High ozone concentrations observed along the Persian Gulf coast by Ozone Monitoring Instrument

High ozone concentrations observed along the Persian Gulf coast by Ozone Monitoring Instrument | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Short Report High ozone concentrations observed along the Persian Gulf coast by Ozone Monitoring Instrument Akiko Kagawa, Sachiko Hayashida, Hikaru Araki, Juseon Bak, Kai Yang, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7877632/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract To reveal air pollution conditions in the rapidly urbanizing and industrializing Persian Gulf coastal region, seven years of ozone profile data from the newly improved Ozone Monitoring Instrument (OMI) V2 product were analyzed; a significant summer (June–September) increase in lowermost tropospheric (0–3 km altitude) ozone indicated the presence of hotspots in this region. The ozone enhancement correlates with the distribution of emission hotspots for ozone precursors observed by OMI, suggesting the validity of OMI ozone observations in the lowermost layer. OMI ozone observation data were compared with In-service Aircraft for a Global Observing System (IAGOS) measurements, confirming correspondence with OMI during several ozone high-concentration events. Same-day IAGOS-OMI comparisons showed a positive correlation with a slope of nearly unity, confirming OMI data reliability. This study demonstrates that lower-level ozone observations using the ultraviolet-visible spectrum is highly effective for understanding air pollution issues in rapidly growing urban areas such as the Persian Gulf region. tropospheric ozone air pollution satellite data analysis Middle East atmospheric chemistry Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1 Introduction In recent years, increases in tropospheric ozone have been recorded in urban areas worldwide, highlighting the importance of global observations because of its adverse effects on human and plant health (e.g., Turnock et al. 2025), and its climate impact as a greenhouse gas (e.g., Gaudel et al. 2018; TOAR-II 2025). Satellite measurements are strategically advantageous to ozone research, as they can continuously and repeatedly observe large geographic regions over extended time periods. However, as approximately 90% of total ozone reflects stratospheric ozone amounts, it is very challenging to monitor tropospheric ozone, especially near the ground, which is of concern for air pollution. Many researchers have sought to derive ozone profiles from the ultraviolet-visible solar spectrum (e.g., Liu et al. 2005; Miles et al. 2015) or from infrared thermal radiation observation spectra (e.g., Oetjen et al. 2016). The Smithsonian Astrophysical Observatory (SAO) provides ozone profile products from multiple satellite missions, including direct contributions to GOME 1 , GOME-2, OMI, and TEMPO, and collaborative efforts involving OMPS, TROPOMI, and GEMS (Liu et al. 2005; 2010; Cai et al. 2012; Bak et al. 2017; 2024; 2025a, 2025b; Zoogman et al. 2017; Zhao et al. 2021). The OMI/SAO ozone profiles have been used to investigate the spatiotemporal variability of tropospheric ozone over East Asia and revealed a significant enhancement of lower tropospheric ozone over central China during summer (Hayashida et al. 2015; 2018), and its usefulness was verified by regional model simulations (Kajino et al. 2019). While previous studies have focused primarily on ozone pollution over East Asia, our study shifts attention to the Persian Gulf coast. This region, home to some of the world’s largest oil fields, has experienced rapid urbanization and population growth in recent decades. For example, the population of Kuwait City grew from 1.3 million in 2000 to 3.4 million in 2025 (PopulationStat 2025) accompanied by severe air pollution (e.g., Farahat, 2016). High summer ozone concentrations in the middle troposphere over the Middle East were pointed out by Li et al. (2001) based on model simulations, and afterwards the satellite sensor TES found layers of high ozone concentrations near 100 ppbv in the mid-troposphere (464 hPa) over the Persian Gulf in June, July, and August (Worden et al. 2009). While studies had previously focused mainly on long-range transport (e.g., Liu et al. 2009), Lelieveld et al. (2009) extended their model simulations to near-surface levels and noted that, in the Middle East, relatively high background ozone mixing ratios in the mid-troposphere are creating conditions that are conducive to the expansion of severe local air pollution with strong surface emissions of ozone precursors. OMI's high-spatial-resolution column-level observations of NO₂, HCHO, SO₂, and CHOCHO by OMI identified emission source spots such as urban areas, oil refineries, oil ports, and power plants along the Persian Gulf coast, revealing a degradation in air quality over 2005–2014 in this region (Barkley et al. 2017). Recently, improved OMI/SAO ozone profiles have become available, featuring numerous enhancements to account for instrument degradation over long-term observations and to improve retrieval accuracy through refined auxiliary data and forward model calculation, radiometric calibration (Bak et al. 2024). We used the improved OMI V2 product to reveal ozone enhancements in the Persian Gulf coastal region and locate ozone hotspots. This study demonstrates the potential for detecting high-concentration ozone in the lower troposphere, which has previously been considered challenging, using satellite data. 2 Data and Method 2.1 Data 2.1.1 Satellite Observation Data (OMI) OMI is a sensor onboard the National Aeronautics and Space Administration’s (NASA’s) EOS Aura spacecraft. Launched on July 15, 2004, it observes Earth’s backscattered radiation in the ultraviolet and visible spectrum. Operating in a sun-synchronous, nadir-viewing configuration, OMI provides daily global coverage with an equator crossing time at 13:45 local time (Levelt et al. 2018 ). The SAO ozone profile algorithm, based on the optimal estimation (OE) scheme (Rodgers, 2000 ), was used to derive the earlier version of the OMI/SAO ozone profiles from the Collection 3 Level-1B product (Liu et al. 2010 ). The Level-1B data have since been reprocessed into Collection 4 to correct for gradual optical and electronic degradation accumulated over OMI’s unprecedentedly long operational period, as well as to improve bad pixel flagging—thereby enhancing the overall quality and reliability of the data (Kleipool et al. 2022 ). The latest version of the SAO ozone profiles utilizes the Collection 4 Level-1B product, along with improved implementations in the radiative transfer model, radiometric and wavelength calibration, and a priori ozone information—resulting in more accurate and stable ozone profile retrievals (Bak et al. 2024 ). The improved OMI ozone profiles have demonstrated their potential for use in long-term studies of ozone variability associated with intra- and interannual changes in summer monsoonal meteorology (Bak et al. 2022 ). In this study, we use the bottom layer (0–3 km) of the 24-layer ozone profile, focusing on pollution-enhanced ozone. Due to OMI data processing limitations, we selected seven representative years from the early, middle, and recent periods of the observational record: 2005–2006, 2015–2016–2017, and 2022–2023. To ensure the validity of the analysis, retrievals were excluded if the cloud fraction exceeded 0.2 or if the root mean square of spectral fitting residuals was greater than 2.4%. 