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1.
Article in English | MEDLINE | ID: mdl-39251872

ABSTRACT

BACKGROUND: Geospatial methods are common in environmental exposure assessments and increasingly integrated with health data to generate comprehensive models of environmental impacts on public health. OBJECTIVE: Our objective is to review geospatial exposure models and approaches for health data integration in environmental health applications. METHODS: We conduct a literature review and synthesis. RESULTS: First, we discuss key concepts and terminology for geospatial exposure data and models. Second, we provide an overview of workflows in geospatial exposure model development and health data integration. Third, we review modeling approaches, including proximity-based, statistical, and mechanistic approaches, across diverse exposure types, such as air quality, water quality, climate, and socioeconomic factors. For each model type, we provide descriptions, general equations, and example applications for environmental exposure assessment. Fourth, we discuss the approaches used to integrate geospatial exposure data and health data, such as methods to link data sources with disparate spatial and temporal scales. Fifth, we describe the landscape of open-source tools supporting these workflows.

2.
Am J Epidemiol ; 193(10): 1384-1391, 2024 Oct 07.
Article in English | MEDLINE | ID: mdl-38844537

ABSTRACT

Human-induced climate change has led to more frequent and severe flooding around the globe. We examined the association between flood risk and the prevalence of coronary heart disease, high blood pressure, asthma, and poor mental health in the United States, while taking into account different levels of social vulnerability. We aggregated flood risk variables from First Street Foundation data by census tract and used principal component analysis to derive a set of 5 interpretable flood risk factors. The dependent variables were census-tract level disease prevalences generated by the Centers for Disease Control and Prevention. Bayesian spatial conditional autoregressive models were fit on these data to quantify the relationship between flood risk and health outcomes under different stratifications of social vulnerability. We show that 3 flood risk principal components had small but significant associations with each of the health outcomes across the different stratifications of social vulnerability. Our analysis gives, to our knowledge, the first United States-wide estimates of the associated effects of flood risk on specific health outcomes. We also show that social vulnerability is an important moderator of the relationship between flood risk and health outcomes. Our approach can be extended to other ecological studies that examine the health impacts of climate hazards. This article is part of a Special Collection on Environmental Epidemiology.


Subject(s)
Asthma , Bayes Theorem , Censuses , Floods , Humans , Floods/statistics & numerical data , United States/epidemiology , Asthma/epidemiology , Risk Factors , Hypertension/epidemiology , Coronary Disease/epidemiology , Coronary Disease/etiology , Social Vulnerability , Climate Change , Prevalence , Principal Component Analysis , Mental Health/statistics & numerical data
3.
Cell Genom ; 4(7): 100591, 2024 Jul 10.
Article in English | MEDLINE | ID: mdl-38925123

ABSTRACT

Understanding the complex interplay of genetic and environmental factors in disease etiology and the role of gene-environment interactions (GEIs) across human development stages is important. We review the state of GEI research, including challenges in measuring environmental factors and advantages of GEI analysis in understanding disease mechanisms. We discuss the evolution of GEI studies from candidate gene-environment studies to genome-wide interaction studies (GWISs) and the role of multi-omics in mediating GEI effects. We review advancements in GEI analysis methods and the importance of large-scale datasets. We also address the translation of GEI findings into precision environmental health (PEH), showcasing real-world applications in healthcare and disease prevention. Additionally, we highlight societal considerations in GEI research, including environmental justice, the return of results to participants, and data privacy. Overall, we underscore the significance of GEI for disease prediction and prevention and advocate for integrating the exposome into PEH omics studies.


Subject(s)
Environmental Health , Gene-Environment Interaction , Precision Medicine , Humans , Precision Medicine/methods , Genome-Wide Association Study , Environmental Exposure/adverse effects
4.
Sci Total Environ ; 855: 158905, 2023 Jan 10.
Article in English | MEDLINE | ID: mdl-36152849

