Key Takeaways & Executive Findings
- •• • In-situ ecological risk: ≥97.83% of sediment samples posed moderate or higher potential ecological risk under both national and Yunnan background values; Hg and Cd were dominant contributors, with Hg exhibiting higher risk, underscoring the necessity for dredging to mitigate internal pollution. • • Ex-situ agricultural risk: 95.65% of sampling points were unacceptable for both paddy and other agricultural uses; pollution intensity order was Cd>Cu>Zn>As>Cr, with Cd showing the highest pollution coefficient, indicating that sediment unsuitable for in-situ risk may still require treatment before agricultural reuse. • • Ex-situ construction risk: For Class I construction land, As was the sole target pollutant with 93.48% of points unacceptable, while Class II land was fully acceptable; this distinction is critical for cost-effective remediation and land-use planning. • • Vertical stratification: The polluted layer exhibited 11.58% higher Hg and 24.19% higher Cd potential ecological risk coefficients, and 43.27% higher comprehensive potential ecological risk index than the transition layer; pollution coefficients for Cd, Zn, Cu, and As increased by 123.02%, 6.3%, 4.1%, and 41.02%, respectively, confirming the need for layer-specific dredging and treatment.
Abstract
Sediment risk assessment for lakes and rivers often neglects the distinction between in-situ ecological risks and ex-situ comprehensive utilization risks. This study proposes a multi-scenario risk assessment model incorporating both in-situ and ex-situ contexts. For the in-situ scenario, a dual-background-value potential ecological risk assessment was applied using national and Yunnan soil background values. For ex-situ scenarios, pollution risk assessment models were established for agricultural land (paddy and other) and construction land (Class I and II). Taking the Dianchi Lake dredging project as a case study, 92 sediment samples from polluted and transition layers at 46 grid points were analyzed for six heavy metals (As, Hg, Cd, Zn, Cu, Cr). Results showed: (1) Under different background values, ≥97.83% of samples exhibited moderate or higher ecological risk, with Hg and Cd as main contributors, Hg posing higher risk. (2) In agricultural scenarios (paddy/orchard and other), 95.65% of points had unacceptable risk, with comprehensive pollution intensity order Cd>Cu>Zn>As>Cr; for Class I construction land, As was the target pollutant with 93.48% of points unacceptable, while Class II land was acceptable. (3) Spatial heterogeneity was significant: horizontal risk was higher at the lake outlet than at the Cixiang River inlet; vertical risk was higher in the polluted layer than in the transition layer, with Hg and Cd potential ecological risk coefficients 11.58% and 24.19% higher, and comprehensive potential ecological risk index 43.27% higher. Pollution coefficients for Cd, Zn, Cu (agricultural) and As (Class I construction) increased by 123.02%, 6.3%, 4.1%, and 41.02% in the polluted layer. (4) Different evaluation methods yielded significantly different results, indicating that metals without potential ecological risk may still pose pollution risk in utilization scenarios, necessitating comprehensive consideration in remediation.
1. Introduction
Heavy metal contamination in lake and river sediments poses a dual threat: as an internal pollution source to aquatic ecosystems and as a constraint on sediment reuse after dredging. Traditional risk assessments often apply a single set of criteria, either focusing on in-situ ecological risk or on ex-situ soil quality standards, leading to either over- or under-estimation of actual risks. This disconnect hampers the precise delineation of dredging areas and the selection of appropriate remediation targets, particularly in large-scale engineering projects like Dianchi Lake, where sediment volumes are massive and disposal options are limited.
This study addresses this bottleneck by developing a multi-scenario risk assessment framework that simultaneously evaluates in-situ potential ecological risk using dual background values (national and Yunnan) and ex-situ pollution risk for agricultural and construction land uses. By applying this framework to 92 sediment samples from Dianchi Lake, the research quantifies the spatial and vertical heterogeneity of heavy metal contamination and identifies the specific metals that drive unacceptable risks in each scenario. The findings provide a scientific basis for optimizing dredging boundaries and tailoring sediment treatment to meet the safety requirements of different reuse pathways, thereby facilitating the transition from coarse to precise sediment management.
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DUAN Huibo, XING Hao (2026). Risk Assessment of Heavy Metal Pollution Characteristics in Environmental Dredging Sediment from Dianchi Lake under Multi-Scenario Land Use. Chinese Journal of Environmental Engineering. https://doi.org/10.12030/j.cjee.202505012
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Frequently Asked Questions
How does the dual-background-value approach affect the classification of ecological risk compared to using a single background value?
The dual-background approach uses both national and Yunnan soil background values. In this study, under both backgrounds, ≥97.83% of samples showed moderate or higher risk, indicating that the choice of background value did not change the overall high-risk classification. However, the magnitude of risk indices varied, with Yunnan background values likely yielding higher indices due to lower regional background concentrations. This dual approach provides a more conservative and regionally relevant risk assessment.
What are the specific pollution coefficients for Cd and As in the polluted layer compared to the transition layer, and why are these differences critical for dredging decisions?
In the polluted layer, the pollution coefficient for Cd increased by 123.02% relative to the transition layer, while As increased by 41.02% in the Class I construction land scenario. These significant vertical differences indicate that the polluted layer is the primary target for removal to mitigate risks. Dredging only the polluted layer can reduce the volume of contaminated sediment requiring treatment, thereby lowering costs and environmental impact.
How do the risk assessment results for agricultural land use differ between paddy and other agricultural scenarios, and what are the implications for sediment reuse?
For paddy scenarios, the unacceptable risk metals were Cd, Zn, and As in order of intensity, while for other agricultural scenarios, the order was Cd, Cu, Zn, As, Cr. This difference arises from the varying bioavailability and toxicity of metals under different soil conditions and crop types. Consequently, sediment that is unsuitable for paddy fields might be acceptable for other agricultural uses after appropriate treatment, or vice versa, guiding targeted remediation strategies.
What is the significance of the finding that some metals without potential ecological risk in-situ still pose pollution risk in ex-situ scenarios?
This finding highlights the necessity of multi-scenario risk assessment. For example, a metal like As may not contribute significantly to the in-situ potential ecological risk index but can be a primary pollutant when sediment is used as construction land soil. This discrepancy arises because the exposure pathways and protection targets differ. Therefore, relying solely on in-situ risk assessment could lead to underestimation of risks during sediment reuse, potentially endangering human health or crop safety.
How does the spatial heterogeneity of heavy metal contamination influence the design of dredging operations?
The study found that horizontal risk was higher at the lake outlet than at the Cixiang River inlet, and vertical risk was higher in the polluted layer. This spatial variability necessitates a flexible dredging plan that prioritizes high-risk zones and layers. Grid-based sampling with 300-500 m spacing allowed for detailed mapping, enabling precise delineation of dredging boundaries and optimization of resource allocation, reducing unnecessary dredging in low-risk areas.
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