Ambient exposure to fine particulate matter (PM2.5) and ground-level ozone (O3) is identified as a leading risk factor for global disease burden. A major limitation to advancing our understanding of the cause and impacts of air pollution is the lack of observations with the spatial and temporal resolution needed to observe variability in emission, chemistry and population exposure. Satellite remote sensing, which fills a spatial gap in ground-based networks, is playing an increasingly important role in atmospheric chemistry. This thesis exploits satellite remote sensing observations to: (1) estimate human exposure to PM2.5 from remotely sensed aerosol optical properties; (2) identify the chemical regimes of surface O3 formation using satellite observations of O3 precursors.
In the first part, we use a forward geophysical approach to derive PM2.5 distributions from satellite AOD at 1 km2 resolution over the northeastern US by applying relationships between PM2.5 and AOD simulated from a regional air quality model (CMAQ). We use multi-platform ground, airborne and radiosonde measurements to quantify multiple sources of uncertainties in the satellite-derived PM2.5. We find that uncertainties in satellite-derived PM2.5 are largely attributed to the varying relationship between PM2.5 and AOD that depends on the aerosol vertical distribution, speciation, aerosol optical properties and ambient relative humidity. To assess the value of remote sensing to improve PM2.5 exposure estimate, we compile multiple PM2.5 products that include information from remote sensing, ground-based observations and models. Evaluating these products using independent observations, we find the inclusion of satellite remote sensing improves the representativeness of surface PM2.5 mostly in the remote areas with sparse monitors. Due to the success of emission control, PM2.5-related mortality burden over NYS decreased by 67% from 8410 (95% confidence interval (CI): 4, 570 – 12, 400) deaths in 2002 to 2750 (95% CI: 700 – 5790) deaths in 2012. We estimate a 28% uncertainty in the state-level PM2.5 mortality burden due to the choice of PM2.5 products, but such uncertainty is much smaller than the uncertainty (130%) associated with the exposure-response function.
The second part of the thesis focuses on ground-level O3. O3 production over urban areas is non-linearly dependent on the availability of its precursors: nitrogen oxides (NOx) and volatile organic compounds (VOCs). A major challenge in lowering ground-level O3 in urban areas is to determine the limiting species for O3 production (NOx-limited or VOC-limited). We use satellite observations of NO2 and HCHO to infer the relative abundance of NOx versus VOCs, thus to identify the O3 chemical regime. We first use a global chemical transport model (GEOS-Chem) to evaluate the uncertainties of using satellite-based HCHO/NO2 to infer O3 sensitivity to precursor emissions. Next, we directly connect this space-based indicator, retrieved consistently from three satellite instruments, to spatiotemporal variations in O3 recorded by on-the-ground monitors from 1996 to 2016. The nationwide emission reduction has led the O3 formation over U.S. urban areas to shift from VOC-limited to NOx-limited regime. Urban O3 monitors reveal trends consistent with this regime transition. Nonetheless, it is a major challenge for these retrievals to accurately depict day-to-day variability within urban cores. TROPOspheric Monitoring Instrument (TROPOMI) which launched in 2017, offers an unprecedented view to infer O3 chemistry at fine spatial and temporal scales. As an example, we use TROPOMI HCHO/NO2 to identify short-term changes in O3 sensitivity during the California Camp Fire. We find that the emissions from wildfires lead to NOx-saturated ozone formation near the fire source but NOx -limited conditions downwind.
This thesis bridges basic research in atmospheric chemistry, which advances the state-of-science related to O3 and PM2.5 pollution from urban to global scales, and applied research in air quality management and public health, by quantifying the health benefits of emission control, and informs policymakers on which emission reductions to focus so as to maximize the cost-effectiveness of pollution controls. We show how space-based measurements can complement in situ networks and model simulations by providing information on the spatial heterogeneity and temporal evolution of PM2.5 exposure and O3 chemical regimes, which will lay the scientific foundation for interpreting future products retrieved from upcoming geostationary platforms.
| Identifer | oai:union.ndltd.org:columbia.edu/oai:academiccommons.columbia.edu:10.7916/d8-cxn5-h474 |
| Date | January 2020 |
| Creators | Jin, Xiaomeng |
| Source Sets | Columbia University |
| Language | English |
| Detected Language | English |
| Type | Theses |
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