Spatial-Temporal Analysis of Particulate Matter (PM10) and Sulphur Dioxide (SO2) Concentrations in Ankara Using Climate Parameters

Abstract

Air pollution in Turkey has become a significant issue, particularly in large cities, due to population growth, unplanned urbanization, and increases in industrial and energy facilities. Particulate Matter (PM10) and Sulphur Dioxide (SO2) concentrations significantly deteriorate air quality due to high emissions from industrial and energy production. Ankara, one of the major cities facing air pollution problems, is noted as having air pollution among the top issues in the 2022 Turkey Environmental Issues and Priorities Assessment Report. This study aims to investigate the spatial-temporal changes in PM10 and SO2 concentrations in Ankara from 2011 to 2014 under the influence of meteorological factors using the Kriging with External Drift (KED) method. In 2011, PM10 and SO2 concentration levels were lower compared to other years but remained above the annual concentration limits set by the World Health Organization (WHO). In 2012 and 2013, increases in PM10 and SO2 concentrations were observed, showing variability across different regions of the city. In 2014, a notable decrease in PM10 and SO2 concentrations was observed, coinciding with increased rainfall and temperature values. When evaluating the performance of the prediction models for PM10 and SO2 concentrations, it is found that the PM10 model explains 66% and the SO2 model explains 78% of the variance. The spatial-temporal KED analysis of PM10 and SO2 concentrations using meteorological factors is crucial for understanding the changes in air pollution and comprehending the relationships between spatial variables and their interactions over time.

Keywords

• Air Pollution
• Particulate Matter (PM10)
• Sulphur Dioxide (SO2)
• Spatio-Temporal Distribution
• Kriging with External Drift (KED)

Ankara’da Partikül Madde (PM10) ve Kükürt Dioksit (SO2) Konsantrasyonlarının İklim Parametreleri İle Mekânsal-Zamansal Analizi

Abstract

Türkiye’de hava kirliliği, özellikle büyük şehirlerde, nüfus artışı, plansız kentleşme, sanayi ve enerji tesislerindeki artış nedeniyle ciddi bir sorun haline gelmiştir. Partikül Madde (PM10) ve Kükürt Dioksit (SO2) konsantrasyonları, sanayi ve enerji üretimindeki yüksek emisyonlar sonucu hava kalitesini önemli ölçüde bozmaktadır. Hava kirliliği sorunuyla karşılaşan büyük şehirlerden biri olan Ankara, 2022 Türkiye Çevre Sorunları ve Öncelikleri Değerlendirme Raporu'nda hava kirliliğinin öncelikli sorunlar arasında ikinci sırada yer aldığı belirtilmiştir. Bu çalışmanın amacı, 2011–2014 yılları arasında Ankara’da PM10 ve SO2 konsantrasyonlarının, meteorolojik faktörlerin etkisi altında mekânsal-zamansal değişimlerini Kriging with External Drift (KED) yöntemi kullanarak incelemektir. 2011 yılında, PM10 ve SO2 konsantrasyon değerleri, diğer yıllara göre daha düşük seviyelerde olup, Dünya Sağlık Örgütü (World Health Organization, WHO) tarafından belirlenen yıllık konsantrasyon değerlerinin üzerinde kalmıştır. 2012 ve 2013 yıllarında, PM10 ve SO2 konsantrasyonlarında artış gözlemlenmiş ve şehrin farklı bölgelerinde değişkenlik göstermiştir. 2014 yılında, artan yağış ve sıcaklık değerleri ile birlikte, PM10 ve SO2 konsantrasyonlarında dikkat çekici bir azalma yaşanmıştır. PM10 ve SO2 konsantrasyonlarına ait tahmin modellerinin performansı değerlendirildiğinde, PM10 modelinin %66, SO2 modelinin %78 oranında açıklayıcı güce sahip olduğu görülmektedir. PM10 ve SO2 konsantrasyonlarının meteorolojik faktörler kullanılarak yapılan mekânsal-zamansal KED analizi, hava kirliliğinin değişimlerini anlamak ve mekânsal değişkenler arasındaki ilişkileri ile zaman içindeki etkileşimleri kavrayabilmek açısından önemlidir.

