@article{gibson_desclos_harrington_mcelmurry_mulhern_2024, title={Effect of Community Water Service on Lead in Drinking Water in an Environmental Justice Community}, volume={58}, ISSN={["1520-5851"]}, DOI={10.1021/acs.est.3c01341}, abstractNote={Multiple recent studies have found elevated lead (Pb) concentrations in tap water in U.S. homes relying on unregulated private wells. The main Pb source is dissolution from household plumbing, fixtures, and well components. Here, we leverage a natural experiment and citizen science approach to evaluate how extending community water service to an environmental justice community relying on private wells affects Pb in household water. We analyzed Pb in 260 first-draw kitchen tap water samples collected by individual homeowners over a 5-month period in residences that did and did not connect to the community system. Before the community water system was extended, 25% of homes had Pb > 15 μg/L (the U.S. regulatory action level for community water systems) in first-draw water samples. Pb was significantly correlated with nickel (ρ = 0.61), zinc (ρ = 0.50), and copper (ρ = 0.40), suggesting that corrosion of brass fittings and fixtures is the main Pb source. Among homes that connected to the community system, Pb decreased rapidly and was sustained at levels well below 15 μg/L over the study period. Overall, connecting to the municipal water supply was associated with a 92.5% decrease in first-draw tap water Pb.}, number={3}, journal={ENVIRONMENTAL SCIENCE & TECHNOLOGY}, author={Gibson, Jacqueline MacDonald and Desclos, April and Harrington, James and Mcelmurry, Shawn P. and Mulhern, Riley}, year={2024}, month={Jan}, pages={1441–1451} }
 @article{mihelcic_barra_brooks_diamond_eckelman_gibson_guidotti_ikeda-araki_kumar_maiga_et al._2023, title={Accelerating Environmental Research to Achieve Sustainable Development Goals}, volume={57}, ISSN={["1520-5851"]}, DOI={10.1021/acs.est.3c08894}, abstractNote={ADVERTISEMENT RETURN TO ISSUEEditorialNEXTAccelerating Environmental Research to Achieve Sustainable Development GoalsJames R. Mihelcic*James R. MihelcicDepartment of Civil & Environmental Engineering, University of South Florida, 4202 E Fowler Ave, ENG 030, Tampa, Florida 33620, United States*[email protected]More by James R. Mihelcichttps://orcid.org/0000-0002-1736-9264, Ricardo O. BarraRicardo O. BarraFaculty of Environmental Sciences and EULA Chile Centre, University of Concepcion, Barrio Universitario s/n, Concepción 4070386, ChileMore by Ricardo O. Barra, Bryan W. BrooksBryan W. BrooksDepartment of Environmental Science, Baylor University, One Bear Place #97266, Waco, Texas 76798-7266, United StatesMore by Bryan W. Brooks, Miriam L. DiamondMiriam L. DiamondDepartment of Earth Sciences and School of the Environment, University of Toronto, Toronto M5S 1A1, ON, CanadaMore by Miriam L. Diamond, Matthew J. EckelmanMatthew J. EckelmanCollege of Engineering, Northeastern University, Boston, Massachusetts 02115, United StatesMore by Matthew J. Eckelman, Jacqueline MacDonald GibsonJacqueline MacDonald GibsonDepartment of Civil, Construction, and Environmental Engineering, North Carolina State University, Fitts-Woolard Hall, Room 3253, 915 Partners Way, Raleigh, North Carolina 27695-7908, United StatesMore by Jacqueline MacDonald Gibson, Sunny GuidottiSunny GuidottiUNICEF Latin America and Caribbean Regional Office, Building 102, Alberto Tejada Street, City of Knowledge 0843, Republic of PanamaMore by Sunny Guidotti, Atsuko Ikeda-ArakiAtsuko Ikeda-ArakiFaculty of Health Sciences, Hokkaido University, Kita 12, Nishi 5, Kitaku, Sapporo 060-0812, JapanMore by Atsuko Ikeda-Araki, Manish KumarManish KumarSustainability Cluster, School of Engineering, University of Petroleum & Energy Studies, Dehradun, Uttarakhand 248007, IndiaEscuela de Ingenieria y Ciencias, Tecnologico de Monterrey, Campus Monterey, Monterrey 64849, Nuevo León, MexicoMore by Manish Kumar, Ynoussa MaigaYnoussa MaigaLaboratory of Microbiology and Microbial Biotechnology, UFR SVT, University Joseph KI-ZERBO, 03 BP 7021 Ouagadougou, Burkina FasoMore by Ynoussa Maiga, Jennifer McConvilleJennifer McConvilleDepartment of Energy and Technology, Swedish University of Agricultural Sciences, Box 7032, Uppsala SE-750 07, SwedenMore by