Exposure Characterization and Prediction of Ambient Particulate Matter: From Boston to the Middle East
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CitationMasri, Shahir Fouad. 2016. Exposure Characterization and Prediction of Ambient Particulate Matter: From Boston to the Middle East. Doctoral dissertation, Harvard T.H. Chan School of Public Health.
AbstractChapter one of this manuscript identifies the sources, composition, and temporal variability of fine (PM2.5) and coarse (PM2.5-10) particles. A Harvard Impactor was used to collect daily particle samples from 2002-2010 at a single site in Boston, MA. Filters were analyzed for elements, black carbon (BC), and total PM mass. Positive Matrix Factorization (PMF) identified BC, S, Pb, V, and Ni to be associated mostly with the fine particle mode, and Ca, Mn (road dust), and Cl (sea salt) mostly with the coarse mode. PMF identified six source types for PM2.5, including regional pollution (48%), motor vehicles (21%), wood burning (19%), oil combustion (8%), crustal/road dust (4%), and sea salt (<1%). Three source types were identified for PM2.5-10, including crustal/road dust (62%), motor vehicles (22%), and sea salt (16%). A linear decrease in PM concentrations with time was observed for both fine (-5.2%/yr) and coarse (-3.6%/yr) particles. That PM2.5 is decreasing more sharply than PM2.5-10 over time suggests the increasing importance of PM2.5-10 and traffic-related sources for PM exposure and future policies.
Chapters two and three use variables related to PM2.5 concentrations to quantify PM2.5 exposure where PM monitoring does not exist. In chapter two, validation of a model calibrating the PM2.5-visibility relationship using data collected in Kuwait from 2004-2005 demonstrated the ability to predict PM2.5 exposure at monitoring sites. In chapter three, validation of a model calibrating the visibility-AOD relationship using data collected from 2006-2007 in Iraq demonstrated the ability to predict visibility between monitoring sites at a fine resolution (1x1 km), and in turn estimate PM2.5 spatially. Applying these relationships to predict PM2.5 at locations exterior to the calibration sites showed high temporal and spatial variability in PM2.5 exposure (10-365 µg/m3).
The ability to obtain precise estimates of PM2.5 concentrations in Southwest Asia and Afghanistan is of high utility for epidemiologists seeking to understand the relationship between chronic PM2.5 exposure and respiratory diseases among deployed military personnel stationed throughout the region. That predicted PM2.5 varies widely over space and time supports the ability to successfully utilize our estimates to understand this relationship in the region.
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