Seasonal Variation of PM₂.₅ Concentrations in Eastern Anatolia and Comparison with WHO/EU Air Quality Thresholds: CAMS EAC4 Reanalysis (2003–2024)

Abstract

The lack of PM2.5 monitoring infrastructure in Eastern Anatolia (Türkiye) limits objective regional assessment of fine particulate matter dynamics. This study uses CAMS EAC4 reanalysis data to examine seasonal and temporal variation of PM2.5 concentrations and comparison with WHO 2021 and EU air quality thresholds for the period 2003–2024. The regional annual mean PM2.5 over the 22-year period was 16.43 µg/m³, which is 3.29 times the WHO 2021 annual guideline of 5 µg/m³. Monthly climatology peaks in April at 23.46 µg/m³, with the spring seasonal mean (22.17 µg/m³) markedly exceeding other seasons, in a pattern consistent with North African and Middle Eastern desert dust transport. Winter recorded the lowest seasonal mean (13.50 µg/m³), contrary to conventional expectations. Mann-Kendall trend tests detected no statistically significant trend at annual or seasonal scale (all P>0.05). Annual mean PM2.5 exceeded both the WHO annual guideline and the EU 2030 annual limit value (10 µg/m³) in all 22 years. At daily scale, the WHO 15 µg/m³ guideline was exceeded on an average of 165 days/year and the EU 2030 daily limit value (25 µg/m³) on 45 days/year. Reaching the WHO guideline requires approximately a 69% reduction in the period mean, while the EU 2030 limit value requires 39%. The absence of a binding national PM2.5 limit in Türkiye represents a critical policy gap for regional air quality management.

Keywords

• CAMS EAC4 reanalysis
• Eastern Anatolia
• Seasonal variation
• Mann-Kendall trend test
• Air quality thresholds
• Desert dust transport

Supporting Institution

Hakkari University

Project Number

FM19BAP

Doğu Anadolu'da PM₂.₅ Konsantrasyonlarının Mevsimsel Değişimi ve WHO/AB Hava Kalitesi Eşikleriyle Karşılaştırılması: CAMS EAC4 Yeniden Analizi (2003–2024)

Abstract

Doğu Anadolu Bölgesi’nde PM2.5 izleme altyapısının yetersizliği, bölgesel ince partikül madde dinamiklerinin değerlendirilmesini güçleştirmektedir. Bu çalışmada Copernicus Atmosphere Monitoring Service (CAMS) EAC4 yeniden analiz verisi kullanılarak 2003–2024 dönemine ait PM2.5 konsantrasyonlarının mevsimsel değişimi ile WHO 2021 ve AB hava kalitesi eşikleriyle karşılaştırılmıştır. Yirmi iki yıllık dönem boyunca bölgesel yıllık ortalama PM2.5 değeri 16,43 µg/m³ olup WHO 2021 yıllık kılavuz değerinin (5 µg/m³) 3,29 katıdır. Aylık klimatoloji Nisan’da 23,46 µg/m³ zirve değeriyle belirgin bir mevsimsel döngü ortaya koymakta; İlkbahar ortalaması (22,17 µg/m³) diğer mevsimleri geride bırakmaktadır. Bu örüntü Kuzey Afrika ve Orta Doğu kökenli çöl tozu taşınımıyla tutarlıdır. Kış mevsimi 13,50 µg/m³ ile beklentilerin aksine yıl içindeki en düşük dönem olmuştur. Mann-Kendall trend testi yıllık ve mevsimsel serilerin hiçbirinde istatistiksel anlamlı eğilim saptamamıştır (tüm p>0,05). İncelenen 22 yılın tamamında yıllık değerler hem WHO yıllık kılavuzunu hem de AB 2030 yıllık limit değerini (10 µg/m³) aşmıştır. Günlük ölçekte WHO 15 µg/m³ kılavuz değeri ortalama 165 gün/yıl, AB 2030 günlük 25 µg/m³ limit değeri ise ortalama 45 gün/yıl aşılmıştır. WHO kılavuzuna ulaşmak için dönem ortalamasında %69, AB 2030 limit değerine ulaşmak için %39 azaltma gerekmektedir. Türkiye’de PM2.5 için bağlayıcı ulusal limit değer bulunmaması bölgesel hava kalitesi yönetiminde kritik bir politika boşluğunu sürdürmektedir.

Keywords

• CAMS EAC4 yeniden analiz
• Doğu Anadolu
• Mevsimsel değişim
• Mann-Kendall trend testi
• Hava kalitesi eşikleri
• Çöl tozu taşınımı

Supporting Institution

Hakkari Üniversitesi

Project Number

FM19BAP

Ethical Statement

Bu araştırmada hayvanlar ve insanlar üzerinde herhangi bir çalışma yapılmadığı için etik kurul onayı alınmamıştır.

Thanks

Bu çalışma, Hakkari Üniversitesi Bilimsel Araştırma Projeleri Koordinasyon Ofisi tarafından desteklenmiştir (Proje No: FM19BAP).

