Moderate Resolution Imaging Spectroradiometer (MODIS) burnt area products are widely used to assess the damaged area after wildfires and agricultural burning have occurred. This study improved the accuracy of the assessment of the burnt areas by using the MCD45A1 and MCD64A1 burnt area products with the finer spatial resolution product from the Landsat-8 Operational Land Imager/Thermal Infrared Sensor (OLI/TIRS) surface reflectance data. Thus, more accurate wildfires and agricultural burning areas in the Greater Mekong Subregion (GMS) for the year 2015 as well as the estimation of the fire emissions were reported. In addition, the results from this study were compared with the data derived from the fourth version of the Global Fire Emissions Database (GFED) that included small fires (GFED4.1s). Upon analysis of the data of the burnt areas, it was found that the burnt areas obtained from the MCD64A1 and MCD45A1 had lower values than the reference fires for all vegetation fires. These results suggested multiplying the MCD64A1 and MCD45A1 for the GMS by the correction factors of 2.11−21.08 depending on the MODIS burnt area product and vegetation fires. After adjusting the burnt areas by the correction factor, the total biomass burnt area in the GMS during the year 2015 was about 33.3 million hectares (Mha), which caused the burning of 109 ± 22 million tons (Mt) of biomass. This burning emitted 178 ± 42 Mt of CO₂, 469 ± 351 kilotons (kt) of CH₄, 18 ± 3 kt of N₂O, 9.4 ± 4.9 Mt of CO, 345 ± 206 kt of NO_X, 46 ± 25 kt of SO₂, 147 ± 117 kt of NH₃, 820 ± 489 kt of PM2.5, 60 ± 32 kt of BC, and 350 ± 205 kt of OC. Furthermore, the emission results of fine particulate matter (PM2.5) in all countries were slightly lower than GFED4.1s in the range between 0.3 and 0.6 times.

Mercury (Hg) is a highly toxic pollutant with a long range transport in the atmosphere resulting in both local and global concerns. Understanding of emissions is required to support an effective control strategy. In this study, atmospheric Hg emissions from power sector in Thailand in 2010 were investigated by using the bottom-up approach to improve the accuracy of the estimate by up to 50% in comparison to those provided in global inventories. The activity data of each individual source were collected and emissions factors were assessed based on local sources, well reflecting the emissions behavior of various emitters in Thailand. The atmospheric Hg emissions from power sector in 2010 amounted to 844.5 kg, in which emissions from coal and lignite power plants constituted up to 92.3% and biomass power plants constituted up to 7.4%. Spatial and temporal distribution analysis indicated high emissions in the Central and Northern regions, and from February to July. Annual trends in emissions from 2010 to 2030 were estimated and discussed.

Accurate, high-resolution emission inventories are essential for air quality modeling and policy evaluation, yet national-scale inventories for Thailand remain limited in spatial and temporal detail. This study develops a comprehensive national emission inventory for Thailand in 2019 (EI–TH 2019), covering 12 major air pollutants and greenhouse gases across key sectors, including energy, transport, industry, agriculture, waste, and residential activities. The inventory is constructed using country-specific activity data from official statistics and sectoral surveys, combined with GAINS-consistent emission factors and control assumptions. Emissions are resolved at 1 × 1 km spatial resolution and monthly temporal resolution to capture Thailand-specific emission dynamics. The results show that emissions across major pollutants are dominated by a limited number of source groups, with biomass burning and residential solid-fuel use driving particulate matter, transport dominating NO_x and CO emissions, large-scale combustion and industry controlling SO₂ emissions, and agriculture contributing the majority of NH₃ emissions. Strong seasonal variability is observed in PM_2.5, CO, and NH₃, primarily driven by dry-season biomass burning, whereas NO_x and SO₂ exhibit relatively stable temporal patterns. The reliability of EI–TH 2019 is supported by a multi-dimensional evaluation framework. Temporal consistency is demonstrated through strong agreement between modeled PM_2.5 emissions and ground-based observations, as well as between NO_x emissions and satellite-derived TROPOMI NO₂ (r = 0.93; ρ = 0.96). Biomass burning timing is further validated using satellite fire activity (VIIRS), showing consistent seasonal patterns. Comparisons with global inventories (EDGAR v8.1, HTAP v3.2, and GFED5.1) reveal systematic differences in sectoral contributions, temporal profiles, and emission magnitudes, particularly for biomass burning, reflecting the importance of country-specific data and assumptions. Overall, EI–TH 2019 provides a robust, high-resolution, and policy-relevant emission dataset that improves the representation of emission processes in Thailand. The results highlight key priority sectors—biomass burning, transport, industry, and agriculture—for targeted emission-reduction strategies and support applications in chemical transport modeling, exposure assessment, and integrated air-quality and climate-policy analysis.

