Start Date
3-7-2022 12:00 AM
End Date
3-8-2022 12:00 AM
Abstract
Urban areas often struggle with deteriorated water quality as a result of complex interactions between landscape factors such as land cover, use, and management as well as climatic variables such as weather, precipitation, and atmospheric conditions. Green stormwater infrastructure (GSI) has been introduced as a strategy to reintroduce pre-development hydrological conditions in cities, but questions remain as to how GSI interacts with other landscape factors to affect water quality. We conducted a statistical analysis of six relevant water quality indicators in 131 water quality stations in four watersheds around Portland, Oregon using data from 2015 to 2021. E. coli and lead in the wet season are negatively correlated with distance to nearest GSI. Spatial lag and spatial error models best explain variations in water quality; when accounting for spatial autocorrelation, up to 43% of variation in water quality can be explained by selected landscape and anthropogenic variables. Future studies should include multi-level analysis at the census block group scale to include sociodemographic variables that demonstrate whether benefits from GSI are equally distributed. Our findings provide valuable insights to city planners and researchers seeking to improve water quality in metropolitan areas by implementing GSI.
Subjects
GIS / modeling, Land/watershed management, Sustainable development
Persistent Identifier
https://archives.pdx.edu/ds/psu/38026
Rights
© Copyright the author(s)
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Spatial Analysis of Landscape Characteristics, Anthropogenic Factors, and Seasonality Effects on Water Quality in Portland, Oregon
Urban areas often struggle with deteriorated water quality as a result of complex interactions between landscape factors such as land cover, use, and management as well as climatic variables such as weather, precipitation, and atmospheric conditions. Green stormwater infrastructure (GSI) has been introduced as a strategy to reintroduce pre-development hydrological conditions in cities, but questions remain as to how GSI interacts with other landscape factors to affect water quality. We conducted a statistical analysis of six relevant water quality indicators in 131 water quality stations in four watersheds around Portland, Oregon using data from 2015 to 2021. E. coli and lead in the wet season are negatively correlated with distance to nearest GSI. Spatial lag and spatial error models best explain variations in water quality; when accounting for spatial autocorrelation, up to 43% of variation in water quality can be explained by selected landscape and anthropogenic variables. Future studies should include multi-level analysis at the census block group scale to include sociodemographic variables that demonstrate whether benefits from GSI are equally distributed. Our findings provide valuable insights to city planners and researchers seeking to improve water quality in metropolitan areas by implementing GSI.