recommended to reach a limit of quantification of 1 mg kg -1 for Hg and Cd. 2 and3. Some results of Lab1 are over-estimated (S1 to S5) or underestimated (S8-TRI).

Figure 3 . Non-extractible organic compounds (Lab2 vs Lab1)

Volatile and semi-volatile organic compounds

These two families were gathered because the same compounds were sometimes assigned by laboratories in different families. Lab1 conducted further analysis "Total hydrocarbons C10-C40" on the liquid samples, but not the "unidentified semi-volatile organic compounds". The Lab2 used the bottle "fresh" or "raw" for the analysis of semi-volatile, instead of using the bottle pretreated and computed the "unidentified semi-volatile organic compounds" of liquid samples as "integration> C10". Therefore, direct comparison of "unidentified semi-volatile organic compounds" is not possible between laboratories. The results for volatile organic compounds and semi-volatile compounds are presented in Figure 4 and in Tables 2 and3. The list of molecules is not consistent between laboratories. For molecules found by the two laboratories, the concentrations are not consistent between laboratories at concentrations below 0.1 %. An improvement could be to use three standards to quantify the substances, in place of one standard. The concordance between laboratories should be improved. The chromatographic conditions used should maybe be closer to the identification parameters of the molecules from the database. It should be noted that a therapeutic substance was detected by the two laboratories at low concentrations in a solvent from a pharmaceutical industry, showing that a fine detection of specific compounds is still possible.

If a petroleum mixture is identified, it could be quantified with the corresponding standard, and the corresponding surface subtracted from the response surface of the unidentified compounds.

Further analyses of substances important in the regulation were performed for some samples, by routine quantitative methods of the laboratories (methanol, dioxins and furans concentrations, isocyanates, organic lead derivatives (tetra methyl, tetra ethyl), PCBs.

Analytical mass balance

The analytical mass balances of the solid sample results are presented in Table 2. Values between 90% and 110% are highlighted in yellow.

For Lab1, in three out of four cases where the mass balance is less than 90%, the "ash content minus the sum of metal" is negative, while there are no cases for Lab2. These 3 cases are samples of shredded packaging with contaminated materials. This indicates, for each sample, an insufficient homogeneity of the laboratory sub-sample. This can be related to the visual high heterogeneity of the samples. It is the recommended to start from a laboratory sample of at least 30 kg of these materials, and to measure the calcinated residue with a test portion of about 30 g of ground (< 1 mm) material.

The mass balance lies between 90% and 110% in 10 cases of 15 for Lab1 and Lab2 (67% of the samples). This seems satisfactory for a first run of the protocol by routine service laboratory with deviations from the recommended methods. For comparison, a campaign analysis of solid and liquid recovered fuels (Hennebert, P., 2011b) showed that the median of the sum of known substances (the analytical mass balance) for 64 samples was 46% by weight.

The analytical mass balances of the liquid samples are presented in Table 3. Values between 90% and 110% are highlighted in yellow. The calculated parameter "ash content -sum of metals" is negative 8 and 6 times for Lab1 and Lab 2 respectively. Lab1 handled the volatile hydrocarbons C5-C10 (volatile Total Petroleum Hydrocarbons) and the standard C10-C40 (TPH) as a result for the "unidentified volatile compounds" and "unidentified semi-volatile compounds" respectively. The mass balances are therefore not strictly comparable. The mass balance relies between 90% and 110 % in 10 cases of 17 for Lab1 and 9 cases of 17 for Lab2 (respectively 59 % and 53 % of the samples). This encouraging result could be improved. When the water content is> 90%, the sum of known compounds exceeds half of the dry matter in 3 cases of 5 to Lab1 and 1 case of 6 for Lab2. For hydrocarbons containing vegetable oil (sample S17), the method must be developed. Oily waste likely to contain vegetable oil should be saponified before injection so that the fatty acid products are detected in the group of semi-volatile. Otherwise, this oil will not be detected. Information on the potential presence of vegetable oil should be provided in advance by the holder of the waste.

So, it is possible with the new pooled parameters to complete the mass balance. Despite discrepancies for some parameters, a satisfactory analytical balance of 90 % is reached for 20 samples of 32 (63 % of the samples) during this first run. Technical improvements of the majority of unsatisfying results are identified.

Classification of hazardousness of waste according to the Seveso Directive

For hazard assessment according to the Seveso Directive (using concentration limits of the Dangerous Preparation Directive -DPD 1999), the total metal content must first be converted into mineral species content, since hazard properties are defined for species and not for element. A first conservative estimation was performed by arithmetically converting the total content of each element in the most hazardous form of each element, taking into account the stoechiometry of the elements in the waste sample [START_REF] Rebischung | Reconstitution d'une spéciation des éléments totaux en minéraux dans les déchets en vue de la détermination d'un potentiel de danger dans un objectif de classement SEVESO[END_REF]. For that, the hazard properties of the elements for human or environmental toxicity listed in the CLP regulation were used (CLP Annex, 2011). Exclusion of some minerals based on basic consideration of pH, pe and knowledge of waste was also integrated in this computation.

The content of organic and mineral compounds were then summed by hazard properties, and compared with the limit values (taking into account eventual additivity of properties) of the CLP regulation. This exercise was done with generic limit values given by the CLP and also, in a second run, with additional minimal aquatic ecotoxicity values EC 50 from the literature (no evaluation of those EC 50 was performed). The resulting classification of the waste (hazardous/non hazardous for human toxicity T+ and T and aquatic environment N R51 and N R50) is the following: Using the CLP data, 23 out of 32 samples are not classified as hazardous from the two sets of data (one set per laboratory), 4 are classified as hazardous by the two sets of data (but largely due to different substances), 1 is classified as hazardous by the two sets of data (but for different hazard), and 4 are classified as hazardous by only one set of data ; Using additional EC 50 data, 13 out of 32 samples are not classified as hazardous for the two sets of data, 14 were classified as hazardous by the two sets of data, and the other 5 were classified as hazardous by only set of data (largely because different substances are found by the two laboratories, and secondly because the concentrations found are different). So using the CLP data and using additional ecotoxicity values, 27 samples out of 32 (84 % of the samples) are classified identically by the two laboratory data sets.

Conclusion

A set of 32 samples was analyzed by 2 service laboratories, giving a total of 7 000 data, from which mass balances were calculated. Despite deviations to the protocol, the mass balance lies between 90 and 110% for 20 samples of 32 (63% of the samples). Most deviations are explained and the protocol has been amended during the campaign to improve this encouraging result. A first exploratory classification of 32 wastes was performed with the protocol results. 27 samples out of 32 (84% of the samples) are classified identically by the two laboratory data set.

A first version of the protocol is included in a French application guideline of the SEVESO II Directive (MEDDTL 2011). The eco-industries started at autumn 2011 a wider analysis campaign, which will probably give insight to additional improvements to cover a wider variety of wastes (i.e. waste from electric and electronic equipment -WEEE).

  Figure 1. Conceptual scheme of waste composition

Figure 2 .

 2 Figure 2 . Sum of ICP-measured element content (Lab 2 vs Lab1)

Figure 4 .

 4 Figure 4 . Sum of organic volatile and semi-volatile substance (Lab 2 vs Lab 1)

Table 1 .

 1 Waste samples

	Solid/Name Waste			European List of Waste code, origin of waste
		Air pollution control	19 01 07* (Wastes from incineration or pyrolysis of waste, Solid
	S1	(APC)	residue,	waste from gas treatment)
		bicarbonate process
	S2	APC residue, lime process 19 01 07* (see above)
	S3	MSWI fly ash			19 01 05* (Filter cake from gas treatment)
	S4	waste #1 APC residue industrial	19 01 07* (see above)
	S5	waste #2 APC residue industrial	19 01 07* (see above)
	S6	ash Industrial waste bottom	19 01 11* (Bottom ash and slag containing dangerous substances)
	S7	aluminum industry Metallic dust	from	10 03 19 * (Flue-gas dust containing dangerous substances)
	S8-DON	Packages and materials #1 No information
	S8-GEO	Packages and materials #2 19 12 11* (Other wastes (including mixtures) from the mechanical treatment of wastes containing hazardous substances)
	S8-SAR	Packages and materials #3 19 12 11* (see above)
	S8-SCO	Packages and materials #4 15 01 10* (Packaging containing residues of hazardous or contaminated by residues).
	S8-TRI	Packages and materials #5 No information
	S9-GEO	Pasty waste #1			19 08 13* (Sludges containing dangerous substances from other industrial water treatment plant)
		Pasty waste #2			08 01 13* (Sludges from paint or varnish containing organic
	S9-SCO				solvents or other dangerous substances), 08 04 11* (Adhesives and sealants sludges containing organic
					solvents or other hazardous substances)
	S18	Solid recovered fuel		19 02 09*Solid fuel waste containing hazardous substances
	Liquid/Name Waste			European List of Waste code, origin of waste
	S9-SAR	Pasty waste #3			Mix of storage tank
	S10	Engine oil			13 02 08* (Other motor oils, gear and lubricating)
	S11	Hydraulic oil			13 01 13* (Other hydraulic oils)
	S12-SON	Hydrocarbon #1			13 07 03* (Wastes of liquid fuels, Other fuels (including mixtures))
	S13-SCO	Hydrocarbon #2			13 05 07* (Water mixed with oil from oil / water separators), 13 07 03* (see above)
	S13-SON	Hydrocarbon #3			Mixture of wastes of oils and liquid fuels without motor and lubricating oil and hydraulic oil
	S14-PCX	Halogenated solvent #1	07 01 03* (Organic halogenated solvents, washing liquids and mother liquors)
	S14-SAN	Halogenated solvent #2	No information
	S14-SAR	Halogenated solvent #3	14 06 02* (Other solvents and mixtures of halogenated solvents)
		Non-halogenated solvent	Other solvents, washing liquids and mother liquors from :
		#1			07 01 04* (Wastes from the manufacture, formulation, distribution
					and use (MFSU) of basic organic chemicals), 07 02 04* (idem of
	S15				plastics, rubber and synthetic fibers), 07 05 04* (idem of
					pharmaceuticals), 07 06 04* (idem of fats, soaps, detergents,
					disinfectants and cosmetics), 07 07 04* (idem of chemicals from the
					fine chemicals and chemical products not elsewhere specified)
	S16-CHI	Waste water #1			16 10 01* mixture of (Aqueous liquid wastes destined for off-site treatment, Aqueous liquid wastes containing dangerous substances).
		Waste water #2, mixture	08 04 16 (Waste from MFSU of adhesives and sealants (including
	S16-DUC	of 13 wastes			waterproofing products, Aqueous liquid waste containing adhesives or sealants other than those mentioned in 08 04 15), 11 01 06*
					(chemical surface treatment and coating of metals), 11 01 11*
					(rinsing liquids), 11 01 13* (degreasing wastes), 12 01 09* (shaping

