Accurate and timely SO2 and NOx emission inventories are required to simulate and forecast SO2 and NO2 concentrations in the atmosphere. However, bottom-up emission inventories have a time lag of at least one year, as it takes time to collect necessary activity rates and emission factors. This thesis focuses on using satellite data from Ozone Monitoring Instrument (OMI), Ozone Mapper and Profile Suite (OMPS), and Visible Infrared Imaging Radiometer Suite (VIIRS) to optimize SO2 and NOx emissions through the GEOS-Chem adjoint model. The optimized emission inventories are further applied to improve air quality simulation and forecasts.
We firstly integrate OMI SO2 satellite measurements and GEOS-Chem adjoint model simulations to constrain monthly anthropogenic SO2 emissions. The effectiveness of this approach is demonstrated for 14 months over China; resultant posterior emissions not only capture a 20% SO2 emission reduction in Beijing during the 2008 Olympic Games but also improve agreement between modeled and in situ surface measurements. Further analysis reveals that posterior emissions estimates, compared to the prior, lead to significant improvements in forecasting monthly surface and columnar SO2.
SO2 and NO2 observations from the newer sensor OMPS are used to optimize SO2 and NOx emissions over China for October 2013 through GEOS-Chem adjoint model. OMPS SO2 and NO2 observations are assimilated separately to optimize corresponding emissions, respectively, and posterior emissions, compared to the prior, yield improvements in simulating columnar SO2 and NO2, which are validated with both OMI and OMPS observations. The posterior emissions from assimilating OMPS SO2 and NO2 simultaneously are within -3% to 15% of separate assimilations for SO2 emissions and ±1% for NOx, and the joint assimilation saves about 50% computational time. Changes of NH3 emissions modify NO2 lifetime, hence affecting posterior NOx emissions in separate assimilations, and having impacts on both posterior SO2 and NOx emissions in joint assimilation. All these assimilation experiments are conducted at coarse (2°×2.5°) spatial resolution to save computational time, but coarse-resolution simulations underestimate hot spots of surface SO2 and NO2. Thus, the posterior coarse-resolution emissions are further efficiently downscaled to fine resolution (0.25°×0.3125°) according to spatial distributions of prior MIX emissions or VIIRS nighttime lights. Posterior fine-resolution simulation and forecasts, validating with in situ surface SO2 and NO2 measurements, improve on the prior ones.
| Identifer | oai:union.ndltd.org:uiowa.edu/oai:ir.uiowa.edu:etd-8543 |
| Date | 01 August 2019 |
| Creators | Wang, Yi |
| Contributors | Wang, Jun |
| Publisher | University of Iowa |
| Source Sets | University of Iowa |
| Language | English |
| Detected Language | English |
| Type | dissertation |
| Format | application/pdf |
| Source | Theses and Dissertations |
| Rights | Copyright © 2019 Yi Wang |
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