Analyzing the Relationship Between Meteorological Elements and Criteria Atmospheric Pollutants in Tabriz Using Statistical Modeling

Document Type : Original Article


1 Department of Natural Geography, Faculty of Earth Sciences, University of Shahid Beheshti, Tehran, Iran

2 ,Department of Forestry, Faculty of Natural Resources, University of Tarbiat Modarres, Mazandaran, Iran



Introduction: The rapid increase in population, growth of urbanization and industrialization in recent years, which is generally associated with an increase in demand and energy consumption, and as a result, an increase in pollutant emission sources, has exacerbated air pollution as one of the biggest current crises of urban societies and consequently health risks and related social inequalities in terms of time and space. On the other hand, meteorological parameters directly affect the amount of pollutants as well as the duration of their presence in the atmosphere, and the present research was conducted in order to investigate this effect and discover the relationships between criteria air pollutants and atmospheric elements.
Material and Methods: In addition to investigating the status of meteorological elements (temperature, precipitation, wind speed, relative humidity, radiation, sunshine hours and cloudiness) and air pollutants (carbon monoxide (CO), nitrogen dioxide (NO2), sulfur dioxide (SO2), ozone (O3) and particulate matters with aerodynamic diameters less than 10 microns and 2.5 microns (PM10 and PM2.5)) in Tabriz city during 2004-2021, the present study has explored the relationships between pollutants and meteorological parameters in monthly and seasonal time scales using Pearson's correlation test at the 95% confidence level and the effect of these elements on the concentration of pollutants using Multiple Linear Regression (MLR) and Generalized Additive Model (GAM) in R 4.3.1 statistical software.
Results and Discussion: Based on the results of Pearson correlation analysis, NO2 and PM2.5, SO2 and PM2.5 pollutants and PM2.5 and PM10 pollutants have shown a significant positive correlation in pairs, so it seems that these pollutants have similar emission sources. Also, the results of this research demonstrate that the concentration of air pollutants in Tabriz was affected by weather conditions during the entire statistical period in the monthly and seasonal time scales. NO2 and PM2.5 pollutants had the most negative monthly correlation with the parameters of temperature, wind speed and sunshine hours and the most positive correlation with relative humidity; PM2.5 had the most positive correlation with pressure; CO and SO2 had the most negative correlation with radiation; O3 had a strong positive correlation with temperature, wind speed and sunny hours and the most negative correlation with pressure, relative humidity and cloudiness; and NO2 and PM10 pollutants had the most positive correlation with cloudiness. The results of fitting Multiple Linear Regression (MLR) and Generalized Additive Model (GAM) for each criteria in Tabriz city indicated the better performance of GAM in analyzing the relationships between all air pollutants and the set of independent variables except NO2.
Conclusion: The results of this research indicate that the effect of atmospheric elements on the concentration of pollutants in Tabriz city is different depending on the type of pollutant and at different times, and it can be acknowledged that the effect of a specific meteorological parameter on air pollution is uncertain. However, wind speed, radiation, temperature and air pressure are the most important meteorological elements related to the concentration of pollutants in Tabriz city. Also, the results suggest that both MLR and GAM can describe the variability of the response variable by a set of predictor variables and explain the linear and non-linear relationships between them. However, considering the non-linear relationship between the concentration of atmospheric pollutants and meteorological elements, GAM is able to justify a higher percentage of changes in all criteria atmospheric pollutants except NO2.


