Urban Aerosol: Spatiotemporal Variation &amp; Source Characterization
Creators: name: Zhongju Li
Publisher: name: Carnegie Mellon University
Year: 2018
Subjects: subject: Mechanical Engineering
subject: FOS: Mechanical engineering
subject: FOS: Mechanical engineering

description: Long and short-term exposure to particulate matter (PM) are linked to adverse heath endpoints. Evidence indicates that PM composition such as metals and organic carbon (OC) might drive the health effects. As airborne pollutants show significant intracity spatiotemporal variation, mobile sampling and distributed monitors are utilized to capture the variation pattern. The measurements are then fed to develop models to better characterize the relationship between exposure and health outcomes. Two sampling campaigns were conducted. One was sole mobile sampling in 2013 summer and winter in Pittsburgh, PA. Thirty-six sites were chosen based on three stratification variables: traffic density, proximity to point sources, and elevation. The other one was hybrid sampling network, incorporating a mobile sampling platform, 15 distributed monitors, and a supersite. We designed two case studies (transect and downtown), selected 14 neighborhoods (~1 km2), and conducted sampling in 2016 summer/fall and winter. Spatial variation of PM2.5 mass and composition was studied in the 2013 campaign. X-ray fluorescence (XRF) was used to analyze concentrations of 26 elements: Na, Mg, Al, Si, S, Cl, K, Ca, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, As, Se, Br, Rb, Sr, Zr, Cd, Sb, and Pb. Trace elements had a broad range of concentrations from 0 to 300 ng/m3. Comparison of data from mobile sampling with stationary monitors showed reasonable agreement. We developed Land use regression (LUR) models to describe spatial variation of PM2.5, Si, S, Cl, K, Ca, Ti, Cr, Fe, Cu, and Zn. Independent variables included traffic influence, land-use type, and facility emissions. Models had an average R2 of 0.57 (SD = 0.16). Traffic related variables explained the most variability with an average R2 contribution of 0.20 (SD = 0.20). Overall, these results demonstrated significant intra-urban spatial variability of fine particle composition. Spatial variation of OC was based on the 2013 campaign as well. We collected organic carbon (OC) on quartz filters, quantified different OC components with thermaloptical analysis, and grouped them based on volatility in decreasing order (OC1, OC2, OC3, OC4, and pyrolyzed carbon (PC)). We compared our ambient OC concentrations (both gas and particle phase) to similar measurements from vehicle dynamometer tests, cooking emissions, biomass burning emissions, and a highway traffic tunnel. OC2 and OC3 loading on ambient filters showed a strong correlation with primary emissions while OC4 and PC were more spatially homogenous. While we tested our hypothesis of OC2 and OC3 as markers of fresh source exposure for Pittsburgh, the relationship seemed to hold at a national level. Land use regression (LUR) models were developed for the OC fractions, and models had an average R2 of 0.64 (SD = 0.09). We demonstrate that OC2 and OC3 can be useful markers for fresh emissions, OC4 is a secondary OC indicator, and PC represents both biomass burning and secondary aerosol. People with higher OC exposure are likely inhaling more fresh OC2 and OC3, since secondary OC4 and PC varies much less drastically in space or with local primary sources. With the 2016 hybrid sampling campaign, we addressed the intracity exposure patterns, as they could be more complex than intercity ones because of local traffic, restaurants, land use, and point sources. This network studied a wide range of pollutants (CO2, CO, NO2, PM1 mass and composition, and particle number PN). Mobile measurements and distributed monitors show good agreement. PN hotspots are strongly associated with restaurants and highway traffic. PN at sites with large local source impacts tends to have larger diurnal variation than daily variation, while CO in downtown center shows the opposite trend. PN exhibits the largest spatial and temporal variations. Spatial variation is generally larger than temporal variation among all five pollutants (CO2, NO2, CO, PN, and PM1). These findings provide quantitative comparison between spatial and temporal variation in different scales, and support the theoretical validity of developing long-term exposure models from short-term mobile measurement. A combined sampling network with mobile and distributed monitor could prove more valuable in studying intracity air pollution. In the 2016 hybrid sampling campaign, we also studied spatial variability of air pollution in the vicinity of monitors. Monitoring network is essential for protecting public health, though evaluation is needed to assess spatial representativeness of monitors in different environments. Mobile sampling was conducted repeatedly around 15 distributed monitors. Substantial short-range spatial variability was observed. Spatial variation was consistently larger than temporal variation for NO2 and CO at different sites. Ultrafine particles were highly dynamic both in space and time. PM1 was less spatially and temporally variable. Urban locations had more frequent episodic source plume events compared with background sites. Using a single monitor measurement to represent surrounding ~1 km2 areas could introduce an average daily exposure misclassification of 46 ppb (SD = 26) for CO (30% of regional background), 3 ppb (SD = 2) for NO2 (43% of background), 4007 #/cm3 (SD = 1909) for ultrafine particle number (64% of background), and 1.2 μg/m3 (SD = 1.0) for PM1 (13% of background). Exposure differences showed fair correlation with traditional land use covariates such as traffic and restaurant density, and the magnitude of misclassification could be even bigger for urban neighborhoods.