Bangkok Metropolitan Region - Thailand (BMR), one of the major metropolitan areas in the world, is a bustling upper-middle class megacity composed of Bangkok city proper and five surrounding provinces. With an actual population of approximately 15 million, it is the central hub for commerce and tourism in the Southeast Asian (SEA) region. In the past decades, Bangkok has experienced extraordinary economic development and urban expansion, all the while displaying notable concern regarding the ambient air pollution and its adverse effects on human health. In this study, 16-years of air quality monitoring data were collected from 25 stations in BMR, which are operated by the Thailand’s Pollution Control Department. Data for O₃, PM₁₀, NOx, CO and SO^₂ were analyzed temporally with a particular focus on long-term, seasonal and diurnal variations. The data were also analyzed spatially to identify the hotspots and flow of air pollution in the region. These investigations were performed to assess the evolution of air quality in BMR over the past 16 years and identify areas of potential concern. Analysis of the data indicated that PM₁₀, NOx , CO and SO₂ were in a moderate decline over the 16 years, while O₃ concentration has risen steadily throughout this time period. The results and findings of these investigations are discussed with a perspective to provide policy recommendations for air quality improvements in BMR.

Air-pollution source control is usually prioritised by emission mass, yet the health damage per tonne emitted varies widely in space. Using agricultural-residue open burning in Northern Thailand as a case study, we couple a GIS emission inventory with a province-resolved damage-function health model to quantify the health and economic burden, quantify how the health damage per tonne emitted varies across provinces, and apportion the co-benefits of diverting residue from open burning between air-quality and climate gains. In 2018, residue burning released about 21 kt of PM_2.5, 3.0 kt of black carbon and 0.84 Mt CO₂-eq of net non-biogenic greenhouse gases (methane and nitrous oxide), together with 6.2 Mt of biogenic CO₂ that is refixed by the following crop and is not a net climate source, with rice, sugarcane and maize contributing 39%, 35% and 20% of fine particles respectively, imposing costs of 59,600 disability-adjusted life years (DALYs; 97.5% from primary PM_2.5) and ~USD 1.07 billion per year. The damage caused per tonne emitted varied 4.8-fold among provinces (1.43–6.85 DALY per tonne of PM_2.5), so the largest emitter (Nakhon Sawan) was not the province whose emissions caused the largest attributable burden (Phetchabun); damage-weighted prioritisation therefore identifies where diversion yields the greatest benefit. Diverting rice and maize residue avoided 12 kt PM_2.5, 1.6 kt black carbon and 0.56 Mt CO₂-eq of net greenhouse gas, and 60% of the health and economic burden; extending diversion to sugarcane raised these to 20 kt, 2.9 kt, 0.79 Mt CO₂-eq and 95% (USD 1.02 billion per year), so a three-crop strategy is required. Rice-only and maize-only diversion were not clearly separable (rice-only was larger in 64% of the Monte Carlo draws).

Air pollution is a serious issue that affects many parts of the world, Southeast Asia in particular. Nitrogen oxides, particulate matter, sulfur dioxide, and other emissions have negative impacts on human health as well as overall environmental quality. The major sources in Thailand are open burning and fossil fuel combustion, i.e. in vehicles, energy use in industries and power generation. Given increasing actual and projected GDP growth, subsequent increases in energy consumption are inevitable. The power generation system must grow and expand as well to meet changes in demand from industrial, commercial, and residential customers. The Ministry of Energy of Thailand has published the Power Development Plan 2015 (PDP 2015) to outline policies and goals of the growing power generation and transmission systems throughout the nation. Notably, the plan involves increasing the use of coal-fired generation. Using both the Greenhouse Gas and Air Pollution Interactions and Synergies Model (GAINS) and the Comprehensive Air Quality Model with Extensions (CAMx), we have compared two different emissions scenarios: one with standard emission control technology, and another with maximum feasible emission controls. The effectiveness of emission control technology varied by region and pollutant. The greatest increase in air quality was located around the Rayong province in central Thailand. For PM₁₀ in the northern Thailand, however, emission control technologies did little to improve the air quality because the main source of pollutant, biomass burning, was left unabaited. This forecast of air quality can show possible impacts from future emissions in Thailand and regions that may benefit from added emission control technology in the future.