Volume 4, Issue 4 (12-2018)                   J. Hum. Environ. Health Promot 2018, 4(4): 164-168 | Back to browse issues page


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Makaremi M, Mansouri N, Vafaeinajad A, Behzadi M H, Mirzahossieni S A. Regression Equation of PM10 Dispersion of Gypsum Industry Emissions by AERMOD Model (Case Study: Zarch, Iran). J. Hum. Environ. Health Promot. 2018; 4 (4) :164-168
URL: http://zums.ac.ir/jhehp/article-1-184-en.html
1- Department of Natural Resources and Environment, Science and Research Branch, Islamic Azad University, Tehran, Iran.
2- Department of Civil, Water and Environmental Engineering, Shahid Beheshti University, Tehran, Iran.
3- Department of Foundation Science, Science and Research Branch, Islamic Azad University, Tehran, Iran.
Abstract:   (198 Views)
Background: Gypsum industry is one of industries which is known as an environment pollutant. In this study distribution and prediction of emitted PM10 from Gypsum factory is studied by modeling. The main goal of this study is to create a regression equation to demonstrate the Zarch factory PM10 dispersion by the AERMOD model.
Methods: In this study distribution and concentration of emitted particulate matter (PM10) is estimated by AERMOD model. The regression equation of emission is resulted by AERMOD estimation and excel software, finally the results of AERMOD model and regression equation was compared to each other.
Results: In this study the regression equation is c = 14.6 x (-1.045) and by this regression equation the particulate matter’s concentration could be estimated around the factory. Comparison of regression equation and AERMOD model represented that 69% of total results are similar in models.
Conclusion: The results of this study represented that by AERMOD outputs, a regression equation could be created which is able to estimate particulate matter’s concentration around the emission sources according to sources properties, meteorological parameters, site topography and etc.
Full-Text [PDF 681 kb]   (47 Downloads)    
Type of Study: Research Article | Subject: Environmental Health, Sciences, and Engineering
Received: 2018/10/17 | Accepted: 2018/12/4 | Published: 2018/12/21

References
1. Keshavarzi B, Tazarvi Z, Rajabzadeh M, Najmeddin A. Chemical Speciation, Human Health Risk Assessment and Pollution Level of Selected Heavy Metals in Urban Street Dust of Shiraz, Iran. J Atmos Environ. 2015; 119 (87): 1-10. [Crossref]
2. Men C, Liu R, Xu F, Wang Q, Shen Z. Pollution Characteristics, Risk Assessment and Source Apportionment of Heavy Metals in Road Dust in Beijing, China. Sci Total Environ. 2018; 612(254): 138-47. [Crossref]
3. Hoveidi H, Aslemand A, Vahidi H, Limode H. Cast Emission of on Human Health Due to the Solid Waste Disposal Scenarios, Case Study: Tehrn, Iran. J Eerth Sci Clim Chang. 2013; 4(1): 55-67.
4. Nadoushan N, Mansouri N, Nezhadkurki F. Assessment of AERMOD Model’s Sensitivity to Terrain Features for Identifying Air Pollutants Receptor Points in Steel Industry. J Fundam Appl Sci. 2016; 8(3s): 1399-413.
5. Environmental Protection Agency (U.S. EPA-a). User’s Guide for the Aermod Meteorological Preprocessor (AERMET) Office of Air Quality Planning and Standards Emissions, Monitoring and Analysis Division Research Triangle Park. Environ Prot Agency: North Carolina. 2004; 27711: EPA-454/B-03-002, 252.
6. Environmental Protection Agency (U.S. EPA-b). User’s Guide for the Aermod Terrain Preprocessor (AERMAP) Office of Air Quality Planning and Standards Emissions, Monitoring and Analysis Division Research Triangle Park. Environ Prot Agency: North Carolina. 2004; 27711: EPA-454/B-03-003, 106.
7. Hays W. Statistics for The Social Science. New York: Holt, Rinehart and Winston, Inc; 2001.
8. Winter BJ. Statistical Principles in Experimental Design. New York: Mc Graw-Hill; 2006.
9. Michanowicz D, Shmool J, Tunno B, Tripathy Sh , Gillooly S, Kinnee E, et al. A hybrid Land Use Regression/ AERMOD Model for Predicting Intra-urban Variation in PM 2.5. J Atmos Environ. 2016; 131(8): 307-15. [Crossref]
10. Lee M, Brauer M, Wong P, Tang R, Tsui T, Choi C, et al . Land Use Regression Modeling of Air Pollution in High Density High Rise Cities: A Case Study in Hong Kong. J Sci total Environ. 2017; 24 (7): 306-15. [Crossref]
11. Mokhtar M, Hassim M, Taib R. Health Risk Assessment of Emissions from a Cool-Fired Power Plant Using AERMOD Modeling. Process Saf Environ Prot. 2014; 92 (5): 476-85. [Crossref]
12. Tartakovsky D, Broday D, Stren E. Evaluation of AERMOD and Calpuff for Predicting Ambient Concentrations of Total Suspended Particulate Matter (TSP) Emissions from a Quarry in Complex Terrain. Environ Pollut. 2013; 179(19): 138-45. [Crossref]
13. Onofrio M, Sparto R, Botta S. The Role of Steel Plant in North-West Italy to the Local Air Concentration of PCDD/FS. Chemosphere. 2011; 82 (5): 708-17. [Crossref]
14. Seangkiatiyuth K, Surapipith V, Tantrakamapa K, Lothongkum A. Application of the AERMOD Modeling System for Environmental Impact Assessment of NO2 Emissions from a Cement Complex. J Environ Sci. 2011; 23(6): 931-40. [Crossref]
15. Kesarkar A, Dalvi M, Kaginalkar A, Ojha A. Coupling of the Weather Research and Forecasting Model with AERMOD for Pollutant Dispersion Modeling A Case study for PM10 Dispersion Over Pune, India. Atmos Environ. 2007; 41(9): 1976-88. [Crossref]
16. Pasquill F. The Estimation of the Dispersion of Windborne Material. J Meteorol Mag. 2007; 34(12): 149-62.

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