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Normalisation internationale - Actualités - Qualité des sols et Sites et Sols Pollués
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Performances of low-cost optical PM sensors for indoor air quality monitoring in mobility situations by car around Paris
No full textInternational audienceIn many industrialized countries, people spend more than one hour a day in their vehicles where particulate matter (PM) could reach high concentrations [1]. Though such a situation has become a matter of public concern, the lack of concentration data makes the monitoring of these ambiances difficult to apprehend. In order to tackle this challenge, attention is redirected towards the low-cost sensing units. Indeed, besides some interesting features such as portability, fast-deployment and cost, these devices can help to remotely characterize and monitor the spatial and temporal patterns of pollution sources [2]. However, once deployed, sensors information may be unreliable if uncertainties or intrinsic sensors’ limitations are not taken into consideration [3]. In this study, the objective is to evaluate performances of low-cost optical PM sensors under running conditions near the Paris ring-road and motorways. An appropriate field test campaign was designed and carried out within an instrumental car equipped with a GPS tracker and, reference and equivalent PM-measurement methods. For PM10 and PM2.5 values, data comparisons were performed across different seasons and pollution contexts (urban and semi-urban). Such an approach allowed to consider the influence of the inside car conditions (temperature and relative humidity) and the one associated to the physico-chemical nature and morphology of the particles encountered in vehicle environment. If this first campaign demonstrated the feasibility of the deployment of micro-sensors for indoor air quality monitoring in mobility situations, the results lead to conclude that some technological improvements have to be implemented to ensure their validation in respect to reference methods
Evaluation of the Density and Absorption Properties of Laboratory-Generated Particulate Organic Nitrates (pON)
No full textInternational audienceAtmospheric particulate organic nitrates (pON) have recently been shown to account for a large fraction of organic aerosol (OA). Through light absorption, especially at short wavelengths, they make up part of atmospheric brown carbon. While pON might be a non-negligible climate-forcing agent, the physical and optical properties are still poorly documented. As part of the Aerosol Chemical Monitor Calibration Centre (ACMCC) pON experiment, measurements have been conducted to characterize the physical and optical properties of laboratory-generated pON, such as density, mass absorption coefficient (MAC) and refractive index (RI). pON were generated in a Potential Aerosol Mass oxidation flow reactor from the reaction of single VOC precursors with NO3 radical, using two biogenic (limonene and b-pinene) and two anthropogenic (acenaphthylene and guaiacol) compounds. In addition to online physicochemical characterization with aerosol mass spectrometers, a suite of instruments was dedicated to study the physical and optical pON properties, including an aerodynamic aerosol classifier (AAC), a centrifugal particle mass analyzer (CPMA), a scanning mobility particle sizer (SMPS), a condensation particle counter (CPC) and a multi-wavelength aethalometer (AE33). Results will be discussed according to different precursors (biogenic/anthropogenic), as well as compared to the literature data from laboratory experiments for the same (and other) precursors but in different chemical conditions, such as OH or NH3 exposure. Results for laboratory-generated pON will also be compared with ambient air observations
Integration of new approach methods in a structure based read-across for DART effects
No full textInternational audienceRead-across is one of the most often applied alternative tools for hazard assessment, in particular for complex endpoints such as toxicity after repeated exposure or developmental and reproductive toxicity. We have applied this approach to a series of six aliphatic carboxylic acids that have developmental toxicity data, some being positive, some negative. For one of these compounds, 2-Methylhexanoic acid (MHA), we have specifically blinded this toxicity data, and we have applied new approach methodologies (NAM) to substantiate the read across of the other compounds (as source compounds) to MHA, and to explore whether these NAM correctly predict the in vivo developmental toxicity of MHA. Thus, we have tested MHA and the five analogues in a battery of in vitro tests with clear relevance to DART, i.e. the Zebrafish Embryo Test (ZET), mouse Embryonic Stem cell Test (mEST), iPSC-based neurodevelopmental model (UKN1), and a series of CALUX Reporter assays, and combined this with toxicokinetic models to calculate effective cellular concentrations and associated in vivo exposure doses. We also included two positive, and one negative control compound in this test. As the histone deacetylase enzyme is postulated to be the molecular initiating target leading to neural tube defects with these compounds, we have also investigated the potential of these six analogues to inhibit this enzyme in ZET, mEST, and UKN1 models. The NAM quite well predicted the in vivo developmental outcome of these six aliphatic carboxylic acids. This presentation will discuss the combining of results from multiple NAMs for predicting the teratogenic properties and potency of this series of structurally related chemicals and how this information can be used to establish a framework of testing for regulatory applications. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 681002
