Ankara İli Meteoroloji Parametrelerinin Hava Kirliliği Üzerindeki Etkilerinin Regresyon Analizi ile İncelenmesi

Yıl 2023, Cilt: 5 Sayı: 2, 135 - 150, 31.12.2023

Ceren Ünal Gamze Özel

https://doi.org/10.51541/nicel.1231668

Öz

Hava kirliliği ile ilişkili riskleri daha iyi anlamak ve yönetmek için, hava kirliliği eğiliminin doğru bir şekilde tahmin edilmesi çok önemlidir. Ankara, Türkiye'nin İç Anadolu Bölgesi'nin merkezinde yer almaktadır. Hızlı nüfus artışı, düzensiz kentleşme ve artan sanayileşme nedeniyle, Ankara'da son yıllarda hava kirliliği ciddi boyutlara ulaşmıştır. Bu çalışma kapsamında, 2018-2021 yılları arasında Ankara iline ait bağıl nem, rüzgâr hızı, rüzgâr yönü, hava sıcaklığı, hava basıncı biçimindeki meteoroloji parametrelerinin, partiküler madde (PM10), kükürt dioksit (SO2), azot (NO), azot dioksit (NO2), azot oksit (NOX), karbon monoksit (CO) biçimindeki hava kirliliği göstergeleri üzerindeki etkilerinin incelenmesi amaçlanmıştır. Meteoroloji parametreleri ile hava kirlilik gösterge değerleri arasındaki ilişkilerin istatistiksel anlamlılığı regresyon analizinden yararlanılmıştır. Bu amaçla, PM10, SO2, NO, NOX, CO verilerine ayrı ayrı regresyon analizleri, meteoroloji parametreleri bağımsız değişkenler olarak alınarak uygulanmıştır. Elde edilen sonuçlar, PM10, SO2, NO, NOX, CO düzeyi ile meteorolojik parametreler olan rüzgâr yönü, rüzgâr hızı, bağıl nem, hava sıcaklığı, hava basıncı arasında negatif yönde ilişki olduğunu göstermiştir.

Anahtar Kelimeler

Regresyon Analizi, Hava Kirliliği, Emisyon, Ankara, Regression Analysis, Air Pollution, Emission

Investigation of the Effects of Ankara Meteorological Parameters on Air Pollution by Regression Analysis

Öz

To better understand and manage the risks associated with air pollution, an accurate estimation of the air pollution trend is crucial. Ankara is located in the centre of Turkey's Central Anatolia Region. Due to rapid population growth, irregular urbanization, and increasing industrialization, air pollution has reached serious levels in Ankara in recent years. Within the scope of this study, meteorological parameters in the form of relative humidity, wind speed, wind direction, air temperature, air pressure, particulate matter (PM10), sulfur dioxide (SO2), nitrogen (NO), nitrogen dioxide, in the province of Ankara between 2018-2021. Nitrogen dioxide (NO2), nitrogen oxide (NOX), and carbon monoxide (CO) in the form of air pollution indicators are the form of examining the effects. The statistical significance of the relations between meteorological parameters and air pollution indicator values benefited from the regression analysis. For this purpose, regression analyses were applied to PM10, SO2, NO, NOX, and CO data separately, taking meteorological parameters as independent variables. The results showed that there is a negative relationship between PM10, SO2, NO, NOX, and CO levels and meteorological parameters such as wind direction, wind speed, relative humidity, air temperature, and air pressure.

