Development of a dynamic model to predict PM10 emissions from swine houses

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 microm (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.     

Definition of yearly emission factor of dust and greenhouse gases through continuous measurements in swine husbandry

The object of this study was to develop an accurate estimation method to evaluate the contribution of the various compartments of swine husbandry to dust and GHG (greenhouse gases, CO₂, CH₄ and N₂O) emission into the atmosphere during one year of observation.A weaning, a gestation, a farrowing and a fattening room in an intensive pig house were observed in three different periods (Autumn–Winter, Springtime and Summer, monitoring at least 60% of each period (20% at the beginning, in the middle and at the end) of each cycle).During monitoring, live weight, average live weight gain, number of animals and its variation, type of feed and feeding time were taken into account to evaluate their influence on PM₁₀, or the fraction of suspended particulate matter with an aerodynamic diameter less than or equal to 10 μm [Emission Inventory Guidebook, 2007. B1100 Particle Emissions from Animal Husbandry Activities. Available from: <http://reports.eea.europa.eu/EMEPCORINAIR5/en/B1100vs1.pdf> (accessed October 2008)] and to define GHG emission.The selected piggery had a ventilation control system using a free running impeller to monitor continuously real-time environmental and management parameters with an accuracy of 5%.PM₁₀ concentration was monitored by a sampler (Haz Dust EPAM 5000), either continuously or through traditional gravimetric technique, and the mean value of dust amount collected on the membranes was utilized as a correction factor to be applied to continuously collected data.PM₁₀ concentration amount incoming from inlets was removed from PM₁₀ emission calculation, to estimate the real contribution of pig house dust pollution into atmosphere.Mean yearly emission factor of PM₁₀ was measured in 2 g d^?1 LU^?1 for the weaning room, 0.09 g d^?1 LU^?1 for the farrowing room, 2.59 g d^?1 LU^?1 for the fattening room and 1.23 g d^?1 LU^?1 for the gestation room. The highest PM₁₀ concentration and emission per LU was recorded in the fattening compartment while the lowest value was recorded in the farrowing room.CO₂, CH₄ and N₂O concentrations were continuously measured in the exhaust ducts using an infrared photoacoustic detector IPD (Brüel & Kjaer, Multi-gas Monitor Type 1302, Multipoint Sampler and Doser Type 1303) sampling data every 15 min, for the 60% of the cycles.Yearly emission factor for CO₂ was measured in 5997 g d^?1 LU^?1 for the weaning room, 1278 g d^?1 LU^?1 for the farrowing room, 13,636 g d^?1 LU^?1 for the fattening room and 8851 g d^?1 LU^?1 for the gestation room.Yearly emission factor for CH₄ was measured in 24.57 g d^?1 LU^?1 for the weaning room, 4.68 g d^?1 LU^?1 for the farrowing room, 189.82 g d^?1 LU^?1 for the fattening room and 132.12 g d^?1 LU^?1 for the gestation room.Yearly emission factor for N₂O was measured in 3.62 g d^?1 LU^?1 for the weaning room, 0.66 g d^?1 LU^?1 for the farrowing room, 3.26 g d^?1 LU^?1 for the fattening room and 2.72 g d^?1 LU^?1 for the gestation room.     

Floating aquatic macrophytes for the treatment of aquaculture effluents

Environmental Science and Pollution Research - Aquaculture is an activity with economic and social importance since it generates food, employment, and income. However, like other human activities,...     

Evaluation of chelating and cytoprotective activity of vanillin against the toxic action of mercuric chloride as an alternative for phytoremediation

Environmental Geochemistry and Health - Mercury is widely found in nature, however, in low concentrations, but anthropological activities have increased its concentration considerably. This causes...     

Influence of population,income and electricity consumption on per capita municipal solid waste generation in São Paulo State,Brazil

Journal of Material Cycles and Waste Management - Predicting municipal solid waste (MSW) generation is fundamental in choosing and scaling the processes involved in municipal management. The...     

Abundance,distribution, and characteristics of microplastics in coastal surface waters of the Colombian Caribbean and Pacific

Environmental Science and Pollution Research - Microplastic pollution has become a global concern due to its distribution, high abundance, and negative impacts on aquatic ecosystems. These...