Effect of Meteorological Parameters on PM10 Concentrations in Ardahan by Wavelet Coherence Analysis

Abstract

In the city of Ardahan, the PM10 concentrations are high especially in winter and autumn due to heating in buildings. This paper investigates the impact of meteorological parameters (air temperature, air pressure, humidity and wind speed) on the PM10 concentrations in the city of Ardahan by using the Wavelet Coherence analysis. The data have been provided from the records of the Ministry of Environment and Urbanization Continuous Monitoring Center and the Turkish State Meteorological Service in between 2010-2020. The results of the study show that selected meteorological parameters have the different effects on the PM10 concentrations in a period of ten years. Wavelet coherence approach presents clearly the influence of meteorological factors on the PM10 concentrations, and the approach is quite useful in terms of the practical explanation of available data, also.

Keywords

• Meteorological parameters,PM10,time series,wavelet coherence

References

1. 1. Miller, BG. Clean Coal Engineering Technology; 2nd Edition, Elsevier Inc.: Pennsylvania, US, 2017; pp. 419-420
2. 2. Cheng, Z, Jiang, J, Fajardo, O, Wang, S and Hao, J. 2013. Characteristics and health impacts of particulate matter pollution in China (2001-2011). Atmospheric Environment; 65:186-194.
3. 3. Kappos AD, Bruckmann P, Eikmann T, Englert N, Uwe H, Höppe P, Koch E, Krause GH, Kreyling WG, Rauchfuss K, Rombout P, Schulz-Klemp V, Thiel WR and Wichmann HE. 2004. Health effects of particles in ambient air. International Journal of Hygiene and Environmental Health; 207(4):399-407.
4. 4. Wilson JG, Kingham S, Pearce J and Sturman AP. 2005. A review of intraurban variations in particulate air pollution: Implications for epidemiological research. Atmospheric Environment; 39: 6444-6462.
5. 5. Ştefan, S, Radu C and Belegante, L. 2013. Analysis of Air Quality In Two Sites With Different Local Conditions. Environmental Engineering and Management Journal; 12(2):371-379.
6. 6. Guerra, SA, Lane, DD, Marotz, GA, Carter, RE, Hol CM and Baldauf, RW. 2006. Effects of Wind Direction on Coarse and Fine Particulate Matter Concentrations in Southeast Kansas. Journal of the Air & Waste Management Association; 56:1525–1531.
7. 7. Galindo, N, Varea, M, Gil-Moltó J and Yubero, E. 2011. The Influence of Meteorology on Particulate Matter Concentrations at an Urban Mediterranean Location. Water Air and Soil Pollution; 2015: 365–372.
8. 8. Bathmanabhan, S and Madanayak, SNS. 2010. Analysis and interpretation of particulate matter – PM10, PM2.5 and PM1 emissions from the heterogeneous traffic near an urban roadway. Atmospheric Pollution Research; 1:184-194.

9. 9. Sajjadi, SA, Zolfagharib, G, Adab, H, Allahabadi A and Delsouza, M. 2017. Measurement and modeling of particulate matter concentrations: Applying spatial analysis and regression techniques to assess air quality. MethodsX; 4:372-390.
10. 10. Tchepel, O and Borrego, C. 2010. Frequency analysis of air quality time series for traffic related pollutants. Journal of Environmental Monitoring; 12: 544–550.
11. 11. Li, L, Qian, J, Ou, CQ, Zhou, YX, Guo, C and Guo, Y. 2014. Spatial and temporal analysis of Air Pollution Index and its timescale-dependent relationship with meteorological factors in Guangzhou, China, 2001-2011. Environmental Pollution; 190:75-81.
12. 12. Anusasananan, P. 2019. Wavelet spectrum analysis of PM10 data in Bangkok, Thailand. Journal of Physics: Conference Series,1380:1-9.
13. 13. Biancofiore, F, Busilacchio, M, Verdecchia, M, Tomassetti, B, Aruffo, E, Bianco, S, Tommaso, SD, Colangeli, C, Rosatelli G and Carlo, PD. 2017. Recursive neural network model for analysis and forecast of PM10 and PM2.5. Atmospheric Pollution Research; 8(4): 652-659.
14. 14. Sifuzzaman, M, Islam, MR and Ali, MZ. 2009. Application of Wavelet Transform and its Advantages Compared to Fourier Transform. Journal of Physical Sciences; 13:121-134.
15. 15. Environment and Urban Ministry, Hava Kalitesi Veri Bankası. http://www.havaizleme.gov.tr/ (accessed at: 05.05. 2020).
16. 16. Torrence, C and Compo, GP. 1998. A practical guide to wavelet analysis. Bulletin of the American Meteorological Society; 79(1):61-78.
17. 17. Nie, Y, Chen, P, Zhang, T and Wang, E. Impacts of international oil price fluctuations on China’s PM2.5 concentrations: a wavelet analysis. 2019. Economic Research-Ekonomska Istrazivanja;1-21. 18. Grossmann, A and Morlet, J. 1984. Decomposition of hardy functions into square integrable wavelets of constant shape. Society for Industrial and Applied Mathematics; 15(4): 723-736.
18. 19. Loh, L. 2013. Co-movement of Asia-pacific with European and us stock market returns: A cross-time-frequency analysis. Research in International Business and Finance; 29:1-13.
19. 20. Environment and Urban Ministry. 2019. Environmental Status Report for Ardahan 2018 (Turkish).
20. 21. Aguiar-Conraria, L, Azevedo N and Soares, MJ. 2008. Using wavelets to decompose the time–frequency effects of monetary policy. Physica A; 387: 2863–2878.