This paper aims to investigate the potential contribution of biomass burning in PM2.5 pollution in Northern Thailand. We applied the coupled atmospheric and air pollution model which is based on the Weather Research and Forecasting Model (WRF) and a Hybrid Single-Particle Lagrangian Integrated Trajectory Model (HYSPLIT). The model output was compared to the ground-based measurements from the Pollution Control Department (PCD) to examine the model performance. As a result of the model evaluation, the meteorological variables agreed well with observations using the Index of Agreement (IOA) with ranges of 0.57 to 0.79 for temperature and 0.32 to 0.54 for wind speed, while the fractional biases of temperature and wind speed were 1.3 to 2.5 °C and 1.2 to 2.1 m/s. Analysis of the model and hotspots from the Moderate Imaging Spectroradiometer (MODIS) found that biomass burning from neighboring countries has greater potential to contribute to air pollution in northern Thailand than national emissions, which is indicated by the number of hotspot locations in Burma being greater than those in Thailand by two times under the influence of two major channels of Asian Monsoons, including easterly and northwesterly winds that bring pollutants from neighboring counties towards northern Thailand.

Climate change has an effect human living in a variety of ways, such as health and food security. This study presents a prediction of crop yields and production risks during the years 2020–2029 in northern Thailand using the coupling of a 1 km resolution regional climate model, which is downscaled using a conservative remapping method, and the Decision Support System for the Transfer of Agrotechnology (DSSAT) modeling system. The accuracy of the climate and agricultural model was appropriate compared with the observations, with an Index of Agreement (IOA) in the range of 0.65–0.89. The results reveal the negative effects of climate change on rice and maize production in northern Thailand. We show that, in northern Thailand, rainfed rice and maize production may be reduced by 5% for rice and 4% for maize. Moreover, rice and maize production risk analysis showed that maize production is at a high risk of low production, while rice production is at a low risk. Additional irrigation, crop diversification, the selection of appropriate planting dates and methods of conservation are promising adaptation strategies in northern Thailand that may improve crop production.

The concentrations of PM2.5 and metallic elements were measured in Rayong during the dry season (November 2021 to April 2022). The mean PM2.5 concentration was 20.1 ± 10.9 µg/m³ (4.9–52.3 µg/m³). Moreover, the percentages of days when those PM2.5 concentrations exceeded the daily WHO and US-EPA NAAQS limit were 56.8% and 10.2%, respectively. However, the levels did not exceed 50 µg/m³, which is the limit of the 24 h standard defined by the PCD in Thailand. The dominant heavy metals and elements in PM2.5 samples were Cr, Cu, Fe, Mn, Pb, V, and Zn, which constituted 70%. In Rayong, the PCA results showed that industrial emissions (Cd, Cu, Fe, Mn, Pb, and Zn) and traffic emissions (As, Cd, Cr, K, and Ni) were the major sources of PM2.5-bound heavy metals. Exposure to toxic metals in PM2.5 through the inhalation pathway in Rayong obviously entails a high potential risk of cancer (>10⁻⁴) based on the total lung cancer risk (TCR_inh). It was found that the TCR_inh values of Cr for combined age groups were higher than 10⁻⁶, which implies a high cancer risk in Rayong.

Applied Science and Engineering Progress

Biogenic volatile organic compounds (BVOC), which are mainly emitted from plants, are a major precursor for the formation of ground-level ozone (O₃) and secondary organic aerosols (SOA). In the northern region of Thailand, 63.8% of the land area is covered by forests. Herein we investigated the effects of biogenic volatile organic compounds (BVOC) emitted from plants and anthropogenic NO_x emissions on ground-level ozone (O₃) and fine particulate matters (PM_2.5) formation. The Weather Research and Forecasting Model coupled with Chemistry (WRF-Chem Model) was applied to simulate three scenarios including baseline, noBio and modiAntho simulations. The modeling results over the northern region of Thailand indicate that BVOC emissions over the northern region of Thailand contributed only 5.3%–5.6% of the total concentrations of PM_2.5 and BVOC had a direct relationship to glyoxal and SOA of glyoxal. The comparison between the observed and the modeled isoprene over the study site showed an underestimation (3- to 4-folds) of the simulated concentrations during the study period (June and November 2021). In June, decreases in anthropogenic NO_x emissions by 40% led to PM_2.5 reductions (5.3%), which corresponded to a zero BVOC emission scenario. While higher PM_2.5 reductions (5.6%) were found to be caused by anthropogenic NO_x reductions in November, small increases in PM_2.5 were observed over the area near a power plant located in Lampang Province. Therefore, both VOC and NO_x emission controls may be necessary for areas near the lignite mine and power plant. Since the areas within the vicinity of the power plant were under VOC-limited regimes, while the other areas were determined to be NO_x-limited.