Association of county level poverty with mortality from primary liver cancers.

BACKGROUND: The highly variable occurrence of primary liver cancers across the United States emphasize the relevance of location-based factors. Social determinants such as income, educational attainment, housing, and other factors may contribute to regional variations in outcomes. To evaluate their impact, this study identified and analyzed clusters of high mortality from primary liver cancers and the association of location-based determinants with mortality across the contiguous United States. METHODS: A geospatial analysis of age-adjusted incidence and standardized mortality rates from primary liver cancers from 2000 to 2020 was performed. Local indicators of spatial association identified hot-spots, clusters of counties with significantly higher mortality. Temporal analysis of locations with persistent poverty, defined as high (>20%) poverty for at least 30 years, was performed. Social determinants were analyzed individually or globally using composite measures such as the social vulnerability index or social deprivation index. Disparities in county level social determinants between hot-spots and non-hot-spots were analyzed by univariate and multivariate logistic regression. RESULTS: There are distinct clusters of liver cancer incidence and mortality, with hotspots in east Texas and Louisiana. The percentage of people living below the poverty line or Hispanics had a significantly higher odds ratio for being in the top quintile for mortality rates in comparison to other quintiles and were highly connected with mortality rates. Current and persistent poverty were both associated with an evolution from non-hotspots to new hotspots of mortality. Hotspots were predominantly associated with locations with significant levels of socioeconomic vulnerability or deprivation. CONCLUSIONS: Poverty at a county level is associated with mortality from primary liver cancer and clusters of higher mortality. These findings emphasize the importance of addressing poverty and related socio-economic determinants as modifiable factors in public health policies and interventions aimed at reducing mortality from primary liver cancers.

A data-driven approach to identifying PFAS water sampling priorities in Colorado, United States.

BACKGROUND: Per and polyfluoroalkyl substances (PFAS), a class of environmentally and biologically persistent chemicals, have been used across many industries since the middle of the 20th century. Some PFAS have been linked to adverse health effects. OBJECTIVE: Our objective was to incorporate known and potential PFAS sources, physical characteristics of the environment, and existing PFAS water sampling results into a PFAS risk prediction map that may be used to develop a PFAS water sampling prioritization plan for the Colorado Department of Public Health and Environment (CDPHE). METHODS: We used random forest classification to develop a predictive surface of potential groundwater contamination from two PFAS, perfluorooctane sulfonate (PFOS) and perfluorooctanoate (PFOA). The model predicted PFAS risk at locations without sampling data into one of three risk categories after being "trained" with existing PFAS water sampling data. We used prediction results, variable importance ranking, and population characteristics to develop recommendations for sampling prioritization. RESULTS: Sensitivity and precision ranged from 58% to 90% in the final models, depending on the risk category. The model and prioritization approach identified private wells in specific census blocks, as well as schools, mobile home parks, and public water systems that rely on groundwater as priority sampling locations. We also identified data gaps including areas of the state with limited sampling and potential source types that need further investigation. IMPACT STATEMENT: This work uses random forest classification to predict the risk of groundwater contamination from two per- and polyfluoroalkyl substances (PFAS) across the state of Colorado, United States. We developed the prediction model using data on known and potential PFAS sources and physical characteristics of the environment, and "trained" the model using existing PFAS water sampling results. This data-driven approach identifies opportunities for PFAS water sampling prioritization as well as information gaps that, if filled, could improve model predictions. This work provides decision-makers information to effectively use limited resources towards protection of populations most susceptible to the impacts of PFAS exposure.

Construction of residential histories to estimate long-term environmental exposures in the California Teachers Study cohort.

