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Association between chronic long-term exposure to airborne dioxins and breast cancer
No full textInternational audienceBreast cancer is the most common type of cancer among women. Environmental pollutants, specifically those with endocrine disrupting properties like dioxins, may impact breast cancer development. Current epidemiological studies on the association between exposure to dioxins and the risk of breast cancer show inconsistent results. To address these uncertainties, our objective was to investigate the impact of airborne dioxin exposure on breast cancer risk within the E3N cohort, encompassing 5222 cases identified during the 1990–2011 follow-up and 5222 matched controls. Airborne dioxin exposure was assessed using a Geographic Information System-based metric considering residential proximity to dioxin emitting sources, their technical characteristics, exposure duration and wind direction. Additional analyses were performed using dioxin concentrations estimated by a chemistry transport model, CHIMERE. The results suggest a slightly increased risk between cumulative dioxin exposure at the residential address and overall breast cancer risk (adjusted odds ratio (OR) = 1.03, 95% confidence interval (CI): 0.99–1.07, for a one standard deviation (SD) increment among controls (14.47 log-μg-TEQ/m2). The associations remained consistent for sources within 3, 5, and 10 km, and when restricting exposure to dioxin emissions from household waste incinerators. Similar OR estimates (OR = 1.02, 95% CI: 0.97–1.07, for a one SD increment) were obtained using the CHIMERE model. The findings of this study suggest the possibility of an increased risk of breast cancer associated with long-term residential exposure to dioxins and emphasize the importance of efforts to mitigate air pollution exposure
Estimation of Ganciclovir Exposure in Adults Transplant Patients by Machine Learning
No full textInternational audienceIntroduction Valganciclovir, a prodrug of ganciclovir (GCV), is used to prevent cytomegalovirus infection after transplantation, with doses adjusted based on creatinine clearance (CrCL) to target GCV AUC0-24 h of 40-60 mg*h/L. This sometimes leads to overexposure or underexposure. This study aimed to train, test and validate machine learning (ML) algorithms for accurate GCV AUC0-24 h estimation in solid organ transplantation. Methods We simulated patients for different dosing regimen (900 mg/24 h, 450 mg/24 h, 450 mg/48 h, 450 mg/72 h) using two literature population pharmacokinetic models, allocating 75% for training and 25% for testing. Simulations from two other literature models and real patients provided validation datasets. Three independent sets of ML algorithms were created for each regimen, incorporating CrCL and 2 or 3 concentrations. We evaluated their performance on testing and validation datasets and compared them with MAP-BE. Results XGBoost using 3 concentrations generated the most accurate predictions. In testing dataset, they exhibited a relative bias of -0.02 to 1.5% and a relative RMSE of 2.6 to 8.5%. In the validation dataset, a relative bias of 1.5 to 5.8% and 8.9 to 16.5%, and a relative RMSE of 8.5 to 9.6% and 10.7% to 19.7% were observed depending on the model used. XGBoost algorithms outperformed or matched MAP-BE, showing enhanced generalization and robustness in their estimates. When applied to real patients' data, algorithms using 2 concentrations showed relative bias of 1.26% and relative RMSE of 12.68%. Conclusions XGBoost ML models accurately estimated GCV AUC0-24 h from limited samples and CrCL, providing a strategy for optimized therapeutic drug monitoring.</div
Influence of vent distribution on the violence of a gas explosion
No full textInternational audienceThe development of new energies has led to their implementation in ISO maritime containers, raising the risk of flammable gas accumulation and explosion. Vent panels are commonly used to release excess gas produced by combustion and limit explosion overpressure. However, explosion discharge orifices are generally concentrated in one area. Little research has been done on the impact of vent distribution across the enclosure's surface. This article presents the results of an experimental study in which 1.2 m2 of vent area was distributed over the surface of a 37 m3 blast chamber. Four vent surface distribution configurations are studied. Two flammable mixtures, 15.5% and 17.4% hydrogen-air, respectively, were used, with two ignition source locations (backwall, central). An experimental study found that vent distribution reduces internal overpressure in the case of backwall ignition but has little influence when the ignition source is central. However, vent distribution plays a significant role in reducing external pressure effects
Differentiating between Euro 5 gasoline and diesel light-duty engine primary and secondary particle emissions using multivariate statistical analysis of high-resolution mass spectrometry (HRMS) fingerprints
