Development of Spatial and Temporal Emission Inventory for Crop Residue Field Burning |
| |
Authors: | Thongchai Kanabkaew Nguyen Thi Kim Oanh |
| |
Institution: | (1) Environmental Engineering and Management, Asian Institute of Technology, Pathumthani, Thailand; |
| |
Abstract: | 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 PM2.5, 128; particulate matter as PM10, 143; sulfur dioxide (SO2), 4; carbon dioxide (CO2), 21,400; carbon monoxide (CO), 1,453; oxides of nitrogen (NOx), 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. |
| |
Keywords: | |
本文献已被 SpringerLink 等数据库收录! |
|