* Liquid fuel waste containing hazardous.

Table 2 .

 2 Analytical mass balance of solid samples

	Concentration (%)	Lab1	Lab2
	Sample	Total	Total
	S1	126.00	99.96
	S2	98.70	99.83
	S3	99.40	99.59
	S4	91.21	99.81
	S5	92.31	99.80
	S6	97.26	95.83
	S7	95.05	101.35
	S8-DON	64.91	80.04
	S8-GEO	75.60	89.60
	S8-SAR	104.59	91.34
	S8-SCO	84.52	100.50
	S8-TRI	40.88	67.45
	S9-GEO	100.03	85.24
	S9-SCO	100.80	79.41
	S18	98.61	94.39

Acknowledgements

This work was supported by the Ministry for Ecology, France (www.ecologie.gouv.fr). The wastes samples have been provided by professional associations SYVED, SYPRED (wastes) and ATILH (cement industry). The protocol uses previous results of Holcim (cement) and Micropolluants Technologie (laboratory) companies. The contributions of Mr François David (Research and Development Manager, SGS Multilab) and Christophe Allamelle (Development Engineer, Eurofins laboratory) were essential to this work. The work of Mrs Pauline Molina (treatment of samples) and Ms. Flore Rebischung (speciation of metals), both from INERIS, was greatly appreciated


T Balouin

A Lahoz

C Bolvin

Y Flauw

Risk Assessment Required In The Framework Of New French Regulation hhdTTjjhkljdjjsgshjhfsdkjhskslsl;s;s;;s;;s;;sjsjkjffffrtttttttfggjfgjgkfkjkjf fffffjfjjfkkfjjj On Dams Methodology Developed By INERIS

Keywords: Major accident, Risk Analysis, Severity, Probability, Risk Acceptability

The new French dam safety regulation classifies each dam in 4 classes depending on its height and the volume of water it stores. The first two classes require each owner to establish a safety report for an authority review. A main objective of this report is to demonstrate that all hazardous scenarios have been taken into account by the dam owner in assessing the risk level and also that sufficient safety measures have been specified to reach a global risk as low as reasonably possible. First, considering all existing data and characteristics of the dam, the owner has to carry out a risk analysis to identify all failure modes that may occur on dam structure and hydraulic equipment. Every potential accident is then characterised in terms of severity of consequences and annual occurrence probability. The severity level is assessed quantitatively or qualitatively, using a combination of maps representing the effects of the flood waves and the Population At Risks. The probability is assessed using a semi-quantitative method analyzing all scenarios and safety measures involved in prevention and protection: the bow-tie approach. This cross analysis implements an iterative approach to highlight complementary measures and recommendations and therefore enhance global safety level.

INTRODUCTION

Dam Safety Regulation

French regulation on hydraulic structures changed in 2007 with the publication of a decree (n°2077-1735, 11 th December 2007) that sets new requirements in terms of dam and dike safety. Hydraulic structures are classified into four classes (A, B, C and D) according to their characteristics. Concerning dams, the considered parameters are the height (H in meters) above the ground surface and the volume (V in cubic meters) of the reservoir. The previously referenced decree, among other mandatory measures, requires the realization by the owner of a safety report for classes A and B dams. This safety report is then analysed by the Authorities who decide if the report is admissible. If it is not, Authorities can ask for supplementary information.

Table 1. Classification of Dams in French Regulation

Objectives of a Safety Report

One of the safety report's objectives is to demonstrate that all risks that may occur on the single dam are identified and managed. The risk of a dam failure must be studied, for example during a flood event or an earthquake, but so must all the events (accidents or incidents) related to current operations be. The safety report also presents the safety measures implemented to avoid or limit the accidents and possible additional measures to reach a level As Low As Reasonably Practicable (ALARP). Chapters 3 to 7 represent input data needed to lead to the assessment and characterisation in terms of severity and annual probability developed in chapters 8 and 9. The Figure 1 below shows the process of a study focused on risk assessment (chapter 8) to determine all the hazards that may be encountered. -Risk analysis, -Consequences assessment, -Probability assessment, -Risk acceptability criteria and risk reduction.

RISK ANALYSIS

General Framework

Risk analysis is the first step of risk assessment. Under the new French regulation, owners have to identify all possible major accident scenarios that could occur on the dam, along with the implemented safety measures that could stop them or limit their consequences. Scenarios leading to a dam failure have to be considered, but not only: all failure modes have to be identified, including emptying valve failure or spillgate rupture. This identification is based on dam characteristics and includes dam environment, as a potential extern aggressor. This identification also integrates field experience.

The French regulation does not make compulsory a specific methodology of risk assessment. The owner is free to use FMEA, Preliminary Hazard Analysis or any other methodology to his or her convenience, as long as this choice is justified and leads to an exhaustive list of dam failure modes and associated scenarios.

INERIS Methodology for Scenarios Identification

Risk estimation is supported by a dedicated working group. This group is composed of experts and operators who have a good knowledge of the dam and the risk analysis methodology. The working group fills up a table in which each line represents a scenario, as presented on Table 2. During this analysis, all using modes of the dam have to be considered, such as usual operation mode, maintenance, emptying and filling of the reservoir, operation in times of floods... The flood following the failure of an upstream dam is also considered. The summary of this table leads to an exhaustive list of accidents, with their kinetics and estimated flow straight to the dam.

This list can now be reduced. As an example, a flood event can lead to a failure mode that would not have stronger effects than those already waited downstream because of the flood event itself. Then, this failure mode is not considered as a potential major accident. Therefore, after this selection, the residual list consists of all major accidents identified for the dam, with estimated flow and kinetics associated. Some of them, at least the dam failure, need modelling to obtain data on their consequences. Major accident implying lower flows may be studied qualitatively, without modelling.

A cross analysis between the consequences of the effects of these major accidents and their annual probability must be done in order to demonstrate the control of dam risks by the owner.

CONSEQUENCES ASSESSMENT

The consequences assessment of a dam major accident by INERIS is based on the superposition of a mapping of the flood wave effects and a mapping of the identified people exposed to these effects.

Effects Evaluation

The effects of major accident consequences are characterised in terms of local kinetics and intensity.

Local kinetics is about the time between the arrival of the first effects of the wave and its top flow. It can be gradual or sudden, depending if the population will be able to be evacuated after the arrival of the first effects or not.

Local kinetics

Local kinetics is assessed for each impacted person according to two parameters: the flow speed, and the flow height, leading to the speed of the rise of water level. The French Prevention of Flood Guidelines indicates that a water height of 0.5 meters makes a man unable to move freely in the flow. It also indicates that a flow speed of 0.5 meters per second is a line above which a man could be hurt by objects carried along the flow or could be carried himself.

If the local flow speed and local speed of the rise of the water are both under the speed lines, local kinetics is gradual, as an evacuation is considered as possible. If not, kinetics is sudden. If speed data are not available, for example if there is no modelling and the accident is studied qualitatively, kinetics are considered as sudden, in a conservative approach.

Local intensity

The intensity level is assessed for each person by comparing local flood flows to reference local return flows, as presented by Table 3.

