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Monitoring Of Co, No2 And So2 Levels During The Covid-19 Pandemic In Iran Using Remote Sensing Imagery

https://doi.org/10.24057/2071-9388-2020-74

Abstract

The COVID-19 pandemic has had a major impact on various sectors. Iran is one of the countries most affected by this pandemic. After considering the huge impact, the government imposed strict rules prohibiting social gatherings and restricting travel for the entire population following the large number of victims in the country. These restrictions lead to changes in the environment, especially air quality. The purpose of this study was to find out how the COVID-19 pandemic affected air quality in Iran following the activity restrictions in the region. The method used in this research was based on the use of multitemporal Sentinel-5P data processing with scripts available on the Google Earth Engine applied on the images, acquired in the period before and after the COVID-19 pandemic. The data used included the image collection of Sentinel-5P NRTI CO: Near Real-Time Carbon Monoxide, Sentinel-5P NRTI NO2: Near Real-Time Nitrogen Dioxide and Sentinel-5P NRTI SO2: Near Real-Time Sulphur Dioxide. The results showed, that for Iran in general, changes in the concentration of CO are clearly visible in urban areas with high population activity such as Tehran, where there was a decrease from 0.05 to 0.0286 mol/m2, while for other areas it is also influenced by the varying climate conditions, which affect the level of pollution. For the NO2 pollutant, there was a significant decrease in pollution levels in big cities such as Tehran, Qom, Isfahan and Mashhad from 0.0002 to 0.000114 mol/m2. For the SO2 pollutant, there was a decrease in pollution levels in Iran’s big cities from 0.0005 to 0.0000714 mol/m2. For Tehran province, which is the most populous and busiest province in Iran, it can be observed that there was also a decrease in the concentration of pollutants after the lockdown compared to the pre-lockdown period. The CO concentration decreased from 0.043 to 0.036 mol/m2, while for the NO2 pollutant there was a decrease from 0.0002 to 0.000142 mol/m2 and for the SO2 pollutant, there was a decrease from 0.0005 to 0.000143 mol/m2.

About the Authors

Nurwita Mustika Sari
Universitas Indonesia; Remote Sensing Application Center, LAPAN
Indonesia

Department of Geography, Faculty of Mathematics and Natural Science

 



Muhammad Nur Sidiq Kuncoro
Universitas Indonesia
Indonesia

Department of Management, Faculty of Economics and Business



References

1. Adams D., Oh D.H., Kim D.W., Lee C.H. & Oh M. (2020). Prediction of SOx–NOx emission from a coal-fired CFB power plant with machine learning: Plant data learned by deep neural network and least square support vector machine. Journal of Cleaner Production, 270, 122310, DOI: 10.1016/j.jclepro.2020.122310.

2. Adnan M., Khan S., Kazmi A., Bashir N., & Siddique R. (2020). COVID-19 infection : Origin , transmission , and characteristics of human coronaviruses. Journal of Advanced Research, 24, 91-98, DOI: 10.1016/j.jare.2020.03.005.

3. Altuğ H., Fuks K.B., Hüls A., Mayer A.K., Tham R., Krutmann J. & Schikowski T. (2020). Air pollution is associated with depressive symptoms in elderly women with cognitive impairment. Environment International, 136(January), DOI: 10.1016/j.envint.2019.105448.

4. Andersson E.M., Ögren M., Molnár P., Segersson D., & Stockfelt L. (2020). Road traffic noise, air pollution and cardiovascular events in a Swedish cohort. Environmental Research, 109446, DOI: 10.1016/j.envres.2020.109446.

5. Azimi M., Feng F., & Yang Y. (2018). Air pollution inequality and its sources in SO2 and NOX emissions among Chinese Provinces from 2006 to 2015. Sustainability (Switzerland), 10(2), DOI: 10.3390/su10020367.

6. Berman J.D., & Ebisu K. (2020). Science of the Total Environment Changes in U . S . air pollution during the COVID-19 pandemic. Science of the Total Environment, 739, 139864, DOI: 10.1016/j.scitotenv.2020.139864.

7. Chen Y., Wang S., Han W., Xiong Y., Wang W., & Tong L. (2017). A New Air Pollution Source Identification Method Based on Remotely Sensed Aerosol and Improved Glowworm Swarm Optimization. 3454 IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 10(8), 3454-3464.

8. Clerbaux C., Bauduin S., Boynard A., Clarisse L., Coheur P., George M., Hadji-lazaro J., Hurtmans D., Safieddine S., Damme M. Van, & Whitburn S. (2017). Observation of Air Pollution over China Using the IASI Thermal Infrared Space Sensor. In Air Pollution in Eastern Asia: An Integrated Perspective, 309-322, DOI: 10.1007/978-3-319-59489-7.

