US drinking water quality: exposure risk profiles for seven legacy and emerging contaminants.

BACKGROUND: Advances in drinking water infrastructure and treatment throughout the 20th and early 21st century dramatically improved water reliability and quality in the United States (US) and other parts of the world. However, numerous chemical contaminants from a range of anthropogenic and natural sources continue to pose chronic health concerns, even in countries with established drinking water regulations, such as the US. OBJECTIVE/METHODS: In this review, we summarize exposure risk profiles and health effects for seven legacy and emerging drinking water contaminants or contaminant groups: arsenic, disinfection by-products, fracking-related substances, lead, nitrate, per- and polyfluorinated alkyl substances (PFAS) and uranium. We begin with an overview of US public water systems, and US and global drinking water regulation. We end with a summary of cross-cutting challenges that burden US drinking water systems: aging and deteriorated water infrastructure, vulnerabilities for children in school and childcare facilities, climate change, disparities in access to safe and reliable drinking water, uneven enforcement of drinking water standards, inadequate health assessments, large numbers of chemicals within a class, a preponderance of small water systems, and issues facing US Indigenous communities. RESULTS: Research and data on US drinking water contamination show that exposure profiles, health risks, and water quality reliability issues vary widely across populations, geographically and by contaminant. Factors include water source, local and regional features, aging water infrastructure, industrial or commercial activities, and social determinants. Understanding the risk profiles of different drinking water contaminants is necessary for anticipating local and general problems, ascertaining the state of drinking water resources, and developing mitigation strategies. IMPACT STATEMENT: Drinking water contamination is widespread, even in the US. Exposure risk profiles vary by contaminant. Understanding the risk profiles of different drinking water contaminants is necessary for anticipating local and general public health problems, ascertaining the state of drinking water resources, and developing mitigation strategies.

Meteorological data source comparison-a case study in geospatial modeling of potential environmental exposure to abandoned uranium mine sites in the Navajo Nation.

Meteorological (MET) data is a crucial input for environmental exposure models. While modeling exposure potential using geospatial technology is a common practice, existing studies infrequently evaluate the impact of input MET data on the level of uncertainty on output results. The objective of this study is to determine the effect of various MET data sources on the potential exposure susceptibility predictions. Three sources of wind data are compared: The North American Regional Reanalysis (NARR) database, meteorological aerodrome reports (METARs) from regional airports, and data from local MET weather stations. These data sources are used as inputs into a machine learning (ML) driven GIS Multi-Criteria Decision Analysis (GIS-MCDA) geospatial model to predict potential exposure to abandoned uranium mine sites in the Navajo Nation. Results indicate significant variations in results derived from different wind data sources. After validating the results from each source using the National Uranium Resource Evaluation (NURE) database in a geographically weighted regression (GWR), METARs data combined with the local MET weather station data showed the highest accuracy, with an average R2 of 0.74. We conclude that local direct measurement-based data (METARs and MET data) produce a more accurate prediction than the other sources evaluated in the study. This study has the potential to inform future data collection methods, leading to more accurate predictions and better-informed policy decisions surrounding environmental exposure susceptibility and risk assessment.

Individual level spatial-temporal modelling of exposure potential of livestock in the Cove Wash watershed, Arizona.