2.1.2 Aircraft observation data (IAGOS) In this study, in addition to OMI observations, we investigated the lower tropospheric ozone concentrations using the In-service Aircraft for a Global Observing System (IAGOS) aircraft observations. IAGOS observes atmospheric trace components using commercial aircraft. The IAGOS project has been publishing reliable datasets on its website ( https://iagos.aeris-data.fr ) from 1994 to the present. During the OMI observation period (2005–2023), there were only nine airports in the Persian Gulf region where IAGOS observations were conducted, as shown in Table 1 (as of May 2025). The amount of data varies by year, with some years having no data at all. In this study, we selected the five years (2005, 2015, 2016, 2017, and 2022) during the seven-year OMI analysis period when sufficient IAGOS data were available for analysis. Table 1 Airports where IAGOS observations were conducted. Airport latitude and longitude were obtained from (Geocoding 2025 ). The “All” column shows the number of annual ozone profile observations for the years 2005, 2015, 2016, 2017, and 2022. Additionally, the “Match” column lists those matched with OMI data pixels (see Section 2.2). All projects under IAGOS—IAGOS-CORE, IAGOS-MOZAIC, and IAGOS-CARIBIC—are included. 2.2 Methods In this study, following from the method used by Hayashida et al. ( 2015 ), we defined the difference between the a priori and retrieved ozone values as ΔO 3 and traced its dynamics at the lowest layer of the ozone profile data (layer 24: corresponding to approximately 0–3 km). The retrieved values appear to exhibit reasonable seasonal variations, but these variations are inherent in the a priori climatology. The ΔO 3 values reflect daily variations that deviate from the climate values. The Persian Gulf area, shown in a green rectangle in Fig. 1 , was selected as the analysis target area where high concentrations of ozone are frequently observed. Airports where IAGOS conducted observations are also shown in Fig. 1 (left panel). We first set a threshold for ΔO₃ and analyzed the areas and timing of high values of ΔO₃. Concurrently, we confirmed the ozone concentration in the lower atmosphere using IAGOS data for the same period. Furthermore, we selected "matching pairs" by choosing the geographically closest OMI pixel center on the same day for each IAGOS observation, filtering based on a distance threshold of 100 km and a time threshold of 10 hours. Here, the latitude and longitude of IAGOS observation points were defined as "at the start of observation” for ascent observations and “at the end of observation” for descent observations. The number of matched observation instances is shown in the “Match” column of Table 1. 3 Results and Discussion 3.1 OMI ΔO 3 MAP Visual inspection of the ΔO 3 distribution in the lower troposphere over the seven-year period from 2005 to 2023 revealed that high values of ΔO 3 were frequently observed in the Persian Gulf, particularly from June to September. The right panel in Fig. 1 shows a map with overlaid observation points where ΔO 3 values are exceeding 2.0 DU km − 1 from June to September 2015. Data in other years are shown in supplementary materials (Fig. S1 ). In all years, high values of ΔO 3 are present in the central region of the Persian Gulf. To statistically investigate the frequency of high-concentration ozone, the analysis target area shown in Fig. 1 was divided into a grid of 1° latitude and 1° longitude, and the number of observation points where ΔO₃ exceeded 2.0 DU km − 1 was counted for each grid over seven years. Among these, the frequency of observations exceeding 2.0 DU km − 1 was significantly higher in the grid at 49°E, 26°N, and the monthly frequency is shown in a histogram in Fig. 2 a. The frequency of observations exceeding 2.0 DU km − 1 is high in June–September in all years, with a peak in August. Additionally, there is a tendency for the occurrence frequency to be higher in the latter half of the period (2017, 2022, and 2023) compared to the first half (2005, 2006, 2015, and 2016). The monthly frequency of high ΔO 3 values for all grids is shown in Fig. 2 b; the frequency of high ΔO 3 values was particularly high in August, with over 100 occurrences near the center of the Persian Gulf, where major cities such as Doha, Bahrain, and Dammam are located. Additionally, approximately 50 to 100 times were observed around Kuwait City, Abu Dhabi, Dubai, and Sharjah (see Fig. S2 for other months). The areas with high ΔO 3 shown in Fig. 2 b are aligned with the areas with high concentrations of NO 2 and SO 2 as reported by Barkley et al. ( 2017 ). This good correlation suggests local ozone formation and reinforces the reliability of OMI detection of ozone at the lowermost layer. The retrieved ozone concentrations and a priori ozone concentrations are shown in Fig. 3 together with ΔO₃. Here, we show the results from August 2017, indicating a significant increase in ΔO 3 around the Persian Gulf.; high ΔO₃ around the Persian Gulf was also found in July and September (Fig. S3). The a priori ozone values generally depend on latitude and do not indicate specific geographical structures, but depend on terrain locally and show structural patterns in some spatial distributions. A band of high-retrieved ozone concentrations appears to extend around the Mediterranean Sea, but it is also seen in the distribution of a priori values. However, when expressed as the difference from the a priori values (ΔO₃), geographical structures that were not present in the a priori values become clearly observable over the Persian Gulf coast. This suggests that OMI observations have captured the actual signal from the high ozone concentrations in this region. 3.2 Ozone concentrations observed by IAGOS in high-concentration areas This section presents the results of the analysis of IAGOS air-borne data targeting OMI high ΔO 3 areas. The monthly average ozone (DU) concentrations at 0–3 km altitude are analyzed for the airports where IAGOS observations were conducted (Table 1), for the years 2005, 2015, 2016, 2017, and 2022. As shown in Fig. 4 a, in Kuwait City (KWI), high ozone concentrations were particularly noticeable in July 2022. Four profiles obtained between the evening of July 1 and midnight on July 2 in Kuwait City (KWI) are shown in Fig. 4 b. In the boundary layer, extremely high ozone concentration exceeding approximately 170 ppbv was observed at 20:28 on July 1 and rapidly decreased until 01:51 on July 2. Fig. S4 shows IAGOS data at the airports within the target area other than Kuwait City. While enhanced ozone levels were also observed in July 2022 at Damman (DMM) and Bahrain (BAH), a detailed trend is not clear because of the limited number of observations. 