Jennifer McConville, Shelly L. MillerShelly L. MillerDepartment of Mechanical Engineering, University of Colorado Boulder, 427 UCB, Boulder, Colorado 80309-0427, United StatesMore by Shelly L. Miller, Valeria PizarroValeria PizarroPerry Institute for Marine Science Windsor School (Albany Campus), Frank Watson Boulevard, Adelaide, The BahamasMore by Valeria Pizarro, Fernando Rosario-OrtizFernando Rosario-OrtizDepartment of Civil, Environmental and Architectural Engineering, Environmental Engineering Program, University of Colorado, Boulder, Colorado 80309, United StatesMore by Fernando Rosario-Ortiz, Shuxiao WangShuxiao WangState Key Joint Laboratory of Environment, Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, ChinaState Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, ChinaMore by Shuxiao Wang, and Julie B. ZimmermanJulie B. ZimmermanSchool of Forestry and Environmental Studies, Department of Chemical and Environmental Engineering, Yale University, New Haven, Connecticut 06511, United StatesMore by Julie B. ZimmermanCite this: Environ. Sci. Technol. 2023, 57, 45, 17167–17168Publication Date (Web):November 14, 2023Publication History Received25 October 2023Published online14 November 2023Published inissue 14 November 2023https://doi.org/10.1021/acs.est.3c08894Copyright © Published 2023 by American Chemical SocietyRequest reuse permissions This publication is free to access through this site. Learn MoreArticle Views1042Altmetric-Citations-LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InReddit PDF (902 KB) Get e-AlertscloseSUBJECTS:Climate,Ecotoxicology,Energy,Environmental science,Sustainability Get e-Alerts}, number={45}, journal={ENVIRONMENTAL SCIENCE & TECHNOLOGY}, author={Mihelcic, James R. and Barra, Ricardo O. and Brooks, Bryan W. and Diamond, Miriam L. and Eckelman, Matthew J. and Gibson, Jacqueline MacDonald and Guidotti, Sunny and Ikeda-Araki, Atsuko and Kumar, Manish and Maiga, Ynoussa and et al.}, year={2023}, month={Nov}, pages={17167–17168} }
 @misc{lee_gibson_brown_habtewold_murphy_2023, title={Burden of disease from contaminated drinking water in countries with high access to safely managed water: A systematic review}, volume={242}, ISSN={["1879-2448"]}, DOI={10.1016/j.watres.2023.120244}, abstractNote={The vast majority of residents of high-income countries (≥90%) reportedly have high access to safely managed drinking water. Owing perhaps to the widely held perception of near universal access to high-quality water services in these countries, the burden of waterborne disease in these contexts is understudied. This systematic review aimed to: identify population-scale estimates of waterborne disease in countries with high access to safely managed drinking water, compare methods to quantify disease burden, and identify gaps in available burden estimates. We conducted a systematic review of population-scale disease burden estimates attributed to drinking water in countries where ≥90% of the population has access to safely managed drinking water per official United Nations monitoring. We identified 24 studies reporting estimates for disease burden attributable to microbial contaminants. Across these studies, the median burden of gastrointestinal illness risks attributed to drinking water was ∼2,720 annual cases per 100,000 population. Beyond exposure to infectious agents, we identified 10 studies reporting disease burden-predominantly, cancer risks-associated with chemical contaminants. Across these studies, the median excess cancer cases attributable to drinking water was 1.2 annual cancer cases per 100,000 population. These median estimates slightly exceed WHO-recommended normative targets for disease burden attributable to drinking water and these results highlight that there remains important preventable disease burden in these contexts, particularly among marginalized populations. However, the available literature was scant and limited in geographic scope, disease outcomes, range of microbial and chemical contaminants, and inclusion of subpopulations (rural, low-income communities; Indigenous or Aboriginal peoples; and populations marginalized due to discrimination by race, ethnicity, or socioeconomic status) that could most benefit from water infrastructure investments. Studies quantifying drinking water-associated disease burden in countries with reportedly high access to safe drinking water, focusing on specific subpopulations lacking access to safe water supplies and promoting environmental justice, are needed.}, journal={WATER RESEARCH}, author={Lee, Debbie and Gibson, Jacqueline MacDonald and Brown, Joe and Habtewold, Jemaneh and Murphy, Heather M.}, year={2023}, month={Aug} }
