Development And Application Of Petroleum Contaminated Groundwater Management Models With Consideration Of Health Risk

Groundwater contaminated by petroleum has been of much concern in recent years. Contaminated groundwater has heavily threatened environmental quality and human health. It is hard to monitor groundwater quality and remove contaminants due to its subsurface characteristic. Contamination remediation is a time-consuming and costly process. It is difficult to remedy and manage groundwater only depending on engineering techniques, and optimal design is thus desirable. In order to provide reliable decision support for remediation and management of petroleum-contaminated site, this dissertation focuses on simulation of fate and transfer of petroleum contaminants, system optimal design and health risk assessment; the research on uncertainty of health risk assessment is also conducted.Simulation-optimization models are usually used for optimal groundwater remediation design. However, there is much uncertainty information and huge computational efforts in the numerical simulation process. Therefore, a series of surrogates are developed to reflect the relationship between pumping rate and contaminant concentration through stepwise quadratic response surface analysis. A nonlinear goal programming model for groundwater remediation design is then formulated based on the surrogates. The optimal remediation strategies are obtained through uncertainty analysis on remediation cost, environmental standards and technical requirements. The statistical test ensures the reliability and accuracy of the surrogates. The nonlinear goal programming model can overcome the low accuracy of traditional models and greatly reduce computational efforts.Petroleum in groundwater has high toxicity and carcinogenicity. This study integrates health risk assessment into groundwater remediation framework and regards it as one of constraints in optimal remediation design. Parameter uncertainty is addressed by chance constrained programming. Furthermore, a stochastic goal programming based groundwater remediation model is developed. Stochastic analysis and goal programming are introduced into a general framework to handle the uncertainty of economic, environmental, technical and risk factors. Sound strategies are then obtained with uncertainty analysis. Health risk of petroleum contaminants is also assessed, showing that it is a more powerful and strict constraint than environmental standard. Health risk constraint could lead to the increase from 15% to 50% of remediation cost. But the corresponding carcinogenic risk can be reduced by eight times. Optimal strategies based on health risk assessment would lead to the increase of remediation cost; however, contaminant concentrations and health risk levels can be greatly reduced at the same time. Therefore, such increased cost is acceptable for site managers because of the decrease in potential health risk violation.Multiple uncertainties regarding remediation effects, economic benefits and environmental and health risks should be considered in the process of groundwater remediation design. Another concern is usually raised by decision makers from different levels, who concern about distinct issues. Thus, this study develops a bi-level optimal groundwater remediation management model, wherein environmental and health-risk concerns are considered in the upper level, while economic concerns are in the lower one. In the bi-level decision-making process, economic concerns must follow environmental and health-risk ones, which in turn must satisfy economic concerns in an incentive manner for their targets to be achieved. Then, the satisfactory degree is communicated into the solution process for measuring to what extent the constraints are met and the objective reaches its optima. Results indicate that the remediation cost can be reduced by 50%-70%, while the contamination concentration can be reduced by more than 50%. The bi-level model can not only handle various concerns as the targets, but also reflect the relationship between different decision makers as compared with single-objective models. The optimal remediation strategies with both lower economic costs and lower contamination are not only consistent with the lower-level decision maker’s expectations, but also corresponding to the upper-level decision maker’s intentions, which can avoid the extreme results generated from single-level models.A modified fuzzy credibility constrained programming model for groundwater remediation optimization is finally proposed under consideration of parameter uncertainty and standard inconsistency in health risk assessment for contaminated sites in China. Modified fuzzy credibility is introduced to not only deal with parameter uncertainty represented as fuzzy sets, but also provide a credibility level for decision makers. A series of assessment standards are considered and used as constraints in the groundwater remediation framework. Optimal strategies are obtained in the combination of different credibility levels and various contributions from possibility and necessity to credibility. Results show the carcenigenic risk can be controlled from 1×10-6-1×10-4, wich provide theoretical foundation and data support for groundwater managers.