Dataset Information

A Bayesian Downscaler Model to Estimate Daily PM_2.5 Levels in the Conterminous US.

ABSTRACT: There has been growing interest in extending the coverage of ground particulate matter with aerodynamic diameter ? 2.5 ?m (PM_2.5) monitoring networks based on satellite remote sensing data. With broad spatial and temporal coverage, a satellite-based monitoring network has a strong potential to complement the ground monitor system in terms of the spatiotemporal availability of the air quality data. However, most existing calibration models focus on a relatively small spatial domain and cannot be generalized to a national study. In this paper, we proposed a statistically reliable and interpretable national modeling framework based on Bayesian downscaling methods to be applied to the calibration of the daily ground PM_2.5 concentrations across the conterminous United States using satellite-retrieved aerosol optical depth (AOD) and other ancillary predictors in 2011. Our approach flexibly models the PM_2.5 versus AOD and the potential related geographical factors varying across the climate regions and yields spatial- and temporal-specific parameters to enhance model interpretability. Moreover, our model accurately predicted the national PM_2.5 with an R² at 70% and generated reliable annual and seasonal PM_2.5 concentration maps with its SD. Overall, this modeling framework can be applied to national-scale PM_2.5 exposure assessments and can also quantify the prediction errors.

SUBMITTER: Wang Y

PROVIDER: S-EPMC6164266 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature

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A Bayesian Downscaler Model to Estimate Daily PM<sub>2.5</sub> Levels in the Conterminous US.

Wang Yikai Y Hu Xuefei X Chang Howard H HH Waller Lance A LA Belle Jessica H JH Liu Yang Y

International journal of environmental research and public health 20180913 9

There has been growing interest in extending the coverage of ground particulate matter with aerodynamic diameter ≤ 2.5 μm (PM<sub>2.5</sub>) monitoring networks based on satellite remote sensing data. With broad spatial and temporal coverage, a satellite-based monitoring network has a strong potential to complement the ground monitor system in terms of the spatiotemporal availability of the air quality data. However, most existing calibration models focus on a relatively small spatial domain and ...[more]

PMID: 30217060

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Project description:Ambient particulate matter of aerodynamic diameter less than 2.5 microns PM2.5) levels in Delhi routinely exceed World Health Organization (WHO) guidelines and Indian National Ambient Air Quality Standards (NAAQS) for acceptable levels of daily exposure. Only a handful of studies have examined the short-term mortality effects of PM in India, with none from Delhi examining the contribution of PM2.5.ObjectivesWe aimed to analyze the association between short-term PM2.5 exposures and daily nonaccidental mortality in Delhi, India.MethodsUsing generalized additive Poisson regression models, we examined the association between daily PM2.5 exposures and nonaccidental mortality between June 2010 and December 2016. Daily exposures to PM2.5 were estimated using an ensemble averaging technique developed by our research group, and mortality data were obtained from the Municipal Corporations of Delhi and the New Delhi Municipal Council.ResultsMedian exposures to PM2.5 were 91.1 µg/m3 (interquartile range = 68.9, 126.2), with minimum and maximum exposures of 21.4 µg/m3 and 276.7 µg/m3, respectively. Total nonaccidental deaths recorded in Delhi during the study period were 700,512. Each 25 µg/m3 increment in exposure was associated with a 0.8% (95% confidence intervals [CI] = 0.3, 1.3%) increase in daily nonaccidental mortality in the study population and a 1.5% (95% CI = 0.8, 2.2%) increase in mortality among those with 60 years of age or over. The exposure-response relationship was nonlinear in nature, with relative risk rising rapidly before tapering off above 125 µg/m3. Meeting WHO guidelines for acceptable levels of exposure over the study period would have likely averted 17,526 (95% CI = 6,837, 25,589) premature deaths, with older and male populations disproportionately affected.DiscussionThis study provides robust evidence of the impact of short-term exposure to PM2.5 on nonaccidental mortality with important considerations for various stakeholders including policymakers and physicians. Most importantly, we find that reducing exposures significantly below current levels would substantially decrease the mortality burden associated with PM2.5.

Project description:BackgroundThe respiratory system of children is vulnerable to exposure to particulate matter (PM) with a diameter of less than 2.5 and 10 μm (PM2.5 and PM10) or even lower.ObjectiveThis study assessed PM10 and PM2.5 levels and respiratory health impacts on children in schools located in an industrialized suburb in Kenya.MethodThe PM10 and PM2.5 levels were sampled from five public primary schools in Athi River Township and a control school during the wet and dry seasons. Outdoor and classroom samples were collected concurrently on an 8-hour mean during school hours on two consecutive days in each school and analyzed using gravimetric techniques. Five hundred and seventy-eight (n = 578) pupils aged 9-14 years from these schools were also evaluated for symptoms of respiratory illnesses and lung function using a questionnaire and spirometric method, respectively, during the same periods.ResultsIndoor median PM10 levels (μg/m3) ranged from 60.8-269.1 and 52.8-232.3 and PM2.5 values (μg/m3) of 17.7-52.4 and 28.5-75.5 during the dry and wet seasons, respectively. The control classrooms had significantly (p <0.05) lower median PM10 levels (μg/m3) of 5.2 and 4.2, and PM2.5 levels (μg/m3) of 3.5 and 3.0 during the respective seasons. Nearly all the classrooms in Athi River schools had PM2.5 and PM10 median levels that exceeded the World Health Organization (WHO) recommended levels. The indoor-to-outdoor ratios varied from 0.35-1.40 and 0.80-2.40 for PM10 and 0.30-0.80 and 0.80-1.40 for PM2.5 during the dry and wet seasons, respectively, suggesting higher levels in the classrooms during the wet season. The relative risk (RR) and odds ratio (OR) presented higher prevalence of respiratory diseases following PM exposure in all the Athi River schools than the control during the dry and wet seasons. At 95% CI, the RR and OR showed strong associations between high PM10 and PM2.5 levels and lung function deficits and vice versa. The association was more prevalent during the wet season.ConclusionsThe study calls for effective indoor air management programs in school environments to reduce PM exposure and respiratory health impacts.Participant consentObtained.Ethics approvalThe research permit and approvals were obtained from the University of Nairobi/Kenyatta National Hospital Ethics and Research Committee (KNH-UoN ERC Reference: P599/08/2016) and the National Commission for Science, Technology and Innovation (Reference: NACOSTI/P/18/4268/25724).Competing interestsThe authors declare no competing financial interests.

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A Bayesian Downscaler Model to Estimate Daily PM_2.5 Levels in the Conterminous US.

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A Bayesian Downscaler Model to Estimate Daily PM<sub>2.5</sub> Levels in the Conterminous US.

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A Bayesian Downscaler Model to Estimate Daily PM2.5 Levels in the Conterminous US.

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A Bayesian Downscaler Model to Estimate Daily PM<sub>2.5</sub> Levels in the Conterminous US.

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OmicsDI is part of the ELIXIR infrastructure

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A Bayesian Downscaler Model to Estimate Daily PM_2.5 Levels in the Conterminous US.