Trajectory-Based Models and Remote Sensing for Biomass Burning Assessment in Bangladesh

Abstract

Biomass burning is a major global source of fine primary carbonaceous particles including strongly light absorbing compounds and marker compounds. In a prior study, particulate matter (PM) sampling was conducted during 2010-2012 period at sites in Rajshahi, Dhaka, Khulna, and Chittagong. PM samples were collected using dichotomous samplers in the PM 2.5 and PM 2.5-10 size fractions. The samples were analyzed for mass, black carbon at 370 nm (UVBC) and 880 nm (BC), Delta-C (UVBC-BC), and elemental compositions with X-ray fluorescence. Source apportionment using PMF was performed to identify and quantify the PM sources. Results showed that biomass burning contributions during winters in Rajshahi were substantially higher than in Dhaka, Khulna, or Chittagong. Agricultural burning areas of the Indo-Gangetic Plain were hypnotized as the primary source region. The present study explores the relationships between the source regions using trajectory ensemble models and determines if transported biomass PM has disproportionately affected air quality in Rajshahi. The probable source locations that were identified included Pakistan, northern India, Nepal, Bangladesh, Northeastern India, and Myanmar. To assess the model results, satellite measurements of fire radiative power (FRP) were calculated based on fire data acquired by the MODerate-resolution Imaging Spectro-radiometer (MODIS) sensor in six defined areas. High fire occurrences from MODIS coincident with the source regions identified in Nepal, Northeastern India and Myanmar in winter. The instantaneous FRP values ranged between 4.4 MW and 2449 MW. The mean winter FRP values for Nepal and Northeastern India were higher than for the other regions with Nepal having the overall highest value. Fire locations with their mean power, NASA Satellite pictures and particles speed along trajectories have been analyzed. In summary, the integrated outcome of the different techniques has identified Northern India and Nepal as the main source area responsible for the increased biomass burning concentration difference at Rajshahi.

Evaluating the variability, transport and periodicity of particulate matter over smart city Bhubaneswar, a tropical coastal station of eastern India

“…Transported pollutants at any site may also increase the load of contaminants, and the source-apportionment studies are found to be useful for finding the regions, which are responsible for transported pollution to any location (e.g. [25,40]). This situation, however, may be different at coastal stations during the winter season, where land-sea breeze interacts with the atmospheric boundary layer variation, and pollutants concentrations are changing during diurnal cycle (e.g.…”

Characterization of Inhalable Ground-Level Ambient Particulate Matter in Dhaka City, Bangladesh

“…During the winter season brick kilns start their production which contributes to total air pollution with the other sources in Dhaka city [29]. The evidence also showed that high concentration of PM during the winter season is caused by the seasonal fluctuations and transportation of the pollutants from the north and northwesterly directions of air [30,31] that also indicates the PM concentration is influenced by transboundary emission, especially in winter season [30].…”

Effects of Seasonality and Transport Route on Chemical Characteristics of PM2.5 and PM2.5-10 in the East Asian Pacific Rim Region

“…Detailed information regarding the trajectory model and these data sets can be found on the NOAA Web site (http://www.arl.noaa.gov/re ady/hysplit4.html). This type of analysis has been widely applied in the environmental sciences; for instances, to investigate the long-range transport of pollutants and to establish the source-receptor relationships of air pollutants (Stohl, 1998;Xin et al, 2016;Ommi et al, 2017;Yang et al, 2017). Different air masses travel through different regions and therefore bring different chemical constituents with them, leading to different air quality characteristics in downwind areas (Abdalmogith and Harrison, 2005;Wang et al, 2006b;Kong et al, 2010).…”