State of knowledge and future research needs on microplastics in groundwater.

Microplastics (MPs) are widespread in aquatic and soil environments. This study targets the issue of MPs' transfer from soil to groundwater. Scientific papers were collected and analyzed using a text-mining approach that classifies text segments. This allowed the identification of four research topics and the organization of the results into a summarizing table. Those four topics are sources of groundwater MPs, main types of MPs (physico-chemical properties, polymer units, shapes, and size), human exposure (mainly drinking water), and potential environmental and human effects. Compared to the research of MP on aquatic or soil compartments, scientific data on MP in groundwater are less substantial. Current results show a divergence due to differences in context (alluvial aquifer, fractured rock aquifer, karst aquifer, etc), collecting, sampling, and analytical methods. This divergence requires further research with standardized analytic protocols and reference materials. The associated research gaps were identified by using the same approach. The following five topics emerged: (1) the transfer of MPs from soil to underground, (2) the contribution of groundwater to drinking water microplastic pollution, (3) the interaction with other contaminants, (4) the human and environmental effects, and (5) the protective and remediation solutions.

Building spatial composite indicators to analyze environmental health inequalities on a regional scale.

BACKGROUND: Reducing health inequalities involves the identification and characterization of social and exposure factors and the way they accumulate in a given area. The areas of accumulation then allow for prioritization of interventions. The present study aims to build spatial composite indicators based on the aggregation of environmental, social and health indicators and their inter-relationships. METHOD: Preliminary work was carried out firstly to homogenize spatial coverage, and secondly to study spatial variation of environmental (EI), socioeconomic (SI) and health (HI) indicators. The aggregation of the different indicators was performed using several methodologies for which results and decision-makers' usability were compared. RESULTS: Four methodologies were tested: 1) A simple summation of normalized HI, EI and SI indicators (IC), 2) the sum of the normalized HI, EI and SI indicators weighted by the first principal component of a Principal Component Analysis (IC PCA), 3) the sum of normalized and weighted indicators of the first principal component of Local Principal Component Analysis (IC LPCA), and 4) the sum of normalized and weighted indicators of the first principal component of a Geographically Weighted Principal Component Analysis (IC GWPCA). CONCLUSION: The GWPCA is particularly adapted to taking into account the spatial heterogeneity and the spatial autocorrelation between SI, EI and HI. This approach invalidates the basic assumptions of many standard statistical analyses. Where socioeconomic indicators present high deprivation and where they are associated with potential modifiable health determinants, decision-makers can prioritize these areas for reducing inequalities by controlling the socioeconomic and health determinants.

Spatial relationship quantification between environmental, socioeconomic and health data at different geographic levels.

Spatial health inequalities have often been analyzed in terms of socioeconomic and environmental factors. The present study aimed to evaluate spatial relationships between spatial data collected at different spatial scales. The approach was illustrated using health outcomes (mortality attributable to cancer) initially aggregated to the county level, district socioeconomic covariates, and exposure data modeled on a regular grid. Geographically weighted regression (GWR) was used to quantify spatial relationships. The strongest associations were found when low deprivation was associated with lower lip, oral cavity and pharynx cancer mortality and when low environmental pollution was associated with low pleural cancer mortality. However, applying this approach to other areas or to other causes of death or with other indicators requires continuous exploratory analysis to assess the role of the modifiable areal unit problem (MAUP) and downscaling the health data on the study of the relationship, which will allow decision-makers to develop interventions where they are most needed.