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Spatio-temporal analysis of PM2.5 pollutant in Tehran metropolis during the years 2014-2017

Document Type : Original quantitative and Qualitative Research Article

Authors

1 Department of Environmental Health, School of Public Health and Safety, Shahid Beheshti University of Medical Sciences, Tehran, Iran

2 Department of Environmental Health, Health, and safety Faculty, Shahid Beheshti University of Medical Science, Tehran, Iran

Abstract

Background and Aim: Tehran, as the largest and most populous city in Iran, has encountered many problems, of which air pollution is the most common. In recent years, PM2.5 has been the cause of the unhealthiest days in terms of air pollution in Tehran; For this purpose, the present study was conducted with the aim of Spatio-temporal analysis of PM2.5 pollutant in the Tehran metropolis during the years 2014-2017 using GIS.
Materials and Methods: In this study, Inverse Distance Weighting (IDW) and HotSpots methods have been used to predict and zoning of PM2.5 concentrations during four consecutive years (2014-2017).
Results: The results of hotspot analysis and Getis-Ord-Gi index showed that the southern and southwestern regions with a confidence level above 90% and a concentration of more than 50 μg/m3 are the most polluted areas. And to a lesser extent, the western and central areas are next.; Also, the concentration of PM2.5 pollutants shows an increasing trend from north to south and east to west.
Conclusion: South, southwest, west, and center are the most polluted areas; However, the present study only shows the zoning of PM2.5 concentration and does not identify the various factors and the contribution of each of them in the production of this pollutant; And it is necessary to conduct studies to identify sources and their contribution to control and reduce the concentration of PM2.5 in the Tehran metropolitan.

