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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...

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Autores principales: 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.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Chemical Society 2023
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.
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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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