Development of Spatial and Temporal Emission Inventory for Crop Residue Field Burning

Accurate emission inventory (EI) is the foremost requirement for air quality management. Specifically, air quality modeling requires EI with adequate spatial and temporal distributions. The development of such EI is always challenging, especially for sporadic emission sources such as biomass open burning. The country of Thailand produces a large amount of various crops annually, of which rough (unmilled) rice alone accounted for over 30 million tonnes in 2007. The crop residues are normally burned in the field that generates large emissions of air pollutants and climate forcers. We present here an attempt at a multipollutant EI for crop residue field burning in Thailand. Available country-specific and regional primary data were thoroughly scrutinized to select the most realistic values for the best, low and high emission estimates. In the base year of 2007, the best emission estimates in Gigagrams were as follows: particulate matter as PM_2.5, 128; particulate matter as PM₁₀, 143; sulfur dioxide (SO₂), 4; carbon dioxide (CO₂), 21,400; carbon monoxide (CO), 1,453; oxides of nitrogen (NO_x), 42; ammonia (NH₃), 59; methane (CH₄), 132; non-methane volatile organic compounds (NMVOC), 108; elemental carbon (EC), 10; and organic carbon (OC), 54. Rice straw burning was by far the largest contributor to the total emissions, especially during the dry season and in the central part of the country. Only a limited number of EIs for crop residue open burning were reported for Thailand but with significant discrepancies. Our best estimates were comparable but generally higher than other studies. Analysis for emission uncertainty, taking into account possible variations in activity data and emission factors, shows considerable gaps between low and high estimates. The difference between the low and high EI estimates for particulate matter and for particulate EC and OC varied between −80% and +80% while those for CO₂ and CO varied between −60% and +230%. Further, the crop production data of Thailand were used as a proxy to disaggregate the emissions to obtain spatial (76 provinces) and temporal (monthly) distribution. The provincial emissions were also disaggregated on a 0.1° × 0.1° grid net and to hourly profiles that can be directly used for dispersion modeling.