No full textInternational audienceAbstract. Emissions from gasoline and diesel vehicles are predominant anthropogenic sources in ambient air, and their accurate source apportionment is a major concern for air quality policymakers aiming to implement effective strategies to reduce air pollution. Recent studies indicate that particulate matter (PM) emissions from modern cars equipped with the latest after-treatment technologies are mainly related to secondary organic aerosol (SOA) production, particularly in the case of gasoline vehicles. However, distinguishing between emissions from gasoline and diesel vehicles in ambient air remains challenging and is rarely achieved. This study aimed to evaluate the potential of non-targeted-screening (NTS) analyses for determining specific organic molecular markers of primary organic aerosol (POA) and SOA from gasoline and diesel vehicles, which could enhance PM source apportionment efforts. Experiments were conducted using a chassis dynamometer with Euro 5 gasoline and diesel vehicles under three different driving cycles. Exhaust emissions were diluted before being introduced into a potential aerosol mass oxidation flow reactor (PAM-OFR) to simulate atmospheric ageing and SOA formation. Samples were collected both upstream and downstream of the PAM-OFR and analysed using NTS approaches with liquid- and gas-chromatography coupled to quadrupole time-of-flight mass spectrometry (LC- and GC-QToF-MS). The chemical fingerprints obtained were compared using multivariate statistical analyses, including principal component analysis (PCA), hierarchical clustering analysis (HCA), and partial least-square discriminant analysis (PLS-DA). Results revealed specific fingerprints of POA and SOA for each type of vehicle tested and about 10 markers unique to each fraction of diesel and gasoline vehicles. This study demonstrates the promise of combining high-resolution mass-spectrometry-based NTS with advanced multivariate statistical analyses to differentiate between OA fingerprints and to discover specific markers of diesel and gasoline vehicular sources for further use in PM source apportionment studies
Machine-learning-driven reconstruction of organic aerosol sources across dense monitoring networks in Europe
No full textInternational audienceFine particulate matter (PM) poses a major threat to public health, with organic aerosol (OA) being a key component. Major OA sources, hydrocarbon-like OA (HOA), biomass burning OA (BBOA), and oxygenated OA (OOA), have distinct health and environmental impacts. However, OA source apportionment via positive matrix factorization (PMF) applied to aerosol mass spectrometry (AMS) or aerosol chemical speciation monitoring (ACSM) data is costly and limited to a few supersites, leaving over 80% of OA data uncategorized in global monitoring networks. To address this gap, we trained machine learning models to predict HOA, BBOA, and OOA using limited OA source apportionment data and widely available organic carbon (OC) measurements across Europe (2010–2019). Our best performing model expanded the OA source data set 4-fold, yielding 85 000 daily apportionment values across 180 sites. Results show that HOA and BBOA peak in winter, particularly in urban areas, while OOA, consistently the dominant fraction, is more regionally distributed with less seasonal variability. This study provides a significantly expanded OA source data set, enabling better identification of pollution hotspots and supporting high-resolution exposure assessments
Thermo-hydro mechanical coupling in a discrete modelling: Large-scale 3D application to thermal hydrofracturing
No full textInternational audienceThis work aims to model an in-situ heating test (CRQ) conducted by the French National Radioactive WasteManagement Agency (Andra) within the Meuse/Haute-Marne Underground Research Laboratory (MHM URL),using a discrete approach. The modelling of the CRQ test is part of numerical simulations performed through theinternational research project DECOVALEX. The goal of the CRQ test is to study the conditions under whichthermal hydrofracturing can occur in the Callovo-Oxfordian claystone (COx) formation and to identify its in-fluence on pore pressure evolution. The present discrete model introduces thermo-hydro-mechanical (THM)coupling into the Itasca discrete code 3DEC, which represents an assembly of elastic deformable blocks withinterfaces modelled as joints. The THM formulation is implemented in the 3DEC code using an iterativeapproach. At each step, this iterative numerical solving starts by the thermal simulation. Then, the hydro-mechanical calculation is carried out by a series of hydraulic and mechanical computations until equilibriumis reached. This iterative process repeats at each timestep until the final calculation time is achieved. To modelthe fracturing process in the COx, a failure criterion based on Mohr Coulomb with tensile cut-off is used for thejoints. The THM coupling implementation is first validated against a poro-elastic closed-form solution byconsidering a heat source within an infinite saturated porous medium. Afterwards, the CRQ experiment isconsidered with particular attention to the phenomenon of thermal fracturing, as the main advantage of thediscrete model lies in its explicitly representation of fractures. This study also demonstrates the ability of adiscrete model in dealing with a large model that includes multiple processes (THM coupling and rock failure)
Enhanced Daytime Production of Airborne Pollutants by SO 2 Oxidation at Ocean Surface Over Eastern Asia
No full textInternational audienceSulfur dioxide (SO 2 ) is oxidized to sulfate within atmospheric aerosols. However, it is also deposited on the ocean surface by dry deposition thereby reacting with the enriched organic material within sea‐surface microlayer (SML) such as unsaturated fatty acids and humic substances. Here, we performed dedicated experiments on SO 2 oxidation chemistry at authentic SML sampled from 10 sites in coastal area and open sea of the South China Sea, in dark and under simulated sunlight irradiation. Real‐time measurements of volatile organic compounds (VOCs) formed by SO 2 oxidation chemistry were performed by using ultra‐high resolution mass spectrometry. Intriguingly, a total of 104 product compounds were identified in dark whereas 843 compounds were produced upon sunlight irradiation, during the SO 2 oxidation of the SML samples. We evaluated the potential influence of the SO 2 oxidation on SML under light irradiation as a new marine VOCs source by a chemical transport model. The simulations suggest a significant change of oxygenated VOCs in marine atmosphere over Eastern Asia that potentially alters the budgets of HO x and RO x radicals and consequently the lifetime of methane
