Spatial modelling of individual arsenic exposure via well water: evaluation of arsenic in urine, main water source and influence of neighbourhood water sources in rural Bangladesh

Abstract

Arsenic concentrations in well water often vary even within limited geographic areas. This makes it difficult to obtain valid estimates of the actual exposure, as people may take their drinking water from different wells. We evaluated a spatial model for estimation of the influence of multiple neighbourhood water sources on the actual exposure, as assessed by concentrations in urine in a population in rural Bangladesh. In total 1307 individuals (one per bari, group of families) were randomly selected. Arsenic concentrations of urine and water were analysed. Simple average and inverse distance weighted average of arsenic concentrations in the five nearest water sources were calculated for each individual. Spatial autocorrelation was evaluated using Moran's I statistics, and spatial regression models were employed to account for spatial autocorrelation. The average distance from a household to the nearest tube-well was 32 metres (Inter-Quartile Range 1–49 metres). Water arsenic concentrations of the reported main water sources were significantly correlated with concentrations in urine (R² = 0.41, ρ < 0.0001, R² for women = 0.45 and for men = 0.36). General model fit improved only slightly after spatial adjustment for neighbouring water sources (pseudo-R² = 0.53, spatial lag model), compared to covariate adjusted regression coefficient (R² = 0.46). Arsenic concentration in urine was higher than arsenic in main water source with an intercept of 57 µg L⁻¹, indicating exposure from food. A suitable way of estimating an individual's past exposure to arsenic in this rural setting, where influence of neighbouring water sources was minimal, was to consider the reported main source of drinking water.