Cargando…
Spatiotemporal analysis and forecasting of air quality in the greater Dhaka region and assessment of a novel particulate matter filtration unit
Bangladesh is one of the most polluted nations in the world, with an average Air Quality Index (AQI) of 161 in 2021; its capital, Dhaka, has the worst air quality of any major city in the world. The present study aims to analyze the spatiotemporal distribution of air quality indicators in the greate...
Autores principales: | , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer International Publishing
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10250181/ https://www.ncbi.nlm.nih.gov/pubmed/37291439 http://dx.doi.org/10.1007/s10661-023-11370-y |
_version_ | 1785055700109492224 |
---|---|
author | Rahman, R-Rafiul Kabir, Alamgir |
author_facet | Rahman, R-Rafiul Kabir, Alamgir |
author_sort | Rahman, R-Rafiul |
collection | PubMed |
description | Bangladesh is one of the most polluted nations in the world, with an average Air Quality Index (AQI) of 161 in 2021; its capital, Dhaka, has the worst air quality of any major city in the world. The present study aims to analyze the spatiotemporal distribution of air quality indicators in the greater Dhaka region, forecast weekly AQI, and assess the performance of a novel particulate matter filtration unit in removing particulate matter. Air quality indicators remained highest during the dry season with an average of 128.5 μm/m(3,) while the lowest concentration was found in the monsoon season with an average of 19.096 μm/m(3). Analysis revealed a statistically significant annual increasing trend of CO, which was associated with the growing number of brick kilns and usage of high-sulfur diesel. Except for the pre-monsoon AQI, concentrations of both seasonal and yearly AQI and PM(2.5) showed decreasing trend, though predominantly insignificant, demonstrating the improvement in air quality. Prevailing winds influenced the seasonal distribution of tropospheric CO & NO(2). The study also employed a seasonal autoregressive integrated moving average (ARIMA) model to forecast weekly AQI values. ARIMA (3,0,4) (3,1,3) at the 7-periodicity level performed best forecasting the AQI values among all developed models with low root mean square error (RMSE)-29.42 and mean absolute percentage error (MAPE)-13.11 values. The predicted AQI values suggested that the air quality would remain unhealthy for most weeks. The experimental simulation of the particulate matter filtration unit, designed in the shape of a road divider, generated substantial cyclonic motion while maintaining a very minimal pressure drop. In the real-world scenario, using only cyclonic separation and dry deposition, the suggested air filtration system removed 40%, 44%, and 42% of PM(2.5), PM(10), and TSP, respectively. Without employing filters, the device removed significant amounts of particulate matter, implying enormous potential to be used in the study area. The study could be useful for policy makers to improve urban air quality and public health in Bangladesh and in other developing countries. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s10661-023-11370-y. |
format | Online Article Text |
id | pubmed-10250181 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Springer International Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-102501812023-06-12 Spatiotemporal analysis and forecasting of air quality in the greater Dhaka region and assessment of a novel particulate matter filtration unit Rahman, R-Rafiul Kabir, Alamgir Environ Monit Assess Research Bangladesh is one of the most polluted nations in the world, with an average Air Quality Index (AQI) of 161 in 2021; its capital, Dhaka, has the worst air quality of any major city in the world. The present study aims to analyze the spatiotemporal distribution of air quality indicators in the greater Dhaka region, forecast weekly AQI, and assess the performance of a novel particulate matter filtration unit in removing particulate matter. Air quality indicators remained highest during the dry season with an average of 128.5 μm/m(3,) while the lowest concentration was found in the monsoon season with an average of 19.096 μm/m(3). Analysis revealed a statistically significant annual increasing trend of CO, which was associated with the growing number of brick kilns and usage of high-sulfur diesel. Except for the pre-monsoon AQI, concentrations of both seasonal and yearly AQI and PM(2.5) showed decreasing trend, though predominantly insignificant, demonstrating the improvement in air quality. Prevailing winds influenced the seasonal distribution of tropospheric CO & NO(2). The study also employed a seasonal autoregressive integrated moving average (ARIMA) model to forecast weekly AQI values. ARIMA (3,0,4) (3,1,3) at the 7-periodicity level performed best forecasting the AQI values among all developed models with low root mean square error (RMSE)-29.42 and mean absolute percentage error (MAPE)-13.11 values. The predicted AQI values suggested that the air quality would remain unhealthy for most weeks. The experimental simulation of the particulate matter filtration unit, designed in the shape of a road divider, generated substantial cyclonic motion while maintaining a very minimal pressure drop. In the real-world scenario, using only cyclonic separation and dry deposition, the suggested air filtration system removed 40%, 44%, and 42% of PM(2.5), PM(10), and TSP, respectively. Without employing filters, the device removed significant amounts of particulate matter, implying enormous potential to be used in the study area. The study could be useful for policy makers to improve urban air quality and public health in Bangladesh and in other developing countries. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s10661-023-11370-y. Springer International Publishing 2023-06-09 2023 /pmc/articles/PMC10250181/ /pubmed/37291439 http://dx.doi.org/10.1007/s10661-023-11370-y Text en © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Research Rahman, R-Rafiul Kabir, Alamgir Spatiotemporal analysis and forecasting of air quality in the greater Dhaka region and assessment of a novel particulate matter filtration unit |
title | Spatiotemporal analysis and forecasting of air quality in the greater Dhaka region and assessment of a novel particulate matter filtration unit |
title_full | Spatiotemporal analysis and forecasting of air quality in the greater Dhaka region and assessment of a novel particulate matter filtration unit |
title_fullStr | Spatiotemporal analysis and forecasting of air quality in the greater Dhaka region and assessment of a novel particulate matter filtration unit |
title_full_unstemmed | Spatiotemporal analysis and forecasting of air quality in the greater Dhaka region and assessment of a novel particulate matter filtration unit |
title_short | Spatiotemporal analysis and forecasting of air quality in the greater Dhaka region and assessment of a novel particulate matter filtration unit |
title_sort | spatiotemporal analysis and forecasting of air quality in the greater dhaka region and assessment of a novel particulate matter filtration unit |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10250181/ https://www.ncbi.nlm.nih.gov/pubmed/37291439 http://dx.doi.org/10.1007/s10661-023-11370-y |
work_keys_str_mv | AT rahmanrrafiul spatiotemporalanalysisandforecastingofairqualityinthegreaterdhakaregionandassessmentofanovelparticulatematterfiltrationunit AT kabiralamgir spatiotemporalanalysisandforecastingofairqualityinthegreaterdhakaregionandassessmentofanovelparticulatematterfiltrationunit |