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Low-Cost Sensor Performance Intercomparison, Correction Factor Development, and 2+ Years of Ambient PM2.5 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.
Afiliação
  • Raheja G; Department of Earth and Environmental Sciences, Columbia University, New York, New York 10027, United States.
  • Nimo J; Lamont-Doherty Earth Observatory of Columbia University, Palisades, New York 10964, United States.
  • Appoh EK; Department of Physics, University of Ghana, Legon, Ghana, Ghana.
  • Essien B; African Institute of Mathematical Sciences, Kigali, Rwanda.
  • Sunu M; Ghana Environmental Protection Agency, Accra, Ghana.
  • Nyante J; Ghana Environmental Protection Agency, Accra, Ghana.
  • Amegah M; Ghana Environmental Protection Agency, Accra, Ghana.
  • Quansah R; Ghana Environmental Protection Agency, Accra, Ghana.
  • Arku RE; Ghana Environmental Protection Agency, Accra, Ghana.
  • Penn SL; School of Public Health, University of Ghana, Accra, Ghana.
  • Giordano MR; Department of Environmental Health Sciences, School of Public Health and Health Sciences, University of Massachusetts, Amherst, Massachusetts 01003, United States.
  • Zheng Z; Industrial Economics, Inc, Cambridge, Massachusetts 02140, United States.
  • Jack D; Univ Paris Est Creteil, CNRS UMS 3563, Ecole Nationale des Ponts et Chaussés, Université de Paris, OSU-EFLUVE─Observatoire Sciences de L'Univers-Envelopes Fluides de La Ville à L'Exobiologie, F-94010 Créteil, France.
  • Chillrud S; Department of Earth and Environmental Sciences, The University of Manchester, Manchester M13 9PL, U.K.
  • Amegah K; Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, New York 10032, United States.
  • Subramanian R; Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, New York 10032, United States.
  • Pinder R; University of Cape Coast, Cape Coast, Ghana.
  • Appah-Sampong E; Univ Paris Est Creteil, CNRS UMS 3563, Ecole Nationale des Ponts et Chaussés, Université de Paris, OSU-EFLUVE─Observatoire Sciences de L'Univers-Envelopes Fluides de La Ville à L'Exobiologie, F-94010 Créteil, France.
  • Tetteh EN; Kigali Collaborative Research Centre, Kigali, Rwanda.
  • Borketey MA; Environmental Protection Agency, Raleigh, North Carolina 27709, United States.
  • Hughes AF; Ghana Environmental Protection Agency, Accra, Ghana.
  • Westervelt DM; Ghana Environmental Protection Agency, Accra, Ghana.
Environ Sci Technol ; 57(29): 10708-10720, 2023 07 25.
Article em En | MEDLINE | ID: mdl-37437161
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 PM2.5 is strongly correlated with reference PM2.5, 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/m3, followed by PurpleAir PA-II (4.54 µg/m3) and Clarity Node-S (13.68 µg/m3). 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 (R2: 0.97, 0.94, 0.96; mean absolute error: 0.56, 0.80, and 0.68 µg/m3 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 PM2.5 concentration in Accra is 23.4 µg/m3, which is 1.6 times the World Health Organization Daily PM2.5 guideline of 15 µg/m3. 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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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Poluentes Atmosféricos / Poluição do Ar Tipo de estudo: Health_economic_evaluation / Prognostic_studies País como assunto: Africa Idioma: En Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Poluentes Atmosféricos / Poluição do Ar Tipo de estudo: Health_economic_evaluation / Prognostic_studies País como assunto: Africa Idioma: En Ano de publicação: 2023 Tipo de documento: Article