Constraining NOx Emissions Using Satellite NO2 Column Measurements Over The Southeast Texas
Ozone production depends not only on availability of Volatile Organic Compounds (VOCs) and Nitrogen Oxides (NOx) but also on their relative concentrations, which can be expressed as a VOC/NOx ratio. Over or under prediction of either component in an air quality model changes the VOC/NOx ratio and limits the capability of an air quality model to predict ozone properly. Additionally, accurate predictions of meteorological variables are crucial to simulate atmospheric chemistry and consequently properly simulate ozone concentrations. In addition to ground and aircraft measurements obtained in Houston during the Deriving Information on Surface Conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) campaign in September 2013, remote sensing data of NO2 are available from Aura Ozone Monitoring Instrument (OMI) NO2 column data products and can be used as a proxy for NOx and their values in air quality models can be quantified and thus constrained. In this project, an analysis of the archived in-situ aircraft and ground measurements will be performed and satellite measurements of NO2 will be utilized to improve the bottom-up NOx emission inventories and study the impact of these improved emissions on ozone predictions. Objective analysis (OA) of meteorological simulations will be applied to improve predictions of meteorological parameters as well as ozone predictions.
The primary objectives of this project are to: (1) utilize satellite measurements of tropospheric NO2 columns to quantify surface NOx anthropogenic and soil emissions using inverse modeling; (2) evaluate model-simulated formaldehyde and isoprene concentrations (key drivers for ozone) using in-situ ground and/or aircraft measurements; (3) examine how the ratio of model-simulated NO2/HCHO in Air Quality Forecasting system at UH (AQF-UH) varies and corresponds to remote sensing NO2/HCHO column measurements, and (4) perform objective analysis (OA) of meteorological predictions to improve their predictions, and consequently, ozone predictions. The Air Quality Forecasting System will use the Community Multiscale Air Quality (CMAQ) Model with a 4 km resolution for Southeast Texas. The meteorological inputs will be provided by the Weather Research and Forecasting (WRF) model.