Application of Gaussian Mixture Regression for the Correction of Low Cost PM2.5 Monitoring Data in Accra, Ghana

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dc.contributor.authorMcFarlane, C.
dc.contributor.authorRaheja, G.
dc.contributor.authorMalings, C.
dc.contributor.authorAppoh, E.K.E.
dc.contributor.authorHughes, A.F.
dc.contributor.authorWestervelt, D.M.
dc.date.accessioned2021-10-27T11:19:38Z
dc.date.available2021-10-27T11:19:38Z
dc.date.issued2021
dc.descriptionResearch Articleen_US
dc.description.abstractABSTRACT: Low-cost sensors (LCSs) for air quality monitoring have enormous potential to improve air quality data coverage in resource-limited parts of the world such as sub-Saharan Africa. LCSs, however, are affected by environment and source conditions. To establish high-quality data, LCSs must be collocated and calibrated with reference grade PM2.5 monitors. From March 2020, a low-cost PurpleAir PM2.5 monitor was collocated with a Met One Beta Attenuation Monitor 1020 in Accra, Ghana. While previous studies have shown that multiple linear regression (MLR) and random forest regression (RF) can improve accuracy and correlation between PurpleAir and reference data, MLR and RF yielded suboptimal improvement in the Accra collocation (R2 = 0.81 and R2 = 0.81, respectively). We present the first application of Gaussian mixture regression (GMR) to air quality data calibration and demonstrate improvement over traditional methods by increasing the collocated PM2.5 correlation and accuracy to R2 = 0.88 and MAE = 2.2 μg m−3. Gaussian mixture models (GMMs) are a probability density estimator and clustering method from which nonlinear regressions that tolerate missing inputs can be derived. We find that even when given missing inputs, GMR provides better correlation than MLR and RF performed with complete data. GMR also allows us to estimate calibration certainty. When evaluated, 95% confidence intervals agreed with reference PM2.5 data 96% of the time, suggesting that the model accurately assesses its own confidence. Additionally, clustering within the GMM is consistent with climate characteristics, providing confidence that the calibration approach can learn underlying relationships in data.en_US
dc.identifier.urihttp://ugspace.ug.edu.gh/handle/123456789/36895
dc.language.isoenen_US
dc.publisherACS Earth and Space Chemistryen_US
dc.subjectlow-cost sensorsen_US
dc.subjectparticulate matteren_US
dc.subjectair qualityen_US
dc.subjectAfricaen_US
dc.subjectGaussian mixture regressionen_US
dc.titleApplication of Gaussian Mixture Regression for the Correction of Low Cost PM2.5 Monitoring Data in Accra, Ghanaen_US
dc.typeArticleen_US

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