Anahtar Kelimeler

Regression Analysis, Air Pollution, Emission, Ankara

Kaynakça

• Akman, Y. (1990), İklim ve Biyoiklim (Biyoiklim Metodları ve Türkiye İklimleri), Palme Yayınevi, Ankara.
• Andersen, T.K., Radcliffe, D.E. and Shepherd, J.M. (2013), Quantifying surface energy fluxes in the vicinity of inland-tracking tropical cyclones, Journal of Applied Meteorology and Climatology, 52, 2797-2808.
• Atalay, İ.E. ve Neslihanoğlu, S. (2021), Türkiye’deki illerin partikül madde (pm10) miktarının değerlendirilmesi ve r programlama dili ile görselleştirilmesi, Doğal Afetler ve Çevre Dergisi, 7(2), 354-361.
• Bayram, H. (2005), Türkiye’de hava kirliliği sorunu: nedenleri, alınan önlemler ve mevcut durum, Toraks Dergisi, 6(2),159-165.
• Bayram, H., Dörtbudak, Z., Fişekçi, E.F., Kargın, M. ve Bülbül, B. (2006), Hava kirliliğinin insan sağlığına etkileri, dünyada, ülkemizde ve bölgemizde hava kirliliği sorunu, Dicle Tıp Dergisi, 33(2), 105-112.
• Brunekreef, B. ve Forsberg, B. (2005), Epidemiological evidence of effects of coarse airborne particles on health, European Respiratory Journal, 26, 309–318.
• Brokamp C., Jandarov R., Rao M.B., LeMasters G. ve Ryan P. (2017), Exposure assessment models for elemental components of particulate matter in an urban environment: A comparison of regression and random forest approaches, Atmospheric Environment, 151, 1-11.
• Cazorla M. (2016), Air quality over a populated Andean region: Insights from measurements of ozone, NO, and boundary layer depths, Atmospheric Pollution Research, 7, 66-74.
• Cekim, H.O. (2020), Forecasting PM10 concentrations using time series models: A case of the most polluted cities in Turkey, Environmental Science and Pollution Research, 27(20), 25612-25624.
• Çiçek, İ., Türkoğlu, N. ve Gürgen, G. (2004), Ankara’da Hava Kirliliğinin İstatistiksel Analizi, Fırat Üniversitesi Sosyal Bilimler Dergisi, 14(2), 1-18.
• Dündar, E., Dursun, Ş. ve Toros, H. (2020), Air pollution in Ankara during COVID-19, Journal of Research in Atmospheric Science, 2(1), 24-30.
• Hu, J., Chen, J., Ying, Q., and Zhang, H. (2016), One-year simulation of ozone and particulate matter in China using WRF/CMAQ modeling system, Atmospheric Chemistry and Physics, 16, 10333–10350.
• Garaga, R., Sahu, S.K. ve Kota, S.H. (2018), A review of air quality modeling studies in ındia: local and regional scale, Current Pollution Reports, 4, 59-73.
• Graff, D.W., Cascio, W.E., Rappold, A., Zhou, H., Huang, Y.C.T ve Devlin, R.B. (2009), Exposure to concentrated coarse air pollution particles causes mild cardiopulmonary effects in healthy young adults, Environmental Health Perspectives, 117, 1089–1094.
• Goudarzi, G., Mohammadi, M., Angali, K.A., Mohammadi, B., Soleimani, Z., Babaei, A., Neisi, A. ve Geravandi, S. (2013), Estimation of number of cardiovascular death, myocardial infarction and chronic obstructive pulmonary disease (COPD) from NO2 exposure using air Q model in Ahvaz City during 2009, Iranian Journal of Health and Environment, 6, 91–102.
• Hava Kalitesi İndeksi, http://www.havaizleme.gov.tr/hava.html. Erişim tarihi: 03.01.2023.
• Host, S., Larrieu, S., Pascal, L., Blanchard, M., Declercq, C., Fabre, P., Jusot. J.F., Chardon, B., Le Tertre, A., Wagner, V., Prouvost, H. ve Lefranc, A. (2008), Short-term associations between fine and coarse particles and hospital admissions for cardiorespiratory diseases in six French cities, Occupational and Environmental Medicine, 5, 544-551.
• Jaafari, J., Naddafi, K., Yunesian, M., Nabizadeh, R., Hassanvand, M.S., Ghozikali, M.G., Nazmara, S., Shamsollahi, H.R. ve Yaghmaeian, K. (2018), Study of PM10, PM2.5, and PM1 levels in during dust storms and local air pollution events in urban and rural sites in Tehran, Human and Ecological Risk Assessment: An International Journal, 24(2), 482-493.
• Li Y, Chen Q, Zhao H, Wang L. ve Tao R. (2015), Variations in PM10, PM2.5 and PM1.0 in an urban area of the Sichuan basin and their relation to meteorological factors, Atmosphere, 6, 150-163.
