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Use of Geographically Weighted Regression (GWR) to Reveal Spatially Varying Relationships between Cd Accumulation and Soil Properties at Field Scale

Author

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  • Zhifan Chen

    (College of Geography and Environmental Science, Henan University, Kaifeng 475004, China
    Henan Engineering Research Center for Control & Remediation of Soil Heavy Metal Pollution, Henan University, Kaifeng 475004, China
    Henan Key Laboratory of Earth System Observation and Modeling, Henan University, Kaifeng 475004, China)

  • Sen Zhang

    (College of Geography and Environmental Science, Henan University, Kaifeng 475004, China)

  • Wencai Geng

    (School of Economics, Henan University, Jinming District, Kaifeng 475004, China)

  • Yongfeng Ding

    (College of Geography and Environmental Science, Henan University, Kaifeng 475004, China)

  • Xingyuan Jiang

    (College of Geography and Environmental Science, Henan University, Kaifeng 475004, China)

Abstract

The spatial variation of correlation between Cd accumulation and its impact factors plays an important role in precise management of Cd contaminated farmland. Samples of topsoils ( n = 247) were collected from suburban farmland located at the junction of the Yellow River Basin and the Huaihe River Basin in China using a 200 m × 200 m grid system. The total and available contents of Cd (T-Cd and A-Cd) in topsoils were analyzed by ICP-MS, and their spatial distribution was analyzed using kriging interpolation with the GIS technique. Geographically weighted regression (GWR) models were applied to explore the spatial variation and their influencing mechanisms of relationships between major environmental factors (pH, organic matter, available phosphorus (A-P)) and Cd accumulation. Spatial distribution showed that T-Cd, A-Cd and their influencing factors had obvious spatial variability, and high value areas primarily cluster near industrial agglomeration areas and irrigation canals. GWR analysis revealed that relationships between T-Cd, A-Cd and their environmental factors presented obvious spatial heterogeneity. Notably, there was a significant negative correlation between soil pH and T-Cd, A-Cd, but with the increase of pH in soil the correlation decreased. A novel finding of a positive correlation between OM and T-Cd, A-Cd was observed, but significant positive correlation only occurred in the high anthropogenic input area due to the complex effects of organic matter on Cd activity. The influence intensity of pH and OM on T-Cd and A-Cd increases under the strong influence of anthropogenic sources. Additionally, T-Cd and A-Cd were totally positively related to soil A-P, but mostly not significantly, which was attributed to the complexity of the available phosphorus source and the differences in Cd contents in chemical fertilizer. Furthermore, clay content might be an important factor affecting the correlation between Cd and soil properties, considering that the correlation between Cd and pH, SOM, A-P was significantly lower in areas with lower clay particles. This study suggested that GWR was an effective tool to reveal spatially varying relationships at field scale, which provided a new idea to further explore the related influencing factors on spatial distribution of contaminants and to realize precise management of a farmland environment.

Suggested Citation

  • Zhifan Chen & Sen Zhang & Wencai Geng & Yongfeng Ding & Xingyuan Jiang, 2022. "Use of Geographically Weighted Regression (GWR) to Reveal Spatially Varying Relationships between Cd Accumulation and Soil Properties at Field Scale," Land, MDPI, vol. 11(5), pages 1-18, April.
    • Handle: RePEc:gam:jlands:v:11:y:2022:i:5:p:635-:d:802252
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    References listed on IDEAS

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    1. Sagarika Patowary & Arup Kumar Sarma, 2018. "GIS-Based Estimation of Soil Loss from Hilly Urban Area Incorporating Hill Cut Factor into RUSLE," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(10), pages 3535-3547, August.
    2. Evert Meijers & Martijn Burger & Mark Thissen & Thomas Graaff & Frank Oort, 2016. "Competitive network positions in trade and structural economic growth: A geographically weighted regression analysis for European regions," Papers in Regional Science, Wiley Blackwell, vol. 95(1), pages 159-180, March.
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    Cited by:

    1. Zhifan Chen & Wencai Geng & Xingyuan Jiang & Xinling Ruan & Di Wu & Yipeng Li, 2022. "A New Sight of Influencing Effects of Major Factors on Cd Transfer from Soil to Wheat ( Triticum aestivum L.): Based on Threshold Regression Model," IJERPH, MDPI, vol. 19(19), pages 1-15, September.
    2. Wenhui Zhang & Liangwei Cheng & Ruitao Xu & Xiaohua He & Weihan Mo & Jianbo Xu, 2023. "Assessing Spatial Variation and Driving Factors of Available Phosphorus in a Hilly Area (Gaozhou, South China) Using Modeling Approaches and Digital Soil Mapping," Agriculture, MDPI, vol. 13(8), pages 1-18, August.

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