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Updated: Feb 7, 2026

Quantification of Heavy Metals and Other Inorganic Contaminants on the Productivity of Microalgae
Published on: July 10, 2015
Risk assessment and early warning of soil heavy metal contamination based on the environmental capacity and Bayesian
Ting Zhu1, Zhenyi Jia1, Xiaolei Chen2
1College of Geography and Environmental Sciences, Zhejiang Normal University, Jinhua 321004, China; Zhejiang Key Laboratory of Digital Intelligence Monitoring and Restoration of Watershed Environment, Zhejiang Normal University, Jinhua 321004, China.
Abstract:
Early warning of soil heavy metal (HM) pollution is essential for regional prevention and control. This study proposes an integrated framework for regional assessment and early warning of soil HM contamination. Pollution thresholds derived from a finite mixture distribution model (FMDM) were incorporated as optimized input parameters into an environmental capacity (EC) model to assess soil HM risks in Jiande City, southeastern China, and to forecast trends from 2018 to 2026. Bayesian maximum entropy (BME) was applied to characterize the spatiotemporal evolution of EC-based risk zones. Results revealed severe Cd pollution in 2018 and an overall medium EC level. From 2018-2026, EC continuously declined, accompanied by expansion of overload zones. At the 95 % confidence level, overload areas expanded from 139 km² to 404 km², and at the 85 % level from 176 km² to 610 km². Overload zones initially clustered in the northeast and southwest (2018-2022), primarily driven by high geochemical background, as supported by Cd isotope tracing, while emerging central hotspots after 2022 reflected growing anthropogenic inputs. Model validation using 2022 observations achieved an accuracy of 88.9 %. Long-term simulations also highlighted limitations of the EC model. These findings support early-warning strategies for regional soil HM pollution.
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