Chlorinated benzenes are common groundwater contaminants in the United States, so demonstrating whether they undergo degradation in the subsurface is important in determining the best remedy for this contamination. The purpose of this work was to use a new data mining approach to investigate chlorinated benzene degradation pathways in the subsurface. Positive Matrix Factorization (PMF) was used to analyze long-term measurements of chlorinated benzene concentrations in groundwater at a contaminated site in New Jersey. A dataset containing 597 groundwater samples and 5 chlorinated benzenes and benzene collected from 144 wells over 20 years was investigated using PMF2 software. Despite the heterogeneity of this dataset, PMF analysis revealed patterns indicative of microbial dechlorination in the groundwater and provided insight about where dechlorination is occurring, to what extent, and under which geochemical conditions. PMF resolved a factor indicative of a source of 1,2,4-trichlorobenzene and 1,2-dichlorobenzene and two factors representing stages of dechlorination, one more advanced than the other. The PMF results indicated that virtually all of the 1,2-dichlorobenzene at the site arises from its use onsite, not from the dechlorination of trichlorobenzenes. Factors were further interpreted using ancillary data such as geochemical indicators and field parameters also measured in the samples. Analysis suggested that the partial and advanced dechlorination signals occur under different subsurface physical conditions. The results provided field validation of the current understanding of anaerobic dechlorination of chlorinated benzenes in the subsurface developed from laboratory studies. PMF is thereby shown to be a useful tool for investigating chlorinated benzene dechlorination.
All Science Journal Classification (ASJC) codes
- Environmental Engineering
- Environmental Chemistry
- Health, Toxicology and Mutagenesis
- Chlorinated benzenes
- Data mining
- Positive matrix factorization