Identifying the Local Influencing Factors of Arsenic Concentration in Suburban Soil: A Multiscale Geographically Weighted Regression Approach
Exploring the local influencing factors and sources of soil arsenic (As) is crucial for reducing As pollution, protecting soil ecology, and ensuring human health. Based on geographically weighted regression (GWR), multiscale GWR (MGWR) considers the different influence ranges of explanatory variable...
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MDPI AG
2024-03-01
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| Series: | Toxics |
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| Online Access: | https://www.mdpi.com/2305-6304/12/3/229 |
| _version_ | 1827304896222724096 |
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| author | Yuanli Zhu Bo Liu Gui Jin Zihao Wu Dongyan Wang |
| author_facet | Yuanli Zhu Bo Liu Gui Jin Zihao Wu Dongyan Wang |
| author_sort | Yuanli Zhu |
| collection | DOAJ |
| description | Exploring the local influencing factors and sources of soil arsenic (As) is crucial for reducing As pollution, protecting soil ecology, and ensuring human health. Based on geographically weighted regression (GWR), multiscale GWR (MGWR) considers the different influence ranges of explanatory variables and thus adopts an adaptative bandwidth. It is an effective model in many fields but has not been used in exploring local influencing factors and sources of As. Therefore, using 200 samples collected from the northeastern black soil zone of China, this study examined the effectiveness of MGWR, revealed the spatial non-stationary relationship between As and environmental variables, and determined the local impact factors and pollution sources of As. The results showed that 49% of the samples had arsenic content exceeding the background value, and these samples were mainly distributed in the central and southern parts of the region. MGWR outperformed GWR with the adaptative bandwidth, with a lower Moran’s I of residuals and a higher R<sup>2</sup> (0.559). The MGWR model revealed the spatially heterogeneous relationship between As and explanatory variables. Specifically, the road density and total nitrogen, clay, and silt contents were the primary or secondary influencing factors at most points. The distance from an industrial enterprise was the secondary influencing factor at only a few points. The main pollution sources of As were thus inferred as traffic and fertilizer, and industrial emissions were also included in the southern region. These findings highlight the importance of considering adaptative bandwidths for independent variables and demonstrate the effectiveness of MGWR in exploring local sources of soil pollutants. |
| first_indexed | 2024-04-24T17:46:56Z |
| format | Article |
| id | doaj.art-1fbe24961f134ef5aee0ad9ad99af6fd |
| institution | Directory Open Access Journal |
| issn | 2305-6304 |
| language | English |
| last_indexed | 2024-04-24T17:46:56Z |
| publishDate | 2024-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Toxics |
| spelling | doaj.art-1fbe24961f134ef5aee0ad9ad99af6fd2024-03-27T14:06:17ZengMDPI AGToxics2305-63042024-03-0112322910.3390/toxics12030229Identifying the Local Influencing Factors of Arsenic Concentration in Suburban Soil: A Multiscale Geographically Weighted Regression ApproachYuanli Zhu0Bo Liu1Gui Jin2Zihao Wu3Dongyan Wang4School of Public Policy & Management, China University of Mining and Technology, Xuzhou 221116, ChinaSchool of Public Policy & Management, China University of Mining and Technology, Xuzhou 221116, ChinaSchool of Public Policy & Management, China University of Mining and Technology, Xuzhou 221116, ChinaSchool of Public Policy & Management, China University of Mining and Technology, Xuzhou 221116, ChinaCollege of Earth Sciences, Jilin University, Changchun 130061, ChinaExploring the local influencing factors and sources of soil arsenic (As) is crucial for reducing As pollution, protecting soil ecology, and ensuring human health. Based on geographically weighted regression (GWR), multiscale GWR (MGWR) considers the different influence ranges of explanatory variables and thus adopts an adaptative bandwidth. It is an effective model in many fields but has not been used in exploring local influencing factors and sources of As. Therefore, using 200 samples collected from the northeastern black soil zone of China, this study examined the effectiveness of MGWR, revealed the spatial non-stationary relationship between As and environmental variables, and determined the local impact factors and pollution sources of As. The results showed that 49% of the samples had arsenic content exceeding the background value, and these samples were mainly distributed in the central and southern parts of the region. MGWR outperformed GWR with the adaptative bandwidth, with a lower Moran’s I of residuals and a higher R<sup>2</sup> (0.559). The MGWR model revealed the spatially heterogeneous relationship between As and explanatory variables. Specifically, the road density and total nitrogen, clay, and silt contents were the primary or secondary influencing factors at most points. The distance from an industrial enterprise was the secondary influencing factor at only a few points. The main pollution sources of As were thus inferred as traffic and fertilizer, and industrial emissions were also included in the southern region. These findings highlight the importance of considering adaptative bandwidths for independent variables and demonstrate the effectiveness of MGWR in exploring local sources of soil pollutants.https://www.mdpi.com/2305-6304/12/3/229arsenicmultiscale geographically weighted regressionadaptative bandwidthlocal influencing factorspatial heterogeneity |
| spellingShingle | Yuanli Zhu Bo Liu Gui Jin Zihao Wu Dongyan Wang Identifying the Local Influencing Factors of Arsenic Concentration in Suburban Soil: A Multiscale Geographically Weighted Regression Approach Toxics arsenic multiscale geographically weighted regression adaptative bandwidth local influencing factor spatial heterogeneity |
| title | Identifying the Local Influencing Factors of Arsenic Concentration in Suburban Soil: A Multiscale Geographically Weighted Regression Approach |
| title_full | Identifying the Local Influencing Factors of Arsenic Concentration in Suburban Soil: A Multiscale Geographically Weighted Regression Approach |
| title_fullStr | Identifying the Local Influencing Factors of Arsenic Concentration in Suburban Soil: A Multiscale Geographically Weighted Regression Approach |
| title_full_unstemmed | Identifying the Local Influencing Factors of Arsenic Concentration in Suburban Soil: A Multiscale Geographically Weighted Regression Approach |
| title_short | Identifying the Local Influencing Factors of Arsenic Concentration in Suburban Soil: A Multiscale Geographically Weighted Regression Approach |
| title_sort | identifying the local influencing factors of arsenic concentration in suburban soil a multiscale geographically weighted regression approach |
| topic | arsenic multiscale geographically weighted regression adaptative bandwidth local influencing factor spatial heterogeneity |
| url | https://www.mdpi.com/2305-6304/12/3/229 |
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