The identification and assessment of ecological and environmental damage are inherently uncertain due to various factors, including the spatiotemporal variability of pollutant distribution, the simulation models used to assess damage scope, and the parameters employed for value quantification. This study aims to establish a robust methodological framework for uncertainty analysis in these processes, providing a theoretical foundation and technical support for damage compensation negotiations and judicial litigation. The research outlines the primary sources of uncertainty and the prevailing analytical methods utilized in soil and groundwater damage assessment. Additionally, a case study of soil environmental damage is conducted employing Gaussian sequential simulation and Monte Carlo simulation to quantitatively assess uncertainties in physical and value quantification stages. Through additional sampling in high-uncertainty zones and optimizing the unit price range for soil remediation, the uncertainty ranges for damaged areas and areas to be remediated in the soil environmental damage case decreased to 39.36% and 65.38% of their original levels, respectively. Post-optimization, the damage amount decreased by 69.99% at a 90% probability level, enhancing the accuracy of data crucial for subsequent compensation negotiations and legal proceedings. This research confirms that targeted supplementary investigations in critical uncertain areas and the judicious refinement of key assessment parameters, such as the unit price for remediation, can effectively reduce the variability in assessment outcomes, significantly enhancing the scientific rigor and reliability of conclusions in ecological and environmental damage identification and assessment.
ZHAO Dan
,
ZHI Yan
,
YU Fang
,
WU Wei-da
,
ZHANG Wen-qi
. Method and Application of Uncertainty Analysis for Key Processes in Identifying and Assessing Environmental Damage: A Case Study of Soil and Groundwater Environmental Damage[J]. Journal of University of Chinese Academy of Sciences, 0
: 37
.
DOI: 10.7523/j.ucas.2026.037
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