Volume 6, Issue 2 (2018)                   ECOPERSIA 2018, 6(2): 91-100 | Back to browse issues page

XML Print


Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Farhadi R, Hadavifar M, Moeinaddini M, Amintoosi M. Sensitivity Analysis of Meteorological Parameters and Instability ‎Indices on Concentration of Carbon Monoxide, Particulate ‎Matter, and Air Quality Index in Tehran. ECOPERSIA 2018; 6 (2) :91-100
URL: http://ecopersia.modares.ac.ir/article-24-16537-en.html
1- ‎Department of Environmental Science, Faculty of Geographical and Environmental Sciences, Hakim ‎Sabzevari University, Sabzevar, Iran
2- ‎Department of Environmental Science, Faculty of Geographical and Environmental Sciences, Hakim ‎Sabzevari University, Sabzevar, Iran , mhadavifar@yahoo.com
3- Environmental Sciences Department, Faculty of Natural Resources, University of Tehran, Tehran, Iran
4- Department of Mathematic, Faculty of Mathematics Hakim Sabzevari University, Sabzevar, Iran‎
Abstract:   (5639 Views)
Aims: Nowadays, dangerous chemical pollutants by a numerous of natural and synthetic sources are produced and released to the environment. These pollutants have short-term and long-term effects on human health. The purpose of this paper is to examine the impact of climate parameters and instability indices on air pollution in Tehran-Iran.
Materials and Methods: To evaluate the impact of meteorological parameters and indices of stability and instability on sensitivity analysis in Tehran-Iran, the Sharif University monitoring station was selected for air sampling and analysis. Sampling was performed from March 2011 to July 2012 in Tehran.
Findings: Results of sensitivity analysis showed that average daily change of the concentration of pollutants throughout the year was very different and intensively influenced by meteorological parameters. Results showed that wind direction (WD) (82%) and relative humidity (32%) and temperature (20%) have the most influence on the concentration values of pollutants carbon monoxide (CO), particulate matter (PM10), and air quality index (AQI). The highest concentrations of CO occurred in summer and lowest in winter, and maximum concentration of PM10 was in autumn, and its lowest concentration was in spring. Results revealed that the lowest average of AQI occurred in the spring, while in autumn, winter, and summer have almost equal values, but in winter AQI has slightly higher values.
Conclusion: According to the results of this research in Sharif station Tehran, the WD has the highest impact percentage (82%) on the concentration of pollutants. The highest concentrations of CO occurred in summer, and maximum concentration of PM10 was in autumn.
Full-Text [PDF 489 kb]   (1410 Downloads)    
Article Type: مقاله Ø§Ø³ØªØ®Ø±Ø§Ø Ø´Ø¯Ù‡ از پایان نامه | Subject: Aquatic Ecology
Received: 2017/10/16 | Accepted: 2018/07/17 | Published: 2018/07/17
* Corresponding Author Address: Department of Environmental Science, Hakim Sabzevari University, Sabzevar, Iran.‎

