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

摘要

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.