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Volume 4, Issue 4 (12-2018)
jhehp 2018, 4(4): 164-168 |
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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). jhehp 2018; 4 (4) :164-168
URL: http://jhehp.zums.ac.ir/article-1-184-en.html
URL: http://jhehp.zums.ac.ir/article-1-184-en.html
Marzieh Makaremi1 
, Nabiollah Mansouri *1 , Alireza Vafaeinajad2 , Mohammad Hasan Behzadi3 , Seyed Alireza Mirzahossieni1
, Nabiollah Mansouri *1 , Alireza Vafaeinajad2 , Mohammad Hasan Behzadi3 , Seyed Alireza Mirzahossieni1
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.
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: (10230 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.
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.
Type of Study: Original Article |
Subject:
Environmental Health, Sciences, and Engineering
Received: 2018/10/17 | Accepted: 2018/12/4 | Published: 2018/12/21
Received: 2018/10/17 | Accepted: 2018/12/4 | Published: 2018/12/21
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