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Low-Cost Sensor Performance Intercomparison, Correction Factor Development, and 2+ Years of Ambient PM(2.5) Monitoring in Accra, Ghana
[Image: see text] Particulate matter air pollution is a leading cause of global mortality, particularly in Asia and Africa. Addressing the high and wide-ranging air pollution levels requires ambient monitoring, but many low- and middle-income countries (LMICs) remain scarcely monitored. To address t...
Autores principales: | , , , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Chemical Society
2023
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10373484/ https://www.ncbi.nlm.nih.gov/pubmed/37437161 http://dx.doi.org/10.1021/acs.est.2c09264 |
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author | Raheja, Garima Nimo, James Appoh, Emmanuel K.-E. Essien, Benjamin Sunu, Maxwell Nyante, John Amegah, Mawuli Quansah, Reginald Arku, Raphael E. Penn, Stefani L. Giordano, Michael R. Zheng, Zhonghua Jack, Darby Chillrud, Steven Amegah, Kofi Subramanian, R. Pinder, Robert Appah-Sampong, Ebenezer Tetteh, Esi Nerquaye Borketey, Mathias A. Hughes, Allison Felix Westervelt, Daniel M. |
author_facet | Raheja, Garima Nimo, James Appoh, Emmanuel K.-E. Essien, Benjamin Sunu, Maxwell Nyante, John Amegah, Mawuli Quansah, Reginald Arku, Raphael E. Penn, Stefani L. Giordano, Michael R. Zheng, Zhonghua Jack, Darby Chillrud, Steven Amegah, Kofi Subramanian, R. Pinder, Robert Appah-Sampong, Ebenezer Tetteh, Esi Nerquaye Borketey, Mathias A. Hughes, Allison Felix Westervelt, Daniel M. |
author_sort | Raheja, Garima |
collection | PubMed |
description | [Image: see text] Particulate matter air pollution is a leading cause of global mortality, particularly in Asia and Africa. Addressing the high and wide-ranging air pollution levels requires ambient monitoring, but many low- and middle-income countries (LMICs) remain scarcely monitored. To address these data gaps, recent studies have utilized low-cost sensors. These sensors have varied performance, and little literature exists about sensor intercomparison in Africa. By colocating 2 QuantAQ Modulair-PM, 2 PurpleAir PA-II SD, and 16 Clarity Node-S Generation II monitors with a reference-grade Teledyne monitor in Accra, Ghana, we present the first intercomparisons of different brands of low-cost sensors in Africa, demonstrating that each type of low-cost sensor PM(2.5) is strongly correlated with reference PM(2.5), but biased high for ambient mixture of sources found in Accra. When compared to a reference monitor, the QuantAQ Modulair-PM has the lowest mean absolute error at 3.04 μg/m(3), followed by PurpleAir PA-II (4.54 μg/m(3)) and Clarity Node-S (13.68 μg/m(3)). We also compare the usage of 4 statistical or machine learning models (Multiple Linear Regression, Random Forest, Gaussian Mixture Regression, and XGBoost) to correct low-cost sensors data, and find that XGBoost performs the best in testing (R(2): 0.97, 0.94, 0.96; mean absolute error: 0.56, 0.80, and 0.68 μg/m(3) for PurpleAir PA-II, Clarity Node-S, and Modulair-PM, respectively), but tree-based models do not perform well when correcting data outside the range of the colocation training. Therefore, we used Gaussian Mixture Regression to correct data from the network of 17 Clarity Node-S monitors deployed around Accra, Ghana, from 2018 to 2021. We find that the network daily average PM(2.5) concentration in Accra is 23.4 μg/m(3), which is 1.6 times the World Health Organization Daily PM(2.5) guideline of 15 μg/m(3). While this level is lower than those seen in some larger African cities (such as Kinshasa, Democratic Republic of the Congo), mitigation strategies should be developed soon to prevent further impairment to air quality as Accra, and Ghana as a whole, rapidly grow. |
format | Online Article Text |
id | pubmed-10373484 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | American Chemical Society |
record_format | MEDLINE/PubMed |
