Factors affecting the ammonia content in the air surrounding a broiler farm

The emission and dispersion of ammonia (NH3) in the atmosphere near a large livestock facility was investigated for its direct impact on the environment. Ammonia emission and dispersion are influenced by various local factors. The major factors affecting the NH3 concentrations in the air around the broiler farm over an entire growing cycle were monitored. Both internal and external factors in connection with the broiler barns were considered. Ten factors-of-influence (FOI) that strongly influence the dispersion of NH3 were selected. The FOI employed included: NH3-mass-flow, internal and external temperatures, mean and turbulent wind components in horizontal and vertical directions, atmospheric stability, day-of-growing cycle, and exhaust air height. Multiple linear regressions (MLR) were implemented to analyse the relationship between FOI and the NH3 measured in the air. Different approaches of MLR were applied, considering the entire data range and subsets of data to determine a general model and other more restrictive models that can be used. Overall, the precision of the predictive models of atmospheric NH3 dispersion, based on different datasets, was satisfactory (coefficient of determination R2 > 0.83). Irrespective of the models applied, the mean vertical wind component proved to have significant influence on the concentration of NH3 in the air. The results of this study show that the monitoring of specific FOI can be used to predict the concentration of NH3 in the air surrounding a broiler farm. This particular monitoring practice enables investigators to assess the possible negative impact of NH3 on the environment.

► This study reports a parallel observation of the time-series of NH3 emissions inside and outside of a broiler farm during one growing cycle.
► The variance in the measurements of NH3 in the air is described by certain factors-of-influence.
► Multiple linear regressions were implemented to analyse the relationship between these factors and the NH3 in the air.
► The precision of the predictive models was good (R2 > 0.83).
► In nearly all models, the mean vertical wind component played a role and proved to be the most influential factor.