Lake Champlain is significantly impaired by excess phosphorus loading, requiring frequent lake-wide monitoring for eutrophic conditions and algal blooms. Satellite remote sensing provides regular, synoptic coverage of algal production over large areas with better spatial and temporal resolution compared with in situ monitoring. This study developed two algal production models using Landsat Enhanced Thematic Mapper Plus (ETM(+)) satellite imagery: a single band model and a band ratio model. The models predicted chlorophyll a concentrations to estimate algal cell densities throughout Lake Champlain. Each model was calibrated with in situ data compiled from summer 2006 (July 24 to September 10), and then validated with data for individual days in August 2007 and 2008. Validation results for the final single band and band ratio models produced Nash-Sutcliffe efficiency (NSE) coefficients of 0.65 and 0.66, respectively, confirming satisfactory model performance for both models. Because these models have been validated over multiple days and years, they can be applied for continuous monitoring of the lake.

This study assesses the performance of stormwater best management practices (BMPs) in industrial sectors and their effluent quality to facilitate the development of technology-based numerical effluent criteria. Generally, retention ponds outperform other BMP types for reducing total suspended solids, and media filter and wetland basins outperform other BMPs for metal removal. Detention basins were not effective in reducing stormwater pollution although they can retain the stormwater before entering surface waters. However, many BMPs show high variability of influent and effluent concentrations and no significant difference between them, which makes it difficult to determine the effectiveness of the BMP. In some cases, low influent concentrations govern the distribution of effluent concentrations and effluent concentrations are often greater than inflow concentrations. The analysis results can be used to assist in the developing a watershed based multisector industrial stormwater general permit to ensure compliance with total maximum daily loads. The results also suggest the need for additional monitoring data.

This study utilized spatial analysis to identify hotspots for endocrine disrupting chemicals (EDCs), pharmaceuticals, and personal care products (PPCPs) using data from potential sources including wastewater treatment plants, National Pollutant Discharge Elimination System (NPDES)-permitted pollution sources, septic systems, and agricultural and grazing areas. The study area is Lake Mead, to which the return of treated effluent is one of the largest water reuse practices in the USA. Based on Getis-Ord's Gi* statistic, clusters of pollution sources were identified based on the values of each feature and its neighboring features. Spatial analysis was applied to evaluate the impact from point and nonpoint source pollution. The results of spatial statistical analyses were used to evaluate the existing sampling locations in Las Vegas Wash. The results indicated that sampling locations with highest concentrations of EDCs/PPCPs were close to the outlets of subbasins with high susceptibility to EDCs/PPCPs, which confirms the suitability of sampling locations.