Estimation of particulate matter (PM2.5, PM10) concentration and its variation over urban sites in Bangladesh

Gupta, Amitesh; Moniruzzaman, Md; Hande, Avinash; Rousta, Imán; Ólafsson, Haraldur; Mondal, Karno Kumar

doi:10.1007/s42452-020-03829-1

Cited by 22 publications

(12 citation statements)

References 119 publications

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“…(c) PM 2.5 PM 2.5 is hazardous to both the environment and to human health [73][74][75]. Particulate matter has high environmental value, but it is also a significant contributor to air pollution, contributing to climate variability.…”

Section: Covid-19 Lockdown Impact On Atmospheric Pollutionmentioning

confidence: 99%

Situation of Urban Mobility in Pakistan: Before, during, and after the COVID-19 Lockdown with Climatic Risk Perceptions

et al. 2021

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The coronavirus pandemic (COVID-19) has impacted the usual global movement patterns, atmospheric pollutants, and climatic parameters. The current study sought to assess the impact of the COVID-19 lockdown on urban mobility, atmospheric pollutants, and Pakistan’s climate. For the air pollution assessment, total column ozone (O3), sulphur dioxide (SO2), and tropospheric column nitrogen dioxide (NO2) data from the Ozone Monitoring Instrument (OMI), aerosol optical depth (AOD) data from the Multi-angle Imaging Spectroradiometer (MISR), and dust column mass density (PM2.5) data from the MERRA-2 satellite were used. Furthermore, these datasets are linked to climatic parameters (temperature, precipitation, wind speed). The Kruskal–Wallis H test (KWt) is used to compare medians among k groups (k > 2), and the Wilcoxon signed-rank sum test (WRST) is for analyzing the differences between the medians of two datasets. To make the analysis more effective, and to justify that the variations in air quality parameters are due to the COVID-19 pandemic, a Generalized Linear Model (GLM) was used. The findings revealed that the limitations on human mobility have lowered emissions, which has improved the air quality in Pakistan. The results of the study showed that the climatic parameters (precipitation, Tmax, Tmin, and Tmean) have a positive correlation and wind speed has a negative correlation with NO2 and AOD. This study found a significant decrease in air pollutants (NO2, SO2, O3, AOD) of 30–40% in Pakistan during the strict lockdown period. In this duration, the highest drop of about 28% in NO2 concentrations has been found in Karachi. Total column O3 did not show any reduction during the strict lockdown, but a minor decline was depicted as 0.38% in Lahore and 0.55% in Islamabad during the loosening lockdown. During strict lockdown, AOD was reduced up to 23% in Islamabad and 14.46% in Lahore. The results of KWt and WRST evident that all the mobility indices are significant (p < 0.05) in nature. The GLM justified that restraining human activities during the lockdown has decreased anthropogenic emissions and, as a result, improved air quality, particularly in metropolitan areas.

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Section: Covid-19 Lockdown Impact On Atmospheric Pollutionmentioning

confidence: 99%

Situation of Urban Mobility in Pakistan: Before, during, and after the COVID-19 Lockdown with Climatic Risk Perceptions

et al. 2021

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“…According to Akimoto et al, 2015;Desideri et al, 2007;Steffens, 2020;Wang and Su, 2020, the O 3 decrease during the presence of higher humidity levels, hence the O 3 formation also got reduced during the rainy season and depleted by depositing on water droplets. The negative correlation between ne PM and precipitation is often registered in umpteen studies (Gupta et al, 2020e;Pandey et al, 2017;Wang and Ogawa, 2015;Wu et al, 2018b). Thus the concentration of ne PM including the dust which was earlier conjectured for probable expeditious recrudescence of COVID-19 also got reduced during the monsoon precipitation.…”

Section: Resultsmentioning

confidence: 82%

Does Environment has any role in recovery from COVID-19 pandemic? A case study from India.