 @article{mihelcic_barra_brooks_diamond_eckelman_gibson_guidotti_ikeda-araki_kumar_maiga_et al._2023, title={Environmental Research Addressing Sustainable Development Goals}, ISSN={["1520-5851"]}, DOI={10.1021/acs.est.3c01070}, abstractNote={ADVERTISEMENT RETURN TO ISSUEEditorialNEXTEnvironmental Research Addressing Sustainable Development GoalsJames R. Mihelcic*James R. MihelcicDepartment of Civil & Environmental Engineering, University of South Florida, 4202 E Fowler Ave, Tampa 33620, Florida, United StatesMore by James R. MihelcicView Biographyhttps://orcid.org/0000-0002-1736-9264, Ricardo O. BarraRicardo O. BarraFaculty of Environmental Sciences and EULA Chile Centre, University of Concepcion, Barrio Universitario s/n, Concepción 4070386, ChileMore by Ricardo O. Barrahttps://orcid.org/0000-0002-1567-7722, Bryan W. BrooksBryan W. BrooksDepartment of Environmental Science, Baylor University, One Bear Place #97266, Waco 76798-7266, Texas, United StatesMore by Bryan W. Brookshttps://orcid.org/0000-0002-6277-9852, Miriam L. DiamondMiriam L. DiamondDepartment of Earth Sciences and School of the Environment, University of Toronto, Toronto M5S 1A1, ON, CanadaMore by Miriam L. Diamondhttps://orcid.org/0000-0001-6296-6431, Matthew J. EckelmanMatthew J. EckelmanCollege of Engineering, Northeastern University, Boston 02115, Massachusetts, United StatesMore by Matthew J. Eckelmanhttps://orcid.org/0000-0002-0595-3682, Jacqueline MacDonald GibsonJacqueline MacDonald GibsonDepartment of Civil, Construction, and Environmental Engineering, North Carolina State University, Fitts-Woolard Hall, Room 3253, 915 Partners Way, Raleigh 27695-7908, North Carolina, United StatesMore by Jacqueline MacDonald Gibsonhttps://orcid.org/0000-0002-5468-0713, Sunny GuidottiSunny GuidottiUNICEF Latin America and Caribbean Regional Office, Building 102, Alberto Tejada St., City of Knowledge 0843, Panama, Republic of PanamaMore by Sunny Guidotti, Atsuko Ikeda-ArakiAtsuko Ikeda-ArakiFaculty of Health Sciences, Hokkaido University, Kita 12, Nishi 5, Kita-ku, Sapporo 060-0812, JapanMore by Atsuko Ikeda-Arakihttps://orcid.org/0000-0002-3065-262X, Manish KumarManish KumarSustainability Cluster, School of Engineering, University of Petroleum & Energy Studies, Dehradun Uttarakhand, 248007, IndiaEscuela de Ingeniería y Ciencias, Tecnologico de Monterrey, Campus Monterey, Monterrey 64849, Nuevo León, MéxicoMore by Manish Kumarhttps://orcid.org/0000-0002-3351-7298, Ynoussa MaigaYnoussa MaigaLaboratory of Microbiology and Microbial Biotechnology, UFR SVT, University Joseph KI-ZERBO, Ouagadougou CFX2+7R6, Burkina FasoMore by Ynoussa Maigahttps://orcid.org/0000-0002-8077-9952, Jennifer McConvilleJennifer McConvilleDepartment of Energy and Technology, Swedish University of Agricultural Sciences, Box 7032, Uppsala SE-750 07, SwedenMore by Jennifer McConvillehttps://orcid.org/0000-0003-0373-685X, Shelly L. MillerShelly L. MillerDepartment of Mechanical Engineering, University of Colorado at Boulder, 112 ECES Engineering Center, Boulder 80309, Colorado, United StatesMore by Shelly L. Millerhttps://orcid.org/0000-0002-1967-7551, Valeria PizarroValeria PizarroPerry Institute for Marine Science Windsor School (Albany Campus), Frank Watson Boulevard, Adelaide 00000, The BahamasMore by Valeria Pizarro, Fernando Rosario-OrtizFernando Rosario-OrtizDepartment of Civil, Environmental and Architectural Engineering, Environmental Engineering Program, University of Colorado, Boulder 80309, Colorado, United StatesMore by Fernando Rosario-Ortizhttps://orcid.org/0000-0002-3311-9089, Shuxiao WangShuxiao WangState Key Joint Laboratory of Environment, Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, ChinaState Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing100084, ChinaMore by Shuxiao Wanghttps://orcid.org/0000-0001-9727-1963, and Julie B. ZimmermanJulie B. ZimmermanSchool of Forestry and Environmental Studies, Department of Chemical and Environmental Engineering, Yale University, New Haven 06511, Connecticut, United StatesMore by Julie B. Zimmermanhttps://orcid.org/0000-0002-5392-312XCite this: Environ. Sci. Technol. 