Improving Constraints on Aerosols in the United States Using Ground Based Observations, Satellite Retrievals, and a Chemical Transport Model
AdvisorArellano, Avelino F.
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PublisherThe University of Arizona.
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AbstractKnowledge of distributions of aerosols is critical to human health, Earth's radiative budget, and air quality. However, the lack of sufficient direct measurements of aerosol type, number, mass concentrations and current limitations of satellite retrievals make it challenging to accurately model the aerosol variability. Such measurement gaps also hinder evaluation of aerosol source budget from emission inventories, modeling of aerosol chemistry, and sinks. In this context, the first study characterizes the potential of multivariate relationships between Aerosol Optical Depth (AOD), a quantity that represents light extinction by aerosols in the atmospheric column and a suite of surface and atmospheric parameters (e.g., vegetation, precipitation, fire characteristics) in order to assess trends in AOD anomalies for the U.S Southwest. This study covers the area that experiences North American Monsoon (NAM) and examines trends in AOD across different aerosol sources in this region such as dust storms, biomass burning, and anthropogenic emissions. We find that aerosols from anthropogenic processes and biomass burning exhibited a strong declining trend in AOD whereas trends along the NAM alley were obfuscated by the monsoon precipitation (sink) and convective dust storms (sources). In the second study, we develop constraints to improve characterization of anthropogenic apparent Elemental Carbon (ECa) using coemitted combustion products such as Carbon Monoxide (CO) and Nitrogen Oxides (NOx). We compare observational ratios of ECa vs CO and ECa vs NOx against those from emission inventories. We find that the observational ratios have increased at sites in the Urban-West due to increase in ECa from 2000-2007 to 2008-2015. Further, emission ratios do not match with observational ratios. We recommend that rigorous efforts are needed to better quantify and monitor the changes in these species in the Urban-West particularly for non-road and residential combustion sectors. The final study of this dissertation discusses a technique to produce forecasts of AOD by combining satellite retrievals and a chemical transport model in an analog based framework. We use model forecasts of AOD, particulate matter (PM) concentrations, and meteorological parameters from Weather Research and Forecasting model with Chemistry (WRF-Chem) to train the framework for choosing analogs (past forecasts similar to current simulations). MODIS Terra and Aqua satellite retrievals of AOD for analog days are then used in a Kalman Filter (KF) framework to determine the forecast error and referred to as KFAN. The analog based estimates better forecasts of AOD for the Western US compared to the East and the mean bias in AOD forecasts are reduced to the range of 0.001-0.1. The reduction in positive bias in AOD is drastic and the method captures the decrease in AOD from morning to afternoon. We find that higher root mean square error (RMSE) values in the East are due to the inability of KFAN to capture the AOD peaks during biomass burning episodes and AOD lows during days of high precipitation rates. A systematic statistical analysis using step-wise linear regression models also show that in the East, there is a stronger dependence of aerosol loading on meteorological factors such as air temperature, precipitation, and relative humidity. As a consequence, overall quality of the analogs in the East is impacted when uncertainties in the simulated meteorological fields are higher. Overall, this study shows that the correlative information from multi-satellite remote sensing retrievals and models provide additional constraints on aerosols using composition/source identification (e.g., aerosol type, landcover, emission sources, fuel consumption), coemitted gas phase species (e.g., CO and NOx), and meteorological parameters (e.g., wind speed, TPW). The synergy of information from these datasets can be beneficial for design of future remote sensing missions, deployment of ground networks, and studies related to feedbacks between meteorology and aerosols.
Degree ProgramGraduate College