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/m3/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
1. World Health Organization. Coronavirus disease 2019 (COVID-19), Situation Report 65 [Internet]. 2020. Available from: https://www.who.int/publications/m/item/situation-report--65
2. Colombopage [Internet]. Available from: www.colombopage.com
3. Bao R, Zhang A. Does lockdown reduce air pollution? Evidence from 44 cities in northern China. Sci Total Environ [Internet]. 2020;731(1954):139052. Available from: https://doi.org/10.1016/j.scitotenv.2020.139052
4. Bauwens M, Compernolle S, Stavrakou T, Müller JF, van Gent J, Eskes H, et al. Impact of Coronavirus outbreak on NO2 pollution asessed using TROPOMI and OMI observations. Geophys Res Lett. 2020 Jun 16;47(11):e2020GL087978.
5. Ghahremanloo M, Lops Y, Choi Y, Mousavinezhad S. Impact of the COVID-19 outbreak on air pollution levels in East Asia. 2021 Feb 1;754:142226.
6. Hashim BM, Al-Naseri SK, Al-Maliki A, Al-Ansari N. Impact of COVID-19 lockdown on NO2, O3, PM2.5 and PM10 concentrations and assessing air quality changes in Baghdad, Iraq. Sci Total Environ [Internet]. 2021 Feb 1;754:141978. Available from: https://doi.org/10.1016/j.scitotenv.2020.141978
7. Ju MJ, Oh J, Choi YH. Changes in air pollution levels after COVID-19 outbreak in Korea. Sci Total Environ [Internet]. 2021;750:141521. Available from: https://doi.org/10.1016/j.scitotenv.2020.141521
8. Muhammad S, Long X, Salman M. COVID-19 pandemic and environmental pollution: A blessing in disguise? Sci Total Environ [Internet]. 2020;728:138820. Available from: https://doi.org/10.1016/j.scitotenv.2020.138820
9. Patel H, Talbot N, Salmond J, Dirks K, Xie S, Davy P. Changes in air quality during lockdown in Auckland (New Zealand) in response to the 2020 SARS-COV-2 pandemic. Air Quality and Climate Change. 2022 Jun 1;56(2):20-1.
10. Ropkins K, Tate JE. Early observations on the impact of the COVID-19 lockdown on air quality trends across the UK. Sci Total Environ [Internet]. 2020;754(January 2020):142374. Available from: https://doi.org/10.1016/j.scitotenv.2020.142374
11. Shen L, Zhao T, Wang H, Liu J, Bai Y, Kong S, Zheng H, Zhu Y, Shu Z. Importance of meteorology in air pollution events during the city lockdown for COVID-19 in Hubei Province, Central China. Science of the Total Environment. 2021 Feb 1;754:142227.
12. Kandari R, Kumar A. COVID-19 pandemic lockdown: effects on the air quality of South Asia. Environ Sustain [Internet]. 2021;(January 2020). Available from: https://doi.org/10.1007/s42398-020-00154-6
13. Mongabay. Crediting the lockdown for Sri Lanka's cleaner air masks the real problem (Commentary) [Internet]. Available from: https://news.mongabay.com/2020/06/crediting-the-lockdown-for-sri-lankas-cleaner-air-masks-the-real-problem-commentary/
14. Grange SK, Carslaw DC, Lewis AC, Boleti E, Hueglin C. Random forest meteorological normalization models for Swiss PM10 trend analysis. Atmos Chem Phys. 2018 May 3;18(9):6223-39.
15. Grange SK, Carslaw DC. Using meteorological normalization to detect interventions in air quality time series. Sci Total Environ [Internet]. 2019;653:578–88. Available from: https://doi.org/10.1016/j.scitotenv.2018.10.344
16. Barmpadimos I, Hueglin C, Keller J, Henne S, Prévôt ASH. Influence of meteorology on PM10 trends and variability in Switzerland from 1991 to 2008. Atmos Chem Phys. 2011 Feb 28;11(4):1813-35.
17. Friedman, J., Hastie, T., Tibshirani R. The Elements of Statistical Learning. 2nd ed., Data Mining, Inference, and Prediction. Springer series in statistics Springer, Berlin. 2001.
18. Dirks KN, Johns MD, Hay JE, Sturman AP. A semi-empirical model for predicting the effect of changes in traffic flow patterns on carbon monoxide concentrations. 2003;37:2719–24.
19. Dirks KN, Nanni A, Dirks VI. Modelling and predicting urban atmospheric pollutants in the Aosta Valley region of Italy using a site-optimized model. Atmos Sci Lett. 2006;7(1):15–20.
20. Elangasinghe MA, Singhal N, Dirks KN, Salmond JA, Samarasinghe S. Complex time series analysis of PM10 and PM2.5 for a coastal site using artificial neural network modelling and k-means clustering. Atmos Environ [Internet]. 2014;94:106–16. Available from: http://dx.doi.org/10.1016/j.atmosenv.2014.04.051.