Keywords

• Hava Kirliliği
• Partikül Madde (PM10)
• Kükürt Dioksit (SO2)
• Mekânsal-Zamansal Dağılım
• Kriging with External Drift (KED)

References

1. Adhikary, S. K., Muttil, N., & Yilmaz, A. G. (2017). Cokriging for enhanced spatial interpolation of rainfall in two Australian catchments. Hydrological Process, 31(12), 2129–2315. https://doi.org/10.1002/hyp.11163
2. Akbal, Y., & Ünlü, K. D. (2022). A deep learning approach to model daily particular matter of Ankara: key features and forecasting. International Journal of Environmental Science and Technology, 19, 5911–5927. https://doi.org/10.1007/s13762-021-03730-3
3. Alahmad, B., Khraishah, H., Althalji, K., MPhil, W. B., Al-Mulla, F., & Koutrakis, P. (2023). Connections between air pollution, climate change, and cardiovascular health. Canadian Journal of Cardiology, 39(9), 1182–1190. https://doi.org/10.1016/j.cjca.2023.03.025
4. Arslan, H., Ağır, A., & Demir, G. (2024). Impacts of PM10 exposure on hospitalization for acute bronchitis in Ankara, Türkiye. Frontiers in Life Sciences and Related Technologies, 5(1), 1–5. https://doi.org/10.51753/flsrt.1322260
5. Arslan, M., & Dursun, D. (2024). Planlı gelişme alanlarının hava kirliliğine olası etkilerinin değerlendirilmesi. Doğal Afetler ve Çevre Dergisi, 10(1), 125–139. http://doi.org/10.21324/dacd.1360742
6. Aydın, O., & Özgür, E. M. (2009). Ankara’nın kentsel gelişiminin uzaktan algılama ve coğrafi bilgi sistemleriyle ölçülmesi. e-Journal of New World Sciences Academy Nature Sciences, 4(4), 215–242.
7. Aydin, O., & Çiçek, I. (2015). Geostatistical interpolation of precipitation in Turkey. Lambert Academic Publishing.
8. Aydın, N. (2023). Investigating the Relationship between urban environment, air quality and childhood asthma: the case of Ankara. [Doctoral dissertation, Middle East Technical University]. OpenMETU. https://open.metu.edu.tr/handle/11511/102564

