Effect of Community Water Service on Lead in Drinking Water in an Environmental Justice Community.

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.

Improved Decision Making for Water Lead Testing in U.S. Child Care Facilities Using Machine-Learned Bayesian Networks.

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' Fß-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.

Lead Levels in Tap Water at Licensed North Carolina Child Care Facilities, 2020-2021.

Objectives. To evaluate lead levels in tap water at licensed North Carolina child care facilities. Methods. Between July 2020 and October 2021, we enrolled 4005 facilities in a grant-funded, participatory science testing program. We identified risk factors associated with elevated first-draw lead levels using multiple logistic regression analysis. Results. By sample (n = 22 943), 3% of tap water sources exceeded the 10 parts per billion (ppb) North Carolina hazard level, whereas 25% of tap water sources exceeded 1 ppb, the American Academy of Pediatrics' reference level. By facility, at least 1 tap water source exceeded 1 ppb and 10 ppb at 56% and 12% of facilities, respectively. Well water reliance was the largest risk factor, followed by participation in Head Start programs and building age. We observed large variability between tap water sources within the same facility. Conclusions. Tap water in child care facilities is a potential lead exposure source for children. Given variability among tap water sources, it is imperative to test every source used for drinking and cooking so appropriate action can be taken to protect children's health. (Am J Public Health. 2022;112(S7):S695-S705. https://doi.org/10.2105/AJPH.2022.307003).

A Participatory Science Approach to Evaluating Factors Associated with the Occurrence of Metals and PFAS in Guatemala City Tap Water.

Limited information is available regarding chemical water quality at the tap in Guatemala City, preventing individuals, water utilities, and public health authorities from making data-driven decisions related to water quality. To address this need, 113 participants among households served by a range of water providers across the Guatemala City metropolitan area were recruited as participatory scientists to collect first-draw and flushed tap water samples at their residence. Samples were transported to the U.S. and analyzed for 20 metals and 25 per- and polyfluoroalkyl substances (PFAS). At least one metal exceeded the Guatemalan Maximum Permissible Limit (MPL) for drinking water in 63% of households (n = 71). Arsenic and lead exceeded the MPL in 33.6% (n = 38) and 8.9% (n = 10) of samples, respectively. Arsenic was strongly associated with groundwater while lead occurrence was not associated with location, water source, or provider. One or more PFAS were detected in 19% of samples (n = 21, range 2.1-64.2 ppt). PFAS were significantly associated with the use of plastic water storage tanks but not with location, water source, or provider. Overall, the high prevalence of arsenic above the MPL in Guatemala City tap water represents a potential health risk that current water treatment processes are not optimized to remove. Furthermore, potential contaminants from premise plumbing and storage, including lead and PFAS, represent additional risks requiring further investigation and public engagement.

A new approach to a legacy concern: Evaluating machine-learned Bayesian networks to predict childhood lead exposure risk from community water systems.

Lead in drinking water continues to put children at risk of irreversible neurological impairment. Understanding drinking water system characteristics that influence blood lead levels is needed to prevent ongoing exposures. This study sought to assess the relationship between children's blood lead levels and drinking water system characteristics using machine-learned Bayesian networks. Blood lead records from 2003 to 2017 for 40,742 children in Wake County, North Carolina were matched with the characteristics of 178 community water systems and sociodemographic characteristics of each child's neighborhood. Bayesian networks were machine-learned to evaluate the drinking water variables associated with blood lead levels ≥2 µg/dL and ≥5 µg/dL. The model was used to predict geographic areas and water utilities with increased lead exposure risk. Drinking water characteristics were not significantly associated with children's blood lead levels ≥5 µg/dL but were important predictors of blood lead levels ≥2 µg/dL. Whether 10% of water samples exceeded 2 ppb of lead in the most recent year prior to the blood test was the most important water system predictor and increased the risk of blood lead levels ≥2 µg/dL by 42%. The model achieved an area under the receiver operating characteristic curve of 0.792 (±0.8%) during ten-fold cross validation, indicating good predictive performance. Water system characteristics may thus be used to predict areas that are at risk of higher blood lead levels. Current drinking water regulatory thresholds for lead may be insufficient to detect the levels in drinking water associated with children's blood lead levels.

User experience of point-of-use water treatment for private wells in North Carolina: Implications for outreach and well stewardship.