Personal exposure studies suffer from uncertainty issues, largely stemming from individual behavior uncertainties. Built on spatial-temporal exposure analysis and methods, this study proposed a novel approach to spatial-temporal modeling that incorporated behavior classifications taking into account uncertainties, to estimate individual livestock exposure potential. The new approach was applied in a community-based research project with a Tribal community in the southwest United States. The community project examined the geospatial and temporal grazing patterns of domesticated livestock in a watershed containing 52 abandoned uranium mines (AUMs). Thus, the study aimed to 1) classify Global Positioning System (GPS) data from livestock into three behavior subgroups - grazing, traveling or resting; 2) calculate the daily cumulative exposure potential for livestock; 3) assess the performance of the computational method with and without behavior classifications. Using Lotek Litetrack GPS collars, we collected data at a 20-minute-interval for 2 flocks of sheep and goats during the spring and summer of 2019. Analysis and modeling of GPS data demonstrated no significant difference in individual cumulative exposure potential within each flock when animal behaviors with probability/uncertainties were considered. However, when daily cumulative exposure potential was calculated without consideration of animal behavior or probability/uncertainties, significant differences among animals within a herd were observed, which does not match animal grazing behaviors reported by livestock owners. These results suggest that the proposed method of including behavior subgroups with probability/uncertainties more closely resembled the observed grazing behaviors reported by livestock owners. Results from the research may be used for future intervention and policy-making on remediation efforts in communities where grazing livestock may encounter environmental contaminants. This research also demonstrates a novel robust geographic information system (GIS)-based framework to estimate cumulative exposure potential to environmental contaminants and provides critical information to address community questions on livestock exposure to AUMs.

Understanding Public Perspectives on Fracking in the United States using Social Media Big Data.

People's attitudes toward hydraulic fracturing (i.e., "fracking") to extract fossil fuels can be shaped by factors associated with socio-demographics, economic development, social equity and politics, environmental impacts, and fracking-related information obtainment. Existing research typically conducts surveys and interviews to study public attitudes toward fracking among a small group of individuals in a specific geographic area, where limited samples may introduce bias. Here, we compiled geo-referenced social media big data from Twitter during 2018-2019 for the entire United States to present a more holistic picture of people's attitudes toward fracking. We used a multiscale geographically weighted regression (MGWR) to investigate county-level relationships between the aforementioned factors and percentages of negative tweets concerning fracking. Results clearly depict spatial heterogeneity and varying scales of those associations. Counties with higher median household income, larger African American populations, and/or lower educational level are less likely to oppose fracking, and these associations show global stationarity in all contiguous U.S. counties. Eastern and Central U.S. counties with higher unemployment rate, counties east of the Great Plains with less fracking sites nearby, and Western and Gulf Coast region counties with higher health insurance enrollments are more likely to oppose fracking activities. These three variables show clear East-West geographical divides in influencing public perspective on fracking. In counties across the southern Great Plains, negative attitudes toward fracking are less often vocalized on Twitter as the share of Republican voters increases. These findings have implications for both predicting public perspectives and needed policy adjustments. The methodology can also be conveniently applied to investigate public perspectives on other controversial topics.

Social Vulnerabilities and Spatial Access to Primary Healthcare through Car and Public Transportation System in the Albuquerque, NM, Metropolitan Area: Assessing Disparities through GIS and Multilevel Modeling.

Primary healthcare (PHC) is a keystone component of population health. However, inequities in public transportation access hinder equitable usage of PHC services by minoritized populations. Using the multimodal enhanced 2-step floating catchment area method and data in 2018 and 2019 for spatial access to PHC providers (n = 1166) and social vulnerability markers through census block (n = 543) and tract data (n = 226), a generalized linear mixed-effect model (GLMEM) was constructed to test the effects of sociodemographic and community area correlates on both car and bus transit spatial access to PHC in the Albuquerque, New Mexico (NM) metropolitan area. Results for bus spatial access to PHC showed lower access for Hispanics (B = - 0.097 ± 0.029 [- 0.154, - 0.040]) and non-Hispanic Whites (B = - 0.106 ± 0.032 [- 0.169, - 0.043]) and a positive association between single-family households and bus spatial access (B = 1.573 ± 0.349 [0.866, 2.261]). Greater disability vulnerability (B = - 0.569 ± 0.173 [- 0.919, - 0.259]) and language vulnerability (B = - 0.569 ± 0.173 [- 0.919, - 0.259]) were associated with decreased bus spatial access. For car spatial access to PHC, greater SES vulnerability (B = - 0.338 ± 0.021 [- 1.568, -0.143]), disability (B = - 0.721 ± .092 [- 0.862, - 0.50 9]), and language vulnerability (B = - 0.686 ± 0.172 [- 1.044, - 0.362]) were associated with less car spatial access. Results indicate a disproportionate burden of low PHC access among disadvantaged population groups who rely heavily on public transportation. These results necessitate targeted interventions to reduce these disparities in access to PHC.

