Electrochemical deposition of amorphous aluminum oxides on lead pipes to prevent lead leaching into the drinking water.

Over 5000 public drinking water systems in the US are out of compliance with the Lead and Copper Rule. Lead leaching from lead pipes is limited by the solubility of a naturally occurring scale. Changes in water quality may cause this scale to become more soluble, releasing lead into the drinking water. We propose a novel electrochemical method to prevent lead leaching from lead pipes. In this method, an aluminum wire and an alkaline phosphate electrolyte are inserted into the pipes. The pipes are then anodized for 2 h by using an external power supply, resulting in the electrodeposition of an insoluble aluminum oxide layer on top of the preexisting scale. This technology was tested on lead pipes from the EBMUD water distribution systems located in Berkeley, CA, using recirculating synthetic and actual tap water for 120 days. The untreated pipes leached an average of 23 ppb and 38 ppb of lead respectively, when using free chlorine and monochloramine as disinfection residuals. In contrast, the treated pipes leached 3 ppb and 5 ppb respectively. These results suggest that the proposed treatment has the potential to prevent lead leaching regardless of the disinfection residual and thus should be further explored in a field trial.

Identifying schools at high-risk for elevated lead in drinking water using only publicly available data.

Estimating the risk of lead contamination of schools' drinking water at the State level is a complex, important, and unexplored challenge. Variable water quality among water systems and changes in water chemistry during distribution affect lead dissolution rates from pipes and fittings. In addition, the locations of lead-bearing plumbing materials are uncertain. We tested the capability of six machine learning models to predict the likelihood of lead contamination of drinking water at the schools' taps using only publicly available datasets. The predictive features used in the models correspond to those with a proven correlation to the dominant, but commonly unavailable, factors that govern lead leaching: the presence of lead-bearing plumbing materials and water quality conducive to lead corrosion. By combining water chemistry data from public reports, socioeconomic information from the US census, and spatial features using Geographic Information Systems, we trained and tested models to estimate the likelihood of lead contaminated tap water in over 8,000 schools across California and Massachusetts. Our best-performing model was a Random Forest, with a 10-fold cross validation score of 0.88 for Massachusetts and 0.78 for California using the average Area Under the Receiver Operating Characteristic Curve (ROC AUC) metric. The model was then used to assign a lead leaching risk category to half of the schools across California (the other half was used for training). There was good agreement between the modeled risk categories and the actual lead leaching outcomes for every school; however, the model overestimated the lead leaching risk in up to 17% of the schools. This model is the first of its kind to offer a tool to predict the risk of lead leaching in schools at the State level. Further use of this model can help deploy limited resources more effectively to prevent childhood lead exposure from school drinking water.

Arsenic hyperaccumulator Pteris vittata shows reduced biomass in soils with high arsenic and low nutrient availability, leading to increased arsenic leaching from soil.

Plant-soil interactions affect arsenic and nutrient availability in arsenic-contaminated soils, with implications for arsenic uptake and tolerance in plants, and leaching from soil. In 22-week column experiments, we grew the arsenic hyperaccumulating fern Pteris vittata in a coarse- and a medium-textured soil to determine the effects of phosphorus fertilization and mycorrhizal fungi inoculation on P. vittata arsenic uptake and arsenic leaching. We investigated soil arsenic speciation using synchrotron-based spectromicroscopy. Greater soil arsenic availability and lower nutrient content in the coarse-textured soil were associated with greater fern arsenic uptake, lower biomass (apparently a metabolic cost of tolerance), and arsenic leaching from soil, due to lower transpiration. P. vittata hyperaccumulated arsenic from coarse- but not medium-textured soil. Mass of plant-accumulated arsenic was 1.2 to 2.4 times greater, but aboveground biomass was 74% smaller, in ferns growing in coarse-textured soil. In the presence of ferns, mean arsenic loss by leaching was 195% greater from coarse- compared to the medium-textured soil, and lower across both soils compared to the absence of ferns. In the medium-textured soil arsenic concentrations in leachate were higher in the presence of ferns. Fern arsenic uptake was always greater than loss by leaching. Most arsenic (>66%) accumulated in P. vittata appeared of rhizosphere origin. In the medium-textured soil with more clay and higher nutrient content, successful iron scavenging increased arsenic release from soil for leaching, but transpiration curtailed leaching.

Predicting childhood lead exposure at an aggregated level using machine learning.

Childhood lead exposure affects over 500,000 children under 6 years old in the US; however, only 14 states recommend regular universal blood screening. Several studies have reported on the use of predictive models to estimate lead exposure of individual children, albeit with limited success: lead exposure can vary greatly among individuals, individual data is not easily accessible, and models trained in one location do not always perform well in another. We report on a novel approach that uses machine learning to accurately predict elevated Blood Lead Levels (BLLs) in large groups of children, using aggregated data. To that end, we used publicly available zip code and city/town BLL data from the states of New York (n = 1642, excluding New York City) and Massachusetts (n = 352), respectively. Five machine learning models were used to predict childhood lead exposure by using socioeconomic, housing, and water quality predictive features. The best-performing model was a Random Forest, with a 10-fold cross validation ROC AUC score of 0.91 and 0.85 for the Massachusetts and New York datasets, respectively. The model was then tested with New York City data and the results compared to measured BLLs at a borough level. The model yielded predictions in excellent agreement with measured data: at a city level it predicted elevated BLL rates of 1.72% for the children in New York City, which is close to the measured value of 1.73%. Predictive models, such as the one presented here, have the potential to help identify geographical hotspots with significantly large occurrence of elevated lead blood levels in children so that limited resources may be deployed to those who are most at risk.