ESTIMATION OF THE AIR QUALITY TRENDS IN ISTANBUL

Air pollution is a problem that accumulates with urbanization and threatens human health. The quality of the air we breathe is more important for the cities like İstanbul, having the busiest traffic flow and highest population in Turkey. In this work, in order to measure the air quality of İstanbul, the trend of air quality data will be estimated. Turkey, according to the data of Environment Performance Index 2014 prepared by Yale University, is listed low especially on air quality. In accordance with the agreements signed with the European Union the environmental indicators and thus our air quality should be improved. For this reason, in order to reduce air pollution a program is prepared and an Air Quality Index is formed Turkey-wide. Also in İstanbul, via many stations the air pollutants are being monitored. To increase the air quality, it is necessary to monitor the change within time of these pollutants and estimate their trend. However, the pollutants listed in the air quality index, as in all the environmental data, is a data that consists outliers and missing observations, asymmetrical, seasonal, serial dependent, do not fit the normal distribution. As of these properties, the trend analysis of the air pollutants are done via tests that are nonparametric In detecting the existence of a trend in the air quality data carrying seasonal effect the Seasonal Kendall Test is being used.In this study the trend of air pollutants for İstanbul is tested via the monthly data gathered through 2005-2014 from different monitoring stations established by the İstanbul Metropolitan Municipality,. Among the pollutants especially in SO2 emission, in all monitoring stations, statistically significant negative seasonal trend is seen. CO emission is also determined to be in decrease. However PM10 and NO2 emissions are determined to be increasing in some stations. Besides, the homogeneity testing of pollutants between seasons and stations is also performed. According to this test, SO2 has a homogeneous trend between stations and seasons.

ESTIMATION OF THE AIR QUALITY TRENDS IN ISTANBUL

Air pollution is a problem that accumulates with urbanization and threatens human health. The quality of the air we breathe is more important for the cities like İstanbul, having the busiest traffic flow and highest population in Turkey. In this work, in order to measure the air quality of İstanbul, the trend of air quality data will be estimated. Turkey, according to the data of Environment Performance Index 2014 prepared by Yale University, is listed low especially on air quality. In accordance with the agreements signed with the European Union the environmental indicators and thus our air quality should be improved. For this reason, in order to reduce air pollution a program is prepared and an Air Quality Index is formed Turkey-wide. Also in İstanbul, via many stations the air pollutants are being monitored. To increase the air quality, it is necessary to monitor the change within time of these pollutants and estimate their trend. However, the pollutants listed in the air quality index, as in all the environmental data, is a data that consists outliers and missing observations, asymmetrical, seasonal, serial dependent, do not fit the normal distribution. As of these properties, the trend analysis of the air pollutants are done via tests that are nonparametric In detecting the existence of a trend in the air quality data carrying seasonal effect the Seasonal Kendall Test is being used.In this study the trend of air pollutants for İstanbul is tested via the monthly data gathered through 2005-2014 from different monitoring stations established by the İstanbul Metropolitan Municipality,. Among the pollutants especially in SO2 emission, in all monitoring stations, statistically significant negative seasonal trend is seen. CO emission is also determined to be in decrease. However PM10 and NO2 emissions are determined to be increasing in some stations. Besides, the homogeneity testing of pollutants between seasons and stations is also performed. According to this test, SO2 has a homogeneous trend between stations and seasons.

Kaynakça

AY, E.F., Balta, M., Çolak, M., Semercioğlu, H., “Hava Kirliliği ve Modellemesi”, Sakarya Üniversitesi, 2010, http://www.sahakk.sakarya.edu.tr/documents/hava%20kirliligi%20ve%20modellemesi%20II.pdf, Accessed (18.02.2015)

BAYRAM H., “Türkiye’de Hava Kirliliği Sorunu: Nedenleri, Alınan Önlemler ve Mevcut Durum”, Türk Toraks Dergisi August, 6(2), 2005, pp: 159-165

COSUN, F., Karabulut M., “Kahramanmaraş’ta Ortalama, Minimum ve Maksimum Sıcaklıkların Trend Analizi”, Türk Coğrafya Dergisi, 53, 2005, pp.41-50

DÜNDAR, C., K. Oğuz, G. Güllü,“Toz Taşınımı Mekanizmalarındaki Farklılıklar: İki Farklı Toz Taşınımı Olayı”, 6. Ulusal Hava Kirliliği ve Kontrolü Sempozyumu,, Bildiriler Kitabı, 7-9 Ekim 2015, İzmir, pp.18-28

ELBİR, T. , Müzezzinoğlu, A., Bayram, A., “Evaluation of Some Air Pollution Indicators in Turkey”, Environmental International, 26, 2000, pp.5-10

Environmental Performance Index, 2014, http://epi.yale.edu/epi/country-profile/turkey, Accessed (06.03.2015)

European Commision, http://ec.europa.eu/environment/air/quality/standards.htm, Accessed (12.03.2015)

European Environment Agency www.eea.europa.eu, Accessed (12.03.2015)

GILBERT, R.O., Statistical Methods for Environmental Pollution Monitoring, Von Nostrand Reinhold Company Inc., New York, 1987

GÜMÜŞ O, Alver Şahin, Ü., Onat, B., Özçelik, R., Gedik, E., Solakoğlu, İ., Taş, N., “Marmara Bölgesi Hava Kalitesinin İstatistiksel Yöntemlerle Analizi”, 6. Ulusal Hava Kirliliği ve Kontrolü Sempozyumu, 7-9 Ekim 2015, pp.780-793

