Mapping Heavy Metals in Agricultural Soils Using a Hybrid HASM–ANN Model: A Case Study of the Eastern Longquan Mountain Region, China

作者：Kun Wang, Yuanfeng Li, Q Y Liu, Kun Mao, Yuan Yao · 发表于：Applied Sciences · 年份：2026 · DOI：10.3390/app16115402 · 被引用次数：1 · 研究领域：Soil Geostatistics and Mapping、Heavy metals in environment、Geochemistry and Geologic Mapping

Mitigating heavy metal (HM) contamination in soil is vital for ecological and food security. Accurately mapping these pollutants and understanding their drivers are essential prerequisites for informed regional environmental governance. However, conventional spatial interpolation techniques used to estimate HM concentrations are susceptible to systematic biases and inadequate spatial resolution. To address these limitations, this study developed a novel hybrid model, termed HASM–ANN, coupling high-accuracy surface modeling (HASM) with artificial neural networks (ANNs). This approach generated high-resolution spatial distributions of HMs (As, Cd, Cu, Hg, Cr, and Pb) in agricultural soils of the Eastern Longquan Mountain region, Chengdu, China. Furthermore, the geographical detector (GD) and the Multiscale geographically weighted regression (MGWR) models were employed to explore driving mechanisms. Results indicate that HASM–ANN significantly outperformed conventional interpolations (ordinary/universal kriging, IDW) and HASM–coupled other machine learning downscaling methods. The proposed model demonstrated high predictive accuracy, yielding R2 values between 0.75 and 0.86, and consistently achieved a significantly lower RMSE across all targeted soil heavy metals compared to the HASM. Analysis of the explanatory power (q) revealed that soil As was primarily influenced by clay content (CC, q = 0.45) and available phosphorus (AP, q = 0.42), whereas Cd was mainly driven by AP (q =...