Bayesian Maximum Entropy space/time estimation of surface water chloride in Maryland using river distances.

Widespread contamination of surface water chloride is an emerging environmental concern. Consequently accurate and cost-effective methods are needed to estimate chloride along all river miles of potentially contaminated watersheds. Here we introduce a Bayesian Maximum Entropy (BME) space/time geostatistical estimation framework that uses river distances, and we compare it with Euclidean BME to estimate surface water chloride from 2005 to 2014 in the Gunpowder-Patapsco, Severn, and Patuxent subbasins in Maryland. River BME improves the cross-validation R2 by 23.67% over Euclidean BME, and river BME maps are significantly different than Euclidean BME maps, indicating that it is important to use river BME maps to assess water quality impairment. The river BME maps of chloride concentration show wide contamination throughout Baltimore and Columbia-Ellicott cities, the disappearance of a clean buffer separating these two large urban areas, and the emergence of multiple localized pockets of contamination in surrounding areas. The number of impaired river miles increased by 0.55% per year in 2005-2009 and by 1.23% per year in 2011-2014, corresponding to a marked acceleration of the rate of impairment. Our results support the need for control measures and increased monitoring of unassessed river miles.

Burden of disease attributed to anthropogenic air pollution in the United Arab Emirates: estimates based on observed air quality data.

This study quantifies the national burden of disease attributed to particulate matter (PM) and ozone (O(3)) in ambient air in the United Arab Emirates (UAE), a rapidly growing nation in which economic development and climatic conditions pose important challenges for air quality management. Estimates of population exposure to these air pollutants are based on observed air quality data from fixed-site monitoring stations. We divide the UAE into small grid cells and use spatial-statistical methods to estimate the ambient pollutant concentrations in each cell based on the observed data. Premature deaths attributed to PM and O(3) are computed for each grid cell and then aggregated across grid cells and over a year to estimate the total number of excess deaths attributable to ambient air pollution. Our best estimate is that approximately 545 (95% CI: 132-1224) excess deaths in the UAE in the year 2007 are attributable to PM in ambient air. These excess deaths represent approximately 7% (95% CI: 2-17%) of the total deaths that year. We attribute approximately 62 premature deaths (95% CI: 17-127) to ground-level O(3) for the year 2007. Uncertainty in the natural background level of PM, due to the frequent dust storms occurring in the region, has significant impacts on the attributed mortality estimates. Despite the uncertainties associated with the integrated assessment framework, we conclude that anthropogenic ambient air pollution, in particular PM, causes a considerable public health impact in the UAE in terms of premature deaths. We discuss important uncertainties and scientific hypotheses to be investigated in future work that might help reduce the uncertainties in the burden of disease estimates.