Environmental epidemiologic studies using geospatial data often estimate exposure at a participant's residence upon enrollment, but mobility during the exposure period can lead to misclassification. We aimed to mitigate this issue by constructing residential histories for participants in the California Teachers Study through follow-up (1995-2018). Address records have been collected from the US Postal Service, LexisNexis, Experian, and California Cancer Registry. We identified records of the same address based on geo-coordinate distance (≤250 m) and street name similarity. We consolidated addresses, prioritizing those confirmed by participants during follow-up questionnaires, and estimating the duration lived at each address using dates associated with records (e.g., date-first-seen). During 23 years of follow-up, about half of participants moved (48%, including 14% out-of-state). We observed greater mobility among younger women, Hispanic/Latino women, and those in metropolitan and lower socioeconomic status areas. The cumulative proportion of in-state movers remaining eligible for analysis was 21%, 32%, and 41% at 5, 10, and 20 years post enrollment, respectively. Using self-reported information collected 10 years after enrollment, we correctly identified 94% of movers and 95% of non-movers as having moved or not moved from their enrollment address. This dataset provides a foundation for estimating long-term environmental exposures in diverse epidemiologic studies in this cohort. IMPACT: Our efforts in constructing residential histories for California Teachers Study participants through follow-up (1995-2018) benefit future environmental epidemiologic studies. Address availability during the exposure period can mitigate misclassification due to residential changes, especially when evaluating long-term exposures and chronic health outcomes. This can reduce differential misclassification among more mobile subgroups, including younger women and those from lower socioeconomic and urban areas. Our approach to consolidating addresses from multiple sources showed high accuracy in comparison to self-reported residential information. The residential dataset produced from this analysis provides a valuable tool for future studies, ultimately enhancing our understanding of environmental health impacts.

Local exposure misclassification in national models: relationships with urban infrastructure and demographics.

BACKGROUND: National-scale linear regression-based modeling may mischaracterize localized patterns, including hyperlocal peaks and neighborhood- to regional-scale gradients. For studies focused on within-city differences, this mischaracterization poses a risk of exposure misclassification, affecting epidemiological and environmental justice conclusions. OBJECTIVE: Characterize the difference between intraurban pollution patterns predicted by national-scale land use regression modeling and observation-based estimates within a localized domain and examine the relationship between that difference and urban infrastructure and demographics. METHODS: We compare highly resolved (0.01 km2) observations of NO2 mixing ratio and ultrafine particle (UFP) count obtained via mobile monitoring with national model predictions in thirteen neighborhoods in the San Francisco Bay Area. Grid cell-level divergence between modeled and observed concentrations is termed "localized difference." We use a flexible machine learning modeling technique, Bayesian Additive Regression Trees, to investigate potentially nonlinear relationships between discrepancy between localized difference and known local emission sources as well as census block group racial/ethnic composition. RESULTS: We find that observed local pollution extremes are not represented by land use regression predictions and that observed UFP count significantly exceeds regression predictions. Machine learning models show significant nonlinear relationships among localized differences between predictions and observations and the density of several types of pollution-related infrastructure (roadways, commercial and industrial operations). In addition, localized difference was greater in areas with higher population density and a lower share of white non-Hispanic residents, indicating that exposure misclassification by national models differs among subpopulations. IMPACT: Comparing national-scale pollution predictions with hyperlocal observations in the San Francisco Bay Area, we find greater discrepancies near major roadways and food service locations and systematic underestimation of concentrations in neighborhoods with a lower share of non-Hispanic white residents. These findings carry implications for using national-scale models in intraurban epidemiological and environmental justice applications and establish the potential utility of supplementing large-scale estimates with publicly available urban infrastructure and pollution source information.

Geospatial determinants of maternal and child exposure to fine particulate matter in Kintampo, Ghana: Levels within the household and community, by surrounding building density and near roadways.

BACKGROUND: Personal exposure to fine particulate matter (PM2.5) from household air pollution is well-documented in sub-Saharan Africa, but spatiotemporal patterns of exposure are poorly characterized. OBJECTIVE: We used paired GPS and personal PM2.5 data to evaluate changes in exposure across location-time environments (e.g., household and community, during cooking and non-cooking hours), building density and proximity to roadways. METHODS: Our study included 259 sessions of geolocated, gravimetrically-calibrated one-minute personal PM2.5 measurements from participants in the GRAPHS Child Lung Function Study. The household vicinity was defined using a 50-meter buffer around participants' homes. Community boundaries were developed using a spatial clustering algorithm applied to an open-source dataset of building footprints in Africa. For each GPS location, we estimated building density (500 m buffer) and proximity to roadways (100 m buffer). We estimated changes in PM2.5 exposure by location (household, community), time of day (morning/evening cooking hours, night), building density, and proximity to roadways using linear mixed effect models. RESULTS: Relative to nighttime household exposure, PM2.5 exposure during evening cooking hours was 2.84 (95%CI = 2.70-2.98) and 1.80 (95%CI = 1.54-2.10) times higher in the household and community, respectively. Exposures were elevated in areas with the highest versus lowest quartile of building density (FactorQ1vsQ4 = 1.60, 95%CI = 1.42-1.80). The effect of building density was strongest during evening cooking hours, and influenced levels in both the household and community (31% and 65% relative increase from Q1 to Q4, respectively). Being proximal to a trunk, tertiary or track roadway increased exposure by a factor of 1.16 (95%CI = 1.07-1.25), 1.68 (95%CI = 1.45-1.95) and 1.27 (95%CI = 1.06-1.53), respectively. IMPACT: Household air pollution from cooking with solid fuels in sub-Saharan Africa is a major environmental concern for maternal and child health. Our study advances previous knowledge by quantifying the impact of household cooking activities on air pollution levels in the community, and identifying two geographic features, building density and roadways, that contribute to maternal and child daily exposure. Household cooking contributes to higher air pollution levels in the community especially in areas with greater building density. Findings underscore the need for equitable clean household energy transitions that reach entire communities to reduce health risks from household and outdoor air pollution.