21. Ranathunga N, Perera P, Nandasena S, Sathiakumar N, Kasturiratne A, Wickremasinghe R. Effect of household air pollution due to solid fuel combustion on childhood respiratory diseases in a semi urban population in Sri Lanka. BMC Pediatr. 2019;19(1):1–12.
22. Zubair L. Diurnal and seasonal variation in surface wind at Sita Eliya, Sri Lanka. Theor Appl Climatol. 2002;71(1–2):119–27.
23. Air Quality Open Data Platform – Worldwide COVID 19 Data Set [Internet]. Available from: https://aqicn.org/data-platform/COVID19.
24. Weather Data & API [Internet]. Available from: www.visualcrossing.com.
25. Carslaw DC, Ropkins K. Openair-An r package for air quality data analysis. Environ Model Softw [Internet]. 2012;27–28:52–61. Available from: http://dx.doi.org/10.1016/j.envsoft.2011.09.008.
26. Open Air [Internet]. Available from: http://www.openair-project.org.
27. David Carslaw. The openair manual 2019. 2019;(November):203. Available from: https://github.com/davidcarslaw/openair.
28. Theil H. A rank-invariant method of linear and polynomial regression analysis. Indagationes mathematicae. 1950;12(85):173.
29. Sen PK. Estimates of the Regression Coefficient Based on Kendall's Tau. J Am Stat Assoc. 1968;63(324):1379–89.
30. Carslaw DC, Beevers SD, Ropkins K, Bell MC. Detecting and quantifying aircraft and other on-airport contributions to ambient nitrogen oxides in the vicinity of a large international airport. Atmos Environ. 2006;40(28):5424–34.
31. Elangasinghe MA, Dirks KN, Singhal N, Costello SB, Longley I, Salmond JA. A simple semi-empirical technique for apportioning the impact of roadways on air quality in an urban neighbourhood. Atmos Environ [Internet]. 2014;83(January 2022):99–108. Available from: http://dx.doi.org/10.1016/j.atmosenv.2013.11.005
32. Senarathna M, Jayaratne R, Morawska L, Guo Y, Bui D, Abeysundara S, et al. Impact of COVID-19 lockdown on air quality of Sri Lankan cities. Int J Environ Pollut Remediat. 2021;12–21.
2. Colombopage [Internet]. Available from: www.colombopage.com
3. Bao R, Zhang A. Does lockdown reduce air pollution? Evidence from 44 cities in northern China. Sci Total Environ [Internet]. 2020;731(1954):139052. Available from: https://doi.org/10.1016/j.scitotenv.2020.139052
4. Bauwens M, Compernolle S, Stavrakou T, Müller JF, van Gent J, Eskes H, et al. Impact of Coronavirus outbreak on NO2 pollution asessed using TROPOMI and OMI observations. Geophys Res Lett. 2020 Jun 16;47(11):e2020GL087978.
5. Ghahremanloo M, Lops Y, Choi Y, Mousavinezhad S. Impact of the COVID-19 outbreak on air pollution levels in East Asia. 2021 Feb 1;754:142226.
6. Hashim BM, Al-Naseri SK, Al-Maliki A, Al-Ansari N. Impact of COVID-19 lockdown on NO2, O3, PM2.5 and PM10 concentrations and assessing air quality changes in Baghdad, Iraq. Sci Total Environ [Internet]. 2021 Feb 1;754:141978. Available from: https://doi.org/10.1016/j.scitotenv.2020.141978
7. Ju MJ, Oh J, Choi YH. Changes in air pollution levels after COVID-19 outbreak in Korea. Sci Total Environ [Internet]. 2021;750:141521. Available from: https://doi.org/10.1016/j.scitotenv.2020.141521
8. Muhammad S, Long X, Salman M. COVID-19 pandemic and environmental pollution: A blessing in disguise? Sci Total Environ [Internet]. 2020;728:138820. Available from: https://doi.org/10.1016/j.scitotenv.2020.138820
9. Patel H, Talbot N, Salmond J, Dirks K, Xie S, Davy P. Changes in air quality during lockdown in Auckland (New Zealand) in response to the 2020 SARS-COV-2 pandemic. Air Quality and Climate Change. 2022 Jun 1;56(2):20-1.
10. Ropkins K, Tate JE. Early observations on the impact of the COVID-19 lockdown on air quality trends across the UK. Sci Total Environ [Internet]. 2020;754(January 2020):142374. Available from: https://doi.org/10.1016/j.scitotenv.2020.142374
11. Shen L, Zhao T, Wang H, Liu J, Bai Y, Kong S, Zheng H, Zhu Y, Shu Z. Importance of meteorology in air pollution events during the city lockdown for COVID-19 in Hubei Province, Central China. Science of the Total Environment. 2021 Feb 1;754:142227.
