Investigating the correlation coefficient of carbon monoxide pollutant concentration from air pollution measurement stations and Sentinel 5 satellite (case study of Mashhad city)

Authors

1 PHD Student, Tehran University

2 Environmental Faculty, Tehran University

3 Faculty of Biological Sciences, Khwarazmi University, Tehran, Iran

Abstract
Carbon monoxide gas is classified as one of the most dangerous gaseous pollutants in the form of air, which causes poisoning and even death of people through absorption in blood oxygen and the formation of COHB The usual ways to measure carbon monoxide pollutant in general and to check the air pollution index are air pollution measuring stations. However, for some places such as hospitals, educational centers, universities, military barracks offices, nursing homes, private homes, etc., carbon monoxide detectors or mobile measuring devices are used. . High costs, high error and lack of access of the general public to this type of measuring devices made the aim of this research to obtain a coefficient with high accuracy to calculate CO error in any place with the least cost, high accuracy and easy access. be placed by even non-experts. In order to find this coefficient, the data of the measurement stations of Mashhad city in a specific time period and simultaneously the data of Sentinel 5 satellite in the same time period were used and we named this coefficient as alpha. By multiplying alpha by the numerical value of the CO gas measured by the satellite, the actual size of the CO gas error will be calculated at any point and place on the earth.

Keywords


1. Du, W., et al., Deciphering urban traffic impacts on air quality by deep learning and emission inventory. journal of environmental sciences, 2023. 124: p. 745-757. https://doi.org/10.1016/j.jes.2021.12.035 
2. Yang, S., et al., Global evaluation of carbon neutrality and peak carbon dioxide emissions: Current challenges and future outlook. Environmental Science and Pollution Research, 2023. 30(34): p. 81725-81744. https://doi.org/10.1007/s11356-022-19764-0 
3. Nguyen, H.M., J. He, and M.J. Wooster, Biomass burning CO, PM and fuel consumption per unit burned area estimates derived across Africa using geostationary SEVIRI fire radiative power and Sentinel-5P CO data. Atmospheric Chemistry and Physics, 2023. 23(3): p. 2089-2118. https://doi.org/10.5194/acp-23-2089-2023 
4. Yilmaz, O.S., et al., Mapping burn severity and monitoring CO content in Türkiye’s 2021 Wildfires, using Sentinel-2 and Sentinel-5P satellite data on the GEE platform. Earth Science Informatics, 2023: p. 1-20. https://doi.org/10.1007/s12145-023-00933-9 
5. Magro, C., et al., Atmospheric trends of CO and CH4 from extreme wildfires in Portugal using Sentinel-5P TROPOMI level-2 data. Fire, 2021. 4(2): p. 25. https://doi.org/10.3390/fire4020025 
6. Ghaedrahmati, S. and M. Hajilou, Analyzing the effects of air pollution on life expectancy in Tehran, Iran. International Journal of Environmental Science and Technology, 2022. 19(8): p. 7009-7018. https://doi.org/10.1007/s13762-021-03877-z 
7. Keshtkar, M., H. Heidari, N. Moazzeni, and H. Azadi, Analysis of changes in air pollution quality and impact of COVID-19 on environmental health in Iran: application of interpolation models and spatial autocorrelation. Environmental Science and Pollution Research, 2022. 29(25): p. 38505-38526. https://doi.org/10.1007/s11356-021-17955-9 
8. Jamshidi Kalajahi, M., L. Khazini, Y. Rashidi, and S. Zeinali Heris, Development of reduction scenarios based on urban emission estimation and dispersion of exhaust pollutants from light duty public transport: case of Tabriz, Iran. Emission Control Science and Technology, 2020. 6: p. 86-104. https://doi.org/10.1007/s40825-019-00135-0 
9. Borgschulte, M., D. Molitor, and E.Y. Zou, Air pollution and the labor market: Evidence from wildfire smoke. Review of Economics and Statistics, 2022: p. 1-46. https://doi.org/10.1162/rest_a_01243 
10. Gharibi, S. and K. Shayesteh, Application of Sentinel 5 satellite imagery in identifying air pollutants Hotspots in Iran. Journal of Spatial Analysis Environmental hazarts, 2021. 8(3): p. 123-138. DOI: 10.52547/jsaeh.8.3.123 
11. Kazemi Garajeh, M., et al., Monitoring Trends of CO, NO2, SO2, and O3 Pollutants Using Time Series Sentinel-5 Images Based on Google Earth Engine. Pollutants, 2023. 3(2): p. 255-279. https://doi.org/10.3390/pollutants3020019 
12. Lu, L., et al., Spatiotemporal variation of surface urban heat islands in relation to land cover composition and configuration: A multi-scale case study of Xi’an, China. Remote Sensing, 2020. 12(17): p. 2713. https://doi.org/10.3390/rs12172713 
13. Nair, A.P., et al., Optical pressure sensing at MHz rates via collisional line broadening of carbon monoxide: uncertainty quantification in reacting flows. Applied Physics B, 2023. 129(4): p. 51. https://doi.org/10.1007/s00340-023-07985-1 
14. Azizi Jalilian, F., et al., Evaluation of SARS-CoV-2 in Indoor Air of Sina and Shahid Beheshti Hospitals and Patients' Houses. Food and Environmental Virology, 2022. 14(2): p. 190-198. https://doi.org/10.1007/s12560-022-09515-2 
15. Tian, Y., et al., Satellite observations reveal a large CO emission discrepancy from industrial point sources over China. Geophysical Research Letters, 2022. 49(5): p. e2021GL097312. https://doi.org/10.1029/2021GL097312 
16. Safarianzengir, V., B. Sobhani, M.H. Yazdani, and M. Kianian, Monitoring, analysis and spatial and temporal zoning of air pollution (carbon monoxide) using Sentinel-5 satellite data for health management in Iran, located in the Middle East. Air Quality, Atmosphere & Health, 2020. 13: p. 709-719. https://doi.org/10.1007/s11869-020-00827-5 
17. Lalitaporn, P. and T. Mekaumnuaychai, Satellite measurements of aerosol optical depth and carbon monoxide and comparison with ground data. Environmental Monitoring and Assessment, 2020. 192: p. 1-19. https://doi.org/10.1007/s10661-020-08346-7 
18. Ghannadi, M.A., M. Shahri, and A. Moradi, Air pollution monitoring using Sentinel-5 (Case study: Big industrial cities of Iran). Environmental Sciences, 2022. 20(2): p. 81-98. DOI: 10.52547/ENVS.2022.1026 
19. Seifi, M., et al., Exposure to ambient air pollution and socio-economic status on intelligence quotient among schoolchildren in a developing country. Environmental Science and Pollution Research, 2022. 29: p. 2024-2034. https://doi.org/10.1007/s11356-021-15827-w 
20. Rahnama, M.R., Forecasting land-use changes in Mashhad Metropolitan area using Cellular Automata and Markov chain model for 2016-2030. Sustainable Cities and Society, 2021. 64: p. 102548. https://doi.org/10.1016/j.scs.2020.102548 
21. Sha, M.K., et al., Validation of methane and carbon monoxide from Sentinel-5 Precursor using TCCON and NDACC-IRWG stations. Atmospheric Measurement Techniques, 2021. 14(9): p. 6249-6304. https://doi.org/10.5194/amt-14-6249-2021

  • Receive Date 22 December 2023
  • Revise Date 07 January 2024
  • Accept Date 14 April 2024
  • First Publish Date 14 April 2024
  • Publish Date 20 March 2024