Spatio-temporal assessment of pregnant women exposure to chlorpyrifos at a regional scale.

BACKGROUND: The aim of this study was to use an integrated exposure assessment approach, combining spatiotemporal modeling of environmental exposure and fate of the chemical to assess the exposure of vulnerable populations. In this study, chlorpyrifos exposure of pregnant women in Picardy was evaluated at a regional scale during 1 year. This approach provided a mapping of exposure indicators of pregnant women to chlorpyrifos over fine spatial and temporal resolutions using a GIS environment. METHODS: Fate and transport models (emission, atmospheric dispersion, multimedia exposure, PBPK) were combined with environmental databases in a GIS environment. Quantities spread over agricultural fields were simulated and integrated into a modeling chain coupling models. The fate and transport of chlorpyrifos was characterized by an atmospheric dispersion statistical metamodel and the dynamiCROP model. Then, the multimedia model Modul'ERS was used to predict chlorpyrifos daily exposure doses which were integrated in a PBPK model to compute biomarker of exposure (TCPy urinary concentrations). For the concentration predictions, two scenarios (lower bound and upper bound) were built. RESULTS: At fine spatio-temporal resolutions, the cartography of biomarkers in the lower bound scenario clearly highlights agricultural areas. In these maps, some specific areas and hotspots appear as potentially more exposed specifically during application period. Overall, predictions were close to biomonitoring data and ingestion route was the main contributor to chlorpyrifos exposure. CONCLUSIONS: This study demonstrated the feasibility of an integrated approach for the evaluation of chlorpyrifos exposure which allows the comparison between modeled predictions and biomonitoring data.

Maternal Ambient Exposure to Atmospheric Pollutants during Pregnancy and Offspring Term Birth Weight in the Nationwide ELFE Cohort.

BACKGROUND: Studies have reported associations between maternal exposure to atmospheric pollution and lower birth weight. However, the evidence is not consistent and uncertainties remain. We used advanced statistical approaches to robustly estimate the association of atmospheric pollutant exposure during specific pregnancy time windows with term birth weight (TBW) in a nationwide study. METHODS: Among 13,334 women from the French Longitudinal Study of Children (ELFE) cohort, exposures to PM2.5, PM10 (particles < 2.5 µm and <10 µm) and NO2 (nitrogen dioxide) were estimated using a fine spatio-temporal exposure model. We used inverse probability scores and doubly robust methods in generalized additive models accounting for spatial autocorrelation to study the association of such exposures with TBW. RESULTS: First trimester exposures were associated with an increased TBW. Second trimester exposures were associated with a decreased TBW by 17.1 g (95% CI, -26.8, -7.3) and by 18.0 g (-26.6, -9.4) for each 5 µg/m3 increase in PM2.5 and PM10, respectively, and by 15.9 g (-27.6, -4.2) for each 10 µg/m3 increase in NO2. Third trimester exposures (truncated at 37 gestational weeks) were associated with a decreased TBW by 48.1 g (-58.1, -38.0) for PM2.5, 38.1 g (-46.7, -29.6) for PM10 and 14.7 g (-25.3, -4.0) for NO2. Effects of pollutants on TBW were larger in rural areas. CONCLUSIONS: Our results support an adverse effect of air pollutant exposure on TBW. We highlighted a larger effect of air pollutants on TBW among women living in rural areas compared to women living in urban areas.

Characterizing environmental geographic inequalities using an integrated exposure assessment.

BACKGROUND: At a regional or continental scale, the characterization of environmental health inequities (EHI) expresses the idea that populations are not equal in the face of pollution. It implies an analysis be conducted in order to identify and manage the areas at risk of overexposure where an increasing risk to human health is suspected. The development of methods is a prerequisite for implementing public health activities aimed at protecting populations. METHODS: This paper presents the methodological framework developed by INERIS (French National Institute for Industrial Environment and Risks) to identify a common framework for a structured and operationalized assessment of human exposure. An integrated exposure assessment approach has been developed to integrate the multiplicity of exposure pathways from various sources, through a series of models enabling the final exposure of a population to be defined. RESULTS: Measured data from environmental networks reflecting the actual contamination of the environment are used to gauge the population's exposure. Sophisticated methods of spatial analysis are applied to include additional information and take benefit of spatial and inter-variable correlation to improve data representativeness and characterize the associated uncertainty. Integrated approaches bring together all the information available for assessing the source-to-human-dose continuum using a Geographic Information System, multimedia exposure and toxicokinetic model. DISCUSSION: One of the objectives of the integrated approach was to demonstrate the feasibility of building complex realistic exposure scenarios satisfying the needs of stakeholders and the accuracy of the modelling predictions at a fine spatial-temporal resolution. A case study is presented to provide a specific application of the proposed framework and how the results could be used to identify an overexposed population. CONCLUSION: This framework could be used for many purposes, such as mapping EHI, identifying vulnerable populations and providing determinants of exposure to manage and plan remedial actions and to assess the spatial relationships between health and the environment to identify factors that influence the variability of disease patterns.

