TY - JOUR
T1 - Low-Cost PM2.5 Sensor Performance Characteristics against Meteorological Influence in Sub-Saharan Africa
T2 - Evidence from the Air Sensor Evaluation and Training Facility for the West Africa Project
AU - Nimo, James
AU - Borketey, Mathias A.
AU - Appoh, Emmanuel K.E.
AU - Morrison, Abena Kyerewaa
AU - Ibrahim-Anyass, Yussif
AU - Owusu Tawiah, Audrey
AU - Arku, Raphael E.
AU - Amoah, Selina
AU - Tetteh, Esi Nerquaye
AU - Brown, Tim
AU - Presto, Albert A.
AU - Subramanian, R.
AU - Westervelt, Daniel M.
AU - Giordano, Michael R.
AU - Hughes, Allison Felix
N1 - Publisher Copyright:
© 2025 American Chemical Society.
PY - 2025
Y1 - 2025
N2 - Fine particulate matter (PM2.5) 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 PM2.5 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, PM2.5 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 (R2) 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 PM2.5 measured in the wet period, levels that were more common during the dry period typically characterized by long-range transport of PM2.5 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.
AB - Fine particulate matter (PM2.5) 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 PM2.5 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, PM2.5 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 (R2) 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 PM2.5 measured in the wet period, levels that were more common during the dry period typically characterized by long-range transport of PM2.5 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.
KW - Afri-SET
KW - air quality
KW - Air Quality Egg
KW - Air Visual Outdoor
KW - AirBeam3
KW - Airly PM
KW - low-cost sensors
KW - ModulAir-PM
KW - Open Air
KW - Praxis Urban
KW - Sensor.Africa
KW - sub-Saharan Africa
UR - https://www.scopus.com/pages/publications/105000735243
U2 - 10.1021/acs.est.4c09752
DO - 10.1021/acs.est.4c09752
M3 - Article
AN - SCOPUS:105000735243
SN - 0013-936X
JO - Environmental Science and Technology
JF - Environmental Science and Technology
ER -