3.3 Comparison of OMI and IAGOS ozone OMI observation data were validated by comparing them with IAGOS data observed on the same day. The matching method for OMI and IAGOS was described in Section 2.2. OMI's observation time is 13:45 (LT), but there are few daytime observation data close to this time in IAGOS; many cases were selected from nighttime observations. The number of matching observations with OMI is shown in Table 1. Many pairs were matched in 2016 because IAGOS observations were more frequent. Throughout the entire period, 458 observations (32% of the total 1,434 observations) were selected as matching pairs. One example of a matching pair is shown in Fig. S5, the IAGOS and OMI profiles for Bahrain on July 22, 2015. The IAGOS observation time was 13:54, a rare daytime event. The observed ozone showed a large value of 180 ppbv near the ground. CO similarly increased near the ground, suggesting the presence of a boundary layer up to approximately 1 km. While the OMI a priori ozone mixing ratio in the 24th layer was approximately 30 ppbv, the retrieved ozone mixing ratio was over 60 ppbv, approaching the IAGOS ozone mixing ratio of approximately 90 ppbv averaged over 0–3 km. This case is a good example of how OMI observations have corrected prior values and detected high ozone concentrations. Analyses for all matching pairs were summarized in Fig. 5 as a scatter plot of 458 pairs from IAGOS and OMI. The IAGOS ozone profiles were smoothed using averaging kernels obtained by corresponding OMI observations. In all layers 22–24, both O 3 and ΔO 3 showed a gradient close to unity. On the other hand, the R 2 values were around 0.5, indicating that the data were relatively scattered. This may be due to the incongruity between OMI and IAGOS’s typical measurement times. OMI observations are taken around 13:45 LT, but many IAGOS observations are taken during nighttime due to aircraft schedules, with most occurring between 19:00 LT and 4:00 LT the following morning. Ozone has a short lifetime, so even if concentrations are high during the day, they may decrease at night (as shown in Fig. 4 b). In the case of IAGOS observations during the daytime (Fig. S5), OMI ozone concentrations corresponded with the IAGOS data well. If more IAGOS observation data had been obtained at near 13:45, a better correlation between the two would likely have been found. Figure 6 shows the OMI ozone profile and averaging kernel for the observation case on July 22, 2015 (corresponding to Fig. S5). The 24th-layer averaging kernel (red solid line) takes values close to 0.2 at around 0 to 12 km. Those values resemble those of Fig. 4 by Hayashida et al. ( 2015 ) when significant ozone enhancement was observed in East Asia, indicating that actual ozone change from the a priori in the 24th layer can be retrieved. As observed by TES, ozone in the mid-troposphere tends to enhanced condition over the Middle East (Worden et al. 2009 ; Li et al. 2009 ); some part of the ΔO₃ shown in this study may be slightly influenced by the mid-troposphere during the retrieval process. However, the frequent occurrence of high ozone concentrations along the Persian Gulf coast clearly indicates a link to local pollutant emissions in this region, which is supported by OMI's observations of NO 2 and other ozone precursors (Barkley et al. 2017 ), along with IAGOS observations. The model simulation by Lelieveld et al. ( 2009 ) predicted high ozone concentrations over the Persian Gulf during the summer (Fig. 8 of Lelieveld et al. ( 2009 )), and OMI observations shown in this study confirmed it for the first time. Near the ground, wind systems and transport processes must differ from those previously identified in the middle troposphere. When examining surface winds in each major city (WeatherSpark 2025 ) in the target area, they are often found to be blowing from the Persian Gulf toward land during the summer; this is likely to be due to the Persian Gulf forming a localized high-pressure zone similar to a basin. To understand local variations in ozone concentrations in this region, it is necessary to analyze meteorological data in more detail, which is a challenge for the future. 4 Conclusions Analysis of seven years of the latest ozone research product, OMI V2 revealed that in the lowermost layer, corresponding to an altitude of approximately 0–3 km above the ground, significant ozone enhancement was clearly detected over the Persian Gulf area, which is home to numerous oil fields and refineries and has recently experienced significant population growth and urbanization. This high concentration of ozone appeared prominently in summer (June to September). Comparison with IAGOS observations supported OMI ozone detection. The frequent occurrence of high ozone concentrations over the Persian Gulf area clearly indicates a link to local pollutant emissions in this region, which is supported by OMI's observations of NO 2 and other ozone precursors. While satellite-based measurements of ozone concentrations in the middle troposphere have been made using infrared sensors such as TES, the development of techniques to estimate ozone concentrations in lower layers using ultraviolet-visible spectra is crucial for understanding air pollution issues in rapidly developing urban regions like the Persian Gulf coast. Declarations Funding This work was supported by JSPS KAKENHI Grant Number JP23K03496. Competing interests The authors declare that they have no competing interests. Author Contributions The research concept was proposed by S. H., and the specific methodology was proposed by A. K.. The drafting of the manuscript was primarily led by A. K. and S. H., with all authors contributing to the writing. OMI V2 data was provided by J. B., K. Y. and X. L.. Data analysis and visualisation were primarily carried out by H. A.. M. K. and T. T S. analysed the meteorological conditions. All co-authors participated in the discussions for the preparation of the paper. Acknowledgement This work was supported by JSPS KAKENHI Grant Number JP23K03496. The authors would like to express our gratitude to Dr. Tomohiro Sato of NICT for valuable discussions. MOZAIC/CARIBIC/IAGOS data were created with support from the European Commission, national agencies in Germany (BMBF), France (MESR), and the UK (NERC), and the IAGOS member institutions (http://www.iagos.org/partners). 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Jones, Jane Liu, Mark Parrington, Kevin Bowman, Ivanka Stajner, Reinhard Beer, Jonathan Jiang, Valérie Thouret, Susan Kulawik, Jui‐Lin F. Li, Sunita Verma, Helen Worden (2009), Observed vertical distribution of tropospheric ozone during the Asian summertime monsoon, J. Geophys. Res., https://doi.org/10.1029/2008JD010560, 114, D13. Zhao, F., Liu, C., Cai, Z., Liu, X., Bak, J., Kim, J., Hu, Q., Xia, C., Zhang, C., Sun, Y., Wang, W., and Liu, J. (2021), Ozone profile retrievals from TROPOMI: Implication for the variation of tropospheric ozone during the outbreak of COVID-19 in China, Sci. Total Environ., 764, 142886, https://doi.org/10.1016/j.scitotenv.2020.142886. Zoogman, P., X. Liu, R.M. Suleiman, W.F. Pennington, D.E. Flittner, J.A. Al-Saadi, B.B. Hilton, D.K. Nicks, M.J. Newchurch, J.L. Carr, S.J. Janz, M.R. Andraschko, A. Arola, B.D. Baker, B.P. Canova, C. Chan Miller, R.C. Cohen, J.E. Davis, M.E. Dussault, D.P. Edwards, J. Fishman, A. Ghulam, G. González Abad, M. Grutter, J.R. Herman, J. Houck, D.J. Jacob, J. Joiner, B.J. Kerridge, J. Kim, N.A. Krotkov, L. Lamsal, C. Li, A. Lindfors, R.V. Martin, C.T. McElroy, C. McLinden, V. Natraj, D.O. Neil, C.R. Nowlan, E.J. O׳Sullivan, P.I. Palmer, R.B. Pierce, M.R. Pippin, A. Saiz-Lopez, R.J.D. Spurr, J.J. Szykman, O. Torres, J.P. Veefkind, B. Veihelmann, H. Wang, J. Wang, K. Chance (2017), Tropospheric emissions: Monitoring of pollution (TEMPO), Journal of Quantitative Spectroscopy and Radiative Transfer, Volume 186, Pages 17-39, ISSN 0022-4073, https://doi.org/10.1016/j.jqsrt.2016.05.008. Footnotes All abbreviations for satellite sensors are listed with their full names in Table S1 . Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterialTableS1FigS1FigS5rev.pdf Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 27 Oct, 2025 Reviews received at journal 23 Oct, 2025 Reviews received at journal 20 Oct, 2025 Reviewers agreed at journal 19 Oct, 2025 Reviewers agreed at journal 17 Oct, 2025 Reviewers invited by journal 15 Oct, 2025 Submission checks completed at journal 13 Oct, 2025 First submitted to journal 13 Oct, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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1","display":"","copyAsset":false,"role":"figure","size":659377,"visible":true,"origin":"","legend":"\u003cp\u003e(left) Locations of the airports from Table 1. The analysis target area is indicated by the green frame. The black square locates the pixel where the frequency of high ΔO\u003csub\u003e3\u003c/sub\u003e was significant at 49°E, 26°N. (right) Locations where the ΔO₃ (units: DU km\u003csup\u003e-1\u003c/sup\u003e) in the 24th layer of OMI during June–September in 2015 exceeded 2.0 DU km\u003csup\u003e-1\u003c/sup\u003e.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7877632/v1/8d354e120431c69ed3ecba9f.jpeg"},{"id":94784801,"identity":"6af40906-88c6-4e77-9352-d0fbdb0529b4","added_by":"auto","created_at":"2025-10-30 16:24:44","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":165791,"visible":true,"origin":"","legend":"\u003cp\u003eOzone profile and averaging kernels (AKs) for July 22, 2015 at Bahrain. (left) OMI a priori ozone profile (blue); profile retrieved from OMI spectra (red), IAGOS-adjusted values (blue stars), IAGOS values convolved with the AKs (violet triangles). (middle) Normalized AK rows for the six layers of OMI retrieval. Violet, blue, light blue, green, orange, and red lines correspond to the 19th, 20th, 21st, 22nd, 23rd, and 24th layers, respectively. (right) Corresponding AK columns normalized by the a priori error.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7877632/v1/9c41b627e2a295544d6853bc.png"},{"id":94784798,"identity":"80f567b9-594a-40bb-96e0-f52292d3f58b","added_by":"auto","created_at":"2025-10-30 16:24:43","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":124152,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Monthly count of observations with the ΔO₃ \u0026gt; 2.0 DU km\u003csup\u003e-1\u003c/sup\u003e at 49°E, 26°N. (b) Spatial distribution of observation points where ΔO\u003csub\u003e3\u003c/sub\u003e exceeded 2.0 DU km\u003csup\u003e-1\u003c/sup\u003e within the target area over seven years: (from left) June, July, August, and September. Other months shown in Fig. S2.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7877632/v1/3680fb963ccd067c8d59979f.png"},{"id":94784804,"identity":"f1a10bff-51ae-4cc3-b499-1c2a3cb444ec","added_by":"auto","created_at":"2025-10-30 16:24:44","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":182757,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Monthly average ozone (DU km\u003csup\u003e-1\u003c/sup\u003e) in the lowest layer (approximately 0-3 km) in August 2017, (b) a priori ozone (DU km\u003csup\u003e-1\u003c/sup\u003e) for the same month as (a), and (c) ΔO\u003csub\u003e3\u003c/sub\u003e (DU km\u003csup\u003e-1\u003c/sup\u003e)\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7877632/v1/34783903be1f39d9ec12bcfe.png"},{"id":94784805,"identity":"1cd80019-da1e-4ace-8120-6c57218327fa","added_by":"auto","created_at":"2025-10-30 16:24:44","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":281992,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Monthly average ozone concentrations (DU) integrated in the 0–3 km altitude at Kuwait City airport (KWI). The horizontal axis represents months, and the vertical axis represents years from top to bottom: 2005, 2015, 2016, 2017, and 2022. (b) Ozone profile observed by IAGOS in Kuwait City (KWI) in July 2022. (blue) 20:28LT, July 1st, (orange) 21:32LT, July 1st, (green) 00:42 LT, July 2nd, and (red) 01:51LT, July 2nd.\u003c/p\u003e","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7877632/v1/13fbaff40675405a89d36621.jpeg"},{"id":94784803,"identity":"d830f1b1-cc0f-4e93-bf1c-bb0d33b7dabd","added_by":"auto","created_at":"2025-10-30 16:24:44","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":238624,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Scatter plot of 458 matching pairs between IAGOS and OMI in 2005, 2015, 2016, 2017, and 2022for 24th layer (approx. 0–3 km). IAGOS values are converted to column abundances (DU) corresponding to the OMI layer and averaged using the averaging kernels obtained from corresponding OMI observations. (b) Comparison of ΔO₃ for IAGOS data for 24th layer, the same prior value as OMI was used to calculate the difference. Plotted by month with different colors and showing linear regression lines. R² is shown at the top of each panel. X-Xa means IAGOS data subtract the same prior value as OMI.\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-7877632/v1/832af9f694c62b2c0d884bd4.png"},{"id":94984654,"identity":"62863fa1-71b0-4bed-94de-62d1a8049c4c","added_by":"auto","created_at":"2025-11-03 06:54:53","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2264199,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7877632/v1/85f5ced2-13cb-4a8d-b004-b848cdacf163.pdf"},{"id":94784824,"identity":"4852bbf9-583f-4fa1-95af-90c06f25c3e1","added_by":"auto","created_at":"2025-10-30 16:24:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":2270606,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterialTableS1FigS1FigS5rev.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7877632/v1/ebdeca3b973f17351112ec83.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"High ozone concentrations observed along the Persian Gulf coast by Ozone Monitoring Instrument","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eIn recent years, increases in tropospheric ozone have been recorded in urban areas worldwide, highlighting the importance of global observations because of its adverse effects on human and plant health (e.g., Turnock et al. 2025), and its climate impact as a greenhouse gas (e.g., Gaudel et al. 2018; TOAR-II 2025).\u003c/p\u003e\n\u003cp\u003eSatellite measurements are strategically advantageous to ozone research, as they can continuously and repeatedly observe large geographic regions over extended time periods. However, as approximately 90% of total ozone reflects stratospheric ozone amounts, it is very challenging to monitor tropospheric ozone, especially near the ground, which is of concern for air pollution. Many researchers have sought to derive ozone profiles from the ultraviolet-visible solar spectrum (e.g., Liu et al. 2005; Miles et al. 2015) or from infrared thermal radiation observation spectra (e.g., Oetjen et al. 2016).\u003c/p\u003e\n\u003cp\u003eThe Smithsonian Astrophysical Observatory (SAO) provides ozone profile products from multiple satellite missions, including direct contributions to GOME\u003ca href=\"#_ftn1\" name=\"_ftnref1\" title=\"\"\u003e\u003c/a\u003e\u003csup\u003e1\u003c/sup\u003e, GOME-2, OMI, and TEMPO, and collaborative efforts involving OMPS, TROPOMI, and GEMS (Liu et al. 2005; 2010; Cai et al. 2012; Bak et al. 2017; 2024; 2025a, 2025b; Zoogman et al. 2017; Zhao et al. 2021). The OMI/SAO ozone profiles have been used to investigate the spatiotemporal variability of tropospheric ozone over East Asia and revealed a significant enhancement of lower tropospheric ozone over central China during summer (Hayashida et al. 2015; 2018), and its usefulness was verified by regional model simulations (Kajino et al. 2019). While previous studies have focused primarily on ozone pollution over East Asia, our study shifts attention to the Persian Gulf coast. This region, home to some of the world\u0026rsquo;s largest oil fields, has experienced rapid urbanization and population growth in recent decades. For example, the population of Kuwait City grew from 1.3 million in 2000 to 3.4 million in 2025 (PopulationStat 2025) accompanied by severe air pollution (e.g., Farahat, 2016).