ARSENIC IN THE SHALLOW GROUND WATERS OF CONTERMINOUS UNITED STATES: ASSESSMENT,HEALTH RISKS,AND COSTS FOR MCL COMPLIANCE1

A methodology consisting of ordinal logistic regression (OLR) is used to predict the probability of occurrence of arsenic concentrations in different threshold limits in shallow ground waters of the conterminous United States (CONUS) subject to a set of influencing variables. The analysis considered a number of maximum contaminant level (MCL) options as threshold values to estimate the probabilities of occurrence of arsenic in ranges defined by a given MCL of 3, 5, 10, 20, and 50 μg/l and a detection limit of 1 μg/l. The fit between the observed and predicted probability of occurrence was around 83 percent for all MCL options. The estimated probabilities were used to estimate the median background concentration of arsenic in the CONUS. The shallow ground water of the western United States is more vulnerable than the eastern United States. Arizona, Utah, Nevada, and California in particular are hotspots for arsenic contamination. The risk assessment showed that counties in southern California, Arizona, Florida, and Washington and a few others scattered throughout the CONUS face a high risk from arsenic exposure through untreated ground water consumption. A simple cost effectiveness analysis was performed to understand the household costs for MCL compliance in using arsenic contaminated ground water. The results showed that the current MCL of 10 μg/l is a good compromise based on existing treatment technologies.     

Sustainability of ground water quality considering land use changes and public health risks

One of the major environmental issues of concern to policy-makers is the increased vulnerability of ground water quality (GWQ). Another issue of equal interest is the sustainability of natural resources for future generations. To understand the sustainability of the natural resources such as water in general, one needs to understand the impact of future land use changes on the natural resources. This work proposes a methodology to address sustainability of GWQ considering land use changes, aquifer vulnerability to multiple contaminants, and public health risks. The methodology was demonstrated for the Sumas-Blaine aquifer in Washington State. The land transformation model predicted that nearly 60 percent of the land use practices would change in the Sumas-Blaine Aquifer by the year 2015. The accuracy of the LTM model predictions increased to greater levels as the spatial resolution was decreased. Aquifer vulnerability analysis was performed for major contaminants using the binary logistic regression (LR) method. The LR model, along with the predicted future land use, was used to estimate the future GWQ using two indices-carcinogenic and non-carcinogenic ground water qualities. Sustainability of GWQ was then analyzed using the concept of 'strong' sustainability. The sustainability map of GWQ showed improvements in many areas where urbanization is expected to occur. The positive impact of urbanization on GWQ is an indication of the extensive damage caused by existing agricultural activities in the study area.