Private well users are potentially exposed to a range of chemical contaminants through their drinking water. Point-of-use (POU) water treatment represents one potential solution to reduce harmful exposures through well water, but well users frequently do not adopt household treatment even if they learn their water is contaminated. This study elucidates the experiences, perceptions, and beliefs of 17 households on private wells in North Carolina that participated in a pilot-scale POU water treatment intervention to better understand the drivers and barriers of POU treatment adoption among well users. The intervention consisted of an under-sink activated carbon block POU filter designed to remove lead and two long-chain perfluoroalkyl acids. Filter effluents and influents were tested monthly for eight months. Questionnaires administered before and after the intervention showed a significant decrease in participants' perceived vulnerability to well water contamination, with 77% feeling vulnerable to poor well water quality before, compared to 23% after the filter was installed. However, the POU filters did not fully eliminate feelings of water insecurity (for example, concerns about exposure to contaminants when bathing remained). Lack of knowledge and skills associated with installing and maintaining POU treatment were important barriers to adoption for some well users. Perceptions of POU treatment were also significantly correlated with the intent to implement other well stewardship behaviors such as well water testing. The results highlight the need for strengthened outreach and support programs that provide technical assistance, education, and financial support for households relying on private wells.

Are carbon water filters safe for private wells? Evaluating the occurrence of microbial indicator organisms in private well water treated by point-of-use activated carbon block filters.

Point-of-use (POU) water treatment is highly relevant to private well users vulnerable to chemical contamination, but uncertainty remains around the effects of activated carbon based POU devices on the microbial quality of the treated water. In this study, under-sink activated carbon block water filters were installed in 17 homes relying on private well water in North Carolina. The influent and effluent water in each home was evaluated for bacterial and viral microbial indicator organisms monthly for five months. Multiple logistic regression was used to identify water quality and water usage variables that were significant predictors of each indicator organism occurring in the filter effluent. The odds of total coliforms occurring in the effluent decreased by 84% with each 1-log10 increase in the influent HPC (p < 0.05), suggesting a protective effect by native heterotrophic bacteria, but increased by over 50 times with low cumulative water use (p < 0.05). The filters were not protective against coliphages in the influent and viral shedding may occur after periods of increased virus concentrations in the raw well water. Specific bacteria were also found to increase in the effluent, causing a shift in the bacterial community composition, although potential opportunistic pathogens were detected in both the influent and the effluent. Overall, under normal conditions of use, the filters tested in this study did not represent a significant additional risk for well users beyond the existing exposures from undisinfected well water alone.

Occurrence of male-specific and somatic coliphages and relationship with rainfall in privately-owned wells from peri­urban and rural households.

Privately-owned drinking water wells serving fewer than 25 people (private wells) are prevalent and understudied across most of the US. Private wells primarily serve rural households located outside of municipal drinking water and sewerage service coverage areas. These wells are not regulated by United States Environmental Protection Agency (EPA) under the Safe Drinking Water Act, are not regularly monitored by any public agency or utility, and generally do not undergo disinfection treatment. Coliphages are a group of viruses that infect coliform bacteria and are useful viral surrogates for fecal contamination in water systems in much the same way that fecal indicator bacteria (FIB), such as E. coli and to a lesser extent total coliforms, are used to quantify fecal contamination. Coliphages are approved by the EPA for regulatory monitoring in groundwater wells in the USA, but are not routinely used for this purpose. The present study characterizes the occurrence of male-specific and somatic coliphages, along with FIB, in private wells (n = 122) across two different counties in North Carolina. While occurrences of E. coli were rare and frequency of total coliform was generally low (~20%), male-specific and somatic coliphages were detectable in 66% and 54% of samples, respectively. Concentrations of male-specific coliphages were higher than somatics at each county and on a monthly basis. Rainfall appears to be partly influencing higher coliphage concentrations in December, January and February. This research underscores the need for increased surveillance in private wells and consideration of using coliphages in order to better characterize occurrence of fecal contamination at the time of sampling, especially during rainier months.

Predicting the risk of GenX contamination in private well water using a machine-learned Bayesian network model.

Per- and polyfluoroalkyl substances (PFAS) are emerging contaminants that pose significant challenges in mechanistic fate and transport modeling due to their diverse and complex chemical characteristics. Machine learning provides a novel approach for predicting the spatial distribution of PFAS in the environment. We used spatial location information to link PFAS measurements from 1207 private drinking water wells around a fluorochemical manufacturing facility to a mechanistic model of PFAS air deposition and to publicly available data on soil, land use, topography, weather, and proximity to multiple PFAS sources. We used the resulting linked data set to train a Bayesian network model to predict the risk that GenX, a member of the PFAS class, would exceed a state provisional health goal (140 ng/L) in private well water. The model had high accuracy (ROC curve index for five-fold cross-validation of 0.85, 90% CI 0.84-0.87). Among factors significantly associated with GenX risk in private wells, the most important was the historic rate of atmospheric deposition of GenX from the fluorochemical manufacturing facility. The model output was used to generate spatial risk predictions for the study area to aid in risk assessment, environmental investigations, and targeted public health interventions.