9. Beauchamp, M., & Bessagnet, B. (2023). An iterative optimization scheme to accommodate inequality constraints in air quality geostatistical estimation of multivariate PM. Heliyon, 9, Article e17413. https://doi.org/10.1016/j.heliyon.2023.e17413
10. Bozdağ, A., Dokuz, Y., & Gökçek, Ö. B. (2020). Spatial prediction of PM10 concentration using machine learning algorithms in Ankara, Turkey. Environmental Pollution, 263, Article 114635. https://doi.org/10.1016/j.envpol.2020.114635
11. Butenko, O., & Topchıy, A. (2023). Analysis of pollutants in air within the territory of Ukraine using geostatistical methods. Radioelectronic and Computer Systems, 3(107), 226–237. http://doi.org/0.32620/reks.2023.3.18 Breeze Technologies. (2021). The new 2021 WHO air quality guideline limits. Breeze Tech. https://www.breeze-technologies.de/blog/new-2021-who-air-quality-guideline-limits/
12. Cenlin, H., Rajesh, K., Wenfu, T., Gabriele, P., Yangyang, X., Yun, Q., & Guy, B. (2024). Air pollution interactions with weather and climate extremes: current knowledge, gaps, and future directions. Current Pollution Reports, 10(3), 430–442. http://doi.org/10.1007/s40726-024-00296-9
13. Çevre ve Şehircilik Bakanlığı. (2013). Kentlerde hava kalitesi değerlendirme sisteminin geliştirilmesi projesi (KENTAIR), Ankara hava kalitesi değerlendirme raporu. https://webdosya.csb.gov.tr/db/cygm/editordosya/Ankara%20Kentair%20Raporu.pdf
14. Çevre, Şehircilik ve İklim Değişikliği Bakanlığı. (2022). Türkiye çevre sorunları ve öncelikleri değerlendirme raporu. https://webdosya.csb.gov.tr/db/ced/icerikler/turk-ye-cevre-sorunlari-ve-oncel-kler-_2022_3_ver3.logoduzenlendi 20230901135641.pdf
15. Çevre, Şehircilik ve İklim Değişikliği Bakanlığı. (t.y.). Ulusal hava kalite izleme ağı. https://sim.csb.gov.tr/Services/AirQuality
16. Delbari, M., Afrasiab, P., & Jahani, S. (2013). Spatial interpolation of monthly and annual rainfall in northeast of Iran. Meteorology and Atmospheric Physics, 122(1), 103–113. https://doi.org/10.1007/s00703‐013‐0273‐5 Demuzere, M., Trigo, R. M., Vila-Guerau de Arellano, J., & van Lipzig, N. P. M. (2009). The impact of weather and atmospheric circulation on O3 and PM10 levels at a rural mid-latitude site. Atmospheric Chemistry and Physics, 9, 2695–2714.
17. Erol, O., (1968). The growth of Ankara city and the geomorphology of its site. Colloque İnternational de Gèographie Appliquèe, 48, 231–245.
18. Eshraghi, M. (2023). Automatic modelling and mapping of Paticular Matter (PM10) [Master dissertation, University of Twente]. Essay.utwente.n. https://purl.utwente.nl/essays/93966
19. Fan, H., Zhao, C., & Yang, Y. (2020). A comprehensive analysis of the spatio-temporal variation of urban air pollution in China during 2014–2018. Atmospheric Environment, 220, Article 117066. https://doi.org/10.1016/j.atmosenv.2019.117066
20. Feng, T., Sun, Y., Shi, Y., Ma, J., Feng, C., & Chen, Z. (2024). Air pollution control policies and impacts: A review. Renewable and Sustainable Energy Reviews, 191, Article 11407. https://doi.org/10.1016/j.rser.2023.114071
21. Genc, D. D., Yesilyurt, C., & Tuncel, G. (2010). Air pollution forecasting in Ankara, Turkey using air pollution index and its relation to assimilative capacity of the atmosphere. Environmental Monitoring and Assessment, 166, 11–27. http://doi.org/0.1007/s10661-009-0981-y
22. Goovaerts, P. (1997). Geostatistics for natural resources evaluation. Oxford University Press.
23. Greenpeace. (2022, Ekim 13). Türkiye’de Hava Kirliliği Yükü–2021 Raporu. https://www.greenpeace.org/turkey/raporlar/turkiyede-hava-kirliligi-yuku-2021/
24. Hengl, T., Heuvelink, G., & Rossiter, D. G. (2007). About regression-kriging: From equations to case studies. Computers & Geosciences, 33(10), 1301–1315. http://doi.org/10.1016/j.cageo.2007.05.001
25. Hooyberghs, J., Mensink, C., Dumont, G., Fierens, F., & Brasseur, O. (2005). A neural network forecast for Daily average PM10 concentrations in Belgium. Atmospheric Environment, 39, 3279–3289. http://doi/10.1016/j.atmosenv.2005.01.050
26. Idir, Y. M., Olivier, O., Judalet, V., Sagot, B., & Chatellier, P. (2021). Mapping urban air quality from mobile sensors using spatio-temporal geostatistics. Sensors, 21, Article 4717. http://doi.org/10.3390/s21144717
