Prediction and spatiotemporal analysis of atmospheric Fine Particles and their effect on temperature and vegetation cover in Iran using Exponential Smoothing approach in Python

Document Type : Research Paper


1 Department of Civil-Environmental Engineering, School of Environment, University of Tehran, Tehran, Iran.

2 Faculty of Natural Resources and Environment, Science and Research Branch, Islamic Azad University, Tehran, Iran. Head of the General Department of Environment and Sustainable Development, Deputy of Municipal Service



A relatively accurate prediction of the concentration of pollutants and environmental variables, short-term and long-term, is an important step in reducing the damage caused by poor air quality. In this study, first by spatiotemporal analysis and then, using prediction techniques, to predict the concentration of fine atmospheric particles (PM2.5), temperature and vegetation cover index (NDVI) on the trend of PM2.5 in a period of 5 years (2017-2022) was discussed at the level of Iran. The data of PM2.5 concentration, temperature and vegetation index were extracted based on MERRA-2, FLDAS and MODIS satellite models. In the five-year study period, a somewhat downward trend was observed for the air concentration of PM2.5. The results showed the lowest annual average concentration of fine atmospheric particles in 2019 and 2020. Also, a strong correlation between PM2.5 concentration and temperature was obtained. The highest average concentration of PM2.5 occurred in the northwest, west, and southwest of Iran. In the next step, in order to predict the future concentration of PM2.5 air particles, temperature and vegetation index, the Exponential Smoothing approach was used in the Python statistical library (Statsmodels) to model monthly time series. Evaluation of the models with two criteria of root mean square error (RMSE) and coefficient of determination (R2) was done to minimize the estimation error and find the most suitable model among the eleven predicted models. The results show that double exponential smoothing models are more suitable for predicting PM2.5 concentration and triple exponential smoothing models with Holt-Winter trend are more suitable for predicting temperature and NDVI data. This study can help public and private institutions to better understand economic, health and environmental condition affected by air pollution effects by predicting the period when air pollution levels may be particularly high.


Al-Ansari, N., Adamo, N., Knutsson, S., Laue, J., 2018. Geopolitics of the Tigris and Euphrates basins. Journal of Earth Sciences and Geotechnical Engineering 8(3), 187-222.
Allen, R.G., Pruitt, W.O., 1986. Rational use of the FAO Blaney-Criddle formula. Journal of Irrigation and Drainage Engineering 112(2), 139-155.
Anbari, K., Sicard, P., Omidi Khaniabadi, Y., Raja Naqvi, H., Rashidi, R., 2022. Assessing the effect of COVID-19 pandemic on air quality change and human health outcomes in a capital city, southwestern Iran. International Journal of Environmental Health Research 1-12.
Asna-ashary, M., Farzanegan, M.R., Feizi, M., Sadati, S.M., 2020. COVID-19 outbreak and air pollution in Iran: a panel VAR analysis (No. 16-2020). MAGKS Joint Discussion Paper Series in Economics.
Baker, L.H., Collins, W.J., Olivié, D.J.L., Cherian, R., Hodnebrog, Ø., Myhre, G., Quaas, J., 2015. Climate responses to anthropogenic emissions of short-lived climate pollutants. Atmospheric Chemistry and Physics 15(14), 8201-8216.
Beckerman, B.S., Jerrett, M., Martin, R.V., van Donkelaar, A., Ross, Z., Burnett, R.T., 2013. Application of the deletion/substitution/addition algorithm to selecting land use regression models for interpolating air pollution measurements in California. Atmospheric Environment 77(2), 172-177.
Bilen, K., Ozyurt, O., Bakırcı, K., Karslı, S., Erdogan, S., Yılmaz, M., Comaklı, O., 2008. Energy production, consumption, and environmental pollution for sustainable development: A case study in Turkey. Renewable and Sustainable Energy Reviews, 12(6), 1529-1561.