9. Dahlmann K., Grewe V., Ponater M., & Matthes S. (2011). Quantifying the contributions of individual NOx sources to the trend in ozone radiative forcing. Atmospheric Environment, 45(17), 2860-2868, DOI: 10.1016/j.atmosenv.2011.02.071.

10. European Space Agency. (2021). Sentinel. https://sentinel.esa.int/web/sentinel/missions/sentinel-5p accessed on June 12th, 2021.

11. Filonchyk M., Yan H., Yang S., & Lu X. (2017). Detection of aerosol pollution sources during sandstorms in Northwestern China using remote sensed and model simulated data. Advances in Space Research, DOI: 10.1016/j.asr.2017.11.037.

12. Fu M., Kelly J.A., & Clinch J.P. (2020). Prediction of PM2.5 daily concentrations for grid points throughout a vast area using remote sensing data and an improved dynamic spatial panel model. Atmospheric Environment, 117667, DOI: 10.1016/j.atmosenv.2020.117667.

13. Google Earth Engine (2020). Sentinel-5P NRTI CO Near Real-Time Carbon Monoxide. Earth Engine Data Catalog.

14. Huang Y., Organ B., Zhou J.L., Surawski N.C., Hong G., Chan F.C., & Yam Y.S. (2018). Remote sensing of on-road vehicle emissions: mechanism, applications and a case study from Hong Kong. Atmospheric Environment, DOI: 10.1016/j.atmosenv.2018.03.035.

15. Srivastava I.N., Yarragunta S., Kumar Y., & Mitra D. (2020). Distribution of surface carbon monoxide over the Indian subcontinent: Investigation of source contributions using WRF-Chem. Atmospheric Environment, 243, DOI: 10.1016/j.atmosenv.2020.117838.

16. Kurata M., Takahashi K., & Hibiki A. (2020). Gender differences in associations of household and ambient air pollution with child health: Evidence from household and satellite-based data in Bangladesh. World Development, 128, 104779, DOI: 10.1016/j.worlddev.2019.104779.

17. Leifer I., Melton C., Chatfield R., Cui X., Fischer M.L., Fladeland M., Gore W., Hlavka D.L., Iraci L., Marrero J., Ryoo M., Tanaka T., Yates E., & Yorks J. (2019). Air pollution inputs to the Mojave Desert by fusing surface mobile and airborne in situ and airborne and satellite remote sensing: A case study of interbasin transport with numerical model validation. Atmospheric Environment, 117184, DOI: 10.1016/j.atmosenv.2019.117184.

18. Li J. (Jie), Massa M., Zhang H., & Zhang J. (2019). Air pollution, behavioral bias, and the disposition effect in China. Journal of Financial Economics, xxxx, DOI: 10.1016/j.jfineco.2019.09.003.

19. Li X., & Zhang X. (2019). Predicting ground-level PM 2 . 5 concentrations in the Beijing-Tianjin- Hebei region : A hybrid remote sensing and machine learning. Environmental Pollution, 249, 735-749, DOI: 10.1016/j.envpol.2019.03.068.

20. Miller M.R. (2020). Oxidative stress and the cardiovascular effects of air pollution. Free Radical Biology and Medicine, January, DOI: 10.1016/j.freeradbiomed.2020.01.004.

21. NASA (2011). Effects of Changing the Carbon Cycle. In Earth Observatory Omrani H., Omrani B., Parmentier B., & Helbich M. (2020). Spatio-temporal data on the air pollutant nitrogen dioxide derived from Sentinel satellite for France. Data in Brief, 28, 105089, DOI: 10.1016/j.dib.2019.105089.

22. Pacheco H., Díaz-López S., Jarre E., Pacheco H., Méndez W., Zamora-Ledezma E. (2020). NO2 levels after the COVID-19 lockdown in Ecuador: A trade-off between environment and human health. Urban Climate, 34(December), 100674, DOI: 10.1016/j.uclim.2020.100674.

23. Petrosillo N., Viceconte G., Ergonul O., Ippolito G., & Petersen E. (2020). COVID-19, SARS and MERS: are they closely related? Clinical Microbiology and Infection, DOI: 10.1016/j.cmi.2020.03.026.

24. Prud G., Dobbin N.A., Sun L., Burnett R.T., Martin R.V, Davidson A., Cakmak S., Villeneuve P.J., Lamsal L.N., Donkelaar A.Van, Peters P.A., & Johnson M. (2013). Comparison of remote sensing and fixed-site monitoring approaches for examining air pollution and health in a national study population. Atmospheric Environment, 80, 161-171, DOI: 10.1016/j.atmosenv.2013.07.020.

