Oyediran Kayode Owoade1, Pelumi Olaitan Abiodun1, Opeyemi R. Omokungbe1, Olusegun Gabriel Fawole1,2, Felix Samuel Olise1, Olalekan O.M. Popoola3, Roderic L. Jones3, Philip K. Hopke This email address is being protected from spambots. You need JavaScript enabled to view it.4,5

1 Environmental Pollution Laboratory, Department of Physics and Engineering Physics, Obafemi Awolowo University, Ile-Ife, Nigeria
2 Atmospheric Science Unit, Department of Environmental Sciences, Stockholm University, Stockholm, Sweden
3 Department of Chemistry, University of Cambridge, Cambridge, UK
4 Institute for a Sustainable Environment, Clarkson University, Potsdam, NY, USA
5 Department of Public Health Sciences, University of Rochester School of Medicine and Dentistry, Rochester, NY, USA


Received: October 19, 2020
Revised: July 11, 2021
Accepted: July 18, 2021

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.


Download Citation: ||https://doi.org/10.4209/aaqr.200598  


Cite this article:

Owoade, O.K., Abiodun, P.O., Omokungbe, O.R., Fawole, O.G., Olise, F.S., Popoola, O.O.M., Jones, R.L., Hopke, P.K. (2021). Spatial-temporal Variation and Local Source Identification of Air Pollutants in a Semi-urban Settlement in Nigeria Using Low-cost Sensors. Aerosol Air Qual. Res. 21, 200598. https://doi.org/10.4209/aaqr.200598


HIGHLIGHTS

  • Low-cost sensors measured the spatial-temporal variability of air pollutants in Ile-Ife Nigeria.
  • Distinct diel temporal pollutant variations were observed within the study area.
  • Residential and other related sources of pollution were identified in the study area.
  • Transport of emissions from a local iron and smelting factory were identified.
 

ABSTRACT


Low-cost sensors were deployed at five locations in a growing, semi-urban settlement in southwest Nigeria between June 8 and July 31, 2018 to measure particulate matter (PM2.5 and PM10), gaseous pollutants (CO, NO, NO2, O3 and CO2), and meteorological variables (air temperature, relative humidity, wind speed and wind-direction). The spatial and temporal variations of measured pollutants were determined, and the probable sources of pollutants were inferred using conditional bivariate probability function (CBPF). Hourly PM2.5 and PM10 concentrations ranged from 20.7 ± 0.7 to 36.3 ± 1.6 µg m–3 and 47.5 ± 1.5 to 102.9 ± 5.6 µg m–3, respectively. Hourly gaseous pollutant concentrations ranged from 348 ± 132 to 542 ± 200 ppb CO, 21.5 ± 7.2 ppb NO2 and 57.5 ± 11.3 to 64.4 ± 14.0 ppb O3. Kruskal-Wallis ANOVA on ranks determined statistically significant spatial differences in the hourly-average pollutant concentrations. Diel variation analyses indicated that CO2, PM2.5, and PM10 peaked in the early hours of most days, O3 at noon while NO, NO2, and CO peaked in the evening. Most pollutants were of anthropogenic origins and exhibited the highest contributions from the southwest at most sampling locations. There were strong similarities between pollutants source contribution at two of the monitoring sites that were in residential areas with a frequently used paved road. Mitigation strategies need to be established to avoid further deterioration of ambient air quality that negatively affect public health.


Keywords: Temporal variation, Low-cost sensors, Particulate matter, CBPF, Source identification




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