TY - JOUR

T1 - Current Status and Future Forecast of Short-lived Climate-Forced Ozone in Tehran, Iran, derived from Ground-Based and Satellite Observations

AU - Borhani, Faezeh

AU - Motlagh, Majid Shafiepour

AU - Ehsani, Amir Houshang

AU - Rashidi, Yousef

AU - Ghahremanloo, Masoud

AU - Amani, Meisam

AU - Moghimi, Armin

N1 - Funding Information: Special thanks are extended to the Tehran Air Quality Control Company (AQCC), Department of Environment (DOE), Institute of Geophysics of the University of Tehran (IGUT), and Iran Meteorological Organization (IRIMO) for efficient help in providing the database of this study.

PY - 2023/2/15

Y1 - 2023/2/15

N2 - In this study, the distribution and alterations of ozone concentrations in Tehran, Iran, in 2021 were investigated. The impacts of precursors (i.e., CO, NO 2, 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 O 3 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.

AB - In this study, the distribution and alterations of ozone concentrations in Tehran, Iran, in 2021 were investigated. The impacts of precursors (i.e., CO, NO 2, 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 O 3 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.

KW - Air quality

KW - Heatmap of concentrations

KW - Ozone precursors

KW - SARIMA model

KW - Sentinel-5P

KW - Short-lived climate tropospheric ozone

UR - http://www.scopus.com/inward/record.url?scp=85160025730&partnerID=8YFLogxK

U2 - 10.1007/s11270-023-06138-6

DO - 10.1007/s11270-023-06138-6

M3 - Article

VL - 234

JO - Water, Air, & Soil Pollution

JF - Water, Air, & Soil Pollution

SN - 0049-6979

IS - 2

M1 - 134

ER -