27. Ignaccolo, R., Mateu, J., & Giraldo, R. (2014). Kriging with external drift for functional data for air quality monitoring. Stochastic Environmental Research and Risk Assessment, 28, 1171–1186. http://doi.org/10.1007/s00477-013-0806-y
28. Isaaks, E., & Srivastava, R. (1989). An introduction to applied geostatistics. Oxford University Press.
29. İpek, Z., & Uyanık, İ. (2022). Sanayi kaynaklı noktasal emisyonların hava kalitesine katkısı: Kayseri İli örneği. Doğal Afetler ve Çevre Dergisi, 8(2), 341–350. http://doi.org/10.21324/dacd.1056806
30. Koçak, E., & Balcılar, İ. (2024). Spatio-temporal variation of particulate matter with health impact assessment and long-range transport case study: Ankara, Türkiye. Science of the Total Environment, 938, Article 173650. https://doi.org/10.1016/j.scitotenv.2024.173650
31. Kongsanun, C., Chutsagulprom, N., & Moonchai, S. (2024). Spatio-temporal dual kriging with adaptive coefficient drift function. Mathematics, 12, 400, 1–21. http://doi.org/10.3390/math12030400
32. Köse, Y., Şahin, Ş., & Müftüoğlu, V. (2024). Ankara Çayı Havzası’nın kentsel planlama kapsamında taşkın duyarlılığı açısından değerlendirilmesi. İdealkent, 43(16), 512–543. http://doi.org/10.31198/idealkent.1360600
33. Liang, F., Gao, M., Xiao, Q., Carmichael, G. R., Pan, X., & Liu, Y. (2017). Evaluation of a data fusion approach to estimate daily PM2.5 levels in North China. Environmental Research, 158, 54–60. https://doi.org/10.1016/j.envres.2017.06.001
34. Lichtenstern, A. (2013). Kriging methods in spatial statistics [Bachelor’s dissertation, Technische Universität München]. mediatum.ub.tum.de. https://www.semanticscholar.org/paper/Kriging-methods-in-spatial-statistics-Lichtenstern/129ba812abb cb734a973e51f102d60227d5e6f6e
35. Meteoroloji Genel Müdürlüğü. (t.y.). İllere ait mevsim normalleri (1991–2020). https://www.mgm.gov.tr/veridegerlendirme/il-ve-ilceler-istatistik.aspx?m=ANKARA
36. Oral, H. V., Ulusoy, İ., Özelkan, E., & Toros, H. (2015, April 28–30). Ankara ve çevre illerde hava kirliliği dağılımı ve emisyon envanterinin belirlenmesi [Conference presentation]. VII. Atmospheric Science Symposium, İstanbul, Türkiye.
37. Otto, P., Moro, A. F., Rodeschini, J., Shaboviq, Q., Ignaccolo, R., Golini, N., Cameletti, M., Maranzano, P., Finazzi, F., & Fassò, A. (2024). Spatiotemporal modelling of PM2.5 concentrations in Lombardy (Italy): a comparative study. Environmental and Ecological Statistics, 31, 245–272. https://doi.org/10.1007/s10651-023-00589-0
38. Özgür, E.M. (1995). Türkiye’deki iç göçlerde Ankara İli’nin yeri. Ankara Üniversitesi Türkiye Coğrafyası Araştırma ve Uygulama Merkezi Dergisi, 4, 63–76.
39. Pearce, J. L., Rathbun, S. L., Aguilar-Villalobos, M., & Naeher, L. P. (2009). Characterizing the spatiotemporal variability of PM2.5 in Cusco, Peru using kriging with external drift. Atmospheric Environment, 43, 2060–2069. http://doi.org/10.1016/j.atmosenv.2008.10.060
40. Pebesma, E. J. (2004). Multivariable geostatistics in S: the gstat package. Computer Geoscience, 30(7), 683–691.
41. Pebesma, E. (2012). Spacetime: spatio-temporal data in R. Journal of Statistical Software, 51(7), 1–30
42. Pei, H., & Shiliang, W. (2016). Long-term changes in extreme air pollution meteorology and the implications for air quality. Scientific Reports, 6, Article 23792. http://doi.org/10.1038/srep23792
43. Pinho-Gomes, A.-C., Roaf, E., Fuller, G., Fowler, D., Lewis, A., ApSimon, H., Noakes, C., Johnstone, P., & Holgate, S. (2023). Air pollution and climate change. The LANCET Planetary Health, 7(9), 7–8. https://doi.org/10.1016/S2542-5196(23)00189-4
44. R Development Core Team. (2016). The R project for statistical computing. https://www.r-project.org/
45. Raja, N. B, & Aydin, O. (2017). New approaches to flash flood forecasting in the Mediterranean Region. Lambert Academic Publishing.
46. Raja, N. B., Aydin, O., Türkoğlu, N., & Çiçek, İ. (2018). Characterising the seasonal variations and spatial distribution of ambient PM10 in urban Ankara, Turkey. Environmental Processes, 5(1), 349–362. http://doi.org/10.1007/s40710-018-0305-8
47. Rahaman, S.N., Nelson, J., Ali, A.A.B., Shermin, N., Pricope, N. G., Kafy, A. A., Sabuj, M. S., & Toa, S. S. (in press). A multivariate geostatistical framework to assess the spatio-temporal dynamics of air pollution and land surface temperature in Bangladesh. Earth Systems Environment. https://doi.org/10.1007/s41748-024-00418-9
