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Working Paper Series "Sustainable Lives - Food Choices" Project website This Working Paper was presented at the 69th Annual International Communication Association (ICA) Conference 2019 in Washington, D.C.

Working Paper Series "Sustainable Lives - Food Choices" Project website This Working Paper was presented at the 69th Annual International Communication Association (ICA) Conference 2019 in Washington, D.C.

The main goal of TERENO was to create observation platforms on the basis of an interdisciplinary and long‐term aimed research program. By the aid of SoilCan‐lysimeters, an ideal network was built between the four different TERENO‐observatories with their highly instrumented test sites. Within the four TERENO‐observatories at 13 different experimental sites, in total 126 lysimeters were filled monolithically and the measurements were started in October 2010. The lysimeters have been cultivated as grassland or arable land with a standardized crop rotation: winter wheat – winter barley – winter rye – oat.

The main goal of TERENO was to create observation platforms on the basis of an interdisciplinary and long‐term aimed research program. By the aid of SoilCan‐lysimeters, an ideal network was built between the four different TERENO‐observatories with their highly instrumented test sites. Within the four TERENO‐observatories at 13 different experimental sites, in total 126 lysimeters were filled monolithically and the measurements were started in October 2010. The lysimeters have been cultivated as grassland or arable land with a standardized crop rotation: winter wheat – winter barley – winter rye – oat.

For an enhancement in the time-space-yield of one-stage agricultural biogas plants, experiments for developing early warning indicators (EWI) for process failures and countermeasures for process stabilization and for adapting the substrate composition were conducted. A new EWI was developed, the A/elCon, the proportion of the concentration of organic acids to the electrical conductivity, which warned most effectively in lab- and full-scale experiments. As a countermeasure against process failures, the addition of different additives was tested. In general, the addition of additives counteracted over-acidifications at high organic loading rates (OLR). In contrast to increased OLRs without countermeasures, the microbial communities adapted to high OLRs, if additives were added, by an increase in the relative abundance of hydrogenotrophic and acid tolerant mixotrophic methanogens. The combination of trace elements and NaOH was most beneficial for stabilizing agricultural biogas processes and was successfully used in combination with the A/elCon to maintain a long-term high-performance digestion (up to OLR = 7.5 kg VS m 3 d 1, t > 100 d). A further increase in the time-space-yield was achieved by co-digesting energy-rich lipid-containing substrates. As known from the digestion of biological waste, the high-performance co-digestions were stabilized by CaO additions leading to an aggregate formation. The stabilization was based on the precipitation of toxic long-chain fatty acids with calcium and thus the formation of surfaces for biofilms. Even a long-term high-performance co-digestion (up to OLR = 9 kg VS m 3 d 1, t > 100 d) was established by adding CaO. In this connection the formation of highly effective fatty acid degrading microbial consortia was detected consisting of syntrophic bacteria and hydrogenotrophic archaea that contributed to the process stability. Analyzing the microbial communities the results of the metagenome analysis and of the classical genetic fingerprinting via DGGE were compared. Both methods showed an increase in acidogenic bacteria and thereby especially in lactic acid producing bacteria with increasing loading rates. The results of the metagenome analysis were much more detailed. Zur Steigerung der Raum-Zeit-Ausbeute in einstufigen, landwirtschaftlichen Biogasanlagen wurden Versuche zur Entwicklung von Frühwarnindikatoren (FWI) für Prozessstörungen und von Maßnahmen zur Prozessstabilisierung, sowie zur Substratzusammensetzung durchgeführt. Ein neuer FWI wurde entwickelt, der A/elCon, das Verhältnis aus der Konzentration organischer Säuren (engl.: org. acids) zur elektrischen Leitfähigkeit (engl.: electr. conductivity), der im Labor und in der Großtechnik am effektivsten vor drohenden Prozessstörungen warnte. Als Maßnahme zur Prozessstabilisierung wurde die Zugabe von Additiven untersucht. Grundsätzlich wurde durch die Additivzugabe eine Übersäuerung bei hohen Raumbelastungen vermieden. Im Unterschied zur Leistungssteigerung ohne Additivzugabe, passte sich die mikrobielle Lebensgemeinschaft durch Zunahme der relativen Abundanz hydrogenotropher und säuretoleranter mixotropher Methanogener an hohe Raumbelastungen (BR) an. Eine Kombination aus Spurenelementen und NaOH eignete sich am besten zur Prozessstabilisierung und wurde zusammen mit dem A/elCon erfolgreich zur Etablierung einer Langzeit-Hochlast-Vergärung (bis BR = 7,5 kg oTS m 3 d 1, t > 100 d) von NawaRo eingesetzt. Eine weitere Leistungssteigerung wurde durch Co-Vergärung energiereicher lipidhaltiger Substrate erzielt. Wie aus Anlagen der Abfallwirtschaft bekannt, wurden die Hochlast-Co-Vergärungen bei CaO-Zugabe durch eine Aggregatbildung stabilisiert. Die Aggregate binden toxische, langkettige Fettsäuren und bilden Oberflächen für Biofilme. Es wurde auch eine Langzeit-Hochlast-Co-Vergärung (t > 100 d) durch Zugabe von CaO etabliert. Dabei wurde die Ausbildung einer hocheffektiven fettsäureabbauenden mikrobiellen Lebensgemeinschaft, bestehend aus syntrophen Bacteria und hydrogenotrophen Archaea detektiert, die entscheidend zur Prozessstabilität beitrug. Bei der Charakterisierung der mikrobiellen Biozönosen wurden die Ergebnisse der Metagenomanalyse und des klassischen genetischen Fingerprintings mittels DGGE verglichen. Beide Methoden zeigten eine Zunahme acidogener Bacteria und dabei speziell von Milchsäurebakterien mit zunehmender Raumbelastung. Die Ergebnisse der Metagenomanalyse waren deutlich detaillierter.

