Identification of the primary pollution sources and dominant influencing factors of soil heavy metals using a random forest model optimized by genetic algorithm coupled with geodetector
Identifying and quantifying the dominant factors influencing heavy metal (HM) pollution sources are essential for maintaining soil ecological health and implementing effective pollution control measures. This study analyzed soil HM samples from 53 different land use types in Jiaozuo City, Henan Prov...
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| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-01-01
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| Series: | Ecotoxicology and Environmental Safety |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S0147651325000673 |
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| Summary: | Identifying and quantifying the dominant factors influencing heavy metal (HM) pollution sources are essential for maintaining soil ecological health and implementing effective pollution control measures. This study analyzed soil HM samples from 53 different land use types in Jiaozuo City, Henan Province, China. Pollution sources were identified using Absolute Principal Component Score (APCS), with 8 anthropogenic factors, 9 natural factors, and 4 soil physicochemical properties mapped using Geographic Information System (GIS) kernel density estimation. Geodetector and a genetic algorithm optimized random forest model (GA-RF) were employed to quantify the dominant factors and precisely identify pollution sources. A Monte Carlo model was further applied to assess source-oriented health risk probabilities across age groups in the study area. The results revealed three principal components representing pollution sources, with contribution rates of 47.2 %, 33.3 %, and 19.5 %, respectively. For pollution source 1, industrial activities were dominant, with factory density (27.7 %) and distance from the factory (36.3 %) identified as the main factors. Cr, Cu, Mn, and Ni had high loads in this source. Pollution source 2, a combination of natural and transportation influences, was primarily affected by the normalized difference vegetation index (NDVI, 37.8 %), road network density (16.8 %), and proximity to roads (15.3 %). Pollution source 3 was linked to agricultural activities, with cultivated land density (CLD) contributing 39.1 %. As exhibited a high load (91.1 %) in this source, with an exceedance rate of 93 % in cultivated soil, a moderate enrichment factor of 2.33, and a strong ecological risk index of 615.72, making it the most polluted metal in the area. The source-oriented Health Risk Assessment (HRA) showed that agricultural activities contributed 88.7 % to the carcinogenic risk from As in cultivated land. Overall, 99.3 % of the population faced an acceptable cancer risk level. Unlike traditional source apportionment methods, the GA-RF model effectively quantified the contributions of specific influencing factors (e.g., factory density) to pollution sources, rather than merely estimating the percentage contributions of the sources themselves. This approach provides a novel perspective for HM source apportionment under complex environmental conditions. |
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| ISSN: | 0147-6513 |