Data fusion for air quality mapping using low-cost sensor observations: Feasibility and added-value.

In Nantes, low-cost sensors were installed in the city center and deployed on driving school cars, ambulances and service vehicles to measure PM10 concentrations. This work aims to use the large amount of observations provided by the sensors for air quality mapping at the urban scale in order to show the potential added-value with respect to the dispersion model (ADMS-Urban) calculations. A preprocessing is applied to the raw sensor dataset to remove the unreliable observations based on an outlier detection technique and to compensate for the measurement drift by adjusting the estimated daily variation of the underlying background PM10 concentrations with the reference stations. Then, data fusion is performed by combining the preprocessed fixed and mobile low-cost sensor observations and the 2016 annual average of the ADMS-Urban outputs. The measurement uncertainty related to the low-cost sensors and the dispersion of the data are considered in data fusion as the Variance of Measurement Error (VME). The spatial interpolation is achieved at hourly resolution and results are presented for November 29th, 2018 from 7 am to 7 pm. Hourly fused maps show disparate responses to data fusion mainly depending on the variability of the sensor data and the correlation between the sensor observations and the drift. The data fusion performance has been investigated by comparing the daily average of the estimated concentrations, the reference observations and the hourly model outputs at each station of the Air Pays de la Loire network. Results show that considering the model alone implies 8% bias whereas including the LCS observations reduces the bias to 2.5%. However, the concentration distributions related to the data fusion are characterized by a lower dispersion than the reference observations and the model estimation. Thus, the fusion smooths the PM10 peaks. In addition, the effect of the measurement uncertainty has been investigated by doubling it or reducing it to the reference station measurement uncertainty. The sensitivity study demonstrates that the performance is increasing by reducing the uncertainty. This highlights the importance to estimate accurately the measurement uncertainty of the devices to ensure relevant air quality mapping. The method efficiency is also quite limited by the low correlation between the sensor observations and the model used as external drift in the kriging that may be explained by the remaining bias on LCS data. Efforts on this issue might increase the performance of the spatial interpolation.

Characterization of Environmental Health Inequalities Due to Polyaromatic Hydrocarbon Exposure in France.

Reducing environmental health inequalities has become a major focus of public health efforts in France, as evidenced by the French action plans for health and the environment. To evaluate environmental inequalities, routine monitoring networks provide a valuable source of data on environmental contamination, which can be used in integrated assessments, to identify overexposed populations and prioritize actions. However, available databases generally do not meet sufficient spatial representativeness to characterize population exposure, as they are usually not assembled for this specific purpose. The aim of this study was to develop geoprocessing procedures and statistical methods to build spatial environmental variables (water, air, soil, and food pollutant concentrations) at a fine resolution, and provide appropriate input for the exposure modelling. Those methods were designed to combine in situ monitoring data with correlated auxiliary information (for example, atmospheric emissions, population, and altitude), in order to better represent the variability of the environmental compartment quality. The MODUL'ERS multimedia exposure model developed by INERIS (French Institute for industrial Environment and Risks) was then used to assess the transfer of substances from the environment to humans, through inhalation and ingestion pathway characterization. We applied the methodology to a carcinogenic Polycyclic Aromatic Hydrocarbon substance, benzo[a]pyrene(B[a]P), to map spatialized exposure indicators, at the national scale. The largest environmental contribution corresponded to the ingestion pathway. Data processing algorithms and calculation of exposure will be integrated into the French coordinated integrated environment and health platform PLAINE (PLteforme intégrée d'Analyse des INégalités Environnementales) which has been developed to map and analyze environmental health inequalities.

A necessary distinction between spatial representativeness of an air quality monitoring station and the delimitation of exceedance areas.

