Status of Ambient Particulate Matter and Black Carbon Concentrations in Rajshahi Air, Bangladesh

Begum, Bilkis A.; Ahmed, K Shahin; Sarkar, Mousumi; Islam, Jahida Binte; Rahman, Akm Lutfor

doi:10.3329/jbas.v39i2.25948

Cited by 4 publications

(2 citation statements)

References 9 publications

(8 reference statements)

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“…The study area is located in the urban area, and BC is increasing (Begum et al, 2015); thus, it may significantly lead to a rising CH4. Cofala et al (2007) found a similar relationship between BC and CH4 in Asian urban and urban residential areas.…”

Section: Correlations Between Ch4 and Air Pollutantsmentioning

confidence: 99%

Probable nexus between Methane and Air Pollution in Bangladesh using Machine Learning and Geographically Weighted Regression Modeling

Islam

Bhuiyan

2023

Jour. Hypers. Rem. Sensg.

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This paper investigates the probable nexus between methane (CH4) and air pollutants, a public health hazard in Bangladesh. The hypothesis considers that the concentration of CH4 is dependent on the ten air pollutants found in the five districts in Dhaka Division, a major urban and industrial area in Bangladesh. These pollutants are: Particular matters (PM2.5), Nitrogen dioxide (NO2), Nitrogen oxide (NOx), Aerosol optical thickness (AOT), Sulfur dioxide (SO2), Carbon monoxide (CO), Ozone (O3), Black carbon (BC), Formaldehyde (HCHO) and Dust. The study applies Machine Learning (ML) technique and Geographically Weighted Regression (GWR) Modeling. Temporal CH4 datasets from the Sentinel-5P sensor are classified to estimate the annual CH4 concentration during 2019-2021.Seven supervised classifiers of ML coupled with the GWR model are used to predict the statistical and spatial relationships. CH4 increases gradually during 2018-2021 in Dhaka, Gazipur, and Munshiganj Districts. It relates differently with various air pollutants, e.g., positively with BC, Dust, NO2, PM2.5, O3, and AOT, and negatively with NOx, CO, HCHO, and SO2.This study results that Rational quadratic (RMSE-0.001, MAE-0.001, R2-0.96), Random Forest (RMSE-0.004, MAE-0.003, R2-0.91), and Stepwise regression (RMSE-0.002, MAE-0.002, R2-0.87) are the suitable method in ML. The highest goodness-of-fit (R2) of 82%-96% is found in Dhaka and Narshingdi Districts. The key findings may help formulate the appropriate action plan to mitigate ongoing and future air pollution in Bangladesh. In addition, the methodology of the research may be applicable elsewhere nationally and internationally for air pollution research.

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Section: Correlations Between Ch4 and Air Pollutantsmentioning

confidence: 99%

Probable nexus between Methane and Air Pollution in Bangladesh using Machine Learning and Geographically Weighted Regression Modeling

Islam

Bhuiyan

2023

Jour. Hypers. Rem. Sensg.

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“…From the above discussion, it was observed that although particulate matter has significant impact on human health, very limited work have been conducted upon it. Considering PM, most of the researches focused only in urban background (Hasan et al, 2016;Begum et al, 2015) and traffic areas (Jahida et al, 2014and Begum et al, 2012, 2008 whereas research related to PM focusing on Power Company limited has not been well reported so far. Therefore, this study was carried out to i) observe the PM concentration in midland power company Ltd. at Ashuganj, in Brahmanbaria and to ii) determine BC concentration within the atmospheric PM.…”

Section: Introductionmentioning

confidence: 99%

Status of atmospheric particulate matter and black carbon concentration at Ashuganj in Brahmanbaria

Baitun

Begum

Khan

2018

J Bangladesh Agric Univ

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Atmospheric particulate matter (PM) samples (both PM10 and PM2.5) were collected simultaneously from Midland Power Company Limited at Ashuganj, in Brahmanbaria to monitor the seasonal fluctuations of particulate matter and black carbon (BC) concentrations from air samples. Total eight samples were collected from January 2016 to December 2017 using two Air Metrics MiniVol samplers and the quantity of PM2.5 and PM10 was detected by weighing the filters before and after exposure. The concentration of PM (PM2.5 and PM10) and BC were analyzed at the laboratory of Chemistry Division, Atomic Energy Centre, Dhaka. The black carbon concentration from both PM2.5 and PM10 samples were also determined by reflectance measurement. The concentrations of PM2.5 varied from 12.2–145 μgm−3 where mean value was 67.09 μgm−3. The mean concentration of PM10 was 103.64 μgm−3 whereas the concentration ranged from 20.5–220 μgm−3. The ratios of PM2.5/PM10 fluctuated during different seasons, winter showed higher concentration and summer showed relatively lower concentration for both PM2.5 and PM10. Atmospheric dispersion due to increased wind speeds and wider mixing layer heights are recognized to be responsible for the lower PM concentrations in warmer months. The concentrations of BC in PM2.5 also varied from 2.44–33.4 μgm−3 and BC in PM10 varied from 4.51–57.2 μgm−3, respectively. The concentrations of particulate matter exceeded the Bangladesh National Ambient Air Quality Standard.J. Bangladesh Agril. Univ. 16(2): 232-236, August 2018

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Atmospheric Particulate Matter and Associated Trace Elements Pollution in Bangladesh: A Comparative Study with Global Megacities

Kumar,

Saha,

Mohana

et al. 2024

Water Air Soil Pollut

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Air quality degradation due to high levels of atmospheric particulate matter (PM) of various size fractions and the associated potentially toxic trace elements (PTEs) is a global concern. This article provides a thorough review and analysis of the temporal and spatial distribution of PM and PTEs in Bangladesh, offering a comprehensive assessment with other megacities worldwide based on existing literature. This study provides insights into the sources and transport mechanisms of PM and their link to human health. The level of PM was consistently high in Dhaka (capital of Bangladesh), with occasional higher levels in the surrounding cities. Different functional areas within Bangladesh show varying levels of PM, with total suspended particulates (TSP) being notably prevalent. When compared to megacities worldwide, African and Asian megacities, like India, Pakistan, Nigeria, and Egypt, exhibited higher PM concentrations. The concentration of PM-associated PTEs varies significantly among megacities and PM10 tends to have relatively higher concentrations of PTEs compared to other fractions in Bangladesh. Pb in ambient air was found across most megacities, with a temporal increase in Bangladesh. TSP exhibited the highest relative Pb content, followed by PM10 and PM2.5. Temporal factors, geographic locations, meteorological conditions, and anthropogenic activities contribute to the variation in PM and associated PTEs concentration in Bangladesh and global megacities. Ultimately, this study would aid policymakers in assessing the magnitude of PM pollution in Bangladesh compared to other megacities considering regional factors.

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Status of Ambient Particulate Matter and Black Carbon Concentrations in Rajshahi Air, Bangladesh

Abstract: Air borne particulate matter (PM) samples (both PM 10 and PM 2.5 ) were collected simultaneously from continuous air quality monitoring station (CAMS) at Rajshahi city from

Cited by 4 publications

References 9 publications

Probable nexus between Methane and Air Pollution in Bangladesh using Machine Learning and Geographically Weighted Regression Modeling

Probable nexus between Methane and Air Pollution in Bangladesh using Machine Learning and Geographically Weighted Regression Modeling

Status of atmospheric particulate matter and black carbon concentration at Ashuganj in Brahmanbaria

Atmospheric Particulate Matter and Associated Trace Elements Pollution in Bangladesh: A Comparative Study with Global Megacities

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