Composite Indices of Social Determinants of Health: Overview, Measurement Gaps, and Research Priorities for Health Equity.

The goal of health equity is for all people to have opportunities and resources for optimal health outcomes regardless of their social identities, residence in marginalized communities, and/or experience with oppressive systems. Social determinants of health (SDOH)-the conditions in which we are born, grow, live, work, and age-are inextricably tied to health equity. Advancing health equity thus requires reliable measures of SDOH. In the United States, comprehensive individual-level data on SDOH are difficult to collect, may be inaccurate, and do not capture all dimensions of inequitable outcomes. Individual area-based indicators are widely available, but difficult to use in practice. Numerous area-level composite indices are available to describe SDOH, but there is no consensus on which indices are most appropriate to use. This article presents an analytic taxonomy of currently available SDOH composite indices and compares their components and predictive ability, providing insights into gaps and areas for further research.

Characterizing temporal trends in populations exposed to aircraft noise around U.S. airports: 1995-2015.

BACKGROUND: Aircraft noise is a key concern for communities surrounding airports, with increasing evidence for health effects and inequitable distributions of exposure. However, there have been limited national-scale assessments of aircraft noise exposure over time and across noise metrics, limiting evaluation of population exposure patterns. OBJECTIVE: We evaluated national-scale temporal trends in aviation noise exposure by airport characteristics and across racial/ethnic populations in the U.S. METHODS: Noise contours were modeled for 90 U.S. airports in 5-year intervals between 1995 and 2015 using the Federal Aviation Administration's Aviation Environmental Design Tool. We utilized linear fixed effects models to estimate changes in noise exposure areas for day-night average sound levels (DNL) of 45, 65, and a nighttime equivalent sound level (Lnight) of 45 A-weighted decibels (dB[A]). We used group-based trajectory modeling to identify distinct groups of airports sharing underlying characteristics. We overlaid noise contours and Census tract data from the U.S. Census Bureau and American Community Surveys for 2000 to 2015 to estimate exposure changes overall and by race/ethnicity. RESULTS: National-scale analyses showed non-monotonic trends in mean exposed areas that peaked in 2000, followed by a 37% decrease from 2005 to 2010 and a subsequent increase in 2015. We identified four distinct trajectory groups of airports sharing latent characteristics related to size and activity patterns. Those populations identifying as minority (e.g., Hispanic/Latino, Black/African American, Asian) experienced higher proportions of exposure relative to their subgroup populations compared to non-Hispanic or White populations across all years, indicating ethnic and racial disparities in airport noise exposure that persist over time. SIGNIFICANCE: Overall, these data identified differential exposure trends across airports and subpopulations, helping to identify vulnerable communities for aviation noise in the U.S. IMPACT STATEMENT: We conducted a descriptive analysis of temporal trends in aviation noise exposure in the U.S. at a national level. Using data from 90 U.S. airports over a span of two decades, we characterized the noise exposure trends overall and by airport characteristics, while estimating the numbers of exposed by population demographics to help identify the impact on vulnerable communities who may bear the burden of aircraft noise exposure.

Predictors of pesticide levels in carpet dust collected from child care centers in Northern California, USA.