References

1. Arıkan İspir, D., & Yıldız, F. (2025). Air quality of Bursa: Temporal and spatial evaluation of PM10, PM2.5, NO2 and SO2 pollutants using IDW geostatistical technique. Doğal Afetler ve Çevre Dergisi, 11(1), 181–193. https://doi.org/10.21324/dacd.1562860
2. Aslanoğlu, S. Y., Proestakis, E., Gkikas, A., Güllü, G., & Amiridis, V. (2022). Dust climatology of Turkey as a part of the Eastern Mediterranean Basin via 9-year CALIPSO-derived product. Atmosphere, 13(5), Article 733. https://doi.org/10.3390/atmos13050733
3. Bahşi, K., Ustaoğlu, B., Aksoy, S., & Sertel, E. (2023). Estimation of emissions from crop residue burning in Türkiye using remotely sensed data and the Google Earth Engine platform. Geocarto International, 38(1), Article 2157052. https://doi.org/10.1080/10106049.2022.2157052
4. Baltaci, H., & Ezber, Y. (2022). Characterization of atmospheric mechanisms that cause the transport of Arabian dust particles to the southeastern region of Turkey. Environmental Science and Pollution Research, 29(15), 22771–22784. https://doi.org/10.1007/s11356-021-17526-y
5. Benjamini, Y., & Hochberg, Y. (1995). Controlling the false discovery rate: A practical and powerful approach to multiple testing. Journal of the Royal Statistical Society: Series B (Methodological), 57(1), 289–300. https://doi.org/10.1111/j.2517-6161.1995.tb02031.x
6. Bimenyimana, E., Sciare, J., Pikridas, M., Oikonomou, K., Iakovides, M., Vasiliadou, E., Savvides, C., & Mihalopoulos, N. (2026). Persistent high PM pollution in the Eastern Mediterranean and Middle East: Insights from long-term observations and source apportionment in Cyprus. Atmospheric Chemistry and Physics, 26(2), 1605–1622. https://doi.org/10.5194/acp-26-1605-2026
7. Cohen, A. J., Brauer, M., Burnett, R., Anderson, H. R., Frostad, J., Estep, K., Balakrishnan, K., Brunekreef, B., Dandona, L., Dandona, R., Feigin, V., Freedman, G., Hubbell, B., Jobling, A., Kan, H., Knibbs, L., Liu, Y., Martin, R., Morawska, L., ... Forouzanfar, M. H. (2017). Estimates and 25-year trends of the global burden of disease attributable to ambient air pollution: An analysis of data from the Global Burden of Diseases Study 2015. The Lancet, 389(10082), 1907–1918. https://doi.org/10.1016/S0140-6736(17)30505-6
8. Dimitriou, K., Tsagkaraki, M., Tavernaraki, K., Papoutsidaki, K., Malandraki, M. A., Petrinoli, K., Liakakou, E., Bougiatioti, A., & Mihalopoulos, N. (2024). The effect of mixed layer across air mass trajectory pathways on PM2.5 constituent levels: The case of a major urban center in the Eastern Mediterranean. Atmospheric Pollution Research, 15(6), Article 102129. https://doi.org/10.1016/j.apr.2024.102129

