Ammonia (NH₃) is a key precursor to secondary particulate matter in Southeast Asia, yet Thailand has lacked a country-specific, policy-focused emission inventory. This study creates the first spatially gridded (12 × 12 km) and monthly resolved national NH₃ inventory for 2019, using detailed agricultural activity data, survey-based livestock management practices, and crop-specific fertilizer application profiles. Satellite-derived burned-area data were included to constrain emissions from open burning. National NH₃ emissions are estimated at 459.1 kt per year, with an overall uncertainty of ±15.3%. Agriculture accounts for 95.8% of total emissions. Livestock and manure management contribute 225.3 kt per year (49.1%), reflecting high densities of poultry, cattle, and pigs, as well as regional differences in manure handling and storage practices that enhance ammonia volatilization. Fertilizer-related emissions total 192.4 kt per year (41.9%), with seasonal peaks during primary planting cycles, in contrast to the more episodic biomass-burning emissions. Comparison with the global EDGARv8.1 inventory shows significant sectoral and temporal differences, including considerably higher livestock emissions and lower fertilizer emissions in this study, due to Thailand-specific emission factors and temporal emission allocation methods. These findings clarify the spatial and temporal drivers of NH₃ emissions in Thailand and offer actionable insights for targeted mitigation—notably improved manure management and optimized nitrogen use in regions where dry-season emissions coincide with severe PM_2.5 episodes. The THAI-NH₃ Inventory provides a strong foundation for chemical-transport modeling and evidence-based policymaking to reduce ammonia-related haze in Thailand.

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.