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHigh summer ozone concentrations in the middle troposphere over the Middle East were pointed out by Li et al. (2001) based on model simulations, and afterwards the satellite sensor TES found layers of high ozone concentrations near 100 ppbv in the mid-troposphere (464 hPa) over the Persian Gulf in June, July, and August (Worden et al. 2009). While studies had previously focused mainly on long-range transport (e.g., Liu et al. 2009), Lelieveld et al. (2009) extended their model simulations to near-surface levels and noted that, in the Middle East, relatively high background ozone mixing ratios in the mid-troposphere are creating conditions that are conducive to the expansion of severe local air pollution with strong surface emissions of ozone precursors. OMI\u0026apos;s high-spatial-resolution column-level observations of NO₂, HCHO, SO₂, and CHOCHO by OMI identified emission source spots such as urban areas, oil refineries, oil ports, and power plants along the Persian Gulf coast, revealing a degradation in air quality over 2005\u0026ndash;2014 in this region (Barkley et al. 2017).\u003c/p\u003e\n\u003cp\u003eRecently, improved OMI/SAO ozone profiles have become available, featuring numerous enhancements to account for instrument degradation over long-term observations and to improve retrieval accuracy through refined auxiliary data and forward model calculation, radiometric calibration (Bak et al. 2024). We used the improved OMI V2 product to reveal ozone enhancements in the Persian Gulf coastal region and locate ozone hotspots. This study demonstrates the potential for detecting high-concentration ozone in the lower troposphere, which has previously been considered challenging, using satellite data.\u003c/p\u003e"},{"header":"2 Data and Method","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e2.1 Data\u003c/h2\u003e\n \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e\n \u003ch2\u003e2.1.1 Satellite Observation Data (OMI)\u003c/h2\u003e\n \u003cp\u003eOMI is a sensor onboard the National Aeronautics and Space Administration\u0026rsquo;s (NASA\u0026rsquo;s) EOS Aura spacecraft. Launched on July 15, 2004, it observes Earth\u0026rsquo;s backscattered radiation in the ultraviolet and visible spectrum. Operating in a sun-synchronous, nadir-viewing configuration, OMI provides daily global coverage with an equator crossing time at 13:45 local time (Levelt et al. \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e). The SAO ozone profile algorithm, based on the optimal estimation (OE) scheme (Rodgers, \u003cspan class=\"CitationRef\"\u003e2000\u003c/span\u003e), was used to derive the earlier version of the OMI/SAO ozone profiles from the Collection 3 Level-1B product (Liu et al. \u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e). The Level-1B data have since been reprocessed into Collection 4 to correct for gradual optical and electronic degradation accumulated over OMI\u0026rsquo;s unprecedentedly long operational period, as well as to improve bad pixel flagging\u0026mdash;thereby enhancing the overall quality and reliability of the data (Kleipool et al. \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). The latest version of the SAO ozone profiles utilizes the Collection 4 Level-1B product, along with improved implementations in the radiative transfer model, radiometric and wavelength calibration, and a priori ozone information\u0026mdash;resulting in more accurate and stable ozone profile retrievals (Bak et al. \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). The improved OMI ozone profiles have demonstrated their potential for use in long-term studies of ozone variability associated with intra- and interannual changes in summer monsoonal meteorology (Bak et al. \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eIn this study, we use the bottom layer (0\u0026ndash;3 km) of the 24-layer ozone profile, focusing on pollution-enhanced ozone. Due to OMI data processing limitations, we selected seven representative years from the early, middle, and recent periods of the observational record: 2005\u0026ndash;2006, 2015\u0026ndash;2016\u0026ndash;2017, and 2022\u0026ndash;2023. To ensure the validity of the analysis, retrievals were excluded if the cloud fraction exceeded 0.2 or if the root mean square of spectral fitting residuals was greater than 2.4%.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e\n \u003ch2\u003e2.1.2 Aircraft observation data (IAGOS)\u003c/h2\u003e\n \u003cp\u003eIn this study, in addition to OMI observations, we investigated the lower tropospheric ozone concentrations using the In-service Aircraft for a Global Observing System (IAGOS) aircraft observations. IAGOS observes atmospheric trace components using commercial aircraft. The IAGOS project has been publishing reliable datasets on its website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://iagos.aeris-data.fr\u003c/span\u003e\u003c/span\u003e) from 1994 to the present. During the OMI observation period (2005\u0026ndash;2023), there were only nine airports in the Persian Gulf region where IAGOS observations were conducted, as shown in Table\u0026nbsp;1 (as of May 2025). The amount of data varies by year, with some years having no data at all. In this study, we selected the five years (2005, 2015, 2016, 2017, and 2022) during the seven-year OMI analysis period when sufficient IAGOS data were available for analysis.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eTable 1\u0026nbsp;\u003c/strong\u003eAirports where IAGOS observations were conducted. Airport latitude and longitude were obtained from (Geocoding \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e). The \u0026ldquo;All\u0026rdquo; column shows the number of annual ozone profile observations for the years 2005, 2015, 2016, 2017, and 2022. Additionally, the \u0026ldquo;Match\u0026rdquo; column lists those matched with OMI data pixels (see Section 2.2). All projects under IAGOS\u0026mdash;IAGOS-CORE, IAGOS-MOZAIC, and IAGOS-CARIBIC\u0026mdash;are included.\u003c/p\u003e\n \u003cp\u003e\u003cimg src=\"https://myfiles.space/user_files/58895_8739fc6c57c1c19a/58895_custom_files/img1761841068.png\" width=\"1059\" height=\"414\"\u003e\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003e2.2 Methods\u003c/h2\u003e\n \u003cp\u003eIn this study, following from the method used by Hayashida et al. (\u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e), we defined the difference between the a priori and retrieved ozone values as \u0026Delta;O\u003csub\u003e3\u003c/sub\u003e and traced its dynamics at the lowest layer of the ozone profile data (layer 24: corresponding to approximately 0\u0026ndash;3 km). The retrieved values appear to exhibit reasonable seasonal variations, but these variations are inherent in the a priori climatology. The \u0026Delta;O\u003csub\u003e3\u003c/sub\u003e values reflect daily variations that deviate from the climate values.