New Homogeneous Spatial Areas Identified Using Case-Crossover Spatial Lag Grid Differences between Aerosol Optical Depth-PM2.5 and Respiratory-Cardiovascular Emergency Department Visits and Hospitalizations.

Optimal use of Hierarchical Bayesian Model (HBM)-assembled aerosol optical depth (AOD)-PM2.5 fused surfaces in epidemiologic studies requires homogeneous temporal and spatial fused surfaces. No analytical method is available to evaluate spatial heterogeneity. The temporal case-crossover design was modified to assess the spatial association between four experimental AOD-PM2.5 fused surfaces and four respiratory-cardiovascular hospital events in 12 km2 grids. The maximum number of adjacent lag grids with significant odds ratios (ORs) identified homogeneous spatial areas (HOSAs). The largest HOSA included five grids (lag grids 04; 720 km2) and the smallest HOSA contained two grids (lag grids 01; 288 km2). Emergency department asthma and inpatient asthma, myocardial infarction, and heart failure ORs were significantly higher in rural grids without air monitors than in urban grids with air monitors at lag grids 0, 1, and 01. Rural grids had higher AOD-PM2.5 concentration levels, population density, and poverty percentages than urban grids. Warm season ORs were significantly higher than cold season ORs for all health outcomes at lag grids 0, 1, 01, and 04. The possibility of elevated fine and ultrafine PM and other demographic and environmental risk factors synergistically contributing to elevated respiratory-cardiovascular chronic diseases in persons residing in rural areas was discussed.

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

The fine particulate matter baseline (PMB), which includes PM2.5 monitor readings fused with Community Multiscale Air Quality (CMAQ) model predictions, using the Hierarchical Bayesian Model (HBM), is less accurate in rural areas without monitors. To address this issue, an upgraded HBM was used to form four experimental aerosol optical depth (AOD)-PM2.5 concentration surfaces. A case-crossover design and conditional logistic regression evaluated the contribution of the AOD-PM2.5 surfaces and PMB to four respiratory-cardiovascular hospital events in all 99 12 km2 CMAQ grids, and in grids with and without ambient air monitors. For all four health outcomes, only two AOD-PM2.5 surfaces, one not kriged (PMC) and the other kriged (PMCK), had significantly higher Odds Ratios (ORs) on lag days 0, 1, and 01 than PMB in all grids, and in grids without monitors. In grids with monitors, emergency department (ED) asthma PMCK on lag days 0, 1 and 01 and inpatient (IP) heart failure (HF) PMCK ORs on lag days 01 were significantly higher than PMB ORs. Warm season ORs were significantly higher than cold season ORs. Independent confirmation of these results should include AOD-PM2.5 concentration surfaces with greater temporal-spatial resolution, now easily available from geostationary satellites, such as GOES-16 and GOES-17.

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.

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 PM2.5 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 PM2.5 in areas with and without air quality monitors by combining PM2.5 concentrations measured by monitors, PM2.5 concentration estimates derived from satellite aerosol optical depth (AOD) data, and Community-Multiscale Air Quality (CMAQ) model predictions of PM2.5 concentrations. This methodology represents a substantial step forward in the approach for developing representative PM2.5 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 PM2.5 monitors and predictions from CMAQ. The second objective was to determine if inclusion of AOD surfaces in HBM model algorithms results in PM2.5 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 PM2.5 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 PM2.5 concentrations from satellite data can be used to supplement PM2.5 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 areas with fewer pollutant monitors or over wider geographic areas.

An improved method for estimating surface fine particle concentrations using seasonally adjusted satellite aerosol optical depth.

Using satellite observations of aerosol optical depth (AOD) to estimate surface concentrations of fine particulate matter (PM2.5) is a well-established technique in the air quality community. In this study, the relationships between PM2.5 concentrations measured at five monitor locations in the Baltimore, MD/Washington, DC region and AOD from Moderate Resolution Imaging Spectroradiometer (MODIS), Multi-Angle Imaging Spectroradiometer (MISR), and Geostationary Operational Environmental Satellite (GOES) were calculated for the summer of 2004 and all of 2005. Linear regression methods were used to determine the direct quantitative relationships between the satellite AOD values and PM2.5 concentration measurements. Results show that correlations between AOD and surface PM2.5 concentrations range from 0.46 to 0.84 for the analyzed time period. Correlations with AOD from MODIS and MISR were higher than those from GOES, likely because of variations in the algorithms used by the different instruments. To determine the relative usefulness of platform- and season-specific AOD PM2.5 regression analysis, the results from this study were used to estimate surface PM2.5 concentrations for two representative case studies. This analysis of case studies demonstrates that it is necessary to include season and satellite platform information for more accurate estimates of surface PM2.5 concentrations from satellite AOD data. Consequently, tools that currently use a constant relationship to estimate surface PM2.5 concentrations from satellite AOD data, such as the Infusing satellite Data into Environmental Applications (IDEA) website, may need to be revised to include parameters that allow the relationships to vary with season and satellite platform to provide more accurate results.

Compilation and assessment of recent positive matrix factorization and UNMIX receptor model studies on fine particulate matter source apportionment for the eastern United States.

In 1997, the U.S. Environmental Protection Agency (EPA) revised its particulate matter standards to include an annual standard for fine particulate matter (PM2.5; 15 microg/m3) and a 24-hr standard (65 microg/m3). The 24-hr standard was lowered to 35 microg/m3 in 2006 in an effort to further reduce overall ambient PM2.5 concentrations. Identifying and quantifying sources of particulate matter affecting a particular location through source apportionment methods is now an important component of the information available to decision makers when evaluating the new standards. This literature compilation summarizes a subset of the source apportionment research and general findings on fine particulate matter in the eastern half of the United States using Positive Matrix Factorization. The results between studies are generally comparable when comparable datasets are used; however, methodologies vary considerably. Commonly identified source categories include: secondary sulfate/coal burning (sometimes over 50% of total mass), secondary organic carbon/mobile sources, crustal sources, biomass burning, nitrate, various industrial processes, and sea salt. The source apportionment tools and methodologies have passed the proof-of-concept stage and are now being used to understand the ambient composition of particulate matter for sites across the United States and the spatial relationship of sources to the receptor. Recommendations are made for further and standardized method development for source apportionment studies, and specific research areas of interest for the eastern United States are proposed.