Jacqueline Gibson

Gibson, J. M. D., Desclos, A., Harrington, J., Mcelmurry, S. P., & Mulhern, R. (2024). Effect of Community Water Service on Lead in Drinking Water in an Environmental Justice Community. ENVIRONMENTAL SCIENCE & TECHNOLOGY, 58(3), 1441–1451. https://doi.org/10.1021/acs.est.3c01341

Mihelcic, J. R., Barra, R. O., Brooks, B. W., Diamond, M. L., Eckelman, M. J., Gibson, J. M. D., … Zimmerman, J. B. (2023, November 14). Accelerating Environmental Research to Achieve Sustainable Development Goals. ENVIRONMENTAL SCIENCE & TECHNOLOGY, Vol. 57, pp. 17167–17168. https://doi.org/10.1021/acs.est.3c08894

Lee, D., Gibson, J. M. D., Brown, J., Habtewold, J., & Murphy, H. M. (2023). [Review of Burden of disease from contaminated drinking water in countries with high access to safely managed water: A systematic review]. WATER RESEARCH, 242. https://doi.org/10.1016/j.watres.2023.120244

Mihelcic, J. R., Barra, R. O., Brooks, B. W., Diamond, M. L., Eckelman, M. J., Gibson, J. M. D., … Zimmerman, J. B. (2023, February 22). Environmental Research Addressing Sustainable Development Goals. ENVIRONMENTAL SCIENCE & TECHNOLOGY. https://doi.org/10.1021/acs.est.3c01070

Gibson, J. M. D., Osman, K. K., Conroy-Ben, O., & Giang, A. (2023, July 27). Environmental Science for the Betterment of All. ENVIRONMENTAL SCIENCE & TECHNOLOGY. https://doi.org/10.1021/acs.est.3c05429

Mulhern, R. E., Kondash, A. J., Norman, E., Johnson, J., Levine, K., McWilliams, A., … Redmon, J. H. (2023, March 18). Improved Decision Making for Water Lead Testing in US Child Care Facilities Using Machine-Learned Bayesian Networks. ENVIRONMENTAL SCIENCE & TECHNOLOGY. https://doi.org/10.1021/acs.est.2c07477

Li, R., & Gibson, J. M. D. (2023, August 18). Predicting Groundwater PFOA Exposure Risks with Bayesian Networks: Empirical Impact of Data Preprocessing on Model Performance. ENVIRONMENTAL SCIENCE & TECHNOLOGY. https://doi.org/10.1021/acs.est.3c00348

Li, R., & Gibson, J. M. D. (2022). Predicting the occurrence of short-chain PFAS in groundwater using machine-learned Bayesian networks. FRONTIERS IN ENVIRONMENTAL SCIENCE, 10. https://doi.org/10.3389/fenvs.2022.958784