Separating signal from noise in wastewater data: An algorithm to identify community-level COVID-19 surges in real time.

Wastewater monitoring has provided health officials with early warnings for new COVID-19 outbreaks, but to date, no approach has been validated to distinguish signal (sustained surges) from noise (background variability) in wastewater data to alert officials to the need for heightened public health response. We analyzed 62 wk of data from 19 sites participating in the North Carolina Wastewater Monitoring Network to characterize wastewater metrics around the Delta and Omicron surges. We found that wastewater data identified outbreaks 4 to 5 d before case data (reported on the earlier of the symptom start date or test collection date), on average. At most sites, correlations between wastewater and case data were similar regardless of how wastewater concentrations were normalized and whether calculated with county-level or sewershed-level cases, suggesting that officials may not need to geospatially align case data with sewershed boundaries to gain insights into disease transmission. Although wastewater trend lines captured clear differences in the Delta versus Omicron surge trajectories, no single wastewater metric (detectability, percent change, or flow-population normalized viral concentrations) reliably signaled when these surges started. After iteratively examining different combinations of these three metrics, we developed the Covid-SURGE (Signaling Unprecedented Rises in Groupwide Exposure) algorithm, which identifies unprecedented signals in the wastewater data. With a true positive rate of 82%, a false positive rate of 7%, and strong performance during both surges and in small and large sites, our algorithm provides public health officials with an automated way to flag community-level COVID-19 surges in real time.

Sociodemographic Disparities in Mercury Exposure from United States Coal-Fired Power Plants.

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.

The Utility of Machine Learning Models for Predicting Chemical Contaminants in Drinking Water: Promise, Challenges, and Opportunities.

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. 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.

Lower Intent to Comply with COVID-19 Public Health Recommendations Correlates to Higher Disease Burden in Following 30 Days.

OBJECTIVES: We sought to determine whether self-reported intent to comply with public health recommendations correlates with future coronavirus disease 2019 (COVID-19) disease burden. METHODS: A cross-sectional, online survey of US adults, recruited by snowball sampling, from April 9 to July 12, 2020. Primary measurements were participant survey responses about their intent to comply with public health recommendations. Each participant's intent to comply was compared with his or her local COVID-19 case trajectory, measured as the 7-day rolling median percentage change in COVID-19 confirmed cases within participants' 3-digit ZIP code area, using public county-level data, 30 days after participants completed the survey. RESULTS: After applying raking techniques, the 10,650-participant sample was representative of US adults with respect to age, sex, race, and ethnicity. Intent to comply varied significantly by state and sex. Lower reported intent to comply was associated with higher COVID-19 case increases during the following 30 days. For every 3% increase in intent to comply with public health recommendations, which could be achieved by improving average compliance by a single point for a single item, we estimate a 9% reduction in new COVID-19 cases during the subsequent 30 days. CONCLUSIONS: Self-reported intent to comply with public health recommendations may be used to predict COVID-19 disease burden. Measuring compliance intention offers an inexpensive, readily available method of predicting disease burden that can also identify populations most in need of public health education aimed at behavior change.

Developing and testing an automated qualitative assistant (AQUA) to support qualitative analysis.

Qualitative research remains underused, in part due to the time and cost of annotating qualitative data (coding). Artificial intelligence (AI) has been suggested as a means to reduce those burdens, and has been used in exploratory studies to reduce the burden of coding. However, methods to date use AI analytical techniques that lack transparency, potentially limiting acceptance of results. We developed an automated qualitative assistant (AQUA) using a semiclassical approach, replacing Latent Semantic Indexing/Latent Dirichlet Allocation with a more transparent graph-theoretic topic extraction and clustering method. Applied to a large dataset of free-text survey responses, AQUA generated unsupervised topic categories and circle hierarchical representations of free-text responses, enabling rapid interpretation of data. When tasked with coding a subset of free-text data into user-defined qualitative categories, AQUA demonstrated intercoder reliability in several multicategory combinations with a Cohen's kappa comparable to human coders (0.62-0.72), enabling researchers to automate coding on those categories for the entire dataset. The aim of this manuscript is to describe pertinent components of best practices of AI/machine learning (ML)-assisted qualitative methods, illustrating how primary care researchers may use AQUA to rapidly and accurately code large text datasets. The contribution of this article is providing guidance that should increase AI/ML transparency and reproducibility.

Developing a Flexible National Wastewater Surveillance System for COVID-19 and Beyond.

