Low-Cost Sensor Performance Intercomparison, Correction Factor Development, and 2+ Years of Ambient PM<sub>2.5</sub> Monitoring in Accra, Ghana
Garima Raheja, James Nimo, Emmanuel K-E Appoh, Benjamin Essien, M.Pd . I Gst. Ketut Arya Sunu., John Kofi Nyante, Mawuli Amegah, Reginald Quansah, Raphael E. Arku, Stefani L. Penn, Michael R. Giordano, Zhonghua Zheng, Darby Jack, Steven N. Chillrud, Kofi Amegah, R. Subramanian, R. W. Pinder, Ebenezer Kwabena Appah-Sampong, Esi Nerquaye Tetteh, Mathias A. Borketey, Allison Hughes, Daniel M. Westervelt
Abstract
High Resolution Image Download MS PowerPoint Slide 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.