9. Faridi, S., Krzyzanowski, M., Cohen, A. J., Malkawi, M., Safi, H. A. M., Yousefian, F., Azimi, F., Naddafi, K., Momeniha, F., Niazi, S., Amini, H., Künzli, N., Shamsipour, M., Mokammel, A., Roostaei, V., & Hassanvand, M. S. (2023). Ambient air quality standards and policies in Eastern Mediterranean countries: A review. International Journal of Public Health, 68, Article 1605352. https://doi.org/10.3389/ijph.2023.1605352
10. Francis, D., Fonseca, R., Nelli, N., Bozkurt, D., Cuesta, J., & Bosc, E. (2023). On the Middle East’s severe dust storms in spring 2022: Triggers and impacts. Atmospheric Environment, 296, Article 119539. https://doi.org/10.1016/j.atmosenv.2022.119539
11. Im, U., Ye, Z., Schuhen, N., Chowdhury, S., Christensen, J. H., Geels, C., Hänninen, R., Hodnebrog, Ø., Marelle, L., Sofiev, M., Brandt, J., & Aunan, K. (2025). Europe will struggle to meet the new WHO air quality guidelines. Npj Clean Air, 1(1), Article 13. https://doi.org/10.1038/s44407-025-00013-w
12. Inness, A., Ades, M., Agustí-Panareda, A., Barré, J., Benedictow, A., Blechschmidt, A., Dominguez, J. J., Engelen, R., Eskes, H., Flemming, J., Huijnen, V., Jones, L., Kipling, Z., Massart, S., Parrington, M., Peuch, V., Razinger, M., Remy, S., Schulz, M., & Suttie, M. (2019). The CAMS reanalysis of atmospheric composition. Atmospheric Chemistry and Physics, 19(6), 3515–3556. https://doi.org/10.5194/acp-19-3515-2019
13. Jin, Caiyi, Wang, Y., Li, T., & Yuan, Q. (2022). Global validation and hybrid calibration of CAMS and MERRA-2 PM2.5 reanalysis products based on OpenAQ platform. Atmospheric Environment, 274, Article 118972. https://doi.org/10.1016/j.atmosenv.2022.118972
14. Kendall, M. G. (1975). Rank correlation methods. Griffin.
15. Lelieveld, J., Pozzer, A., Pöschl, U., Fnais, M., Haines, A., & Münzel, T. (2020). Loss of life expectancy from air pollution compared to other risk factors: A worldwide perspective. Cardiovascular Research, 116(11), 1910–1917. https://doi.org/10.1093/cvr/cvaa025
16. Mann, H. B. (1945). Nonparametric tests against trend. Econometrica, 13(3), 245–259. https://doi.org/10.2307/1907187
17. Millet, T., Arola, A., Benas, N., Benedictow, A., Bennouna, Y., Bouarar, I., Bowdalo, D., Clerbaux, C., Emili, E., Errera, Q., Escribano, J., Eskes, H. J., Gauss, M., Gkikas, A., Griesfeller, J., Kapsomenakis, J., Kouyate, M., Langerock, B., Mortier, A., ... Zerefos, C. (2026). Validation report of the CAMS near-real-time global atmospheric composition service: Period September – November 2025 (Technical Report). Copernicus Atmosphere Monitoring Service. https://doi.org/10.24380/YGQA-B1NH
18. Pai, S. J., Carter, T. S., Heald, C. L., & Kroll, J. (2022). Updated World Health Organization air quality guidelines highlight the importance of non-anthropogenic PM2.5. Environmental Science & Technology Letters, 9(6), 501–506. https://doi.org/10.1021/acs.estlett.2c00203
19. Papadimas, C. D., Hatzianastassiou, N., Mihalopoulos, N., Querol, X., & Vardavas, I. (2008). Spatial and temporal variability in aerosol properties over the Mediterranean Basin based on 6-year (2000–2006) MODIS data. Journal of Geophysical Research: Atmospheres, 113(D11). https://doi.org/10.1029/2007JD009189
20. Pettitt, A. N. (1979). A non-parametric approach to the change-point problem. Journal of the Royal Statistical Society. Series C (Applied Statistics), 28(2), 126–135. https://doi.org/10.2307/2346729
21. Sen, P. K. (1968). Estimates of the regression coefficient based on Kendall’s tau. Journal of the American Statistical Association, 63(324), 1379–1389. https://doi.org/10.1080/01621459.1968.10480934
22. Theil, H. (1992). A rank-invariant method of linear and polynomial regression analysis. In Henri Theil’s contributions to economics and econometrics: Econometric theory and methodology (pp. 345–381). Springer.
23. Tuna Tuygun, G., & Elbir, T. (2024). Long-term spatiotemporal variation in atmospheric aerosol properties over Türkiye based on MERRA-2 reanalysis data: Aerosol classification based on city type. Environmental Science and Pollution Research, 31(28), 40655–40668. https://doi.org/10.1007/s11356-023-27920-3
24. Tuna Tuygun, G., Gündoğdu, S., & Elbir, T. (2022). Calibrating MERRA-2 PM2.5 concentrations with aerosol diagnostics: Testing different machine learning approaches in the Eastern Mediterranean. Air Quality, Atmosphere & Health, 15(12), 2283–2297. https://doi.org/10.1007/s11869-022-01250-8
25. Uzunpinar, E. S., Imamoglu, I., Rahmani, A., & Tuncel, G. (2023). Modification of Saharan dust size distribution during its transport over the Anatolian Plateau. Science of The Total Environment, 892, Article 164646. https://doi.org/10.1016/j.scitotenv.2023.164646
26. WHO Guidelines Approved by the Guidelines Review Committee. (2021). WHO global air quality guidelines: Particulate matter (PM2.5 and PM10), ozone, nitrogen dioxide, sulfur dioxide and carbon monoxide. World Health Organization.
27. Wu, C., Li, K., & Bai, K. (2020). Validation and calibration of CAMS PM2.5 forecasts using in situ PM2.5 measurements in China and United States. Remote Sensing, 12(22), Article 3813. https://doi.org/10.3390/rs12223813
28. Zeydan, Ö., & Tariq, S. (2026). Satellite-based mapping of PM2.5 pollution in Türkiye: Overcoming gaps in ground-level measurements. Journal of Atmospheric Chemistry, 83(1), Article 4. https://doi.org/10.1007/s10874-026-09489-6
29. Zeydan, Ö., & Yıldırım, Y. (2024). Impact of dust transport events on PM10 concentrations on the western coasts of Turkey: A case of April 2022. Air Quality, Atmosphere & Health, 17(4), 799–811. https://doi.org/10.1007/s11869-023-01481-3