title: Assessing the impact of fine particulate matter (PM2.5) on respiratory-cardiovascular chronic diseases in the New York City Metropolitan area using Hierarchical Bayesian Model estimates.
abstract: An enhanced research paradigm is presented to address the spatial and temporal gaps in fine particulate matter (PM2.5) measurements and generate realistic and representative concentration fields for use in epidemiological studies of human exposure to ambient air particulate concentrations. The general approach for research designed to analyze health impacts of exposure to PM2.5is to use concentration data from the nearest ground-based air quality monitor(s), which typically have missing data on the temporal and spatial scales due to filter sampling schedules and monitor placement, respectively. To circumvent these data gaps, this research project uses a Hierarchical Bayesian Model (HBM) to generate estimates of PM2.5in areas with and without air quality monitors by combining PM2.5concentrations measured by monitors, PM2.5concentration estimates derived from satellite aerosol optical depth (AOD) data, and Community-Multiscale Air Quality (CMAQ) model predictions of PM2.5concentrations. This methodology represents a substantial step forward in the approach for developing representative PM2.5concentration datasets to correlate with inpatient hospitalizations and emergency room visits data for asthma and inpatient hospitalizations for myocardial infarction (MI) and heart failure (HF) using case-crossover analysis. There were two key objective of this current study. First was to show that the inputs to the HBM could be expanded to include AOD data in addition to data from PM2.5monitors and predictions from CMAQ. The second objective was to determine if inclusion of AOD surfaces in HBM model algorithms results in PM2.5air pollutant concentration surfaces which more accurately predict hospital admittance and emergency room visits for MI, asthma, and HF. This study focuses on the New York City, NY metropolitan and surrounding areas during the 2004-2006 time period, in order to compare the health outcome impacts with those from previous studies and focus on any benefits derived from the changes in the HBM model surfaces. Consistent with previous studies, the results show high PM2.5exposure is associated with increased risk of asthma, myocardial infarction and heart failure. The estimates derived from concentration surfaces that incorporate AOD had a similar model fit and estimate of risk as compared to those derived from combining monitor and CMAQ data alone. Thus, this study demonstrates that estimates of PM2.5concentrations from satellite data can be used to supplement PM2.5monitor data in the estimates of risk associated with three common health outcomes. Results from this study were inconclusive regarding the potential benefits derived from adding AOD data to the HBM, as the addition of the satellite data did not significantly increase model performance. However, this study was limited to one metropolitan area over a short two-year time period. The use of next-generation, high temporal and spatial resolution satellite AOD data from geostationary and polar-orbiting satellites is expected to improve predictions in epidemiological studies in areas with fewer pollutant monitors or over wider geographic areas.
authors.affiliation: Battelle Memorial Institute, Columbus, OH, United States. Electronic address: WeberS@battelle.org.
authors.forename: Stephanie A
authors.initials: SA
authors.lastname: Weber
authors.affiliation: New York State Department of Health, Albany, NY, United States; School of Public Health, University at Albany, SUNY, Rensselaer, NY, United States.
authors.forename: Tabassum Z
authors.initials: TZ
authors.lastname: Insaf
authors.affiliation: US Environmental Protection Agency, Research Triangle Park, NC, United States.
authors.forename: Eric S
authors.initials: ES
authors.lastname: Hall
authors.affiliation: New York State Department of Health, Albany, NY, United States; School of Public Health, University at Albany, SUNY, Rensselaer, NY, United States.
authors.forename: Thomas O
authors.initials: TO
authors.lastname: Talbot
authors.affiliation: Pennsylvania State University, University Park, PA, United States.
authors.forename: Amy K
authors.initials: AK
authors.lastname: Huff
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D001249:Asthma / Q000453:epidemiology / Q000209:etiology*; D001499:Bayes Theorem; D002908:Chronic Disease; D006333:Heart Failure / Q000453:epidemiology / Q000209:etiology*; D006801:Humans; D008297:Male; D009203:Myocardial Infarction / Q000453:epidemiology / Q000209:etiology*; D009519:New York City / Q000453:epidemiology; D052638:Particulate Matter / Q000009:adverse effects*
chemical_list: D052638:Particulate Matter
keywords: Aerosol optical depth (AOD); Air Quality System (AQS); Air quality model; Asthma; Case crossover; Community Multi-Scale Air Quality (CMAQ) model; Fine particulate matter (PM(2.5)); Heart failure (HF); Hierarchical Bayesian Model (HBM); Myocardial infarction (MI)