2023, 57, 9, 3457–3460Publication Date (Web):February 22, 2023Publication History Received8 February 2023Published online22 February 2023Published inissue 7 March 2023https://doi.org/10.1021/acs.est.3c01070Copyright © Published 2023 by American Chemical SocietyRequest reuse permissions This publication is free to access through this site. Learn MoreArticle Views4638Altmetric-Citations-LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InReddit PDF (2 MB) Get e-AlertscloseSUBJECTS:Air pollution,Energy,Environmental science,Planets,Water treatment Get e-Alerts}, journal={ENVIRONMENTAL SCIENCE & TECHNOLOGY}, author={Mihelcic, James R. and Barra, Ricardo O. and Brooks, Bryan W. and Diamond, Miriam L. and Eckelman, Matthew J. and Gibson, Jacqueline MacDonald and Guidotti, Sunny and Ikeda-Araki, Atsuko and Kumar, Manish and Maiga, Ynoussa and et al.}, year={2023}, month={Feb} }
 @article{gibson_osman_conroy-ben_giang_2023, title={Environmental Science for the Betterment of All}, ISSN={["1520-5851"]}, DOI={10.1021/acs.est.3c05429}, abstractNote={ADVERTISEMENT RETURN TO ISSUEEditorialNEXTEnvironmental Science for the Betterment of AllJacqueline MacDonald Gibson*Jacqueline MacDonald Gibson*[email protected]More by Jacqueline MacDonald Gibsonhttps://orcid.org/0000-0002-5468-0713, Khalid K OsmanKhalid K OsmanMore by Khalid K Osman, Otakuye Conroy-BenOtakuye Conroy-BenMore by Otakuye Conroy-Ben, and Amanda GiangAmanda GiangMore by Amanda GiangCite this: Environ. Sci. Technol. 2023, 57, 36, 13267–13269Publication Date (Web):July 27, 2023Publication History Received10 July 2023Published online27 July 2023Published inissue 12 September 2023https://doi.org/10.1021/acs.est.3c05429Copyright © Published 2023 by American Chemical SocietyRequest reuse permissions This publication is free to access through this site. Learn MoreArticle Views355Altmetric-Citations-LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InReddit PDF (4 MB) Get e-AlertscloseSUBJECTS:Air pollution,Color,Environmental pollution,Environmental science,Impurities Get e-Alerts}, journal={ENVIRONMENTAL SCIENCE & TECHNOLOGY}, author={Gibson, Jacqueline MacDonald and Osman, Khalid K. and Conroy-Ben, Otakuye and Giang, Amanda}, year={2023}, month={Jul} }
 @article{mulhern_kondash_norman_johnson_levine_mcwilliams_napier_weber_stella_wood_et al._2023, title={Improved Decision Making for Water Lead Testing in US Child Care Facilities Using Machine-Learned Bayesian Networks}, ISSN={["1520-5851"]}, DOI={10.1021/acs.est.2c07477}, abstractNote={Tap water lead testing programs in the U.S. need improved methods for identifying high-risk facilities to optimize limited resources. In this study, machine-learned Bayesian network (BN) models were used to predict building-wide water lead risk in over 4,000 child care facilities in North Carolina according to maximum and 90th percentile lead levels from water lead concentrations at 22,943 taps. The performance of the BN models was compared to common alternative risk factors, or heuristics, used to inform water lead testing programs among child care facilities including building age, water source, and Head Start program status. The BN models identified a range of variables associated with building-wide water lead, with facilities that serve low-income families, rely on groundwater, and have more taps exhibiting greater risk. Models predicting the probability of a single tap exceeding each target concentration performed better than models predicting facilities with clustered high-risk taps. The BN models’ Fβ-scores outperformed each of the alternative heuristics by 118–213%. This represents up to a 60% increase in the number of high-risk facilities that could be identified and up to a 49% decrease in the number of samples that would need to be collected by using BN model-informed sampling compared to using simple heuristics. Overall, this study demonstrates the value of machine-learning approaches for identifying high water lead risk that could improve lead testing programs nationwide.}, journal={ENVIRONMENTAL SCIENCE & TECHNOLOGY}, author={Mulhern, Riley E. and Kondash, A. J. and Norman, Ed and Johnson, Joseph and Levine, Keith and McWilliams, Andrea and Napier, Melanie and Weber, Frank and Stella, Laurie and Wood, Erica and et al.}, year={2023}, month={Mar} }
