Journal
JOURNAL OF HAZARDOUS MATERIALS
Volume 209, Issue -, Pages 48-58Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.jhazmat.2011.12.068
Keywords
Geographically weighted regression; Water quality; Development
Categories
Funding
- US National Science Foundation [0948983]
- Portland State University
- Oregon State University
- Washington State University
- Reed College
- Direct For Social, Behav & Economic Scie
- Division Of Behavioral and Cognitive Sci [0948983] Funding Source: National Science Foundation
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The relationship among land cover, topography, built structure and stream water quality in the Portland Metro region of Oregon and Clark County,Washington areas, USA, is analyzed using ordinary least squares (OLS) and geographically weighted (GWR) multiple regression models. Two scales of analysis, a sectional watershed and a buffer, offered a local and a global investigation of the sources of stream pollutants. Model accuracy, measured by R-2 values, fluctuated according to the scale, season, and regression method used. While most wet season water quality parameters are associated with urban land covers, most dry season water quality parameters are related topographic features such as elevation and slope. GWR models, which take into consideration local relations of spatial autocorrelation, had stronger results than OLS regression models. In the multiple regression models, sectioned watershed results were consistently better than the sectioned buffer results, except for dry season pH and stream temperature parameters. This suggests that while riparian land cover does have an effect on water quality, a wider contributing area needs to be included in order to account for distant sources of pollutants. (C) 2012 Elsevier B.V. All rights reserved.
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