Document Type : Original Article


1 Department of RS and GIS, Faculty of Earth Science, Shahid Chamran University, Ahvaz, Iran

2 Department of Geology, Faculty of Earth Science, Shahid Chamran University, Ahvaz, Iran


Introduction: Particulate matters are one of the main air pollutants in urban areas, which are usually produced from various sources such as urban vehicles, fossil fuels, industrial activities. They may cause respiratory diseases, cardiovascular disease and death. It is, therefore, very important to be aware of spatial changes in these pollutants in areas with high levels of pollution. In this regard, the present study was conducted with the aim of spatio-temporal evaluation of the PM2.5 index in the period 1998 to 2016 in Khuzestan Province
Material and methods: For this study, first, precipitation, land surface temperature (LST), wind speed, Digital Elevation Model (DEM) and vegetation cover parameters were prepared using four satellites i.e. Terra, Landsat 8, SRTM and GPM, and ground data. Then PM2.5 index for four periods of 1998, 2004, 2010 and 2016 was extracted using satellite products for Khuzestan Province. Also, information on the distribution of the population and industries of the province was received from the relevant organizations. Finally, after providing the spatio-temporal changes of PM2.5 index in Khuzestan Province, the spatial changes of this index were studied with the mentioned parameters to evaluate the effect of each of these parameters on the contamination degree of this index.
Results and discussion: The results of the present study showed that the southern cities of the province such as Mahshahr, Abadan and Shadegan are regions with higher potential in terms of particles smaller than 2.5 microns in size. The results of the study of population density and industries of this province showed that most of the cities in which the air pollution rate was high due to the PM2.5 index, had more industries and population density. The results also showed that in all study periods, in the northern and northeastern parts of the province, the amount of air pollution caused by this index was much lower than other regions of the province and the reason for this could be the low density of industries and population of these cities, among which we can mention the cities of Lali and Indika. In addition to the direct relationship between industry and human activities in increasing and decreasing the concentration of PM2.5 index, the relationship between this index and several factors (DEM, wind speed, precipitation, temperature and vegetation cover) was investigated. The correlation results between the mentioned parameters and PM2.5 concentration showed that the highest correlation was between PM2.5 concentration and precipitation and this relationship was inverse.
Conclusion: It can be concluded that the concentration of PM2.5 pollutants in the southern and central areas is much higher than other areas and this could be due to the high density of power plants, industries and vehicle pollution in these areas‌. In addition, environmental and climatic factors can play an important role in the persistence and spread of the air pollution layer of this index. It should be noted that this research can be used as the basis for decision-making for air pollution management, which is an important step towards overcoming the crisis of air pollution.


Ahmadi, M., Dadashirodbari, A. and Jafari, M., 2019. The effect of boundary layer height on dust storm in southwest of Iran. Journal of Natural Environmental Hazards. 8(19), 151-174. (In Persian with English abstract).
Amarloei, A., Jonidi Jafari, A., Asilian Mohabadi, H. and Asadollahi, K., 2014. The evaluation of PM10, PM2.5 and PM1 concentration during dust storm events in Ilam city, from Mar 2013 through Feb 2014. Ilam University of Medical Sciences. 22 (4), 240-259. (In Persian with English abstract).
Arami, S.A., Ownegh, M., Mohammadian Behbahani, A., Akbari, M. and Zarasvandi, A., 2018. The analysis of dust hazard studies in southwest region of Iran in 22 years (1996-2017). Journal of Spatial Analysis Environmental Hazards. 5(1), 39-66. (In Persian with English abstract).
Azadi Mubaraky, M. and Ahmadi, M., 2020. Long-term variability of particulate matter (PM2.5) in Tabriz using remote sensing data. Physical Geography Research Quarterly. 52(3), 467-480. (In Persian with English abstract).
Barzeghar, V., Sarbakhsh, P., Hassanvand, M.S., Faridi, S. and Gholampour, A., 2020. Long-term trend of ambient air PM10, PM2.5, and O3 and their health effects in Tabriz city, Iran, during 2006–2017. Sustainable Cities and Society. 54, 101988.
Basheer, S., Rashid, H., Nasir, A. and Nawaz, R.A., 2019. Spatial and temporal variability analysis of PM2. 5 concentration in Lahore city. Environmental Contaminants Reviews. 2(1), 6-10.
