Retrospective simulations of ozone and aerosols utilizing the improved model inputs from global model, in-situ and satellite measurements

Air pollution has been a serious problem in many big cities around world. Air quality in Eastern Texas over the Dallas-Fort Worth and Houston-Galveston areas has been considered among the worst in the United States. Air quality models, such as U.S. EPA's CMAQ, represent the complex physical and chemical processes in the atmosphere affecting air quality such as ozone, particulate matter, regional haze, acid deposition, and air toxins. These models are used to understand the causal relations of various air quality problems affecting human health and to develop air quality management strategies to mitigate their harmful effects. The essential inputs for air quality simulation are meteorological data, emissions data, and initial and boundary conditions of pollutants. One of the fundamental issues in the air quality modeling is related to these model inputs, because the models cannot simulate air quality accurately if these input data are not appropriate and reliable.;Benefits of using satellite-data assimilated global scale RAQMS boundary conditions for the regional CMAQ predictions were verified with various measurement data over the conterminous US (CONUS) domain. To quantify impacts of emissions uncertainty on the predictability of the air quality in the HGB domain, several simulations were performed with forecast and improved meteorological inputs and using different emission inventories. We also investigated ways to improve air quality predictions from an air quality model which lacks proper event-based emission inputs (such as wildland fires) and dynamic boundary conditions representing real-life long-range transport of pollutants. It is shown that use of the aerosol initial conditions adjusted by AOD can help to improve PM2.5 simulations although further refinements of the vertical distribution of aerosols are critically needed.;The goal of this study is to improve air quality predictions by utilizing more reliable model inputs. We focus on three distinctive research areas: (1) improvement of boundary conditions for air quality models with RAQMS global chemistry model outputs incorporating in-situ and satellite measurements, (2) assessment of impacts of utilizing retrospective meteorological and emissions inputs on CMAQ predictions, and (3) evaluation of CMAQ aerosol predictions with MODIS satellite-derived aerosol optical depth (AOD).