Prediction Of Air Pollutant From Poultry Houses By A Modified Gaussian Plume Model
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Concentrated animal feeding operations release a variety of potential pollutants, such as ammonia and particulate matters (PM). Field measurements are time consuming, costly, and only provide a limited amount of spatial and temporal information. Air dispersion models can serve as an alternative solution, especially if coupled with field sampling. The Gaussian plume model (GPM) is a mathematical model that assumes steady state condition. Previous studies have used the GPM to evaluate and analyze source. However, much less is known about utilizing GPM to simulate plumes from horizontal sources, such as the exhaust fans from poultry houses. The purpose of this study is to modify and validate a GPM to predict air pollutant emissions from the poultry houses. Two major assumptions were applied on the model, 1) a virtual releasing point was proposed behind the ventilation fan, and 2) ventilation fan was considered as the dominant wind direction in the model for short distance (< 50 m). The modified model was validated with field experimental data. Performance and sensitivity of the model were also evaluated. Fraction of predictions within a factor of two of observations (FAC2) of NH3 and PM were 0.609 and 0.625. Model-predicted concentrations of NH3 were 1.5 times of the measured values on average. Model-predicted concentrations of PM was 0.98 times of the observed values on average.