BACKGROUND: Young children may be exposed to pesticides in child care centers, but little is known about determinants of pesticide contamination in these environments. OBJECTIVE: Characterize pesticide contamination in early care and education (ECE) centers and identify predictors of pesticide concentrations and loading in dust collected from classroom carpets. METHODS: Carpet dust samples were collected from 51 licensed child care centers in Northern California and analyzed for 14 structural and agricultural pesticides. Program characteristics were collected through administration of director interviews and observational surveys, including an integrated pest management (IPM) inspection. Pesticide use information for the prior year was obtained from the California Department of Pesticide Regulation to characterize structural applications and nearby agricultural pesticide use. RESULTS: The most frequently detected pesticides were cis-permethrin (98%), trans-permethrin (98%), bifenthrin (94%), fipronil (94%), and chlorpyrifos (88%). Higher bifenthrin levels were correlated with agricultural applications within 3 kilometers, and higher fipronil levels were correlated with professional pesticide applications in the prior year. In multivariable models, higher IPM Checklist scores were associated with lower loading of chlorpyrifos and permethrin. Placement of the sampled area carpet was also a predictor of chlorpyrifos loading. The strongest predictor of higher pesticide loading for the most frequently detected pesticides was location in California's San Joaquin Valley. SIGNIFICANCE: Our findings contribute to the growing understanding that pesticides are ubiquitous in children's environments. Pesticide levels in carpet dust were associated with some factors that ECE directors may have control over, such as IPM practices, and others that are beyond their control, such as geographic location. IPM is an important tool that has the potential to reduce pesticide exposures in ECE environments, even for pesticides no longer in use. IMPACT: One million children in California under six years old attend child care programs where they may spend up to 40 h per week. Children are uniquely vulnerable to environmental contaminants; however early care settings are under researched in environmental health studies. Little is known about predictors of pesticide levels found in environmental samples from child care facilities. This study aims to identify behavioral and environmental determinants of pesticide contamination in California child care centers. Findings can empower child care providers and consumers and inform decision makers to reduce children's exposures to pesticides and promote lifelong health.

Validation of a neighborhood sentiment and safety index derived from existing data repositories.

BACKGROUND: The communities we live in are central to our health. Neighborhood disadvantage is associated with worse physical and mental health and even early mortality, while resident sense of safety and positive neighborhood sentiment has been repeatedly linked to better physical and mental health outcomes. Therefore, understanding where negative neighborhood sentiment and safety are salient concerns can help inform public health interventions and as a result, improve health outcomes. To date, fear of crime and neighborhood sentiment data or indices have largely been based on the administration of time consuming and costly standardized surveys. OBJECTIVE: The current study aims to develop a Neighborhood Sentiment and Safety Index (NSSI) at the census tract level, building on publicly available data repositories, including the US Census and ACS surveys, Data Axle, and ESRI repositories. METHODS: The NSSI was created using Principal Component Analysis. Mineigen and minimum loading values were 1 and 0.3, respectively. Throughout the step-wise PCA process, variables were excluded if their loading value was below 0.3 or if variables loaded into multiple components. RESULTS: The novel index was validated against standardized survey items from a longitudinal cohort study in the Northeastern United States characterizing experiences of (1) Neighborhood Characteristics with a Pearson correlation of -0.34 (p < 0.001) and, (2) Neighborhood Behavior Impact with a Pearson correlation of -0.33 (p < 0.001). It also accurately predicted the Share Care Community Well Being Index (Spearman correlation = 0.46) and the neighborhood deprivation index (NDI) (Spearman correlation = -0.75). SIGNIFICANCE: Our NSSI can serve as a predictor of neighborhood experience where data is either unavailable or too resource consuming to practically implement in planned studies. IMPACT STATEMENT: To date, fear of crime and neighborhood sentiment data or indices have largely been based on the administration of time consuming and costly standardized surveys. The current study aims to develop a Neighborhood Sentiment and Safety Index (NSSI) at the census tract level, building on publicly available data repositories, including the US Census and ACS surveys, Data Axle, and ESRI repositories. The NSSI was validated against four separate measures and can serve as a predictor of neighborhood experience where data is either unavailable or too resource consuming to practically implement in planned studies.

The skin is no barrier to mixtures: Air pollutant mixtures and reported psoriasis or eczema in the Personalized Environment and Genes Study (PEGS).

