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How the Selection of Training Data and Modeling Approach Affects the Estimation of Ammonia Emissions from a Naturally Ventilated Dairy Barn—Classical Statistics versus Machine Learning
Environmental protection efforts can only be effective in the long term with a reliable quantification of pollutant gas emissions as a first step to mitigation. Measurement and analysis strategies must permit the accurate extrapolation of emission values. We systematically analyzed the added value of applying modern machine learning methods in the process of monitoring emissions from naturally ventilated livestock buildings to the atmosphere. We considered almost 40 weeks of hourly emission values from a naturally ventilated dairy cattle barn in Northern Germany. We compared model predictions using 27 different scenarios of temporal sampling, multiple measures of model accuracy, and eight different regression approaches. The error of the predicted emission values with the tested measurement protocols was, on average, well below 20%. The sensitivity of the prediction to the selected training dataset was worse for the ordinary multilinear regression. Gradient boosting and random forests provided the most accurate and robust emission value predictions, accompanied by the second-smallest model errors. Most of the highly ranked scenarios involved six measurement periods, while the scenario with the best overall performance was: One measurement period in summer and three in the transition periods, each lasting for 14 days.
Direct Measurements of the Volume Flow Rate and Emissions in a Large Naturally Ventilated Building
The direct measurement of emissions from naturally ventilated dairy barns is challenging due to their large openings and the turbulent and unsteady airflow at the inlets and outlets. The aim of this study was to quantify the impacts of the number and positions of sensors on the estimation of volume flow rate and emissions. High resolution measurements of a naturally ventilated scaled building model in an atmospheric boundary layer wind tunnel were done. Tracer gas was released inside the model and measured at the outlet area, using a fast flame ionization detector (FFID). Additionally, the normal velocity on the area was measured using laser Doppler anemometry (LDA). In total, for a matrix of 65 × 4 sensor positions, the mean normal velocities and the mean concentrations were measured and used to calculate the volume flow rate and the emissions. This dataset was used as a reference to assess the accuracy while systematically reducing the number of sensors and varying the positions of them. The results showed systematic errors in the emission estimation up to +97%, when measurements of concentration and velocity were done at one constant height. This error could be lowered under 5%, when the concentrations were measured as a vertical composite sample.
Particulate matter emissions during field application of poultry manure - The influence of moisture content and treatment
Along with industry and transportation, agriculture is one of the main sources of primary particulate matter (PM) emissions worldwide. Bioaerosol formation and PM release during livestock manure field application and the associated threats to environmental and human health are rarely investigated. In the temperate climate zone, field fertilization with manure seasonally contributes to local PM air pollution regularly twice per year (spring and autumn). Measurements in a wind tunnel, in the field and computational fluid dynamics (CFD) simulations were performed to analyze PM aerosolization during poultry manure application and the influence of manure moisture content and treatment. A positive correlation between manure dry matter content (DM) and PM release was observed. Therefore, treatments strongly increasing the DM of poultry manure should be avoided. However, high manure DM led to reduced microbial abundance and, therefore, to a lower risk of environmental pathogen dispersion. Considering the findings of PM and microbial measurements, the optimal poultry manure DM range for field fertilization was identified as 50–70%. Maximum PM10 concentrations of approx. 10 mg per m3 of air were measured during the spreading of dried manure (DM 80%), a concentration that is classified as strongly harmful. The modeling of PM aerosolization processes indicated a low health risk beyond a distance of 400 m from the manure application source. The detailed knowledge about PM aerosolization during manure field application was improved with this study, enabling manure management optimization for lower PM aerosolization and pathogenic release into the environment.
Methane Emission Characteristics of Naturally Ventilated Cattle Buildings
The mandate to limit global temperature rise calls for a reliable quantification of gaseous pollutant emissions as a basis for effective mitigation. Methane emissions from ruminant fermentation are of particular relevance in the context of greenhouse gas mitigation. The emission dynamics are so far insufficiently understood. We analyzed hourly methane emission data collected during contrasting seasons from two naturally ventilated dairy cattle buildings with concrete floor and performed a second order polynomial regression. We found a parabolic temperature dependence of the methane emissions irrespective of the measurement site and setup. The position of the parabola vertex varied when considering different hours of the day. The circadian rhythm of methane emissions was represented by the pattern of the fitted values of the constant term of the polynomial and could be well explained by feeding management and air flow conditions. We found barn specific emission minima at ambient temperatures around 10 °C to 15 °C. As this identified temperature optimum coincides with the welfare temperature of dairy cows, we concluded that temperature regulation of dairy cow buildings with concrete floor should be considered and further investigated as an emission mitigation measure. Our results further indicated that empirical modeling of methane emissions from the considered type of buildings with a second order polynomial for the independent variable air temperature can increase the accuracy of predicted long-term emission values for regions with pronounced seasonal temperature fluctuations
The impact of atmospheric boundary layer, opening configuration and presence of animals on the ventilation of a cattle barn
Naturally ventilated livestock buildings (NVLB) represent one of the most significant sources of ammonia emissions. However, even the dispersion of passive gas in an NVLB is still not well understood. In this paper, we present a detailed investigation of passive pollutant dispersion in a model of a cattle barn using the wind tunnel experiment method. We simulated the pollution of the barn by a ground-level planar source. We used the time-resolved particle image velocimetry (TR-PIV) and the fast flame ionisation detector (FFID) to study the flow and dispersion processes at high spatial and temporal resolution. We employed the Proper Orthogonal Decomposition (POD) and Oscillating Patterns Decomposition (OPD) methods to detect the coherent structures of the flow. The results show that the type of atmospheric boundary layer (ABL) and sidewall opening height have a significant impact on the pollutant dispersion in the barn, while the presence of animals and doors openings are insignificant under conditions of winds perpendicular to the sidewall openings. We found that the dynamic coherent structures, developed by the Kelvin-Helmholtz instability, contribute to the pollutant transport in the barn. We demonstrate that in any of the studied cases the pollutant was not well mixed within the barn and that a significant underestimation (up to by a factor 3) of the barn ventilation might be obtained using, e.g. tracer gas method. © 2020 The Authors