Site-Specific PM2.5 Estimation at Three Urban Scales

Fine particulate matter, also known as PM2.5, is one of the major risk factors to human health. Because of their small size, these particles travel deep within human lungs and pose a variety of health problems. A primary source of acquiring PM2.5 exposure is based on the nearest groundlevel air quality monitoring station. However, these stations are often few and sparsely located due to their high costs for installation and maintenance. This study addresses three challenges related to PM2.5. First, the number of air-quality monitoring sites is insufficient to acquire the complex spatial variability of PM2.5. Therefore, in-situ ground observations fail to characterize PM2.5 distribution, and hence exposure, adequately. The shortfall calls for models capable of estimating PM2.5 at unmonitored locations. Satellite-based Aerosol Optical Depth (AOD) serves as a proxy to estimate PM2.5. Second, although satellite data can supplement PM2.5 estimates at unmonitored locations, the spatial resolutions of satellite-based estimates of PM2.5 are in the order of kilometers. These spatial grains are too coarse to capture PM2.5’s spatial variation caused by contextual geographic factors such as buildings, and subsequently the estimates’ applicabilities to support environmental exposome on health effects. Third, the current standards measure PM2.5 in terms of mass per volume, but findings from some recent studies suggest that alternative measures of PM2.5 are also strongly associated with adverse health outcomes. However, observations in terms of these measures are not available. The dissertation research aimed to address the three challenges in three studies. The first study evaluated the potential of the Convolutional Neural Network (CNN) approach to downscale PM2.5 using satellite-based AOD and meteorological data using Dallas-Fort Worth as a case study. The study developed a model capable of estimating PM2.5 corresponding to the hour of satellite overpass time and examined environmental predictors commonly available for all monitored or non-monitored locations. In particular, the study investigated the effect of the spatial extent to which predictors from the surrounding area influenced the PM2.5 estimates at a location. The results showed that the proposed CNN model effectively estimates PM2.5 concentration with correlation coefficient (R) of 0.87 and root mean squared error (RMSE) of 2.57 μg/m3 . Moreover, spatially lagged variables from a wider area around an estimation location improved the model performance. As most monitoring stations were in open areas, data from these stations could not be used to examine the effect of contextual factors, such as the building on PM2.5. The second study evaluated the effects of contextual geographic factors on PM2.5 in mass per volume (i.e., standard measures) in pedestrian-friendly areas on the University of Texas at Dallas campus. The study used a mobile sensor to collect spatial and temporal fineresolution PM2.5 data on the campus. The study found very low spatial variation in the study area less than 1km2 . Furthermore, weather-related variables played a dominant role in PM2.5 distribution as temporal variation over-powered spatial variation in PM2.5 data. The study employed a fixed effect model to assess the effect of time-invariant building morphological characteristics on PM2.5 and found that building’s morphological characteristics explained 33.22% variation in the fixed effects in the model. Furthermore, openness in the direction of wind elevated the PM2.5 concentration. The third study investigated the potential of AOD to downscale Particle Number (PN) concentration, an alternative measure of PM2.5, and the effect of building morphology on PN concentration using PN measurements collected across the streets of San Francisco by the Google streetcar. The study showed that AOD remained useful to estimate street-level PN concentration across five different particle sizes. The subsequent analysis of variable importance revealed that AOD and AOD-related variables were more important than building morphology but less important than meteorological variables in the estimation of PN concentration.