Q ≥ Q1000-year Strong Q100-year ≤ Q < Q1000-year Moderate Q10-year ≤ Q < Q100-year Weak Q < Q10-year
If local flow data are not available, a conservative approach is used, depending on the wave flow at the level of the dam. If this flow is inferior to the ten-yearly flood flow straight to the dam, INERIS considers that the accident wave will deaden very quickly downstream and be contained in the river bed. If this flow is superior to the ten-yearly flood flow, INERIS considers that the flow remains constant along its travel, and this travel stops when the flow becomes inferior to the local ten-yearly flood flow. The flow is supposed to be contained in the river bed, except in some critic points (bridges, roads...) where this containment should be confirmed. A special attention should be paid for this confirmation, as the considered flow should be the wave flow added to the natural river flow.

Zoning

The characterisation in local kinetics and intensity leads to a zoning of the effects map: INERIS chose to define 3 zones, based on a methodology already used for the establishment of French emergency plans. This zoning is presented by Table 4. A zone is attributed to each person, and so the mapping can be zoned as shows the figure below. According to the wave modelling maps, the number of people likely to be impacted by the accident flood wave is counted. The regulation does not require a precise number, but asks for the counting mesh to be fine enough to obtain a good estimation of this maximal PAR. The census can be based on the Corine Land Cover database, which shows land cover mapping. It can also be done by adapting the counting methodology used for critical industrial infrastructures presented in the section "Elements for severity level assessment in safety reports" of the circular of May 10 th , 2010 which summarizes the methodological rules applicable to safety reports, the approach of risk reduction at source and plans for prevention of technological risks. This methodology recommends for example to count 2.5 persons for each house, the whole capacity for buildings receiving public, etc.

Once the max PAR is determined, global kinetics of the accident can justify the use of reduction factors. Global kinetics can be of two types: pre-accidental kinetics and post-accidental kinetics. Pre-accidental kinetics is the time for the accident to occur once pre-incidental drifts have been detected. If a pre-incidental drift can be detected and therefore the accident predicted soon enough to allow an evacuation before the accident actually occurs, a reduction factor may be applied to the whole max PAR. Post-accidental kinetics is the time of the arrival of the wave first effects straight to the impacted people. A reduction factor may be applied to the part of the max PAR located far enough from the dam to be impacted by the first effects of the wave in a time longer than the time needed for evacuation.

However, the evaluation of a emergency evacuation plan is not easy and reduction factors have to be assessed carefully. If this evaluation cannot be done, or if the available data cannot be trusted because of too many uncertainties, it is safer to consider the max PAR in a conservative approach.

Severity Levels

Once zoning and PAR are available, the severity level can be assessed, using the scale as shown in Table 5. To each zone is given a Severity Level (SL) depending on its PAR. The global severity level of the accident is the maxima of the severity levels of each zone. As a result, the Severity Level of an accident whose zoning and PAR would be as shown on Fig. 3 is SL3.

PROBABILITY ASSESSMENT

The risk analysis usually identifies the possible central events (example: loss of containment) and their consequences (dangerous phenomena), but also, the initiating events leading to them. In order to present the results of this identification process, INERIS uses bow-tie diagrams. The bow-tie diagram is a combination of a simplified failure tree and an event tree. It is composed of initiating events in the left side of the diagram. Examples of initiating events could be a human error in a filling procedure, an impact resulting of a vehicle collision, etc. These initiating events lead to a central hazardous event which is often a loss of containment. Then, the central event leads to several dangerous phenomena.

The qualitative identification of initiating events, central hazardous events, prevention and protection safety barriers and possible consequences realized through the implementation of the bow-tie diagram has to be considered as a major step of the safety report, leading to a probabilistic quantification of the scenarios.

To realize this quantification, when the identification step is achieved, the ability of the barriers to prevent the occurrence of scenario or to limit the consequences has to be evaluated.

Safety Barriers Evaluation

The barrier analysis is a main issue for the demonstration of the risk control, as they usually bring the proof that the safety system meets the ALARP requirements. Three kinds of safety barriers have been identified: technical barriers, human barriers and barriers which gather both technical and human elements, as shown on Fig. 5.

Figure 5. Safety barrier typology as developed in the INERIS methodology

In order to be considered in the French regulatory framework, a safety barrier has to meet the following requirements. It has to be:

-Independent: the safety barrier must be independent of the cause of the scenario or of the scenario itself; -Effective: able to fulfil the safety function that it was chosen for, in its usage context, for a period of operation process independent and independent of other safety barriers; -With a response time in accordance with the kinetics of the scenario; -Testable; -Covered by preventive maintenance designed to guarantee that performance levels are maintained over time.

If these requirements are met, the safety barrier can be taken into account in the probabilistic quantification of a dangerous phenomenon, as it reduces the occurrence frequency of a given scenario. The point is then to assess the failure probability of a barrier, and so the impact of the barrier on the occurrence frequency of an accident.

There are few data about the failure probability on-demand of a specific barrier. Moreover, the available data are generally an average of failure rates and are not applicable for a specific facility, in a specific environment.

The INERIS methodology is based on the "level of confidence". The risk reduction factor of an active barrier is calculated using an extrapolation of SIL (Safety Integrity level) defined in the norms NF EN 61 508 and NF EN 61 511. The approach described in these norms has been extended to all active barriers. Concerning passive barriers and human barriers, a maximum level of confidence has been defined through literature researches. The level of confidence is then reduced on the basis of different criteria (for more details, see Ω10 and Ω20 reports, INERIS 2008 and 2009, available on INERIS website www.ineris.fr).

When all identified barriers are evaluated, the frequency of major accident can be assessed.

Dangerous Phenomena Probability

As owners are free to choose the methodology to be used in the safety report for assessing the occurrence probability of a dangerous phenomenon, the methodologies can significantly vary. Since 2005, INERIS uses the quantitative evaluation "from initiating events to dangerous phenomena", based on the bow-tie representation. Two main approaches are used in order to estimate the frequency of initiating events.

A first one uses reliability data, or generic frequencies obtained by using reliability data. These data can be available for failure related to equipment. However, data related to human and organizational failures are very limited. A second one, used by INERIS, is the assessment of the frequencies through a questioning process applied to a working group (which could gather for example the risk manager of the facility, people from the maintenance, operators, Each initiating event is studied by the working group, using frequency classes. Table 6 presents the scale of frequency classes usually used.

Table 6. Frequency classes used by INERIS to quantify initiating events

Frequency class Failure frequency

F-1

Between 1 and 10 per year F0 Between 10 -1 and 1 per year F1 Between 10 -2 and 10 -1 per year F2 Between 10 -3 and 10 -2 per year F3 Between 10 -4 and 10 -3 per year

Frequencies of initiating events leading to a common central event are combined using AND and OR gates:

-If any of the initiating event can cause the central hazardous event, an OR gate is used. In that case, the frequency class of the central event is equal to the minimum frequency class of the initiating event; -If multiple initiating events (and the frequency of these events are below 10-1) are required for the occurrence of the central hazardous event, an AND gate is used. In this case, the frequency class of the central event is equal to the sum of the frequency classes of the required initiating event;

-If a prevention barrier exists, the risk reduction factor of the barrier is added to the frequency class of the cause, which gives the frequency class of the intermediate event.

One of the more obvious advantages of this methodology is that it results from a deep analysis of the safety and of accident scenarios that could occur, their causes and barriers that prevent their occurrence. Therefore, dam specific aspects (example: domino effects) and risk prevention issues are explicitly taken into account in probabilistic calculations. In this framework, operator's safety efforts are well promoted and their effectiveness is demonstrated qualitatively and quantitatively. The main possible causes of an accident are identified and graded in function of their frequency. The operator can, on this basis, target the future implementation of its prevention system.

As frequency classes are used, this method has several limits: the assessment does not aim to be as accurate as an assessment using frequency values. This implies an increase of uncertainties when there is a need for aggregating the probabilities of different dangerous phenomena. Besides, some initiating events are difficult to assess. This refers mainly to rare events that the working group have not observed in their facility or in similar facilities. In these cases, generic data on initiating events and reliability data can be used, although it also implies uncertainties due to the use of generic data on a specific situation. An alternative way to assess the frequency of these events can also be the use of expert judgements.

RISK ACCEPTABILITY CRITERIA AND RISK REDUCTION

Once severity of consequences and probability of each accident determined, the last step of the methodology is to assess the risk acceptability for the studied dam.

First, each accident is positioned in a matrix corresponding to its severity level (5 levels) and its annual probability of occurrence class (5 classes). Then, the dam owner defined criteria of acceptability in order to support the discussions in terms of reducing risk 10 -5 10 -4 10 -3 10 -2 following the positioning of the various consequences of hazards in the matrix. Since there is no compulsory matrix yet, owners generally decide to consider the risk acceptance matrix indicated in another French circular (circular of May 10 th , 2010 which summarizes the methodological rules applicable to safety reports, the approach of risk reduction at source and plans for prevention of technological risks) in which explicit classes or additional compensatory measures are implemented. This matrix is given below, as an example: This gradation corresponds to the priority that can be given to risk reduction, focusing first on reducing the greatest risks: -High risk: Additional Measures to reduce risk at source to emerge from the class "High". -Intermediate risk: Measures to control the risk and possible implementation of those whose cost is not disproportionate to the expected benefits, -Low risk: Given the measures to control the risk, no obligation to further reduction, but recommendations can be made.

