2018-09-052018-09-052018-082018-08August 201http://hdl.handle.net/2346/74518Fine particulate matter with aerosol dynamic diameters equal to or less than 2.5 micrometers (PM2.5) is a major component of air pollutants widely threatening public health. To control and mitigate its adverse effects on human health, it is essentially important to explore the potential factors influencing ground-level PM2.5 concentrations and the associations between long-term PM2.5 exposure and its health outcomes. In my dissertation, I incorporate the spatial synoptic classification weather type data to investigate the impacts of meteorological factors on ground-level PM2.5 concentrations in a holistic fashion rather than individual meteorological variables separately. It was found that tropical (polar) weather types have positive (negative) effects on the ground-level PM2.5 concentrations and these positive (negative) effects varied seasonally and geographically. Accurate mapping of ground-level PM2.5 concentrations is the prerequisite for investigating the adverse effect of PM2.5 exposure on human health. However, the current PM2.5 monitoring networks leave many people unmonitored. Satellite-derived gridded PM2.5 images from chemical transport models (CTM) have demonstrated unique attractiveness in terms of their geographic and temporal coverage but often yield results with a coarse spatial resolution and tend to ignore or simplify the impact of geographic and socioeconomic factors on PM2.5 concentrations. In the second part of my dissertation, a random forests-based regression kriging (RFRK) approach was developed to improve the spatial resolution of a CTM-derived PM2.5 dataset from 0.1° to 0.01° with a combined use of in situ PM2.5 observations, brightness of nighttime lights, vegetation index, and elevation. The accuracy and advantages of the proposed approach are demonstrated by comparing the results with an existing PM2.5 dataset with the same spatial resolution. The effectiveness of the geographical variables in long-term PM2.5 mapping were highlighted and the contribution of each variable to the spatial distribution of PM2.5 concentrations was discussed. The third part of my dissertation targets on mapping the distribution of PM2.5-attributable mortality for detecting the potential benefits of PM2.5 control. To highlight the impact of geographic scales and variations of geospatial datasets on the estimation of PM2.5-attributable mortalities, I compared the estimations derived from PM2.5 concentration datasets at different spatial resolutions (i.e., 0.01° and 0.1°) and mortality statistics at different geographic scales (i.e., sub-regional and county-level). Using ischemic heart diseases (IHD) in the contiguous United States (U.S.) as a case study, it was found that the estimated PM2.5-IHD mortalities from the 0.1° PM2.5 dataset tend to be smaller than the estimations from the 0.01° PM2.5 datasets, while the estimated PM2.5-IHD mortalities from the sub-regional-level mortality rates tend to be larger than the estimations from the county-level ones. Simultaneously, the spatiotemporal change of PM2.5-attributable IHD mortality were extracted during 2000 and 2015 and it showed the PM2.5-IHD deaths decreased approximate 50%. A scenario analysis indicated up to 90% deaths could be avoided with the PM2.5 concentration decreased by 4 μg/m3 throughout the country. Influences of long-term PM2.5 exposure on public health have been investigated by many previous studies. However, reliability of those studies may be affected by limited measurements or inaccurate PM2.5 estimations. The last part of my dissertation linked the RFRK-refined PM2.5 dataset with fine spatial resolution and high accuracy to the hospital admission databases for Arizona. Relative risks (RRs) of PM2.5-attributable morbidity were calculated for all-cause, skin cancer, asthma, cerebrovascular, chronic respiratory, and heart diseases. Logarithmic risk functions for all-cause, skin cancer, asthma, and heart diseases and polynomial risk functions for respiratory and cerebrovascular diseases are developed for the total population. I also examined whether long-term PM2.5 exposure had varied intensities on human health among different subpopulations. Female had significantly higher risk of PM2.5-attributable morbidity than those of male for all-cause and heart diseases. African Americans are more vulnerable than Whites and Hispanics for all-cause, heart, and respiratory diseases. Hispanics more easily suffer from skin cancer than Whites from PM2.5 exposure, while Whites’ RRs for cerebrovascular diseases are apparently higher than those of Hispanics.application/pdfengPM2.5High resolution mappingRelative riskMortalityMorbidityDissertation2018-09-05Restricted from online display. For access, please request a copy.