\u003c/p\u003e\n \u003cp\u003eThe Persian Gulf area, shown in a green rectangle in Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e, was selected as the analysis target area where high concentrations of ozone are frequently observed. Airports where IAGOS conducted observations are also shown in Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e (left panel). We first set a threshold for \u0026Delta;O₃ and analyzed the areas and timing of high values of \u0026Delta;O₃. Concurrently, we confirmed the ozone concentration in the lower atmosphere using IAGOS data for the same period. Furthermore, we selected \u0026quot;matching pairs\u0026quot; by choosing the geographically closest OMI pixel center on the same day for each IAGOS observation, filtering based on a distance threshold of 100 km and a time threshold of 10 hours. Here, the latitude and longitude of IAGOS observation points were defined as \u0026quot;at the start of observation\u0026rdquo; for ascent observations and \u0026ldquo;at the end of observation\u0026rdquo; for descent observations. The number of matched observation instances is shown in the \u0026ldquo;Match\u0026rdquo; column of Table 1.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3 Results and Discussion","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e3.1 \u003cb\u003eOMI ΔO\u003c/b\u003e\u003csub\u003e3\u003c/sub\u003e \u003cb\u003eMAP\u003c/b\u003e\u003c/h2\u003e\u003cp\u003eVisual inspection of the ΔO\u003csub\u003e3\u003c/sub\u003e distribution in the lower troposphere over the seven-year period from 2005 to 2023 revealed that high values of ΔO\u003csub\u003e3\u003c/sub\u003e were frequently observed in the Persian Gulf, particularly from June to September. The right panel in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows a map with overlaid observation points where ΔO\u003csub\u003e3\u003c/sub\u003e values are exceeding 2.0 DU km\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e from June to September 2015. Data in other years are shown in supplementary materials (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). In all years, high values of ΔO\u003csub\u003e3\u003c/sub\u003e are present in the central region of the Persian Gulf.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTo statistically investigate the frequency of high-concentration ozone, the analysis target area shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e was divided into a grid of 1\u0026deg; latitude and 1\u0026deg; longitude, and the number of observation points where ΔO₃ exceeded 2.0 DU km\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e was counted for each grid over seven years. Among these, the frequency of observations exceeding 2.0 DU km\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e was significantly higher in the grid at 49\u0026deg;E, 26\u0026deg;N, and the monthly frequency is shown in a histogram in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea. The frequency of observations exceeding 2.0 DU km\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e is high in June\u0026ndash;September in all years, with a peak in August. Additionally, there is a tendency for the occurrence frequency to be higher in the latter half of the period (2017, 2022, and 2023) compared to the first half (2005, 2006, 2015, and 2016). The monthly frequency of high ΔO\u003csub\u003e3\u003c/sub\u003e values for all grids is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb; the frequency of high ΔO\u003csub\u003e3\u003c/sub\u003e values was particularly high in August, with over 100 occurrences near the center of the Persian Gulf, where major cities such as Doha, Bahrain, and Dammam are located. Additionally, approximately 50 to 100 times were observed around Kuwait City, Abu Dhabi, Dubai, and Sharjah (see Fig. S2 for other months). The areas with high ΔO\u003csub\u003e3\u003c/sub\u003e shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb are aligned with the areas with high concentrations of NO\u003csub\u003e2\u003c/sub\u003e and SO\u003csub\u003e2\u003c/sub\u003e as reported by Barkley et al. (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). This good correlation suggests local ozone formation and reinforces the reliability of OMI detection of ozone at the lowermost layer.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe retrieved ozone concentrations and a priori ozone concentrations are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e together with ΔO₃. Here, we show the results from August 2017, indicating a significant increase in ΔO\u003csub\u003e3\u003c/sub\u003e around the Persian Gulf.; high ΔO₃ around the Persian Gulf was also found in July and September (Fig. S3). The a priori ozone values generally depend on latitude and do not indicate specific geographical structures, but depend on terrain locally and show structural patterns in some spatial distributions. A band of high-retrieved ozone concentrations appears to extend around the Mediterranean Sea, but it is also seen in the distribution of a priori values. However, when expressed as the difference from the a priori values (ΔO₃), geographical structures that were not present in the a priori values become clearly observable over the Persian Gulf coast. This suggests that OMI observations have captured the actual signal from the high ozone concentrations in this region.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Ozone concentrations observed by IAGOS in high-concentration areas\u003c/h2\u003e\u003cp\u003eThis section presents the results of the analysis of IAGOS air-borne data targeting OMI high ΔO\u003csub\u003e3\u003c/sub\u003e areas. The monthly average ozone (DU) concentrations at 0\u0026ndash;3 km altitude are analyzed for the airports where IAGOS observations were conducted (Table\u0026nbsp;1), for the years 2005, 2015, 2016, 2017, and 2022. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea, in Kuwait City (KWI), high ozone concentrations were particularly noticeable in July 2022. Four profiles obtained between the evening of July 1 and midnight on July 2 in Kuwait City (KWI) are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb. In the boundary layer, extremely high ozone concentration exceeding approximately 170 ppbv was observed at 20:28 on July 1 and rapidly decreased until 01:51 on July 2. Fig. S4 shows IAGOS data at the airports within the target area other than Kuwait City. While enhanced ozone levels were also observed in July 2022 at Damman (DMM) and Bahrain (BAH), a detailed trend is not clear because of the limited number of observations.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Comparison of OMI and IAGOS ozone\u003c/h2\u003e\u003cp\u003eOMI observation data were validated by comparing them with IAGOS data observed on the same day. The matching method for OMI and IAGOS was described in Section 2.2. OMI's observation time is 13:45 (LT), but there are few daytime observation data close to this time in IAGOS; many cases were selected from nighttime observations. The number of matching observations with OMI is shown in Table\u0026nbsp;1. Many pairs were matched in 2016 because IAGOS observations were more frequent. Throughout the entire period, 458 observations (32% of the total 1,434 observations) were selected as matching pairs.