For any accident located in "High Risk" or "Intermediate Risk", the owner has to propose a list of recommendations to improve existing safety barriers and to identify additional measures if necessary. The aim is to reduce the probability and/or the severity level and slide the accident to a safer zone. This residual risk level is compared with the risk acceptability criteria. Additional measures could be established with the aim of reducing risk (damages and/or probability) and make if As Low As Reasonably Possible.

CONCLUSION

To produce dam safety report according to the new French regulation, INERIS has developed a methodology responding to all required criteria. This methodology has been tested on several case studies and it proved efficient. However, there is still work to do relative to global kinetics to help reduce the max PAR and obtain a better approximation of impacted people. This supposes to define criteria relative to flow speed and flow height, and to be able to assess the time needed to alert and evacuate the population. Furthermore, this assessment done, reduction factors have still to be decided. Besides, another field of study is the probabilistic assessment of initiating events, which remains complicated for natural hazards and human factors.

It is important to note that the French dam regulation is relatively recent and does not define severity level scale, probability scale or risk acceptability matrix yet. To be able to compare several safety reports, these scales could be regulated in the future. The regulation is expected to evolve and therefore INERIS methodology may be adapted in the years to come.

Figure 1 .

 1 Figure 1. Structure of the Safety Report This article describes the methodology developed by INERIS (from the risk assessment step to the risk reduction phase) to identify and quantify all the accidents. It is divided into 4 parts:-Risk analysis, -Consequences assessment, -Probability assessment, -Risk acceptability criteria and risk reduction.

Figure 2 .

 2 Figure 2. Simplified representation of a zoned effects map3.2. Impacted People CensusOnce the effects are characterised, an identification of the impacted people is necessary to assess the severity level. INERIS methodology is based on the Population At Risk (PAR) parameter.

Figure 3 .

 3 Figure 3. Example of a simplified zoning and PAR associated

Figure 4 .

 4 Figure 4. Example of a bow-tie diagram

  Three classes are defined:-Zone at high risk (red); -Zone at intermediate risk (orange); -Zone at low risk (green).

Table 2 .

 2 Example of table used for scenarios identification

	N°	Central event	Failure mode	Cause	Safety barriers	Kinetic and flow	Impacted people

Table 3 .

 3 Intensity levels used by INERIS

	Intensity level	Local flow
	Very strong

Table 4 .

 4 Zoning based on intensity and kinetics levels

		Sudden kinetics	Gradual kinetics
	Very strong	A	A
	Strong	A	B
	Moderate	B	C
	Weak	B	C

Table 5 .

 5 Severity Levels scale used by INERIS (unit: people exposed)

	SL5 SL4 SL3 SL2	Zone A ≥ 1,000 ≥ 100 and < 1,000 ≥ 10 and < 100 < 10	Zone B ≥ 10,000 ≥ 1,000 and < 10,000 ≥ 100 and < 1,000 ≥ 10 and < 100	Zone C ≥ 100,000 ≥ 10,000 and < 100,000 ≥ 1,000 and < 10,000 ≥ 100 and < 1,000
	SL1		< 10	< 100

Table 7 .

 7 Risk Matrix

			Annual Probability of Occurrence
			(E = Very Low ; A = Very High)
	Severity	E	D	C	B	A
	5
	4
	3
	2
	1

Table 8 .

 8 Example of Risk Acceptability Matrix


Dr Isabelle Zdanevitch
email: isabelle.zdanevitch@ineris.fr

N Auvinet

P Mallard

O Bour

ON-SITE ASSESSMENT OF METHODS TO MEASURE GASEOUS EMISSIONS FROM BIOLOGICAL TREATMENT OF WASTE

Landfilling of biodegradable waste must decrease to fulfil the Council Directive 99/31/EC on landfills, in order to reduce the emission of gaseous and liquid pollutants during the landfill lifetime. Therefore, pre-treatment of the organic fraction of municipal waste prior to landfilling is being developed in several countries. In France, the organic fraction is either separated and treated through selective collection of biowaste, or through mechanical sorting in the plant followed by biological treatments (anaerobic or aerobic), the refuses only being landfilled. Or the mixed waste is stabilized by an aerobic process before landfilling. These different processes emit gases which may be harmful for health or the environment (toxic, explosive, odorants, greenhouse gases...). Some of the emissions can be collected and treated through biofilters, while other gases are emitted by surfaces (typically, compost windrows) and cannot be collected unless they are enclosed. Also, the efficiency of the biofilters must be assessed. IRSTEA and INERIS have been working together for several years on the use, comparison and improvement of surface emission measurement methods, applied to biological treatment plants of solid waste. Gaseous emissions were studied on: composting process of pre-sorted organic matter from mixed waste, with a small or larger mesh and porosity, in either turned or aerated windrows, on biofilters, and on landfills which are located beside the composting plants. Depending on the ventilation air flux, different measurement methods were used: static (accumulation), dynamic or chimney type chambers, and a total cover of a biofilter with a plastic tarb. Several of these measurements were undertaken in order to evaluate the global gaseous emissions from those sites, to provide data to an environmental technology validation exercise (ETV). Measurement campaigns presented here comprise: comparison of fluxes measurement techniques, calculation of gas fluxes (CO 2 , CH 4 , NH 3 and N 2 O) emitted from composting windrows and biofilters, calculation of biogas emission (methane + CO2) before and after a final cover was set on a landfill. Comparisons of the two first chambers have been made since 2007 on several sites (composting of the organic fraction of municipal solid waste or stabilization prior to landfilling). On the first site (non aerated windrows and small mesh) the difference between the measured fluxes was a factor of 2. This factor is rather small: differences between flux measurements using different devices can lead to differences as large as a factor of 100. More recent tests, presented here, show a better agreement: the difference between the two techniques lies within the measurement uncertainty. Comparison of surface air speed measured by two different chimney chambers lead to comparable results. During one experiment, the global air flow interpolated from chamber data was underestimated compared to input flow measurement, because of preferred pathways of the air flow along the wall of the biofilter. When the border effect is correctly taken into account, the total gas flow measured with the chimney chamber and the one measured by a total cover of the biofilter show a good agreement. Biogas surface emissions were measured with the static chamber, on a landfill which receives biologically stabilized waste. This landfill was partly uncovered, so only a part of the biogas was collected and flared. After the final cover was installed, the total biogas flow which was collected and flared was comparable to the sum of (the surface emissions + the collected biogas) without the total cover. The results presented here show that on different sites, different emission measurement methods were used, and that generally there is a good agreement between the methods, providing the care of use are respected. Advantages and care of use for the different methods, depending on the aeration conditions, have been established and some recommendations are given.

INTRODUCTION

Landfilling of biodegradable waste must decrease to fulfil the Council Directive 99/31/EC on landfills, in order to reduce the emission of gaseous and liquid pollutants during the landfill lifetime. Therefore, pre-treatment (sorting or stabilization) of the organic fraction of municipal solid waste prior to landfilling is being developed in several countries, named mechanical-biological treatment (MBT). In several locations in France, the organic fraction is separated and treated either through selective collection of biowaste, or through mechanical sorting in the plant followed by biological treatments (anaerobic or aerobic), in order to produce compost. In the latter case only the sorting refuses are landfilled. In some plants, mixed waste containing organic matter is stabilized by an aerobic process before landfilling. These different processes emit gases which may be harmful for health or the environment (toxic, explosive, odorants, greenhouse gases...). Some of the emissions may be collected and treated through biofilters, while other gases are emitted by surfaces (typically, compost windrows) and cannot be collected unless they are enclosed. Also, the efficiency of the biofilters must be assessed.

Background

The environmental assessment of the biological treatment of biodegradable waste takes into account all the emissions of the treatment plants and the outputs when they are valorised or landfilled. Among them, surface gaseous emissions may be important and need to be evaluated. Standard methods exist for the precise determination of exhaust or flare emissions, but surface emissions are more difficult to measure quantitatively. IRSTEA and INERIS have been working together for several years on the use, comparison and improvement of surface emission measurement methods, applied to biological treatment plants of solid waste

Research objectives

The research project, conducted in the partnership aimed to compare different surface emission measurement chambers that were available, and, depending on the process or area to be monitored, to give recommendation for the selection and use of the appropriated chamber(s). Gaseous emissions were established on different MBT plants and associated landfills in order to provide data for an environmental technology validation exercise.

METHODOLOGY

Gaseous emissions were studied on different plants, comprising: composting of pre-sorted organic matter from mixed waste, with a small or larger mesh and porosity, in either turned or aerated windrows, on biofilters, and on emissions from the treatment buildings or devices such as rotating drum bioreactors. Surface emissions of landfills which receive either stabilised waste or sorting refuses, associated to the biological treatment plants, were also investigated.