BACKGROUND: Autoimmune (AI) diseases appear to be a product of genetic predisposition and environmental triggers. Disruption of the skin barrier causes exacerbation of psoriasis/eczema. Oxidative stress is a mechanistic pathway for pathogenesis of the disease and is also a primary mechanism for the detrimental effects of air pollution. METHODS: We evaluated the association between autoimmune skin diseases (psoriasis or eczema) and air pollutant mixtures in 9060 subjects from the Personalized Environment and Genes Study (PEGS) cohort. Pollutant exposure data on six criteria air pollutants are publicly available from the Center for Air, Climate, and Energy Solutions and the Atmospheric Composition Analysis Group. For increased spatial resolution, we included spatially cumulative exposure to volatile organic compounds from sites in the United States Environmental Protection Agency Toxic Release Inventory and the density of major roads within a 5 km radius of a participant's address from the United States Geological Survey. We applied logistic regression with quantile g-computation, adjusting for age, sex, diagnosis with an autoimmune disease in family or self, and smoking history to evaluate the relationship between self-reported diagnosis of an AI skin condition and air pollution mixtures. RESULTS: Only one air pollution variable, sulfate, was significant individually (OR = 1.06, p = 3.99E-2); however, the conditional odds ratio for the combined mixture components of PM2.5 (black carbon, sulfate, sea salt, and soil), CO, SO2, benzene, toluene, and ethylbenzene is 1.10 (p-value = 5.4E-3). SIGNIFICANCE: While the etiology of autoimmune skin disorders is not clear, this study provides evidence that air pollutants are associated with an increased prevalence of these disorders. The results provide further evidence of potential health impacts of air pollution exposures on life-altering diseases. SIGNIFICANCE AND IMPACT STATEMENT: The impact of air pollution on non-pulmonary and cardiovascular diseases is understudied and under-reported. We find that air pollution significantly increased the odds of psoriasis or eczema in our cohort and the magnitude is comparable to the risk associated with smoking exposure. Autoimmune diseases like psoriasis and eczema are likely impacted by air pollution, particularly complex mixtures and our study underscores the importance of quantifying air pollution-associated risks in autoimmune disease.

Personal exposure monitoring using GPS-enabled portable air pollution sensors: A strategy to promote citizen awareness and behavioral changes regarding indoor and outdoor air pollution.

BACKGROUND: Little is known about how individuals are exposed to air pollution in various daily activity spaces due to a lack of data collected in the full range of spatial contexts in which they spend their time. The limited understanding makes it difficult for people to act in informed ways to reduce their exposure both indoors and outdoors. OBJECTIVE: This study aimed to (1) assess whether personalized air quality data collected using GPS-enabled portable monitors (GeoAir2), coupled with travel-activity diaries, promote people's awareness and behavioral changes regarding indoor and outdoor air pollution and (2) demonstrate the effect of places and activities on personal exposure by analyzing individual exposure profiles. METHODS: 44 participants carried GeoAir2 to collect geo-referenced air pollution data and completed travel-activity diaries for three days. These data were then combined for spatial data analysis and visualization. Participants also completed pre- and post-session surveys about awareness and behaviors regarding air pollution. Paired-sample t-tests were performed to evaluate changes in knowledge, attitudes/perceptions, and behavioral intentions/practices, respectively. Lastly, follow-up interviews were conducted with a subset of participants. RESULTS: Most participants experienced PM2.5 peaks indoors, especially when cooking at home, and had the lowest exposure in transit. Participants reported becoming more aware of air quality in their surroundings and more concerned about its health effects (t = 3.92, p = 0.000) and took more action or were more motivated to alter their behaviors to mitigate their exposure (t = 3.40, p = 0.000) after the intervention than before. However, there was no significant improvement in knowledge (t = 0.897; p = 0.187). SIGNIFICANCE: Personal exposure monitoring, combined with travel-activity diaries, leads to positive changes in attitudes, perceptions, and behaviors related to air pollution. This study highlights the importance of citizen engagement in air monitoring for effective risk communication and air pollution management.

Historic redlining and the siting of oil and gas wells in the United States.

BACKGROUND: The presence of active or inactive (i.e., postproduction) oil and gas wells in neighborhoods may contribute to ongoing pollution. Racially discriminatory neighborhood security maps developed by the Home-Owners Loan Corporation (HOLC) in the 1930s may contribute to environmental exposure disparities. OBJECTIVE: To determine whether receiving worse HOLC grades was associated with exposure to more oil and gas wells. METHODS: We assessed exposure to oil and gas wells among HOLC-graded neighborhoods in 33 cities from 13 states where urban oil and gas wells were drilled and operated. Among the 17 cities for which 1940 census data were available, we used propensity score restriction and matching to compare well exposure neighborhoods that were similar on observed 1940 sociodemographic characteristics but that received different grades. RESULTS: Across all included cities, redlined D-graded neighborhoods had 12.2 ± 27.2 wells km-2, nearly twice the density in neighborhoods graded A (6.8 ± 8.9 wells km-2). In propensity score restricted and matched analyses, redlined neighborhoods had 2.0 (1.3, 2.7) more wells than comparable neighborhoods with a better grade. SIGNIFICANCE: Our study adds to the evidence that structural racism in federal policy is associated with the disproportionate siting of oil and gas wells in marginalized neighborhoods.

