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Accelerating Discoveries in Traffic Science

Accelerating Discoveries in Traffic Science

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PUBLISHED
30.09.2022
DOI
https://doi.org/10.7307/ptt.v34i5.4121
ISSUE
2022 (Vol 34), Issue 5
LICENSE
Copyright (c) 2022 David Jesenko, Domen Mongus, Uroš Lešnik
Creative Commons 4.0 logo
This work is licensed under a
Creative Commons Attribution-NonCommercial-
NoDerivatives 4.0 International License.

The Influence of COVID-19 on Particulate Matter Concentrations in a Medium-Sized Town

Authors:David Jesenko, Domen Mongus, Uroš Lešnik

Abstract

The pandemic caused by the coronavirus COVID-19 is having a worldwide impact that affects health, economy and air pollution in cities indirectly. In Slovenia, as well as in all other countries, the number of cases of infected people increased continually in 2020, which affected the health system and caused movement restrictions, which, in turn, affected the air pollution in the country. This article presents the indirect effect produced by this pandemic on air pollution in Maribor, Slovenia. Traffic and air quality data were used to perform the evaluation, in particular PM10 and PM2.5 daily concentrations from the monitoring station in Maribor. By observing the detailed traffic data and particulate matter concentrations acquired in the Maribor city centre before and during the pandemic times, we show the influence of COVID-19 on particulate matter concentrations in that part of the town. The results show slightly lower particulate matter concentrations, which could be explained by the significantly lower traffic volume values in the lockdown months.

