Browsing by Author "Tang, Wei"

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    Incorporation of Satellite Observations into Texas Ozone Attainment Modeling
    (2014-04-15) Tang, Wei; Cohan, Daniel S.; Griffin, Robert J.; Masiello, Caroline A.; Pour-Biazar, Arastoo
    Uncertain photolysis rates and nitrogen oxides (NOx) emission inventories impair the accuracy of ozone (O3) regulatory modeling. Satellite-observed clouds have been used to correct model-predicted photolysis rates, and satellite-constrained top-down NOx emissions have been used to identify and reduce uncertainties in bottom-up NOx emissions. However, studies on using multiple satellite-derived model inputs to improve O3 State Implementation Plan (SIP) modeling are rare. In this thesis, observations of clouds from the Geostationary Operational Environmental Satellite (GOES) and of NO2 from the Ozone Monitoring Instrument (OMI) are used to adjust the inputs to SIP modeling of O3 in Texas. The discrete Kalman filter (DKF) inversion approach is successfully applied with decoupled direct method (DDM) sensitivities in the Comprehensive Air Quality Model with extensions (CAMx) model to adjust Texas NOx emissions in designated emission regions and categories to better match OMI NO2 data. The NO2 vertical column densities (VCD) gap between OMI and CAMx over rural areas is alleviated by adding missing lightning and aviation and underestimated soil NOx emissions to the base regulatory emission inventory and further reduced by increasing modeled NOx lifetime and adding an artificial NO2 layer in the upper troposphere. The region-based DKF inversion using OMI NO2 tends to scale up NOx emissions in most regions, which conflicts with the inversion results using ground NO2 measurements and fails to improve the ground-level O3 simulations. The sector-based DKF inversion using OMI NO2 suggests scaling down area and non-road NOx emissions by 50%, leading to approximately 2-5ppb decrease in ground 8-h O3 concentrations, and improving both hourly ground-level NO2 and O3 simulations by reducing biases by 0.25 and 0.04 and errors by 0.13 and 0.04, respectively. Finally, using both GOES-derived photolysis rates and OMI-constrained NOx emissions reduces modeled bias and error by 0.05, and increases the model correlations in simulating ground O3 measurements and makes O3 more sensitive to NOx emissions in the O3 nonattainment areas.
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    Influence of uncertainties in vertical mixing algorithms on an air quality model
    (2010) Tang, Wei; Cohan, Daniel S.
    Vertical diffusion of trace pollutants is a very important physical process that influences pollutant concentrations. However, there are large uncertainties in the numerical modeling of this process, which could affect model predictions of pollutant levels and their responsiveness to emission controls. Uncertainties could result from the formulation of vertical diffusion schemes or from errors in eddy diffusivity and dry deposition velocity parameters associated with this process. Inter-comparisons between different model configurations and sensitivity analysis of model parameters can be used to help quantify these uncertainties. In this study, a comprehensive evaluation of two vertical diffusion schemes, EDDY and ACM2, was performed by comparing ground-level concentrations and vertical profiles generated using the CMAQ model with measurement data from the Texas Air Quality Study II. In addition, new capabilities of conducting sensitivity analysis to dry deposition velocity and eddy diffusivity were implemented into the CMAQ-DDM model. The results show that the ACM2 scheme tends to predict larger secondary pollutant concentrations and smaller primary pollutant concentrations at the surface compared to the EDDY scheme. Differences between the two vertical diffusion schemes and uncertainties in dry deposition velocity may cause temporal variations in the responsiveness of ozone to both NOx and VOC control respectively.