\u003c/p\u003e\u003cp\u003eOne example of a matching pair is shown in Fig. S5, the IAGOS and OMI profiles for Bahrain on July 22, 2015. The IAGOS observation time was 13:54, a rare daytime event. The observed ozone showed a large value of 180 ppbv near the ground. CO similarly increased near the ground, suggesting the presence of a boundary layer up to approximately 1 km. While the OMI a priori ozone mixing ratio in the 24th layer was approximately 30 ppbv, the retrieved ozone mixing ratio was over 60 ppbv, approaching the IAGOS ozone mixing ratio of approximately 90 ppbv averaged over 0\u0026ndash;3 km. This case is a good example of how OMI observations have corrected prior values and detected high ozone concentrations.\u003c/p\u003e\u003cp\u003eAnalyses for all matching pairs were summarized in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e as a scatter plot of 458 pairs from IAGOS and OMI. The IAGOS ozone profiles were smoothed using averaging kernels obtained by corresponding OMI observations. In all layers 22\u0026ndash;24, both O\u003csub\u003e3\u003c/sub\u003e and ΔO\u003csub\u003e3\u003c/sub\u003e showed a gradient close to unity. On the other hand, the R\u003csup\u003e2\u003c/sup\u003e values were around 0.5, indicating that the data were relatively scattered. This may be due to the incongruity between OMI and IAGOS\u0026rsquo;s typical measurement times. OMI observations are taken around 13:45 LT, but many IAGOS observations are taken during nighttime due to aircraft schedules, with most occurring between 19:00 LT and 4:00 LT the following morning. Ozone has a short lifetime, so even if concentrations are high during the day, they may decrease at night (as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb). In the case of IAGOS observations during the daytime (Fig. S5), OMI ozone concentrations corresponded with the IAGOS data well. If more IAGOS observation data had been obtained at near 13:45, a better correlation between the two would likely have been found.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e shows the OMI ozone profile and averaging kernel for the observation case on July 22, 2015 (corresponding to Fig. S5). The 24th-layer averaging kernel (red solid line) takes values close to 0.2 at around 0 to 12 km. Those values resemble those of Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e by Hayashida et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) when significant ozone enhancement was observed in East Asia, indicating that actual ozone change from the a priori in the 24th layer can be retrieved. As observed by TES, ozone in the mid-troposphere tends to enhanced condition over the Middle East (Worden et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Li et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2009\u003c/span\u003e); some part of the ΔO₃ shown in this study may be slightly influenced by the mid-troposphere during the retrieval process. However, the frequent occurrence of high ozone concentrations along the Persian Gulf coast clearly indicates a link to local pollutant emissions in this region, which is supported by OMI's observations of NO\u003csub\u003e2\u003c/sub\u003e and other ozone precursors (Barkley et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), along with IAGOS observations. The model simulation by Lelieveld et al. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) predicted high ozone concentrations over the Persian Gulf during the summer (Fig.\u0026nbsp;8 of Lelieveld et al. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2009\u003c/span\u003e)), and OMI observations shown in this study confirmed it for the first time. Near the ground, wind systems and transport processes must differ from those previously identified in the middle troposphere. When examining surface winds in each major city (WeatherSpark \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) in the target area, they are often found to be blowing from the Persian Gulf toward land during the summer; this is likely to be due to the Persian Gulf forming a localized high-pressure zone similar to a basin. To understand local variations in ozone concentrations in this region, it is necessary to analyze meteorological data in more detail, which is a challenge for the future.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4 Conclusions","content":"\u003cp\u003eAnalysis of seven years of the latest ozone research product, OMI V2 revealed that in the lowermost layer, corresponding to an altitude of approximately 0\u0026ndash;3 km above the ground, significant ozone enhancement was clearly detected over the Persian Gulf area, which is home to numerous oil fields and refineries and has recently experienced significant population growth and urbanization. This high concentration of ozone appeared prominently in summer (June to September). Comparison with IAGOS observations supported OMI ozone detection. The frequent occurrence of high ozone concentrations over the Persian Gulf area clearly indicates a link to local pollutant emissions in this region, which is supported by OMI's observations of NO\u003csub\u003e2\u003c/sub\u003e and other ozone precursors. While satellite-based measurements of ozone concentrations in the middle troposphere have been made using infrared sensors such as TES, the development of techniques to estimate ozone concentrations in lower layers using ultraviolet-visible spectra is crucial for understanding air pollution issues in rapidly developing urban regions like the Persian Gulf coast.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by JSPS KAKENHI Grant Number JP23K03496.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe research concept was proposed by S. H., and the specific methodology was proposed by A. K.. The drafting of the manuscript was primarily led by A. K. and S. H., with all authors contributing to the writing. OMI V2 data was provided by J. B., K. Y. and X. L.. \u0026nbsp;Data analysis and visualisation were primarily carried out by H. A.. M. K. and T. T S. analysed the meteorological conditions. All co-authors participated in the discussions for the preparation of the paper.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThis work was supported by JSPS KAKENHI Grant Number JP23K03496. The authors would like to express our gratitude to Dr. Tomohiro Sato of NICT for valuable discussions. MOZAIC/CARIBIC/IAGOS data were created with support from the European Commission, national agencies in Germany (BMBF), France (MESR), and the UK (NERC), and the IAGOS member institutions (http://www.iagos.org/partners). The participating airlines (Lufthansa, Air France, Austrian, China Airlines, Hawaiian Airlines, Air Canada, Iberia, Eurowings Discover, Cathay Pacific, Air Namibia, Sabena) supported IAGOS by carrying the measurement equipment free of charge since 1994. The data are available at http://www.iagos.fr thanks to additional support from AERIS.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBak, J., Liu, X., Kim, J.-H., Haffner, D. P., Chance, K., Yang, K., and Sun, K. (2017), Characterization and correction of OMPS nadir mapper measurements for ozone profile retrievals, Atmos. Meas. Tech., 10, 4373\u0026ndash;4388, https://doi.org/10.5194/amt-10-4373-2017.\u003c/li\u003e\n\u003cli\u003eBak, J., Song, E.-J., Lee, H.-J., Liu, X., Koo, J.-H., Kim, J., Jeon, W., Kim, J.-H., and Kim, C.-H. 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(2022), Ozone Monitoring Instrument (OMI) collection 4: establishing a 17-year-long series of detrended level-1b data, Atmos. Meas. Tech., 15(11), 3527-3553. https://doi.org/10.5194/amt-15-3527-2022\u003c/li\u003e\n\u003cli\u003eLelieveld, J., P. Hoor, P. J\u0026uml;ockel, A. Pozzer, P. Hadjinicolaou, J.