Borhani, F., Ehsani, A.H., Shafiepour Motlagh, M., Rashidi, Y., 2023a. Estimate Ground‑based PM2.5 concentrations with Merra‑2 aerosol components in Tehran, Iran: Merra‑2 PM2.5 concentrations verification and meteorological dependence. Environment, Development and Sustainability 1-42.
Borhani, F., Mirmohammadi, M., Aslemand, A., 2017. Experimental study of benzene, toluene, ethylbenzene and xylene (BTEX) concentrations in the air pollution of Tehran, Iran. Journal of Research in Environmental Health 3(2), 105-115.
Borhani, F., Noorpoor, A., 2017. Cancer risk assessment Benzene, Toluene, Ethylbenzene and Xylene (BTEX) in the production of insulation bituminous. Environmental Energy and Economic Research 1(3), 311-320.
Borhani, F., Noorpoor, A., 2020. Measurement of Air pollution Emissions from Chimneys of Production Units Moisture Insulation (Isogam) Delijan. Journal of Environmental Science and Technology 21(12), 57-71.
Borhani, F., Noorpoor, A., Khalili, K., 2016. Measuring and evaluation of non-hydrocarbon air pollutants emitted in the production of insulation bituminous (Isogam) exhaust flue gas. Education 335-343.
Borhani, F., Shafiepour Motlagh, M., Ehsani, A. H., Rashidi, Y., Ghahremanloo, M., Amani, M., Moghimi, A., 2023b. Current Status and Future Forecast of Short-lived Climate-Forced Ozone in Tehran, Iran, derived from Ground-Based and Satellite Observations. Water, Air, & Soil Pollution 234(2), 134.
Borhani, F., Shafiepour Motlagh, M., Ehsani, A. H., Rashidi, Y., Noorpoor, A., Maddah, S., 2023c. Optimization Models for Reducing the Air Pollutants Emission in the Production of Insulation Bituminous. Environmental Energy and Economic Research 7(2), 1-14.
Borhani, F., Shafiepour Motlagh, M., Ehsani, A.H., Rashidi, Y., 2022b. Evaluation of short-lived atmospheric fine particles in Tehran, Iran. Arabian Journal of Geosciences 15(16), 1-10.
Borhani, F., Shafiepour Motlagh, M., Ehsani, A.H., Rashidi, Y., Maddah, S., Mousavi, S.M., 2022c. On the predictability of short-lived particulate matter around a cement plant in Kerman, Iran: machine learning analysis. International Journal of Environmental Science and Technology 1-14.
Borhani, F., Shafiepour Motlagh, M., Rashidi, Y., Ehsani, A.H., 2022a. Estimation of short-lived climate forced sulfur dioxide in Tehran, Iran, using machine learning analysis. Stochastic Environmental Research and Risk Assessment 1-14.
Borhani, F., Shafiepour Motlagh, M., Stohl, A., Rashidi, Y., Ehsani, A. H., 2021. Changes in short-lived climate pollutants during the COVID-19 pandemic in Tehran, Iran. Environmental Monitoring and Assessment 193(6), 1-12.
Borhani, F., Shafiepour Motlagh, M., Stohl, A., Rashidi, Y., Ehsani, A. H., 2022d. Tropospheric Ozone in Tehran, Iran, during the last 20 years. Environmental Geochemistry and Health, 44(10), 3615-3637.
Borhani, F., Zahed, F., Noorpoor, A., 2019. Modeling and evaluating the contribution of NOX and CO pollutants emitted in the insulation Bituminous units (Isogam) exhaust flue gas on the around area (Case study: Delijan City). New Science and Technology 1(2), 91-100.
Broomandi, P., Karaca, F., Nikfal, A., Jahanbakhshi, A., Tamjidi, M., Kim, J.R., 2020. Impact of COVID-19 event on the air quality in Iran. Aerosol and Air Quality Research 20(8), 1793-1804.
Cheraghi, A., Borhani, F., 2016a. Assessing the effects of air pollution on Four Methods of pavement by using Four Methods of Multi-Criteria Decision in Iran. Journal of Environmental Science Studies 1(1), 59-71.
