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

Abstract

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.

Operationalizing the Health-Environment Nexus: Measuring Environmental Health Inequalities to Inform Policy

“…As an illustration, Ioannidou et al (2018) built a Polycyclic Aromatic Hydrocarbon (PAH) exposure indicator at the national level in France, with a fine resolution (9 km 2 ). The PAH congener benzo[a]pyrene (BaP) was used as a marker for PAHs since BaP constitutes a substantial proportion of the total carcinogenic potential of the total PAH burden (Delgado-Saborit et al 2011).…”

“…Data from the different environmental compartments (water, air, soil, food) are available in France in different databases. In order to construct exposure maps from spatialized databases when assessing the EHIs, methods have been developed to process and harmonize the available data in the same resolution and geographic support (Ioannidou et al 2018), with respect to their specificities (missing values, limited number of observations, etc.). These methods made it possible to construct the representative spatial database used to perform the integrated exposure assessment.…”

Characterizing environmental geographic inequalities using an integrated exposure assessment

“…The spatial estimation of water concentrations was carried out by taking into account the multi-annual data and the network water distribution complexity using a bootstrap based expectation-maximization algorithm. The above methods permitted the construction of a representative spatial database in a 9 km 2 grid of reference for the whole of France (550,000 km 2 ) used to perform the integrated exposure assessment [ 21 ].…”

“…As an example, INERIS used BaP as a tracer of the carcinogenic risk associated with PAH. This has been the subject of several recent studies using the CHIMERE model at the European scale [ 21 , 23 ]. The population exposure estimate shows that 20% of the European population is exposed to BaP background ambient concentrations above the EU target value and only 7% live in areas with concentrations under the estimated acceptable risk level of 0.12 ng.m − 3 .…”

“… Van de Kassteele et al, 2009 [ 19 ] Spatial environmental contamination Emission, topology, meteorological, air concentation External drift kriging method Combination of observations and a deterministic dispersion modeldescription to propose a model-based geostatistical interpolation procedure. Breiman, 2001 [ 20 ] Spatial environmental contamination 14 variables about physicochemical soil properties Hybrid regression-kriging fitted using Random Forest models Application of machine learning methods for solving the problems of spatial dimension on environmental thematic Ioannidou et al, 2018 [ 21 ] Integrated spatial human exposure Water, air, soil, food, behavorial data PLAINE and Modul’ERS Proposition of an aggregated exposure assessment approach based on on modeling and monitoring network at a national scale. Adapted method for each environmental compartment are adapted for existing monitoring networks Guerreiro et al, 2016 [ 22 ] Health impact Emission, topology, meteorological, air concentation Chimere and kriging model Combining observations and chemical transport models through the use of spatial interpolation methods at a continental scale Ratola et Jiménez-Guerrero, 2015 [ 23 ] Spatial environmental contamination Emission, topology, meteorological, air concentation Chimere and vegetation transfer model Combining venegetation concentration observations and chemical transport models through the use of transfer model Pennington et al, 2005 [ 24 ] Spatial human exposure Emission, topology, meteorological, air concentation IMPACT Western Europe The model facilitates estimation of concentration profiles of dispersed contaminants and human intake at the population level.…”