Geochemical-integrated machine learning approach predicts the distribution of cadmium speciation in European and Chinese topsoils
2024
ZHANG Naichi | LV Chen | LI Yan | PANAGOS Panos | BALLABIO Cristiano | MAN Jun | GU Xueyuan | ZHAO Fang-Jie | WANG Peng | LIU Xingmei | QIAN Yifan | CUI Peixin | WU Tongliang | HUANG Meiying | LIU Cun | WANG Yujun
Evaluating heavy metals bioavailable is crucial for comprehensive soil contamination assessment but challenging at large scales due to complex and resource-intensive analytical procedures, and the amount of dissolved metal in soils represents the relative solubility and potential mobility of cadmium, which is a key factor determining bioavailability. Here, we developed a geochemical-integrated machine learning framework using multi-source data to predict cadmium speciation distribution in European and Chinese non-industrial topsoils. Average total cadmium content in Chinese topsoils (0.41 mg kg−1) was ~10.8% higher than the Europe, while average dissolved cadmium content (113.2 μg L−1) was ~16.8% higher. Mechanistic interpretation revealed that lower pH, soil organic matter, andamorphous ferrihydrite contents mainly attributed to the higher bioavailability in China. The framework, coupled with knowledge transfer bridging the knowledge gap between geochemical processes and crop uptake, would facilitate the informed decision-making and targeted remediation measures for sustainable agricultural practices and long-term environmental health.
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