Low-CostPM<sub>2.5</sub> Sensor Performance Characteristicsagainst Meteorological Influence in Sub-Saharan Africa: Evidence fromthe Air Sensor Evaluation and Training Facility for the West AfricaProject

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.

Publication Details

Journal
Figshare
Published
2025-03-25
DOI
https://doi.org/10.1021/acs.est.4c09752.s001
Primary Topic
Air Quality Monitoring and Forecasting
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article
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article

Low-CostPM2.5 Sensor Performance Characteristicsagainst Meteorological Influence in Sub-Saharan Africa: Evidence fromthe Air Sensor Evaluation and Training Facility for the West AfricaProject

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Air Quality Monitoring and Forecasting
article

Low-CostPM2.5 Sensor Performance Characteristicsagainst Meteorological Influence in Sub-Saharan Africa: Evidence fromthe Air Sensor Evaluation and Training Facility for the West AfricaProject

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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.

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Low-CostPM<sub>2.5</sub> Sensor Performance Characteristicsagainst Meteorological Influence in Sub-Saharan Africa: Evidence fromthe Air Sensor Evaluation and Training Facility for the West AfricaProject · Figshare (2025) | TGRS Research Map | TGRS