Assessing community-level exposure to social vulnerability and isolation: spatial patterning and urban-rural differences.

BACKGROUND: Environmental health disparity research involves the use of metrics to assess exposure to community-level vulnerabilities or inequities. While numerous vulnerability indices have been developed, there is no agreement on standardization or appropriate use, they have largely been applied in urban areas, and their interpretation and utility likely vary across different geographies. OBJECTIVE: We evaluated the spatial distribution, variability, and relationships among different metrics of social vulnerability and isolation across urban and rural settings to inform interpretation and selection of metrics for environmental disparity research. METHODS: For all census tracts in North Carolina, we conducted a principal components analysis using 23 socioeconomic/demographic variables from the 2010 United States Census and American Community Survey. We calculated or obtained the neighborhood deprivation index (NDI), residential racial isolation index (RI), educational isolation index (EI), Gini coefficient, and social vulnerability index (SVI). Statistical analyses included Moran's I for spatial clustering, t-tests for urban-rural differences, Pearson correlation coefficients, and changes in ranking of tracts across metrics. RESULTS: Social vulnerability metrics exhibited clear spatial patterning (Moran's I ≥ 0.30, p < 0.01). Greater educational isolation and more intense neighborhood deprivation was observed in rural areas and greater racial isolation in urban areas. Single-domain metrics were not highly correlated with each other (rho ≤ 0.36), while composite metrics (i.e., NDI, SVI, principal components analysis) were highly correlated (rho > 0.80). Composite metrics were more highly correlated with the racial isolation metric in urban (rho: 0.54-0.64) versus rural tracts (rho: 0.36-0.48). Census tract rankings changed considerably based on which metric was being applied. SIGNIFICANCE: High correlations between composite metrics within urban and rural tracts suggests they could be used interchangeably; single domain metrics cannot. Composite metrics capture different facets of vulnerabilities in urban and rural settings, and these complexities should be examined by researchers applying metrics to areas of diverse urban and rural forms.

Clean fleets, different streets: evaluating the effect of New York City's clean bus program on changes to estimated ambient air pollution.

BACKGROUND: Motor vehicles, including public transit buses, are a major source of air pollution in New York City (NYC) and worldwide. To address this problem, governments and transit agencies have implemented policies to introduce cleaner vehicles into transit fleets. Beginning in 2000, the Metropolitan Transit Agency began deploying compressed natural gas, hybrid electric, and low-sulfur diesel buses to reduce urban air pollution. OBJECTIVE: We hypothesized that bus fleet changes incorporating cleaner vehicles would have detectable effects on air pollution concentrations between 2009 and 2014, as measured by the New York City Community Air Survey (NYCCAS). METHODS: Depot- and route-specific information allowed identification of areas with larger or smaller changes in the proportion of distance traveled by clean buses. Data were assembled for 9670 300 m × 300 m grid cell areas with annual concentration estimates for nitrogen oxide (NO), nitrogen dioxide (NO2), and black carbon (BC) from NYCCAS. Spatial error models adjusted for truck route presence and total traffic volume. RESULTS: While concentrations of all three pollutants declined between 2009 and 2014 even in the 39.7% of cells without bus service, the decline in concentrations of NO and NO2 was greater in areas with more bus service and with higher proportional shifts toward clean buses. Conversely, the decline in BC concentration was slower in areas with more bus service and higher proportional clean bus shifts. SIGNIFICANCE: These results provide evidence that the NYC clean bus program impacted concentrations of air pollution, particularly in reductions of NO2. Further work can investigate the potential impact of these changes on health outcomes in NYC residents. IMPACT STATEMENT: Urban air pollution from diesel-burning buses is an important health exposure. The New York Metropolitan Transit Agency has worked to deploy cleaner buses into their fleet, but the impact of this policy has not been evaluated. Successful reductions in air pollution are critical for public health.

Non-linear probabilistic calibration of low-cost environmental air pollution sensor networks for neighborhood level spatiotemporal exposure assessment.

