Exploring spatially varying and scale-dependent relationships between soil contamination and landscape patterns using geographically weighted regression |
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| Institution: | 1. Guangdong Key Laboratory of Integrated Agro-environmental Pollution Control and Management, Guangdong Institute of Eco-Environmental Science and Technology, Guangzhou, 510650, China;2. School of Geographical Sciences, Guangzhou University, Guangzhou, 510006, China;1. Key Laboratory of Mollisols Agroecology, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130012, China;2. Greenhouse and Processing Crops Research Centre, Agriculture and Agri-Food Canada, Ontario N0R IG0, Canada;3. University of Chinese Academy of Sciences, Beijing 100049, China;1. State Key Laboratory of Forest and Soil Ecology, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110164, China;2. Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130012, China;3. Key Laboratory of Mountain Ecological Restoration and Bioresource Utilization and Ecological Restoration Biodiversity Conservation, Key Laboratory of Sichuan Province, Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu 610041, China;4. University of Chinese Academy of Sciences, Beijing 100049, China;1. Economics School, Zhongnan University of Economics and Law, Wuhan, 430074, China;2. Key Laboratory of Mathematical Economics and Quantitative Finance, Ministry of Education, Peking University, Beijing, 100871, China;3. School of Statistics, Dongbei University of Finance and Economics, Dalian, 116025, China;1. School of Geographical Sciences and Urban Planning, Arizona State University, Coor Hall, 5th Floor, Tempe, AZ 85281, USA;2. Department of Geography, Virginia Tech, 115 Major Williams Hall, Blacksburg, VA 24061, USA;3. Spatial Analysis Unit, Teagasc Ashtown Research Centre, Ashtown, Dublin 15, Ireland;4. Department of Crop & Soil Environmental Sciences, Virginia Tech, 239 Smyth Hall, Blacksburg, VA 24061, USA |
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| Abstract: | Landscape pattern is an important determinant of soil contamination at multiple scales, and a proper understanding of their relationship is essential for alleviating soil contamination and making decisions for land planners. Both soil contamination and landscape patterns are heterogeneous across spaces and scale-dependent, but most studies were carried out on a single scale and used the conventional multivariate analyses (e.g. correlation analysis, ordinary least squared regression-OLS) that ignored the issue of spatial autocorrelation. To move forward, this paper examined spatially varying relationships between agricultural soil trace metal contamination and landscape patterns at three block scales (i.e. 5 km × 5 km, 10 km × 10 km, 15 km × 15 km) in the Pearl River Delta (PRD), south China, using geographically weighted regression (GWR). This paper found that GWR performed better than OLS in terms of increasing R square of the model, lowering Akaike Information Criterion values and reducing spatial autocorrelation. GWR results revealed great spatial variations in the relationships across scales, with an increasing explanatory power of the model from small to large block scales. Despite a few negative correlations, more positive correlations were found between soil contamination and different aspects of landscape patterns of water, urban land and the whole landscape (i.e. the proportion, mean patch area, the degree of landscape fragmentation, landscape-level structural complexity, aggregation/connectivity, road density and river density). Similarly, more negative correlations were found between soil contamination and landscape patterns of forest and the distance to the river and industry land (p < 0.05). Furthermore, most significant correlations between soil contamination and landscape variables occurred in the western PRD across scales, which could be explained by the prevailing wind, the distribution of pollutant sources and the pathway of trace metal inputs. |
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| Keywords: | Soil contamination Landscape pattern Geographically weighted regression Pearl River Delta |
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