BACKGROUND: Wastewater testing offers a cost-effective strategy for measuring population disease prevalence and health behaviors. For COVID-19, wastewater surveillance addresses testing gaps and provides an early warning for outbreaks. As U.S. federal agencies build a National Wastewater Surveillance System around the pandemic, thinking through ways to develop flexible frameworks for wastewater sampling, testing, and reporting can avoid unnecessary system overhauls for future infectious disease, chronic disease, and drug epidemics. OBJECTIVES: We discuss ways to transform a historically academic exercise into a tool for epidemic response. We generalize lessons learned by a global network of wastewater researchers around validation and implementation for COVID-19 and opioids while also drawing on our experience with wastewater-based epidemiology in the United States. DISCUSSION: Sustainable wastewater surveillance requires coordination between health and safety officials, utilities, labs, and researchers. Adapting sampling frequency, type, and location to threat level, community vulnerability, biomarker properties, and decisions that wastewater data will inform can increase the practical value of the data. Marketplace instabilities, coupled with a fragmented testing landscape due to specialization, may require officials to engage multiple labs to test for known and unknown threats. Government funding can stabilize the market, balancing commercial pressures with public good, and incentivize data sharing. When reporting results, standardizing metrics and contextualizing wastewater data with health resource data can provide insights into a community's vulnerability and identify strategies to prevent health care systems from being overwhelmed. If wastewater data will inform policy decisions for an entire community, comparing characteristics of the wastewater treatment plant's service population to those of the larger community can help determine whether the wastewater data are generalizable. Ethical protocols may be needed to protect privacy and avoid stigmatization. With data-driven approaches to sample collection, analysis, and interpretation, officials can use wastewater surveillance for adaptive resource allocation, pandemic management, and program evaluation. https://doi.org/10.1289/EHP8572.

Isolating the AFFF Signature in Coastal Watersheds Using Oxidizable PFAS Precursors and Unexplained Organofluorine.

Water supplies for millions of U.S. individuals exceed maximum contaminant levels for per- and polyfluoroalkyl substances (PFAS). Contemporary and legacy use of aqueous film forming foams (AFFF) is a major contamination source. However, diverse PFAS sources are present within watersheds, making it difficult to isolate their predominant origins. Here we examine PFAS source signatures among six adjacent coastal watersheds on Cape Cod, MA, U.S.A. using multivariate clustering techniques. A distinct signature of AFFF contamination enriched in precursors with six perfluorinated carbons (C6) was identified in watersheds with an AFFF source, while others were enriched in C4 precursors. Principal component analysis of PFAS composition in impacted watersheds showed a decline in precursor composition relative to AFFF stocks and a corresponding increase in terminal perfluoroalkyl sulfonates with < C6 but not those with ≥ C6. Prior work shows that in AFFF stocks, all extractable organofluorine (EOF) can be explained by targeted PFAS and precursors inferred using Bayesian inference on the total oxidizable precursor assay. Using the same techniques for the first time in impacted watersheds, we find that only 24%-63% of the EOF can be explained by targeted PFAS and oxidizable precursors. Our work thus indicates the presence of large non-AFFF organofluorine sources in these coastal watersheds.

A Statistical Approach for Identifying Private Wells Susceptible to Perfluoroalkyl Substances (PFAS) Contamination.

Drinking water concentrations of per- and polyfluoroalkyl substances (PFAS) exceed provisional guidelines for millions of Americans. Data on private well PFAS concentrations are limited in many regions and monitoring initiatives are costly and time-consuming. Here we examine modeling approaches for predicting private wells likely to have detectable PFAS concentrations that could be used to prioritize monitoring initiatives. We used nationally available data on PFAS sources, and geologic, hydrologic and soil properties that affect PFAS transport as predictors and trained and evaluated models using PFAS data (n~2300 wells) collected by the state of New Hampshire between 2014 and 2017. Models were developed for the five most frequently detected PFAS: perfluoropentanoate, perfluorohexanoate, perfluoroheptanoate, perfluorooctanoate, and perfluorooctane sulfonate. Classification random forest models that allow non-linearity in interactions among predictors performed the best (area under the receiver operating characteristics curve: 0.74 - 0.86). Point sources such as the plastics/rubber and textile industries accounted for the highest contribution to accuracy. Groundwater recharge, precipitation, soil sand content, and hydraulic conductivity were secondary predictors. Our study demonstrates the utility of machine learning models for predicting PFAS in private wells and the classification random forest model based on nationally available predictors is readily extendable to other regions.

PFAS Exposure Pathways for Humans and Wildlife: A Synthesis of Current Knowledge and Key Gaps in Understanding.