Keywords:PM10, PM2.5, COVID-19, traffic, particulate matter

References

  1. [1] WHO. WHO global urban ambient air pollution database. World Health Organization; 2016.
  2. [2] Loomis D, et al. The carcinogenicity of outdoor air pollution. Lancet Oncology. 2013;14(13): 1262-1263. doi: 10.1016/s1470-2045(13)70487-x.
  3. [3] Marco G, Bo X. Air quality legislation and standards in the European union: Background, status and public participation. Advances in Climate Change Research. 2013;4(1): 50-59. doi: 10.3724/SP.J.1248.2013.050.
  4. [4] WHO. Air quality guidelines: Global update 2005. Particulate matter, ozone, nitrogen dioxide and sulfur dioxide. World Health Organization; 2006.
  5. [5] Guerreiro CBB, Foltescu V, De Leeuw F. Air quality status and trends in Europe. Atmospheric Environment. 2014;98(1): 376-384. doi: 10.1016/j.atmosenv.2014.09.017.
  6. [6] Cui J, Li F, Shi ZL. Origin and evolution of pathogenic coronaviruses. Nature Reviews Microbiology. 2019;17(3): 181-192. https://www.nature.com/articles/s41579-018-0118-9.
  7. [7] Hu B, Guo H, Zhou P, Shi ZL. Characteristics of SARS-CoV-2 and COVID-19. Nature Reviews Microbiology. 2021;19(3): 141-154. https://www.nature.com/articles/s41579-020-00459-7.
  8. [8] Wu JT, Leung K, Leung GM. Nowcasting and forecasting the potential domestic and international spread of the 2019-nCoV outbreak originating in Wuhan, China: A modelling study. The Lancet. 2020;395(10225): 689-697. doi: 10.1016/S0140-6736(20)30260-9.
  9. [9] Hui DS, et al. The continuing 2019-nCoV epidemic threat of novel coronaviruses to global health - The latest 2019 novel coronavirus outbreak in Wuhan, China. International Journal of Infectious Diseases. 2020;91(1): 264-266. doi: 10.1016/j.ijid.2020.01.009.
  10. [10] Vodičar PM, et al. Low prevalence of active COVID-19 in Slovenia: A nationwide population study of a probability-based sample. Clinical Microbiology and Infection. 2020;26(11): 1514-1519. doi: 10.1016/j.cmi.2020.07.013.
  11. [11] WHO. Coronavirus disease 2019 (COVID-19): Situation report. World Health Organization, 2020.
  12. [12] Fajersztajn L, Veras M, Barrozo LV, Saldiva P. Air pollution: A potentially modifiable risk factor for lung cancer. Nature Reviews Cancer. 2013;13(9): 674-678. https://www.nature.com/articles/nrc3572.
  13. [13] Jesenko D, et al. Visualization and analytics tool for multi-dimensional data. Proceedings of the 2018 International Conference on Big Data and Education. 2018;1(1): 1-5.
  14. [14] Lešnik U, Mongus D, Jesenko D. Predictive analytics of PM10 concentration levels using detailed traffic data. Transportation Research Part D: Transport and Environment. 2018;67(1): 131-141. doi: 10.1016/j.trd.2018.11.015.
  15. [15] Shakil MH, Munim ZH, Tasnia M, Sarowar S. COVID-19 and the environment: A critical review and research agenda. Science of the Total Environment. 2020;745(1): 141022. doi: 10.1016/j.scitotenv.2020.141022.
  16. [16] Wang Q, Su M. A preliminary assessment of the impact of COVID-19 on environment - A case study of China. Science of the Total Environment. 2020;728(1): 138915. doi: 10.1016/j.scitotenv.2020.138915.
  17. [17] Zambrano-Monserrate MA, Ruano MA, Sanchez-Alcalde L. Indirect effects of COVID-19 on the environment. Science of the Total Environment. 2020;728(1): 138813. doi: 10.1016/j.scitotenv.2020.138813.
  18. [18] Saadat S, Rawtani D, Hussain CM. Environmental perspective of COVID-19. Science of the Total Environment. 2020;728(1): 138870. doi: 10.1016/j.scitotenv.2020.138870.
  19. [19] Bashir MF, et al. Correlation between climate indicators and COVID-19 pandemic in New York, USA. Science of the Total Environment. 2020;728(1): 138835. doi: 10.1016/j.scitotenv.2020.138835.
  20. [20] Tosepu R, et al. Correlation between weather and Covid-19 pandemic in Jakarta, Indonesia. Science of the Total Environment. 2020;725(1): 138436. doi: 10.1016/j.scitotenv.2020.138436.
  21. [21] Sahin M. Impact of weather on COVID-19 pandemic in Turkey. Science of the Total Environment. 2020;728(1): 138810. doi: 10.1016/j.scitotenv.2020.138810.
  22. [22] Qi H, et al. COVID-19 transmission in Mainland China is associated with temperature and humidity: A time-series analysis. Science of the Total Environment. 2020;728(1): 138778. doi: 10.1016/j.scitotenv.2020.138778.
  23. [23] Gupta S, Raghuwanshi GS, Chanda A. Effect of weather on COVID-19 spread in the US: A prediction model for India in 2020. Science of the Total Environment. 2020;728(1): 138860. doi: 10.1016/j.scitotenv.2020.138860.
  24. [24] Sobral MFF, et al. Association between climate variables and global transmission of SARS-CoV-2. Science of the Total Environment. 2020;729(1): 138997. doi: 10.1016/j.scitotenv.2020.138997.
  25. [25] Abdullah S, et al. Air quality status during 2020 Malaysia Movement Control Order (MCO) due to 2019 novel coronavirus (2019-nCoV) pandemic. Science of the Total Environment. 2020;729(1): 139022. doi: 10.1016/j.scitotenv.2020.139022.
  26. [26] Dantas G, et al. The impact of COVID-19 partial lockdown on the air quality of the city of Rio de Janeiro, Brazil. Science of the Total Environment. 2020;729(1): 139085. doi: 10.1016/j.scitotenv.2020.139085.
  27. [27] Tobias A, et al. Changes in air quality during the lockdown in Barcelona (Spain) one month into the SARS-CoV-2 epidemic. Science of the Total Environment. 2020;726(1): 138540. doi: 10.1016/j.scitotenv.2020.138540.
  28. [28] Muhammad S, Long X, Salman M. COVID-19 pandemic and environmental pollution: A blessing in disguise? Science of the Total Environment. 2020;728(1): 138820. doi: 10.1016/j.scitotenv.2020.138820.