Dust pollution substantially weakens the impact of ammonia emission reduction on particulate nitrate formation
No full textInternational audienceDust emissions significantly influence air quality and contribute to nitrate aerosol pollution by altering aerosol acidity. Understanding how dust interacts with ammonia emission controls is crucial for managing particulate nitrate pollution, especially in urban areas. In this study, we conducted field measurements of aerosol components and gases across three cities in Eastern China during the spring of 2023. By combining an aerosol thermodynamic model with machine learning, we assessed the contribution of dust to aerosol pH and its impact on nitrate formation. Our results show that changes in ammonia, both in the gas and particle phases, were the main factors affecting aerosol pH, with dust particles contributing to about 7 % of the total pH variation. During dust events, high concentrations of non-volatile ions increased aerosol pH, leading to higher nitrate levels in particulate form. Machine learning analysis revealed that extreme dust storms caused a significant change in aerosol pH, enhancing nitrate partitioning. Further simulations indicated that while reducing ammonia emissions is effective in lowering nitrate levels under normal conditions, this effect is significantly reduced in dust-affected environments. Dust particles act as a buffer, reducing the sensitivity of nitrate formation to ammonia emission reductions. These findings emphasize the need to consider dust pollution when designing strategies for controlling particulate nitrate levels and highlight the complex interactions between dust and anthropogenic emissions
Construction of copper, iron and manganese anthropogenic emission inventories for Europe
No full textInternational audienceTrace metal elements in atmospheric particulate matter (PM) have significant adverse health effects. However, emissions of some metals, such as copper (Cu), are notyet consistently reported to the EMEP program by European countries under the Convention on Long-range Transboundary Air Pollution (CLRTAP), as their reportingis only encouraged but not mandatory. Other unregulated metals such as iron (Fe) and manganese (Mn), are not considered at all. In this study, we improved thecurrent European Cu inventory by correcting and adjusting existing data and completing the inventory through gap-filling. In addition, we developed the firstEuropean anthropogenic inventories for Fe and Mn, considering key anthropogenic sources such as brake wear, road abrasion, engine lubricant combustion, fuelcombustion, waste incineration and rail wear. These emissions were geographically distributed based on the spatialization of PM10 emissions proposed in thereference inventories. For Cu, our new inventory shows that the heterogeneity of emissions between different countries is greatly reduced; for Fe and Mn, the inventoryconfirms the importance of abrasion processes in the rail and road traffic sectors, as well as that of combustion processes. To evaluate the emission inventory,the Fe/Cu and Mn/Cu ratios in the emissions were directly compared to ambient measurements. The results indicate an underestimation of Fe and Mn emissionscompared to Cu, potentially due to the omission of certain emission sources, such as industrial activities or resuspension by traffic, or to contributions from naturalsources such as desert mineral dust. We also compared our inventory with existing national and global inventories. This comparison suggests an underestimation ofemissions from industrial activities in our inventory but also a potential misrepresentation of road traffic (or railway) sources in other inventories. Further work,including simulations using a chemistry and transport model, and comparison with extended concentration data, is needed to better assess and improve the accuracyof these inventories
The use of diagnostic tools to assess the risks of chemicals to freshwater ecosystems: towards a unified evaluation framework
No full textInternational audienceThe risk assessment of chemicals relies on multiple tools to quantify the ecological responses of ecosystems to existing chemical pollution. These tools are broadly categorized into three major groups: toxic pressure assessments, bioassays, and ecological monitoring. Here, we examine the strengths and limitations of these approaches, their current level of implementation for freshwater ecosystems across Europe, and their ability to evaluate the impacts of chemicals under field conditions. Additionally, we analyze the correspondence between results obtained from these tools when applied to a monitoring dataset from German streams. Our evaluation showed that no single tool can perfectly characterize the environmental impacts of chemical mixtures. However, each provides distinct lines of evidence, enabling the identification of chemicals driving ecological risks and the biological endpoints most likely to be affected, with ecological monitoring tools having the potential to show long-term ecosystem impairment. Finally, we propose recommendations to better understand the discrepancies between the outcomes of different methods and explore their potential integration into a unified water quality evaluation framewor