12. Kandari R, Kumar A. COVID-19 pandemic lockdown: effects on the air quality of South Asia. Environ Sustain [Internet]. 2021;(January 2020). Available from: https://doi.org/10.1007/s42398-020-00154-6
13. Mongabay. Crediting the lockdown for Sri Lanka's cleaner air masks the real problem (Commentary) [Internet]. Available from: https://news.mongabay.com/2020/06/crediting-the-lockdown-for-sri-lankas-cleaner-air-masks-the-real-problem-commentary/
14. Grange SK, Carslaw DC, Lewis AC, Boleti E, Hueglin C. Random forest meteorological normalization models for Swiss PM10 trend analysis. Atmos Chem Phys. 2018 May 3;18(9):6223-39.
15. Grange SK, Carslaw DC. Using meteorological normalization to detect interventions in air quality time series. Sci Total Environ [Internet]. 2019;653:578–88. Available from: https://doi.org/10.1016/j.scitotenv.2018.10.344
16. Barmpadimos I, Hueglin C, Keller J, Henne S, Prévôt ASH. Influence of meteorology on PM10 trends and variability in Switzerland from 1991 to 2008. Atmos Chem Phys. 2011 Feb 28;11(4):1813-35.
17. Friedman, J., Hastie, T., Tibshirani R. The Elements of Statistical Learning. 2nd ed., Data Mining, Inference, and Prediction. Springer series in statistics Springer, Berlin. 2001.
18. Dirks KN, Johns MD, Hay JE, Sturman AP. A semi-empirical model for predicting the effect of changes in traffic flow patterns on carbon monoxide concentrations. 2003;37:2719–24.
19. Dirks KN, Nanni A, Dirks VI. Modelling and predicting urban atmospheric pollutants in the Aosta Valley region of Italy using a site-optimized model. Atmos Sci Lett. 2006;7(1):15–20.
20. Elangasinghe MA, Singhal N, Dirks KN, Salmond JA, Samarasinghe S. Complex time series analysis of PM10 and PM2.5 for a coastal site using artificial neural network modelling and k-means clustering. Atmos Environ [Internet]. 2014;94:106–16. Available from: http://dx.doi.org/10.1016/j.atmosenv.2014.04.051.
21. Ranathunga N, Perera P, Nandasena S, Sathiakumar N, Kasturiratne A, Wickremasinghe R. Effect of household air pollution due to solid fuel combustion on childhood respiratory diseases in a semi urban population in Sri Lanka. BMC Pediatr. 2019;19(1):1–12.
22. Zubair L. Diurnal and seasonal variation in surface wind at Sita Eliya, Sri Lanka. Theor Appl Climatol. 2002;71(1–2):119–27.
23. Air Quality Open Data Platform – Worldwide COVID 19 Data Set [Internet]. Available from: https://aqicn.org/data-platform/COVID19.
24. Weather Data & API [Internet]. Available from: www.visualcrossing.com.
25. Carslaw DC, Ropkins K. Openair-An r package for air quality data analysis. Environ Model Softw [Internet]. 2012;27–28:52–61. Available from: http://dx.doi.org/10.1016/j.envsoft.2011.09.008.
26. Open Air [Internet]. Available from: http://www.openair-project.org.
27. David Carslaw. The openair manual 2019. 2019;(November):203. Available from: https://github.com/davidcarslaw/openair.
28. Theil H. A rank-invariant method of linear and polynomial regression analysis. Indagationes mathematicae. 1950;12(85):173.
29. Sen PK. Estimates of the Regression Coefficient Based on Kendall's Tau. J Am Stat Assoc. 1968;63(324):1379–89.
30. Carslaw DC, Beevers SD, Ropkins K, Bell MC. Detecting and quantifying aircraft and other on-airport contributions to ambient nitrogen oxides in the vicinity of a large international airport. Atmos Environ. 2006;40(28):5424–34.
31. Elangasinghe MA, Dirks KN, Singhal N, Costello SB, Longley I, Salmond JA. A simple semi-empirical technique for apportioning the impact of roadways on air quality in an urban neighbourhood. Atmos Environ [Internet]. 2014;83(January 2022):99–108. Available from: http://dx.doi.org/10.1016/j.atmosenv.2013.11.005
32. Senarathna M, Jayaratne R, Morawska L, Guo Y, Bui D, Abeysundara S, et al. Impact of COVID-19 lockdown on air quality of Sri Lankan cities. Int J Environ Pollut Remediat. 2021;12–21.
| Files | ||
| Issue | Vol 8 No 3 (2023): Summer 2023 | |
| Section | Original Research | |
| DOI | https://doi.org/10.18502/japh.v8i3.13787 | |
| Keywords | ||
| COVID-19 lockdown; Sri Lanka; Air pollution; De-weathering; Particulate matter | ||
| Rights and permissions | |
|
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
How to Cite
1.
Jayasundara P, Elangasinghe A, Dirks K. Disentangling the impact of COVID-19 lockdown and meteorological factors on air quality in Colombo, Sri Lanka: A data clustering approach. JAPH. 2023;8(3):299-314.
