Impact of short-term soil disturbance on cadmium remobilization and associated risk in vulnerable regions
A comprehensive understanding of cadmium (Cd) migration in soils near contaminated hotspots is crucial for optimizing remediation efforts and ensuring crop health. This study investigates agricultural soils from four sites in mining and sewage-irrigation areas, assessing the impact of inorganic and...
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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/S0147651325000351 |
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| Summary: | A comprehensive understanding of cadmium (Cd) migration in soils near contaminated hotspots is crucial for optimizing remediation efforts and ensuring crop health. This study investigates agricultural soils from four sites in mining and sewage-irrigation areas, assessing the impact of inorganic and organic fertilizer application on soil Cd remobilization. Results revealed that fertilization, particularly with mineral phosphorus, disrupts soil stability, substantially increases short-term Cd mobility in vulnerable regions. Random Forest analysis identified elevated dissolved organic matter and pH changes as key drivers of Cd remobilization. Monte Carlo simulation, integrating Michaelis-Menten reaction kinetics model, further accessed the potential risk of Cd remobilization. The model predicted that the probabilities of grain exceeding Cd thresholds ranged from 021.6 % for rice, 13.8 %100 % for wheat, and 084.2 % for maize in the absence of fertilizer use. Fertilization significantly increased these exceedance probabilities by 6.1 %87.4 %, with the highest risks observed in irrigation-contaminated soils, particularly under mineral phosphorus fertilization. Nevertheless, it recommended that while fertilization can elevate Cd remobilization risk in hotspots, remediation strategies might not always be necessary. This study highlights the potential of hybrid data-driven approaches, combining machine learning, mechanistic model and stochastic prediction to simplify the complex environmental process, allowing for integrated risk evaluations. |
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| ISSN: | 0147-6513 |