Measurement of surface flux gases

Depending on the ventilation air flow, different measurement methods were used: 1) a static flux chamber developed by INERIS allows short time measurements on rather small or medium fluxes (typically less than 5 L/m 2 /min). In this chamber, gas emitted by the soil or compost surface accumulates This method, applied to a great number of measurement points, gives a set of local fluxes which can be interpolated by kriging, in order to cartography a zone and calculate the total flux over the area. It has been used for the emission measurement on landfills surface, and on non-aerated or aerated windrows with low aeration speeds. 2 analyzers measure methane and CO 2 simultaneously; 2) a dynamic flux chamber developed by IRSTEA takes longer time for one measurement, but allows measurements on higher fluxes and can be used for monitoring the fluxes variations over a period of time. In this chamber, there is an admission and an extraction of the outside air, with a control of the pressure equilibrium between the inlet, outlet and outside of the chamber. The measurement can be done without effect of overpressure (under-estimation of gases emissions) or depression (over-estimation of gases emissions) in the chamber. It was designed to capture convective emissions and has been also used on non-aerated and aerated windrows. Gaseous concentrations are measured by an INNOVA analyzer, measuring on the same time CO 2 , CH 4 , NH 3 , and N 2 O (possibly SF 6 ) in the ppm range; 3) for larger emitted air flows (typically, on biofilters) a chimney chamber is more adapted. The chamber outlet is constituted of a reduced tubing, increasing the gas speed, which is measured more accurately. Both INERIS and IRSTEA have such a chamber, designed in different geometries and materials. Different types of analyzers can be connected to these devices (the first measurement is the air speed).

Measurement campaigns

Measurement campaigns were undertaken on several waste treatment plants. They are shortly described thereafter.

Site A

This site is a composting plant which processes municipal solid waste. Raw waste is pre-treated in a rotating bioreactor (drum) for 4 days. During this time, waste bags are opened, and paper and cardboards are somewhat decomposed in small pieces. After this pre-treatment, waste is carefully sorted through different devices in order to obtain a clean and fine organic fraction. Before 2010, this fraction was mixed with a bulking agent (shredded green waste) and disposed in windrows which were turned twice a week with an automatic windrow turner (A1). Since 2010, the mixture is disposed in successively 2 aerated cells, with a discontinuous aeration, for 3-4 weeks each. After that, the compost is stored outside without any turning until it is taken by local farmers to be used on fields (A2). Polluted air from the composting boxes and the rotating drum is treated by biofilters (A3 and A4 respectively). The refuses of sorting are stored in a landfill which sits on the same site (A5).

Site B

Site B is an anaerobic digestion (AD) plant which also processes raw MSW. The pre-treatment and sorting of the organic matter is quite similar to the one of site A, but after the sorting the organic matter is hydrolyzed (dry conditions, around 30 % dry matter) and sent to large digesters (volume approximately 1500 m 3 ) for 1 month. Feeding of the reactor is semi -continuous and temperature is kept at 37 °C (mesophilic conditions). The digestate is then dried by pressing, mixed with crushed green waste, and composted in aerated boxes for 14 days: air from this process is treated through an acid washing tower followed by a biofilter (B1). The compost is then stored for maturation without turning (B2, before 2012), until it is used by local farmers, between 1 and 3 months after. Since our measurements, the site manager has decided to turn the maturating compost every week or so, in order to limit the methane emissions to the atmosphere. Efficiency of this new procedure will be evaluated. Refuses of sorting are sent to an outside landfill or incinerated: no emission measurement was possible on this fraction.

Site C

Site C processes MSW, but do not prepare a compost for agricultural purpose. The waste is biologically stabilized before landfilling. The larger fraction of waste (> 50 and 70 mm typically) is not stabilized. A part of the plastics (fraction between 70 and 450 mm) is sorted and recycled. The larger fraction, and the refuses of the intermediary fraction, are landfilled without treatment. The smaller fraction, which is rich in organic matter, is treated in aerated boxes for 2.5 months (C1). Aeration is discontinuous, with a 3 minutes-3 minutes cycle. Treated matter is moved from one box to another one, every 2 weeks. Stabilized matter is used as a temporary cover, every week or so, on the landfill cells (open cell: C2, closed cells: C3)

SURFACE EMISSION MEASUREMENT ON NON AERATED WINDROWS

Emissions from composting and maturation windrows, composting site

The first measurements were performed in 2007 on site A. Methane and carbon dioxide were measured with both a static chamber and a dynamic chamber on several windrows of different ages (A1, A2) and on the landfill cells (A5): "EMISITE", 2007, and Zdanevitch et al, 2009) Individual measurements taken with the static chamber were used for emission cartography by kriging, using Surfer 8. Figures 4 and5 show methane and CO 2 emissions on the same windrow. Table 1 reports the total methane and CO 2 fluxes for windrows of different ages. Discussion: CO 2 emissions from composting at different ages were quite similar. The maturation windrow was larger but also higher than the composting one (6 and 7), so CO 2 emission, related to the quantity of matter, is a little lower, but in the same order of magnitude. This suggests that the first stage of composting is not sufficiently efficient. Methane emissions are more scattered: they are very low on young compost (less than 2 weeks). They increase with the age of the compost, and also on the maturation windrow, which is not turned.

Comparison of results between fluxes measured by the static chamber and the dynamic chamber led to differences between 1 and 5 (mean value around 2: "Emisite", 2007). These differences could not be completely understood, so comparison was repeated during other campaigns (see chapter 4).

Emissions from maturation windrows

As we noticed in 2007, methane emissions can be large on undisturbed windrows. Site A, after a modification of the composting conditions, and site B, store compost in large windrows, without turning, for 1 to 3 months before its use on agriculture soils. We measured methane and CO 2 emissions on maturation windrows on these two sites (A2, B2) with the same method, the static flux chamber. Results are presented in table 2. Discussion: methane emissions are rather important, showing that anaerobic conditions prevail inside the composting windrows, and that the compost is not stabilized despite its age. Emissions are very similar between the two sites, e.g. there is no influence of the anaerobic digestion prior to composting on the methane emission potential of the compost. Nevertheless, CO 2 emissions are similar between windrows of the same site, but 2 to 3 times lower on site B (with AD) than on site A (composting alone). This shows that 1 month of AD followed by two weeks of aerated composting probably biodegrades more the organic matter than the 2 months of aerated composting. In order to compare the results, emissions have been reported to the surface. They should be reported to the mass of compost, but this information is not yet available.

SURFACE EMISSION MEASUREMENT ON AERATED WINDROWS

Parallel measurements using static and the dynamic chambers, were undertaken on the composting boxes of site C (C1).

The measures where done on three boxes, which contained the fine waste fraction of different ages. The aeration cycle has been lengthened from the standard one: 3/3 minutes with/without aeration, to 15/15 minutes, to allow the measurement with the dynamic chamber and INNOVA analyzer. Measurements with the static chamber being faster, several measurements can be made successively during the aeration or non-aeration stage. Figures 6 and7 report measurements made with the 2 chambers on the same windrow, the same day, during non aerated and aerated cycles. Despite different measuring times (2 minutes for the static chamber, 10-20 minutes for the dynamic chamber), it is interesting to notice that the results of the two chambers are similar. The static flux chamber is able to differentiate emissions with and without aeration, although this chamber was not initially designed for aerated surface sources The difference between the mean flux (sum of the aerated and non-aerated periods) is as low as 13 %. It should be noticed that the overall uncertainty of the static flux chamber measurement technique (assuming that measurements are made over several days periods) has been evaluated around 20 %. Knowing the total surface of the windrows (corresponding to a given mass of compost) allowed us to calculate the total CO 2 emissions for the whole composting plant, and to compare it with the emissions from the landfilled stabilized waste. These results will be used for an environmental technology validation exercise (ETV) and will be published shortly.

SURFACE EMISSION MEASUREMENTS ON BIOFILTERS

Biofilter treating the air from the composting boxes on site A (A3)

Gaseous fluxes cartographies (kriging interpolations) were established on this biofilter using a chimney chamber. During a previous experiment, the global air flow interpolated from chamber data was underestimated comparing to input flow measurement, because of preferred pathways of the air flow along the wall of the biofilter. On this site, care has been taken on this point. Two different analysers were connected to the chamber: an ECOPROBE 5 (infrared), measuring CO 2 (and hydrocarbons but at rather high concentrations, useless here), and an INNOVA (photacoustic) which measures simultaneously 4 gases (CO 2 , CH 4 , NH 3 , N 2 O) in the ppm range. Along with the cartography of the biofilter, the results gave a comparison of CO 2 fluxes measured on the same device with the two analyzers: see table 4. The difference between CO 2 fluxes measured by the two analyzers is 16 %. It is similar to the difference that was measured on the aerated windrows with two types of chambers. It means that the difference between flux measurements is probably due to analyzers uncertainties, as well as the difference between techniques. CO 2 flux is 16 times larger than CH 4 flux, a thousand times larger than NH 3 flux, and 3 thousand times larger than N 2 O flux. This allows the calculation of warming effect due to greenhouses gases emissions from this biofilter (NH 3 is not a GHG but has also environmental impacts). Once again, these results will soon be used in an ETV exercise.

Biofilter treating the air from the rotating drum on site A (A4)

On the same site, surface CO 2 emissions were measured on a biofilter treating the air from a rotating drum. Different methods were tested to follow the gas emission evolution. First, a cartography of the biofilter CO 2 emissions was established using the chimney chamber (see figure 8). The other method consisted in covering the whole biofilter surface with a plastic tarp, in aim to measure all the gas emissions. With these two methods, it was possible both to cartography the biofilter emissions, and to follow the total emissions during time and characterize the treatment efficiency of the biofilter.