Keywords

References
1. Annual Report Of Air Quality Company, Tehran 1393.
2. Amini H, Hosseini V, Schindler C, Hassankhany H, Yunesian M, Henderson SB, Künzli N. Spatiotemporal description of BTEX volatile organic compounds in a Middle Eastern megacity: Tehran Study of Exposure Prediction for Environmental Health Research (Tehran SEPEHR). Environ Pollut. 2017; 226:219-29.
3. Nadian M, Mirzaei R, Soltani Mohammadi S, Application of Moran'sI Autocorrelation in Spatial-Temporal Analysis of PM2.5 Pollutant (A case Study: Tehran City). Journal of Environmental Health Engineering, May 2018; Vol. 5, No. 3: 197-213.
3. Annual Report Of Air Quality Company, Tehran 1393.
4. Shahbazi H, Taghvaee S, Hosseini V, Afshin H. A GIS based emission inventory development for Tehran. Urban Climate. 2016; 17:216-29.
5. Pahlavani P, Sheikhian H, Bigdeli B. Assessment of an air pollution monitoring network to generate urban air pollution maps using Shannon information index, fuzzy overlay, and Dempster-Shafer theory, A case study: Tehran, Iran. Atmos Environ. 2017; 167:254-69.
6. Arhami M, Hosseini V, Shahne MZ, Bigdeli M, Lai A, Schauer JJ. Seasonal trends, chemical speciation and source apportionment of fine PM in Tehran. Atmos Environ. 2017; 153:70-82.
7. Bahari RA, Abaspour RA, Pahlavani P. Zoning of Particulate Matters (PM) Pollution Using Local Statistical Models in GIS (Case Study: Tehran Metropolisies). J.Geomat. Sci.Technol. 2016; 5(3):165-74. [in Persian].
8. Kampa M, Castanas E. Human health effects of air pollution. Environ Pollut. 2008; 151(2):362-7.
9. Sharma M, Maloo S. Assessment of ambient air PM 10 and PM 2.5 and characterization of PM 10 in the city of Kanpur, India. . Atmos Environ. 2005; 39(33):6015-26.
10. John H. Seinfeld Spyros N. Pandis. 1997. Atmospheric Chemistry and Physics:  From Air Pollution to Climate Change. Wiley-VCH:  New York. 1997 1326 pp.
11. Fang C, Wang Z, Xu G. Spatial-temporal characteristics of PM2. 5 in China: A city-level perspective analysis. J Geogr Sci. 2016; 26(11):1519-32.
12. Shah ASV, Langrish JP, Nair H, McAllister DA, Hunter AL, Donaldson K, et al. Global association of air pollution and heart failure: a systematic review and meta-analysis.The Lancet 2013;382:1039–48.
13. Cadelis G, Tourres R, Molinie J. Short-term effects of the particulate pollutants contained in Saharan dust on the visits of children to the emergency department due to asthmatic conditions in Guadeloupe (French Archipelago of the Caribbean). PLoS ONE 2014;9(3):e91136. http://dx.doi.org/10.1371/journal.pone.0091136.
14. Chu HJ, Huang B, Lin CY. Modeling the spatio-temporal heterogeneity in the PM10-PM2. 5 relationship. Atmos Environ. 2015; 102:176-82.
15. Sharipour Z, AkbariBidokhti AA. Investigation of spatial and temporal distributions of air pollutants over Tehran in cold months of 2011-2013. J Environ Sci Technol. 2014; 16(1):149-166. [in Persian].
16. Alizadeh-Choobari O, Bidokhti AA, Ghafarian P, Najafi MS. Temporal and spatial variations of particulate matter and gaseous pollutants in the urban area of Tehran. Atmos Environ. 2016; 141:443-53.
17. Zhang C, Luo L, Xu W, Ledwith V. Use of local Moran's I and GIS to identify pollution hotspots of Pb in urban soils of Galway, Ireland. Sci Total Environ. 2008; 398(1):212-21.
18. Fang C, Wang Z, Xu G. Spatial-temporal characteristics of PM2. 5 in China: A city-level perspective analysis. J Geogr Sci. 2016; 26(11):1519-32.
19. Peeters A, Zude M, Käthner J, Ünlü M, Kanber R, Hetzroni A, Gebbers R, Ben-Gal A. Getis–Ord’s hot-and cold-spot statistics as a basis for multivariate spatial clustering of orchard tree data. Comput Electron Agric. 2015; 111:140-50.
20. Wang ZB, Fang CL. Spatial-temporal characteristics and determinants of PM 2.5 in the Bohai Rim Urban Agglomeration. Chemosphere. 2016; 148:148-62.
21. Gong G, Mattevada S, O’Bryant SE. Comparison of the accuracy of kriging and IDW interpolations in estimating groundwater arsenic concentrations in Texas. Environ Res 2014;130(0):59-69.
22. Gemmer M, Becker S, Jiang T. Observed monthly precipitation trends in China 1951–2002. Theor Appl Climatol. 2004; 77(1):39-45.
23. Jain, S., Sharma, S. K., Vijayan, N., & Mandal, T. K. (2020). Seasonal characteristics of aerosols (PM2. 5 and PM10) and their source apportionment using PMF: A four-year study over Delhi, India. Environmental Pollution, 114337.
24. Altuwayjiri, A., Taghvaee, S., Mousavi, A., Sowlat, M. H., Hassanvand, M. S., Kashani, H., ... & Sioutas, C. (2020). Association of systemic inflammation and coagulation biomarkers with source-specific PM2. 5 mass concentrations among elderly and young subjects in central Tehran. Journal of the Air & Waste Management Association.
25. Ali-Taleshi, M. S., Moeinaddini, M., Bakhtiari, A. R., Feiznia, S., Squizzato, S., & Bourliva, A. (2020). A one-year monitoring of spatiotemporal variations of PM2. 5-bound PAHs in Tehran, Iran: Source apportionment, local and regional sources origins and source-specific cancer risk assessment. Environmental Pollution, 115883.
 
Journal of Research in Environmental Health
Volume 6, Issue 3
December 2020
Pages 211-220
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