Cheraghi, A., Borhani, F., 2016b. Evaluation of Environmental and Sustainable Development of Four Pavements in Iran by Four Method of Multi-Criteria Analysis. Journal of Environmental Science Studies 1(2), 51-62.
Dohrmann, M., Hatem, R., 2014. The impact of hydro-politics on the relations of Turkey, Iraq, and Syria. The Middle East Journal 68(4), 567-583.
Drack, J. M. E., Vázquez, D. P., 2018. Morphological response of a cactus to cement dust pollution. Ecotoxicology and Environmental Safety 148, 571-577.
Ebrahimikhusfi, Z., Dargahian, F., 2018. Investigation of the Climatic parameters Effect on the Concentration Change of Particles Matter less than 10 μm and its Relation to Wind Erosion Occurrence in Arid Regions. Journal of Arid Regions Geographics Studies 9(34), 76-92.
Elavarasan, D., Vincent, D. R., Sharma, V., Zomaya, A. Y., Srinivasan, K., 2018. Forecasting yield by integrating agrarian factors and machine learning models: A survey. Computers and Electronics in Agriculture 155, 257-282.
Fazel Dehkordi, L., Azarnivand, H., Zare Chahouki, M.A., Mahmoudi Kohan, F., Khalighi Sigaroudi, S., 2016. Drought Monitoring Using Vegetation Index (NDVI) (Case study: Rangelands of Ilam Province). Journal of Range and Watershed Managment 69(1), 141-154.
Gao, S., 2015. Managing short-lived climate forcers in curbing climate change: an atmospheric chemistry synopsis. Journal of Environmental Studies and Sciences 5(2), 130-137.
Giovanni, NASA's Goddard Earth Sciences Data and Information Services Center 2022.
Goap, A., Sharma, D., Shukla, A.K., Krishna, C.R., 2018. An IoT based smart irrigation management system using Machine learning and open-source technologies. Computers and Electronics in Agriculture 155, 41-49.
Golkar Hamzee Yazd, H.R., Rezayinezhad, M., Tavousi, M., 2016. Climatic zoning of South Khorasan province with GIS software. Journal of Soil and Water Resources Conservation 6(1), 47-62.
Holmes, C. W., Miller, R., 2004. Atmospherically transported elements and deposition in the Southeastern United States: local or transoceanic?. Applied Geochemistry 19(7), 1189-1200.
Hoveidi, H., Aslemand, A., Borhani, F., Naghadeh, S. F., 2017. Emission and health costs estimation for air pollutants from municipal solid waste management scenarios, case study: NOX and SOX pollutants, Urmia. Iran. Journal of Environmental Treatment Techniques 5(1), 59-64.
Jalali, N., Iranmanesh, F., Davoodi, M., 2017. Identification of origin and areas affected by dust storms in southwestern Iran using Madis images. Journal of Watershed Engineering and Management 9(4), 218-331.
Khan, R.K., Strand, M.A., 2018. Road dust and its effect on human health: a literature review. Epidemiology and Health 40.
Khodarahmi, F., Goudarzi, G., Hashemi Shahraki, A., Alavi, N., Ahmadi Angali, K., Dehghani, M., 2013. Study of environmental parameters effect on particulate matters and bacterial concentration in Ahvaz city during different seasons. New Cellular and Molecular Biotechnology Journal 3(11), 83-90.
Maddah, S., Bidhendi, G.N., Borhani, F., Taleizadeh, A.A., 2022. Resilient-Sustainable Supplier Selection Considering Health-Safety-Environment Performance Indices: A Case Study in Automobile Industry.
Mirakbari, M., Ebrahimi Khusfi, Z., 2020. Investigation of spatial and temporal changes in atmospheric aerosol using aerosol optical depth in Southeastern Iran. Journal of RS and GIS for Natural Resources 11(3), 87-105.