FIGURE 5 CO 2 fluxes determined with a chimney chamber FIGURE 6 Gases emissions monitoring on the rotating drums biofilter

Results on figure 6 show that we obtain similar values for the first 2 two days (April 21-22) but the following days, the plastic sheeting method shows a trend of decrease. The decrease can be explained for the three following days by the week-end (from the 23 to 26 th of April), and the fact that there is no new matter entering in the rotating drum. It is more complicated to explain why this decrease continued on the following days, when the site activities came back. Comparing to the gaseous emissions of the two biofilters of site A shows that the rotating drum's biofilter presents higher gases emissions. It can be explained for a part by the fact that, at this time, the composting boxes where open, due to a functioning problem. So, the composting biofilter treated only a part of the gaseous emissions. New measurement should be done on composting biofilter with closed boxes.

SURFACE EMISSION MEASUREMENT ON LANDFILL CELLS VS FLARED BIOGAS

On site C, "stabilized" waste is landfilled. In fact, even after 2.5 months of active composting, the organic fraction of MSW is not completely stabilized, and when it is landfilled under anaerobic conditions, biogas builts up. This biogas contains approximately 55-60 % v/v methane, 35-40 % v/v CO 2 , and trace gases like H 2 S up to 800 ppm. Initially on this site, biogas was not burned as the total flux was supposed to be low. But neighbours reported odour problems due to H 2 S, therefore a flare has been installed. When we measured the surface gaseous emissions of the landfill (July 2011), the flare had been used for 4 months and biogas was pumped only on closed cells (C3). The static chamber was used to measure biogas (methane + CO 2 ) emissions on the surface of the landfill (C2 and C3). (a) Though biogas is richer in methane than in CO 2 , CO 2 surface fluxes are higher than methane ones. It implies that a fraction of methane is oxidised within the surface layers (constituted of mixed sorting refuses and compost) before it is emitted to the atmosphere.

4 months after our measurement campaign, the exploited zone of July had its final cover and biogas was pumped through 14 biogas wells distributed all over the landfill area. The mean biogas flux burned by the flare was then 70-80 m 3 /h. This represents a difference with the estimated total flux of July, of about 6 to 17 %. Once again, this difference lies within the normal uncertainty of the static chamber measurements.

RECOMMANDATIONS

The choice of the measurement technique (type of chamber, gas analyzer) depends on the application. First of all, it is important to know what are the aim and the uses of the measures. If the aim is to cartography an area, choices are static chamber for a compost windrow or chimney chamber for a biofilter. There must be a careful consideration on the measurement strategy to place the points of measurements (on a windrow, at least one row on the top and one, or better, two rows on the edges). The number of points depends on the dimensions of the area and on the variation of the fluxes between points. Even if the area is small, a minimum of points is needed in order to get a good kriging. Typically, a minimum of 20 to 30 points give good results when fluxes are rather stable over the area. Border effects must also be taken into account, especially on biofilters, depending on the type of filtering material. If it is rather coarse, preferred pathways can exist along the wall of the filter. The chamber must be placed in contact to the wall in order to take this phenomenon into account.

Measurements with a static chamber are fast and allow a large number of measures in a short time; but if there are quick changes in the flux, this technique can lead to wrong results. On the other hand, it is possible to repeat measurements and to calculate the mean value.

Measurements with a chimney type chambers take longer. These chambers are very useful for flux measurements on biofilters, but there is little information on this technique in the literature. The following points are important: first, to measure the gas concentration correctly, the volume of the chamber must have been replaced at least 6 times; this depends on the volume of the chamber and on the gas flux. The air speed may have changed between the start and the end of the measurement: it must be controlled, and if the difference is too high, the measurement should not be kept.

To measure fluxes along time, the dynamic chamber is well suited. It allows long time measurements, which is useful to integrate changes in emissions along the changes in process (as example aerated windrow). The measure depends on the pressure inside the chamber which must be controlled and measured. On a biofilter, covering the whole surface with a plastic tarb is a good alternative to follow emissions of a process along time, but the installation is tricky and takes a long time.

CONCLUSIONS

They are several available techniques for gas fluxes measurements and the choice will depend on the application. The strategy of measurement is important and must be thought before the on-site campaign. All the campaigns run together by INERIS and IRSTEA allowed us to raise some recommendation about gas measurements. When there is high variability in the gaseous emissions in a process, measures will be more representative if the emissions can be followed along time. Depending on the process, short measurement times can give random results which can introduce a misunderstanding of the process gases emissions, or mistakes in the calculation of gaseous emissions.

The static chamber is a good method to cartography large surfaces (like landfills) because of its quickness. It can also be used on compost windrows. It allows the measurement on a large number of points over a short period of time, which allows precise determination of highly emissive points ("hot spots").

The dynamic chamber is better used for a continuous monitoring over a long period of time. Chimney chamber is useful on processes with high aeration speeds as biofilters.

Comparison of methods, between static chamber and dynamic chamber, gave recently good results on some sites, but more tests should be necessary to get a better comprehension of the differences observed in the past.

FIGURE 2 CO 2

 22 FIGURE 2

  FIGURE 3 CO 2 flux measured with the static chamber FIGURE 4 CO 2 flux measured with the dynamic chamber

  FIGURE 7Methane surface emissions on site C (landfill cells)

TABLE 1 Methane and CO2 emissions from non aerated windrows, depending on the age of the compost

 1

	Windrow	Age	Surface, m 2	CH 4 flux, m 3 /h	CO 2 flux, m 3 /h
	7	15 days	366	0.04	4.5
	6	1 month	294	0.34	4.6
	Mat	3 months	432	0.82	4.3

TABLE 2 Methane and CO 2 emissions measured on maturation windrows Surface, m 2 CH 4 flux, m 3 /h CO 2 flux, m 3 /h Relative CH 4 , m 3 /h/m 2 Relative CO 2 , m 3 /h/m 2 Site A (MBT, composting alone)

 2

		Windrow 1	272	7.2	26	0.03	0.10
		Windrow 2	380	9.4	40.2	0.02	0.11
	Site B (MBT, AD and composting	2 windrows (Oct. 2010) 1 windrow (May 2011)	500 300	13.7 6.1	22.4 11.5	0.03 0.02	0.04 0.04

TABLE 4 Emission measurements (CO 2 , methane, NH 3 , N 2 O) on the composting boxes biofilter on site A

 4

	Biofilter (Air from compost. boxes, S = 216 m 2 )	CO 2 (ECOPROBE)	CO 2 (INNOVA)	CH 4 (INNOVA)	NH 3 (INNOVA)	N 2 O (INNOVA)
	Flux, m 3 /h	9,1	10,6	0,59	0,009	0,003

TABLE 5 Emissions measurements (CO 2 , CH 4 , NH 3 , N 2 O) on the rotating drums biofilter on site A

 5

	Biofilter (Air rotating drums, S = 84 m 2 )	CO 2 (SURFER) 2011/04/21	CO 2 (INNOVA) 2011/04/22	CH 4 (INNOVA) 2011/04/22	NH 3 (INNOVA) 2011/04/22	N 2 O (INNOVA) 2011/04/22
	Flux, m 3 /h	39	34,82	0,58	0,19	0,01


Yann Flauw

Clement Lenoble

The use of field experience to assess the probabilities of major accidents

Keywords: Probability assessment, Frequency, Field experience

In France, a law published the 30th of July, 2003 introduced prevention and repair of damage caused by industrial and natural disasters. This law has initiated the use of frequencies and probabilities in the risk assessments performed in safety reports. The French legislation requires to assess the probabilities of major accidents when doing a risk analysis. Several approaches are available to estimate the probabilities of accidents. One of the most famous is the bow-tie method. Another approach is also authorized: the use of field experience. For example, a duty holder who has experienced one fire in his installation during the last ten years can use this information to assess the probability of a fire within the following year. This approach is indicated in the French legislation, however no methodology is given or advised on how to use that experience collected by operators. In most cases, one will merely calculate the ratio between the number of accidents and the database timeframe. Unfortunately, doing so barely gives a gross approximation of the expected value, without taking into account the size of the dataset, nor any potential uncertainty implied by it. This paper develops the methodology proposed in France through a series of formulas making it possible to assess the probabilities of major accidents by using field experience and taking the above considerations into account. These formulas mostly depend on the work contextassessing an ignition probability and assessing a leak frequency require different mathematical hypothesis and modelsand on the quality of the field experience data consideredwith a conservative approach: using a very reliable database should lead to a lower probability than using an incomplete one.

INTRODUCTION

In France, regulations regarding risk prevention and risk management in industrial facilities are the result of more than 200 years of legislation, the evolution of which has often been consecutive to industrial accidents. Two years after the industrial accident of AZF in Toulouse (French initials for AZote Fertilisant), a new law was introduced on July 30, 2003. It deals with both prevention and repair of the damage caused by industrial and natural disasters. Since then, regulations have been made considerably tighter and the entire approach towards risk assessment has changed. This law has developed new tools for risk assessment and risk management. One of the evolutions implemented is the use of probability/frequency based risk assessments for safety report and land-use planning issues. This is a major change in France: consequence based risk assessments had been used for more than ten years. Lenoble and Durand [START_REF] Lenoble | Introduction of frequency in France following the AZF accident[END_REF] have developed the subject further.

