Low-Cost PM<sub>2.5</sub> Sensor Performance Characteristics against Meteorological Influence in Sub-Saharan Africa: Evidence from the Air Sensor Evaluation and Training Facility for the West Africa Project

Fine particulate matter (PM<sub>2.5</sub>) pollution represents a major environmental health risk in Africa. The use of low-cost sensors (LCS) for air quality monitoring for policy and civic engagement in sub-Saharan Africa (SSA) has become paramount, as access to traditional reference-grade instruments is still sparse. Yet, studies pertaining to sensor performance under SSA's meteorological conditions and diverse emission sources are limited. Hence, we tested eight low-cost PM<sub>2.5</sub> sensors on the market from different manufacturers containing Plantower PMS, Alphasense OPC-N3, and AVO-Sensor sensors by collocating them with the federal equivalent method Teledyne T640 to ascertain data accuracy, reliability, and responsiveness during wet and dry periods. After 6 months of collocation, PM<sub>2.5</sub> concentrations from the LCS showed low intrasensor variability in both the wet and dry periods, but high intersensor variability with the Teledyne T640. A strong relationship existed between the LCS and Teledyne T640, with average coefficient of determination (<i>R</i><sup>2</sup>) values of 0.7 (range: 05-0.9) and 0.8 (0.64-0.97) in the wet and dry periods, respectively. Larger errors were also associated with LCS data during the dry than the wet period, with the average mean absolute error and root mean squared error, respectively, 4.5 and 5.3 times higher in the dry period. Uncertainties with large errors were also observed with high PM<sub>2.5</sub> measured in the wet period, levels that were more common during the dry period typically characterized by long-range transport of PM<sub>2.5</sub> pollution. The results show that season significantly affects LCS performance and data quality and that care must be taken during deployment and data usage in SSA, with regular maintenance, particularly in the dry season. Strong collaborative efforts between governmental agencies, industries, and civil society are needed to come up with an effective framework for their application.

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Publication Details

Journal
Environmental Science & Technology
Published
2025-03-25
DOI
https://doi.org/10.1021/acs.est.4c09752
Citations
8
Primary Topic
Air Quality Monitoring and Forecasting
Type
article
Field-Weighted Citation Impact
6.39
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article

Low-Cost PM2.5 Sensor Performance Characteristics against Meteorological Influence in Sub-Saharan Africa: Evidence from the Air Sensor Evaluation and Training Facility for the West Africa Project

Allison Felix Hughes, Tim Brown, Samuel Amoah, James Nimo et al.
8 citations
Environmental Science & Technology
Air Quality Monitoring and Forecasting
6.39
article

Low-Cost PM2.5 Sensor Performance Characteristics against Meteorological Influence in Sub-Saharan Africa: Evidence from the Air Sensor Evaluation and Training Facility for the West Africa Project

Allison Felix Hughes, Tim Brown, Samuel Amoah, James Nimo, Michael R. Giordano, Daniel M. Westervelt, Albert A. Presto, R. Subramanian, Raphael Edem Arku, Emmanuel K-E Appoh, Mathias A. Borketey, Andrew Morrison, Esi Nerquaye Tetteh, Yussif Ibrahim-Anyass, Audrey Owusu Tawiah
article en
8 citations

Abstract

Fine particulate matter (PM<sub>2.5</sub>) pollution represents a major environmental health risk in Africa. The use of low-cost sensors (LCS) for air quality monitoring for policy and civic engagement in sub-Saharan Africa (SSA) has become paramount, as access to traditional reference-grade instruments is still sparse. Yet, studies pertaining to sensor performance under SSA's meteorological conditions and diverse emission sources are limited. Hence, we tested eight low-cost PM<sub>2.5</sub> sensors on the market from different manufacturers containing Plantower PMS, Alphasense OPC-N3, and AVO-Sensor sensors by collocating them with the federal equivalent method Teledyne T640 to ascertain data accuracy, reliability, and responsiveness during wet and dry periods. After 6 months of collocation, PM<sub>2.5</sub> concentrations from the LCS showed low intrasensor variability in both the wet and dry periods, but high intersensor variability with the Teledyne T640. A strong relationship existed between the LCS and Teledyne T640, with average coefficient of determination (<i>R</i><sup>2</sup>) values of 0.7 (range: 05-0.9) and 0.8 (0.64-0.97) in the wet and dry periods, respectively. Larger errors were also associated with LCS data during the dry than the wet period, with the average mean absolute error and root mean squared error, respectively, 4.5 and 5.3 times higher in the dry period. Uncertainties with large errors were also observed with high PM<sub>2.5</sub> measured in the wet period, levels that were more common during the dry period typically characterized by long-range transport of PM<sub>2.5</sub> pollution. The results show that season significantly affects LCS performance and data quality and that care must be taken during deployment and data usage in SSA, with regular maintenance, particularly in the dry season. Strong collaborative efforts between governmental agencies, industries, and civil society are needed to come up with an effective framework for their application.

Environmental Science & TechnologyVol. 59(13)
Science and Technology Policy Institute (KR), State University of New York (US), University of Ghana (GH), Lamont-Doherty Earth Observatory (US), Center for Study of Science Technology and Policy (IN), Université Mohammed VI Polytechnique (MA), Carnegie Mellon University (US), Columbia University (US)
Climate action
Openalex Percentile: Top 3%
Air Quality Monitoring and Forecasting
6.39
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