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Author: Janke, David Ă— End date: 2024 Ă— Start date: 2020 Ă— DDC: 333.7 Ă— Has files: Yes Ă— Version: publishedVersion Ă—

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    Particulate matter emissions during field application of poultry manure - The influence of moisture content and treatment
    (Amsterdam [u.a.] : Elsevier Science, 2021) Kabelitz, Tina; Biniasch, Oliver; Ammon, Christian; NĂ¼bel, Ulrich; Thiel, Nadine; Janke, David; Swaminathan, Senthilathiban; Funk, Roger; MĂ¼nch, Steffen; Rösler, Uwe; Siller, Paul; Amon, Barbara; Aarnink, AndrĂ© J. A.; Amon, Thomas
    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.
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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
    (Basel : MDPI AG, 2020) Hempel, Sabrina; Adolphs, Julian; Landwehr, Niels; Janke, David; Amon, Thomas
    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.