Surface water from 38 streams nationwide was assessed using 14 target-organic methods (719 compounds). Designed-bioactive anthropogenic contaminants (biocides, pharmaceuticals) comprised 57% of 406 organics detected at least once. The 10 most-frequently detected anthropogenic-organics included eight pesticides (desulfinylfipronil, AMPA, chlorpyrifos, dieldrin, metolachlor, atrazine, CIAT, glyphosate) and two pharmaceuticals (caffeine, metformin) with detection frequencies ranging 66-84% of all sites. Detected contaminant concentrations varied from less than 1 ng L to greater than 10 μg L, with 77 and 278 having median detected concentrations greater than 100 ng L and 10 ng L, respectively. Cumulative detections and concentrations ranged 4-161 compounds (median 70) and 8.5-102 847 ng L, respectively, and correlated significantly with wastewater discharge, watershed development, and toxic release inventory metrics. Log concentrations of widely monitored HHCB, triclosan, and carbamazepine explained 71-82% of the variability in the total number of compounds detected (linear regression; p-values: < 0.001-0.012), providing a statistical inference tool for unmonitored contaminants. Due to multiple modes of action, high bioactivity, biorecalcitrance, and direct environment application (pesticides), designed-bioactive organics (median 41 per site at μg L cumulative concentrations) in developed watersheds present aquatic health concerns, given their acknowledged potential for sublethal effects to sensitive species and lifecycle stages at low ng L.

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Due to a combination of factors, such as a new coastal/aerosol band and improved radiometric sensitivity of the Operational Land Imager aboard Landsat 8, the atmospherically-corrected Surface Reflectance product for Landsat data, and the growing availability of corrected fDOM data from U.S. Geological Survey gaging stations, moderate-resolution remote sensing of fDOM may now be achievable. This paper explores the background of previous efforts and shows preliminary examples of the remote sensing and data relationships between corrected fDOM and Landsat 8 reflectance values. Although preliminary results before and after Hurricane Sandy are encouraging, more research is needed to explore the full potential of Landsat 8 to continuously map fDOM in a number of water profiles.

Arsenic from geologic sources is widespread in groundwater within the United States (U.S.). In several areas, groundwater arsenic concentrations exceed the U.S. Environmental Protection Agency maximum contaminant level of 10 μg per liter (μg/L). However, this standard applies only to public-supply drinking water and not to private-supply, which is not federally regulated and is rarely monitored. As a result, arsenic exposure from private wells is a potentially substantial, but largely hidden, public health concern. Machine learning models using boosted regression trees (BRT) and random forest classification (RFC) techniques were developed to estimate probabilities and concentration ranges of arsenic in private wells throughout the conterminous U.S. Three BRT models were fit separately to estimate the probability of private well arsenic concentrations exceeding 1, 5, or 10 μg/L whereas the RFC model estimates the most probable category (≤5, >5 to ≤10, or >10 μg/L). Overall, the models perform best at identifying areas with low concentrations of arsenic in private wells. The BRT 10 μg/L model estimates for testing data have an overall accuracy of 91.2%, sensitivity of 33.9%, and specificity of 98.2%. Influential variables identified across all models included average annual precipitation and soil geochemistry. Models were developed in collaboration with public health experts to support U.S.-based studies focused on health effects from arsenic exposure.

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