Purpose of Review This review aims to better understand the utility of machine learning algorithms for predicting spatial patterns of contaminants in the United States (U.S.) drinking water. Recent Findings We found 27 U.S. drinking water studies in the past ten years that used machine learning algorithms to predict water quality. Most studies (42%) developed random forest classification models for groundwater. Continuous models show low predictive power, suggesting that larger datasets and additional predictors are needed. Categorical/classification models for arsenic and nitrate that predict exceedances of pollution thresholds are most common in the literature because of good national scale data coverage and priority as environmental health concerns. Most groundwater data used to develop models were obtained from the United States Geological Survey (USGS) National Water Information System (NWIS). Predictors were similar across contaminants but challenges are posed by the lack of a standard methodology for imputation, pre-processing, and differing availability of data across regions. Summary We reviewed 27 articles that focused on seven drinking water contaminants. Good performance metrics were reported for binary models that classified chemical concentrations above a threshold value by finding significant predictors. Classification models are especially useful for assisting in the design of sampling efforts by identifying high-risk areas. Only a few studies have developed continuous models and obtaining good predictive performance for such models is still challenging. Improving continuous models is important for potential future use in epidemiological studies to supplement data gaps in exposure assessments for drinking water contaminants. While significant progress has been made over the past decade, methodological advances are still needed for selecting appropriate model performance metrics and accounting for spatial autocorrelations in data. Finally, improved infrastructure for code and data sharing would spearhead more rapid advances in machine-learning models for drinking water quality.

The switch to online instruction during the COVID-19 pandemic forced educators to adapt hands-on environmental engineering experiments to a remote curriculum previously conducted in a laboratory using expensive analytical instruments (> $2000 per device). Here, we describe how we developed a low-cost (<$200) aerosol sensor platform as a successful solution for supporting remote laboratories on air quality for environmental engineering courses in Spring 2021, and continued for in-person classes in Spring 2022. This sensor platform, called HazeL (Haze Laser Sensor), consists of an externally mounted aerosol sensor, a GPS receiver, and temperature and pressure sensors coupled to an Arduino MKR WiFi 1010 microcontroller connected via a Grove system. Using a project-based learning approach and implementing the scientific method, students worked asynchronously to design experiments, collect aerosol measurements, and analyze and visualize data using the R programming language. Students generated hypotheses regarding factors affecting air pollution, measured ≥0.3 μm particles in different locations, tested differences between samples, and rejected the null hypothesis if appropriate. HazeL was also used for projects on data processing and statistical inference in an upper-level computational course. We present an instructional guide on manufacturing the HazeL platform and using it as a teaching tool for enhancing student experiential learning, participation, and engagement.

Hazardous air pollutants emitted by United States (U.S) coal-fired power plants have been controlled by the Mercury and Air Toxics Standards (MATS) since 2012. Sociodemographic disparities in traditional air pollutant exposures from U.S. power plants are known to occur but have not been evaluated for mercury (Hg), a neurotoxicant that bioaccumulates in food webs. Atmospheric Hg deposition from domestic power plants decreased by 91% across the contiguous U.S. from 6.4 Mg in 2010 to 0.55 Mg in 2020. Prior to MATS, populations living within 5 km of power plants (n = 507) included greater proportions of frequent fish consumers, individuals with low annual income and less than a high school education, and limited English-proficiency households compared to the US general population. These results reinforce a lack of distributional justice in plant siting found in prior work. Significantly greater proportions of low-income individuals lived within 5 km of active facilities in 2020 (n = 277) compared to plants that retired after 2010, suggesting that socioeconomic status may have played a role in retirement. Despite large deposition declines, an end-member scenario for remaining exposures from the largest active power plants for individuals consuming self-caught fish suggests they could still exceed the U.S. Environmental Protection Agency reference dose for methylmercury.

Background Aggregate exposure, the combined exposures to a single chemical from all pathways, is a critical children’s health issue. Objective The primary objective is to develop a tool to illustrate potential differences in aggregate exposure at various childhood lifestages and the adult lifestage. Methods We developed ExpoKids (an R-based tool) using oral exposure estimates across lifestages generated by US EPA’s Exposure Factors Interactive Resource for Scenarios Tool (ExpoFIRST). Results ExpoKids is applied to illustrate aggregate oral exposure, for ten media, as average daily doses (ADD) and lifetime average daily doses (LADD) in five graphs organized across seven postnatal childhood lifestages and the adult lifestage. This data visualization tool conveys ExpoFIRST findings, from available exposure data, to highlight the relative contributions of media and lifestages to chemical exposure. To evaluate the effectiveness of ExpoKids, three chemical case examples (di[2-ethylhexyl] phthalate [DEHP], manganese, and endosulfan) were explored. Data available from the published literature and databases for each case example were used to explore research questions regarding media and lifestage contributions to aggregate exposure. Significance These illustrative case examples demonstrate ExpoKids’ versatile application to explore a diverse set of children’s health risk assessment and management questions by visually depicting specific media and lifestage contributions to aggregate exposure.

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