48. Rahim, U. A., & Masseran, N. (2023). State-space time series analysis on air pollution data. Environment and Ecology Research, 11(1), 155–164. http://doi.org/10.13189/eer.2023.110111
49. Ramos, Y., St-Onge, B., Blanchet, J-P., & Smargiassi, A. (2016). Spatio-temporal models to estimate daily concentrations of fine particulate matter in Montreal: Kriging with external drift and inverse distance-weighted approaches. Journal of Exposure Science and Environmental Epidemiology, 26, 405–414. https://doi.org/10.1038/jes.2015.79
50. Rusmilli, S. H. A., Hamzah, F. M., Choy, L. K., Azizah, R., Sulistyorini, L., Yudhastuti, R., Diyanah, K. C., Adriyani, R., & Latif, M. T. (2023). Ground-level particulate matter (PM2.5) concentration mapping in the central and south zones of Peninsular Malaysia using a geostatistical approach. Sustainability, 15(23), Article 1616. https://doi.org/10.3390/su152316169
51. Ulutas, K., Abujayyab, S. K. M., & Abu Amr, S. (2021). Evaluation of the major air pollutants levels and its interactions with meteorological parameters in Ankara. Mühendislik Bilimleri ve Tasarım Dergisi, 9(4), 1284–1295. http://doi.org/10.21923/jesd.939724
52. Sahin, M., Incecik, S., Topcu, S., & Yildirim, A. (2001). Analysis of atmospheric conditions during air pollution episodes in Ankara, Turkey. Journal of the Air & Waste Management Association, 51(7), 972–982. https://doi.org/10.1080/10473289.2001.10464334
53. Shaman, J., & Kohn, M., (2009). Absolute humidity modulates influenza survival, transmission, and seasonality. Proceedings of the National Academy of Sciences, 106(9), 3243–3248. https://doi.org/10.1073/pnas.0806852106
54. Temiz Hava Hakkı Platformu. (2023, Mart). Kara rapor 2022: hava kirliliği ve sağlık etkileri. https://www.temizhavahakki.org/wp-content/uploads/2023/03/KaraRapor_v6.pdf
55. Toros, H., Bağış, S., & Gemici, Z. (2018). Ankara’da hava kirliliği mekânsal dağılımının modellenmesi. Ulusal Çevre Bilimleri Araştırma Dergisi, 1(1), 20–53.
56. Tran, H. M., Tsai, F. J., Lee, Y. L., Chang, J. H., Chang, T. Y., Chung, K. F., Kuo, H. P., Lee, K. Y., Chuang, K. J., & Chuang, H. C. (2023). The impact of air pollution on respiratory diseases in an era of climate change: a review of the current evidence. Science of the Total Environment, 898, Article 166340. https://doi.org/10.1016/j.scitotenv.2023.166340
57. Turgut, D., & Temiz, İ. (2015). Ankara'daki hava kirliliği için zaman serileri analizi ve tahmin: box-jenkins yaklaşımı. Alphanumeric Journal, 3(2), 131–138. http://doi.org/10.17093-aj.2015.3.2.5000148347-270497.pdf
58. Türkiye İstatistik Kurumu. (2024a, Temmuz 9). Dünya nüfus günü, 2024. https://data.tuik.gov.tr/Bulten/Index?p=Dunya-Nufus-Gunu-2024-53680
59. Türkiye İstatistik Kurumu. (2024b, Şubat 6). Adrese Dayalı Nüfus Kayıt Sistemi Sonuçları, 2023. https://data.tuik.gov.tr/Bulten/Index?p=Adrese-Dayali-Nufus-Kayit-Sistemi-Sonuclari-2023-49684
60. Türkoğlu, N., Aydın, O., Duman, N., & Çiçek, İ. (2016). Türkiye’de yağışın farklı mekânsal enterpolasyon yöntemleriyle karşılaştırılması. Journal of Human Science, 13(3), 5636–5659. http://doi.org/10.14687/jhs.v13i3.4173
61. Webster, R., & Oliver, M. A. (2007). Geostatistics for environmental scientists. Wiley.
62. World Health Organization. (2021, October 21). Air pollution causes 13 deaths per minute worldwide. https://www.who.int/multi-media/details/air-pollution-climate-change
63. World Health Organization. (2023, January 26). World Health Organization (WHO) air quality guidelines (AQGs) and estimated reference levels (RLs). https://www.eea.europa.eu/publications/status-of-air-quality-in-Europe-2022/europes-air-quality-status-2022/world-health-organization-who-air
64. Van de Kassteele, J., Stein, A., Dekkers, A. L. M., & Velders, G. J. M. (2009). External drift kriging of NOX concentrations with dispersion model output in a reduced air quality monitoring network. Environmental and Ecological Statistics, 16(3), 321–339. http://doi.org/10.1007/s10651-007-0052-x
65. Yan, J., Chen, W. Y., Zhang, Z., Zhao, W., Liu, M., & Yin, S. (2024). Mitigating PM2.5 exposure with vegetation barrier and building designs in urban open-road environments based on numerical simulations. Landscape and Urban Planning, 241, 104918, 1–14. https://doi.org/10.1016/j.landurbplan.2023.104918