With regard to risk assessments performed in the framework of the safety reports (SEVESO Directive), industrialists are asked to assess the probability of each possible major accident in their facilities. To achieve this objective, they have to assess in most cases the frequencies of initiating events given by a fault-tree analysis or the probabilities of secondary events. For example, industrialists are often required to assess a leak frequency (annual, or per operation), a blowout or an ignition probability, a probability of failure on demand for a safety instrumented function, etc.

Data and methodologies to be used are free as long as they are justified. Most of the methodologies used are based on event and/or failure trees or bow tie diagrams. Data such as loss of containment frequencies, ignition probabilities, failure of a safety measures and human error are most of the time extracted from generic data sources. Some examples of them are the Bevi, OREDA, EREIDA, etc. However, the French regulation allows them to use their own field experience to assess the probabilities and frequencies of accidents, without giving specific guidelines.

The French National Institute on Industrial Environment and Risks (INERIS) is developing a methodology which aims at helping industrialists in the use of field experience for events frequency assessment.

Such a methodology needs to address two specific issues:

-the uncertainties linked to the lack of experience: initiating events of major accidents are hopefully rare; -the quality of data: high quality data is rare as long as it needs systematic record of events that occur in facility. This may be hard and expensive to implement in some facilities.

The methodology proposes a set of formulas depending on the characteristics of the studied event (whether it can occur all the time: continuous mode, or at fixed moments: on demand mode)and on the quality of the field experience used to assess the frequency.

It is worth noting this methodology can be used to assess the frequency or probability of any kind of event, as long as the required hypotheses are met.

CONTINUOUS MODE

Notations and hypotheses

The objective is to assess the annual frequency of occurrence of an event which can occur at any time, given a fixed field experience. Let A be this event. Let E be the experience. E is defined as the sum of all the lengths of observation, and is expressed in units of time. For example, if A is a warehouse fire and 100 warehouses are observed during 5 years, E is equal to 500 years. Let N be the number of occurrences of A during E. N has no dimension. Let f A be the real annual frequency of occurrence of A. f A is unknown and cannot be calculated. This methodology aims at giving a relevant estimator of f A . This relevant estimator is noted <f A >. f A and <f A > are expressed in / unit of time.

The frequency of occurrence of A is supposed to be homogeneous within the field experience sample and constant during the observation timeframe. This hypothesis is questionable but common in this field of application (see Procaccia [START_REF] Procaccia | Introduction à l'analyse probabiliste des risques industriels[END_REF]). INERIS has chosen not to take aging into account in the current version of the methodology; that would have lead to using Weibull distributions and resulted in much more complicated formulas. This is not the objective of the methodology. This choice is discussed in section 6.

Under the above assumption, the random variable N(t)number of occurrences of A during a time tfollows a Poisson distribution with parameter f A t. The time between two consecutive occurrences of A follows an exponential distribution with parameter f A .

Frequency assessment

Under the previous assumptions, this methodology proposes to select the upper bound of the γ one-sided confidence interval. With the notations of section 2.1., this leads to (IRSN, [START_REF]Rapport DSU n°223, Avis sur les intervalles de confiance à retenir dans le cadre des études de sécurité des canalisations de transport[END_REF]):

<f A > = (1/2E)Chi-squared γ (2N+2), (1)
where Chi-squared γ (2N+2) is the γ-quantile of a Chi-squared distribution with 2N+2 degrees of freedom.

The choice of a one-sided interval rather than a two-sided interval is based on a conservative approach due to the French regulation. Two-sided intervals contain more information than one-sided intervals. However, the lower bound will be useless in a safety report. In fact, the French regulation demands industrialists to justify the frequencies of major accidents are not higher than a threshold value. In this context, one-sided intervals seem more relevant.

The equation (1) implies that using a field experience with inputs N and E to assess the frequency of A according to this methodology is equivalent to using a field experience with inputs N' and E and selecting the unbiased estimator, where N'= 0.5 Chi-squared γ (2N+2) > N:

<f A > = N'/E (2)
Using a confidence intervals amounts to overestimating the number of occurrences of A reported by the field experience. The table below gives some values of N' depending on N and γ.

Table 1. Values of N' used in equation ( 2)

Table 1. underlines the impact of parameter γ on the selected estimator, especially when N is low. For N = 0, <f A > is more than tripled between γ = 50% and γ = 90%. The choice of γ is therefore essential.

Recommendations on how to choose it are given in section 4.

ON DEMAND MODE

Notations and hypotheses

The objective is to assess the probability of occurrence of an event which can only occur at specific times, given a fixed field experience. Let A be this event. Let E be the experience. E is defined as the total number of observations, and is dimensionless. Let N be the number of occurrences of A during E. Let p A be the real probability of occurrence of A. p A is unknown and cannot be calculated. This methodology aims at giving a relevant estimator of p A . This relevant estimator is noted <p A >. p A and <p A > are dimensionless.

The probability of occurrence of A is supposed to be homogeneous within the field experience sample and constant for each observation. Under this assumption, the random variable N(e)number of occurrences of A during e observationsfollows a binomial distribution with parameters e and p A .

Probability assessment

Under the previous assumptions, this methodology proposes to select the upper bound of the γ one-sided confidence interval. With the notations of section 2.1., this leads to:

If E < 30 or N > E -5, then <p A > = 1, ( 3

) If (E > 30 and N < 5) or (E > 50 and N/E < 0.1), then <p

A > = (1/2E)Chi-squared γ (2N+2), (4) Else, <p A > = (N+u γ Sqrt(N(E-N)/E))/E, ( 5
)
where u γ is the γ-quantile of the standard normal distribution.

These formulas lead to three zones depending on N and E. Equation ( 3) is associated to zone 1, equation ( 4) to zone 2 and equation ( 5) to zone 3. The zones are drawn on the figure below: Figure 1. Domains of definition for formulas (3), ( 4) and ( 5)

It is worth noting that equation ( 4) will be used in most cases. In fact, this methodology aims at assessing accidents and failures probabilities, therefore N/E is likely to be smaller than 0.1. In this case, the formula for the on demand mode is the same than equation (1), except <p A > is dimensionless.

As underlined in section 2.2., the choice of parameter γ is essential. Equations ( 3) through ( 5) are monotonically increasing with γ. γ can therefore be interpreted as an indicator of the degree of reliability of the used field experience. A higher γ decreases the chances of underestimating p A by leading to a higher value of <p A >. Recommendations on how to choose it are given in the following section.

DATABASE QUALITY

This methodology proposes 3 degrees of quality for a field experience database. Each case will be associated with a value for parameter γ and N will be artificially increased in the worst case. Databases are considered to be either very reliable, reliable or moderately reliable. A very reliable database means the user has a total confidence in parameters N and E: all failures are reported; the observation timeframe is homogeneous; reporting methods have not been modified; the same group of people does the reporting; the database is not too old, etc. A reliable database means we are almost sure all failures have been reported, but the user cannot guarantee it. For example, the failure has extreme consequences (meaning lack of reporting is very unlikely), but the database is old and does not cover a continuous timeframe. At last, a moderately reliable database means we strongly suspect some failures have not been reported, or the characteristics of the failure have been modified during the observation timeframe; for example, a pipe leak database where the minimal diameter of the reported leaks has decreased over time.

If the field experience is very reliable, choosing γ = 90% might be unnecessarily restrictive. On the other hand, choosing γ = 50% is questionable: the real value of the assessed frequency or probability is underestimated half of the time. In addition, when N is low, this underestimation can be huge. According to table 1., if N = 0 and we choose γ = 50%, <f A > = 0.7/E. With γ = 70%, <f A > = 1.2/E. This means that in 30% of the cases, f A > 1.2/E which is almost two times the "relevant estimator" <f A >. With a conservative approach, choosing γ = 50% cannot be allowed. INERIS proposes the value γ = 70% when using a very reliable field experience.

If the field experience is reliable but the user cannot guarantee a complete exactness of parameters N and E, this methodology proposes the value γ = 90%. This choice increases the value of <f A > or <p A >. It therefore covers any small uncertainty that might exist on the value of N or E. In fact, choosing γ = 90% with the values N and E is equivalent to choosing γ = 70% with a slightly higher value of N, or a slightly lower value of E.

At last, if the field experience is moderately reliable, using this field experience to assess a probability or a frequency is very questionable. In those cases, using a generic database is recommended. However, if there is no other option, INERIS proposes to choose γ = 90% and to increase artificially the value of N to cover the uncertainties. This methodology does not propose a new value of N since it is very dependent on the bad quality of the database. This is a subjective decision operators have to take.

NUMERICAL EXAMPLES

Continuous mode

An industrialist owns 100 identical warehouses. He points out no fire has been signaled within the 5 years of use of each warehouse. He is completely certain of this information, since a fire cannot go unnoticed. In this case, N = 0, E = 500 years and the database is very reliable: γ = 70%. Equation (1) leads to the following estimator for the annual frequency of occurrence of a fire for one warehouse: <f A > = (1/1000)Chisquared 70% (2) = 2.41 10 -3 / year.

