Climate Change Research

Climate Change Research

Analysis of Temporal Trends and Seasonal Variability in Column-Averaged Methane Concentrations (XCH4) over the Middle East Using Sentinel-5P/TROPOMI Data during 2018–2023

Document Type : Original Article

Authors
1 M.Sc. in Environmental Engineering, Department of Environmental Engineering, Faculty of Environment, University of Tehran, Tehran, Iran
2 Assistant Professor, Department of Environmental Engineering, Faculty of Environment, University of Tehran, Tehran, Iran
3 Department of Environmental Sciences, Faculty of Natural Resources, University of Guilan, Rasht, Iran
10.30488/ccr.2026.580388.1332
Abstract
Methane (CH₄) is one of the most important short-lived greenhouse gases affecting climate change, and its monitoring in energy-intensive regions is of particular importance. The aim of this research is to analyze the temporal trend and seasonal pattern of XCH₄ column concentration in the Middle East during the period 2018-2023 using Sentinel-5P/TROPOMI satellite data. In this study, daily Level 2 XCH4 data were used, and after processing in a Python environment, XCH4 values were aggregated at monthly, seasonal, and annual scales. The trend of changes was then evaluated using the Mann-Kendall test, Sen's slope estimator, and Ordinary Least Squares (OLS) regression. The results showed that XCH₄ in the Middle East during the study period has an increasing, monotonic, and statistically significant trend; its annual average increased from 1862.14 ppb in 2018 to 1918.77 ppb in 2023. The Mann-Kendall test showed Z = 9.64, p-value = 3.04×10⁻²¹, and τ = 0.75, indicating a strong and significant increasing trend over the entire study period. Sen's slope estimator and the OLS model also showed that XCH₄ in the Middle East increased by an average of about 12 to 13 ppb annually. Seasonally, the minimum mean XCH₄ was observed in winter (1876.2 ppb) and its maximum in summer (1898.3 ppb). Monthly analysis also confirmed this pattern, with the lowest value recorded in February and the highest in August–September. Additionally, annual and seasonal average maps indicated spatial heterogeneity of XCH₄ and higher values around the Persian Gulf and parts of the Arabian Peninsula. In summary, the results showed that the methane column concentration in the Middle East has followed a consistent increasing trend and a regular seasonal cycle in recent years. These findings indicate the suitable capacity of satellite monitoring for continuous observation of the temporal and spatial behavior of methane at a regional scale
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. حجام، سهراب؛ خوشخو، یونس؛ و شمس‌الدین‌وندی، رضا. (۱۳۸۷). تحلیل روند تغییرات بارندگی‌های فصلی و سالانه چند ایستگاه منتخب در حوزه مرکزی ایران با استفاده از روش‌های ناپارامتری. پژوهش‌های جغرافیای طبیعی، (۶۳)، ۱۵۱۱۵۹. https://sid.ir/paper/5428/fa
2. ملکی نژاد، حسین؛ سلیمانی مطلق، مهدی؛ جایدری، اعظم؛ شاطر آبشوری، سمیه. (1391). تحلیل روند تغییهرات بارندگی و خشکسالی با استفاده از آزمونهای من-کندال و سن در استان تهران. نیوار، 37(81-80)، 43-54 .
3.Adame, J. A., Padilla, R., Parker, R. J., & Hidalgo, P. J. (2025). Spatial distribution pattern and long-term trend of atmospheric methane in the Atlantic-Mediterranean transition region based on TROPOMI and GOSAT measurements. Science of the Total Environment, 958, 178006. https://doi.org/10.1016/j.scitotenv.2024.178006
4.Chen, Z., Jacob, D. J., Gautam, R., Omara, M., Stavins, R. N., Stowe, R. C., Nesser, H., Sulprizio, M. P., Lorente, A., Varon, D. J., Lu, X., Shen, L., Qu, Z., Pendergrass, D. C., & Hancock, S. (2023). Satellite quantification of methane emissions and oil-gas methane intensities from individual countries in the Middle East and North Africa: Implications for climate action. Atmospheric Chemistry and Physics, 23, 5945–5967. https://doi.org/10.5194/acp-23-5945-2023
5.Cruz, J.-L., & Rossi-Hansberg, E. (2024). The economic geography of global warming. The Review of Economic Studies, 91(2), 899–939. https://doi.org/10.1093/restud/rdad042
6.Dogniaux, M., Maasakkers, J. D., Varon, D. J., & Aben, I. (2024). Report on Landsat 8 and Sentinel-2B observations of the Nord Stream 2 pipeline methane leak. Atmospheric Measurement Techniques, 17, 2777–2787. https://doi.org/10.5194/amt-17-2777-2024
7.He, J., Naik, V., Horowitz, L. W., Dlugokencky, E., & Thoning, K. (2020). Investigation of the global methane budget over 1980–2017 using GFDL-AM4.1. Atmospheric Chemistry and Physics, 20, 805–827. https://doi.org/10.5194/acp-20-805-2020
