Evaluation of concentrations and source contribution of PM10 and SO2 emitted from industrial complexes in Ulsan, Korea: Interfacing of the WRF–CALPUFF modeling tools

The Ulsan metropolitan city in Korea includes two national industrial complexes [Ulsan Petrochemical Industrial Complex (UPIC) and On–San Industrial Complex (OSIC)] that produce various industrial products. Air pollution from these industrial complexes may pose potential health risks to nearby residential areas. Therefore, WRF–CALPUFF (Weather Research and Forecasting–California PUFF) modeling systems were used to simulate concentration distributions of typical air pollutants (PM10 and SO2), and statistics are computed to determine the models' ability to simulate observations. Finally, we classified the type of business and districts in the region and evaluated their contribution to air pollutant concentrations. Five statistical metrics [Index of Agreement (IOA), Fractional Bias (FB), Normalized Mean Square Error (NMSE), and Pearson correlation coefficient (R)] indicated that the simulated values using CALMET was determined to have sufficient reliability to predict CALPUFF, and simulated concentration field using CALPUFF showed a good agreement [typical values: IOA (0.284 to 0.850 for PM10, 0.412 to 0.895 for SO2), and FB (0.043 to 0.821 for PM10, –0.393 to 0.638 for SO2)] with the observed concentrations. The maximum concentrations of PM10 and SO2 using CALPUFF were predicted to be located around OSIC and UPIC, respectively. We compared the simulated values with observed values at 14 monitoring stations, and the SO2 tended to display better agreement to observed SO2 values than modeled and observed PM10. The source contribution analysis found that PM10 and SO2 were mostly influenced by group B (35.1%) including steel, machinery, and electronic industry nearby OSIC and group A (40.6%) including chemical industry nearby UPIC, respectively. Finally, the correlations between simulated concentrations of PM10 and SO2 and corresponding emission quantities were 0.663 and 0.528, respectively. Overall, the results of this study could be useful for designing appropriate seasonal regulations to reduce ambient concentrations of air pollutants and assisting environmental administrators to control the sources that contribute the most to degradation of air quality.