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Seasonal, Temporal and Spatial Variation of Particulate Matter Concentration in Bangladesh: A Longitudinal Analysis
IEEE Region 10 Symposium (TENSYMP) - Good Technologies for Creating Future ; 2021.
Article in English | Web of Science | ID: covidwho-1853492
ABSTRACT
Particulate matters having diameters of 2.5 micrometers or less (PM2.5) have been linked with life threatening health issues worldwide. Data centric approach to ascertain the patterns in the propagation of PM2.5 materials in the atmosphere of a region can help policy makers take informed decisions to take proper action. In this paper, we analyze and identify seasonal, hourly, and regional patterns of PM2.5 propagation in Bangladesh from 2017 to 2020 using the Berkeley Earth dataset. We observe that the concentration of PM2.5 particles has a nationwide median value of about 50 mu gm(-3), which is unhealthy for sensitive individuals. The concentration varies seasonally and diurnally. We observe that the concentrations of PM2.5 in the air is around five times more in winter than in summer. The mean PM2.5 concentration inside Dhaka is significantly worse around 70 mu gm(-3), which is 1.25 times than the average concentration throughout Bangladesh. We also observe average concentration dropped during the covid-19 pandemic due to lockdown. Using cross correlation analysis, we observed how spikes in PM2.5 concentration levels in one zone may correspond with peaked concentrations in a different zone a few hours later, indicating that air currents may cause the particles to move in certain directions. Our exploratory analysis serves as the first cross-country data centric study of the state and propagation patterns of PM2.5 particles within Bangladesh and our findings can serve as foundation for further research on the topic.
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Full text: Available Collection: Databases of international organizations Database: Web of Science Type of study: Prognostic study Language: English Journal: IEEE Region 10 Symposium (TENSYMP) - Good Technologies for Creating Future Year: 2021 Document Type: Article

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Full text: Available Collection: Databases of international organizations Database: Web of Science Type of study: Prognostic study Language: English Journal: IEEE Region 10 Symposium (TENSYMP) - Good Technologies for Creating Future Year: 2021 Document Type: Article