BACKGROUND: Low-cost sensor networks for monitoring air pollution are an effective tool for expanding spatial resolution beyond the capabilities of existing state and federal reference monitoring stations. However, low-cost sensor data commonly exhibit non-linear biases with respect to environmental conditions that cannot be captured by linear models, therefore requiring extensive lab calibration. Further, these calibration models traditionally produce point estimates or uniform variance predictions which limits their downstream in exposure assessment. OBJECTIVE: Build direct field-calibration models using probabilistic gradient boosted decision trees (GBDT) that eliminate the need for resource-intensive lab calibration and that can be used to conduct probabilistic exposure assessments on the neighborhood level. METHODS: Using data from Plantower A003 particulate matter (PM) sensors deployed in Baltimore, MD from November 2018 through November 2019, a fully probabilistic NGBoost GBDT was trained on raw data from sensors co-located with a federal reference monitoring station and compared against linear regression trained on lab calibrated sensor data. The NGBoost predictions were then used in a Monte Carlo interpolation process to generate high spatial resolution probabilistic exposure gradients across Baltimore. RESULTS: We demonstrate that direct field-calibration of the raw PM2.5 sensor data using a probabilistic GBDT has improved point and distribution accuracies compared to the linear model, particularly at reference measurements exceeding 25 µg/m3, and also on monitors not included in the training set. SIGNIFICANCE: We provide a framework for utilizing the GBDT to conduct probabilistic spatial assessments of human exposure with inverse distance weighting that predicts the probability of a given location exceeding an exposure threshold and provides percentiles of exposure. These probabilistic spatial exposure assessments can be scaled by time and space with minimal modifications. Here, we used the probabilistic exposure assessment methodology to create high quality spatial-temporal PM2.5 maps on the neighborhood-scale in Baltimore, MD. IMPACT STATEMENT: We demonstrate how the use of open-source probabilistic machine learning models for in-place sensor calibration outperforms traditional linear models and does not require an initial laboratory calibration step. Further, these probabilistic models can create uniquely probabilistic spatial exposure assessments following a Monte Carlo interpolation process.

Evaluating the accuracy of satellite-based methods to estimate residential proximity to agricultural crops.

BACKGROUND: Epidemiologic investigations increasingly employ remote sensing data to estimate residential proximity to agriculture as a means of approximating individual-level pesticide exposure. Few studies have examined the accuracy of these methods and the implications for exposure misclassification. OBJECTIVES: Compare metrics of residential proximity to agricultural land between a groundtruth approach and commonly-used satellite-based estimates. METHODS: We inspected 349 fields and identified crops in current production within a 0.5 km radius of 40 residences in Idaho. We calculated the distance from each home to the nearest agricultural field and the total acreage of agricultural fields within a 0.5 km buffer. We compared these groundtruth estimates to satellite-derived estimates from three widely used datasets: CropScape, the National Land Cover Database (NLCD), and Landsat imagery (using Normalized Difference Vegetation Index thresholds). RESULTS: We found poor to moderate agreement between the classification of individuals living within 0.5 km of an agricultural field between the groundtruth method and the comparison datasets (53.1-77.6%). All satellite-derived estimates overestimated the acreage of agricultural land within 0.5 km of each home (average = 82.8-148.9%). Using two satellite-derived datasets in conjunction resulted in substantial improvements; specifically, combining CropScape or NLCD with Landsat imagery had the highest percent agreement with the groundtruth data (92.8-93.8% agreement). SIGNIFICANCE: Residential proximity to agriculture is frequently used as a proxy for pesticide exposure in epidemiologic investigations, and remote sensing-derived datasets are often the only practical means of identifying cultivated land. We found that estimates of agricultural proximity obtained from commonly-used satellite-based datasets are likely to result in exposure misclassification. We propose a novel approach that capitalizes on the complementary strengths of different sources of satellite imagery, and suggest the combined use of one dataset with high temporal resolution (e.g., Landsat imagery) in conjunction with a second dataset that delineates agricultural land use (e.g., CropScape or NLCD).

Spatial and Temporal Analysis of Impacts of Hurricane Florence on Criteria Air Pollutants and Air Toxics in Eastern North Carolina.

Natural and anthropogenic disasters are associated with air quality concerns due to the potential redistribution of pollutants in the environment. Our objective was to conduct a spatiotemporal analysis of air concentrations of benzene, toluene, ethylbenzne, and xylene (BTEX) and criteria air pollutants in North Carolina during and after Hurricane Florence. Three sampling campaigns were carried out immediately after the storm (September 2018) and at four-month intervals. BTEX were measured along major roads. Concurrent criteria air pollutant concentrations were predicted from modeling. Correlation between air pollutants and possible point sources was conducted using spatial regression. Exceedances of ambient air criteria were observed for benzene (in all sampling periods) and PM2.5 (mostly immediately after Florence). For both, there was an association between higher concentrations and fueling stations, particularly immediately after Florence. For other pollutants, concentrations were generally below levels of regulatory concern. Through characterization of air quality under both disaster and "normal" conditions, this study demonstrates spatial and temporal variation in air pollutants. We found that only benzene and PM2.5 were present at levels of potential concern, and there were localized increases immediately after the hurricane. These substances warrant particular attention in future disaster response research (DR2) investigations.