Cai, K., Zhang, Q., Li, S., Li, Y. and Ge, W., 2018. Spatial–temporal variations in NO2 and PM2.5 over the Chengdu–Chongqing economic zone in China during 2005–2015 based on satellite remote sensing. Sensors. 18(11), 1-16.
Chand, D., Anderson, T.L., Wood, R., Charlson, R.J., Hu, Y., Liu, Z. and Vaughan, M., 2008. Quantifying above‐cloud aerosol using space borne LIDAR for improved understanding of cloudy‐sky direct climate forcing. Journal of Geophysical Research. 113(13), 1-12.
Chen, L., Zhang, M., Zhu, J. and Skorokhod, A., 2017. Model analysis of soil dust impacts on the boundary layer meteorology and air quality over East Asia in April 2015. Atmospheric Research. 187, 42-56.
Chen, Y., Cai, Q. and Tang, H., 2003. Dust storm as an environmental problem in north China. Environmental Management. 32(4), 413-417.
Contini, D., Gambaro, A., Belosi, F., De Pieri, S., Cairns, W. R. L., Donateo, A., and Citron, M., 2011. The direct influence of ship traffic on atmospheric PM2.5, PM10 and PAH in Venice. Journal of Environmental Management. 92(9), 2119-2129.
Coronas, M.V., Rocha, J.A.V., Salvadori, D.M.F. and Vargas, V.M.F., 2016. Evaluation of area contaminated by wood treatment activities: Genetic markers in the environment and in the child population. Chemosphere. 144, 1207-1215.
Dai, W., Gao, J., Q. Wang, B. and Ouyang, F., 2013. Statistical analysis of weather effects on PM2.5. In advanced materials research. 610, 1033-1040. Trans Tech Publications Ltd.
Daniali, M., Mohamadnezhad, B. and Karimi, N., 2018. Evaluation of vegetation health based on the resilience in arid lands. Journal of Remote Sensing and GIS for Natural Resources. 9(1), 58-73. (In Persian with English abstract).
De Hoogh, K., Héritier, H., Stafoggia, M., Künzli, N. and Kloog, I., 2018. Modelling daily PM2.5 concentrations at high spatio-temporal resolution across Switzerland. Environmental Pollution. 233, 1147-1154.
Ding, Y., Zhang, M., Chen, S., Wang, W. and Nie, R., 2019. The environmental Kuznets curve for PM2.5 pollution in Beijing-Tianjin-Hebei region of China. Journal of Cleaner Production. 220, 984-994.
Duan, J., Chen, Y., Fang, W., and Su, Z., 2015. Characteristics and relationship of PM, PM10, PM2.5 concentration in a polluted city in northern China. Procedia Engineering. 102, 1150-1155.
Escudero, M., Querol, X., Ávila, A. and Cuevas, E., 2007. Origin of the exceedances of the European daily PM limit value in regional background areas of Spain. Atmospheric Environment. 41(4), 730-744.
Etchie, T. O., Etchie, A.T., Adewuyi, G. O., Pillarisetti, A., Sivanesan, S., Krishnamurthi, K. and Arora, N.K., 2018. The gains in life expectancy by ambient PM2.5  pollution reductions in localities in Nigeria. Environmental Pollution. 236, 146-157.
Fann, N., Coffman, E., Timin, B. and Kelly, J. T., 2018. The estimated change in the level and distribution of PM2.5 attributable health impacts in the United States: 2005–2014. Environmental Research. 167, 506-514.
Fenech, S., Doherty, R.M., Heaviside, C., Macintyre, H.L., O'Connor, F.M., Vardoulakis, S. and Agnew, P., 2019. Meteorological drivers and mortality associated with O3 and PM2.5 air pollution episodes in the UK in 2006. Atmospheric Environment. 213, 699-710.
Gao, T., Han, J., Wang, Y., Pei, H. and Lu, S., 2012. Impacts of climate abnormality on remarkable dust storm increase of the Hunshdak Sandy Lands in northern China during 2001–2008. Meteorological Applications. 19(3), 265-278.
Geng, G., Zheng, Y., Zhang, Q., Xue, T., Zhao, H., Tong, D. and Davis, S. J., 2021. Drivers of PM2.5 air pollution deaths in China 2002–2017. Nature Geoscience. 14(9), 645-650.
Goudarzi, G., Daryanoosh, S.M., Godini, H., Hopke, P.K., Sicard, P., De Marco, A., Rad, H.D., Harbizadeh, A., Jahedi, F., Mohammadi, M. J. and Savari, J., 2017. Health risk assessment of exposure to the Middle-Eastern dust storms in the Iranian megacity of Kermanshah. Public health. 148, 109-116.