The European legislation on ambient air quality introduces the concepts of spatial representativeness of a monitoring station and spatial extent of an exceedance zone. Spatial representativeness is an essential macro-scale siting criterion which should be evaluated before the setting-up and during the life of a monitoring point. As for the exceedance area, it has to be defined each time an environmental objective is exceeded in an assessment zone. No specific approach is prescribed to delimit such areas. A probabilistic methodology is presented, based on a preliminary kriging estimation of atmospheric concentrations at each point of the domain. It is applied to NO2 pollution on the urban scale. In the proposed approach, a point belongs to the area of representativeness of a station if its concentration differs from the station measurement by less than a given threshold. To take the estimation uncertainty into account, the standard deviation of the kriging error is used in a probabilistic framework. The choice of the criteria used to deal with overlapping areas is first tested on NO2 annual mean concentration maps of France, built by combining surface monitoring observations and outputs from the CHIMERE chemistry transport model. At the local scale, data from passive sampling surveys and high -resolution auxiliary variables are used to provide a more precise estimation of the background pollution in different French cities. The traffic-related pollution can also be accounted for in the map by additional predictors such as distance to the road, and traffic-related NOx emissions. Similarly, the proposed approach is implemented to identify the points, at a given statistical risk, where the NO2 concentration is above the annual limit value.

Is atmospheric pollution exposure during pregnancy associated with individual and contextual characteristics? A nationwide study in France.

BACKGROUND: Exposure to atmospheric pollutants is a danger for the health of pregnant mother and children. Our objective was to identify individual (socioeconomic and behavioural) and contextual factors associated with atmospheric pollution pregnancy exposure at the nationwide level. METHOD: Among 14 921 women from the French nationwide ELFE (French Longitudinal Study of Children) mother-child cohort recruited in 2011, outdoor exposure levels of PM2.5, PM10 (particulate matter <2.5 µm and <10 µm in diameter) and NO2 (nitrogen dioxide) were estimated at the pregnancy home address from a dispersion model with 1 km resolution. We used classification and regression trees (CART) and linear regression to characterise the association of atmospheric pollutants with individual (maternal age, body mass index, parity, education level, relationship status, smoking status) and contextual (European Deprivation Index, urbanisation level) factors. RESULTS: Patterns of associations were globally similar across pollutants. For the CART approach, the highest tertile of exposure included mainly women not in a relationship living in urban and socially deprived areas, with lower education level. Linear regression models identified different determinants of atmospheric pollutants exposure according to the residential urbanisation level. In urban areas, atmospheric pollutants exposure increased with social deprivation, while in rural areas a U-shaped relationship was observed. CONCLUSION: We highlighted social inequalities in atmospheric pollutants exposure according to contextual characteristics such as urbanisation level and social deprivation and also according to individual characteristics such as education, being in a relationship and smoking status. In French urban areas, pregnant women from the most deprived neighbourhoods were those most exposed to health-threatening atmospheric pollutants.

Chronic effects of air pollution on lung function after lung transplantation in the Systems prediction of Chronic Lung Allograft Dysfunction (SysCLAD) study.

An irreversible loss in lung function limits the long-term success in lung transplantation. We evaluated the role of chronic exposure to ambient air pollution on lung function levels in lung transplant recipients (LTRs).The lung function of 520 LTRs from the Cohort in Lung Transplantation (COLT) study was measured every 6 months. The levels of air pollutants (nitrogen dioxide (NO2), particulate matter with an aerodynamic cut-off diameter of x µm (PMx) and ozone (O3)) at the patients' home address were averaged in the 12 months before each spirometry test. The effects of air pollutants on forced expiratory volume in 1 s (FEV1) and forced vital capacity (FVC) in % predicted were estimated using mixed linear regressions. We assessed the effect modification of macrolide antibiotics in this relationship.Increased 12-month levels of pollutants were associated with lower levels of FVC % pred (-2.56%, 95% CI -3.86--1.25 for 5 µg·m-3 of PM10; -0.75%, 95% CI -1.38--0.12 for 2 µg·m-3 of PM2.5 and -2.58%, 95% CI -4.63--0.53 for 10 µg·m-3 of NO2). In patients not taking macrolides, the deleterious association between PM and FVC tended to be stronger and PM10 was associated with lower FEV1Our study suggests a deleterious effect of chronic exposure to air pollutants on lung function levels in LTRs, which might be modified with macrolides.