Development of qualitative and quantitative AOPs and their integration into risk assessment
No full textInternational audienceChemical hazard assessment can directly use qualitative adverse outcome pathways (AOPs) to integrate data generated by alternative methods or in vivo testing. Risk assessment requires quantitative relationships from exposure to effect timing and magnitude: quantitative AOPs (qAOPs) should be able to provide such dose-time-response predictions. There is also an intermediate level of quantification, in which qAOPs are able to make predictions about the probability of a chemical to belong to a category such as toxic/nontoxic, or low/ medium/high toxicity. Bayesian networks have typically been used in the latter case, and are suitable for refined hazard assessment. We will first briefly review the various methods and their main applications so far. In EU-ToxRisk, we have extended the Bayesian network (BN) approach to encompass continuous dose-time-outcome qAOPs. We compared BN to empirical dose-response modeling and to systems biology (SB) modeling. This was done for an oxidative stress induced chronic kidney disease AOP, using in vitro data obtained on RPTEC/ TERT1 cells exposed to potassium bromate. We showed that, despite the fact that dose-response models give adequate fits to the data they should be accompanied by mechanistic modeling to gain a proper understanding of domain of applicability of the quantification. BNs can be both more precise than dose-response models and simpler than SB models, but more experience with their use is needed. We have since extended our work to qAOPs of mitochondrial disruption induced toxic effects in HepG2 (liver), RPTEC/TERT1 (kidney) and LUHMES (neuronal) cells, after exposure to several chemicals, and present those new results in this session. Comparison of the results across cell types and chemicals will be discussed, together with the assumption of chemical independence of the qAOPs developed. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 681002 as well as from the Innovative Medicines Initiative 2 Joint Undertaking (IMI2/JU) under grant agreement No 777365
Different KNIME workflows for read-across and successive use for weight-of-evidence strategy
No full textInternational audienceThe evaluation of the toxic effects of substances is a complex task, due to the huge amount of factors involved in the biological processes at the basis of the effect. This requires taking advantage of all elements that can be used in the assessment of the property values. The read-across approach and the in silico methods, collectively called non-testing methods, can be integrated within a weight-ofevidence strategy. This integration is typically performed manually. Furthermore, also the read-across process in most of the cases relies on expert decisions, which may be subjective, and based on some initial choices. In this approach, there is a risk of making poorly reproducible results besides losing important pieces of information. In addition, a main shortcoming in read-across is that the process may not identify some of the relevant source compounds. In order to cope with these problems, we explored software tools able to assist the expert. The factors related to similarity which we used to select source compounds were: structural, physico-chemical, toxicological and pharmacokinetic features. These tools analyse the similarities of the compounds in “full or partial” way, i.e. merging all the features or selecting only those more relevant. Furthermore, the steps of the process can be done in a parallel or sequential way. Finally, we combined the results of the read-across procedure with those from in silico models. We will describe the added value of these programs, implemented in KNIME. We acknowledge the project EU-ToxRisk (a project funded by the European Union’s Horizon 2020 research and innovation program under grant agreement No 681002)
Methodology for engineered nanomaterial stack emissions and ambient atmosphere : measurements and multi scale modeling
No full textInternational audienceDuring production and use phase of Engineered nanomaterial (ENM), a part of these materials is released into air as ultrafine particles and form a potential environmental contamination. Delivering models and methods to predict engineered nanomaterials’ (ENM) fate in the environment is the core task of the H2020 EU-funded NanoFASE project. In that context, WP6 focused on the experimental investigation of the emissions and transformations of ENM released from industrial sites. Several field campaigns were performed to quantify and characterize the emissions at stack and to measure the concentration in the environment and ground deposition in the surrounding of the sites. We will present the methodology used experimentally to estimate the features of ENM stack emissions and their fate in the ambient atmosphere. Despite a broad range in the nature and morphology of the emitted ENMs, chimney emissions were well characterized in terms of nature, morphology and quantities of particles (metal oxide (M-O)) emitted. Experimental observations in terms of fluxes, chemical nature of substances, particle sizes, morphologies allowed to provide qualitative and quantitative parameters required to describe a source term for atmospheric dispersion modelling of stack emissions during production. Based on these results, several atmospheric models were tested in order to evaluate the impact within plume of the potential evolution of ENM at various time and space scale. Finally, specific challenges were identified and are discussed for future experimental and modeling work