ABSTRACT

In the real world, individuals are exposed to chemicals from sources that vary over space and time. However, traditional risk assessments based on in vivo animal studies typically use a chemical-by-chemical approach and apical disease endpoints. New approach methodologies (NAMs) in toxicology, such as in vitro high-throughput (HTS) assays generated in Tox21 and ToxCast, can more readily provide mechanistic chemical hazard information for chemicals with no existing data than in vivo methods. In this paper, we establish a workflow to assess the joint action of 41 modeled ambient chemical exposures in the air from the USA-wide National Air Toxics Assessment by integrating human exposures with hazard data from curated HTS (cHTS) assays to identify counties where exposure to the local chemical mixture may perturb a common biological target. We exemplify this proof-of-concept using CYP1A1 mRNA up-regulation. We first estimate internal exposure and then convert the inhaled concentration to a steady state plasma concentration using physiologically based toxicokinetic modeling parameterized with county-specific information on ages and body weights. We then use the estimated blood plasma concentration and the concentration-response curve from the in vitro cHTS assay to determine the chemical-specific effects of the mixture components. Three mixture modeling methods were used to estimate the joint effect from exposure to the chemical mixture on the activity levels, which were geospatially mapped. Finally, a Monte Carlo uncertainty analysis was performed to quantify the influence of each parameter on the combined effects. This workflow demonstrates how NAMs can be used to predict early-stage biological perturbations that can lead to adverse health outcomes that result from exposure to chemical mixtures. As a result, this work will advance mixture risk assessment and other early events in the effects of chemicals.


Subject(s)
Biological Assay , Environmental Exposure , Humans , Animals , Risk Assessment , Monte Carlo Method , Environmental Exposure/analysis
5.
J Expo Sci Environ Epidemiol ; 33(3): 474-481, 2023 05.
Article in English | MEDLINE | ID: mdl-36460922

ABSTRACT

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.


Subject(s)
Air Pollutants , Air Pollution , Eczema , Psoriasis , Humans , United States/epidemiology , Air Pollutants/adverse effects , Air Pollutants/analysis , Particulate Matter/adverse effects , Particulate Matter/analysis , Environmental Exposure/adverse effects , Environmental Exposure/analysis , Air Pollution/adverse effects , Air Pollution/analysis , Eczema/chemically induced , Eczema/epidemiology , Psoriasis/chemically induced , Psoriasis/epidemiology , Psoriasis/genetics
6.
Environ Epidemiol ; 6(5): e220, 2022 Oct.
Article in English | MEDLINE | ID: mdl-36249270

ABSTRACT

Hawai'i has the highest prevalence of nontuberculous mycobacterial (NTM) pulmonary disease in the United States. Previous studies indicate that certain trace metals in surface water increase the risk of NTM infection. Objective: To identify whether trace metals influence the risk of NTM infection in O'ahu, Hawai'i. Methods: A population-based ecologic cohort study was conducted using NTM infection incidence data from patients enrolled at Kaiser Permanente Hawai'i during 2005-2019. We obtained sociodemographic, microbiologic, and geocoded residential data for all Kaiser Permanente Hawai'i beneficiaries. To estimate the risk of NTM pulmonary infection from exposure to groundwater constituents, we obtained groundwater data from three data sources: (1) Water Quality Portal; (2) the Hawai'i Department of Health; and (3) Brigham Young University, Department of Geological Science faculty. Data were aggregated by an aquifer and were associated with the corresponding beneficiary aquifer of residence. We used Poisson regression models with backward elimination to generate models for NTM infection risk as a function of groundwater constituents. We modeled two outcomes: Mycobacterium avium complex (MAC) species and Mycobacterium abscessus group species. Results: For every 1-unit increase in the log concentration of vanadium in groundwater at the aquifer level, infection risk increased by 22% among MAC patients. We did not observe significant associations between water-quality constituents and infection risk among M. abscessus patients. Conclusions: Concentrations of vanadium in groundwater were associated with MAC pulmonary infection in O'ahu, Hawai'i. These findings provide evidence that naturally occurring trace metals influence the presence of NTM in water sources that supply municipal water systems.

7.
Toxics ; 10(7)2022 Jul 20.
Article in English | MEDLINE | ID: mdl-35878308

ABSTRACT

Quantifying the exposome is key to understanding how the environment impacts human health and disease. However, accurately, and cost-effectively quantifying exposure in large population health studies remains a major challenge. Geospatial technologies offer one mechanism to integrate high-dimensional environmental data into epidemiology studies, but can present several challenges. In June 2021, the National Institute of Environmental Health Sciences (NIEHS) held a workshop bringing together experts in exposure science, geospatial technologies, data science and population health to address the need for integrating multiscale geospatial environmental data into large population health studies. The primary objectives of the workshop were to highlight recent applications of geospatial technologies to examine the relationships between environmental exposures and health outcomes; identify research gaps and discuss future directions for exposure modeling, data integration and data analysis strategies; and facilitate communications and collaborations across geospatial and population health experts. This commentary provides a high-level overview of the scientific topics covered by the workshop and themes that emerged as areas for future work, including reducing measurement errors and uncertainty in exposure estimates, and improving data accessibility, data interoperability, and computational approaches for more effective multiscale and multi-source data integration, along with potential solutions.

