Control of hydrogen sulphide, ammonia and odour emissions from swine barns using zinc oxide nanoparticles

Application of zinc oxide (ZnO) nanoparticles was evaluated as a possible measure to mitigate the levels of hydrogen sulphide (H2S), ammonia (NH3) and odour in swine facilities. Two deployment techniques were investigated: direct mixing of zinc oxide nanoparticles into the slurry, and filtration with nanoparticles as filtering media for the manure gases. The overall goal of this work was to determine the impact of the treatments on hydrogen sulphide, ammonia and odour emissions, pig performance and manure characteristics as well as to assess the feasibility of the application of this technology in a typical swine barn. Semi-pilot scale tests were conducted to evaluate operational factors in open system conditions, the results of which showed that the mixing method required a particle-to-slurry ratio of 3 grams of zinc oxide per litre of slurry to control hydrogen sulphide and ammonia levels. Using the air filtration technique, a fluidized bed filter design with a 0.28 g/cm2 loading rate and rated at 0.5 m/s face velocity was found to be the most effective combination for controlling gas levels. Room-scale experiments were conducted in specially designed chambers to assess the effectiveness of the treatments under conditions that represent commercial swine production. The addition of zinc oxide nanoparticles into the manure achieved more than 95% reduction in hydrogen sulphide levels while no significant effects on ammonia concentrations were observed. Zinc oxide nanoparticles were persistent in maintaining low hydrogen sulphide levels up to 15 days after treatment application. On the other hand, the ventilation air recirculation system with a zinc oxide filter achieved significant reduction in both hydrogen sulphide and ammonia concentrations at the animal- and human-occupied zones. Neither treatment had any significant impact on pig performance and manure nutrient characteristics. Estimates of the cost of application of the treatments in a 100-head grow-finish room showed that employing the air filtration method amounted to around 3.8% of the average total cost of production, which was economically more feasible than the mixing method; however, various options can be pursued to further reduce the cost of application of both treatments

Air pollution and livestock production

The air in a livestock farming environment contains high concentrations of dust particles and gaseous pollutants. The total inhalable dust can enter the nose and mouth during normal breathing and the thoracic dust can reach into the lungs. However, it is the respirable dust particles that can penetrate further into the gas-exchange region, making it the most hazardous dust component. Prolonged exposure to high concentrations of dust particles can lead to respiratory health issues for both livestock and farming staff. Ammonia, an example of a gaseous pollutant, is derived from the decomposition of nitrous compounds. Increased exposure to ammonia may also have an effect on the health of humans and livestock. There are a number of technologies available to ensure exposure to these pollutants is minimised. Through proactive means, (the optimal design and management of livestock buildings) air quality can be improved to reduce the likelihood of risks associated with sub-optimal air quality. Once air problems have taken hold, other reduction methods need to be applied utilising a more reactive approach. A key requirement for the control of concentration and exposure of airborne pollutants to an acceptable level is to be able to conduct real-time measurements of these pollutants. This paper provides a review of airborne pollution including methods to both measure and control the concentration of pollutants in livestock buildings

Simulation of site-specific irrigation control strategies with sparse input data

Crop and irrigation water use efficiencies may be improved by managing irrigation application timing and volumes using physical and agronomic principles. However, the crop water requirement may be spatially variable due to different soil properties and genetic variations in the crop across the field. Adaptive control strategies can be used to locally control water applications in response to in-field temporal and spatial variability with the aim of maximising both crop development and water use efficiency. A simulation framework ‘VARIwise’ has been created to aid the development, evaluation and management of spatially and temporally varied adaptive irrigation control strategies (McCarthy et al., 2010). VARIwise enables alternative control strategies to be simulated with different crop and environmental conditions and at a range of spatial resolutions. An iterative learning controller and model predictive controller have been implemented in VARIwise to improve the irrigation of cotton. The iterative learning control strategy involves using the soil moisture response to the previous irrigation volume to adjust the applied irrigation volume applied at the next irrigation event. For field implementation this controller has low data requirements as only soil moisture data is required after each irrigation event. In contrast, a model predictive controller has high data requirements as measured soil and plant data are required at a high spatial resolution in a field implementation. Model predictive control involves using a calibrated model to determine the irrigation application and/or timing which results in the highest predicted yield or water use efficiency. The implementation of these strategies is described and a case study is presented to demonstrate the operation of the strategies with various levels of data availability. It is concluded that in situations of sparse data, the iterative learning controller performs significantly better than a model predictive controller

