Quantifying the contributions of local emissions and regional transport to elemental carbon in Thailand

We used the Weather Research and Forecasting Model coupled with Chemistry (WRF-Chem) to simulate elemental carbon (EC) concentrations in Thailand in 2017. The goals were to quantify the respective contributions of local emissions and regional transport outside Thailand to EC pollution in Thailand, and to identify the most effective emission control strategy for decreasing EC pollution. The simulated EC concentrations in Chiang Mai, Bangkok, and Phuket were comparable with the observation data. The correlation coefficient between the simulated and observed EC concentrations was 0.84, providing a good basis for evaluating EC sources in Thailand. The simulated mean EC concentration over the whole country was the highest (1.38 μg m⁻³) in spring, and the lowest (0.51 μg m⁻³) in summer. We conducted several sensitivity simulations to evaluate EC sources. Local emissions (including anthropogenic and biomass burning emissions) and regional transport outside Thailand contributed 81.2% and 18.8% to the annual mean EC concentrations, respectively, indicating that local sources played the dominant role for EC pollution in Thailand. Among the local sources, anthropogenic emissions (including the industry, power plant, residential, and transportation sectors) and biomass burning contributed 75.1% and 6.1% to the annual mean EC concentrations, respectively. As the anthropogenic emissions dominated the EC pollution, we performed four sensitivity simulations by reducing 30% of the emissions from each of the industry, power plant, residential, and transportation sectors in Thailand. The results indicated that controlling transportation emissions in Thailand was the most effective way in reducing the EC pollution. The 30% reduction of transportation emissions decreased the annual mean EC concentrations by 12.1%. In contrast, 30% reductions of the residential, industry, and power plant emissions caused 8.4%, 6.4%, and 4.0% decreases in the annual mean EC concentrations, respectively. The model results could potentially provide useful information for air pollution control strategies in Thailand.