Urban areas often struggle with deteriorated water quality because of complex interactions between landscape factors and climatic variables. However, few studies have considered the effects of landscape variables on water quality at a sub-500-m scale. We conducted a spatial statistical analysis of six pollutants for 128 water quality stations in four watersheds around Portland, Oregon, using data from 2015 to 2021 for the wet season at two microscales (100 m and 250 m buffers). E. coli was associated with land cover, soil type, topography, and pipe length, while lead variations were best explained by topographic variables. Developed land cover and impervious surface explained variations in nitrate, while orthophosphate was associated with mean elevation. Models for zinc included land cover and topographic variables in addition to pipe length. Spatial regression models better explain variations in water quality than ordinary least squares models, indicating strong spatial autocorrelation for some variables. Our findings provide valuable insights to city planners and researchers seeking to improve water quality in metropolitan areas by manipulating city landscapes.
Keywords: E. coli; Heavy metals; Nutrients; Spatial analysis; Total suspended solids; Urban land use.
© 2022. The Author(s), under exclusive licence to Springer Nature Switzerland AG.