For an enhancement in the time-space-yield of one-stage agricultural biogas plants, experiments for developing early warning indicators (EWI) for process failures and countermeasures for process stabilization and for adapting the substrate composition were conducted. A new EWI was developed, the A/elCon, the proportion of the concentration of organic acids to the electrical conductivity, which warned most effectively in lab- and full-scale experiments. As a countermeasure against process failures, the addition of different additives was tested. In general, the addition of additives counteracted over-acidifications at high organic loading rates (OLR). In contrast to increased OLRs without countermeasures, the microbial communities adapted to high OLRs, if additives were added, by an increase in the relative abundance of hydrogenotrophic and acid tolerant mixotrophic methanogens. The combination of trace elements and NaOH was most beneficial for stabilizing agricultural biogas processes and was successfully used in combination with the A/elCon to maintain a long-term high-performance digestion (up to OLR = 7.5 kg VS m 3 d 1, t > 100 d). A further increase in the time-space-yield was achieved by co-digesting energy-rich lipid-containing substrates. As known from the digestion of biological waste, the high-performance co-digestions were stabilized by CaO additions leading to an aggregate formation. The stabilization was based on the precipitation of toxic long-chain fatty acids with calcium and thus the formation of surfaces for biofilms. Even a long-term high-performance co-digestion (up to OLR = 9 kg VS m 3 d 1, t > 100 d) was established by adding CaO. In this connection the formation of highly effective fatty acid degrading microbial consortia was detected consisting of syntrophic bacteria and hydrogenotrophic archaea that contributed to the process stability. Analyzing the microbial communities the results of the metagenome analysis and of the classical genetic fingerprinting via DGGE were compared. Both methods showed an increase in acidogenic bacteria and thereby especially in lactic acid producing bacteria with increasing loading rates. The results of the metagenome analysis were much more detailed. Zur Steigerung der Raum-Zeit-Ausbeute in einstufigen, landwirtschaftlichen Biogasanlagen wurden Versuche zur Entwicklung von Frühwarnindikatoren (FWI) für Prozessstörungen und von Maßnahmen zur Prozessstabilisierung, sowie zur Substratzusammensetzung durchgeführt. Ein neuer FWI wurde entwickelt, der A/elCon, das Verhältnis aus der Konzentration organischer Säuren (engl.: org. acids) zur elektrischen Leitfähigkeit (engl.: electr. conductivity), der im Labor und in der Großtechnik am effektivsten vor drohenden Prozessstörungen warnte. Als Maßnahme zur Prozessstabilisierung wurde die Zugabe von Additiven untersucht. Grundsätzlich wurde durch die Additivzugabe eine Übersäuerung bei hohen Raumbelastungen vermieden. Im Unterschied zur Leistungssteigerung ohne Additivzugabe, passte sich die mikrobielle Lebensgemeinschaft durch Zunahme der relativen Abundanz hydrogenotropher und säuretoleranter mixotropher Methanogener an hohe Raumbelastungen (BR) an. Eine Kombination aus Spurenelementen und NaOH eignete sich am besten zur Prozessstabilisierung und wurde zusammen mit dem A/elCon erfolgreich zur Etablierung einer Langzeit-Hochlast-Vergärung (bis BR = 7,5 kg oTS m 3 d 1, t > 100 d) von NawaRo eingesetzt. Eine weitere Leistungssteigerung wurde durch Co-Vergärung energiereicher lipidhaltiger Substrate erzielt. Wie aus Anlagen der Abfallwirtschaft bekannt, wurden die Hochlast-Co-Vergärungen bei CaO-Zugabe durch eine Aggregatbildung stabilisiert. Die Aggregate binden toxische, langkettige Fettsäuren und bilden Oberflächen für Biofilme. Es wurde auch eine Langzeit-Hochlast-Co-Vergärung (t > 100 d) durch Zugabe von CaO etabliert. Dabei wurde die Ausbildung einer hocheffektiven fettsäureabbauenden mikrobiellen Lebensgemeinschaft, bestehend aus syntrophen Bacteria und hydrogenotrophen Archaea detektiert, die entscheidend zur Prozessstabilität beitrug. Bei der Charakterisierung der mikrobiellen Biozönosen wurden die Ergebnisse der Metagenomanalyse und des klassischen genetischen Fingerprintings mittels DGGE verglichen. Beide Methoden zeigten eine Zunahme acidogener Bacteria und dabei speziell von Milchsäurebakterien mit zunehmender Raumbelastung. Die Ergebnisse der Metagenomanalyse waren deutlich detaillierter.

