Development of a Dynamic Model to Predict PM10 Emissions from Swine Houses

• Published in: Journal of environmental quality 2008-03, Vol.37 (2), p.557-564
• Main Authors: Haeussermann, A, Costa, A, Aerts, J.M, Hartung, E, Jungbluth, T, Guarino, M, Berckmans, D
• Format: Article
• Language: English
• Subjects: Air Pollutants - analysis; animal housing; Animal Husbandry - methods; Animals; dust emissions; dynamic models; Environmental Monitoring; Germany; Housing, Animal; Humidity; Italy; mathematical models; Models, Theoretical; particle size; particulate emissions; Particulate Matter - analysis; prediction; Swine; Temperature; Ventilation
• ISSN: 0047-2425; 1537-2537
• DOI: 10.2134/jeq2006.0416

Influences on dust emissions from livestock operations are number, weight, and kind of animals and characteristics of the housing system. Differences between facilities cannot be explained solely by mechanistic input variables. The objective of this study was to characterize the main input variables for modeling emissions of particulate matter with a mass median diameter < or = 10 μm (PM10) from swine facilities using a data-based model. Investigations were performed in mechanically ventilated facilities for weaning, growing-finishing, and sows in Italy and Germany. The measurements included inside and outside concentration of airborne PM10 particles (scatter light photometry), ventilation rate (calibrated measuring fans), indoor air climate at a measuring frequency of 60 s, feeding times, and animal-related data such as weight and animal activity. Dust concentration and emission were simulated using a dynamic transfer function. The results indicated that the average PM10 emission rate was influenced considerably by housing system. The simulation of the PM10 emission rate resulted in a mean percentage error per data set of 21 to 39%, whereas the average simulated and measured emission rate per data set differed by about 4 to 19%. High prediction errors occurred especially during situations in which the absolute level and spatial location of the measured activity peaks did not correspond with the measured dust peaks. Further recommendations of the study were to improve continuous and accurate measurements of input variables, such as the activity level in animal houses, and to optimize the amount of measuring days in relation to the model accuracy.