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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

  • James Nimo
  • , Mathias A. Borketey
  • , Emmanuel K.E. Appoh
  • , Abena Kyerewaa Morrison
  • , Yussif Ibrahim-Anyass
  • , Audrey Owusu Tawiah
  • , Raphael E. Arku
  • , Selina Amoah
  • , Esi Nerquaye Tetteh
  • , Tim Brown
  • , Albert A. Presto
  • , R. Subramanian
  • , Daniel M. Westervelt
  • , Michael R. Giordano
  • , Allison Felix Hughes
  • State University of New York
  • University of Ghana
  • University of Massachusetts Amherst
  • Ghana Environmental Protection Authority
  • Kigali Collaborative Research Centre
  • Carnegie Mellon University
  • Center for Study of Science
  • Columbia University
  • Mohammed VI Polytechnic University
  • AfriqAir

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)6623-6635
Number of pages13
JournalEnvironmental Science and Technology
Volume59
Issue number13
DOIs
Publication statusPublished - 8 Apr 2025

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being
  2. SDG 11 - Sustainable Cities and Communities
    SDG 11 Sustainable Cities and Communities

Keywords

  • Afri-SET
  • Air Quality Egg
  • Air Visual Outdoor
  • AirBeam3
  • Airly PM
  • ModulAir-PM
  • Open Air
  • Praxis Urban
  • Sensor.Africa
  • air quality
  • low-cost sensors
  • sub-Saharan Africa

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