Lead Pollution, Demographics, and Environmental Health Risks: The Case of Philadelphia, USA.

Lead (Pb) soil contamination in urban environments represents a considerable health risk for exposed populations, which often include environmental justice communities. In Philadelphia, Pennsylvania (PA), Pb pollution is a major concern primarily due to extensive historical Pb-smelting/processing activity and legacy use of Pb-based paints and leaded gasoline. The U.S. Environmental Protection Agency (USEPA) organized and/or compiled community-driven soil sampling campaigns to investigate Pb content in surface soils across Philadelphia. Using these data (n = 1277), combined with our own dataset (n = 1388), we explored the spatial distribution of Pb content in soils across the city using ArcGIS. While assessing Zone Improvement Plan (ZIP)-code level data, we found strong correlations between factors, such as the percentage of children with elevated blood lead levels (% EBLL) and % minority population as well as between % EBLL and % children in poverty. We developed a "Lead Index" that took demographics, median measured Pb-in-soil content, and % EBLLs into account to identify ZIP codes in need of further assessment. Our results will be used to help lower the Pb-exposure risk for vulnerable children living in disproportionately burdened communities.

Identification of persistent oil residues in Prince William Sound, Alaska using rapid spectroscopic techniques.

Spectroscopic techniques including X-ray fluorescence (XRF) and attenuated total reflectance - Fourier transform infrared spectroscopy (ATR-FTIR) are used to examine oil residues persisting on shorelines in Prince William Sound that originate from the 1989 Exxon Valdez oil spill and oil released as a consequence of the 1964 Great Alaska earthquake. When coupled to classification models, ATR-FTIR and XRF spectral data can be used to distinguish between the two sources of oil with 92% and 86% success rates for the two techniques respectively. Models indicate that the ATR-FTIR data used to determine oil source includes the CO stretch, the twisting-scissoring of the CH2 group, and the CC stretch. For XRF data, decision tree models primarily utilize the abundance of nickel and zinc present in the oil as a means to classify source. This approach highlights the utility of rapid, field-based spectroscopic techniques to distinguish different inputs of oil to coastal environments.