The evaluation of AERMOD through the Quantile-Quantile (Q-Q) plots showed that most of the predicted and observed pair points are lying close to the one-to-one line indicating that there is a good agreement between predicted and observed concentrations in the study. The results showed that the maximum ground level concentration of SO2, NO2 and PM10 simulated through AERMOD-WRF in the industrial area was 36.2, 59.8 and 5.4 μg/m³, respectively. The results of comparing the wind roses from the observed and simulated data in Pasir Gudang station shows the difficulty of prognostic WRF model in predicting wind direction in Malaysia located in a coastal site. The output parameters from WRF such as temperature, wind speed and wind direction were also statistically evaluated in the study.
The WRF model was applied to simulate the hourly surface and upper air meteorological variables for the period of two weeks. In this study, the spatial variations of SO2, NO2 and PM10 emitted from multiple industrial sources in Pasir Gudang industrial area, Johor, Malaysia were predicted using American Meteorological Society/Environmental Protection Regulatory Model (AERMOD) air dispersion model coupled with Weather Research and Forecasting (WRF). The investigation of pollutants concentrations affected by the multiple sources is essential for air quality management. Results have also suggested that the model does not predictĭust dispersion accurately for smaller values. Model performs well for the Indian geo-mining conditions for daily and monthlyĪveraging time period. Study area due to multiple dust sources availability. Statistical parameters were considered for the AERMOD is a model, widely used in the world for dispersion Model evaluation studies are essential required to model application in the mining Simulate the dust concentration in and around a surface mine. Indian geo-miningĬonditions are different from other countries. The closer of mine due to inappropriate mitigative measures.
Without proper evaluation may lead to wrong decisions which can further result in
These results not only help us better understand the performance of AERMOD but also provide useful information to researchers who are interested in applying AERMOD in various applications, such as the utilization of AERMOD in chronic exposure assessment in epidemiological studies where long-term (i.e., monthly and/or annual) air pollution concentration estimations are often used.ĭispersion models are a useful tool for air quality management in the mining Results also suggest that, at the monthly scale, AERMOD performs much better in simulating the high end of the spectrum of SO2 concentrations in the study area compared to results at the 1, 3, 8h, and daily scales. In addition, AERMOD performs better in simulating SO2 concentrations when combined point and mobile emission sources are used as model inputs rather than using point or mobile emission sources alone. Results suggest that SO2 concentrations simulated by AERMOD at the 8h, daily, monthly, and annual intervals match their respective observed concentrations much better compared with the simulated 1 and 3h SO2 concentrations. This paper evaluates the performance of AERMOD in estimating sulfur dioxide (SO2) concentrations in Dallas and Ellis counties in Texas. The AMS/EPA Regulatory Model (AERMOD) is a fairly recent and promising model for estimating concentrations of air pollutants, but the effectiveness of this model at different time scales remains to be verified. As high-density monitoring networks observing pollutant concentrations are costly to establish and maintain, researchers often employ various models to estimate concentrations of air pollutants.
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