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Monitoring, analysis and spatial and temporal zoning of air pollution (carbon monoxide) using Sentinel-5 satellite data for health management in Iran, located in the Middle East

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Abstract

Various natural and anthropogenic factors are effective in causing air pollution. The effects of air pollution are more pronounced on the health of living things, especially on the mental and physical health. Therefore, this issue is important because of the importance of health and well-being. The purpose of the present study is to investigate the spatial and temporal monitoring and analysis of air pollution (carbon monoxide, CO) using satellite and remote sensing data in Iran. To do this, Sentinel-5 satellite data for air pollution (CO) monitoring over a 14-month period (November 2018 to December 2019) and Terra satellite and MODIS satellite data with LST index for monitoring changes of daily and nocturnal temperatures (Kelvin unit) over a 24-month period (January 2018 to December 2019) were used. The results showed that the highest amount of air pollution (CO) was obtained in April 2019 with 0.39 mol/m2. However, the highest amount of air pollution in spatial monitoring of CO was obtained for Tehran and Guilan provinces with values of 0.51 mol/m2 and 0.49 mol/m2 respectively, while the lowest amount of CO was 0.19 mol/m2 for December 2019. Northwestern Iran, south and west areas of the central part of Ardebil province, south half and eastern part of East Azarbaijan province, Sahand mountain range, and western part of the border section of West Azerbaijan province were exposed to air pollution with average values of 0 to 0.21 mol/m2. Since the highest amounts of air pollution (CO) occur during the cold months of the year, people who are vulnerable to this phenomenon should travel to the polluted sites with observing hygiene principles.

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References

  • Bart S, Nandin ET, Andreas V, Johannes R (2020) Mapping wetland characteristics using temporally dense Sentinel-1 and Sentinel-2 data: a case study in the St. Lucia wetlands, South Africa. Int J Appl Earth Obs Geoinf 86:141–189. https://doi.org/10.1016/j.jag.2019.102009

    Article  Google Scholar 

  • Blosch G, Parajka J (2008) The value of MODIS snow covers data in validating and calibrating conceptual hydrologic models. J Hydrol 14:240–258

    Google Scholar 

  • Cheung CW, He G, Pan Y (2020) Mitigating the air pollution effect? The remarkable decline in the pollution-mortality relationship in Hong Kong. J Environ Econ Manag (2020). https://doi.org/10.1016/j.jeem.2020.102316

  • Emre A, Akyu Z, Ormanc AS, Ensoyc AS, Orman AU (2005) Using MODIS snow cover maps in modeling snowmelt runoff process in the eastern part of Turkey. Remote Sens Environ 97:216–230

    Article  Google Scholar 

  • Fan H, Zhao Ch, Yang Y (2020) A comprehensive analysis of the spatio-temporal variation of urban air pollution in China during 2014–2018. Atmos Environ 220:127–201. https://doi.org/10.1016/j.atmosenv.2019.117066

    Article  CAS  Google Scholar 

  • Fouladi F, Bailey MJ, Patterson WB, Sioda M, Blakley IC, Fodor AA, Jones RB, Chen Z, Kim JS, Lurmann F, Martino C, Knight R, Gilliland FD, Alderete TL (2020) Air pollution exposure is associated with the gut microbiome as revealed by shotgun metagenomic sequencing. Environ Int 138. https://doi.org/10.1016/j.envint.2020.105604

  • Ghorbani R, Mobasheri MR, Rahimzadegan M (2010) Capability of Modis sensing data for qualitative and quantitative analyzes of air quality in urban areas. J Climatol Res 34:1–14

    Google Scholar 

  • Ghorbani R, Mobasheri MR, Rahimzadegan M (2012) Rapid method in estimation of suspended particle concentration using modis sensor in Tehran. Hakim Research Journal 15(2)

  • Golbaz S, Farzadkia M, Kermani M (2009) Survey of Tehran air sanitary quality in 2008 based on air quality index. Iran Occup Health J 6:12–45

    Google Scholar 

  • Govender P, Sivakumar V (2020) Application of k-means and hierarchical clustering techniques for analysis of air pollution: a review (1980–2019). Atmos Pollut Res 1:40–56. https://doi.org/10.1016/j.apr.2019.09.009

    Article  CAS  Google Scholar 

  • Ibbetson A, Symonds P, Hutchinson E (2020) Data to support small area health impact modelling of air pollution in the United Kingdom. Data Brief 29(2020):105148

    Article  Google Scholar 

  • Jeniffer S, Ch Z, Tanya L, Alderete C, Toledo-Corral F, Lurmann K, Berhane F, Gilliland D (2019) Associations of air pollution, obesity and cardiometabolic health in young adults: the Meta-AIR study. Environ Int 133:78–120. https://doi.org/10.1016/j.envint.2019.105180

