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Contribution of Aerosol Optical Depth (AOD) Fine Particulate Matter (PM2.5) Fused Surfaces in Assessing Risk of Respiratory-Cardiovascular Hospitalizations and Emergency Room Visits in Baltimore (2019 NEHA AEC)
Citation:
Hall, EricS. Contribution of Aerosol Optical Depth (AOD) Fine Particulate Matter (PM2.5) Fused Surfaces in Assessing Risk of Respiratory-Cardiovascular Hospitalizations and Emergency Room Visits in Baltimore (2019 NEHA AEC). 2019 National Environmental Health Association (NEHA) Annual Education Conference (AEC), Nashville, TN, July 08 - 12, 2019.
Impact/Purpose:
This is a conference presentation to introduce and describe the Hierarchical Bayesian Model used to develop the (in-preparation) journal article, "Contribution of Aerosol Optical Depth (AOD) Fine Particulate Matter (PM2.5) Fused Surfaces to Respiratory-Cardiovascular Chronic Disease Hospitalizations in Baltimore", to health and environmental scientists.
Description:
Advancements in air pollution monitoring using (satellite) remote sensing of fine particulate matter, i.e., Aerosol Optical Depth PM2.5 (AOD PM2.5), along with ground-based air quality monitors, air quality models, and Hierarchical Bayesian Models (HBMs), have been used to combine monitor and satellite measurements, with air quality model predictions, to assess air quality. This study examined Baltimore Maryland, during 2004 – 2006, and complements a previous study conducted in the New York City metropolitan area, presented at the 82nd NEHA Annual Educational Conference (AEC), (Weber et al., 2016: DOI: 10.1016/j.envres.2016.07.012; http://www.sciencedirect.com/science/article/pii/S0013935116302961). The objective was to determine if combining these three data sources enhances understanding of the influence of PM2.5 concentration on the risk of respiratory and cardiovascular diseases. This study used a case-crossover design, and conditional logistic regression analysis, to determine the contribution of PM2.5, to asthma emergency department (ED) visits, and inpatient hospitalizations for asthma, myocardial infarction (MI) and heart failure (HF) in Baltimore. The analysis used five HBM air pollution concentration surfaces. Four surfaces contained AOD PM2.5 measurements, monitor measurements, and air quality model estimates (two of these surfaces used kriging to interpolate/estimate missing AOD PM2.5 data due to cloud obstruction), and one surface represented the ‘baseline’ (monitor measurements and air quality model estimates only). The Baltimore study area covered an 11 (north-south) by 9 (east-west) Community Multiscale Air Quality Modeling System (CMAQ) 12 km grid region. Of the 99 CMAQ grids in Baltimore, 17 grids contained PM2.5 air pollution monitors. There were two major differences between the previous New York City study and the Baltimore study First, there were more asthma, MI and HF cases in New York City than in Baltimore, which was expected, since New York City’s population is larger than the population of the state of Maryland. Second, the mean PM2.5 values (in μg/m3 ), for all five surfaces, were significantly higher in Baltimore than in New York City. This may be due to coal-fired power plants in the Ohio Valley generating PM2.5 that traverses state boundaries, adversely impacting Baltimore’s air quality. Results indicate a significant elevated risk in same-day occurrences for all health outcomes due to ambient PM2.5, for all five air pollution concentration surfaces. This approach can improve health studies by providing an alternative to central site monitors as a surrogate for air pollution exposure.
