Assessing the impact of fine particulate matter (PM 2.5 ) 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 (PM) 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 PM is 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 PM in areas with and without air quality monitors by combining PM concentrations measured by monitors, PM concentration estimates derived from satellite aerosol optical depth (AOD) data, and Community-Multiscale Air Quality (CMAQ) model predictions of PM concentrations. This methodology represents a substantial step forward in the approach for developing representative PM concentration 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 PM monitors and predictions from CMAQ. The second objective was to determine if inclusion of AOD surfaces in HBM model algorithms results in PM air 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 PM exposure 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 PM concentrations from satellite data can be used to supplement PM monitor 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 are...

Allergic disease associations with regional and localized estimates of air pollution

“…These effects were substantially higher than those seen from similar studies examining ambient air pollution measures of fine particulate matter and asthma outcomes. Weber et al (2016) used the USEPA's hierarchical baysian model (HBM) of fine particulate matter and linked daily measures preceding hospital and emergency room visits related to asthma and found a 10-unit change in PM 2.5 resulted in odds ratios of inpatient and emergency room visits slightly over 1.00 for those older than 14 years of age. Nachman and Paker (2012) assessed fine particulate matter and respiratory outcomes in adults using two national cross-sectional datasets and annual average PM 2.5 data derived from USEPA AirData and found an adjusted odds ratio for current asthma of 0.97 (95% CI=0.87, 1.07).…”

Contribution of Satellite-Derived Aerosol Optical Depth PM2.5 Bayesian Concentration Surfaces to Respiratory-Cardiovascular Chronic Disease Hospitalizations in Baltimore, Maryland

“…Ambient air monitors are usually found in urban areas. In the last 15 years, PMB has turned out to be a more representative PM 2.5 concentration surface, compared to the interpolation of PM 2.5 monitor data, as a method to resolve spatial gaps between ambient air monitors [16,18,22]. CDC subsequently incorporated PMB into its Environmental Public Health Tracking (EPHT) network of state and New York City partners [16,18,22,26].…”

“…In the last 15 years, PMB has turned out to be a more representative PM 2.5 concentration surface, compared to the interpolation of PM 2.5 monitor data, as a method to resolve spatial gaps between ambient air monitors [16,18,22]. CDC subsequently incorporated PMB into its Environmental Public Health Tracking (EPHT) network of state and New York City partners [16,18,22,26]. To date, PMB has been used by federal and state epidemiologists completing EPHT projects in different parts of the US [16,18,22,26].Within this decade, the availability and use of satellite AOD data have become more routine [6,16,[27][28][29][30][31].…”

Numerical Simulation of Particulate Matter 2.5 Distribution in a Roadway

“…Respirable dust is a main factor which has led to coal workers’ pneumoconiosis 3 . PM 2.5 is classified as a respirable dust, and is known to have toxic effects on the cardiovascular system by directly entering into the human pulmonary alveolus 4 . Therefore, in order to ensure the health and safety of China’s miners, more attention should be paid to the PM 2.5 dust particles generated in underground roadway blasting processes.…”