Spatial Analysis of PM2.5 and Its Statistical Correlation with Meteorological Parameters in Tehran
Authors
1
Full Professor, Department of Remote Sensing and Geographical Information System, University of Tabriz, Tabriz, Iran.
2
Ph.D. Student in RS & GIS, Department of Remote Sensing and Geographical Information System, University of Tabriz, Tabriz, Iran.
3
2- Ph.D. Student in RS & GIS, Department of Remote Sensing and Geographical Information System, University of Tabriz, Tabriz, Iran.
10.22034/eiat.2025.217693
Abstract
Air pollution has become one of the major challenges associated with urbanization and industrialization. This study aims to conduct a spatial analysis of PM2.5 pollution and examine its statistical relationship with meteorological parameters in the metropolitan area of Tehran. To achieve these objectives, two data sets were utilized: (1) daily PM2.5 concentration data obtained from the Air Quality Control Company and (2) daily meteorological data (including rainfall, evaporation, maximum wind speed, minimum and maximum relative humidity, sunshine hours, and minimum and maximum temperatures) provided by the Meteorological Organization. The inverse distance weighting (IDW) method was used to map the spatial distribution of PM2.5 concentrations. The IDW results revealed that regions 16, 19, 20, and 9 had the highest levels of particulate matter pollution, while regions 8, 22, and 15 had the lowest concentrations. Pearson correlation and multiple linear regression analyses were used to investigate the statistical relationship between PM2.5 levels and meteorological parameters. The Pearson correlation results indicated a positive correlation between PM2.5 and minimum/maximum temperature, as well as sunshine hours, and a negative correlation with maximum wind speed and rainfall. Two linear regression models (Enter and Stepwise) were employed to establish the relationship between PM2.5 (dependent variable) and meteorological parameters (independent variables). The results showed regression coefficients of 0.6 for the Enter model and 0.565 for the Stepwise model, indicating that both models performed well in predicting PM2.5 concentrations. To determine the more suitable model, the estimated standard error was compared, with the Stepwise model showing a lower error (5.84) than the Enter model (6.0089). Therefore, the Stepwise model was deemed more appropriate for predicting PM2.5 concentrations.
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Ahmadi, H.; Ahmadi, T.; Shahmoradi, B.; Mohammadi, S. & Kohzadi, S. 2018. The effect of climatic parameters on air pollution in Sanandaj, Iran. Journal of Advances in Environmental Health Research, 3(1): 49-61.
Ju Wang, Jiatong Han, Tongnan Li, Tong Wu, Chunsheng Fang. 2023Impact analysis of meteorological variables on PM2.5 pollution in the most polluted cities in China.Heliyon VOLUME 9, ISSUE 7, E17609, JULY.
Goudie AS, Middleton N. Desert Dust in the Global System. Springer 2006;1st ed. Berlin: 278-153.
Gu J, Bai Z, Liu A, Wu L, Xie Y, Li W. 2017.Characterization of Atmospheric Organic Carbon and Element Carbon of PM2. 5 and PM10 at Tianjin, China. Aerosol Air Qual Res. 10:167-76.
Horak FSM, Gartner C, Spengler JD, Tauber E, Urbanek R, Veiter A, Fischer T. 2012. Particulate matter and lung function growth in children: a 3-yr follow-up study in Austrian schoolchildren. Eur Respir J. 19:838–45.
Kermani, M., Jonidi Jafari, A., Gholami, M., Fanaei, F., Arfaeinia, H. 2021. Association between Meteorological Parameter and PM2.5 Concentration in Karaj, Iran International Journal of Environmental Health Engineering.
Ketabi, D., Esmaili, R., Alidadi, H., Peirovi, R. & Joulaei, F. 2015. Evaluation of Mashhad City Air Quality based on Air Quality Index (AQI). Journal of Research in Environmental Health; 2(3): 236-228. (Persian)
Kiker. G. A., Bridges, T. S., Varghese, A., Seager, T. P., & Linkovjj, I. 2015. Application of multicriteria decision analysis in environmental decision making. Integrated Environmental Assessment and Management, 1(2), 95-108.
Kuo, C-Y., Chen, P-T., Lin, Y-C., Lin, C-Y., Chen, H-H. & Shih, J-F. 2008. Factors affecting the concentrations of PM10 in central Taiwan. Chemosphere. 70(7):1273-9.