We synthesize current understanding of the magnitudes and methods for assessing human and wildlife exposures to poly- and perfluoroalkyl substances (PFAS). Most human exposure assessments have focused on 2 to 5 legacy PFAS, and wildlife assessments are typically limited to targeted PFAS (up to ~30 substances). However, shifts in chemical production are occurring rapidly, and targeted methods for detecting PFAS have not kept pace with these changes. Total fluorine measurements complemented by suspect screening using high-resolution mass spectrometry are thus emerging as essential tools for PFAS exposure assessment. Such methods enable researchers to better understand contributions from precursor compounds that degrade into terminal perfluoroalkyl acids. Available data suggest that diet is the major human exposure pathway for some PFAS, but there is large variability across populations and PFAS compounds. Additional data on total fluorine in exposure media and the fraction of unidentified organofluorine are needed. Drinking water has been established as the major exposure source in contaminated communities. As water supplies are remediated, for the general population, exposures from dust, personal care products, indoor environments, and other sources may be more important. A major challenge for exposure assessments is the lack of statistically representative population surveys. For wildlife, bioaccumulation processes differ substantially between PFAS and neutral lipophilic organic compounds, prompting a reevaluation of traditional bioaccumulation metrics. There is evidence that both phospholipids and proteins are important for the tissue partitioning and accumulation of PFAS. New mechanistic models for PFAS bioaccumulation are being developed that will assist in wildlife risk evaluations. Environ Toxicol Chem 2021;40:631-657. © 2020 SETAC.

Tap Water Contributions to Plasma Concentrations of Poly- and Perfluoroalkyl Substances (PFAS) in a Nationwide Prospective Cohort of U.S. Women.

BACKGROUND: Between 2013 and 2015, concentrations of poly- and perfluoroalkyl substances (PFAS) in public drinking water supplies serving at least six million individuals exceeded the level set forth in the health advisory established by the U.S. Environmental Protection Agency. Other than data reported for contaminated sites, no systematic or prospective data exist on the relative source contribution (RSC) of drinking water to human PFAS exposures. OBJECTIVES: This study estimates the RSC of tap water to overall PFAS exposure among members of the general U.S. METHODS: We measured concentrations of 15 PFAS in home tap water samples collected in 1989-1990 from 225 participants in a nationwide prospective cohort of U.S. women: the Nurses' Health Study (NHS). We used a one-compartment toxicokinetic model to estimate plasma concentrations corresponding to tap water intake of PFAS. We compared modeled results with measured plasma PFAS concentrations among a subset of 110 NHS participants. RESULTS: Tap water perfluorooctanoic acid (PFOA) and perfluorononanoic acid (PFNA) were statistically significant predictors of plasma concentrations among individuals who consumed [Formula: see text] cups of tap water per day. Modeled median contributions of tap water to measured plasma concentrations were: PFOA 12% (95% probability interval 11%-14%), PFNA 13% (8.7%-21%), linear perfluorooctanesulfonic acid (nPFOS) 2.2% (2.0%-2.5%), branched perfluorooctanesulfonic acid (brPFOS) 3.0% (2.5%-3.2%), and perfluorohexanesulfonic acid (PFHxS) 34% (29%-39%). In five locations, comparisons of PFASs in community tap water collected in the period 2013-2016 with samples from 1989-1990 indicated increases in quantifiable PFAS and extractable organic fluorine (a proxy for unquantified PFAS). CONCLUSIONS: Our results for 1989-1990 compare well with the default RSC of 20% used in risk assessments for legacy PFAS by many agencies. Future evaluation of drinking water exposures should incorporate emerging PFAS. https://doi.org/10.1289/EHP4093.

A review of the pathways of human exposure to poly- and perfluoroalkyl substances (PFASs) and present understanding of health effects.

