Characteristics of Particulate Matter Pollution (PM10) and Their Relationships with Meteorological Elements in Micro-Climate Conditions: Example of Iğdır

Öz

In this study, in seasonal periods in one year, it was examined the relationship between the particulate matter (PM₁₀) concentration and meteorological data at the micro-climate conditions in Iğdır province. For this purpose, it was evaluated relative humidity, wind direction, wind speed, air pressure and air temperature as meteorological data. In addition; the concentration of PM₁₀ in air was taken into account as particulate matter pollution degree. Spearman's correlation test was applied in the factors, owing to having non-parametric properties, to determine the relationship between the meteorological factors and PM₁₀ concentrations. The most effective meteorological parameters to the PM₁₀ level were determined air pressure and wind speed in the winter and autumn, air humidity and wind direction in the spring, and also wind direction and wind speed in the summer.  In addition, average amount of PM₁₀ was found 164.7 μgm^-3, 84 μgm^-3, 117 μgm^-3, and 181 μgm^-3 in winter, spring, summer and autumn periods, respectively, and also, the mean annual PM₁₀ value was 182.23 μgm^-3. When this value is taken into consideration, it is concluded that the PM₁₀ level in Iğdır province is much higher than 50 μgm^-3, which is the 24-hour PM₁₀ limit value determined by the air quality assessment and management regulation. This exceedance was observed during the year.

Anahtar Kelimeler

• PM10,climate,wind direction

Mikroklima Özelliğine Sahip İklim Koşullarında Meteorolojik Verilerle İlişkili Partiküler Kirlilik (PM10) Karakteristikleri: Iğdır Örneği

Öz

Bu çalışmada mikroklima özelliğine sahip Iğdır ilinde meteorolojik faktörlerin partiküler kirlilik (PM₁₀) miktarındaki değişimleri bir yıllık zaman periyodunda mevsimsel olarak incelenmiştir. Araştırmada bağıl nem, rüzgâr yönü, rüzgâr hızı, hava basıncı ve hava sıcaklığı meteorolojik veriler; atmosferdeki PM₁₀ konsantrasyonu ise partiküler kirlilik düzeyi olarak dikkate alınmıştır. Faktörler parametrik olmayan özelliğe sahip olması nedeniyle Spearman’s korelasyon testine tabi tutularak birbirleriyle olan değişimleri belirlenmiştir. Yapılan korelasyon testi sonuçlarına göre il genelinde PM₁₀ değişimine kış ve sonbahar dönemlerinde; hava basıncı ve rüzgâr hızı, ilkbaharda; nem ve rüzgâr yönü, yaz periyodunda ise; rüzgâr yönü ve rüzgâr hızı en çok etkili olan meteorolojik parametreler olarak tespit edilmiştir. Ayrıca ortalama PM₁₀ miktarının kış, ilkbahar, yaz ve sonbahar periyotlarında sırasıyla 164.7 μgm^-3, 84 μgm^-3, 117 μgm^-3 ve 181 μgm^-3 ve yıllık ortalama PM₁₀ değerinin 182.23 μgm^-3 olduğu belirlenmiştir. Bu değer dikkate alındığında Iğdır ilindeki PM₁₀ düzeyinin, Hava Kalitesi Değerlendirme ve Yönetimi Yönetmeliğinin belirlemiş olduğu 24 saatlik PM₁₀ limit değeri olan 50 μgm^-3’den çok daha fazla olduğu sonucuna varılmıştır. Bu aşım yılın tüm günlerinde gözlenmiştir.

Anahtar Kelimeler

• PM10,iklim,rüzgâr yönü,rüzgâr hızı,hava basıncı

Kaynakça

1. Akyüz, M., Çabuk, H., 2009. “Meteorological Variations of PM2.5/PM10 Concentrations and Particle-Associated Polycyclic Aromatic Hydrocarbons in the Atmospheric Environment of Zonguldak, Turkey”, J. Hazard. Mate,. 170, 13-21.
2. ÇŞB (Çevre ve Şehircilik Bakanlığı), 2018. T.C. “Çevre ve Şehircilik Bakanlığı Ulusal Hava İzleme Ağı” http://www.havaizleme.gov.tr/Services/AirQuality# erişim tarihi: 11/11/2018.
3. Bai Yun, Yong Li, Xiaoxue Wang, Jingjing Xie and Chuan Li 2016. “Air Pollutants Concentrations Forecasting Using Back Propagation Neural Network Based on Wavelet Decomposition with Meteorological Conditions”, Atmospheric Pollution Research, 7(3), 557–566.
4. Ceylan, Ζ. and Bulkan S., 2018. “Forecasting PM10 Levels Using ANN and MLR: A Case Study for Sakarya City. Global NEST Journal, 20 (2), 281-290.
5. Choi, Y.S., Ho, C.H., Chen, D., Noh, Y.H. and Song, C.K., 2008. “Spectral Analysis of Weekly Variation in PM10 Mass Concentration and Meteorological Conditions over China”, Atmos. Environ., 42, 655-666.
6. Garcia-Menendez, F., Saari, R.K., Monier, E., Selin, N.E, 2015. “U.S. Air Quality and Health Benefits from Avoided Climate Change under Greenhouse Gas Mitigation”, Environ. Sci. Technol, 49, 7580–7588.
7. Januchs, M.G.C., Dominguez. J.Q., Corona, A.V. and Andina, D., 2015. Development of a Model for Forecasting of PM10 Concentrations in Salamanca, Mexico. Atmospheric Pollution Research, 6: 626-634.
8. Kim Oanh, N.T., Chutimon, P., Ekbordin, W. and Supat, W., 2005. “Meteorological Pattern Classification and Application for Forecasting Air Pollution Episode Potential in a Mountain-Valley Area”, Atmos. Environ., 39, 1211-1225.

9. Kolehmainen, M., Martikainen, H. and Ruuskanen, J., 2001. “Neural Networks and Periodic Components Used in Air Quality Forecasting”, Atmospheric Environment, 35, 815-825.
10. Liu, Y., Park, R.J., Jacob, D.J., Li, Q., Kilaru, V. and Sarnat, J.A., 2004. “Mapping Annual Mean Ground-Level PM2.5 Concentrations Using Multiangle Imaging Spectroradiometer Aerosol Optical Thickness over The Contiguous United States”, J. Geophys,. Res. 109, 1-10.
11. Tambo, E., Duo-Quan, W. and Zhou, X.N., 2016. Tackling Air Pollution and Extreme Climate Changes in China: Implementing The Paris Climate Change Agreement. Environ. Int., 95, 152–156.
12. Tian,G., Qiao, Z. and Xu, X., 2014. “Characteristics of Particulate Matter (PM10) And Its Relationship With Meteorological Factors During 2001-2012 In Beijing”. Environmental Pollution, 192: 266-274.
13. Zheng, J., Jiang, P., Qiao, W., Zhu, Y., and Kennedy, E., 2016. “Analysis of air pollution reduction and climate change mitigation in the industry sector of Yangtze River Delta in China”, J. Clean. Prod., 114, 314–322.