Articles | Volume 10, issue 2
Geosci. Instrum. Method. Data Syst., 10, 265–285, 2021
Geosci. Instrum. Method. Data Syst., 10, 265–285, 2021

Research article 03 Nov 2021

Research article | 03 Nov 2021

Evaluation of multivariate time series clustering for imputation of air pollution data

Wedad Alahamade et al.

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Cited articles

Alahamade, W.: Wedad-O-A/Modelled-concentrations-: Modelled_Concentration_Air_Qaulity (v3.5.2), Zenodo [code and data set],, 2021. a
Alahamade, W., Lake, I., Reeves, C. E., and De La Iglesia, B.: Clustering Imputation for Air Pollution Data, in: International Conference on Hybrid Artificial Intelligence Systems, Lecture Notes in Computer Science, 585–597,, Springer, Cham, 2020. a, b
Alahamade, W., Lake, I., Reeves, C. E., and De La Iglesia, B.: A Multi-variate Time Series clustering approach based on Intermediate Fusion A case study in air pollution data imputation, Neurocomputing, in press, 2021. a, b, c
Austin, E., Coull, B. A., Zanobetti, A., and Koutrakis, P.: A framework to spatially cluster air pollution monitoring sites in US based on the PM2.5 composition, Environ. Int., 59, 244–254, 2013. a, b
Carbajal-Hernández, J. J., Sánchez-Fernández, L. P., Carrasco-Ochoa, J. A., and Martínez-Trinidad, J. F.: Assessment and prediction of air quality using fuzzy logic and autoregressive models, Atmos. Environ., 60, 37–50, 2012. a
Short summary
The goal is to reduce the uncertainty in air quality assessment by imputing all missing pollutants in monitoring stations and identify where more measurements can be beneficial. The proposed approach is based on spatial or temporal similarity between stations. Our proposed approach enables us to impute and estimate plausible concentrations of multiple pollutants at stations across the UK, and the modelled concentrations from the selected models correlated well with the observed concentrations.