- Related Research Areas
- Atmospheric Composition
OBJECTIVES: The proposed work will apply satellite data to address two leading causes of uncertainty in the photochemical modeling that informs ozone attainment planning in Texas -- photolysis rates and nitrogen oxide (NOx) emissions inventories. The specific objectives are to: 1) Assimilate GOES satellite cloud data to improve the photolysis rates in actual ozone modeling episodes used for Texas attainment planning; 2) Conduct inverse modeling using OMI satellite NO2 measurements and other data to create an a posteriori NOx emissions inventory for the region; and 3) Conduct photochemical modeling with high-order sensitivity analysis to assess how the satellite-derived model inputs influence predictions of ozone responsiveness metrics used in attainment planning. APPROACH: Baseline model inputs will be taken from recent regulatory modeling by the Texas Commission on Environmental Quality (TCEQ), focusing on the period coinciding with the 2006 Texas Air Quality Study (TexAQS-II) field campaign. GOES satellite data for cloud radiative and optical properties will be assimilated to improve the photolysis rates in the CAMx model. An iterative inverse method using a Kalman filter will be applied to adjust various components of the NOx emissions inventory for optimal agreement with OMI-retrieved NO2 tropospheric column densities and other NOx measurements available during TexAQS-II. High-order sensitivity analysis will then gauge how the satellite-derived photolysis rates and NOx emissions adjustment factors influence model estimates of two sensitivity metrics that are essential to attainment planning: the relative reduction factor of ozone under projected emission trends, and the sensitivity of ozone to additional emission controls. Finally, the satellite-based input data and associated modeling results will be provided to TCEQ modelers and interested stakeholders for direct incorporation into upcoming attainment planning efforts. SIGNIFICANCE: By addressing two leading causes of uncertainty in ozone sensitivity modeling, this study will enable more reliable predictions of how ozone will respond to future emissions trends and control measures. This more reliable ozone sensitivity modeling will be better able to inform decision-making about the amounts and types of emissions reductions needed to achieve ongoing attainment of ozone standards in a costeffective manner. Close partnership between project scientists and TCEQ officials and stakeholders will ensure that our modeling targets scenarios of greatest interest to Texas decision-makers, and that results and data are readily transferred to interested parties.
Project PI: Daniel Cohan/Rice University
Assistant Professor Dept. of Civil & Environmental Engineering Rice University MS 317 Houston, Texas 77005
Phone: (713) 348‐5129
Fax: (713) 348‐5203
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