รายละเอียดเอกสาร

The biomass burning emission inventory reported by the Global Fire Emissions Database is at a coarse temporal (monthly) and spatial resolution (0.25??0.25?) and may not be appropriate for a model simulation or disease burden investigation. This study estimated an emission inventory of PM10 and PM2.5 caused by biomass burning in the nine provinces of Northern Thailand during 2012?2016 based on daily, monthly, and annual. The Visible Infrared Imaging Radiometer Suite (VIIRS) fire counts (375 m-resolutions), land uses, emission factors, and activity data were applied for the bottom-up estimation. According to the findings, Mae Hong Son (29%), Chiang Mai (20%), and Chiang Rai (38%), respectively, had the highest proportion of forest fires, savanna and grassland fires, and agricultural fires. There was a consistent trend between estimated emission and measured PM. The difference in emission ratio of PM2.5 between the current study and GFED4 was 1.2?2.6, 1.4?2.2, and 1.4?2.5 for forest, savannas and grasslands, and agricultural lands, respectively. The uncertainty range of PM2.5 and PM10 emissions from the three types of biomasses were the same with relative errors of ?15% to 27%, ?8% to 7%, and ?17% to 10%, respectively. The VIIRS fire count can be used for estimation of biomass burning with finer resolution in both temporal and spatial terms.