Development Of Simulation-Based Risk Assessment And Remediation Process-Optimization Technologies For Petroleum-Contaminated Sites

Management of petroleum-contaminated groundwater systems has been of much concern in recent years since pollution from petroleum industries may lead to a variety of impacts, risks, and liabilities. Therefore, this dissertation focuses on risk assessment, remediation process optimization and multi-criteria decision analysis for groundwater contamination in order to provide effective decision support for site-management practices. The main contents of this thesis are summarized as follows:(1) Risk assessment of contaminated sites is crucial for evaluating adverse health impacts on local communities, and provides effective decision support for remediation design. A coupled simulation and fuzzy-rule-based risk assessment method was proposed for health risk assessment of groundwater contamination, and applied to a petroleum contaminated site. Remediation efficiencies under two remediation scenarios (ie., natural attenuation and pump-and-treat remediation) at different time intervals (1,10,20,30, and60years later) were examined. The obtained benzene concentrations were then used to quantify the health risks through the excess lifetime cancer risk (ELCR) model. The study results indicated that the proposed method was useful for assessing potential human health effects when the groundwater was used for drinking water purpose and identifying variation of risk levels under different remediation scenarios.(2) Risk assessment was inherently linked with uncertainty and negligence of such uncertainty in the assessment procedures would bring biased or even false information to the related site managers and eventually harm the appropriateness of the final remediation decisions. Thus, an integrated simulation-assessment approach (ISAA) was developed to systematically tackle multiple uncertainties associated with hydrocarbon contaminant transport in subsurface and assessment of carcinogenic health risk. The fuzzy vertex analysis technique and the Latin hypercube sampling (LHS) based stochastic simulation approach were combined into a fuzzy-Latin hypercube sampling (FLHS) simulation model and was used for predicting contaminant transport in subsurface under coupled fuzzy and stochastic uncertainties. The fuzzy-rule-based risk assessment (FRRA) was used for interpreting the general risk level through fuzzy inference to deal with possibilistic uncertainties associated with both FLHS simulations and health-risk criteria. A study case involving health risk assessment for a benzene-contaminated site was examined. The study results demonstrated the proposed ISAA was useful for evaluating risks within a system containing complicated uncertainties and interactions and providing supports for identifying cost-effective site management strategies. (3) Since many process variables in a groundwater remediation system, such as well location, pumping rate, oxygen-addition rate, and additive-addition rate, may have significant impacts on the performance of remediation systems. The deficiency in understanding the processes controlling the fate of contaminants may lead to over-or under-estimation of system cost in remediation efforts. A fuzzy simulation-based optimization approach (FSOA) was developed for identifying optimal design of a benzene-contaminated groundwater remediation system under uncertainty. FSOA integrated remediation processes (i.e., biodegradation and pump-and-treat), fuzzy simulation, and fuzzy-mean-value-based optimization technique into a general management framework. This approach offered the advantages of:(a) considering an integrated remediation alternative,(b) handling simulation and optimization problems under uncertainty, and (c) providing a direct linkage between control strategies and remediation performance through proxy models. The results demonstrated that optimal remediation alternatives could be obtained to mitigate benzene concentration to satisfy environmental standards with a minimum system cost.(4) Due to complexity of remediation management processes, decision-makers were often faced with difficulties in identifying the most desirable remediation strategy from a pool containing a wide range of options involving consideration of multiple factors such as environmental impact, social acceptance, and system cost. A simulation-based fuzzy multi-criteria decision analysis (SFMCDA) method was developed for supporting the selection of remediation strategies for petroleum contaminated sites. SFMCDA integrated process modeling (using BIOPLUME III) and fuzzy ranking (based on fuzzy TOPSIS) into a general management framework, and could compare various remediation alternatives, in light of both cost-risk tradeoffs and uncertainty impacts. The proposed method was applied to a hypothetical contaminated site suffering from a benzene leakage problem. Six remediation alternatives were taken into consideration, including natural attenuation (NA), pump-and-treat (PAT), enhanced natural attenuation (ENA), and a number of their combinations. Six fuzzy criteria, including both cost and risk information, were used to compare different alternatives through fuzzy TOPSIS. The results demonstrated that the proposed method can help systematically analyze fuzzy inputs from contaminant transport modeling, cost implications and stakeholders’ preferences, and provided useful ranking information covering a variety of dec is ion-re levant remediation options for decision makers.(5) Based on the above-mentioned research works, a real-world petroleum contaminated site in Liaohe oil field was investigated. Two remediation techniques dual-phase vacuum extraction (DPVE) and biodegradation were used to control contaminant transport. A multi-phase multi-standard environment risk assessment approach was used to analyze the remediation efficiency and obtain the key remediation area in order to provide decision support tor further remediation. Then, a simulation-based fuzzy multi-objective optimization (SFMOP) model was developed for process optimization of combined pump-and-treat and biodegradation operations. SFMOP integrated remediation processes modeling, fuzzy multi-objective programming into a general management framework. To solve the SFMOP model, the stepwise cluster analysis (SCA) was used to create a set of proxy simulators for quantifying the relationships between operating conditions (i.e., pumping rate) and benzene concentrations. To alleviate the subjectivity in the selection of objective weighting, the fuzzy multi-objective programming method was used to convert the multi-objective model into a single-objective one. Through the SFMOP model, we obtained the optimal three-days remediation alternatives:0.008,0,0.0024and0.0056m3/s (with cost being42,726.5RMB), and the optimal seven-day remediation alternative:0.0044,0,0.0012and0.0032m3/s (with cost being54,832.4RMB). The results demonstrated that, if the pollution needs to be controlled within a shorter period of time, it would take greater manpower and material resources.