Evaluation of WRF-Chem PM2.5 simulations in Thailand with different anthropogenic and biomass-burning emissions

Thailand experiences severe air quality issues, predominantly due to PM_2.5 pollution that surpasses WHO guidelines. The main sources are attributed to energy production, industrial activities, vehicular emissions, agricultural burning, and transboundary transport of pollutants. Understanding the transport and transformation of these pollutants is necessary for addressing air quality issues. The Weather Research and Forecasting Model coupled with Chemistry (WRF-Chem) provides information about meteorology, chemical reactions, and transport of trace gases and aerosols. The accuracy of WRF-Chem simulations greatly depends on the choice of anthropogenic and biomass burning emissions inventories. This study provides a detailed evaluation of these inventories to model PM_2.5 concentrations in Thailand during both haze and off-haze seasons in 2019. We evaluated WRF-Chem using four anthropogenic emission inventories—CAMS-GLOB-ANT, ECLIPSE, HTAP, and REAS—and four biomass burning emissions inventories—FINN1.5, FINN2.5 MOD, FINN2.5 MODVAR, and QFED—using data from ground-based air quality stations, MODIS AOD, and MOPITT CO satellite data. Our findings suggest CAMS-GLOB-ANT performs optimally for North Thailand, while HTAP and REAS are more effective in Eastern Thailand. For biomass burning, FINN1.5 shows superior performance. The study also highlights the challenge in capturing PM_2.5 diurnal variability, particularly due to inaccuracies in simulating the planetary boundary layer height during nighttime in complex terrains. Moreover, our analysis exhibits moderate model performances during the off-haze season while using global and regional anthropogenic emissions in Thailand, emphasizing the need for improving anthropogenic inventories for reliable air quality prediction. For biomass burning emissions, updating emission factors to reflect Thailand's specific vegetation types is recommended to improve WRF-Chem's representation of PM_2.5 levels.