Arsenic contamination in rainwater harvesting tanks around Lake Poopó in Oruro, Bolivia: An unrecognized health risk.

Drinking water sources used by largely rural and indigenous communities around Lake Poopó in the Bolivian Altiplano are impacted by drought and a combination of natural and anthropogenic mining-related contaminants putting the long-term health and sustainability of these communities at risk. As an alternative drinking water source, 18 rainwater harvesting tanks connected to corrugated iron roofs, each with a first-flush system, were installed in 5 communities around the lake. The water quality of these tanks was monitored over 22 months and compared to alternative unprotected surface and groundwater sources the communities previously relied upon. The rainwater quality was found to be within the Bolivian and World Health Organization (WHO) limits, except for elevated arsenic concentrations two times the recommended health limit (0.01 mg/L). Tracing arsenic concentrations through the rainwater flow-path showed that the elevated arsenic concentrations result from mineral dust particles entering the system when rainwater interacts with the roof catchment, with arsenic leaching out. A leaching test showed that 24 h of contact time between 200 mL of water and <1 g of roof dust is enough to raise the arsenic levels of the water above the Bolivian and WHO limit. Currently, no other research exists evaluating the quality of harvested rainwater in the Bolivian Altiplano for human consumption or the source of arsenic in harvested water. This represents a significant knowledge gap for future development practitioners and programs addressing water security around Lake Poopó and the wider region. As a result, it is strongly recommended to include arsenic as a standard parameter in water quality monitoring of rainwater harvesting projects, especially in active mining regions, and to optimize strategies to minimize roof dust from entering the collection system.

Does Granular Activated Carbon with Chlorination Produce Safer Drinking Water? From Disinfection Byproducts and Total Organic Halogen to Calculated Toxicity.

Granular activated carbon (GAC) adsorption is well-established for controlling regulated disinfection byproducts (DBPs), but its effectiveness for unregulated DBPs and DBP-associated toxicity is unclear. In this study, GAC treatment was evaluated at three full-scale chlorination drinking water treatment plants over different GAC service lives for controlling 61 unregulated DBPs, 9 regulated DBPs, and speciated total organic halogen (total organic chlorine, bromine, and iodine). The plants represented a range of impacts, including algal, agricultural, and industrial wastewater. This study represents the most extensive full-scale study of its kind and seeks to address the question of whether GAC can make drinking water safer from a DBP perspective. Overall, GAC was effective for removing DBP precursors and reducing DBP formation and total organic halogen, even after >22 000 bed volumes of treated water. GAC also effectively removed preformed DBPs at plants using prechlorination, including highly toxic iodoacetic acids and haloacetonitriles. However, 7 DBPs (mostly brominated and nitrogenous) increased in formation after GAC treatment. In one plant, an increase in tribromonitromethane had significant impacts on calculated cytotoxicity, which only had 7-17% reduction following GAC. While these DBPs are highly toxic, the total calculated cytotoxicity and genotoxicity for the GAC treated waters for the other two plants was reduced 32-83% (across young-middle-old GAC). Overall, calculated toxicity was reduced post-GAC, with preoxidation allowing further reductions.

Evaluating Activated Carbon Adsorption of Dissolved Organic Matter and Micropollutants Using Fluorescence Spectroscopy.

Dissolved organic matter (DOM) negatively impacts granular activated carbon (GAC) adsorption of micropollutants and is a disinfection byproduct precursor. DOM from surface waters, wastewater effluent, and 1 kDa size fractions were adsorbed by GAC and characterized using fluorescence spectroscopy, UV-absorption, and size exclusion chromatography (SEC). Fluorescing DOM was preferentially adsorbed relative to UV-absorbing DOM. Humic-like fluorescence (peaks A and C) was selectively adsorbed relative to polyphenol-like fluorescence (peaks T and B) potentially due to size exclusion effects. In the surface waters and size fractions, peak C was preferentially removed relative to peak A, whereas the reverse was found in wastewater effluent, indicating that humic-like fluorescence is associated with different compounds depending on DOM source. Based on specific UV-absorption (SUVA), aromatic DOM was preferentially adsorbed. The fluorescence index (FI), if interpreted as an indicator of aromaticity, indicated the opposite but exhibited a strong relationship with average molecular weight, suggesting that FI might be a better indicator of DOM size than aromaticity. The influence of DOM intermolecular interactions on adsorption were minimal based on SEC analysis. Fluorescence parameters captured the impact of DOM size on the fouling of 2-methylisoborneol and warfarin adsorption and correlated with direct competition and pore blockage indicators.