Investigation of potential application of nanoparticles in reducing gas and odour emission from swine manure slurry

The objective of this research was to determine the effectiveness of nanoparticles for reducing gas and odour emissions from swine manure slurry using three deployment methods: headspace gas filtration, mixing with manure slurry and spraying into the headspace of manure slurry. Filtering manure gas through the zinc oxide (ZnO) filter bed at a flow rate of 500 mL/min reduced ammonia (NH3), hydrogen sulphide (H2S) and odour concentrations by 74 to 99%. Methane (CH4) and carbon dioxide (CO2) concentrations of the filtered manure gas were decreased by 14% and 18%, respectively. Mixing ZnO into the manure slurry significantly reduced odour concentration by 79% and the hedonic tone was improved by 25% at one day after treatment application. Concentrations of CH4 and H2S were reduced by 54% and 98%; however concentrations of NH3 and nitrous oxide (N2O) were increased by 31% and 3%, respectively. Even though mixing of ZnO into the slurry influenced the gas and odour concentration, manure properties such as ammonia as N, TKN, P, K, S, Na, Ca, Mg, Cu, Fe, Mn, Z, total solids, % moisture, pH and EC were not changed except for an increase of 0.2 in pH value. Spraying tungsten oxide (WO3) into the headspace of manure slurry decreased the odour and CO2 concentration by 31 and 10%, but the reduction was not statistically significant (P>0.05).Among the three deployment methods, filtration and mixing methods using ZnO were able to reduce NH3, H2S, and odour concentration. However, surface reactions between the manure gas components and nanoparticles should be investigated to increase the effectiveness of the treatment application. Likewise, knowing these reactions will facilitate the identification and manipulation of factors that influence the effectiveness of the deployment method. Economic, environmental and health assessment should be done to determine the feasibility and overall impact of using nanotechnology in reducing gas and odour emission to the swine industry

Odour Detection Methods: Olfactometry and Chemical Sensors

The complexity of the odours issue arises from the sensory nature of smell. From the evolutionary point of view olfaction is one of the oldest senses, allowing for seeking food, recognizing danger or communication: human olfaction is a protective sense as it allows the detection of potential illnesses or infections by taking into account the odour pleasantness/unpleasantness. Odours are mixtures of light and small molecules that, coming in contact with various human sensory systems, also at very low concentrations in the inhaled air, are able to stimulate an anatomical response: the experienced perception is the odour. Odour assessment is a key point in some industrial production processes (i.e., food, beverages, etc.) and it is acquiring steady importance in unusual technological fields (i.e., indoor air quality); this issue mainly concerns the environmental impact of various industrial activities (i.e., tanneries, refineries, slaughterhouses, distilleries, civil and industrial wastewater treatment plants, landfills and composting plants) as sources of olfactory nuisances, the top air pollution complaint. Although the human olfactory system is still regarded as the most important and effective “analytical instrument” for odour evaluation, the demand for more objective analytical methods, along with the discovery of materials with chemo-electronic properties, has boosted the development of sensor-based machine olfaction potentially imitating the biological system. This review examines the state of the art of both human and instrumental sensing currently used for the detection of odours. The olfactometric techniques employing a panel of trained experts are discussed and the strong and weak points of odour assessment through human detection are highlighted. The main features and the working principles of modern electronic noses (E-Noses) are then described, focusing on their better performances for environmental analysis. Odour emission monitoring carried out through both the techniques is finally reviewed in order to show the complementary responses of human and instrumental sensing