Rahimi, M., Yazdani, M., Asadi, R. M., & Haydari, M.T. 2016. Investigating the Air Pollution of Sanandaj whit emphasis on temporal variation of PM10 concentration. Urban Ecological Research,6(1):99-116
McKendry IG. 2000. PM10 levels in the Lower Fraser Valley, British Columbia, Canada: an overview of spatiotemporal variations and meteorological controls. Journal of the Air & Waste Management Association; 50(3): 452-443
Najafpoor, A.; Jonidi Jafari, A. & Doosti, S. 2015. Analysis of the trend of changes in the concentration of five pollutants of air quality index in Tehran metropolis and their relationship with meteorological data during the years of 2001-2009. Health Journal of Shaheed Beheshti, University of Medical Sciences, Faculty of Health. 3 (2):17-26 (in Persian).
Nasibulina, A. 2015. Education for Sustainable Development and Environmental Ethics. Available online at www.sciencedirect.com, Procedia - Social and Behavioral Sciences, 214– 1082.
Nasibulina, An. 2019. Education for Sustainable Development and Environmental Ethics. Available online at www.sciencedirect.com, Procedia - Social and Behavioral Sciences,
Nazari, Z.; Khorasani, N.; Feyznia, S. & Karami, M. 2013. Investigation of changes in the concentration of PM10 and the effect of meteorological parameters on it during the years 2005-2010. Natural Environment Journal. 66 (1):101-11 (in persian).
Nourpoor, A. & Feyz, A. 2014. Determination of spatial and temporal variations of sulfur dioxide, nitrogen dioxide and various suspended particles by using GIS techniques in Tehran. Journal of Environmental Studies. 40 (3):723-738 (in persian).
Anusasananan, P., Morasum, D., Suwanaratand, S., Thangprasert, N. 2022. Correlation between PM2.5 and meteorological variables in Chiang Mai, Thailand. Journal of Physics: Conference Series 2145. 012045.
Rahimi, M.; Yazdani, M.; Asadi, M. & Heydari, M. 2015. Investigation of Sanandaj air pollution with emphasis on time variation of PM10 concentration. Two letters of research on urban ecology, 6 (1):99-116 (In Persian).
Rezaei, A.; Sayadi, M. & Rezaei, M. 2013. Quantitative and qualitative study of air pollution and its relation with climate factors of Birjand city in 2012. Short report of community health. 7 (4):62-65 (in Persian).
Robles, L. A., Ortega, J. C., Fu, J. S., Reed, G. D., Chow, J. C., Watson, J. G. &Herrera, J. A., 2008. A hybrid ARIMA and artificial neural networks model to forecast particulate matter in urban areas: The case of Temuco, Chile, Atmospheric Environment, 42: 8331- 8340. 18.
Sandstrom, T. FB. 2008. Desert dust, an unrecognized source of dangerous air pollution, Epidemiology, 19: 808-9.
Schaap, M. G., 2016. Using neural network to predict soil water retention and soil hydraulic conductivity, Soil and till Research, 47: 37- 42.
Sousa SIV, Martins FG, Pereira MC, Alvim-Ferraz MCM, Ribeiro H, Oliveira M et al. 2008. Influence of atmospheric ozone, PM10 and meteorological factors on the concentration of airborne pollen and fungal spores. Atmospheric Environment; 42(32): 7464-7452.
Shahsavani, A., Yarahmadi, M., Mesdaghinia, A., Younesian, M., Jaafarzadeh, N., Naimabadi, M., et al. 2011. Analysis of Dust Storms Entering Iran with Emphasis on Khuzestan Province. Hakim Research Journal; 15(3): 202-192.
Wu, Dan, Xu, Yuan, Zhang, Shiqiu. 2015. Will joint regional air pollution control be more cost effective? An empirical study of China's Beijing Tianjin Hebei region. Journal of Environmental Management, 2015, 149 (2015) 27e36
Tian, G., Qiao, Z., Xu, X. Characteristics of particulate matter (PM 10) and its relationship with meteorological factors during 2001–2012 in Beijing. Environmental Pollution.
Tecer, L. H.; Süren, P.; Alagha, O.; Karaca, F. & Tuncel, G. 2018. Effect of meteorological parameters on fine and coarse particulate matter mass concentration in a coal-mining area in Zonguldak, Turkey. Journal of the Air & Waste Management Association, 58(4): 543-552.
Tor, H. O., Markey, j., Kathy, T., & Lsaac N, L. 2015. Assessing traffic and industrial contributions to ambient nitrogen dioxide and volatile organic compounds in a low pollution urban environment, Science of The Total Environment, Volume 529, pp.149–157.
Xing-Ming., X. & P. An-Hua. 2016. Material selection using PROMETHEE combined with analytic network process under hybrid environment. Material and Design., 47., 643-652.
Vassilakos, C., Saraga, D., Maggos, T., Michopoulos, J., Pateraki, S., Helmis, C. 2005.Temporal variations of PM2.5 in the ambient air of a suburban site in Athens, Greece. Science of the Total Environment.; 349(1):223-31.