Modarres, R., Sadeghi, S., 2018. Spatial and temporal trends of dust storms across desert regions of Iran. Natural Hazards 90(1), 101-114.
Mousavi, S. M., Dinan, N. M., Ansarifard, S., Borhani, F., Ezimand, K., Naghibi, A., 2023. Examining the Role of the Main Terrestrial Factors Won the Seasonal Distribution of Atmospheric Carbon Dioxide Concentration over Iran. Journal of the Indian Society of Remote Sensing, 51(4), 865-875.
Özbay, B., 2012. Modeling the effects of meteorological factors on SO2 and PM10 concentrations with statistical approaches. Clean–Soil, Air, Water 40(6), 571-577.
Randall, S., 2008. Baseline assessment of short-lived climate pollutants in Bangladesh. In Proceedings of 3rd International Conference on Environmental Aspects of Bangladesh (p. 33).
Retama, A., Baumgardner, D., Raga, G.B., McMeeking, G.R., Walker, J.W., 2015. Seasonal trends in black carbon properties and co-pollutants in Mexico City. Atmospheric Chemistry & Physics Discussions 15(8), 12539-12582.
Rouse Jr, J. W., Haas, R. H., Deering, D. W., Schell, J. A., Harlan, J. C., 1974. Monitoring the vernal advancement and retrogradation (green wave effect) of natural vegetation (No. E75-10354).
Sanita di Toppi, L., Sanita di Toppi, L., Bellini, E., 2020. Novel coronavirus: How atmospheric particulate affects our environment and health. Challenges 11(1), 6.
Selim, M.E.S., 2009. Environmental security in the Arab World. In facing global environmental change (pp: 843-853). Springer, Berlin, Heidelberg.
Shahsavani, A., Yarahmadi, M., Mesdaghinia, A., Younesian, M., Naimabadi, A., Salesi, M., Naddafi, K., 2012. Analysis of dust storms entering Iran with emphasis on Khuzestan Province. Hakim Research Journal 15(3), 192-202.
Shine, K. P., Berntsen, T. K., Fuglestvedt, J. S., Skeie, R. B., Stuber, N., 2007. Comparing the climate effect of emissions of short-and long-lived climate agents. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 365(1856), 1903-1914.
Sobhani, B., Safarian Zengir, V., Faizollahzadeh, S., 2020. Modeling and prediction of dust in western Iran. Physical Geography Research Quarterly 52(1), 17-35.
Stohl, A., Aamaas, B., Amann, M., Baker, L., Bellouin, N., Berntsen, T.K., Boucher, O., Cherian, R., Collins, W., Daskalakis, N. Dusinska, M., 2015. Evaluating the climate and air quality impacts of short-lived pollutants. Atmospheric Chemistry and Physics 15(18), 10529-10566.
Tucker, C. J., Pinzon, J. E., Brown, M. E., Slayback, D. A., Pak, E. W., Mahoney, R., Vermote, E.F., El Saleous, N., 2005. An extended AVHRR 8‐km NDVI dataset compatible with MODIS and SPOT vegetation NDVI data. International Journal of Remote Sensing 26(20), 4485-4498.
Wu, X., Nethery, R.C., Sabath, B.M., Braun, D., Dominici, F., 2020. Exposure to air pollution and COVID-19 mortality in the United States. medRxiv,
Yongjian, Z., Jingu, X., Fengming, H., Liqing, C., 2020. Association between short-term exposure to air pollution and COVID-19 infection: Evidence from China. Science of the Total Environment 727, 138704.
Zaki, Y., 2020. Hydropolitic of turkey GAP Project and its effect on environmental security of Iraq and Syria. Political Spatial Planning 3(1), 1-9.
Zou, L., Ruan, F., Huang, M., Liang, L., Huang, H., Hong, Z., Yu, J., Kang, M., Song, Y., Xia, J., Guo, Q., 2020. SARS-CoV-2 viral load in upper respiratory specimens of infected patients. New England Journal of Medicine 382 (12), 1177-1179.