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CFD simulation of premixed flames propagating in an obstacles network
No full textInternational audienceThe objective of this work is to study the propagation of a premixed flame through numerous obstacles and to observe the flame/obstacle interactions. Several premixed flame models have been evaluated using the OpenFoam software library. They are all based on the evolution of the progress variable and they take into account the increase of the flame speed due to the effects of folding and stretching due to the turbulence. The studied configuration is based on the expansion of a spherical flame within a network of obstacles whose spherical geometry allows fine statistical analyses along the radial direction of the geometry. Various analyses have been carried out based on the temporal evolution of the flame, on the geometric position of its front and finally on the statistical data of its structure. At a given level of turbulence and for an equal volume obstruction rate, whatever the arrangement of the obstacles, it appears that the flame surface developed over time remains substantially identical even if its local topology is very different. On the other hand, if the volume of obstructions varies, the flame surface and therefore its propagation speed is strongly affected. Different correlations for turbulent combustion models have been used: Gulder, FLACS and Zimont. These are the most used in the most common industrial CFD codes. It has been observed that, by properly adjusting the parameters associated with each model, an identical free flame velocity can be found by each model, however, as soon as the flame crosses obstacles, significant variations are observed. This is a logical observation since these models were developed for flame propagation without obstacles. But it seems necessary to consider extensions of these models that would take into account the presence of a given volume obstruction rate
First QSPR models to predict the thermal stability of potential self-reactive substances
No full textInternational audienceSelf-reactive substances are unstable chemical substances which can easily decompose and may lead to explosion in transport, storage, or process situations. For this reason, their thermal stability properties are required to assess possible process safety issues and for classification purpose. In this study, the first quantitative structure–property relationships (QSPR) dedicated to this class of compounds were developed to predict the heat of decomposition of possible self-reactive substances from their molecular structures. The database used to develop and validate the models was issued from a dedicated experimental campaign on 50 samples using differential scanning calorimetry in homogeneous experimental conditions. QSPR models were derived using the GA-MLR methods (using a genetic algorithm and multi-linear regressions) using molecular descriptors calculated by Dragon software based on two types of inputs: 3D molecular structures determined using the density functional theory (DFT), allowing access to three-dimensional descriptors, and from SMILES codes, favoring the access to simpler models, requiring no preliminary quantum chemical calculations. All models respected the OECD validation guidelines for regulatory acceptability of QSPR models. They were tested by internal and external validation tests and their applicability domains were defined and analyzed
Integration of Genotoxic Biomarkers in Environmental Biomonitoring Analysis Using a Multi-Biomarker Approach in Three-Spined Stickleback (Gasterosteus aculeatus Linnaeus, 1758)
No full textInternational audienceWater is impacted by a variety of increasing pressures, such as contaminants, including genotoxic pollutants. The proposed multi-biomarker approach at a sub-individual level gives a complementary indicator to the chemical and ecological parameters of the Water Framework Directive (WFD, 2000/60/EC). By integrating biomarkers of genotoxicity and erythrocyte necrosis in the sentinel fish species the three-spined stickleback (Gasterosteus aculeatus) through active biomonitoring of six stations of the Artois-Picardie watershed, north France, our work aimed to improve the already existing biomarker approach. Even if fish in all stations had high levels of DNA strand breaks, the multivariate analysis (PCA), followed by hierarchical agglomerative clustering (HAC), improved discrimination among stations by detecting an increase of nuclear DNA content variation (Etaing, St Rémy du Nord, Artres and Biache-St-Vaast) and erythrocyte necrosis (Etaing, St Rémy du Nord). The present work highlighted that the integration of these biomarkers of genotoxicity in a multi-biomarker approach is appropriate to expand physiological parameters which allow the targeting of new potential effects of contaminants
A meta-analysis of ecotoxicological models used for plant protection product risk assessment before their placing on the market
