Proceedings of The 3rd International Academic Conference on Research in Engineering and Technology
Year: 2024
DOI:
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Characterizing the Wastewater Exposome in Je Erson County, Kentucky
Tei Kim, Justin Byun, Douglas I Walker
ABSTRACT:
Wastewater surveillance has emerged as an e ective, cheap and integrated strategy to monitor infectious diseases. Leveraging wastewater samples to comprehensively characterize exposures within a population catchment provides a promising strategy to identify the exposures experienced by a population, and identify groups at risk for high environmental exposures. In collaboration with researchers at the Rollins School of Public Health in Emory University and the University of Kentucky, we performed untargeted high-resolution mass spectrometry of wastewater samples from across 27 locations in Je erson County, Kentucky to detect thousands of organic chemicals that may represent important exposures. We performed the same analysis for two di erent sets of chemicals. Exposures and unique chemicals within the wastewater were identi ed using two di erent approaches that including 1) comparing the peak intensities for detected chemical signals to eld blanks (pure water) and retaining chemical signals 5x higher than the blank and 2) only chemical signals detected in study samples (not present in the eld blank). The results were rst visualized with bar graphs showing the number of chemicals detected at each site, which included hundreds of potential environmental contaminants. To determine factors leading to exposures in the wastewater, we next performed a linear regression analysis to identify which exposures in the wastewater were associated with income, population size and catchment area. Regression analysis showed that there was a statistically signi cant relationship with income (p < 0.05). We also evaluated di erences between the number of chemicals present within samples from catchment areas and at wastewater treatment center in ows and their correlation. The results suggest levels at the individual catchment areas and within the in ow are related. Finally, we assessed the impact of combined sewer over ow systems, which showed that there is a statistically signi cant di erence in the number of chemicals found between sewersheds with and without a combined over ow(p<0.05). This suggests that dilution from the combined over ow reduced the detectable concentrations of toxic metals. The code for this was developed using the programming language R and has been uploaded to GitHub (https://github.com/teikimm307/Wastewater-Analysis). Future studies will further investigate how socioeconomic factors in uence water management practices and analyze the chemicals that exhibited a statistically signi cant correlation across sample times based on the Manhattan plots.
keywords: wastewater surveillance, infectious disease monitoring, environmental exposures, high-resolution mass spectrometry, untargeted analysis, linear regression analysis, r programming