Barzeghar, V., Hassanvand, M.S., Faridi, S., Abbasi, S. and Gholampour, A., 2022. Long-term trends in ambient air pollutants and the effect of meteorological parameters in Tabriz, Iran. Urban Climate. 42, 101-119.
Birinci, E., Deniz, A. and Özdemir, E.T., 2023. The relationship between PM10 and meteorological variables in the mega city Istanbul. Environmental Monitoring and Assessment. 195(2), 304. 10.1007/s10661-022-10866-3.
Chambers, J.M., 2017. Linear models. In: Statistical Models in S. Routledge Press, Abingdon, Oxfordshire, pp. 95-144.
Cui, H., Ma, R. and Gao, F., 2018. Relationship between meteorological factors and diffusion of atmospheric pollutants. Chemical Engineering Transactions. 71, 1417-1422.
Dandotiya, B., Jadon, N. and Sharma, H.K., 2019. Effects of meteorological parameters on gaseous air pollutant concentrations in urban area of Gwalior City, India. Environmental Claims Journal. 31(1), 32-43.
Efron, B., 1983. Estimating the error rate of a prediction rule: improvement on cross-validation. Journal of the American statistical association. 78(382), 316-331.
Fox, J. and Monette, G., 1992. Generalized collinearity diagnostics. Journal of the American Statistical Association. 87(417), 178-183.
Gasmi, K., Aljalal, A., Al-Basheer, W. and Abdulahi, M., 2017. Analysis of NOx, NO and NO2 ambient levels as a function of meteorological parameters in Dhahran, Saudi Arabia. WIT Transactions on Ecology and the Environment. 211, 77-86.
Gorai, A.K., Tuluri, F., Tchounwou, P.B. and Ambinakudige, S., 2015. Influence of local meteorology and NO2 conditions on ground-level ozone concentrations in the eastern part of Texas, USA. Air Quality, Atmosphere and Health. 8, 81-96.
Horne, J.R. and Dabdub, D., 2017. Impact of global climate change on ozone, particulate matter, and secondary organic aerosol concentrations in California: A model perturbation analysis. Atmospheric environment. 153, 1-17.
Jayamurugan, R., Kumaravel, B., Palanivelraja, S. and Chockalingam, M.P., 2013. Influence of temperature, relative humidity and seasonal variability on ambient air quality in a coastal urban area. International Journal of Atmospheric Sciences. 9, 1-7.
Johnson, A.C., 2022. Correlation Study of Meteorological Parameters and Criteria Air Pollutants in Jiangsu Province, China. Pollution. 8(1), 341-354.
Kayes, I., Shahriar, S.A., Hasan, K., Akhter, M., Kabir, M.M. and Salam, M.A., 2019. The relationships between meteorological parameters and air pollutants in an urban environment. Global Journal of Environmental Science and Management. 5(3), 265-278.
Kenty, K.L., Poor, N.D., Kronmiller, K.G., McClenny, W., King, C., Atkeson, T. and Campbell, S.W., 2007. Application of CALINE4 to roadside NO/NO2 transformations. Atmospheric Environment. 41(20), 4270-4280.
Kitamori, K., Manders, T., Dellink, R. and Tabeau, A.A., 2012. OECD environmental outlook to 2050: the consequences of inaction. OECD.
Liu, Y., Zhou, Y. & Lu, J. 2020. Exploring the relationship between air pollution and meteorological conditions in China under environmental governance. Scientific reports. 10(1), 14518.
Ma, Y., Yang, S., Zhou, J., Yu, Z. and Zhou, J., 2018. Effect of ambient air pollution on emergency room admissions for respiratory diseases in Beijing, China. Atmospheric Environment. 191, 320-327.
Mahanta, S.K., Panda, B.S., Pati, S.S., Mallik, M.R., Mahanta, B., Biswas, K. and Sahu, R., 2021. Influence of Meteorological Variables on Ambient Air Pollutants of a Coastal District in Eastern India. Oriental Journal of Chemistry. 37(1), 194-203.
Masoudi, M., Behzadi, F. and Sakhaei, M., 2019. Assessment of NO2 levels as an air pollutant and its statistical modeling using meteorological parameters in Tehran, Iran. Caspian Journal of Environmental Sciences. 17(3), 227-236.
Meng, K., Cheng, X., Xu, X., Qu, X., Ma, C., Zhao, Y., Li, Y., Yang, Y., Zhang, W. and Ding, G., 2017. Spatial-temporal variations of pollutant emission sources inversed by adaptive nudging scheme over Beijing-Tianjin-Hebei region based on the CMAQ model. Acta Scientiae Circumstantiae. 37(1), 52-60.