-P. Cammas, and S. Beirle, (2009), Severe ozone air pollution in the Persian Gulf region, Atmos. Chem. Phys., 9, 1393\u0026ndash;1406, https://doi.org/10.5194/acp-9-1393-2009\u003c/li\u003e\n\u003cli\u003eLevelt, P. F., J. Joiner, J. Tamminen, J. P. Veefkind, P. K. Bhartia, D. C. Stein Zweers, B. N. Duncan, D. G. Streets, H. Eskes, R. van der A, C. McLinden, V. Fioletov, S. Carn, J. de Laat, M. DeLand, S. Marchenko, R. McPeters, J. Ziemke, D. Fu, X. Liu, K. Pickering, A. Apituley, G. Gonz\u0026aacute;lez Abad, A. Arola, F. Boersma, C. Chan Miller, K. Chance, M. de Graaf, J. Hakkarainen, S. Hassinen, I. Ialongo, Q. Kleipool, N. Krotkov, C. Li, L. Lamsal, P. Newman, C. Nowlan, R. Suleiman, L. G. Tilstra, O. Torres, H. Wang and K. Wargan (2018), The Ozone Monitoring Instrument: overview of 14 years in space, Atmos. Chem. Phys, 18(8), 5699-5745, https://doi.org/10.5194/acp-18-5699-2018.\u003c/li\u003e\n\u003cli\u003eLi, Q., Jacob, D. J., Logan, J. A., Bey, I., Yantosca, R. M., Liu, H., Martin, R. V., Fiore, A. M., Field, B. D., Duncan, B. N., \u0026amp; Thouret, V. (2001). A tropospheric ozone maximum over the Middle East, Geophys. Res. Lett., 28(17), 3235-3238. https://doi.org/10.1029/2001gl013134 \u003c/li\u003e\n\u003cli\u003eLi, J., B.E. Carlson, and A.A. Lacis (2009), A study on the temporal and spatial variability of absorbing aerosols using total ozone mapping spectrometer and ozone monitoring instrument aerosol index data. J. Geophys. Res., 114, D09213, https://doi.org/10.1029/2008JD011278.\u003c/li\u003e\n\u003cli\u003eLiu, J. J., D. B. A. Jones, J. R. Worden, D. Noone, M. Parrington, and J. 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Jones, Jane Liu, Mark Parrington, Kevin Bowman, Ivanka Stajner, Reinhard Beer, Jonathan Jiang, Val\u0026eacute;rie Thouret, Susan Kulawik, Jui‐Lin F. Li, Sunita Verma, Helen Worden (2009), Observed vertical distribution of tropospheric ozone during the Asian summertime monsoon, J. Geophys. Res., https://doi.org/10.1029/2008JD010560, 114, D13.\u003c/li\u003e\n\u003cli\u003eZhao, F., Liu, C., Cai, Z., Liu, X., Bak, J., Kim, J., Hu, Q., Xia, C., Zhang, C., Sun, Y., Wang, W., and Liu, J. (2021), Ozone profile retrievals from TROPOMI: Implication for the variation of tropospheric ozone during the outbreak of COVID-19 in China, Sci. Total Environ., 764, 142886, https://doi.org/10.1016/j.scitotenv.2020.142886.\u003c/li\u003e\n\u003cli\u003eZoogman, P., X. Liu, R.M. Suleiman, W.F. Pennington, D.E. Flittner, J.A. Al-Saadi, B.B. Hilton, D.K. Nicks, M.J. Newchurch, J.L. Carr, S.J. Janz, M.R. Andraschko, A. Arola, B.D. Baker, B.P. Canova, C. Chan Miller, R.C. Cohen, J.E. Davis, M.E. Dussault, D.P. Edwards, J. Fishman, A. Ghulam, G. Gonz\u0026aacute;lez Abad, M. Grutter, J.R. Herman, J. Houck, D.J. Jacob, J. Joiner, B.J. Kerridge, J. Kim, N.A. Krotkov, L. Lamsal, C. Li, A. Lindfors, R.V. Martin, C.T. McElroy, C. McLinden, V. Natraj, D.O. Neil, C.R. Nowlan, E.J. O׳Sullivan, P.I. Palmer, R.B. Pierce, M.R. Pippin, A. Saiz-Lopez, R.J.D. Spurr, J.J. Szykman, O. Torres, J.P. Veefkind, B. Veihelmann, H. Wang, J. Wang, K. Chance (2017), Tropospheric emissions: Monitoring of pollution (TEMPO), Journal of Quantitative Spectroscopy and Radiative Transfer, Volume 186, Pages 17-39, ISSN 0022-4073, https://doi.org/10.1016/j.jqsrt.2016.05.008.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Footnotes","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e All abbreviations for satellite sensors are listed with their full names in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"sola","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [SOLA](https://link.springer.com/journal/44393)","snPcode":"44393","submissionUrl":"https://submission.springernature.com/new-submission/44393/3?_gl=1*3gm7xl*_gcl_au*MTQxNDAxMjI3My4xNzYwNjE1NTM2*_ga*MjAwMTA4NDA0NS4xNzE2OTAwNjg2*_ga_B3E4QL2TPR*czE3NjA2MTU1MzckbzkyJGcwJHQxNzYwNjE1NTM3JGo2MCRsMCRoMTYwMDQ5Nzk0NA..","title":"SOLA","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Open","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"tropospheric ozone, air pollution, satellite data analysis, Middle East, atmospheric chemistry","lastPublishedDoi":"10.21203/rs.3.rs-7877632/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7877632/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTo reveal air pollution conditions in the rapidly urbanizing and industrializing Persian Gulf coastal region, seven years of ozone profile data from the newly improved Ozone Monitoring Instrument (OMI) V2 product were analyzed; a significant summer (June\u0026ndash;September) increase in lowermost tropospheric (0\u0026ndash;3 km altitude) ozone indicated the presence of hotspots in this region. The ozone enhancement correlates with the distribution of emission hotspots for ozone precursors observed by OMI, suggesting the validity of OMI ozone observations in the lowermost layer. OMI ozone observation data were compared with In-service Aircraft for a Global Observing System (IAGOS) measurements, confirming correspondence with OMI during several ozone high-concentration events. Same-day IAGOS-OMI comparisons showed a positive correlation with a slope of nearly unity, confirming OMI data reliability. This study demonstrates that lower-level ozone observations using the ultraviolet-visible spectrum is highly effective for understanding air pollution issues in rapidly growing urban areas such as the Persian Gulf region.\u003c/p\u003e","manuscriptTitle":"High ozone concentrations observed along the Persian Gulf coast by Ozone Monitoring Instrument","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-30 16:24:39","doi":"10.21203/rs.3.rs-7877632/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-28T01:59:55+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-24T02:41:48+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-20T22:44:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"275515860896802108504118272890244035507","date":"2025-10-20T00:54:58+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"232684584791003714586964971321728073365","date":"2025-10-17T05:33:06+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-15T04:39:07+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-13T14:25:16+00:00","index":"","fulltext":""},{"type":"submitted","content":"SOLA","date":"2025-10-13T11:49:55+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"sola","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [SOLA](https://link.springer.com/journal/44393)","snPcode":"44393","submissionUrl":"https://submission.springernature.com/new-submission/44393/3?_gl=1*3gm7xl*_gcl_au*MTQxNDAxMjI3My4xNzYwNjE1NTM2*_ga*MjAwMTA4NDA0NS4xNzE2OTAwNjg2*_ga_B3E4QL2TPR*czE3NjA2MTU1MzckbzkyJGcwJHQxNzYwNjE1NTM3JGo2MCRsMCRoMTYwMDQ5Nzk0NA..","title":"SOLA","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Open","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4c3352ae-62fa-4eea-938a-ff03260203ed","owner":[],"postedDate":"October 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-11-12T07:08:23+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-30 16:24:39","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7877632","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7877632","identity":"rs-7877632","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}