  29. [29] Xu K, et al. Impact of the COVID-19 event on air quality in Central China. Aerosol and Air Quality Research. 2020;20(5): 915-929. doi: 10.4209/aaqr.2020.04.0150.
  30. [30] Jain S, Sharma T. Social and travel lockdown impact considering coronavirus disease (COVID-19) on air quality in megacities of India: Present benefits, future challenges, and way forward. Aerosol and Air Quality Research. 2020;20(6): 1222-1236. doi: 10.4209/aaqr.2020.04.0171.
  31. [31] Bacak TN, Toros H. Impact of the COVID-19 event on PM10 air pollution in Istanbul and Ankara. Journal of Research in Atmospheric Science. 2021;3(1): 1-7.
  32. [32] Zareba M, Danek T. Analysis of air pollution migration during COVID-19 lockdown in Krakow, Poland. Aerosol and Air Quality Research. 2022;22(3): 1-22. doi: doi.org/10.4209/aaqr.210275.
  33. [33] Alharbi BH, Alhazmi HA, Aldhafeeri ZM. Air quality of work, residential, and traffic areas during the COVID-19 lockdown with insights to improve air quality. International Journal of Environmental Research and Public Health. 2022;19(2): 727-744. doi: 10.3390/ijerph19020727.
  34. [34] Shukla S, et al. Appraisal of COVID-19 lockdown and unlocking effects on the air quality of North India. Environmental Research. 2022;204(1): 112107. doi: 10.1016/j.envres.2021.112107.
  35. [35] Rojano R, Arregoces H, Frias EG. Changes in ambient particulate matter during the COVID-19 and associations with biomass burning and Sahara dust in northern Colombia. Heliyon. 2021;7(12): e08595. doi: 10.1016/j.heliyon.2021.e08595.
  36. [36] Tekin OF. Evaluation of air pollutants (PM10 and SO2) in the first year of the COVID-19: A city sample from Turkey. World Journal of Advanced Research and Reviews. 2021;10(1): 41-47. doi: 10.30574/wjarr.2021.10.1.0130.
  37. [37] Al-Hemoud A, et al. PM2.5 and PM10 during COVID-19 lockdown in Kuwait: Mixed effect of dust and meteorological covariates. Environmental Challenges. 2021;5(1): 100215. doi: 10.1016/j.envc.2021.100215.
  38. [38] Abdi H, Williams LJ. Principal component analysis. Wiley Interdisciplinary Reviews: Computational Statistics. 2010;2(4): 433-459. doi: 10.1002/wics.101.
  39. [39] Lukač N, Žalik B. Fast approximate k-nearest neighbours search using GPGPU. GPU Computing and Applications. 2015;2(1): 221-234. doi: 10.1007/978-981-287-134-3_14.
  40. [40] Papanastasiou DK, Melas D, Kioutsioukis I. Development and assessment of neural network and multiple regression models in order to predict PM10 levels in a medium-sized Mediterranean city. Water, Air, and Soil Pollution. 2007;182(1): 325-334. doi: 10.1007/s11270-007-9341-0.
  41. [41] Nagendra SM, Khare M. Artificial neural network based line source models for vehicular exhaust emission predictions of an urban roadway. Transportation Research Part D: Transport and Environment. 2004;9(3): 199-208. doi: 10.1016/j.trd.2004.01.002.
  42. [42] Cai M, Yin Y, Xie M. Prediction of hourly air pollutant concentrations near urban arterials using artificial neural network approach. Transportation Research Part D: Transport and Environment. 2009;14(1): 32-41. doi: 10.1016/j.trd.2008.10.004.
  43. [43] Hooyberghs J, et al. A neural network forecast for daily average PM10 concentrations in Belgium. Atmospheric Environment. 2005;39(18): 3279-3289. doi: 10.1016/j.atmosenv.2005.01.050.
  44. [44] Grivas G, Chaloulakou A. Artificial neural network models for prediction of PM10 hourly concentrations, in the Greater Area of Athens, Greece. Atmospheric Environment. 2006;40(7): 1216-1229. doi: 10.1016/j.atmosenv.2005.10.036.
  45. [45] Stadlober E, Hörmann S, Pfeiler B. Quality and performance of a PM10 daily forecasting model. Atmospheric Environment. 2008;42(6): 1098-1109. doi: 10.1016/j.atmosenv.2007.10.073.
  46. [46] Hörmann S, Pfeiler B, Stadlober E. Analysis and prediction of particulate matter PM10 for the winter season in Graz. Austrian Journal of Statistics. 2005;34(4): 307-326. https://graz.pure.elsevier.com/en/publications/analysis-and-prediction-of-particulate-matter-pm10-for-the-winter.
  47. [47] Barmpadimos I, et al. Influence of meteorology on PM 10 trends and variability in Switzerland from 1991 to 2008. Atmospheric Chemistry and Physics. 2011;11(4): 1813-1835. doi: 10.5194/acp-11-1813-2011.
  48. [48] Hoi KI, Yuen KV, Mok KM. Prediction of daily averaged PM10 concentrations by statistical time-varying model. Atmospheric Environment. 2009;43(16): 2579-2581. doi: 10.1016/j.atmosenv.2009.02.020.
  49. [49] Schnelle-Kreis J, et al. Impact of wood combustion on urban PM10 concentration. EGU General Assembly Conference Abstracts. 2009;1(1): 2965. https://meetingorganizer.copernicus.org/EGU2009/EGU2009-2965-1.pdf.
  50. [50] Scheinhardt S, et al. Comprehensive chemical characterisation of size-segregated PM10 in Dresden and estimation of changes due to global warming. Atmospheric Environment. 2013;75(1): 365-373. doi: 10.1016/j.atmosenv.2013.04.059.
  51. [51] Beddows DC, Harrison RM. PM10 and PM2.5 emission factors for non-exhaust particles from road vehicles: Dependence upon vehicle mass and implications for battery electric vehicles. Atmospheric Environment. 2021;244(1): 117886. doi: 10.1016/j.atmosenv.2020.117886.
  52. [52] Gvero P, Radić R, Kotur M, Kardaš D. Urban air pollution caused by the emission of PM10 from the small household devices and abatement measures. Thermal Science. 2018;22(6 Part A): 2325-2333. doi: 10.2298/TSCI180119152G.
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