A quantum-inspired annealing algorithm for cost-efficient sampling and 3D characterization of soil heavy-metal contamination

Résumé fourni par la source

Accurately characterizing the 3D spatial distribution of soil heavy metal contamination is fundamental for remediation-boundary delineation and risk management at industrial legacy sites. However, intensive drilling and sampling campaigns are costly, whereas excessive sample reduction may miss contamination hotspots and distort the spatial distribution and morphology of contamination bodies. To address this issue, this study developed an integrated framework coupling quantum-inspired annealing-based sampling optimization with high-precision 3D contamination characterization. A multi-objective evaluation system integrating spatial distribution similarity, 3D volumetric overlap, pollution-risk consistency, and heavy-metal migration consistency was established, and a quantum-inspired annealing algorithm (QIAA) was employed to identify representative sampling points under 30%–80% retention ratios. The framework was applied to a decommissioned chemical industrial park in southern China, where 1,575 soil samples were analyzed for eight heavy metals. Results showed that retaining 50% of the original sampling locations achieved the optimal balance between cost reduction and information preservation. Under this retention level, the sampling network was reduced to 787 points while preserving essential spatial coverage and vertical distribution characteristics. Compared with genetic algorithm, particle swarm optimization, and simulated annealing, the QIAA-based approach achieved higher reconstruction accuracy and lower prediction error under the same sampling density. The 3D reconstruction and hotspot analysis further confirmed that the optimized sampling design preserved the morphology, spatial extent, and core high-risk clusters of major contamination bodies. Overall, the proposed framework provides a robust, reproducible, and cost-effective approach for contaminated-site investigation, 3D pollution characterization, hotspot identification, and remediation-boundary delineation.

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