Gumede, P. R. and Savage, M. J., 2017. Respiratory health effects associated with indoor particulate matter (PM2.5) in children residing near a landfill site in Durban, South Africa. Air Quality, Atmosphere & Health. 10(7), 853-860.
Hagler, G. S. W., Bergin, M. H., Salmon, L. G., Yu, J. Z., Wan, E. C. H., Zheng, M. and Schauer, J. J., 2007. Local and regional anthropogenic influence on PM2.5 elements in Hong Kong. Atmospheric Environment. 41(28), 5994-6004.
He, J., Gong, S., Yu, Y., Yu, L., Wu, L., Mao, H. and Li, R., 2017. Air pollution characteristics and their relation to meteorological conditions during 2014–2015 in major Chinese cities. Environmental Pollution. 223, 484-496.
Hejazi, A., Mobashari, M. and Abolfazl, A, M., 2012. Spatial distribution map of suspended particles with diameter less than two and a half micrometers in Tehran air using MODIS data. Journal of Applied researches in Geographical Sciences. 12, 161-178. (In Persian with English abstract).
Hoek, G., Krishnan, R. M., Beelen, R., Peters, A., Ostro, B., Brunekreef, B. and Kaufman, J. D., 2013. Long-term air pollution exposure and cardio-respiratory mortality: A Review. Environmental Health. 12(1), 43.
Holben, B.N., Tanre, D., Smirnov, A., Eck, T.F., Slutsker, I., Abuhassan, N., Newcomb, W.W., Schafer, J.S., Chatenet, B., Lavenu, F. and Kaufman, Y.J., 2001. An emerging ground‐based aerosol climatology: Aerosol optical depth from AERONET. Journal of Geophysical Research: Atmospheres. 106, 12067-12097.
Hsu, N.C., Jeong, M.J., Bettenhausen, C., Sayer, A.M., Hansell, R., Seftor, C.S., Huang, J., and Tsay, S.C., 2013. Enhanced Deep Blue aerosol retrieval algorithm: The second generation. Journal of Geophysical Research: Atmospheres.118 (16), 9296-9315.
Jin, J. Q., Du, Y., Xu, L. J., Chen, Z. Y., Chen, J. J., Wu, Y. and Ou, C. Q., 2019. Using Bayesian spatio-temporal model to determine the socio-economic and meteorological factors influencing ambient PM2.5 levels in 109 Chinese cities. Environmental Pollution. 254, 113023.
Jin, Q., Fang, X., Wen, B. and Shan, A., 2017. Spatio-temporal variations of PM2.5 emission in China from 2005 to 2014. Chemosphere. 183, 429-436.
Kabolizadeh, M., Rangzan, K., Rashidian, M. and Delfan, H., 2019. Estimation total dissolved solids and turbidity concentration in Karkheh and Dez dam and Great Karun River by using Sentinel-2 satellite images. Journal of Advanced Applied Geology. 8(4), 17-27. (In Persian with English abstract).
Karami, S., Hossein Hamzeh, N., Sabzezari, H. and Lo Alizadeh, M., 2021. Investigation of trend analysis of the number of dust stormy days and aerosol concentration derived from satellite in Khuzestan province by using non-parametric Mann-Kendall test. Journal of Climate Research. (44), 91-103.
Kassebaum, N. J., Bertozzi-Villa, A., Coggeshall, M. S., Shackelford, K. A., Steiner, C., Heuton, K. R. and Templin, T., 2014. Global, regional, and national levels and causes of maternal mortality during 1990–2013: a systematic analysis for the global burden of disease study 2013. The Lancet. 384(9947), 980-1004.
Kermani, M., Taherain, E. and Izanloo, M., 2016. Analysis of dust and dust storms in Iran, Investigation Internal and external origin of dust storms in Iran using satellite images and control methods. Rahavard Salamat Journal. 2(1), 39-51. (In Persian with English abstract).
Levy, R.C., Mattoo, S., Munchak, L.A., Remer, L.A., Sayer, A.M., Patadia, F. and Hsu, N.C., 2013. The Collection 6 MODIS aerosol products over land and ocean. Atmospheric Measurement Techniques. 6(11), 2989–3034.