    Article  CAS  Google Scholar 

  • John R, Balmes MD (2019) Household air pollution from domestic combustion of solid fuels and health. J Allergy Clin Immunol 143:1979–1987. https://doi.org/10.1016/j.jaci.2019.04.016

    Article  CAS  Google Scholar 

  • Joris K, Ommeren J (2020) Urban air pollution and time losses: evidence from cyclists in London. Reg Sci Urban Econ 81:52–78. https://doi.org/10.1016/j.regsciurbeco.2019.103504

    Article  Google Scholar 

  • Julia C, Fussell F, Kelly J (2019) Oxidative contribution of air pollution to extrinsic skin ageing. Free Radic Biol Med. Available online 23 December 2019. https://doi.org/10.1016/j.freeradbiomed.2019.11.038

  • Kabolizadeh M, Rangzan K, Mohammadi S (2000) Application of integration of Landsat-8 and Sentinel-2 satellite images in environmental monitoring. Remote Sens Geogr Inf Syst Nat Resour 3:53–71

    Google Scholar 

  • Kohavi R, Provost F (1998) Glossary of terms: editorial for the special issue on applications of machine learning and the knowledge discovery process. J Mach Learn 30:271–274. https://doi.org/10.1016/S0146-6453(00)80006-8

    Article  Google Scholar 

  • Kristin A, Halskov Hansen M, Liu Zh, Wang S (2019) The hidden hazard of household air pollution in rural China. Environ Sci Pol 93:27–33. https://doi.org/10.1016/j.envsci.2018.12.004

    Article  CAS  Google Scholar 

  • Kurata M, Takahashi K, Hibiki A (2020) Gender differences in associations of household and ambient air pollution with child health: evidence from household and satellite-based data in Bangladesh. World Dev 128(2020):104779. https://doi.org/10.1016/j.worlddev.2019.104779

    Article  Google Scholar 

  • Lalas D, Katsulis B, Petrakis M (1980) The contribution of the largest local emission sources of sulfur dioxide in Pireaus, Greece. Hellenic Meteorol Soc Bull 3:1–24

    Google Scholar 

  • Lesley G, Jeanine E, David R (2019) Detecting historic informal settlement fires with Sentinel 1 and 2 satellite data - two case studies in Cape Town. Fire Saf J 108(2019):102828. https://doi.org/10.1016/j.firesaf.2019.102828

    Article  Google Scholar 

  • Li N, Chen G, Liu F, Mao S, Liu Y, Liu S, Mao Z, Lu Y, Wang C, Guo Y, Xiang H, Li S (2020) Associations between long-term exposure to air pollution and blood pressure and effect modifications by behavioral factors. Environ Res 182:109109. https://doi.org/10.1016/j.envres.2019.109109

  • Lu M, Schmitz O, Vaartjes I, Karssenberg D (2019) Activity-based air pollution exposure assessment: differences between homemakers and cycling commuters. Health Place 60. https://doi.org/10.1016/j.healthplace.2019.102233

  • Matthias W, Jeroen S, Michael W, Hannes T (2020) Spatial and semantic effects of LUCAS samples on fully automated land use/land cover classification in high-resolution Sentinel-2 data. Int J Appl Earth Obs Geoinf 88. https://doi.org/10.1016/j.jag.2020.102065

  • Miller MR (2020) Oxidative stress and the cardiovascular effects of air pollution. Free Radic Biol Med. https://doi.org/10.1016/j.freeradbiomed.2020.01.004

  • Moon JK (2019) The effects of transboundary air pollution from China on ambient air quality in South Korea. Heliyon 5:196–205. https://doi.org/10.1016/j.heliyon.2019.e02953

    Article  Google Scholar 

  • Mozaffari G, Narangi F, Haghighat Z (2015) Analysis of the relationship between synoptic patterns with particulate matter and carbon monoxide pollutants in Shiraz. Geogr Environ Hazards 14:95–115

    Google Scholar 

  • Nazarian A, Ziaeian P, Jangi AA (2007) Investigating the role of morphological location in Tehran air quality using GIS and satellite data (RS). Geogr Res 61:17–30

    Google Scholar 

  • Nolin A, Liang S (2000) Progress in bidirectional reflectance modeling and application for surface particulate media: snow and soil. Remote Sens Rev 14:307–342

    Article  Google Scholar 

  • Oanh KT, Chutimon P, Ekbordin W, Supat W (2005) Meteorological pattern classification and application for forecasting air pollution episode potential in a mountain-valley area. Atmos Environ 39:1211–1225