8.Heydarizad, M., Gimeno, L., Sorí, R., Minaei, F., & Eskandari Mayvan, J. (2021). The stable isotope characteristics of precipitation in the Middle East highlighting the link between the Köppen climate classifications and the δ18O and δ2H values of precipitation. Water, 13(17), 2397. https://doi.org/10.3390/w13172397
9.Hu, Y., Yue, X., Tian, C., Zhou, H., Fu, W., Zhao, X., Zhao, Y., & Chen, Y. (2023). Identifying the main drivers of the spatiotemporal variations in wetland methane emissions during 2001–2020. Frontiers in Environmental Science, 11, 1275742. https://doi.org/10.3389/fenvs.2023.1275742
10.Jacob, D. J., Turner, A. J., Maasakkers, J. D., Sheng, J., Sun, K., Liu, X., Chance, K., Aben, I., McKeever, J., & Frankenberg, C. (2016). Satellite observations of atmospheric methane and their value for quantifying methane emissions. Atmospheric Chemistry and Physics, 16, 14371–14396. https://doi.org/10.5194/acp-16-14371-2016
11.Kendall, M. G. (1975). Rank correlation methods (4th ed.). Charles Griffin.
12.Lin, X., Zhang, W., Crippa, M., Peng, S., Han, P., Zeng, N., Yu, L., & Wang, G. (2021). A comparative study of anthropogenic CH4 emissions over China based on the ensembles of bottom-up inventories. Earth System Science Data, 13, 1073–1088. https://doi.org/10.5194/essd-13-1073-2021
13.Lorente, A., Borsdorff, T., Butz, A., Hasekamp, O., aan de Brugh, J., Schneider, A., Wu, L., Hase, F., Kivi, R., Wunch, D., Pollard, D. F., Shiomi, K., Deutscher, N. M., Velazco, V. A., Roehl, C. M., Wennberg, P. O., Warneke, T., & Landgraf, J. (2021). Methane retrieved from TROPOMI: Improvement of the data product and validation of the first 2 years of measurements. Atmospheric Measurement Techniques, 14, 665–684. https://doi.org/10.5194/amt-14-665-2021
14.Maasakkers, J. D., Jacob, D. J., Sulprizio, M. P., Scarpelli, T. R., Nesser, H., Sheng, J., Zhang, Y., Lu, X., Bloom, A. A., Bowman, K. W., Worden, J. R., & Parker, R. J. (2021). 2010–2015 North American methane emissions, sectoral contributions, and trends: A high-resolution inversion of GOSAT observations of atmospheric methane. Atmospheric Chemistry and Physics, 21, 4339–4356. https://doi.org/10.5194/acp-21-4339-2021
15.Mousavi, S. M., & Falahatkar, S. (2020). Spatiotemporal distribution patterns of atmospheric methane using GOSAT data in Iran. Environment, Development and Sustainability, 22, 4191–4207. https://doi.org/10.1007/s10668-019-00378-5
16.Mousavi, S. M., Dinan, N. M., Ansarifard, S., Borhani, F., Darvishi, A., Mustafa, F., & Naghibi, A. (2024). Unveiling the drivers of atmospheric methane variability in Iran: A 20-year exploration using spatiotemporal modeling and machine learning. Environmental Challenges, 15, 100946. https://doi.org/10.1016/j.envc.2024.100946
17.Nguyen, N. H. (2024). From source to sink: Measuring and modeling processes affecting methane emissions and loss [Doctoral dissertation, California Institute of Technology].
18.Saunois, M., Stavert, A. R., Poulter, B., Bousquet, P., Canadell, J. G., Jackson, R. B., Raymond, P. A., Dlugokencky, E. J., Houweling, S., Patra, P. K., Ciais, P., Arora, V. K., Bastviken, D., Bergamaschi, P., Blake, D. R., Brailsford, G., Bruhwiler, L., Carlson, K. M., Carrol, M., ... Zhuang, Q. (2020). The global methane budget 2000–2017. Earth System Science Data, 12, 1561–1623. https://doi.org/10.5194/essd-12-1561-2020
19.Seber, G. A. F., & Lee, A. J. (2003). Linear regression analysis (2nd ed.). John Wiley & Sons. https://doi.org/10.1002/9780471722199
20.Tarazkar, M. H., Kargar Dehbidi, N., Ansari, R. A., & Pourghasemi, H. R. (2021). Factors affecting methane emissions in OPEC member countries: Does the agricultural production matter? Environment, Development and Sustainability, 23(5), 6734–6748. https://doi.org/10.1007/s10668-020-00887-8
21.Veefkind, J. P., Aben, I., McMullan, K., Förster, H., de Vries, J., Otter, G., Claas, J., Eskes, H. J., de Haan, J. F., Kleipool, Q., van Weele, M., Hasekamp, O., Hoogeveen, R., Landgraf, J., Snel, R., Tol, P., Ingmann, P., Voors, R., Kruizinga, B., & Levelt, P. F. (2012). TROPOMI on the ESA Sentinel-5 Precursor: A GMES mission for global observations of the atmospheric composition for climate, air quality and ozone layer applications. Remote Sensing of Environment, 120, 70–83. https://doi.org/10.1016/j.rse.2011.09.027
22.World Bank. (2017). World Development Indicators. World Bank. https://www.worldbank.org/
23.World Bank. (2020). World Development Indicators. World Bank. https://www.worldbank.org/