No full textInternational audienceBefore their placing on the market, the safety of plant protection products (PPP) towards both human and animal health, and the environment has to be assessed using experimental and modelling approaches. Models are crucial tools for PPP risk assessment and some even help to avoid animal testing. This review investigated the use of modelling approaches in the ecotoxicology section of PPP active substance assessment reports prepared by the authorities and opened to consultation from 2011 to 2021 in the European Union. Seven categories of models (Structure-Activity, ToxicoKinetic, ToxicoKinetic-ToxicoDynamic, Species Sensitivity Distribution, population, community, and mixture) were searched for into the reports of 317 active substances. At least one model category was found for 44 % of the investigated active substances. The most detected models were Species Sensitivity Distribution, Structure-Activity and ToxicoKinetic for 27, 21 and 15 % of the active substances, respectively. The use of modelling was of particular importance for conventional active substances such as sulfonylurea or carbamates contrary to microorganisms and plant derived substances. This review also highlighted a strong imbalance in model usage among the biological groups considered in the European Regulation (EC) No 1107/2009. For example, models were more often used for aquatic than for terrestrial organisms (e.g., birds, mammals). Finally, a gap between the set of models used in reports and those existing in the literature was observed highlighting the need for the implementation of more sophisticated models into PPP regulation
Monitoring of seismic repeater and aseismic movements in the Garpenberg ore mine, Sweden
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Elucidating the impact of Siberian biomass burning aerosol on the radiative balance in the Arctic: model analysis constrained by observations
No full textInternational audienceSiberian wildfires inject into the atmosphere huge amounts of aerosol particles, part of which are transported into the Arctic. Once in the Arctic, biomass burning (BB) aerosol can contribute to the radiative balance and affect the climate processes in different ways, including the absorption and scattering of the solar radiation, changes in the albedo of the ice/snow surface cover, modification of the optical properties of clouds. However, quantitative knowledge of the role of Siberian BB aerosol in the Arctic is deficient, reflecting major uncertainties in available model representations of its emissions, chemical composition, and optical properties.In this study, the CHIMERE v2020 chemistry transport model (https://www.lmd.polytechnique.fr/chimere/) coupled with the WRF meteorological model was used to examine the effects of aerosol-radiation interactions (the direct aerosol radiative effect and the associated semi-direct effects) due to the transport of BB plumes from Siberia into the Eastern Arctic. The analysis features the use of satellite and in situ observations to constrain the BB aerosol sources and optical properties. Furthermore, the simulations brought together new model representations of the optical properties and aging of the organic component of Siberian BB aerosol [1,2], which were also constrained by satellite and ground-based observations, and recent findings from aerosol chamber experiments [3]. The study focuses on the radiative effects associated with the strong fires that occurred in Siberia in July 2016.It is found that weakly-absorbing Siberian BB aerosol exerted a strong cooling effect in the near-surface layer of the atmosphere and at the top of the atmosphere over large areas on land in the Eastern Arctic. However, the aerosol radiative effects over the ocean were found to be of a mixed character, which is partly due to semi-direct effects triggered by the aerosol absorbing components (black carbon and brown carbon). Overall, our study results indicate that direct and semi-direct radiative effects caused by Siberian BB aerosol constitute a significant part of the evolving natural baseline of the Arctic radiative budget and need to be taken into accounts in analyses and predictions of the Arctic amplification of climate change
Clustering and event similarity based fault characterization of post mining induced seismicity of Gardanne mine, France