On demand mode

An industrialist wants to use his field experience to assess the probability of leakage of a decant pipe when it is used. He has observed 12 leakages in 10 years. Each year, 30 decanting are carried out; each decanting uses only one pipe. The decant pipes are all the same and are checked out by the same operator at fixed intervals. However, the leakages are mostly reported by the truck drivers; more precisely, it might be possible that some limited leakages have not been reported. In this case, N = 12, E = 300 operations, the database is reliable: γ = 90%. Since E > 50 and N/E < 0.1, equation ( 4) leads to the following estimator for the probability of occurrence of a leakage during a decanting operation: <p A > = (1/600)Chi-squared 90% (26) = 5.93 10 -2 .

DISCUSSION

The methodology developed in this paper has some drawbacks, which will lead to complementary works in the upcoming year. Firstly, the methodology supposes the field experience data are homogeneous and the failure rate is constant over time. This is obviously false in reality. Beauzamy [START_REF] Beauzamy | Nouvelles méthodes probabilistes pour l'évaluation des risques[END_REF] explores new methodologies to take uncertainties, lack of homogeneity, missing data, etc. into account. However, the methodology developed in this paper leads to simple formulas that can be easily used by industrialists for their safety reports. This is mainly why it has been favored over other methodologies, more realistic and precise but far more complicated to implement.

Secondly, the choice of γ = 70% for a very reliable database is questionable. At the moment, this value is very subjective: it is the lowest round percentage for which N' (as defined in section 2.2.) is over 1 when N = 0. The idea is to reward very reliable database by allowing lower frequency and probability estimator, while complying with the French conservative approach (thus not taking a too small value for γ). Works will be conducted in 2012 to discuss the relevancy of γ = 70% and propose adapted values for γ.

Another difficulty when using this methodology is to decide whether a field experience is reliable or not. Right now, there is no clear criterion to help an industrialist decide which degree of quality he has to select. INERIS will focus on clarifying this aspect of the methodology. Complementary works will be carried out in 2012.

At last, it is worth noting the mathematical aspects of the methodology can be extended to any type of database, as long as the hypotheses are met: natural risks, functional safety, human reliability, etc.

CONCLUSION

The methodology developed in this paper can be used for frequency and probability assessment of events in the context of French risk assessments of facilities. It allows industrialists to use their own field experience to assess frequencies and probabilities, rather than extracting generic values from aggregated databases. These generic values can be very restrictive for the operator if he has a lower N/E ratio.

Equations (1) through [START_REF] Lenoble | Introduction of frequency in France following the AZF accident[END_REF] give the operators an alternative: selecting the upper bound of a particular onesided confidence interval as the estimator of the frequency or probability they want to assess. However, the degree of reliability of the field experience used in the assessment greatly impacts the results. At the moment, INERIS proposes to distinguish very reliable, reliable and moderately reliable databases, each associated with a different approach as exposed in section 4.

In 2012, INERIS will work on these quality criteria. The objective is to propose the operators a set of questions which will lead to a notation of the used database. This system of notations is associated with a set of formulas, with a main idea: a certain range in the notation equals one formula. The development of this new methodology relies on two steps. Firstly, the questions must be defined. To do so, the parameters defining the reliability of a database must be identified. Some options are: its age; its homogeneity; its sufficiency, etc. Secondly, the impact of each of those parameters must be studied. The objective is to decide how to modify the formulas (increasing γ, increasing N, decreasing E) when a parameter varies.


Laura Chiappini
email: laura.chiappini@ineris.fr

Stéphanie Rossignol

Enrico Mombelli

Claire Greuillet

Jérome Nicolle

Mélanie Nicolas

SOA formation study from limonene ozonolysis in indoor environment: gas and particulate phases chemical characterization and toxicity prediction

Keywords: Field and smog chamber study, reactivity, house cleaning products, health effects

Introduction

Limonene is widely employed in scented products used in indoor environments such as fresheners and household cleaners [START_REF] Nazaroff | Cleaning products and air fresheners: exposure to primary and secondary air pollutants[END_REF]. It also displays one of the highest potential for the formation of Secondary Organic Aerosols (SOAs) following ozonolysis [START_REF] Jaoui | Analysis of Secondary Organic Aerosol Compounds from the Photooxidation of d-Limonene in the Presence of NOX and their Detection in Ambient PM2.5[END_REF][START_REF] Chen | A chamber study of secondary organic aerosol formation by limonene ozonolysis[END_REF]. Besides, indoor ozone concentration, influenced by outdoor concentration and indoor sources, can be quite important to initiate gas phase chemistry [START_REF] Weschler | Ozone in Indoor Environments: Concentration and Chemistry[END_REF] and possibly lead to secondary products formation. This work investigates SOAs formation from the ozonolysis of limonene as emitted from a detergent, in order to gather information on aerosols that are an important source of exposure for people using household products.

Materials/Methods

This work combines simulation chamber ozonolysis experiments in the EUphorePHOtoREactor (EUPHORE) facility (Valencia) to specifically study limonene ozonolysis and field studies in an experimental house (CSTB MARIA) to reproduce real household products use conditions. To describe multiphase chemistry involved in SOAs formation and to take into account gas/particles partitioning phenomena, a new analytical approach is employed. Both gas and particulate phases have been simultaneously collected, respectively on sorbent tubes and filters, and molecular composition is investigated using PFBHA and MTBSTFA derivatisation prior to thermal-desorption coupled with gas chromatography and mass spectrometry (TD-GC-MS) analysis (Rossignol et al, in press). For each experiment, chemical data are coupled with physics characterization of formed particles: mass evolution and size and number distribution evolution. VOC have been sampled on Tenax tubes and analyzed by GC/MS. A pseudocontinuous analyzer (15 min time resolution) has been used to monitor limonene concentrations. Finally, in order to overcome the difficulties related to the achievement of airways exposure experiments, a toxicological profiling of the chemicals was carried out by means of experimental data retrieved from the literature and structure-toxicity analogies based on a series of chemicals that are structurally related to the analyzed ozonolysis products.

Results and Discussion

As soon as the household product is used, limonene concentration increases, going from the background level of 6 µg m -3 to a maximum of 70 µg m -3 . Limonene increase is followed a few minutes later by a PM 2.5 particle number concentration increase going from 2800 particles cm -3 to a maximum value of about 70 000 particles cm -3 . The so formed particles are small, displaying a mode at 25 nm whereas the background mode is about 100 nm. These small particles can be suspected to be secondary organic aerosols formed from limonene ozonolysis. To confirm that hypothesis two experiments have been performed: The same household product use in the MARIA kitchen but without any ozone generation (it has resulted in no particle formation), simultaneous study of the gaseous and particulate phase to search for limonene ozonolysis formation products. 23 carbonyle compounds and 13 hydroxyle and acid carboxylic compounds have been positively or tentatively identified in the gaseous and/or the particulate phases. Most compounds display a concentration increase consequently to the use of the household product. Chemical analysis results from the MARIA experiment have been compared to results obtained in the EUPHORE simulation chamber to ease limonene ozonolysis compounds identification. Some compounds are common to EUPHORE and MARIA experiment revealing the products formed from limonene ozonolysis consequently to the use of the housecleaning product. Among these compounds, the three most abundant have been quantified in both gaseous and particulate phases. The concentration increase of methyl-glyoxal, 4-oxopental and Limononaldehyde are significant ranging from 6 to 200 % for gaseous phase and 8 to 150 % for the particulate phase. Among these three compounds, limononaldehyde is the only limonene ozonolysis specific product. Its identification in both phases confirms SOA formation from limonene ozonolysis consequently to the use of the detergent. Methylglyoxal and 4-oxopental can be formed from the oxidation of other precursors. And indeed, citronelal, myrcene and terpineol have been identified in the gas phase

Conclusions

The formation of secondary organic aerosols from the ozonolysis of limonene emitted consequently to the use of a house cleaning product has been observed and demonstrated by physical (size distribution) and chemical (specific limonene oxidation product quantification in gaseous and particulate phases) particle characterization. Toxicological profiling of the so analysed chemicals indicated a large spectrum of probable toxicological hazards (skin/eye irritation, skin sensitization, mutagenicity carcinogenicity, inhalation toxicity).

Table 1 :

 1 Experimental and predicted toxicological hazards:

		4-oxo-	Methyl-	Limon-
		pentanal	glyoxal	aldehyde
	Skin irritation	Positive ST	Structural Alert + msds	Positive ST
	Skin
	sensiti-	Positive EXP	Positive EXP	Positive ST
	zation
	Eye
	irritation/	Positive ST	Positive EXP	Positive ST
	corrosion
	Muta-genesis	Negative ST	Positive EXP	Negative ST
	Carcino-genicity	Structural Alert	Positive EXP	Structural Alert
	Inhalation toxicity	Positive EXP	Positive EXP	Positive ST
	PositiveEXP experimental evidence exist;
	PositiveST Structure-Toxicity analogies among
	similar chemicals exist to substantiate
	toxicological hazard , NegativeST Structure-
	Toxicity analogies among similar chemicals
	exist to substantiate the absence of toxicological