What question did this study set out to answer?

This study aims to develop a hybrid model for accurately mapping heavy metal concentrations in agricultural soils.

June 4, 2026Applied Sciences1 citationsOpen Access

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

KWKun WangChengdu University YLYuanfeng LiChengdu University QLQ Y LiuChengdu University

Key Points

This study aims to develop a hybrid model for accurately mapping heavy metal concentrations in agricultural soils.
Developed a hybrid HASM–ANN model for spatial distribution of heavy metals.
Utilized geographical detector and Multiscale geographically weighted regression to analyze driving mechanisms.
Compared predictive accuracy against conventional interpolation techniques.
HASM–ANN model achieved R2 values between 0.75 and 0.86, with lower RMSE compared to conventional methods.
As concentration primarily influenced by clay content (q = 0.45) and available phosphorus (q = 0.42).
Cd concentration driven mainly by available phosphorus (q = 0.51) and PM2.5 (q = 0.43).

Abstract

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 = 0.51) and PM2.5 (q = 0.43). The spatial distribution of Hg was largely governed by soil organic matter (SOM, q = 0.53). Additionally, Cu concentrations were determined by SOM (q = 0.38), CC (q = 0.34), and pH (q = 0.31). Notably, Cr was significantly influenced by CC (q = 0.42), pH (q = 0.38), and elevation (q = 0.31), while Pb was further driven by SOM (q = 0.46) and PM2.5 (q = 0.39). By offering high-precision mapping and elucidating the underlying driving mechanisms, this research directly facilitates informed environmental governance to protect ecological integrity and public health.

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