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Exploring key local influencing factors of arsenic content in suburban soils of Changchun city using the GeoShapley approach
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Xin-ying ZOU1, Qing-wu YAN1, Zi-hao WU1, *, Yuan-li ZHU1, Yi-yun CHEN2
China Environmental Science | 2026, 46(1) : 289 - 300
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China Environmental Science | 2026, 46(1): 289-300
• Soil Pollution Control •
Exploring key local influencing factors of arsenic content in suburban soils of Changchun city using the GeoShapley approach
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Xin-ying ZOU1, Qing-wu YAN1, Zi-hao WU1, *, Yuan-li ZHU1, Yi-yun CHEN2
Affiliations
  • 1.School of Public Policy & Management, China University of Mining and Technology, Xuzhou 221116, China
  • 2.School of Resource and Environmental Science, Wuhan University, Wuhan 430079, China
Published: 2026-01-20
Outline
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sing 200soil As samples collected from suburban Changchun, this study combined the extreme gradient boosting tree (XGBoost) and the GeoShapley algorithm to explore the heterogeneity and nonlinear relationship between As and environmental factors, and to identify the local influencing factors and sources of As. The main conclusions are as follows: ① The XGBoost model had a fitted R2 of 0.958, which was better than the geographically weighted regression and multiscale geographically weighted regression models, with fitted and predicted R2 values of 0.958 and 0.729, respectively. ② The results of GeoShapley showed that the average contribution rate of manganese content ranked first and was the most significant characteristic variable affecting soil As, while the spatial location characteristic ranked second in importance, confirming the necessity of considering spatial coordinates. ③ The spatial distribution of GeoShapley values of influencing factors revealed that manganese content, alkali-hydrolyzable nitrogen, and sulfur content were the primary influencing factors for local As content at most sampling points, accounting for 99% of the samples. This indicated that parent material and agricultural production are the main sources of As. ④ The nonlinear relationship plots showed that there was a threshold effect between As content and various environmental variables, especially when the manganese content was higher than 2g/kg, the alkali-hydrolyzable nitrogen was higher than 175mg/kg, and the sulfur content was lower than 20mg/kg, As reached the peak, which should be paid special attention to in pollution prevention and control. This paper confirms the superiority of GeoShapley algorithm and the necessity of considering the spatial location, and provides data support for the governmental departments to formulate the policy of localized prevention and control of soil As pollution.

soil arsenic content  /  suburban area  /  GeoShapley  /  extreme gradient boosting tree  /  local influencing factor
邹心颖, 闫庆武, 吴子豪, 祝元丽, 陈奕云. 基于GeoShapley算法的长春市城郊土壤砷含量局部主控因子探究. 中国环境科学, 2026 , 46 (1) : 289 -300 .
Xin-ying ZOU, Qing-wu YAN, Zi-hao WU, Yuan-li ZHU, Yi-yun CHEN. Exploring key local influencing factors of arsenic content in suburban soils of Changchun city using the GeoShapley approach[J]. China Environmental Science, 2026 , 46 (1) : 289 -300 .
Year 2026 volume 46 Issue 1
50
13
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Article Info
  • Receive Date:2015-05-12
  • Online Date:2026-10-08
  • Published:2026-01-20
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  • Received:2015-05-12
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Affiliations
    1.School of Public Policy & Management, China University of Mining and Technology, Xuzhou 221116, China
    2.School of Resource and Environmental Science, Wuhan University, Wuhan 430079, China
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表12种不同金属材料的力学参数
科
Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)
属
Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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