Disentangling the impact of COVID-19 lockdown and meteorological factors on air quality in Colombo, Sri Lanka: A data clustering approach

Abstract

Introduction: Human activities disrupted by COVID-19 have reduced global air pollution. Meteorological day-to-day and year-to-year variability affects pollution levels and complicates estimating reductions. This paper uses data clustering to remove the complexity of non-linear relationships by separating meteorology from complex emission patterns before modelling. The case study is based on PM2.5 concentration time series data and meteorological data for 2018 to 2021 in Colombo, Sri Lanka.
 Materials and methods: The southwest monsoon brings sea breezes from the Indian Ocean to land from May to October. To separate the effect of the monsoon winds on PM2.5 concentrations, analysis of time series data, polar plots, clusters, and Theil-Sen trends were used based on hourly-average air pollution and meteorological data for the whole dataset.
 Results: Two clear clusters were identified from scatterplots, representing the monsoon and non-monsoon periods. The study suggests that due to the combined effect of the monsoon winds and a reduction in the levels of traffic as a result of perturbations in human activity, the PM2.5 concentrations decreased at an average rate of 10.61 µg/m3/year (95% CI: 12.86 - 8.11) over the four years. During the non-monsoon season, due to traffic reductions alone, PM2.5 concentrations reduced at an average rate of 7.95 µg/m /year (95% CI: 10.07 – 5.51).
 Conclusion: These results are relevant to policymakers in the post pandemic planning of traffic and industry, with the methodology readily adapted for use in other locations where a separation of effects may be beneficial.