Source identification with high-temporal resolution data from low-cost sensors using bivariate polar plots in urban areas of Ghana

C Gameli Hodoli; F Coulon; M I Mead

doi:10.1016/j.envpol.2022.120448

Source identification with high-temporal resolution data from low-cost sensors using bivariate polar plots in urban areas of Ghana

Environ Pollut. 2023 Jan 15:317:120448. doi: 10.1016/j.envpol.2022.120448. Epub 2022 Oct 29.

Authors

C Gameli Hodoli¹, F Coulon², M I Mead³

Affiliations

¹ Cranfield University, School of Water, Energy and Environment, Cranfield, MK43 0AL, UK; University of Environment and Sustainable Development, School of Built Environment, PMB, Somanya, Eastern Region, Ghana.
² Cranfield University, School of Water, Energy and Environment, Cranfield, MK43 0AL, UK.
³ Cranfield University, School of Water, Energy and Environment, Cranfield, MK43 0AL, UK; MRC Centre for Environment and Health, Environmental Research Group, Imperial College London, W12 0BZ, UK. Electronic address: m.mead@imperial.ac.uk.

PMID: 36457223
DOI: 10.1016/j.envpol.2022.120448

Abstract

The emergence of low-cost sensors for atmospheric observations presents a new opportunity for identifying atmospheric emission sources based on high-resolution data reporting. Low-cost sensors have been widely assessed for use in source monitoring and identification of hotspots of key atmospheric species in advanced countries (e.g., for CO, NOx, CO₂, SO₂, O₃, VOCs and PM (PM₁₀, PM_2.5 including emerging PM₁). In contrast, research in recent years has focused on their utility for real-time monitoring, understanding precision and associated calibration requirements in technologically lagging environments. This leads to limited evidence on the utility of high-resolution data from low-cost sensor networks for air pollution source identification in Ghana and more widely across the African continent. In this paper, we demonstrate the potential of low-cost sensors for emission source apportionment in urban areas of Ghana when used with analytical tools such as sectoral and cluster analysis. With a 14-week dataset from a low-cost sensor deployment study at Cape Coast in the Central Region of Ghana, we aimed to identify sources of particulate matter (PM_2.5 and PM₁₀). PM pollution was local (associated with increased PM at wind speeds of ≤2 m s^-1). High levels of PM during this study were associated with transport from the NNE. For coarse PM, hourly levels as high as 125 μg m^-³ were observed at higher wind speeds (7-8 m s^-1) indicating the importance of meteorology in the transport of PM. This study suggests that low-cost sensors could be deployed to (1) augment any existing sparsely distributed air quality monitoring and (2) undertake air quality monitoring for source apportionment studies in areas with no existing air quality observational capability (with appropriate calibration and operation in both cases). The urban landscape monitored in this study is typical of both Ghana and wider areas across Sub-Saharan Africa demonstrating the reproducibility of this study.

Keywords: Cluster analysis; Ghana; Low-cost sensors; Source apportionment; Sub-saharan africa.

Publication types

Observational Study

MeSH terms

Air Pollutants* / analysis
Air Pollution* / analysis
Environmental Monitoring
Ghana
Particulate Matter / analysis
Reproducibility of Results

Substances

Air Pollutants
Particulate Matter

Grants and funding

MR/S019669/1/MRC_/Medical Research Council/United Kingdom