Browsing by Subject "Particulate Matter"

Now showing 1 - 3 of 3
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    Short-term changes in ambient particulate matter and risk of stroke: a systematic review and meta-analysis
    (Ovid Technologies Wolters Kluwer -American Heart Association, 2014-08) Wang, Yi; Eliot, Melissa N.; Wellenius, Gregory A.; Department of Environmental Health Sciences, IU Fairbanks School of Public Health
    BACKGROUND: Stroke is a leading cause of death and long-term disability in the United States. There is a well-documented association between ambient particulate matter air pollution (PM) and cardiovascular disease morbidity and mortality. Given the pathophysiologic mechanisms of these effects, short-term elevations in PM may also increase the risk of ischemic and/or hemorrhagic stroke morbidity and mortality, but the evidence has not been systematically reviewed. METHODS AND RESULTS: We provide a comprehensive review of all observational human studies (January 1966 to January 2014) on the association between short-term changes in ambient PM levels and cerebrovascular events. We also performed meta-analyses to evaluate the evidence for an association between each PM size fraction (PM2.5, PM10, PM2.5-10) and each outcome (total cerebrovascular disease, ischemic stroke/transient ischemic attack, hemorrhagic stroke) separately for mortality and hospital admission. We used a random-effects model to estimate the summary percent change in relative risk of the outcome per 10-μg/m(3) increase in PM. CONCLUSIONS: We found that PM2.5 and PM10 are associated with a 1.4% (95% CI 0.9% to 1.9%) and 0.5% (95% CI 0.3% to 0.7%) higher total cerebrovascular disease mortality, respectively, with evidence of inconsistent, nonsignificant associations for hospital admission for total cerebrovascular disease or ischemic or hemorrhagic stroke. Current limited evidence does not suggest an association between PM2.5-10 and cerebrovascular mortality or morbidity. We discuss the potential sources of variability in results across studies, highlight some observations, and identify gaps in literature and make recommendations for future studies.
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    Soot mass estimation from electrical capacitance tomography imaging for a diesel particulate filter
    (2020-05) Hassan, Salah E.; Anwar, Sohel; El-Mounayri, Hazim; Tovar, Andres
    The Electrical capacitance tomography (ECT) method has recently been adapted to obtain tomographic images of the cross section of a diesel particulate filter (DPF). However, a soot mass estimation algorithm is still needed to translate the ECT image pixel data to obtain soot load in the DPF. In this research, we propose an estimation method to quantify the soot load in a DPF through an inverse algorithm that uses the ECT images commonly generated by a back-projection algorithm. The grayscale pixel data generated from ECT is used in a matrix equation to estimate the permittivity distribution of the cross section of the DPF. Since these permittivity data has direct correlation with the soot mass present inside the DPF, a permittivity to soot mass distribution relationship is established first. A numerical estimation algorithm is then developed to compute the soot mass accounting for the mass distribution across the cross-section of the DPF as well as the dimension of the DPF along the exhaust flow direction. Firstly, ANSYS Electronic Desktop software is used to compute the capacitance matrix for different amounts of soot filled in the DPF, furthermore it also analyzed different soot distribution types applied to the DPF. The Analysis helped in constructing the sensitivity matrix which was used in the numerical estimation algorithm. Experimental data have been further used to verify the proposed soot estimation algorithm which compares the estimated values with the actual measured soot mass to validate the performance of the proposed algorithm.