학술논문
Low-Cost Sensor Performance Intercomparison, Correction Factor Development, and 2+ Years of Ambient PM2.5Monitoring in Accra, Ghana
Document Type
Article
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.
Source
Environmental Science & Technology; July 2023, Vol. 57 Issue: 29 p10708-10720, 13p
Subject
Language
ISSN
0013936X; 15205851
Abstract
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 PM2.5is 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/m3for 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 PM2.5concentration in Accra is 23.4 μg/m3, which is 1.6 times the World Health Organization Daily PM2.5guideline 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.