 @article{li_gibson_2023, title={Predicting Groundwater PFOA Exposure Risks with Bayesian Networks: Empirical Impact of Data Preprocessing on Model Performance}, ISSN={["1520-5851"]}, DOI={10.1021/acs.est.3c00348}, abstractNote={The plethora of data on PFASs in environmental samples collected in response to growing concern about these chemicals could enable the training of machine-learning models for predicting exposure risks. However, differences in sampling and analysis methods across data sets must be reconciled through data preprocessing, and little information is available about how such manipulations affect the resulting models. This study evaluates how data preprocessing influences machine-learned Bayesian network models of PFOA in groundwater. We link 19 years of PFOA measurements from Minnesota, USA, to publicly available information about potential PFOA sources and factors that may influence their environmental fate. Nine different preprocessing methods were tested, and the resulting data sets were used to train models to predict the probability of PFOA ≥ 35 ppt, the 2017 Minnesota health advisory level. Different preprocessing approaches produced varying model structures with significantly different accuracies. Nonetheless, models showed similar relationships between predictor variables and PFOA exposure risks, and all models were relatively accurate, distinguishing wells at high risk from those at low risk for 82.0% to 89.0% of test data samples. There was a trade-off between data quality and model performance since a stricter data screening strategy decreased the sample size for model training.}, journal={ENVIRONMENTAL SCIENCE & TECHNOLOGY}, author={Li, Runwei and Gibson, Jacqueline MacDonald}, year={2023}, month={Aug} }
 @article{li_gibson_2022, title={Predicting the occurrence of short-chain PFAS in groundwater using machine-learned Bayesian networks}, volume={10}, ISSN={["2296-665X"]}, DOI={10.3389/fenvs.2022.958784}, abstractNote={In the past two decades, global manufacturing of per- and polyfluoroalkyl substances (PFAS) has shifted from long-chain compounds to short-chain alternatives in response to evidence of the health hazards of long-chain formulations. However, accumulating data indicate that short-chain PFAS also pose health risks and are highly mobile and persistent in the environment. Because short-chain PFAS are relatively new chemicals, comprehensive knowledge needed to predict their environmental fate is lacking. This study evaluated the capacity of machine-learned Bayesian networks (BNs) to predict risks of exposure to short-chain PFAS in a Minnesota region affected by PFAS releases from the 3M Cottage Grove facility. Models were trained using long-term monitoring data provided by the Minnesota Department of Health (n = 12,406), which we coupled to a comprehensive dataset created by curating 88 other variables that describe potential PFAS sources, soil and hydrogeologic characteristics, and land use. Model performance was assessed using the area under the receiver-operating characteristic curve (AUC), a common measure of the accuracy of machine-learned classification algorithms. In addition, exposure risks were visualized spatially by coupling model predictions to a geographic information system. We found that machine-learned BN models had robust predictive performance, with AUCs above 0.96 in cross-validation. Significant risk factors identified by the BNs include distance to the 3M factory, distance to a former landfill, and areal extent of wetlands and developed land. We also found that risks of exposure to and the areal extent of perfluorosulfonic acids were greater than for perfluorocarboxylic acids with the same carbon number. The results suggest that machine-learned BNs could provide a promising screening tool for assessing short-chain PFAS exposure risks in groundwater.}, journal={FRONTIERS IN ENVIRONMENTAL SCIENCE}, author={Li, Runwei and Gibson, Jacqueline MacDonald}, year={2022}, month={Nov} }