    Article  CAS  Google Scholar 

  • Omrani H, Omrani B, Parmentier B, Marco H (2020) Spatio-temporal data on the air pollutant nitrogen dioxide derived from Sentinel satellite for France. Data Brief 28:105089. https://doi.org/10.1016/j.dib.2019.105089

  • Paola E, Veerle Buffel S, Bracke P (2019) Mental wellbeing, air pollution and the ecological state. Health Place 57:82–91. https://doi.org/10.1016/j.healthplace.2019.03.003

    Article  Google Scholar 

  • Priya D, Barry P, Syed M (2020) Aggressive digital papillary adenocarcinoma and sentinel node biopsy: a case report and literature review. JPRAS Open. https://doi.org/10.1016/j.jpra.2020.03.001

  • Purwadi I, van der Werff HMA, Lievens C (2020) Targeting rare earth element bearing mine tailings on Bangka Island, Indonesia, with Sentinel-2 MSI. Int J Appl Earth Obs Geoinf 88(2020):102055. https://doi.org/10.1016/j.jag.2020.102055

    Article  Google Scholar 

  • Rangzan K, Kabolizadeh M, Rashidian M, Delfan H (2019) Modeling and zoning of water quality parameters using Sentinel-2 satellite imagery and computational intelligence (case study: Karun River). Remote Sens Geogr Inf Syst Nat Resour 4:2137

    Google Scholar 

  • Rohi G, Ejofodomi O, Ofualagba G (2020) Autonomous monitoring, analysis, and countering of air pollution using environmental drones. Heliyon 6(2020):e03252. https://doi.org/10.1016/j.heliyon.2020.e03252

    Article  Google Scholar 

  • Safarianzengir V, Sobhani B (2020) Simulation and analysis of natural hazard phenomenon, drought in southwest of the Caspian Sea, Iran. Carpathian J Earth Environ Sci 15:127–136. https://doi.org/10.26471/cjees/2020/015/115

    Article  Google Scholar 

  • Safarianzengir V, Sobhani B, Asghari S (2019) Modeling and monitoring of drought for forecasting it, to reduce natural hazards atmosphere in western and north western part of Iran, Iran. Air Qual Atmos Health 13:119–130. https://doi.org/10.1007/s11869-019-00776-8

    Article  CAS  Google Scholar 

  • Sager L (2019) Estimating the effect of air pollution on road safety using atmospheric temperature inversions. J Environ Econ Manag 98:2019. https://doi.org/10.1016/j.jeem.2019.102250

    Article  Google Scholar 

  • Saghafi MA, Aliakbari AA (2014) Investigation of circadian and seasonal variations of wind and air temperature and Co and PM10 contaminants in the atmosphere surface layer of Tehran City. Spat Anal Environ Hazards 1:17–34

    Google Scholar 

  • Sandro F, Pellegrini U (2004) Systematic analysis of meteorological conditions causing severe urban air pollution episodes in the central Po valley:9th international conference on harmonization within atmospheric dispersion modeling for regulatory purposes. Garmisch-Partenkir, Germany, pp 1–4

  • Schikowski T, Altug H (2020) The role of air pollution in cognitive impairment and decline. IUF-Leibniz Institute for Environmental Medicine, Duesseldorf, Germany, Neurochem Int 136:104708. https://doi.org/10.1016/j.neuint.2020.104708

  • Shamsipour AA, Amini Z (2013) Simulation of co-dispersion pattern with Envi-met microclimatic model on Azadi-Tehran Pars Route. Geogr Environ Hazards 7:85–103

    Google Scholar 

  • Sobhani B, Safarianzengir V (2019a) Modeling, monitoring and forecasting of drought in south and southwestern Iran, Iran. Model Earth Syst Environ 5:63–71. https://doi.org/10.1007/s40808-019-00655-2

    Article  Google Scholar 

  • Sobhani B, Safarianzengir V (2019b) Investigation hazard effect of monthly ferrrin temperature on agricultural products in north bar of Iran. Iraqi J Agric Sci 50:320–330

    Google Scholar 

  • Sobhani B, Safarianzengir V (2020) Evaluation and zoning of environmental climatic parameters for tourism feasibility in northwestern Iran, located on the western border of Turkey. Model Earth Syst Environ 6:853–864. https://doi.org/10.1007/s40808-020-00712-1

    Article  Google Scholar 

  • Sobhani B, Safarianzengir V, Kianian MK (2018) Potentiometric mapping for wind turbine power plant installation Guilan province in Iran. J Appl Sci Environ Manag 22:1363–1368. https://doi.org/10.4314/jasem.v22i8.36

    Article  Google Scholar 

  • Sobhani B, Safarianzengir V, Kianian MK (2019a) Drought monitoring in the Lake Urmia basin in Iran. Arab J Geosci 12:448. https://doi.org/10.1007/s12517-019-4571-1

