4.8 Article

Spatial analysis of water quality trends in the Han River basin, South Korea

Journal

WATER RESEARCH
Volume 42, Issue 13, Pages 3285-3304

Publisher

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.watres.2008.04.006

Keywords

water quality; trend; urbanization; land cover; spatial regression; spatial analysis; scale; GIS

Funding

  1. Korea Agency for Infrastructure Technology Advancement (KAIA) [09-2007-05-001-00] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)

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Spatial patterns of water quality trends for 118 sites in the Han River basin of South Korea were examined for eight parameters-temperature, pH, dissolved oxygen (DO), biochemical oxygen demand (BOD), chemical oxygen demand (COD), suspended sediment (SS), total phosphorus (TP), and total nitrogen (TN). A non-parametric seasonal Mann-Kendall's test determined the significance of trends for each parameter for each site between 1993 and 2002. There are no significant trends in temperature, but TN concentrations increased for the majority of the monitoring stations. DO, BOD, COD, pH, SS, and TP show increasing or decreasing trends with approximately half of the stations exhibiting no trends. Urban land cover is positively associated with increases in water pollution and included as an important explanatory variable for the variations in all water quality parameters except pH. Topography and soil factors further explain the spatial variations in pH, COD, BOD, and SS. BOD, COD, SS, and TP variations are consistently better explained by 100m buffer scale analysis, but DO are better explained by the whole basin scale analysis. Local water quality management or geology could further explain some variations of water quality. Non-point-source pollution exhibits strong positive spatial autocorrelation as measured by Moran's 1, indicating that the incorporation of spatial dimensions into water quality assessment enhances our understanding of spatial patterns of water quality. The spatial regression models, compared to ordinary least square (OLS) models, always better explain the variations in water quality. This study suggests that spatial analysis of watershed data at different scales should be a vital part of identifying the fundamental spatio-temporal distribution of water quality. (c) 2008 Elsevier Ltd. All rights reserved.

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