科研之友
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摘要
Techniques for monitored natural attenuation usually produce large complex datasets that are difficult to interpret. Here, human health risk assessments and multivariate statistical analyses are combined to extract and analyze useful information from large monitoring datasets to identify the main pollutants in a petroleum-contaminated aquifer in northeast China and the main biogeochemical processes affecting the pollutants. The data included organic and inorganic geochemical species concentrations, physicochemical indicators, C and S stable isotope data collected for four years of more than 10 monitoring. The health risk assessment indicated that benzene was a representative pollutant. Cluster analysis classified the groundwater samples into two groups and indicated strong biodegradation occurred near the core and upgradient of the petroleum hydrocarbon plume. The factors explaining most variability were extracted by principal component analysis, which correlated with biodegradation and mineral dissolution processes. The factor scores and spatial distributions of hydrogeochemical and isotope indicators confirmed that biodegradation effects weakened and mineral dissolution strengthened upgradient to downgradient of the contaminated plume. The analysis method could be useful for rapidly studying pollution characteristics and identifying biodegradation processes in contaminated aquifersfrom large complex datasets. The results will provide a basis for developing an enhanced bioremediation scheme for the study site.
