Volume 11, Issue 3 (12-2018)
ijhe 2018, 11(3): 365-376 |
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Kalhor M, Ghaleh Askari S, Bozorgi M. AERMET performance in evaluation of boundary layer parameters and its effect on carbon monoxide concentration outputs in AERMOD model compared to upper air data. ijhe 2018; 11 (3) :365-376
URL: http://ijhe.tums.ac.ir/article-1-6077-en.html
URL: http://ijhe.tums.ac.ir/article-1-6077-en.html
1- Department of Environmental Engineering, Faculty of Environment, Tehran University, Tehran, Iran , kalhor@ut.ac.ir
2- Department of Environmental Health Engineering, Faculty of Health, Jiroft Medical University, Jiroft, Iran
3- Department of Environment, Faculty of Environmental Science, Yazd University, Yazd, Iran
2- Department of Environmental Health Engineering, Faculty of Health, Jiroft Medical University, Jiroft, Iran
3- Department of Environment, Faculty of Environmental Science, Yazd University, Yazd, Iran
Abstract: (3462 Views)
Background and Objective: Concentration prediction with Gaussian dispersion models is highly sensitive to meteorological data. The lack of sounding data station in developing countries may lead to large error and uncertainty in air pollution modeling results. In this paper, the effects of estimated upper air data on the model output concentration values were investigated.
Materials and Methods: AERMOD model was executed once with real upper air data and also with estimated upper air data separately. T-Student and LEVENE tests were used to evaluate the significant differences between concentrations in two modes of using actual and estimated upper air data.
Results: The results showed that large differences in concentration between the two methods. In long term modeling, there was up to 33% differences between real and estimated upper meteorological data and up to 63% differences for short term modeling. A large difference was also observed between boundary layer parameterization values in each case. The statistical analysis showed a meaningful difference (p=0.00) between the cases. The differences between ZICNV, DT/DZ, W* were 7.1%, 48%, and 19%, respectively.
Conclusion: The use of estimated upper meteorological data in comparison with measured data may lead to a large error. The AERMOD modeling results with estimated meteorological data must be expressed with appropriate uncertainties and confidence interval.
Conclusion: The use of estimated upper meteorological data in comparison with measured data may lead to a large error. The AERMOD modeling results with estimated meteorological data must be expressed with appropriate uncertainties and confidence interval.
Keywords: Air pollution modeling, Gaussian model, Meteorological preprocessor, Carbon monoxide, Upper air data
Type of Study: Research |
Subject:
Air
Received: 2018/05/22 | Accepted: 2018/10/24 | Published: 2018/12/19
Received: 2018/05/22 | Accepted: 2018/10/24 | Published: 2018/12/19
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