Current Status and Future Forecast of Short-lived Climate-Forced Ozone in Tehran, Iran, derived from Ground-Based and Satellite Observations

Faezeh Borhani; Majid Shafiepour Motlagh; Amir Houshang Ehsani; Yousef Rashidi; Masoud Ghahremanloo; Meisam Amani; Armin Moghimi

doi:10.1007/s11270-023-06138-6

Current Status and Future Forecast of Short-lived Climate-Forced Ozone in Tehran, Iran, derived from Ground-Based and Satellite Observations

Water Air Soil Pollut. 2023;234(2):134. doi: 10.1007/s11270-023-06138-6. Epub 2023 Feb 15.

Authors

Faezeh Borhani¹, Majid Shafiepour Motlagh¹, Amir Houshang Ehsani¹, Yousef Rashidi², Masoud Ghahremanloo³, Meisam Amani⁴, Armin Moghimi^{5

6}

Affiliations

¹ School of Environment, College of Engineering, University of Tehran, P.O. Box, Tehran, 14155-6135 Iran.
² Environmental Sciences Research Institute, Shahid Beheshti University, Tehran, Iran.
³ Department of Earth and Atmospheric Sciences, University of Houston, Houston, TX 77004 USA.
⁴ Wood Environment and Infrastructure Solutions, Ottawa, ON K2E 7L5 Canada.
⁵ Department of Remote Sensing and Photogrammetry, Faculty of Geodesy and Geomatics Engineering, Toosi University of Technology, Tehran, K. N Iran.
⁶ Institute of Photogrammetry and GeoInformation, Leibniz Universitat Hannover, Hannover, Germany.

Abstract

In this study, the distribution and alterations of ozone concentrations in Tehran, Iran, in 2021 were investigated. The impacts of precursors (i.e., CO, NO₂, and NO) on ozone were examined using the data collected over 12 months (i.e., January 2021 to December 2021) from 21 stations of the Air Quality Control Company (AQCC). The results of monthly heat mapping of tropospheric ozone concentrations indicated the lowest value in December and the highest value in July. The lowest and highest seasonal concentrations were in winter and summer, respectively. Moreover, there was a negative correlation between ozone and its precursors. The Inverse Distance Weighting (IDW) method was then implemented to obtain air pollution zoning maps. Then, ozone concentration modeled by the IDW method was compared with the average monthly change of total column density of ozone derived from Sentinel-5 satellite data in the Google Earth Engine (GEE) cloud platform. A good agreement was discovered despite the harsh circumstances that both ground-based and satellite measurements were subjected to. The results obtained from both datasets showed that the west of the city of Tehran had the highest averaged O₃ concentration. In this study, the status of the concentration of ozone precursors and tropospheric ozone in 2022 was also predicted. For this purpose, the Box-Jenkins Seasonal Autoregressive Integrated Moving Average (SARIMA) approach was implemented to predict the monthly air quality parameters. Overall, it was observed that the SARIMA approach was an efficient tool for forecasting air quality. Finally, the results showed that the trends of ozone obtained from terrestrial and satellite observations throughout 2021 were slightly different due to the contribution of the tropospheric ozone precursor concentration and meteorology conditions.

Keywords: Air quality; Heatmap of concentrations; Ozone precursors; SARIMA model; Sentinel-5P; Short-lived climate tropospheric ozone.

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