A mass-balance approach to evaluate arsenic intake and excretion in different populations.

Unless a toxicant builds up in a deep compartment, intake by the human body must on average balance the amount that is lost. We apply this idea to assess arsenic (As) exposure misclassification in three previously studied populations in rural Bangladesh (n = 11,224), Navajo Nation in the Southwestern United States (n = 619), and northern Chile (n = 630), under varying assumptions about As sources. Relationships between As intake and excretion were simulated by taking into account additional sources, as well as variability in urine dilution inferred from urinary creatinine. The simulations bring As intake closer to As excretion but also indicate that some exposure misclassification remains. In rural Bangladesh, accounting for intake from more than one well and rice improved the alignment of intake and excretion, especially at low exposure. In Navajo Nation, comparing intake and excretion revealed home dust as an important source. Finally, in northern Chile, while food-frequency questionnaires and urinary As speciation indicate fish and shellfish sources, persistent imbalance of intake and excretion suggests imprecise measures of drinking water arsenic as a major cause of exposure misclassification. The mass-balance approach could prove to be useful for evaluating sources of exposure to toxicants in other settings.

A Secure and Reusable Software Architecture for Supporting Online Data Harmonization.

Retrospective data harmonization across multiple research cohorts and studies is frequently done to increase statistical power, provide comparison analysis, and create a richer data source for data mining. However, when combining disparate data sources, harmonization projects face data management and analysis challenges. These include differences in the data dictionaries and variable definitions, privacy concerns surrounding health data representing sensitive populations, and lack of properly defined data models. With the availability of mature open-source web-based database technologies, developing a complete software architecture to overcome the challenges associated with the harmonization process can alleviate many roadblocks. By leveraging state-of-the-art software engineering and database principles, we can ensure data quality and enable cross-center online access and collaboration. This paper outlines a complete software architecture developed and customized using the Django web framework, leveraged to harmonize sensitive data collected from three NIH-support birth cohorts. We describe our framework and show how we successfully overcame challenges faced when harmonizing data from these cohorts. We discuss our efforts in data cleaning, data sharing, data transformation, data visualization, and analytics, while reflecting on what we have learned to date from these harmonized datasets.

Impact of travel time uncertainties on modeling of spatial accessibility: a comparison of street data sources.

GIS-based spatial access measures have been used extensively to monitor social equity and to help develop policy. However, inherent uncertainties in the road datasets used in spatial access estimates remain largely underreported. These uncertainties might result in unrecognized biases within visualization products and decision-making outcomes that strive to improve social equity based on seemingly egalitarian accessibility metrics. To better understand and address these uncertainties, we evaluated variations in travel impedance for car and bus transportation using proprietary, volunteer-information-based, and free (non-volunteer-information-based) street networks. We then interpreted the measured variations through the lens of street data uncertainty and its propagation in a common E2SFCA model of spatial accessibility. Results indicated that travel impedance disagreement propagates through the modeling process to effect agreement of spatial access index (SPAI) estimates among different street sources, with larger uncertainties observed for bus travel than car travel. Higher impedance coefficients (ß), a model parameter, reduced the impact of street-source variations on estimates. Less urbanized regions were found to experience higher street-source variations when compared with the core-metropolitan area. We also demonstrated that a relative spatial access measure - the spatial access ratio (SPAR) - reduced uncertainties introduced by the choice of street datasets. Careful selection of reliable street sources and model parameters (e.g., higher ß), as well as consideration of the potential for bias, particularly for less urbanized areas and areas reliant on public transportation, is warranted when leveraging SPAI to inform policy. When reliable/accurate road network data is not accessible or data quality information is not available, the SPAR is a suitable alternative or supplement to SPAI for visualization and analyses.

