Application of Statistical Distributions to PM10 Concentrations: Van, Türkiye

Yıl 2023, , 87 - 95, 30.06.2023

Kübra Bağcı

https://doi.org/10.53568/yyusbed.1214637

Öz

Air pollution is one of the most serious environmental pollution problems that adversely affect human health and the environment. Industrializing and frequent use of low-quality coals for heating purposes and incorrect combustion techniques may cause intense air pollution in the winter season. There are several studies on air quality employing different methods using various air pollutants (carbon monoxide, nitrogen oxides, ground-level ozone, and particle pollution) in the related literature. In this study PM_10 (concentration of 10 micrometers or smaller size of air pollutants) levels in Van province which is one of the most crowded provinces in Eastern Anatolia, Türkiye. Due to the fuels used for heating in Van, the air quality may be higher than limits set by regulations several times during the year. In this study, PM_10 levels of Van are modeled using lognormal, Weibull, and Gamma distributions. Information and goodness of fit criteria are used to compare their performance. In addition, predictions of exceedances are provided for the PM_10 concentration higher than given limits. According to the results, the Gamma distribution performed better than the other two distributions in modeling the PM_10 concentrations in Van and predicted the exceedances accurately.

Anahtar Kelimeler

Van., Air pollution, lognormal distribution, Türkiye

PM10 Konsantrasyonunun İstatistiksel Dağılımına İlişkin Bir Uygulama: Van, Türkiye

Öz

Hava kirliliği, insan sağlığını ve çevreyi olumsuz yönde etkileyen en ciddi çevre kirliliği sorunlarından biridir. Sanayileşme ve kalitesiz kömürlerin ısınma amacıyla sıklıkla kullanılması ve yanlış yakma teknikleri kış mevsiminde yoğun hava kirliliğine neden olabilmektedir. İlgili literatürde çeşitli hava kirleticilerine (karbon monoksit, kurşun, nitrojen oksitler, yer seviyesinde ozon ve partikül kirliliği) ait ölçümlerin farklı yöntemler kullanarak modellenmesine ve incelenmesine ilişkin birçok çalışma bulunmaktadır. Bu çalışmada Türkiye'nin Doğu Anadolu bölgesindeki en kalabalık illerden biri olan Van ilindeki PM_10 (10 mikrometreden küçük partikül madde) konsantrasyonu, lognormal, Weibull ve Gamma olasılık fonksiyonları kullanılarak modellenmiştir. Ek olarak, verilen limitlerden daha yüksek PM_10 konsantrasyonu için aşım kestirimleri verilmiştir. Van ilinde ısınma amaçlı kullanılan yakıtlar nedeniyle hava kalitesi yıl içerisinde çeşitli zamanlarda hava kalitesi yönetmelikle belirlenen limitlerinin üzerinde olabilmektedir. Bu sebeple hava kalitesi ile ilgili çalışmalar önem arz etmektedir. Çalışmada kullanılan veri seti, Kasım 2021 ile Mart 2022 tarihleri arasında ölçülen PM_10 konsantrasyonlarını içermektedir. Elde edilen sonuçlara göre Van ili PM_10 konsantrasyonlarının modellenmesinde Gamma dağılımının diğer dağılmlara göre daha bir iyi performans gösterdiği tespit edilmiştir.

Anahtar Kelimeler

Hava kirliliği, lognormal dağılımı, Türkiye, Van.