8.
Curr Epidemiol Rep ; 9(2): 87-107, 2022 Jun.
Article in English | MEDLINE | ID: mdl-35754929

ABSTRACT

Purpose of review: We reviewed the exposure assessments of ambient air pollution used in studies of fertility, fecundability, and pregnancy loss. Recent findings: Comprehensive literature searches were performed in the PUBMED, Web of Science, and Scopus databases. Of 168 total studies, 45 met the eligibility criteria and were included in the review. We find that 69% of fertility and pregnancy loss studies have used one-dimensional proximity models or surface monitor data, while only 35% have used the improved models, such as land-use regression models (4%), dispersion/chemical transport models (11%), or fusion models (20%). No published studies have used personal air monitors. Summary: While air pollution exposure models have vastly improved over the past decade from simple, one-dimensional distance or air monitor data, to models that incorporate physiochemical properties leading to better predictive accuracy, precision, and increased spatiotemporal variability and resolution, the fertility literature has yet to fully incorporate these new methods. We provide descriptions of each of these air pollution exposure models and assess the strengths and limitations of each model, while summarizing the findings of the literature on ambient air pollution and fertility that apply each method.

9.
Proc Natl Acad Sci U S A ; 118(37)2021 09 14.
Article in English | MEDLINE | ID: mdl-34493674

ABSTRACT

Disparity in air pollution exposure arises from variation at multiple spatial scales: along urban-to-rural gradients, between individual cities within a metropolitan region, within individual neighborhoods, and between city blocks. Here, we improve on existing capabilities to systematically compare urban variation at several scales, from hyperlocal (<100 m) to regional (>10 km), and to assess consequences for outdoor air pollution experienced by residents of different races and ethnicities, by creating a set of uniquely extensive and high-resolution observations of spatially variable pollutants: NO, NO2, black carbon (BC), and ultrafine particles (UFP). We conducted full-coverage monitoring of a wide sample of urban and suburban neighborhoods (93 km2 and 450,000 residents) in four counties of the San Francisco Bay Area using Google Street View cars equipped with the Aclima mobile platform. Comparing scales of variation across the sampled population, greater differences arise from localized pollution gradients for BC and NO (pollutants dominated by primary sources) and from regional gradients for UFP and NO2 (pollutants dominated by secondary contributions). Median concentrations of UFP, NO, and NO2 are, for Hispanic and Black populations, 8 to 30% higher than the population average; for White populations, average exposures to these pollutants are 9 to 14% lower than the population average. Systematic racial/ethnic disparities are influenced by regional concentration gradients due to sharp contrasts in demographic composition among cities and urban districts, while within-group extremes arise from local peaks. Our results illustrate how detailed and extensive fine-scale pollution observations can add new insights about differences and disparities in air pollution exposures at the population scale.


Subject(s)
Air Pollutants/analysis , Environmental Monitoring/methods , Ethnicity/statistics & numerical data , Health Status Disparities , Mobile Applications/statistics & numerical data , Social Planning , Urban Renewal , Cities , Environmental Monitoring/instrumentation , Humans
10.
Ann Appl Stat ; 15(2): 688-710, 2021 Jun.
Article in English | MEDLINE | ID: mdl-35069963

ABSTRACT

Nitrogen dioxide (NO2) is a primary constituent of traffic-related air pollution and has well established harmful environmental and human-health impacts. Knowledge of the spatiotemporal distribution of NO2 is critical for exposure and risk assessment. A common approach for assessing air pollution exposure is linear regression involving spatially referenced covariates, known as land-use regression (LUR). We develop a scalable approach for simultaneous variable selection and estimation of LUR models with spatiotemporally correlated errors, by combining a general-Vecchia Gaussian-process approximation with a penalty on the LUR coefficients. In comparisons to existing methods using simulated data, our approach resulted in higher model-selection specificity and sensitivity and in better prediction in terms of calibration and sharpness, for a wide range of relevant settings. In our spatiotemporal analysis of daily, US-wide, ground-level NO2 data, our approach was more accurate, and produced a sparser and more interpretable model. Our daily predictions elucidate spatiotemporal patterns of NO2 concentrations across the United States, including significant variations between cities and intra-urban variation. Thus, our predictions will be useful for epidemiological and risk-assessment studies seeking daily, national-scale predictions, and they can be used in acute-outcome health-risk assessments.