Environmental risk mapping of potential abandoned uranium mine contamination on the Navajo Nation, USA, using a GIS-based multi-criteria decision analysis approach.

The Navajo Nation (NN), a sovereign indigenous tribal nation in the Southwestern United States, is home to 523 abandoned uranium mines (AUMs). Previous health studies have articulated numerous human health hazards associated with AUMs and multiple environmental mechanisms/pathways (e.g., air, water, and soil) for contaminant transport. Despite this evidence, the limited modeling of AUM contamination that exists relies solely on proximity to mines and only considers single rather than combined pathways from which the contamination is a product. In order to better understand the spatial dynamics of contaminant exposure across the NN, we adopted the following established geospatial and computational methods to develop a more sophisticated environmental risk map illustrating the potential for AUM contamination: GIS-based multi-criteria decision analysis (GIS-MCDA), fuzzy logic, and analytic hierarchy process (AHP). Eight criteria layers were selected for the GIS-MCDA model: proximity to AUMs, roadway proximity, drainage proximity, topographic landforms, wind index, topographic wind exposure, vegetation index, and groundwater contamination. Model sensitivity was evaluated using the one-at-a-time method, and statistical validation analysis was conducted using two separate environmental datasets. The sensitivity analysis indicated consistency and reliability of the model. Model results were strongly associated with environmental uranium concentrations. The model classifies 20.2% of the NN as high potential for AUM contamination while 65.7% and 14.1% of the region are at medium and low risk, respectively. This study is entirely a novel application and a crucial first step toward informing future epidemiologic studies and ongoing remediation efforts to reduce human exposure to AUM waste.

High geographic prevalence of pulmonary artery hypertension: associations with ethnicity, drug use, and altitude.

While estimates of pulmonary arterial hypertension incidence and prevalence commonly range from 1-3/million and 15-25/million, respectively, clinical experience at our institution suggested much higher rates. We sought to describe the disease burden of pulmonary arterial hypertension in the geographic area served by our Pulmonary Hypertension Clinic and compare it to the REVEAL registry. Our secondary objectives were to document pulmonary arterial hypertension prevalence in minorities underrepresented in REVEAL (Hispanics and Native Americans) and to address the association of pulmonary arterial hypertension with exposure to drugs and moderately increased residential altitude in this population. Retrospective review of pulmonary arterial hypertension clinic patients alive during 2016 identified 154 patients. Hispanic patients made up 35.7% of the cohort, a much greater percentage than REVEAL, p < .001 but smaller than the percentage of Hispanic patients (48.4%) in geographic area served by the clinic. Pulmonary arterial hypertension due to drug exposure was more common and idiopathic pulmonary arterial hypertension was less common than in REVEAL (p < .001). Overall, pulmonary arterial hypertension incidence was 14 cases per million, greater than the REVEAL registry, odds ratio 6.3 (95% CI: 4.2-9.5), (p < .001). Annual period prevalence of pulmonary arterial hypertension was 93 cases per million, also greater than the REVEAL, odds ratio = 7.5 (95% CI: 6.4-8.8) and remained greater when the clinic cohort was constrained to patients with hemodynamic severity comparable to REVEAL, odds ratio = 3.8 (95% CI: 3.0-4.6), (p < .001). There was a strong association between pulmonary arterial hypertension prevalence and residence at altitude > 4000 ft, odds ratio = 26.6 (95% CI: 8.5-83.5), p < .001; however, this was potentially confounded by pulmonary arterial hypertension treatment referral patterns. These findings document a much higher local pulmonary arterial hypertension incidence and prevalence than previously reported in REVEAL. While population ethnicity differed markedly from REVEAL, the disease burden was not driven by these differences. The possible association of moderately increased residential altitude with pulmonary arterial hypertension warrants further evaluation.