A health risk assessment method for pollutants based on process simulation and uncertainty analysis

Abstract

The invention discloses a pollutant health risk assessment method based on process simulation and uncertainty analysis. The method combines the health risk assessment with the migration process of groundwater pollutants, and uses TOUGH2 software to simulate the temporal and spatial distribution of organic pollutants in groundwater . In order to improve the reliability of the evaluation results and consider the impact of pollutant migration uncertainty on human health risk assessment, the key parameter media permeability was identified through the Markov chain Monte Carlo simulation method. This method improves the reliability of risk assessment results by analyzing the uncertainty of pollutant migration simulation, and provides decision-making information for the management and prevention of polluted sites.

Description

technical field [0001] The invention relates to a health risk assessment method for typical organic pollutants based on process simulation and uncertainty analysis, and belongs to the technical field of pollutant health risk assessment. Background technique [0002] In many water-scarce areas, groundwater is an important source of water for residents' living, irrigation and industrial production. However, with the rapid development of social economy, groundwater pollution has become an important environmental problem that threatens ecosystems and human health. At the same time, various typical organic pollutants with complex physical and chemical properties appear, such as perchloroethylene, polychlorinated biphenyls, etc. These pollutants are denser than water and insoluble in water, which increases the difficulty of monitoring and prevention of groundwater pollution. . In order to reduce the harm of typical pollutants in groundwater to human health and provide decision-m...

AI Technical Summary

Problems solved by technology

[0009] The simulation process of groundwater pollutant migration will be disturbed by various uncertain factors, such as the uncertainty of model parameters, the uncertainty of model structure and the error of observation data

These uncertainties often lead to low reliability of the simulated pollutant concentration, making the final health risk assessment results less accurate, which can easily lead to mistakes in groundwater management and decision-making, and waste manpower and material resources.

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