    Article  Google Scholar 

  • Sobhani B, Safarianzengir V, Kianian MK (2019b) Modeling, monitoring and prediction of drought in Iran. Iran (Iranica) J Energy Environ 10:216–224. https://doi.org/10.5829/ijee.2019.10.03.09

    Article  Google Scholar 

  • Sobhani B, Safarianzengir V, Miridizaj F (2019c) Feasibility study of potato cultivating of Ardabil province in Iran based on VIKOR model. Rev Agric 10:92–102

    Google Scholar 

  • Sobhani B, Jafarzadehaliabad L, Safarianzengir V (2020a) Investigating the effects of drought on the environment in northwestern province of Iran, Ardabil, using combined indices, Iran. Model Earth Syst Environ 6:983–993. https://doi.org/10.1007/s40808-020-00733-w

    Article  Google Scholar 

  • Sobhani B, Safarianzengir V, Yazdani MH (2020b) Modelling, evaluation and simulation of drought in Iran, southwest Asia. J Earth Syst Sci 129:100 (2020). https://doi.org/10.1007/s12040-020-1355-7

    Article  Google Scholar 

  • Soltani T, Mofidi A, Gandomkar A (2015) Synoptic investigation of highly polluted days in Mashhad (case study: 13 and 14 November 2007). J Spat Anal Environ Hazards 4:95–112

    Google Scholar 

  • Song Y, Li M, Zhang M, Sun X (2019) Study on the impact of air pollution control on urban residents’ happiness from microscopic perspective. J Clean Prod 229:1307–1318. https://doi.org/10.1016/j.jclepro.2019.05.070

  • Susan MK, Africa F, Robert EG, Absae S, James N, Robinson M, Ashutosh L, Daniel EI (2018) Mapping threats to agriculture in East Africa: performance of MODIS derived LST for frost identification in Kenyas tea plantations. Int J Appl Earth Obs Geoinf 72:131–139. https://doi.org/10.1016/j.jag.2018.05.009

    Article  Google Scholar 

  • Tiwary A, Colls J (2010) Air pollution: measurement, modelling and mitigation, Third edn. Routledge, Taylor and Francis group, 528 p

  • Tsolmon R, Ochirkhuyag L, Sternberg T (2008) Monitoring the source of trans-national dust storms in North East Asia. Int J Digit Earth 1:119–129

    Article  Google Scholar 

  • Vanino S, Nino P, De Michele C, Falanga Bolognesi S, D'Urso G, Di Bene C, Pennelli B, Vuolo F, Farina R, Pulighe G, Napoli R (2018) Capability of Sentinel-2 data for estimating maximum evapotranspiration and irrigation requirements for tomato crop in Central Italy. Remote Sens Environ 215:452–470. https://doi.org/10.1016/j.rse.2018.06.035

  • Wan Z (1999) MODIS landsurface temperature algorithm theoretical basis document. Institute for Computational Earth System Science University of California Santa Barbara 3:1–77

  • Wang L, Yuesi W, Jinyuan X, Zhanqing L, Xiaoyuan W (2010) Assessment and comparison of three years of Terra and Aqua MODIS Aerosol Optical Depth Retrieval (C005) in Chinese terrestrial regions. Atmos Res 97:229–240

    Article  Google Scholar 

  • Wu Q, Lu J (2020) Air pollution, individual investors, and stock pricing in China. Int Rev Econ Financ 67:267–287. https://doi.org/10.1016/j.iref.2020.02.001

    Article  Google Scholar 

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Acknowledgments

We acknowledge the Sentinel-5 satellite, Terra satellite and MODIS sensor and personnel and scientists for their efforts and data sharing and for the production.

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Authors and Affiliations

  1. Faculty of Literature and Humanities, University of Mohaghegh Ardabili, Ardabil, Iran

    Vahid Safarianzengir, Behrouz Sobhani & Mohammad Hasan Yazdani

  2. Desert Studies Faculty, Semnan University, Semnan, Iran

    Mohammadkia Kianian

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  1. Vahid Safarianzengir
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  2. Behrouz Sobhani
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  3. Mohammad Hasan Yazdani
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  4. Mohammadkia Kianian
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Correspondence to Vahid Safarianzengir.

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Safarianzengir, V., Sobhani, B., Yazdani, M.H. et al. Monitoring, analysis and spatial and temporal zoning of air pollution (carbon monoxide) using Sentinel-5 satellite data for health management in Iran, located in the Middle East. Air Qual Atmos Health 13, 709–719 (2020). https://doi.org/10.1007/s11869-020-00827-5

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