Levy, R.C., Munchak, L.A., Mattoo, S., Patadia, F., Remer, L.A. and Holz, R.E., 2015. Towards a long-term global aerosol optical depth record: applying a consistent aerosol retrieval algorithm to MODIS and VIIRS-observed reflectance. Atmospheric Measurement Techniques Discussions. 8, 4083–4110.
Li, L., Qian, J., Ou, C. Q., Zhou, Y. X., 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.
Li, X., Feng, Y. J. and Liang, H. Y., 2017. The impact of meteorological factors on PM2.5 variations in Hong Kong. In IOP Conf. Series: Earth and Environmental Science. 78(1), 012003.
Li, Y., Liao, Q., Zhao, X., Tao, Y., Bai, Y., and Peng, L., 2020. Premature mortality attributable to PM2.5 pollution in China during 2008–2016: Underlying causes and responses to emission reductions. Chemosphere. 263, 127925.
Lin, G., Fu, J. Jiang, D., Hu, W., Dong, D., Huang, Y. and Zhao, M., 2014. Spatio-temporal variation of PM2.5 concentrations and their relationship with geographic and socioeconomic factors in China. International Journal of Environmental Research and Public Health. 11(1), 173-186.
Liu, Z., Omar, A.H., Hu, Y., Vaughan, M.A., Winker, D.M., Poole, L. and Kovacs, T., 2005. CALIOP algorithm theoretical basis document. Part 3: Scene classification algorithms. NASA-CNES document PC-SCI-203.
Lu, D., Xu, J., Yang, D. and Zhao, J., 2017. Spatio-temporal variation and influence factors of PM2.5 concentrations in China from 1998 to 2014. Atmospheric Pollution Research. 8(6), 1151-1159.
Martonchik, J.V., Kahn, R.A. and Diner, D.J., 2009. Retrieval of aerosol properties over land using MISR observations. In satellite aerosol remote sensing over land. Springer, Berlin, Heidelberg.
Megaritis, A., G. Fountoukis, C., Charalampidis, P. E., Denier Van Der Gon, H. A., C. Pilinis, C. and Pandis, S.N., 2014. Linking climate and air quality over Europe: effects of meteorology on PM2.5 concentrations. Atmospheric Chemistry and Physics. 14(18), 10-283.
Mehrabi, S., Soltani, S. and Jafari, R., 2015. Analyzing the relationship between dust storm occurrence and climatic parameters. Journal of Water and Soil Science(JWSS). 19(71), 69-81. (In Persian with English abstract).
Mokhtari, M., Miri, M., Mohammadi, A., Khorsandi, H., Hajizadeh, Y. and Abdolahnejad, A., 2015. Assessment of air quality index and health impact of PM10, PM2. 5 and SO2 in Yazd, Iran. Journal of Mazandaran University of Medical Sciences. 25(131), 14-23. (In Persian with English abstract).
Nabavi, S., Moradi, H. and Shrifikia, M., 2019. Evaluation of dust storm temporal distribution and the relation of the effective factors with the frequency of occurrence in Khuzestan province from 2000 to 2015'. Scientific- Research Quarterly of Geographical Data (SEPEHR). 28(111), 191-203. (In Persian with English abstract).
Peng, X., Shi, G. L., Zheng, J., Liu, J. Y., Shi, X. R., Xu, J. and Feng, Y. C., 2016. Influence of quarry mining dust on PM2.5 in a city adjacent to a limestone quarry: Seasonal characteristics and source contributions. Science of the Total Environment. 550, 940-949.
Rangzan, K., Zarasvandi, A., Abdulkhani, A.  and Mojaradi, B., 2014. Modeling air pollution using modis images: a case study of dust masses in Khuzestan province. Advanced Applied Geology. 4(4), 38-45. (In Persian with English abstract).
Reff, A., Bhave, P. V., Simon, H., Pace, T. G., Pouliot, G. A., Mobley, J. D. and Houyoux, M., 2009. Emissions inventory of PM2.5 trace elements across the United States. Environmental science & technology. 43(15), 5790-5796.
Rosen, P.A., Hensley, S., Joughin, I.R., Li, F.K., Madsen, S.N., Rodriguez, E. and Goldstein, R.M., 2000. Synthetic aperture radar interferometry. Proceedings of the IEEE. 88(3), 333-382.
Saberi, M., Rangzan, K., Mahjouri, R. and Agriculture, M., 2012. Potential of groundwater resource integration by remote sensing and gis using Hierarchical Analysis method (AHP) in Khuzestan province's Antarctic Anticline. Journal of Advanced Applied Geology. 2(4), 11-20. (In Persian with English abstract).
