Characteristics of Microbial Aerosol Pollution in Pig Houses

[ Objective] To explore the characteristics of microbial aerosol pollution in different pig houses and provide theoretical foundation for risk assessment of microbial aerosols on human and animal health. [Method] Gestation house, nursery house and fattening house in suburb of Beijing were chosen as research objects, and the concentration, size distribution and composition characteristics of the airborne microorganisms were determined. [ Result] The concentrations of airborne microorganisms were completely different in different houses under the influences of ventilation, methods of cleaning manure and pig populations. The highest concentration of airborne microrganisms was the nursery house. The particles with diameter size of 0.65 -2.10 μm carried 26% -27% heterotrophic bacteria and 39% -43% fungi. They could reach alveolus of humans and animals and thus made infectious threat to health of humans and animals. The dominant heterotrophic bacteria genera were composed of Bacil/us and Pseudomonas; and the dominant fungi were Penicillium and Muco. [ Conclusion] Concentrations of airborne microorganisms are completely different in different houses. However. there is no sianificant difference between the microbial composition and tyee of oia houses.

Effect of partial pit exhaust ventilation system on ammonia removal ratio and mass transfer coefficients from different emission sources in pig houses

A partial pit exhaust ventilation(PPEV)system installed below the slatted floor has been widely used in fattening pig barns nowadays in Denmark.Experimental tests showed that annually around 50%of ammonia emissions was collected by PPEV system.However,the percent of emissions collected by PPEV system from different emission sources including slurry manure surfaces,top surfaces,bottom surfaces and side surfaces of the slatted floor has not been investigated as well as the mass transfer coefficients.This study applied CFD modeling to investigate the removal ratio of ammonia emissions from four emission surfaces including the top,side,bottom surfaces of the slatted floor and slurry manure surfaces.The CFD model was validated by experimental air speeds measured in a room equipped with two full-scale pigpens.The validated CFD model was further adopted to simulate cases under five ventilation rates(2000-4000 m^(3)/h),four emission sources and two locations of PPEV system exhaust.The results showed that the removal ratios of ammonia emissions by PPEV system from the four emission sources were generally higher for the cases that the PPEV exhaust was installed opposite to the air supplier than the values of those cases that the PPEV exhaust was located at the same side of side wall air supplier.The removal ratios of ammonia emissions were the highest with the emission source of slurry manure surface and generally 30%higher than the values of other cases.The mass transfer coefficients with the emission sources on the side surfaces of the slatted floor were the largest.The results indicated that the airflow patterns and locations of emission sources greatly influenced the removal ratios of ammonia emissions and ammonia mass transfer coefficients.

基于BP神经网络的猪舍有害气体定量检测模型研究

To find a neural network model suitable to identify the concentration of mixed pernicious gases in pig house, the quantitative detection model of pernicious gases in pig house was set up based on BP （ Back propagation） neural network. The BP neural network was trained separately by the three functions, trainbr, traingdm and trainlm, in order to identify the concentration of mixed pernicious gases composed of ammonia gas and hepatic gas. The neural network toolbox in MATLAB software was used to simulate the detection. The results showed that the neural network trained by trainbr function has high average identification accuracy and faster detection speed, and it is also insensitive to noise; therefore, it is suitable to identify the concentration of pemidous gases in pig house. These data provide a reference for intelligent monitoring of pemicious gases in pigsty.