Here, we review present understanding of sources and trends in human exposure to poly- and perfluoroalkyl substances (PFASs) and epidemiologic evidence for impacts on cancer, immune function, metabolic outcomes, and neurodevelopment. More than 4000 PFASs have been manufactured by humans and hundreds have been detected in environmental samples. Direct exposures due to use in products can be quickly phased out by shifts in chemical production but exposures driven by PFAS accumulation in the ocean and marine food chains and contamination of groundwater persist over long timescales. Serum concentrations of legacy PFASs in humans are declining globally but total exposures to newer PFASs and precursor compounds have not been well characterized. Human exposures to legacy PFASs from seafood and drinking water are stable or increasing in many regions, suggesting observed declines reflect phase-outs in legacy PFAS use in consumer products. Many regions globally are continuing to discover PFAS contaminated sites from aqueous film forming foam (AFFF) use, particularly next to airports and military bases. Exposures from food packaging and indoor environments are uncertain due to a rapidly changing chemical landscape where legacy PFASs have been replaced by diverse precursors and custom molecules that are difficult to detect. Multiple studies find significant associations between PFAS exposure and adverse immune outcomes in children. Dyslipidemia is the strongest metabolic outcome associated with PFAS exposure. Evidence for cancer is limited to manufacturing locations with extremely high exposures and insufficient data are available to characterize impacts of PFAS exposures on neurodevelopment. Preliminary evidence suggests significant health effects associated with exposures to emerging PFASs. Lessons learned from legacy PFASs indicate that limited data should not be used as a justification to delay risk mitigation actions for replacement PFASs.

Shifting Global Exposures to Poly- and Perfluoroalkyl Substances (PFASs) Evident in Longitudinal Birth Cohorts from a Seafood-Consuming Population.

Rapid declines in legacy poly- and perfluoroalkyl substances (PFASs) have been reported in human populations globally following changes in production since 2000. However, changes in exposure sources are not well understood. Here, we report serum concentrations of 19 PFASs (∑19PFAS) measured in children between 1993 and 2012 from a North Atlantic fishing community (Faroe Islands). Median ∑19PFAS concentrations in children (ages 5-13 years) peaked in 2000 (47.7 ng mL-1) and declined significantly by 14.4% year-1 until 2012. Principal component analysis (PCA) identified two groups of PFASs that likely reflect exposures from diverse consumer products and a third group that consisted of perfluorocarboxylic acids (PFCAs) with nine or more carbons (C ≥ 9). These C ≥ 9 PFASs are strongly associated with mercury in children's hair, a well-established proxy for seafood consumption, especially perfluoroundecanoic acid (PFUnDA, r = 0.72). Toxicokinetic modeling shows PFAS exposures from seafood have become increasingly important (53% of perfluorooctanesulfonate, PFOS, in 2012), despite a decline in whale consumption in recent years. We infer that even in a major seafood-consuming population, declines in legacy PFAS exposure after 2000 were achieved by the rapid phase out of PFOS and its precursors in consumer products. These results emphasize the importance of better understanding exposures to replacement PFASs in these sources.

Can profiles of poly- and Perfluoroalkyl substances (PFASs) in human serum provide information on major exposure sources?

BACKGROUND: Humans are exposed to poly- and perfluoroalkyl substances (PFASs) from diverse sources and this has been associated with negative health impacts. Advances in analytical methods have enabled routine detection of more than 15 PFASs in human sera, allowing better profiling of PFAS exposures. The composition of PFASs in human sera reflects the complexity of exposure sources but source identification can be confounded by differences in toxicokinetics affecting uptake, distribution, and elimination. Common PFASs, such as perfluorooctanoic acid (PFOA), perfluorooctane sulfonic acid (PFOS) and their precursors are ubiquitous in multiple exposure sources. However, their composition varies among sources, which may impact associated adverse health effects. METHODS: We use available PFAS concentrations from several demographic groups in a North Atlantic seafood consuming population (Faroe Islands) to explore whether chemical fingerprints in human sera provide insights into predominant exposure sources. We compare serum PFAS profiles from Faroese individuals to other North American populations to investigate commonalities in potential exposure sources. We compare individuals with similar demographic and physiological characteristics and samples from the same years to reduce confounding by toxicokinetic differences and changing environmental releases. RESULTS: Using principal components analysis (PCA) confirmed by hierarchical clustering, we assess variability in serum PFAS concentrations across three Faroese groups. The first principal component (PC)/cluster consists of C9-C12 perfluoroalkyl carboxylates (PFCAs) and is consistent with measured PFAS profiles in consumed seafood. The second PC/cluster includes perfluorohexanesulfonic acid (PFHxS) and the PFOS precursor N-ethyl perfluorooctane sulfonamidoacetate (N-EtFOSAA), which are directly used or metabolized from fluorochemicals in consumer products such as carpet and food packaging. We find that the same compounds are associated with the same exposure sources in two North American populations, suggesting generalizability of results from the Faroese population. CONCLUSIONS: We conclude that PFAS homologue profiles in serum provide valuable information on major exposure sources. It is essential to compare samples collected at similar time periods and to correct for demographic groups that are highly affected by differences in physiological processes (e.g., pregnancy). Information on PFAS homologue profiles is crucial for attributing adverse health effects to the proper mixtures or individual PFASs.

