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Sustainability
Volume 16
Issue 13
10.3390/su16135299
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Article

Application of Integrated Land Use Regression and Geographic Information Systems for Modeling the Spatial Distribution of Chromium in Agricultural Topsoil

by
Meng Cao
1,
Daoyuan Wang
2,
Yichun Qian
1,
Ruyue Yu
3,
Aizhong Ding
4 and
Yuanfang Huang
3,*
1
China International Engineering Consulting Corporation, Beijing 100048, China
2
Department of Environmental Science and Engineering, Shanghai University, Shanghai 200444, China
3
College of Land Science and Technology, China Agricultural University, Beijing 100193, China
4
College of Water Sciences, Beijing Normal University, Beijing 100875, China
*
Author to whom correspondence should be addressed.
Sustainability 2024, 16(13), 5299; https://doi.org/10.3390/su16135299
Submission received: 22 May 2024 / Revised: 14 June 2024 / Accepted: 18 June 2024 / Published: 21 June 2024
(This article belongs to the Special Issue Soil Heavy Metals Threaten Agricultural Sustainability: Translocation Processes and Remediation Strategies)
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Abstract

Chromium (Cr) contamination is widely distributed in agricultural soil and poses a threat to agricultural sustainability. Developing integrated models based on soil survey data can be an effective measure to accurately predict the spatial distribution of Cr. Focused on an agriculturally dominated area, this study presents a novel hybrid mapping model that combines land use regression (LUR) and geostatistical methods to predict Cr distribution in topsoil and examines the influence of various influencing factors on Cr content. The LUR model was first adopted to screen the influencing factors for Cr predictions. Then LUR, was combined with ordinary Kriging (OK_LUR) and geographically weighted regression Kriging (GWRK_LUR) to describe the spatial distribution of Cr. Results showed that Cr distribution was profoundly influenced by soil Cu and Zn content, the distance between the soil sampling and livestock farm, orchard areas within 100 m, and population density within 1000 m. The developed GWRK_LUR model significantly improved the prediction accuracy of the OK_LUR and LUR models (by 9% and 16%, respectively). This model provides a novel route to account for the spatial distribution of Cr in agricultural topsoil at a regional scale, which has potential application in pollution remediation.
Keywords: chromium; land use regression; geographically weighted regression Kriging; spatial distribution; agricultural sustainability chromium; land use regression; geographically weighted regression Kriging; spatial distribution; agricultural sustainability

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MDPI and ACS Style

Cao, M.; Wang, D.; Qian, Y.; Yu, R.; Ding, A.; Huang, Y. Application of Integrated Land Use Regression and Geographic Information Systems for Modeling the Spatial Distribution of Chromium in Agricultural Topsoil. Sustainability 2024, 16, 5299. https://doi.org/10.3390/su16135299

AMA Style

Cao M, Wang D, Qian Y, Yu R, Ding A, Huang Y. Application of Integrated Land Use Regression and Geographic Information Systems for Modeling the Spatial Distribution of Chromium in Agricultural Topsoil. Sustainability. 2024; 16(13):5299. https://doi.org/10.3390/su16135299

Chicago/Turabian Style

Cao, Meng, Daoyuan Wang, Yichun Qian, Ruyue Yu, Aizhong Ding, and Yuanfang Huang. 2024. "Application of Integrated Land Use Regression and Geographic Information Systems for Modeling the Spatial Distribution of Chromium in Agricultural Topsoil" Sustainability 16, no. 13: 5299. https://doi.org/10.3390/su16135299

APA Style

Cao, M., Wang, D., Qian, Y., Yu, R., Ding, A., & Huang, Y. (2024). Application of Integrated Land Use Regression and Geographic Information Systems for Modeling the Spatial Distribution of Chromium in Agricultural Topsoil. Sustainability, 16(13), 5299. https://doi.org/10.3390/su16135299

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