Abstract
Land use in riparian zones is an important factor affecting river water quality. Data of 10 water quality indicators for 2013 were collected from 55 river network water quality monitoring stations in Shanghai. Based on the data, the self-organizing map was used to identify the spatial distribution pattern of water quality in the city. Moreover, the redundancy analysis(RDA)and Spearman rank correlation analysis were used to investigate the relationship and scale effect between water quality and riparian land use(100,200,500,1 000 m buffer). The results show that:(1)The 55 water quality monitoring stations in Shanghai could be divided into 4 clusters, which reveals an evident spatial heterogeneity. The cluster I composed of monitoring stations in the outer suburbs, including Dianshan Lake and Chongming Island, represents the best water quality. Comparatively, water qualities of cluster II distributed along the Suzhou River and cluster III in the suburbs of the city are poor.(2)Among all the spatial scales, the 500 m buffer has the strongest total interpretation of clusters I, III, and IV, and the 1 000 m buffer has the strongest total interpretation of cluster II. (3) On the optimal spatial scale, urban construction land has a high interpretation rate for water quality of each cluster, and is positively correlated with most of the water quality indicators.
| Original language | English |
|---|---|
| Article number | 1673-4831(2019)07-0925-08 |
| Pages (from-to) | 925-932 |
| Number of pages | 8 |
| Journal | Journal of Ecology and Rural Environment |
| Volume | 35 |
| Issue number | 7 |
| DOIs | |
| State | Published - 25 Jul 2019 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 15 Life on Land
Keywords
- K-means algorithm
- Redundancy analysis (RDA)
- Reticular river network area
- Self-organization feature mapping (SOM)
- Spatial heterogeneity
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