Evaluation of AERMOD and CALPUFF for predicting ambient concentrations of total suspended particulate matter (TSP) emissions from a quarry in complex terrain

Tartakovsky, Dmitry; Broday, David M.; Stern, Eli

doi:10.1016/j.envpol.2013.04.023

Cited by 107 publications

(49 citation statements)

References 10 publications

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“…The ratio of the modeled versus observed concentrations of the main pollutants meet the standard accuracy index0.5≤FAC2≤2 [27]. These results confirm the general opinion [20,25,26] about the reliable properties of the CALMET/CALPUFF system, especially in relation to the annual mean concentrations.…”

Section: Discussionsupporting

confidence: 76%

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Emission Data Uncertainty in Urban Air Quality Modeling—Case Study

Holnicki

Nahorski

2015

Environ Model Assess

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Air pollution models are often used to support decisions in air quality management. Due to the complexity of the forecasting system and difficulty in acquiring precise enough input data, an environmental prognosis of air quality with an analytical model of the air pollution dispersion is burdened with a substantial share of uncertainty, especially as regards urban areas. To ignore the uncertainty in the modeling would lead to incorrect policy decisions, with further negative environmental and health consequences. This paper presents a case study which shows how emission uncertainty of air pollutants generated by the industry, traffic, and the municipal sector relates to concentrations measured at receptor points. The computational experiment was implemented in the Warsaw metropolitan area, Poland. The main source of this adverse environmental impact is the transportation system, including the transit traffic. The Monte Carlo technique was used for assessing the key uncertainty factors. Several types of pollution species that are characteristic for the urban atmospheric environment (e.g., PM 10 , PM 2.5 , NO x , SO 2 , Pb) were included in the analysis. The results show significant spatial variability of the modeled uncertainty. The reason of this variability is discussed in detail. It depends not only on the category of the emission source but also on the contributing emission sources and their quantity.

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Section: Discussionsupporting

confidence: 76%

“…Validation studies showed good correlation with the observations, especially for annual mean concentrations [25,26]. This fact has been confirmed also within this study by the model performance estimates presented below (see Table 2 and Fig.…”

Section: Air Pollution Simulation: Selected Resultssupporting

confidence: 71%

Emission Data Uncertainty in Urban Air Quality Modeling—Case Study

Holnicki

Nahorski

2015

Environ Model Assess

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“…The estimation of TSPM concentration was evaluated from quarries and open mining sources in a complex terrain for the year 2010 and 2011 (Tartakovsky et al, 2013) using AERMOD and CALPUFF. Drilling, gathering, Truck loading, transportation, wind erosion and crushing (grinding) were included in emission inventory of TSPM.…”

Section: Comparison Of Aermod With Other Air Quality Modelsmentioning

confidence: 99%

Application of WRF Model for Air Quality Modelling and AERMOD - A Survey

Kumar

Patil

Dikshit

et al. 2017

Aerosol Air Qual. Res.

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Meteorology plays a crucial role in air quality. The presence of uncertainties of a significant nature in the meteorological profile used during air quality model simulation has the potential to affect negatively the results of the simulations. This paper describes a most recent version of the meteorological model called Weather Research and Forecasting (WRF) model and its importance in air quality. The performance of WRF depends upon the intended application and parameterization scheme of physics options. WRF model is also applied to investigate the simulation results with various land surface models (LSMs) and Planetary Boundary Layer (PBL) parameterizations and various set of microphysics options. It predicts various meteorological spatial parameters like mixing layer height, temperature, humidity, rain fall, cloud cover and wind. The WRF results are integrated with air quality model (AQM) and the AQM depends upon the performance of WRF. It has been applied for evaluation of national pollution control policy, behaviour of plume rise, property of aerosols, prediction of Ozone, SO 2 , NO x , PM 10 , PM 2.5 etc. using AQM for various sources. The effect of topography and different seasons on the concentration of pollutants in the atmosphere has also been studied using AQM. AQM AERMOD has also been reviewed with various other AQM models such as ADMS-Urban and CALPUFF. AERMOD has been used for different time scales, health risk assessment, evaluation of various control strategies, Environmental Impact Assessment (EIA) studies and emission factor estimation. This paper presents the importance of meteorological model to AQM as well as many applications of AQM to demonstrate various scientific questions and policies.

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“…Misra et al [11] using a meteorological preprocessor called AERMET to describe the atmospheric conditions in the planetary boundary layer and a land preprocessor called AERMAP used to describe the height of the land [11]. The study of Capelli in [12] &Tartakovsky in [13] is also noticeable. In this paper, after collecting the necessary information from analyzing the chimneys of an industrial plant located in south west of Iran, predictions of the pollution distribution of CO gas was analyzed by the air box model.…”

Section: Introductionmentioning

confidence: 99%

Using Box Model to Estimate the Impact of Air Pollution from an Industrial Plant

MJ¹

2017

MOJCE

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Today, air pollution in developing countries, has imposed a significant negative effects on public health and the environment. Box model is among the techniques that are used for modeling and predicting of air quality. Box model is widely used in modeling of air pollution. In this paper, after collecting the necessary information from analyzing the chimneys of an industrial plant located in south west of Iran, predictions of the pollution distribution of CO gas was analyzed by the air box model. The formula of box model was written by Excel 2013Microsoftsoftware in 20 zones and 14 hours, and by entering the information, three-dimensional graph height, time and zone were obtained. It should be noted that if the selected times and zones are divided into smaller intervals, the accuracy of the estimation of pollution concentration in this model increases. The results showed that by the stability of air and reduced the volume of element, the accuracy prediction increases and the pollution of the industrial plant goes to towards the city. Contamination concentration (CO) after passing through the first zone of the city gradually decreases to the boundary that the concentration will be the same in all zones.

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Evaluation of AERMOD and CALPUFF for predicting ambient concentrations of total suspended particulate matter (TSP) emissions from a quarry in complex terrain

Cited by 107 publications

References 10 publications

Emission Data Uncertainty in Urban Air Quality Modeling—Case Study

Emission Data Uncertainty in Urban Air Quality Modeling—Case Study

Application of WRF Model for Air Quality Modelling and AERMOD - A Survey

Using Box Model to Estimate the Impact of Air Pollution from an Industrial Plant

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