EGU23-3903, updated on 24 Feb 2023
EGU General Assembly 2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Enhancing the spatial and temporal resolution of air quality monitoring in low – and middle-income countries using low-cost sensors 

Collins Gameli Hodoli1,8, Anthony Amoah1, Dominic Buer Boyetey1, Iq Mead2, Frederic Coulon3, Pallavi Pant4, Cesunica E. Ivey5, Victoria Owusu Tawiah6, James Nimoo7, John-Terry Morladza8, Garima Raheja9, Mawuli Amedofu10, Felix Allison Hughes7, Nelson Kowu11, Emmanuel Appoh12, Benjamin Essien12, Carl Malings13, and Daniel M. Westervelt9
Collins Gameli Hodoli et al.
  • 1School of Built Environment, University of Environment and Sustainable Development, PMB, Somanya, Eastern Region, Ghana
  • 2Imperial College, London, UK
  • 3Cranfield University, School of Water, Energy and Environment, Cranfield, MK43 0AL, UK
  • 4Health Effects Institute, Boston, MA, USA
  • 5Civil and Environmental Engineering, University of California, Berkeley, Berkeley, CA, USA
  • 6Ghana Meteorological Agency, Accra, Ghana
  • 7Department of Physics, University of Ghana, Legon, Accra, Ghana
  • 8Clean Air One Atmosphere, Accra, Ghana
  • 9Lamont Doherty Earth Observatory, Columbia University, Palisades, NY, USA
  • 10Kwame Nkrumah University of Science and Technology, KNUST, Kumasi, Ghana
  • 11Dept of Earth and Environmental Sciences, University of West Florida, Pensacola, FL, USA
  • 12Environmental Protection Agency, Accra, Ghana
  • 13Morgan State University, Baltimore, MD, USA & NASA Global Modelling and Assimilation Office, Goddard Space Flight Center, Greenbelt, MD, USA

Air pollution is one of the leading risk factors for poor health in Africa, resulting in millions of premature deaths and economic losses. Of particular interest is exposure to fine particulate matter (PM2.5) which is the driver for a majority of deaths across the continent. However, PM monitoring, and by extension, ground-level data on PM2.5 is very limited; this limits our understanding of the widespread societal and health impacts linked to PM pollution. The robustness of low-cost PM sensors and their ability to report in situ data in tropical environments via internet-based platforms as well as relative affordability has created the opportunity to employ low-cost sensors (LCS) for air quality monitoring but calibration methodologies and the usefulness of the high-temporal resolution data for source identification remain a challenge. Increasingly, local governments in African countries are also turning to low-cost sensors to monitor air quality. In this study, two Airnote PM monitors were colocated with reference-grade Teledyne PM mass monitor T640 for ~4 weeks at the University of Ghana, Accra to establish their performance using a simplified data correction methodology - multiple linear regression (MLR) model. A split ratio of 80% and 20% was used to train and test the populated Airnote PM2.5 data respectively based on measurements from Teledyne T640 with temperature and relative humidity values from the Airnote monitor. Sectoral and calendar analysis with wind component data were used to triangulate the sources of PM2.5. We observed a high consistency between the two Airnote monitors. Hourly and 24-hour average PM2.5 values ranged from 25 to 95 μg/m3, and 29 to 54 μg/m3 respectively, and in most cases, were significantly higher than the WHO Air Quality Guideline. MLR using Pearson’s correlation analysis improved the out-of-the-box quality of low-cost Airnote PM2.5 data; the R2 improved from 0.69 to 0.84 and the mean absolute error from 11.75 to 4.20 μg/m3 respectively. Also, the MLR correction model was found to improve the Airnote PM2.5 data quality for higher relative humidity (between 50 and 90%) but not lower. PM2.5 pollution was local and from N, NE and SW winds for the raw, corrected and Teledyne PM mass monitor T640 measurements. Together, these results indicate that with appropriate corrections, low-cost PM sensors can generate the much needed data for air pollution research and mitigation in areas with limited air quality monitoring and data.

How to cite: Hodoli, C. G., Amoah, A., Buer Boyetey, D., Mead, I., Coulon, F., Pant, P., Ivey, C. E., Owusu Tawiah, V., Nimoo, J., Morladza, J.-T., Raheja, G., Amedofu, M., Allison Hughes, F., Kowu, N., Appoh, E., Essien, B., Malings, C., and Westervelt, D. M.: Enhancing the spatial and temporal resolution of air quality monitoring in low – and middle-income countries using low-cost sensors , EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-3903,, 2023.