11.
Sci Total Environ ; 763: 144552, 2021 Apr 01.
Article in English | MEDLINE | ID: mdl-33383509

ABSTRACT

The prevalence of pulmonary nontuberculous mycobacteria (NTM) disease is increasing in the United States. Associations were evaluated among residents of central North Carolina between pulmonary isolation of NTM and environmental risk factors including: surface water, drinking water source, urbanicity, and exposures to soils favorable to NTM growth. Reports of pulmonary NTM isolation from patients residing in three counties in central North Carolina during 2006-2010 were collected from clinical laboratories and from the State Laboratory of Public Health. This analysis was restricted to patients residing in single family homes with a valid residential street address and conducted at the census block level (n = 13,495 blocks). Negative binomial regression models with thin-plate spline smoothing function of geographic coordinates were applied to assess effects of census block-level environmental characteristics on pulmonary NTM isolation count. Patients (n = 507) resided in 473 (3.4%) blocks within the study area. Blocks with >20% hydric soils had 26.8% (95% confidence interval (CI): 1.8%, 58.0%), p = 0.03, higher adjusted mean patient counts compared to blocks with ≤20% hydric soil, while blocks with >50% acidic soil had 24.8% (-2.4%, 59.6%), p = 0.08 greater mean patient count compared to blocks with ≤50% acidic soil. Isolation rates varied by county after adjusting for covariates. The effects of using disinfected public water supplies vs. private wells, and of various measures of urbanicity were not significantly associated with NTM. Our results suggest that proximity to certain soil types (hydric and acidic) could be a risk factor for pulmonary NTM isolation in central North Carolina.


Subject(s)
Mycobacterium Infections, Nontuberculous , Nontuberculous Mycobacteria , Humans , Lung , North Carolina/epidemiology , Risk Factors , United States
12.
Environ Sci Technol ; 54(13): 7848-7857, 2020 07 07.
Article in English | MEDLINE | ID: mdl-32525662

ABSTRACT

Urban concentrations of black carbon (BC) and other primary pollutants vary on small spatial scales (<100m). Mobile air pollution measurements can provide information on fine-scale spatial variation, thereby informing exposure assessment and mitigation efforts. However, the temporal sparsity of these measurements presents a challenge for estimating representative long-term concentrations. We evaluate the capabilities of mobile monitoring in the represention of time-stable spatial patterns by comparing against a large set of continuous fixed-site measurements from a sampling campaign in West Oakland, California. Custom-built, low-cost aerosol black carbon detectors (ABCDs) provided 100 days of continuous measurements at 97 near-road and 3 background fixed sites during summer 2017; two concurrently operated mobile laboratories collected over 300 h of in-motion measurements using a photoacoustic extinctiometer. The spatial coverage from mobile monitoring reveals patterns missed by the fixed-site network. Time-integrated measurements from mobile lab visits to fixed-site monitors reveal modest correlation (spatial R2 = 0.51) with medians of full daytime fixed-site measurements. Aggregation of mobile monitoring data in space and time can mitigate high levels of uncertainty associated with measurements at precise locations or points in time. However, concentrations estimated by mobile monitoring show a loss of spatial fidelity at spatial aggregations greater than 100 m.


Subject(s)
Air Pollutants , Air Pollution , Air Pollutants/analysis , Air Pollution/analysis , Carbon , Environmental Monitoring , Particulate Matter/analysis , Soot/analysis
13.
J Am Stat Assoc ; 115(531): 1111-1124, 2020.
Article in English | MEDLINE | ID: mdl-33716356