References
1. Holt MS. Sources of chemical contaminants and routes into the freshwater environment. Food Chem ‎Toxicol. 2000;38(1 Suppl):S21-7.‎ [Link]
2. Maji S, Ahmed S, Siddiqui WA, Ghosh S. Short term effects of criteria air pollutants on daily mortality in ‎Delhi, India. Atmos Environ. 2017;150:210-9.‎ [Link] [DOI:10.1016/j.atmosenv.2016.11.044]
3. Piraino F, Aina R, Palin L, Prato N, Sgorbati S, Santagostino A, et al. Air quality biomonitoring: Assessment ‎of air pollution genotoxicity in the Province of Novara (North Italy) by using Trifolium repens L. and ‎molecular markers. Sci Total Environ. 2006;372(1):350-9.‎ [Link]
4. Wark K, Warner CF. Air pollution, its origin and control. New York: IEP; 1976.‎ [Link]
5. Yu T, Wang W, Ciren P, Zhu Y. Assessment of human health impact from exposure to multiple air ‎pollutants in China based on satellite observations. Int J Appl Earth Obs Geoinf. 2016;52:542-53.‎ [Link] [DOI:10.1016/j.jag.2016.07.020]
6. Gulia S, Nagendra SMS, Khare M, Khanna I. Urban air quality management-A review. Atmos Pollut Res. ‎‎2015;6(2):286-304.‎ [Link] [DOI:10.5094/APR.2015.033]
7. Wang S, Zhao M, Xing J, Wu Y, Zhou Y, Lei Y, et al. Quantifying the air pollutants emission reduction during ‎the 2008 Olympic Games in Beijing. Environ Sci Technol. 2010;44(7):2490-6.‎ [Link]
8. Xepapadeas AP. Environmental policy design and dynamic nonpoint-source pollution. J Environ Econ ‎Manag. 1992;23(1):22-39.‎ [Link]
9. Abdul-Wahab SA, Al-Alawi SM. Assessment and prediction of tropospheric ozone concentration levels ‎using artificial neural networks. Environ Model Softw. 2002;17(3):219-28.‎ [Link] [DOI:10.1016/S1364-8152(01)00077-9]
10. Agirre-Basurko E, Ibarra-Berastegi G, Madariaga I. Regression and multilayer perceptron-based models ‎to forecast hourly O3 and NO2 levels in the Bilbao area. Environ Model Softw. 2006;21(4):430-46.‎ [Link] [DOI:10.1016/j.envsoft.2004.07.008]
11. Saide PE, Carmichael GR, Spak SN, Gallardo L, Osses AE, Mena-Carrasco MA, et al. Forecasting urban PM10 ‎and PM2.5 pollution episodes in very stable nocturnal conditions and complex terrain using WRF–Chem CO ‎tracer model. Atmos Environ. 2011;45(16):2769-80.‎ [Link]
12. Antanasijević DZ, Pocajt VV, Povrenović DS, Ristić MĐ, Perić-Grujić AA. PM10 emission forecasting using ‎artificial neural networks and genetic algorithm input variable optimization. Sci Total Environ. ‎‎2013;443:511-9.‎ [Link] [DOI:10.1016/j.scitotenv.2012.10.110]
13. Chen J, Lu J, Avise JC, DaMassa JA, Kleeman MJ, Kaduwela AP. Seasonal modeling of PM2.5 in California's ‎San Joaquin Valley. Atmos Environ. 2014;92:182-90.‎ [Link] [DOI:10.1016/j.atmosenv.2014.04.030]
14. Plaia A, Di Salvo F, Ruggieri M, Agró G. A multisite-ultipollutant air quality index. Atmos Environ. ‎‎2013;70:387-91.‎ [Link] [DOI:10.1016/j.atmosenv.2013.01.028]
15. Domańska D, Wojtylak M. Application of fuzzy time series models for forecasting pollution concentrations. ‎Expert Syst Appl. 2012;39(9):7673-9.‎ [Link] [DOI:10.1016/j.eswa.2012.01.023]
16. Song YY, Lu Y. Decision tree methods: Applications for classification and prediction. Shanghai Arch ‎Psychiatry. 2015;27(2):130-5.‎ [Link]
17. Ostromsky T, Dimov I, Georgieva R, Marinov P, Zlatev Z. High performance computing of data for a new ‎sensitivity analysis algorithm, applied in an air pollution model. In: Dimov I, Faragó I, Vulkov L, editors. ‎Numerical analysis and its applications: 5th international conference, NAA 2012, Lozenetz, Bulgaria, june 15-‎‎20, 2012, revised selected papers. New York: Springer; 2013.‎ [Link]
18. Rivalin, L, Stabat P, Marchio D, Caciolo M, Hopquin F, A ‎comparison of methods for uncertainty and ‎sensitivity analysis applied to ‎the energy performance of new commercial buildings. Energy & ‎Buildings. ‎‎2018;166:489-504.‎ [Link]
19. Tasdemir Y, Cindoruk SS, Esen F. Monitoring of criteria air pollutants in Bursa, Turkey. Environ Monit ‎Assess. 2005;110(1-3):227-41.‎ [Link]
