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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Hydrology and Remote Sensing Laboratory » Research » Publications at this Location » Publication #358089

Research Project: Improving Agroecosystem Services by Measuring, Modeling, and Assessing Conservation Practices

Location: Hydrology and Remote Sensing Laboratory

Title: Modification and Validation of the Gaussian Plume Model (GPM) to Predict Ammonia and Particulate Matter Dispersion

Author
item YANG, Z. - University Of Maryland
item YAO, Q. - University Of Maryland
item Buser, Michael
item Alfieri, Joseph
item LI, H. - University Of Delaware
item TORRENTS, A. - University Of Maryland
item MCCONNELL, L.L. - University Of Maryland
item Downey, Peter
item Hapeman, Cathleen

Submitted to: Popular Publication
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 3/27/2020
Publication Date: 5/1/2020
Citation: Yang, Z., Yao, Q., Buser, M.D., Alfieri, J.G., Li, H., Torrents, A., McConnell, L., Downey, P.M., Hapeman, C.J. 2020. Modification and Validation of the Gaussian Plume Model (GPM) to Predict Ammonia and Particulate Matter Dispersion. Atmospheric Pollution Research. https://doi.org/10.1016/j.apr.2020.03.012.
DOI: https://doi.org/10.1016/j.apr.2020.03.012

Interpretive Summary: Poultry houses in the United States are typically ventilated using tunnel fans to maintain the temperature and a healthy atmosphere for the birds. The emissions from the houses contain ammonia and particulate matter (PM) which can have negative effects on human health and nearby ecosystems. Estimating the transport of these pollutants and assessing the effects of environmental conditions on their transport is needed to develop effective mitigation strategies. Field measurements, however, are time-consuming, costly, and deliver a limited amount of data. Alternatively, air dispersion models can provide more information under a variety of conditions, especially if coupled with field sampling. The Gaussian plume model (GPM) has been widely applied to predict the emission plumes released vertically into the atmosphere and often at long distances (greater than 1 km). But, much less is known about using the GPM at much smaller scales or for the simulation of emission plumes from horizontally-releasing sources, such as the exhaust tunnel fans of poultry houses. Therefore, a study was conducted to modify and to validate the GPM for predicting emissions from a poultry house using observations from a series of previously-reported field measurements. Results showed that the model was very accurate in predicting PM plumes and only slightly overestimated the measurements for ammonia emissions. In addition, the model performance was not sensitive to different sampling scenarios; thus, the model can be applied to other conditions or different experimental setups. This modified model will be useful in examining the effectiveness of practices designed to control or mitigate poultry house emissions.

Technical Abstract: Poultry houses in the U.S. are typically ventilated using tunnel fans to maintain the temperature and a healthy atmosphere for the birds. But, these emissions contain ammonia and particulate matter (PM) which can have negative effects on human health and nearby ecosystems. Estimating the transport of these pollutants and the effects of environmental conditions on their transport is needed to develop effective mitigation strategies. Field measurements, however, are time-consuming, costly, and afford a limited amount of data. Alternatively, air dispersion models can provide more information under a variety of conditions, especially if coupled with field sampling. A study was conducted to modify and to validate the Gaussian plume model to predict poultry house emissions. The most notable modification was the addition of a virtual, emission-release point within the house. The modified model was validated using previously-reported field measurements. The fraction of predictions within a factor of two (FAC2) for both ammonia and PM observations was 0.63. On average, the model predictions were 1.38 times and 0.98 times the measured values for ammonia and PM, respectively. In addition, the model performance was not sensitive to different sampling scenarios; thus, the model can be applied to other conditions or different experimental setups