spelling | pubmed-103734842023-07-28 Low-Cost Sensor Performance Intercomparison, Correction Factor Development, and 2+ Years of Ambient PM(2.5) Monitoring in Accra, Ghana Raheja, Garima Nimo, James Appoh, Emmanuel K.-E. Essien, Benjamin Sunu, Maxwell Nyante, John Amegah, Mawuli Quansah, Reginald Arku, Raphael E. Penn, Stefani L. Giordano, Michael R. Zheng, Zhonghua Jack, Darby Chillrud, Steven Amegah, Kofi Subramanian, R. Pinder, Robert Appah-Sampong, Ebenezer Tetteh, Esi Nerquaye Borketey, Mathias A. Hughes, Allison Felix Westervelt, Daniel M. Environ Sci Technol [Image: see text] Particulate matter air pollution is a leading cause of global mortality, particularly in Asia and Africa. Addressing the high and wide-ranging air pollution levels requires ambient monitoring, but many low- and middle-income countries (LMICs) remain scarcely monitored. To address these data gaps, recent studies have utilized low-cost sensors. These sensors have varied performance, and little literature exists about sensor intercomparison in Africa. By colocating 2 QuantAQ Modulair-PM, 2 PurpleAir PA-II SD, and 16 Clarity Node-S Generation II monitors with a reference-grade Teledyne monitor in Accra, Ghana, we present the first intercomparisons of different brands of low-cost sensors in Africa, demonstrating that each type of low-cost sensor PM(2.5) is strongly correlated with reference PM(2.5), but biased high for ambient mixture of sources found in Accra. When compared to a reference monitor, the QuantAQ Modulair-PM has the lowest mean absolute error at 3.04 μg/m(3), followed by PurpleAir PA-II (4.54 μg/m(3)) and Clarity Node-S (13.68 μg/m(3)). We also compare the usage of 4 statistical or machine learning models (Multiple Linear Regression, Random Forest, Gaussian Mixture Regression, and XGBoost) to correct low-cost sensors data, and find that XGBoost performs the best in testing (R(2): 0.97, 0.94, 0.96; mean absolute error: 0.56, 0.80, and 0.68 μg/m(3) for PurpleAir PA-II, Clarity Node-S, and Modulair-PM, respectively), but tree-based models do not perform well when correcting data outside the range of the colocation training. Therefore, we used Gaussian Mixture Regression to correct data from the network of 17 Clarity Node-S monitors deployed around Accra, Ghana, from 2018 to 2021. We find that the network daily average PM(2.5) concentration in Accra is 23.4 μg/m(3), which is 1.6 times the World Health Organization Daily PM(2.5) guideline of 15 μg/m(3). While this level is lower than those seen in some larger African cities (such as Kinshasa, Democratic Republic of the Congo), mitigation strategies should be developed soon to prevent further impairment to air quality as Accra, and Ghana as a whole, rapidly grow. American Chemical Society 2023-07-12 /pmc/articles/PMC10373484/ /pubmed/37437161 http://dx.doi.org/10.1021/acs.est.2c09264 Text en © 2023 The Authors. Published by American Chemical Society https://creativecommons.org/licenses/by-nc-nd/4.0/Permits non-commercial access and re-use, provided that author attribution and integrity are maintained; but does not permit creation of adaptations or other derivative works (https://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Raheja, Garima Nimo, James Appoh, Emmanuel K.-E. Essien, Benjamin Sunu, Maxwell Nyante, John Amegah, Mawuli Quansah, Reginald Arku, Raphael E. Penn, Stefani L. Giordano, Michael R. Zheng, Zhonghua Jack, Darby Chillrud, Steven Amegah, Kofi Subramanian, R. Pinder, Robert Appah-Sampong, Ebenezer Tetteh, Esi Nerquaye Borketey, Mathias A. Hughes, Allison Felix Westervelt, Daniel M. Low-Cost Sensor Performance Intercomparison, Correction Factor Development, and 2+ Years of Ambient PM(2.5) Monitoring in Accra, Ghana |
title | Low-Cost Sensor
Performance Intercomparison, Correction
Factor Development, and 2+ Years of Ambient PM(2.5) Monitoring
in Accra, Ghana |
title_full | Low-Cost Sensor
Performance Intercomparison, Correction
Factor Development, and 2+ Years of Ambient PM(2.5) Monitoring
in Accra, Ghana |
title_fullStr | Low-Cost Sensor
Performance Intercomparison, Correction
Factor Development, and 2+ Years of Ambient PM(2.5) Monitoring
in Accra, Ghana |
title_full_unstemmed | Low-Cost Sensor
Performance Intercomparison, Correction
Factor Development, and 2+ Years of Ambient PM(2.5) Monitoring
in Accra, Ghana |
title_short | Low-Cost Sensor
Performance Intercomparison, Correction
Factor Development, and 2+ Years of Ambient PM(2.5) Monitoring
in Accra, Ghana |
title_sort | low-cost sensor
performance intercomparison, correction
factor development, and 2+ years of ambient pm(2.5) monitoring
in accra, ghana |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10373484/ https://www.ncbi.nlm.nih.gov/pubmed/37437161 http://dx.doi.org/10.1021/acs.est.2c09264 |
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