Browsing by Author "Coker, Eric S."

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    A Social Vulnerability Index for Air Pollution and Its Spatially Varying Relationship to PM2.5 in Uganda
    (Atmosphere, 2022) Clarke, Kayan; Ash, Kevin; Coker, Eric S.; Sabo-Attwood, Tara; Bainomugisha, Engineer
    Fine particulate matter (PM2.5) is a ubiquitous air pollutant that is harmful to human health. Social vulnerability indices (SVIs) are calculated to determine where vulnerable populations are located. We developed an SVI for Uganda to identify areas with high vulnerability and exposure to air pollution. The 2014 national census was used to create the SVI. Mean PM2.5 at the subcounty level was estimated using global PM2.5 estimates. The mean PM2.5 for Kampala at the parish level was estimated using low-cost PM2.5 sensors and spatial interpolation. A local indicator of spatial association (LISA) was performed to determine significant spatial clusters of social vulnerability, and a bivariate analysis was performed to identify where significant associations were between SVI and annual PM2.5 mean concentrations. The LISA results showed significant clustering of high SVI in the northern and western regions of the country. The spatial bivariate analysis showed positive linear associations between SVI and PM2.5 concentration in subcounties in the northern, western, and central regions of Uganda, as well as in certain northern parishes in Kampala. Our approach identified areas facing both high social vulnerability and air pollution levels. These areas can be prioritized for health interventions and policy to reduce the impact of ambient PM2.5.
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    Using a Network of Locally Developed Low Cost Particulate Matter Sensors for Land Use Regression Modeling of PM2.5 in Urban Uganda
    (2020) Coker, Eric S.; Ssematimba, Joel; Bainomugisha, Engineer
    here are major air pollution monitoring gaps in sub-Saharan Africa. Developing capacity in the region to conduct air monitoring in the region can help estimate exposure to air pollution for epidemiology research. The purpose of our study is to develop a land use regression (LUR) model using low-cost air quality sensors developed by a research group in Uganda (AirQo). Methods Using these low-cost sensors, we collected continuous measurements of fine particulate matter (PM2.5) between May 1, 2019 and February 29, 2020 at 22 monitoring sites across urban municipalities of Uganda. We compared average monthly PM2.5 concentrations from the AirQo sensors with measurements from a BAM-1020 reference monitor operated at the US Embassy in Kampala. Monthly PM2.5 concentrations were used for LUR modeling. We used eight Machine Learning (ML) algorithms and ensemble modeling; using 10-fold cross validation and root mean squared error (RMSE) to evaluate model performance. Results Monthly PM2.5 concentration was 60.2 μg/m3 (IQR: 45.4-73.0 μg/m3; median= 57.5 μg/m3). For the ML LUR models, RMSE values ranged between 5.43 μg/m3 - 15.43 μg/m3 and explained between 28% and 92% of monthly PM2.5 variability. Generalized a 46 dditive models explained the largest amount of PM2.5 variability (R2=0.92) and produced the lowest RMSE (5.43 μg/m3) in the held-out test set. The most important predictors of monthly PM2.5 concentrations included monthly precipitation, major roadway density, population density, latitude, greenness, and percentage of households using solid fuels. Conclusion To our knowledge, ours is the first study to model the spatial distribution of urban air pollution in sub-Saharan Africa using air monitors developed from the region itself. Non-parametric ML for LUR modeling performed with high accuracy for prediction of monthly PM2.5 levels. Our analysis suggests that locally produced low-cost air quality sensors can help build capacity to conduct air pollution epidemiology research in the region.