Flexible geostatistical modeling and risk assessment analysis of lead concentration levels of residential soil in the Coeur D'Alene River Basin
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Soil heavy metals pollution is an urgent problem worldwide. Understanding the spatial distribution of pollutants is critical for environmental management and decision-making. Children and adults are still routinely exposed to very high levels of heavy metals contaminants in some countries, particularly in regions with a long mining history. In this paper, we analyze lead concentration levels from residential soil samples in the Coeur D'Alene River Basin in the United States. The aim of this paper is to estimate the spatial distribution of the lead concentration levels that may affect exposed humans. Geographic coordinates were compiled for a total of 781 residential addresses and 1,075 mine-related sites (e.g. mine tailings, rock dumps, mine wastes, etc.) surrounding the properties. The lead concentration levels analyzed in the study are in general variable within a residential property and measured levels can differ greatly from one residential address to a nearby address. We consider a unified approach to model the lead concentration levels by means of penalized regression splines and tensor product smooths, using generalized additive models as a building block. We also use this approach to perform a risk assessment spatial analysis to map hot spots for lead based on the action levels defined by the US Environmental Protection Agency.