• Linares, C., Tobías, A. ve Díaz, J. (2010), Is there new scientific evidence to justify reconsideration of the current WHO guidelines for particulate matter during dust intrusions?, The Science of the Total Environment, 408(10), 2283–2294.
• Makkonen, M., Berg, M.P., Handa, I.T., Hattenschwiler, S., van Ruijven, J., van Bodegom, P.M. ve Aerts, R. (2012), Highly consistent effects of plant litter identity and functional traits on decomposition across a latitudinal gradient, Ecology Letters, 15, 1033-1041.
• Malig, B.J. ve Ostro, B.D. (2009), Coarse particles and mortality: Evidence from a multi-city study in California, Occupational and Environmental Medicine, 66,832–839.
• Mathur, R., Xing, J., Gilliam, R., Sarwar, G., Hogrefe, C., Pleim, J., Pouliot, G., Roselle, S., Spero, T.L. ve Wong, D.C. (2017), Extending the Community Multiscale Air Quality (CMAQ) modeling system to hemispheric scales: Overview of process considerations and initial applications, Atmospheric Chemistry and Physics, 17, 12449–12474.
• Miller, S.D., Kuciauskas, A.P., Liu, M., Ji, Q., Reid, J., Breed, D., Walker, A. ve Mandoos, A. (2008), Haboob dust storms of the southern Arabian Peninsula, Journal of Geophysical Research-Atmospheres, 113, 1-26.
• Morand, C.P., Maesano, I.A. (2004), Air pollution: from sources of emissions to health effects, Breathe, 1(2), 108-119.
• Ni X., Huang H. ve Du W. (2017), Relevance analysis and short-term prediction of PM2.5 concentrations in Beijing based on multi-source data, Atmospheric Environment, 150, 146-161.
• Ozel, G. ve Cakmakyapan, S. (2015), A new approach to the prediction of PM10 concentrations in Central Anatolia Region, Turkey, Atmospheric Pollution Research, 6(5), 735-741.
• Qiu, H., Yu, I.T.S., Tian, L., Wang, X., Tse, L.A., Tam, W. ve Wong, T.W. (2012), Effects of coarse particulate matter on emergency hospital admissions for respiratory diseases: A timeseries analysis in Hong Kong, Environmental Health Perspectives, 120, 572-576.
• Rafee, S.A., Martins, L.D., Kawashima, A.B., Almeida, D.S., Morais, M., Souza, R., Oliveira, M.B.L., Souza, R.A.F., Medeiros, A.S.S. ve Urbina, V. (2017), Contributions of mobile, stationary and biogenic sources to air pollution in the Amazon rainforest: A numerical study with the WRF-Chem model, Environmental Health Perspectives, 17, 7977–7995.
• Rybarczyk, Y. ve Zalakeviciute, R. (2018), Machine learning approaches for outdoor air quality modelling: A systematic review, Applied Sciences, 8, 2570.
• Taheri Shahraiyni, H. ve Sodoudi, S. (2016), Statistical modeling approaches for PM10 prediction in urban areas; A review of 21st-century studies, Atmosphere, 7, 15.
• Tai, A.P.K., Mickley, L.J. ve Jacob, D.J. (2010), Correlations between fine particulate matter (PM2.5) and meteorological variables in the United States: Implications for the sensitivity of PM2.5 to climate change, Atmospheric Environment, 44, 3976–3984.
• Wang, Z., Feng, J., Fu, Q. ve Gao, S. (2019), Quality control of online monitoring data of air pollutants using artificial neural networks, Air Quality, Atmosphere and Health, 12, 1189–1196.
• Wang, S., Wang, J., Zhou, Z. ve Shang, K. (2005), Regional characteristics of three kinds of dust storm events in China, Atmospheric Environment, 39, 509–520.
• WHO. (2006), Air quality guideline. global update 2005. Copenhagen: World Health Organization Regional Office for Europe.
• Xu, X. ve Ren, W. (2019), Prediction of air pollution concentration based on mRMR and echo state network, Applied Sciences, 9(9), 1811.
• Yeşil, A. (2006), Ankara Metropoliten Alanının Yeşil Alan Sisteminin Analizi, Fen Bilimleri Enstitüsü, Yıldız Teknik Üniversitesi, İstanbul.
• Yu, R., Yang, Y., Yang, L., Han, G. ve Oguti, M. (2016), RAQ–a random forest approach for predicting air quality in urban sensing systems, Sensors, 16, 86–104.