ABSTRACT

People are increasingly concerned with understanding their personal environment, including possible exposure to harmful air pollutants. In order to make informed decisions on their day-to-day activities, they are interested in real-time information on a localized scale. Publicly available, fine-scale, high-quality air pollution measurements acquired using mobile monitors represent a paradigm shift in measurement technologies. A methodological framework utilizing these increasingly fine-scale measurements to provide real-time air pollution maps and short-term air quality forecasts on a fine-resolution spatial scale could prove to be instrumental in increasing public awareness and understanding. The Google Street View study provides a unique source of data with spatial and temporal complexities, with the potential to provide information about commuter exposure and hot spots within city streets with high traffic. We develop a computationally efficient spatiotemporal model for these data and use the model to make short-term forecasts and high-resolution maps of current air pollution levels. We also show via an experiment that mobile networks can provide more nuanced information than an equally-sized fixed-location network. This modeling framework has important real-world implications in understanding citizens' personal environments, as data production and real-time availability continue to be driven by the ongoing development and improvement of mobile measurement technologies.

14.
J Clin Tuberc Other Mycobact Dis ; 17: 100133, 2019 Dec.
Article in English | MEDLINE | ID: mdl-31867444

ABSTRACT

The American Thoracic Society (ATS) and Infectious Diseases Society of America (IDSA) have provided guidelines to assist in the accurate diagnosis of lung disease caused by nontuberculous mycobacteria (NTM). These microbiologic, radiographic, and clinical criteria are considered equally important and all must be met to make the diagnosis of NTM lung disease. To assess the significance of the three criteria, each was evaluated for its contribution to the diagnosis of NTM lung disease in a case series. Laboratory reports of any specimen positive for NTM isolation were collected between January 1, 2006 and December 31, 2010 at a university medical center. Medical records were reviewed in detail using a standardized form. The total number of patients with a culture from any site positive for NTM was 297 while the number from respiratory specimens during the same period was 232 (78%). Samples from two of these patients also yielded M. tuberculosis complex and were excluded. While 128 of the remaining 230 patients (55.7%) in the cohort met the microbiologic criterion for diagnosis of NTM lung disease, 151 (65.6%) and 189 (78.3%) met the radiologic and clinical criteria respectively. Only 78 patients (33.9%) met all three criteria provided by the ATS/IDSA for diagnosis of NTM lung disease. This evaluation reaffirms that defining NTM lung disease using either one or two of the criteria provided by the 2007 ATS/IDSA guidelines may significantly overestimate the number of cases of NTM lung disease. Based on the experience of defining NTM lung disease in this case series, recommendations for modification of the ATS/IDSA guidelines are provided which include expansion of both radiologic patterns and the list of symptoms associated with NTM lung disease.

15.
Sci Total Environ ; 655: 512-519, 2019 Mar 10.
Article in English | MEDLINE | ID: mdl-30476830

ABSTRACT

Unregulated private wells in the United States are susceptible to many groundwater contaminants. Ingestion of nitrate, the most common anthropogenic private well contaminant in the United States, can lead to the endogenous formation of N-nitroso-compounds, which are known human carcinogens. In this study, we expand upon previous efforts to model private well groundwater nitrate concentration in North Carolina by developing multiple machine learning models and testing against out-of-sample prediction. Our purpose was to develop exposure estimates in unmonitored areas for use in the Agricultural Health Study (AHS) cohort. Using approximately 22,000 private well nitrate measurements in North Carolina, we trained and tested continuous models including a censored maximum likelihood-based linear model, random forest, gradient boosted machine, support vector machine, neural networks, and kriging. Continuous nitrate models had low predictive performance (R2 < 0.33), so multiple random forest classification models were also trained and tested. The final classification approach predicted <1 mg/L, 1-5 mg/L, and ≥5 mg/L using a random forest model with 58 variables and maximizing the Cohen's kappa statistic. The final model had an overall accuracy of 0.75 and high specificity for the higher two categories and high sensitivity for the lowest category. The results will be used for the categorical prediction of private well nitrate for AHS cohort participants that reside in North Carolina.