Kaynakça

• Air Quality Monitoring. (2021). https://www.havaizleme.gov.tr.
• Akbulut, İ.B. (2020). Hava Kirliliği Tahmini: Yapay Sinir Ağları ve Regresyon Yöntemleriyle Bir Karşılaştırma. Journal, 3(1), 12–22.
• Aktaş, S. (2021). Bayesian ANOVA(BANOVA): An Application on Air Pollution in Ankara. Journal, 2(1), 8–22.
• Aleksandropoulou, V., Eleftheriadis, K., Diapouli, E., Torseth, K., & Lazaridis, M. (2012). Assessing PM 10 source reduction in urban agglomerations for air quality compliance. Journal of Environmental Monitoring, 14(1), 266–278.
• Bozdağ, A., Dokuz, Y., & Gökçek, Ö. B. (2020). Spatial prediction of PM10 concentration using machine learning algorithms in Ankara, Turkey. Environmental Pollution, 263, 114635.
• Cakmak, S., Dales, R. E., & Coates, F. (2012). Does air pollution increase the effect of aeroallergens on hospitalization for asthma? Journal of Allergy and Clinical Immunology, 129(1), 228–231.
• Choi, S. C., & Wette, R. (1969). Maximum Likelihood Estimation of the Parameters of the Gamma Distribution and Their Bias. Technometrics, 11(4), 683–690.
• Cohen, A. C. (1965). Maximum Likelihood Estimation in the Weibull Distribution Based On Complete and On Censored Samples. Technometrics, 7(4), 579–588.
• Contini, D., Cesari, D., Donateo, A., Chirizzi, D., & Belosi, F. (2014). Characterization of PM10 and PM2.5 and Their Metals Content in Different Typologies of Sites in South-Eastern Italy. Atmosphere, 5(2), 435–453.
• Díaz-Robles, L., Cortés, S., Vergara-Fernández, A., & Ortega, J. C. (2015). Short Term Health Effects of Particulate Matter: A Comparison between Wood Smoke and Multi-Source Polluted Urban Areas in Chile. Aerosol and Air Quality Research, 15(1), 306–318.
• Evans, M., Hastings, N., & Peacock, B. (2001). Statistical distributions. IOP Publishing.
• Gulia, S., Nagendra, S. M. S., & Khare, M. (2017). Extreme Events of Reactive Ambient Air Pollutants and their Distribution Pattern at Urban Hotspots. Aerosol and Air Quality Research, 17(2), 394–405.
• Kahraman, O. (2020). Nevşehir İlinde Hava Kalitesinin ve Meteorolojik Faktörlerin Hava Kirliliği Üzerine Etkilerinin İncelenmesi. Doğal Afetler ve Çevre Dergisi, 6(2), 391–404.
• Karademir, A. (2006). Evaluation of the potential air pollution from fuel combustion in industrial boilers in Kocaeli, Turkey. Fuel, 85(12), 1894–1903.
• Koşan, Z., Kavuncuoğlu, D., Çalıkoğlu, E. O., & Yerli, E. B. (2018). Evaluation of air pollution by PM10 and SO2 levels in Erzurum province, Turkey: Descriptive study. Journal of Surgery and Medicine, 2(3), 265–268.
• Evans, M., Hastings, N., & Peacock, B. (2001). Statistical distributions. IOP Publishing.
• Mijić, Z., Tasić, M., Rajšić, S., & Novaković, V. (2009). The statistical characters of PM10 in Belgrade area. Atmospheric Research, 92(4), 420–426.
• Mishra, V. (2003). Indoor air pollution from biomass combustion and acute respiratory illness in preschool age children in Zimbabwe. International Journal of Epidemiology, 32(5), 847–853.
• Nur Shaziayani, W., Zia Ul-Saufie, A., Libasin, Z., Norsyiha Ahmad Shukri, F., Sarimah Syed Abdullah, S., & Mohamed Noor, N. (2020). A Review of PM10 Concentrations Modelling in Malaysia. IOP Conference Series: Earth and Environmental Science, 616(1), 012008.
• Ozel, G., & Cakmakyapan, S. (2015). A new approach to the prediction of PM10 concentrations in Central Anatolia Region, Turkey. Atmospheric Pollution Research, 6(5), 735–741.
• Öztürk D., & Bayram T. (2019). Van İli Kent Merkezinde Hava Kirliliği. Journal, 8(3), 1142–1153.
• Papanastasiou, D. K., & Melas, D. (2010). Application of PM10′s Statistical Distribution to Air Quality Management—A Case Study in Central Greece. Water, Air, and Soil Pollution, 207(1), 115–122.
• Plocoste, T., Calif, R., Euphrasie-Clotilde, L., & Brute, F.-N. (2020). The statistical behavior of PM10 events over guadeloupean archipelago: Stationarity, modelling and extreme events. Atmospheric Research, 241, 104956.
• Perišić, M., Stojić, A., Stanišić Stojić, S., Šoštarić, A., Mijić, Z., & Rajšić, S. (2015). Estimation of required PM10 emission source reduction on the basis of a 10-year period data. Air Quality, Atmosphere & Health, 8(4), 379–389.
• Sansuddin, N., Ramli, N. A., Yahaya, A. S., Yusof, N. F. F. M. D., Ghazali, N. A., & Madhoun, W. A. al. (2011). Statistical analysis of PM10 concentrations at different locations in Malaysia. Environmental Monitoring and Assessment, 180(1), 573–588.
• Şahin, Ü. A., Scherbakova, K., & Onat, B. (2012). Size distribution and seasonal variation of airborne particulate matter in five areas in Istanbul, Turkey. Environmental Science and Pollution Research, 19(4), 1198–1209.
• Taheri Shahraiyni, H., & Sodoudi, S. (2016). Statistical Modeling Approaches for PM10 Prediction in Urban Areas; A Review of 21st-Century Studies. Atmosphere, 7(2).
• Tayanç, M., Sezen, İ., Ünal, A., Flores, R. M., & Karanfil, S. (2022). A holistic approach to the air quality of Konya City, Turkey. Air Quality, Atmosphere & Health, 15(6), 951–965.
• Todorovic, M. N., Perisic, M. D., Kuzmanoski, M. M., Stojic, A. M., Sostarić, A. I., Mijic, Z. R., & Rajsic, S. F. (2015). Assessment of PM10 pollution level and required source emission reduction in Belgrade area. Journal of Environmental Science and Health, Part A, 50(13), 1351–1359.
• Wang, X., Chen, R. J., Chen, B. H., & Kan, H. D. (2013). Application of Statistical Distribution of PM10 Concentration in Air Quality Management in 5 Representative Cities of China. Biomedical and Environmental Sciences, 26(8), 638–646.
• Yakın, A., & Behçet, R. (2019). Van ili trafik kaynaklı hava kirleticilerinin emisyon envanteri. Journal of the Institute of Science and Technology, 9(3), 1567–1573.
• Yusof, N. F. F. M., Ramli, N. A., Yahaya, A. S., Sansuddin, N., Ghazali, N. A., & al Madhoun, W. (2010). Monsoonal differences and probability distribution of PM 10 concentration. Environmental Monitoring and Assessment, 163(1), 655–667.
• Zeydan, Ö. (2021). Assessment of Particulate Matter (PM10) Pollution in Turkey in 2019. Journal, 11(1), 106–118.