Complexities in attributing lead contamination to specific sources in an industrial area of Philadelphia, PA.

Globally, lead (Pb) contamination is one of the top ten chemical exposure issues affecting public health. The identification of specific Pb sources provides valuable information to determine assignment of liability for site cleanup, improve sampling plans and develop remedial strategies. This paper examines Pb concentrations and Pb isotopic data from samples collected at and near the site of a Pb paint production facility with a long operating history. Although high soil Pb concentrations were found at the site, Pb concentrations in surrounding neighborhoods did not simply decline with distance from the site. We evaluated soil concentrations and isotopic mixing lines to explore potential sources of Pb pollution. Three-isotope plots showed overlap of site samples and the surrounding neighborhood, consistent with pollution from the facility affecting offsite soils. A major challenge in separation of potential sources, however, is that the isotopic signatures of other potential Pb sources fall within the range of the soil data. The long operational site history, soil disturbances, the presence of nearby smelters, and other local and remote sources affect identification of lead sources. This analysis demonstrates that source attribution can be confounded by incomplete site and material sourcing information. An integrated approach that includes in-depth site characterization and an evaluation of historical activities (e.g., Pb ores used over time, amounts of Pb emitted by all area smelters, land use changes, and soil disturbances) is important for determining source attribution. This analysis provides insight into future site investigations where soil lead contamination has resulted from a long industrial history in an urban setting.

Contaminant Back Diffusion from Low-Conductivity Matrices: Case Studies of Remedial Strategies.

Recalcitrant groundwater contamination is a common problem at hazardous waste sites worldwide. Groundwater contamination persists despite decades of remediation efforts at many sites because contaminants sorbed or dissolved within low-conductivity zones can back diffuse into high-conductivity zones, and therefore act as a continuing source of contamination to flowing groundwater. A review of the available literature on remediation of plume persistence due to back diffusion was conducted, and four sites were selected as case studies. Remediation at the sites included pump and treat, enhanced bioremediation, and thermal treatment. Our review highlights that a relatively small number of sites have been studied in sufficient detail to fully evaluate remediation of back diffusion; however, three general conclusions can be made based on the review. First, it is difficult to assess the significance of back diffusion without sufficient data to distinguish between multiple factors contributing to contaminant rebound and plume persistence. Second, high-resolution vertical samples are decidedly valuable for back diffusion assessment but are generally lacking in post-treatment assessments. Third, complete contaminant mass removal from back diffusion sources may not always be possible. Partial contaminant mass removal may nonetheless have potential benefits, similar to partial mass removal from primary DNAPL source zones.

A hierarchical modeling approach for estimating national distributions of chemicals in public drinking water systems.

Water quality studies often include the analytical challenge of incorporating censored data and quantifying error of estimation. Many analytical methods exist for estimating distribution parameters when censored data are present. This paper presents a Bayesian-based hierarchical model for estimating the national distribution of the mean concentrations of chemicals occurring in U.S. public drinking water systems using fluoride and thallium as examples. The data used are Safe Drinking Water Act compliance monitoring data (with a significant proportion of left-censored data). The model, which assumes log-normality, was evaluated using simulated data sets generated from a series of Weibull distributions to illustrate the robustness of the model. The hierarchical model is easily implemented using the Markov chain Monte Carlo simulation method. In addition, the Bayesian method is able to quantify the uncertainty in the estimated cumulative density function. The estimated fluoride and thallium national distributions are presented. Results from this study can be used to develop prior distributions for future U.S. drinking water regulatory studies of contaminant occurrence.