No full textInternational audienceIn post-mining environments, seismic hazard is still not very well understood, as number of research studies remains limited. Seismicity is often considered in post-mining risk mitigation procedures as a precursory of failure initiation in rocks within the mining works leading to ground instabilities. However, flooding of the mines can also lead to perturbations of stress states and pore pressures within the rock mass leading to failure of pre-existing faults, which may have more important impact on public safety due to a potentially longer period of activity and possibly higher magnitudes of the induced seismic events depending on the fault size. In a former coal mine in Gardanne, France, which was abandoned in 2003 and flooded afterwards, seismicity started appearing and raising concerns since 2010, when flooding reached the center of the mining basin. The seismic activity has been occurring approximately every two years in the form of crises. Events were also felt by the local population. A sparse temporary monitoring network has been installed in 2013 in this seismically active area. Based on research results so far, seismicity originates from the reactivation of faults underlying the mining excavations and is influenced by flooding, pumping of the water, and seasonal meteorological conditions. We investigate the clustering behavior and multiplet occurrences within the seismic events recorded by the sparse temporary microseismic network between 2014 and 2017. Detailed cluster analyses, the spatio-temporal distribution, recurrence time patterns, and source parameters help to characterize seismically active structure(s) below the mining works. The triggering of the seismic activity in each cluster appears to be differently influenced by the hydro-meteorological conditions, with some clusters being more affected by rainfall, while other by dry period. The variations of the pumping rate strongly affect the rate of seismicity in this area as well. The analysis is complemented by incorporating a new dataset recorded by an enhanced monitoring network during 2019, which allows to follow the evolution of the cluster activity
Unmanned Aerial Air Quality measurements: the potential for industrial fire plumes characterization with onboard low-cost sensor measurements
No full textInternational audienceDuring an industrial fire, potentially hazardous substances may be released outside the site by atmospheric transfer. In order to characterize their potential impact, it is essential to gather data as quickly and reliably as possible on the consequences of the event on the environment and on the population. However, recent events related to the Lubrizol 2019 factory fire in Rouen have highlighted the difficulty in certain circumstances of listing the composition of fumes and knowing the mechanisms that contributed to their emission. These shortcomings taint the numerical models used to prioritize the fallout zones and thus distort the assessment of the impact of the fire on the environment and the health of the populations. The DESIHR project (Drones en Essaims pour la Surveillance de Sites Industriels à Hauts Risques), winner of an French National Research Agency grant, aims to develop innovative tools and methods based on the use of a fleet of autonomous drones to characterize in real-life situations the substances present in a fire plume as well as their emission and propagation conditions. The definition of the control laws governing the behaviour of each drone must in this context be based on real-time air quality information from sensors onboard each mobile vector. In order to limit the mass and the energy cost, low-cost sensors are among others considered in the DESIHR project. The objective of the work presented here is to address the potential for industrial fire plumes characterization with onboard low-cost sensor measurements in complex environments. On the basis of experiments carried out before the start of the project, it will address issues ranging from the selection of pollutants monitored and subsequent sensors to the use of data by drones, through the definition of innovative protocols for qualifying measurement performance based on real air matrices
GENerator of reduced Organic Aerosol mechanism (GENOA v1.0): an automatic generation tool of semi-explicit mechanisms
No full textInternational audienceThis paper describes the GENerator of reduced Organic Aerosol mechanism (GENOA) that produces semi-explicit mechanisms for simulating the formation and evolution of secondary organic aerosol (SOA) in air quality models. Using a series of predefined reduction strategies and evaluation criteria, GENOA trains and reduces SOA mechanisms from near-explicit chemical mechanisms (e.g., the Master Chemical Mechanism – MCM) under representative atmospheric conditions. As a consequence, these trained SOA mechanisms can preserve the accuracy of detailed gas-phase chemical mechanisms on SOA formation (e.g., molecular structures of crucial organic compounds, the effect of “non-ideality”, and the hydrophilic/hydrophobic partitioning of aerosols), with a size (in terms of reaction and species numbers) that is manageable for three-dimensional (3-D) aerosol modeling (e.g., regional chemical transport models). Applied to the degradation of sesquiterpenes (as β-caryophyllene) from MCM, GENOA builds a concise SOA mechanism (2 % of the MCM size) that consists of 23 reactions and 15 species, with 6 of them being condensable. The generated SOA mechanism has been evaluated regarding its ability to reproduce SOA concentrations under the varying atmospheric conditions encountered over Europe, with an average error lower than 3 %
