MODELING AMMONIA EMISSION FROM SWINE ANAEROBIC LAGOONS

A mathematical model to estimate ammonia emission from anaerobic swine lagoons was developed based on the classical two–film theory. Inputs to the model are wind speed and lagoon liquid properties such as total ammonia nitrogen (TAN) concentration, pH, and temperature. Predicted emission rates of ammonia increase when any of these parameters are increased, but the relationship is linear only with TAN concentration. The dissociation constant (Kd) for ammonia in lagoon liquid is also an important factor, with higher flux predictions for higher Kd. The model was validated by comparing the model outputs to measured fluxes from two lagoons in North Carolina. The predicted ammonia emission fluxes for the two lagoons ranged from 1 to 38 kg NH3–N/ha–d, which was a wider range than the fluxes measured (2.5 to 22 kg N/ha–d) by other researchers using the micrometeorological method. Compared to measured fluxes at each lagoon, the model tended to predict higher ammonia fluxes at lagoon A and lower fluxes at lagoon B when a Kd of 0.5 was used. Additional information is needed regarding ammonia dissociation (Kd) values for anaerobic lagoon liquid. Comparison of the model results with a linear regression equation indicated that the model predicted much higher fluxes at temperatures above 25 ³ C and at upper ranges of pH and wind speed. Finally, the model was used with typical lagoon TAN concentration and pH, and average monthly values for wind speed and estimated liquid temperature at Raleigh, North Carolina, to predict monthly ammonia emissions for a typical anaerobic swine lagoon in North Carolina. The highest and lowest monthly ammonia emission occurred in June and January, respectively. Based on the average monthly emissions, it is estimated that the average annual ammonia nitrogen emission rate from the surface of a typical lagoon in North Carolina would be 234 g/m 2 or 2340 kg/ha. However, the model and results from other researchers indicate that ammonia emission can vary greatly.