Subject(s)
Environmental Monitoring/methods , Groundwater/chemistry , Models, Theoretical , Nitrates/analysis , Water Pollutants, Chemical/analysis , Water Wells , Agriculture , Drinking Water/standards , Machine Learning , North Carolina
16.
Environ Health Perspect ; 126(12): 127007, 2018 12.
Article in English | MEDLINE | ID: mdl-30566375

ABSTRACT

BACKGROUND: There is growing evidence that exposure to ultrafine particles (UFP; particles smaller than [Formula: see text]) may play an underexplored role in the etiology of several illnesses, including cardiovascular disease (CVD). OBJECTIVES: We aimed o investigate the relationship between long-term exposure to ambient UFP and incident cardiovascular and cerebrovascular disease (CVA). As a secondary objective, we sought to compare effect estimates for UFP with those derived for other air pollutants, including estimates from two-pollutant models. METHODS: Using a prospective cohort of 33,831 Dutch residents, we studied the association between long-term exposure to UFP (predicted via land use regression) and incident disease using Cox proportional hazard models. Hazard ratios (HR) for UFP were compared to HRs for more routinely monitored air pollutants, including particulate matter with aerodynamic diameter [Formula: see text] ([Formula: see text]), PM with aerodynamic diameter [Formula: see text] ([Formula: see text]), and [Formula: see text]. RESULTS: Long-term UFP exposure was associated with an increased risk for all incident CVD [[Formula: see text] per [Formula: see text]; 95% confidence interval (CI): 1.03, 1.34], myocardial infarction (MI) ([Formula: see text]; 95% CI: 1.00, 1.79), and heart failure ([Formula: see text]; 95% CI: 1.17, 2.66). Positive associations were also estimated for [Formula: see text] ([Formula: see text]; 95% CI: 1.01, 1.48 per [Formula: see text]) and coarse PM ([Formula: see text]; HR for all [Formula: see text]; 95% CI: 1.01, 1.45 per [Formula: see text]). CVD was not positively associated with [Formula: see text] (HR for all [Formula: see text]; 95% CI: 0.75, 1.28 per [Formula: see text]). HRs for UFP and CVAs were positive, but not significant. In two-pollutant models ([Formula: see text] and [Formula: see text]), positive associations tended to remain for UFP, while HRs for [Formula: see text] and [Formula: see text] generally attenuated towards the null. CONCLUSIONS: These findings strengthen the evidence that UFP exposure plays an important role in cardiovascular health and that risks of ambient air pollution may have been underestimated based on conventional air pollution metrics. https://doi.org/10.1289/EHP3047.


Subject(s)
Cardiovascular Diseases/epidemiology , Cerebrovascular Disorders/epidemiology , Particulate Matter/adverse effects , Adult , Aged , Air Pollution/adverse effects , Environmental Exposure/adverse effects , Female , Humans , Male , Middle Aged , Netherlands/epidemiology , Particle Size , Prospective Studies
17.
Environ Sci Technol ; 52(21): 12563-12572, 2018 11 06.
Article in English | MEDLINE | ID: mdl-30354135

ABSTRACT

Air pollution measurements collected through systematic mobile monitoring campaigns can provide outdoor concentration data at high spatial resolution. We explore approaches to minimize data requirements for mapping a city's air quality using mobile monitors with "data-only" versus predictive modeling approaches. We equipped two Google Street View cars with 1-Hz instruments to collect nitric oxide (NO) and black carbon (BC) measurements in Oakland, CA. We explore two strategies for efficiently mapping spatial air quality patterns through Monte Carlo analyses. First, we explore a "data-only" approach where we attempt to minimize the number of repeated visits needed to reliably estimate concentrations for all roads. Second, we combine our data with a land use regression-kriging (LUR-K) model to predict at unobserved locations; here, measurements from only a subset of roads or repeat visits are considered. Although LUR-K models did not capture the full variability of on-road concentrations, models trained with minimal data consistently captured important covariates and general spatial air pollution trends, with cross-validation R2 for log-transformed NO and BC of 0.65 and 0.43. Data-only mapping performed poorly with few (1-2) repeated drives but obtained better cross-validation R2 than the LUR-K approach within 4 to 8 repeated drive days per road segment.


Subject(s)
Air Pollutants , Air Pollution , Cities , Environmental Monitoring , Particulate Matter
18.
Environ Sci Technol ; 52(14): 7775-7784, 2018 07 17.
Article in English | MEDLINE | ID: mdl-29886747