Mohammad Khorshiddoust, A., Mohammadi, G.H., Aghlmand, F. and Hosseini Sadr, A., 2018. Descriptive-statistical Analysis of the Relationship between Atmospheric Conditions and Urban Pollution in Tabriz. Environmental Management Hazards. 5(2), 217-230. . (In Persian with English abstract).
Mukaka, M.M., 2012. A guide to appropriate use of correlation coefficient in medical research. Malawi medical journal. 24(3), 69-71. PMID: 23638278; PMCID: PMC3576830.
Oji, S. and Adamu, H., 2020. Correlation between air pollutants concentration and meteorological factors on seasonal air quality variation. Journal of air pollution and health. 5(1), 11-32.
Pearson, K., 1895. Notes on Regression and Inheritance in the Case of Two Parents. Proceedings of the Royal Society of London. 58, 240-242.
R Core Team, R., 2023. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria.
Rad, A.K, Shamshiri, R.R., Naghipour, A., Razmi, S.O., Shariati, M., Golkar, F. and Balasundram, S.K., 2022. Machine Learning for Determining Interactions between Air Pollutants and Environmental Parameters in Three Cities of Iran. Sustainability. 14(13), 8027.
Radaideh, J.A., 2017. Effect of meteorological variables on air pollutants variation in arid climates. Journal of Environmental & Analytical Toxicology. 7(4), 1000478.
Ramezani, R., Alijani, B. and Borna, R., 2018. Explaining the Effects of Climate Elements in Tehran’s Metropolis Air Quality. Geographical Researches. 33(3), 154-169. (In Persian with English abstract).
Shahmohammadi, A., Bayat, A. and Mashhadizadeh Maleki, S., 2020. Investigation of nitrogen dioxide behavior in Mashhad and its relationship with Meteorological Parameters. Journal of Applied researches in Geographical Sciences. 20(58), 71-85. (In Persian with English abstract).
Sirithian, D. and Thanatrakolsri, P., 2022. Relationships between Meteorological and Particulate Matter Concentrations (PM2.5 and PM10) during the Haze Period in Urban and Rural Areas, Northern Thailand. Air, Soil and Water Research. 15, 1-15.
Song, R., Yang, L., Liu, M., Li, C. and Yang, Y., 2019. Spatiotemporal distribution of air pollution characteristics in Jiangsu Province, China. Advances in Meteorology. 2019, 1-14.
Tian, J., Fang, C., Qiu, J. and Wang, J., 2021. Analysis of Ozone pollution characteristics and influencing factors in Northeast economic cooperation region, China. Atmosphere. 12(7), 843.
TLboFcbA, M., 2020. leaps: Regression Subset Selection. R package version 3.1,
Venables, W.N. and Ripley, B.D., 2002. Modern Applied Statistics with S, Fourth ed. Springer.
Wei, T., Simko, V. and Package ‘corrplot, R., 2022.’: Visualization of a Correlation Matrix. (Version 0.92).
WHO., 2021. Air pollution and health: Summary. Retrieved March 27, 2021, from
Wood, S.N., 2017. Generalized Additive Models: An Introduction with R, Second ed. Chapman and Hall/CRC Press, Boca Raton, Florida.
Wu, Y., Zhou, B., Zhao, A., Chen, D., Wang, X., and Yan, X., 2017. Characteristics of atmospheric pollutant concentration in urban area of Baoji nd their relationship with meteorological factors. Acta Agriculturae Jiangxi. 29(6), 100-104.
Zhai, S., Jacob, D.J., Wang, X., Shen, L., Li, K., Zhang, Y., Gui, K., Zhao, T. and Liao, H., 2019. Fine particulate matter (PM2.5) trends in China, 2013–2018: separating contributions from anthropogenic emissions and meteorology. Atmospheric Chemistry and Physics. 19(16), 11031-11041.
Zhang, H., Wang, Y., Hu, J., Ying, Q. and Hu, X.M., 2015. Relationships between meteorological parameters and criteria air pollutants in three megacities in China. Environmental research. 140, 242-254.
Zhang, Q., Geng, G., Wang, S., Richter, A. and He, K., 2012. Satellite remote sensing of changes in NOx emissions over China during 1996–2010. Chinese Science Bulletin. 57(22), 2857–2864.
Zhou, W. and Liang, P., 2013. The Possible Effect of Climate Change on Air Quality During Autumn in Shanghai. Resour. Sci. 35(5), 1044-1050.
┼╗yromski, A., Biniak-Pieróg, M., Burszta-Adamiak, E. and Zamiar, Z., 2014. Evaluation of relationship between air pollutant concentration and meteorological elements in winter months. Journal of Water and Land Development. 22(1), 25-32.