Shi, L. N., Xu, X., Zhao, X. D., Dou, X. Y. and Zhao, Q. Q ., 2014. Characteristics of the atmospheric pollution and health risk of arsenic and heavy metals (Cu, Pb, Cr, Ni, Hg) in PM2.5 during heating period in Xining, China. In Advanced Materials Research. 955, 993-1002. Trans Tech Publications Ltd.
Sivakumar, M.V., 2005. Impacts of sand storms/dust storms on agriculture. In Natural disasters and extreme events in agriculture. Springer, Berlin, Heidelberg.
Squizzato, S., Masiol, M., Rich, D. Q. and Hopke, P. K., 2018. PM2.5 and gaseous pollutants in NewYork State during 2005–2016: Spatial variability, temporal trends, and economic influences. Atmospheric Environment. 183, 209-224.
Van Donkelaar, A., Martin, R.V., Brauer, M., Hsu, N.C., Kahn, R.A., Levy, R.C., Lyapustin, A., Sayer, A.M. and Winker, D.M., 2016. Global estimates of fine particulate matter using a combined geophysical-statistical method with information from satellites, models, and monitors. Environmental Science & Technology. 50(7), 3762-3772.
Wang, Y., Stein, A.F., Draxler, R.R., Jesús, D. and Zhang, X., 2011. Global sand and dust storms in 2008: Observation and HYSPLIT model verification. Atmospheric environment. 45(35), 6368-6381.
Winker, D.M., Pelon, J.R. and McCormick, M.P., 2003. CALIPSO mission: space borne LIDAR for observation of aerosols and clouds. International Society for Optics and Photonics. 4893, 1-11.
Winker, D.M., Vaughan, M.A., Omar, A., Hu, Y., Powell, K.A., Liu, Z., Hunt, W.H. and Young, S.A., 2009. Overview of the CALIPSO mission and CALIOP data processing algorithms. Journal of Atmospheric and Oceanic Technology. 26(11), 2310-2323.
Xiao, Q. Geng, G. Liang, F. Wang, X. Lv, Z. Lei, Y. and He, K., 2020. Changes in spatial patterns of PM2.5 pollution in China 2000–2018: Impact of clean air policies. Environment International. 141, 105776.
Xing, Y. F., Xu, Y. H., Shi, M. H. and Lian, Y.X., 2016. The impact of PM2.5  on the human respiratory system. Journal of Thoracic Disease. 8(1), 69-74.
Xu, G., Ren, X., Xiong, K., Li, L., Bi, X. and Wu, Q., 2020. Analysis of the driving factors of PM2.5 concentration in the air: A case study of the Yangtze River Delta, China. Ecological Indicators. 110(3), 105889.
Xu, L.J., Zhou, J.X., Guo, Y., Wu, T. M., Chen, T.T., Zhong, Q. J. and Ou, C.Q., 2017. Spatiotemporal pattern of air quality index and its associated factors in 31 Chinese provincial capital cities. Air Quality, Atmosphere & Health. 10(5), 601-609.
Yadav, R., Sahu, L.K., Beig, G., Tripathi, N. and Jaaffrey, S.N.A., 2017. Ambient particulate matter and carbon monoxide at an urban site of India: influence of anthropogenic emissions and dust storms. Environmental Pollution. 225, 291-303.
Yang, Q., Yuan, Q., Li, T., Shen, H., and Zhang, L., 2017. The relationships between PM2.5 and meteorological factors in China: seasonal and regional variations. International Journal of Environmental Research and Public Health. 14(12), 1510.
Yang, Y., Li, J., Zhu, G. and Yuan, Q., 2019. Spatio–temporal relationship and evolvement of socioeconomic factors and PM2.5 in China during 1998–2016. International Journal of Environmental Research and Public Health. 16(7), 1149.
Zhang, J. and Reid, J.S., 2010. A decadal regional and global trend analysis of the aerosol optical depth using a data-assimilation grade over-water MODIS and Level 2 MISR aerosol products. Atmospheric Chemistry and Physics. 10, 10949–10963.
Zhang, Q., He, K. and Huo, H., 2012. Cleaning China's air. Nature. 484(7393), 161-162.
Zhao, L., Chen, C., Wang, P., Chen, Z., Cao, S., Wang, Q. and Lu, B., 2015. Influence of atmospheric fine particulate matter (PM2.5) pollution on indoor environment during winter in Beijing. Building and Environment. 87, 283-291.