ABSTRACT

Predictive modeling is promising as an inexpensive tool to assess water quality. We developed geostatistical predictive models of microbial water quality that empirically modeled spatiotemporal autocorrelation in measured fecal coliform (FC) bacteria concentrations to improve prediction. We compared five geostatistical models featuring different autocorrelation structures, fit to 676 observations from 19 locations in North Carolina's Jordan Lake watershed using meteorological and land cover predictor variables. Though stream distance metrics (with and without flow-weighting) failed to improve prediction over the Euclidean distance metric, incorporating temporal autocorrelation substantially improved prediction over the space-only models. We predicted FC throughout the stream network daily for one year, designating locations "impaired", "unimpaired", or "unassessed" if the probability of exceeding the state standard was ≥90%, ≤10%, or >10% but <90%, respectively. We could assign impairment status to more of the stream network on days any FC were measured, suggesting frequent sample-based monitoring remains necessary, though implementing spatiotemporal predictive models may reduce the number of concurrent sampling locations required to adequately assess water quality. Together, these results suggest that prioritizing sampling at different times and conditions using geographically sparse monitoring networks is adequate to build robust and informative geostatistical models of water quality impairment.


Subject(s)
Meteorology , Rivers , Environmental Monitoring , Lakes , North Carolina , Water Quality
19.
Environ Health ; 16(1): 108, 2017 10 17.
Article in English | MEDLINE | ID: mdl-29041975

ABSTRACT

BACKGROUND: Influenza peaks during the wintertime in temperate regions and during the annual rainy season in tropical regions - however reasons for the observed differences in disease ecology are poorly understood. We hypothesize that episodes of extreme precipitation also result in increased influenza in the Northeastern United States, but this association is not readily apparent, as no defined 'rainy season' occurs. Our objective was to evaluate the association between extreme precipitation (≥ 99th percentile) events and risk of emergency room (ER) visit for influenza in Massachusetts during 2002-2008. METHODS: A case-crossover analysis of extreme precipitation events and influenza ER visits was conducted using hospital administrative data including patient town of residence, date of visit, age, sex, and associated diagnostic codes. Daily precipitation estimates were generated for each town based upon data from the National Oceanic and Atmospheric Administration. Odds ratio (OR) and 95% confidence intervals (CI) for associations between extreme precipitation and ER visits for influenza were estimated using conditional logistic regression. RESULTS: Extreme precipitation events were associated with an OR = 1.23 (95%CI: 1.16, 1.30) for ER visits for influenza at lag days 0-6. There was significant effect modification by race, with the strongest association observed among Blacks (OR = 1.48 (1.30, 1.68)). CONCLUSIONS: We observed a positive association between extreme precipitation events and ER visits for influenza, particularly among Blacks. Our results suggest that influenza is associated with extreme precipitation in a temperate area; this association could be a result of disease ecology, behavioral changes such as indoor crowding, or both. Extreme precipitation events are expected to increase in the Northeastern United States as climate change progresses. Additional research exploring the basis of this association can inform potential interventions for extreme weather events and influenza transmission.


Subject(s)
Emergency Service, Hospital/statistics & numerical data , Influenza, Human/epidemiology , Weather , Adolescent , Adult , Aged , Child , Child, Preschool , Female , Humans , Infant , Infant, Newborn , Male , Massachusetts/epidemiology , Middle Aged , Odds Ratio , Young Adult
20.
Environ Sci Technol ; 51(12): 6999-7008, 2017 Jun 20.
Article in English | MEDLINE | ID: mdl-28578585

ABSTRACT

Air pollution affects billions of people worldwide, yet ambient pollution measurements are limited for much of the world. Urban air pollution concentrations vary sharply over short distances (≪1 km) owing to unevenly distributed emission sources, dilution, and physicochemical transformations. Accordingly, even where present, conventional fixed-site pollution monitoring methods lack the spatial resolution needed to characterize heterogeneous human exposures and localized pollution hotspots. Here, we demonstrate a measurement approach to reveal urban air pollution patterns at 4-5 orders of magnitude greater spatial precision than possible with current central-site ambient monitoring. We equipped Google Street View vehicles with a fast-response pollution measurement platform and repeatedly sampled every street in a 30-km2 area of Oakland, CA, developing the largest urban air quality data set of its type. Resulting maps of annual daytime NO, NO2, and black carbon at 30 m-scale reveal stable, persistent pollution patterns with surprisingly sharp small-scale variability attributable to local sources, up to 5-8× within individual city blocks. Since local variation in air quality profoundly impacts public health and environmental equity, our results have important implications for how air pollution is measured and managed. If validated elsewhere, this readily scalable measurement approach could address major air quality data gaps worldwide.


Subject(s)
Air Pollutants , Automobiles , Environmental Monitoring/methods , Air Pollution , Humans , Particulate Matter , Public Health
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