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 PM 10 , 143; sulfur dioxide (SO 2 ), 4; carbon dioxide (CO 2 ), 21,400; carbon monoxide (CO), 1,453; oxides of nitrogen (NO x ), 42; ammonia (NH 3 ), 59; methane (CH 4 ), 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 CO 2 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.

No abstract

Brick manufacturing is traditionally a small-scale and improperly managed industry. Air pollutants, i.e. particulate matters (PM), carbon monoxide (CO) and sulfur dioxide (SO 2) were mainly emitted during the brick firing process. The area of Bang Pu, Nakhon Si Thammarat in southern Thailand was observed to have high numbers of the operating kilns. With substantial low height and conventionally unorganized industry, the nearby communities would be exposed to the adverse environmental and health impacts from local air pollutions. This paper was designed to develop a database of air pollutant emissions and to assess pollutant dispersion of PM 10 , CO and SO 2 from brick kilns in Bang Pu, Nakhon Si Thammarat. Air pollution emission was estimated mainly using IPCC and USEPA guidelines. An AERMOD modeling system was used to simulate pollution dispersions. Results showed that emissions for each brick kiln including PM 10 , CO and SO 2 were ranged between 0.0346-0.0751, 1.3022-3.3603 and 0.1736-0.4481 g/s, respectively. For pollutant dispersions, simulated concentrations for a maximum 1-hour, 24-hour and annual averages were as follows: PM 10 (13, 1 and 0.2 µg/m , respectively); CO (487, 35 and 6 µg/m , respectively); and SO 3 3 2 (64, 5 and 1 µg/m 3 , respectively). The overall assessment showed that the concentrations of pollutants (PM 10 , CO and SO 2) met the National Ambient Air Quality Standards of Thailand. However, for long term health impact reduction, the appropriate prevention and mitigation measures should be implemented to control the concentrations of air pollutants from brick kilns in the area.

Na Phra Lan Subdistrict is a pollution control zone with the highest PM10 level in Thailand. Major mobile and industrial sources in the area are related to stone crushing, quarrying and mining. This study used statistical techniques to investigate the potential sources influencing high PM10 levels in Na Phra Lan. Hourly PM10 data and related parameters (PM2.5, PMcoarse and NOx) from 2014–2017 were analysed using time series, bivariate polar plot and conditional bivariate probability function (CBPF). Results of diurnal variation revealed two peaks of PM10 levels from 06:00–10:00 and 19:00–23:00 every month. For seasonal variation, high PM10 concentrations were found from October to February associated with the cool and dry weather during these months. The bivariate polar plot and CBPF confirmed two potential sources, i.e., resuspended dust from mobile sources close to the air quality monitoring station (receptor) and industrial sources of mining, quarrying and stone crushing far from the station on the northeast side. While the industrial source areas played a role in background PM10 concentrations, the influence of mobile sources increased the concentrations resulting in two PM10 peaks daily. From the study results, we proposed that countermeasure activities should focus on potential source areas, resuspended road dust from vehicles and the industrial sources related to quarrying and mining, rather than distributing equal attention to all sources.