Examining spatially varying relationships between land use and water quality using geographically weighted regression I: model design and evaluation.

Traditional regression techniques such as ordinary least squares (OLS) can hide important local variations in the model parameters, and are not able to deal with spatial autocorrelations existing in the variables. A recently developed technique, geographically weighted regression (GWR), is used to examine the relationships between land use and water quality in eastern Massachusetts, USA. GWR models make great improvements of model performance over OLS models, which is proved by F-test and comparisons of model R2 and corrected Akaike Information Criterion (AICc) from both GWR and OLS. GWR models also improve the reliabilities of the relationships by reducing spatial autocorrelations. The application of GWR models finds that the relationships between land use and water quality are not constant over space but show great spatial non-stationarity. GWR models are able to reveal the information previously ignored by OLS models on the local causes of water pollution, and so improve the model ability to explain local situation of water quality. The results of this study suggest that GWR technique has the potential to serve as a useful tool for environmental research and management at watershed, regional, national and even global scales.

Impact of urban sprawl on water quality in eastern Massachusetts, USA.

A study of water quality, land use, and population variations over the past three decades was conducted in eastern Massachusetts to examine the impact of urban sprawl on water quality using geographic information system and statistical analyses. Since 1970, eastern Massachusetts has experienced pronounced urban sprawl, which has a substantial impact on water quality. High spatial correlations are found between water quality indicators (especially specific conductance, dissolved ions, including Ca, Mg, Na, and Cl, and dissolved solid) and urban sprawl indicators. Urbanized watersheds with high population density, high percentage of developed land use, and low per capita developed land use tended to have high concentrations of water pollutants. The impact of urban sprawl also shows clear spatial difference between suburban areas and central cities: The central cities experienced lower increases over time in specific conductance concentration, compared to suburban and rural areas. The impact of urban sprawl on water quality is attributed to the combined effects of population and land-use change. Per capita developed land use is a very important indicator for studying the impact of urban sprawl and improving land use and watershed management, because inclusion of this indicator can better explain the temporal and spatial variations of more water quality parameters than using individual land use or/and population density.