عملکرد پیش‌پردازنده AERMET در محاسبه پارامترهای لایه مرزی و بررسی تاثیر آن بر خروجی‌های غلظت مونوکسید کربن در مدل AERMOD در مقایسه با داده‌های جو بالا

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.