A mass-balance model to assess arsenic exposure from multiple wells in Bangladesh.

BACKGROUND: Water arsenic (As) sources beyond a rural household's primary well may be a significant source for certain individuals, including schoolchildren and men working elsewhere. OBJECTIVE: To improve exposure assessment by estimating the fraction of drinking water that comes from wells other than the household's primary well in a densely populated area. METHODS: We use well water and urinary As data collected in 2000-2001 within a 25 km2 area of Araihazar upazila, Bangladesh, for 11,197 participants in the Health Effects of Arsenic Longitudinal Study (HEALS). We estimate the fraction of water that participants drink from different wells by imposing a long-term mass-balance constraint for both As and water. RESULTS: The mass-balance model suggest that, on average, HEALS participants obtain 60-75% of their drinking water from their primary household wells and 25-40% from other wells, in addition to water from food and cellular respiration. Because of this newly quantified contribution from other wells, As in drinking water rather than rice was identified as the largest source of As exposure at baseline for HEALS participants with a primary household well containing ≤50 µg/L As. SIGNIFICANCE: Dose-response relationships for As based on water As should take into account other wells. The mass-balance approach could be applied to study other toxicants.

Predicting traffic noise using land-use regression-a scalable approach.

BACKGROUND: In modern societies, noise is ubiquitous. It is an annoyance and can have a negative impact on human health as well as on the environment. Despite increasing evidence of its negative impacts, spatial knowledge about noise distribution remains limited. Up to now, noise mapping is frequently inhibited by the necessary resources and therefore limited to selected areas. OBJECTIVE: Based on the assumption, that prevalent noise is determined by the arrangement of sources and the surrounding environment in which the sound propagates, we build a geostatistical model representing these parameters. Aiming for a large-scale noise mapping approach, we utilize publicly available data, context-aware feature engineering and a linear land-use regression (LUR) model. METHODS: Compliant to the European Noise Directive 2002/49/EG, we work at a high spatial granularity of 10 × 10-m resolution. As reference, we use the day-evening-night noise level indicator Lden. Therewith, we carry out 2000 virtual field campaigns simulating different sampling schemes and introduce spatial cross-validation concepts to test the transferability to new areas. RESULTS: The experimental results suggest the necessity for more than 500 samples stratified over the different noise levels to produce a representative model. Eventually, using 21 selected variables, our model was able to explain large proportions of the yearly averaged road noise (Lden) variability (R2 = 0.702) with a mean absolute error of 4.24 dB(A), 3.84 dB(A) for build-up areas, respectively. In applying this best performing model for an area-wide prediction, we spatially close the blank spots in existing noise maps with continuous noise levels for the entire range from 24 to 106 dB(A). SIGNIFICANCE: This data is new, particular for small communities that have not been mapped sufficiently in Europe so far. In conjunction, our findings also supplement conventionally sampled studies using physical microphones and spatially blocked cross-validations.

Effect of heavy metals on protein content of marine unicellular green alga Dunaliella tertiolecta.

Microalgae are rich source of protein containing necessary amino acids at different levels. The present study was designed to assess stimulatory and/or inhibitory impact of five different concentrations (5, 10, 15, 20, and 25 mg/L) of three essential heavy metals (nickel, zinc, and copper) on protein content (soluble, insoluble, and total) of the marine unicellular green alga Dunaliella tertiolecta. Further, geospatial analyses were used to assess the suitability of Qaroun Lake for D. tertiolecta proliferation. The experimental results showed a gradual increase in protein content of D. tertiolecta with low concentrations of the three investigated heavy metals. However, increasing levels of heavy metals led to inhibitory effect on protein synthesis in alga with different grades. Ni, Zn and Cu levels in Qaroun lake were found suitable for the proliferation of Dunaliella (Lower than 5 mg/L). The present study highly recommends the necessity to encourage site selection of optimal marine environments suitable for the proliferation of marine algae rich in protein content.