25. Reuters (2020). Iran reports COVID-19 death every four minutes, extends curbs. In Health News. Rothan H.A., & Byrareddy S.N. (2020). The epidemiology and pathogenesis of coronavirus disease (COVID-19) outbreak. Journal of Autoimmunity, February, 102433, DOI: 10.1016/j.jaut.2020.102433.

26. Shikwambana L., Mhangara P., Mbatha N. (2020). Trend analysis and first time observations of sulphur dioxide and nitrogen dioxide in South Africa using TROPOMI/Sentinel-5 P data. International Journal of Applied Earth Observation and Geoinformation, 91(September), 102130, DOI: 10.1016/j.jag.2020.102130.

27. Smit R., Kingston P., Neale D.W., Brown M.K., Verran B. & Nolan T. (2019). Monitoring on-road air quality and measuring vehicle emissions with remote sensing in an urban area. Atmospheric Environment, 218(September), 116978, DOI: 10.1016/j.atmosenv.2019.116978.

28. Somvanshi S.S., Vashisht A., Chandra U., & Kaushik G. (2019). Delhi Air Pollution Modeling Using Remote Sensing Technique. In Handbook of Environmental Materials Management, 1-27, DOI: 10.1007/978-3-319-58538-3_174-1.

29. Sun S., Li L., Wu Z., Gautam A., Li J., & Zhao W. (2020). Variation of industrial air pollution emissions based on VIIRS thermal anomaly data. Atmospheric Research, 244(May), 105021, DOI: 10.1016/j.atmosres.2020.105021.

30. Tosepu R., Gunawan J., Effendy D.S., Ahmad L.O.A.I., Lestari H., Bahar H., & Asfian P. (2020). Correlation between weather and Covid-19 pandemic in Jakarta, Indonesia. Science of the Total Environment, 138436, DOI: 10.1016/j.scitotenv.2020.138436.

31. TROPOMI (2020). Carbon Monoxide. In Data Products

32. TROPOMI (2020). Nitrogen Dioxide. In Data Products

33. TROPOMI (2020). Sulphur Dioxide. In Data Products

34. ul-Haq Z., Tariq S., Ali M. (2015). Atmospheric variability of methane over Pakistan, Afghanistan and adjoining areas using retrievals from SCIAMACHY/ENVISAT. Journal of Atmospheric and Solar-Terrestrial Physics, 135(December), 161-173, DOI: 10.1016/j.jastp.2015.11.002.

35. Vratolis S., Fetfatzis P., Argyrouli A., Soupiona O., Mylonaki M., & Maroufidis J. (2020). Comparison and complementary use of in situ and remote sensing aerosol measurements in the Athens Metropolitan Area. Atmospheric Environment, 228(March), 117439, DOI: 10.1016/j.atmosenv.2020.117439.

36. Wan Y., Li Y., Liu C., & Li Z. (2020). Is traffic accident related to air pollution? A case report from an island of Taihu Lake, China. Atmospheric Pollution Research, September 2019, 0-1, DOI: 10.1016/j.apr.2020.02.018

37. Wang P., Chen K., Zhu S., Wang P., & Zhang H. (2020). Severe air pollution events not avoided by reduced anthropogenic activities during COVID-19 outbreak. Resources, Conservation and Recycling, 158(March), 104814, DOI: 10.1016/j.resconrec.2020.104814.

38. Wikipedia (2021). Geography of Iran. https://en.wikipedia.org/wiki/Geography_of_Iran accessed on June 14th, 2021.

39. Wikipedia (2021). Tehran Province. https://en.wikipedia.org/wiki/Tehran_Province accessed on June 15th, 2021.

40. Zheng B., Chevallier F., Ciais P., Yin Y., Deeter M.N., Worden H.M., Wang Y., Zhang Q., & He K. (2018). Rapid decline in carbon monoxide emissions and export from East Asia between years 2005 and 2016. Environmental Research Letters, 13(044007) , DOI: 10.1088/1748-9326/aab2b3.

41. Zong Z., Tan Y., Wang X., Tian C., Fang Y., Chen Y., Fang Y., Han G., Li J., & Zhang G. (2018). Assessment and quantification of NOx sources at a regional background site in North China: Comparative results from a Bayesian isotopic mixing model and a positive matrix factorization model. Environmental Pollution, 242, 1379-1386, DOI: 10.1016/j.envpol.2018.08.026.


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For citations:


Sari N.M., Kuncoro M.N. Monitoring Of Co, No2 And So2 Levels During The Covid-19 Pandemic In Iran Using Remote Sensing Imagery. GEOGRAPHY, ENVIRONMENT, SUSTAINABILITY. 2021;14(4):183-191. https://doi.org/10.24057/2071-9388-2020-74

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ISSN 2071-9388 (Print)
ISSN 2542-1565 (Online)