20. Chao Z. Urban climate and air pollution in Shanghai. Energy Build. 1991;16(1-2):647-56.‎ [Link] [DOI:10.1016/0378-7788(91)90033-Y]
21. Tilden JW, Seinfeld JH. Sensitivity analysis of a mathematical model for photochemical air pollution. ‎Atmos Environ. 1982;16(6):1357-64.‎ [Link] [DOI:10.1016/0004-6981(82)90056-7]
22. Ostromsky T, Dimov I, Georgieva R, Zlatev Z. Air pollution modelling, sensitivity analysis and parallel ‎implementation. Int J Environ Pollut. 2011;46(1-2):83-96.‎ [Link]
23. Ostromsky T, Dimov l., Alexandrov V, Zlatev Z. Preparing input data for sensitivity analysis of an air ‎pollution model by using high-performance supercomputers and algorithms Computers & Mathematics with ‎Applications, 2015. 70(11):2773-2782. ‎ [Link]
24. Azid A, Juahir H, Toriman M, Endut A, Abdul Rahman M, Amri Kamarudin M, et al. Selection of the most ‎significant variables of air pollutants using sensitivity analysis. J Test Eval. 2016;44(1):376-84.‎ [Link]
25. Khamooshi S, Panahi F, Vali A, Mousavi SH. Dust storm monitoring using HYSPLIT model and NDDI (Case ‎study: Southern cities of Shiraz, Bushehr and Fasa, Iran). Ecopersia. 2016;4(4):1603-16.‎ [Link] [DOI:10.18869/modares.ecopersia.4.4.1603]
26. Beddows AV, Kitwiroon A, Williams ML, Beevers SD. Emulation and sensitivity analysis of the community ‎Multiscale Air Quality Model for a UK Ozone Pollution Episode. Environ Sci Technol. 2017;51(11):6229-36. ‎ [Link] [DOI:10.1021/acs.est.6b05873]
27. Alexander ER. Sensitivity analysis in complex decision models. J Am Plan Assoc. 1989;55(3):323-33.‎ [Link] [DOI:10.1080/01944368908975419]
28. Huang S, Xiong J, Cai C, Xu W, Zhang Y. Influence of humidity on the initial emittable concentration of ‎formaldehyde and hexaldehyde in building materials: Experimental observation and correlation. Sci Rep. ‎‎2016;6:23388.‎ [Link] [DOI:10.1038/srep23388]
29. Nazari Z, khorasani N, Feyzina S, Karami M. Investigation of PM10 concentration trend during 2005-2010 ‎and impact of meteorological parameters on it. Iran Nat Resour. 2013;66:101-11. ‎ [Link]
30. Guo H, Wang Y, Zhang H. Characterization of criteria air pollutants in Beijing during 2014-2015. Environ ‎Res. 2017;154:334-44.‎ [Link]
31. Dijkema MBA, Van Der Zee SC, Brunekreef B, Van Strien RT. Air quality effects of an urban highway ‎speed limit reduction. Atmos Environ. 2008;42(40):9098-105.‎ [Link] [DOI:10.1016/j.atmosenv.2008.09.039]
32. Afzali A, Rashid M, Sabariah B, Ramli M. PM10 pollution: Its prediction and meteorological influence in ‎Pasir Gudang, Johor. 8th International Symposium of the Digital Earth (ISDE8), IOP Conf Series: Earth and ‎Environmental Science, volume 18, 2014, 012100. Philadelphia: IOP Publishing; 2014.‎ [Link]
33. Ghaffari D, Nouri H. Relative humidity and moisture flux convergence during the dusty days in Alvand ‎Mountain. Ecopersia. 2016;4(4):1527-40.‎ [Link] [DOI:10.18869/modares.ecopersia.4.4.1527]
34. Christine MK, Koonce P, Linda AG. Diurnal and seasonal variations of NO, NO2 and PM2.5 mass as a ‎function of traffic volumes alongside an urban arterial. Atmospheric Environment. 2015;122:133-41.‎ [Link] [DOI:10.1016/j.atmosenv.2015.09.019]
35. Lal S, Naja M, Subbaraya BH. Seasonal variations in surface ozone and its precursors over an urban site in ‎India. Atmos Environ. 2000;34(17):2713-24.‎ [Link] [DOI:10.1016/S1352-2310(99)00510-5]
36. Reizer, M, Juda-Rezler, K. Explaining the high PM10 concentrations observed in Polish urban areas. Air ‎Quality, Atmosphere & Health. 2016;9(5):517-31. ‎ [Link] [DOI:10.1007/s11869-015-0358-z]
37. Hosseinibalam F, Hejazi A. Influence of meteorological parameters on air pollution in Isfahan. IPCBEE. ‎‎2012;46:7-12.‎ [Link]

Add your comments about this article : Your username or Email:
CAPTCHA

Send email to the article author


Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.