Gupta¹,

Saha

2021

Preprint

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The present study has investigated the role of meteorology and air quality for recovering from the COVID-19 pandemic in India. Using Pearson’s correlation method, we look into if there is any significant association occurs between the district level recovery case counts and different remote sensing based environmental variables. Among weather parameters, only precipitation and air temperature found to be significantly correlated with recovery situation. However, all the pollutants’ concentration was negatively correlated with count of recovery cases. It depicts that air quality might has greater importance in recovery from this disease. During late monsoon onwards, recovery rate was getting more than the infections which indicate that lesser temperature and good rainfall could help the air to be freshen. Through air pollution was greater during winter and post monsoon than the summer season in India the higher recovery rate was counted during post-monsoon and winter which suggest that patients may require lesser temperate ambient for better recovery. Spatial patterns also suggest that north-eastern hilly region followed by districts located in the northern mountain had better recovery where the pollutants’ concentration was also quite lower during the study period. Therefore, improving air quality with proper preventive precaution could help to combat the pandemic situation in India.

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“…The ground-level PM 2.5 and PM 10 concentrations have a great deal of variation from year to year. This type of pollution is elevated by the contribution of predominant anthropogenic causes; this is well reflected by the PM 2.5 /PM 10 ratio, which is highest during January and lowest during July (Gupta et al 2020). PM 2.5 emissions show seasonal variations.…”

Section: Introductionmentioning

confidence: 99%

“…From many previous studies, it could be seen that there was a strong seasonal and temporal variation in PM pollution (Rana et al 2016). Nowadays, the MODIS-AOD and machine learning (ML) models are utilized for estimating both PM 2.5 and PM 10 concentrations (Gupta et al 2020, Shahriar et al 2020. However, the relationships between the pollutants and the meteorological parameters are still poorly understood.…”

Section: Introductionmentioning

confidence: 99%

Estimating ground-level PM2.5 using subset regression model and machine learning algorithms in Asian megacity, Dhaka, Bangladesh

Islam

Awadh

Mallick

et al. 2023

Air Qual Atmos Health

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Fine particulate matter (PM 2.5 ) has become a prominent pollutant due to rapid economic development, urbanization, industrialization, and transport activities, which has serious adverse effects on human health and the environment. Many studies have employed traditional statistical models and remote-sensing technologies to estimate PM 2.5 concentrations. However, statistical models have shown inconsistency in PM 2.5 concentration predictions, while machine learning algorithms have excellent predictive capacity, but little research has been done on the complementary advantages of diverse approaches. The present study proposed the best subset regression model and machine learning approaches, including random tree, additive regression, reduced error pruning tree, and random subspace, to estimate the ground-level PM 2.5 concentrations over Dhaka. This study used advanced machine learning algorithms to measure the effects of meteorological factors and air pollutants (NO X , SO 2 , CO, and O 3 ) on the dynamics of PM 2.5 in Dhaka from 2012 to 2020. Results showed that the best subset regression model was well-performed for forecasting PM 2.5 concentrations for all sites based on the integration of precipitation, relative humidity, temperature, wind speed, SO 2 , NO X , and O 3 . Precipitation, relative humidity, and temperature have negative correlations with PM 2.5 . The concentration levels of pollutants are much higher at the beginning and end of the year. Random subspace is the optimal model for estimating PM 2.5 because it has the least statistical error metrics compared to other models. This study suggests ensemble learning models to estimate PM 2.5 concentrations. This study will help quantify ground-level PM 2.5 concentration exposure and recommend regional government actions to prevent and regulate PM 2.5 air pollution. Supplementary Information The online version contains supplementary material available at 10.1007/s11869-023-01329-w.

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Estimation of particulate matter (PM2.5, PM10) concentration and its variation over urban sites in Bangladesh

Cited by 22 publications

References 119 publications

Situation of Urban Mobility in Pakistan: Before, during, and after the COVID-19 Lockdown with Climatic Risk Perceptions

Situation of Urban Mobility in Pakistan: Before, during, and after the COVID-19 Lockdown with Climatic Risk Perceptions

Does Environment has any role in recovery from COVID-19 pandemic? A case study from India.

Estimating ground-level PM2.5 using subset regression model and machine learning algorithms in Asian megacity, Dhaka, Bangladesh

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