Temporal Shifts in Poly- and Perfluoroalkyl Substances (PFASs) in North Atlantic Pilot Whales Indicate Large Contribution of Atmospheric Precursors.

Poly- and perfluoroalkyl substances (PFASs) are persistent, bioaccumulative anthropogenic compounds associated with adverse health impacts on humans and wildlife. PFAS production changed in North America and Europe around the year 2000, but impacts on wildlife appear to vary across species and location. Unlike other mammal species, cetaceans lack the enzyme for transforming an important intermediate precursor (perfluorooctane sulfonamide: FOSA), into a prevalent compound in most wildlife (perfluorooctanesulfonate: PFOS). Thus, their tissue burden differentiates these two compounds while other mammals contain PFOS from both direct exposure and precursor degradation. Here we report temporal trends in 15 PFASs measured in muscle from juvenile male North Atlantic pilot whales (Globicephala melas) harvested between 1986 and 2013. FOSA accounted for a peak of 84% of the 15 PFASs around 2000 but declined to 34% in recent years. PFOS and long-chained PFCAs (C9-C13) increased significantly over the whole period (2.8% yr-1 to 8.3% yr-1), but FOSA declined by 13% yr-1 after 2006. Results from FOSA partitioning and bioaccumulation modeling forced by changes in atmospheric inputs reasonably capture magnitudes and temporal patterns in FOSA concentrations measured in pilot whales. Rapid changes in atmospheric FOSA in polar and subpolar regions around 2000 helps to explain large declines in PFOS exposure for species that metabolize FOSA, including seafood consuming human populations. This work reinforces the importance of accounting for biological exposures to PFAS precursors.

Detection of Poly- and Perfluoroalkyl Substances (PFASs) in U.S. Drinking Water Linked to Industrial Sites, Military Fire Training Areas, and Wastewater Treatment Plants.

Drinking water contamination with poly- and perfluoroalkyl substances (PFASs) poses risks to the developmental, immune, metabolic, and endocrine health of consumers. We present a spatial analysis of 2013-2015 national drinking water PFAS concentrations from the U.S. Environmental Protection Agency's (US EPA) third Unregulated Contaminant Monitoring Rule (UCMR3) program. The number of industrial sites that manufacture or use these compounds, the number of military fire training areas, and the number of wastewater treatment plants are all significant predictors of PFAS detection frequencies and concentrations in public water supplies. Among samples with detectable PFAS levels, each additional military site within a watershed's eight-digit hydrologic unit is associated with a 20% increase in PFHxS, a 10% increase in both PFHpA and PFOA, and a 35% increase in PFOS. The number of civilian airports with personnel trained in the use of aqueous film-forming foams is significantly associated with the detection of PFASs above the minimal reporting level. We find drinking water supplies for 6 million U.S. residents exceed US EPA's lifetime health advisory (70 ng/L) for PFOS and PFOA. Lower analytical reporting limits and additional sampling of smaller utilities serving <10000 individuals and private wells would greatly assist in further identifying PFAS contamination sources.

Source attribution of poly- and perfluoroalkyl substances (PFASs) in surface waters from Rhode Island and the New York Metropolitan Area.

Exposure to poly and perfluoroalkyl substances (PFASs) has been associated with adverse health effects in humans and wildlife. Understanding pollution sources is essential for environmental regulation but source attribution for PFASs has been confounded by limited information on industrial releases and rapid changes in chemical production. Here we use principal component analysis (PCA), hierarchical clustering, and geospatial analysis to understand source contributions to 14 PFASs measured across 37 sites in the Northeastern United States in 2014. PFASs are significantly elevated in urban areas compared to rural sites except for perfluorobutane sulfonate (PFBS), N-methyl perfluorooctanesulfonamidoacetic acid (N-MeFOSAA), perfluoroundecanate (PFUnDA) and perfluorododecanate (PFDoDA). The highest PFAS concentrations across sites were for perfluorooctanate (PFOA, 56 ng L-1) and perfluorohexane sulfonate (PFOS, 43 ng L-1) and PFOS levels are lower than earlier measurements of U.S. surface waters. PCA and cluster analysis indicates three main statistical groupings of PFASs. Geospatial analysis of watersheds reveals the first component/cluster originates from a mixture of contemporary point sources such as airports and textile mills. Atmospheric sources from the waste sector are consistent with the second component, and the metal smelting industry plausibly explains the third component. We find this source-attribution technique is effective for better understanding PFAS sources in urban areas.