HELSEL, D.R. ,R.M. Hirsch, Statistical Methods in Water Resources, Book 4: Hydrologic Analysis and Interpretation, Techniques of Water-Resources Investigations of the United States Geological Survey, 2002

HIRSCH, R.M., “Statistical methods and sampling design for estimating step trends in surface water quality”, Water Resources. Research, 24, 1988, pp.493-503

HIRSCH, R.M., Slack, J.R.Smith, R.A. “Techniques of Trend Analysis for Monthly Water Quality Data”, Water Resources Research, 18(1), 1982, pp.107-121

HIRSCH, R.M., Slack, J.R., “A Nonparametric Trend Test for seasonal Data With Serial Dependence”, Water Resources Research, 20(6), 1984, pp.727-732

İstanbul Büyükşehir Belediyesi, İBB, www.ibb.gov.tr/tr-TR

İBB, “İstanbul’da Hava Kalitesi Yönetimi için CBS Tabanlı Bir Karar Destek Sisteminin Geliştirilmesi”, LIFE06-TCY/TR/000283, Teknik Olmayan Rapor, Nisan, İstanbul, 2009

İÇAĞA, Y., Harmancıoğlu, N., “Yeşilırmak Havzasında Su Kalite Eğilimlerinin Belirlenmesi”, TMMOB, Türkiye İnşaat Mühendisliği 13. Teknik Kong, 481, 1995, pp.481-497

KALAYCI, S., Kahya, E., “Susurluk Havzası Nehirlerinde Su Kalitesi Trendlerinin Belirlenmesi”, Tr. J. of Engineering and Environmental Science, Tübitak, 1998 ,22, pp.503-514

KALAYCI, S., Kahya, E., “Trend analysis of streamflow in Turkey”, Journal of Hydrology, 289, 2004, pp128– 144

KENDALL, M. G., (1975), Rank Correlation Methods, Charles Griffin & Company Limited, London, 2nd ed.

KIROĞLU, G. B., Uygulamalı Parametrik Olmayan İstatistiksel Yöntemler, M.S.Ü. Fen-Edebiyat Fakültesi İstatistik Bölümü, İstanbul, 1996

MALAK U. , Alp, K., “İstanbul Anadolu Yakası Hava Kirliliğinin PM ve PM2.5 açısından Değerlendirilmesi”, 6. Ulusal Hava Kirliliği ve Kontrolü Sempozyumu,, 7-9 Ekim 2015, İzmir, pp:515-525

MANN H. B., “Nonparametric Tests Against Trend”, Econometrica, 13(3), 1945, pp. 245-259

MOZEJKO, Janina, “Detecting and Estimating Trends of Water Quality Parametres”, Water Quality Monitoring and Assessment, Ed. K. Voudouris, D. Voutsa, www.intechopen.com , 2012, Accessed (15.03.2015)

NSW Goverment Office of Environment & Heritage, http://www.environment.nsw.gov.au/AQMS/ dataindex.htm, Accessed (07.03.2015)

ÖNÖZ, B., Bayazıt, M., “The Power of Statistical Tests for Trend Detection”, Turkish Journal of Engineering and Environmental Sciences, TÜBİTAK, 27, 2003, pp:247-251

SCHERTZ, T. L., Alexander, R. B., Ohe, D. J., “The Computer Program Estimate Trend ( Estrend)”, A System For The Detection in Water-Quality Data, U.S. Geological Survey, 1991

SHAPIRO, S. S., Wilk, M. B., “An Analysis of Variance Test for Normality (Complete Samples)”, Biometrika, 52(3/4), Dec., 1965, pp 591-611

TAYANÇ, M., Karaca, M., Yenigün, O., “Annual and Seasonal Air Temperature Trend Patterns of Climate Change and Urbanization Effects in Raletion to Air Pollutants in Turkey”, Journal of Geophysical Research, 102( D2), 1997, pp.1909-1919

TAYANÇ, Mete,“Türkiye’de Hava Kalitesi Modellemesi”, Hava Kirliliği Araştırma Dergisi, 2, 2013, pp.112- 122

T.C. Başbakanlık Mevzuatı Geliştirme ve Yayın Genel Müdürlüğü, “Hava Kirliliği Değerlendirme ve Yönetimi Yönetmeliği”, http://www.mevzuat.gov.tr/Metin. Aspx?MevzuatKod=7.5.12188&sourceXmlSearch=&MevzuatIliski=0 , 2008, ErişimTarihi (25.03.2015)

T.C. Çevre ve Şehircilik Bakanlığı Çevre Yönetimi Genel Müdürlüğü,“Hava Kalitesinin İnsan Sağlığı Üzerine Olan Etkileri” http://www.csb.gov.tr/gm/cygm/index.php?Sayfa=sayfa&Tur=banner&Id=78, Accessed (24.03.2015)

TIAN, J., Fernandez, G.C.J., “Seasonal Trend Analysis of Monthly Water Quality Data”, The Western Users of SAS Software, Los Angeles, US, 1999, pp.1-6

Türkiye İstatistik Kurumu, TUİK, www.tuik.gov.tr

UZGÖREN, E., Yücel, Ö., “Çevre Sorunları Bağlamında Dışsal Ekonomiler ve Ekonomik Etkilerinin Analizi”, Dumlupınar Üniversitesi, Sosyal Bilimler Dergisi, Sayı:3, Kasım, 1999, pp. 97-110

VAN BELLE, Gerald, Hughes, J.P.,“Nonparametric Tests for Trend in Water Quality”, Water Resources Research, Vol:20, No:1, January, 1984, pp.127-136

WONG, R., “A Procedure to Analyze Air Quality Data for the Detection of Linear Time Trends”, Air Policy Branch Alberta Environment, Canada, 2010