Urban air pollution estimation using unscented Kalman filtered inverse modeling with scaled monitoring data. (March 2020) - Metia, S.; Ha, Q.P.; Duc, H.N.; Scorgie, Y.
Highlights: Reduction of uncertainty of emissions inventory achieved in two stages Scaling factors calculated for accuracy improvement in Stage 1. Unscented Kalman Filter with Matérn function covariance used in Stage 2. Abstract: The increasing rate of urbanization requires effective and reliable techniques for air quality monitoring and control. For this, the Air Pollution Model and Chemical Transport Model (TAPM-CTM) has been developed and used in Australia with emissions inventory data, synoptic data and terrain data used as its input parameters. Since large uncertainties exist in the emissions inventory (EI), further refinements and improvements are required for accurate air quality prediction. This study evaluates the performance of urban air quality forecasting, using TAPM-CTM, and improves accuracy of air pollution estimation by using a two-stage optimization technique to upgrade EI with validation from monitoring data. The first stage is based on statistical analysis for EI correction and the second stage is based on the unscented Kalman filter (UKF) to take into account the spatio-temporal distributions of air pollutant levels utilizing a Matérn covariance function. The predicted nitrogen monoxide (NO) and nitrogen dioxide (NO 2 ) concentrations with a priori emissions are first compared with observations at monitoring stations in the New South Wales (NSW). Ozone (O 3 ) is also considered since at the ground level it represents a major air pollutant affecting human health and the environment. In the second stage, with the improved EI, TAPM-CTM model errors are reduced further by using the UKF to calibrate EI. Results obtained show effectiveness of the proposed technique, which is promising for air quality inverse modeling, an important aspect of air pollution control in smart cities to achieve environmental sustainability.