Complex contaminant mixtures in multistressor Appalachian riverscapes

Abstract

Runoff from watersheds altered by mountaintop mining in the Appalachian region (USA) is known to pollute headwater streams, yet regional-scale assessments of water quality have focused on salinization and selenium. The authors conducted a comprehensive survey of inorganic contaminants found in 170 stream segments distributed across a spectrum of historic and contemporary human land use. Principal component analysis identified 3 important dimensions of variation in water chemistry that were significantly correlated with contemporary surface mining (principal component 1: elevated dominant ions, sulfate, alkalinity, and selenium), coal geology and legacy mines (principal component 2: elevated trace metals), and residential development (principal component 3: elevated sodium and chloride). The combination of these 3 dominant sources of pollutants produced a complex stream-to-stream patchwork of contaminant mixtures. Seventy-five percent of headwater streams (catchments < 5 km(2) ) had water chemistries that could be classified as either reference (49%), development only (18%), or mining only (8%). Only 21% of larger streams (catchments > 5 km(2) ) were classified as having reference chemistries, and chemistries indicative of combined mining and development contaminants accounted for 47% of larger streams (compared with 26% of headwater streams). Extreme degradation of larger streams can be attributed to accumulation of contaminants from multiple human land use activities that include contemporary mountaintop mining, underground mining, abandoned mines, and untreated domestic wastewater. Consequently, water quality improvements in this region will require a multicontaminant remediation approach.

Source water vulnerability to elevated total dissolved solids within a mixed-use Appalachian River basin

“…Moreover, elevated salt content can be a concern for people with and exacerbate certain health conditions such as hypertension and cardiovascular and kidney disease [6][7][8]. Mixed and pervasive historic and contemporary land use activities within this and other Appalachian basins have resulted in elevated TDS concentrations with complex chemical mixtures [13,26]. Further study is warranted into how water distribution systems within this and other vulnerable Appalachian watersheds are potentially impacted by elevated TDS.…”

“…The model was optimized by varying tree complexity (i.e., number of splits within each tree and degree of interaction) and learning rate (i.e., contribution of successive trees to the growing model) to minimize predictive error sensu [24]. We set tree complexity to two because previous research with this region indicates two-way interactions are important when predicting TDS from landscape characteristics within this region [26], and performance did not improve with greater interaction depth. We set learning rate to 0.01, which maximized deviance reduction without overfitting (i.e., increase in predictive deviance with successive trees).…”

Watershed Planning within a Quantitative Scenario Analysis Framework

“…We provide and demonstrate protocols for assessing and managing cumulative effects within heavily impacted watersheds. Although the current manuscript focused on construction and implementation of cumulative effects models within a scenario analysis framework, the demonstrated watershed assessment techniques produce data capable of quantifying detailed patterns of physicochemical and biological degradation related to the accumulation of dominant land use activities across larger spatial scales 35 . Consequently, data produced by the study design and sampling protocols described herein have potential management benefits that extend well beyond those discussed.…”