Urban drainage infrastructure has to adapt to challenges such as climate change, but also to trends such as population and settlement development. At the same time, the opportunities offered by digitization can be used to meet these challenges. In this thesis, therefore, two geoinformation-based models are developed, which use the advancing digitization to contribute to the adaptation of urban drainage to demographic and climate change. An important aspect of municipal flood protection are nature-oriented measures for the rainwater management as part of water-sensitive urban development. According to the recognized rules of technology, the overriding goal of rainwater management is to approximate the natural water balance. The estimate of this target value is difficult to determine. The potential offered by decentralized rainwater management measures to achieve or approach this target as well. One geoinformation-based model pursues these two goals. The first goal is the estimation of the water balance deficit. The second goal is a limitation of the potential measures for the best compensation of this deficit. For this purpose, structural, geological and hydrogeological restrictions are taken into account. The model works exclusively with geo-referenced data and thus offers direct localization of the results. Furthermore, the model combines the approach of the empirically determined water balance distribution functions of the DWA-A 102 and a GIS-based calculation. The advantages of this combination are the scaling of a selectable study area and the on the fly consideration of restrictions for the measures of decentralized rainwater management. The second geoinformation-based model is an artificial neural network (ANN) for calculating flood areas caused by heavy rain. The trained ANN is suitable for known areas, works in real time and substitute a conventional hydrodynamic model. Therefore, a supervised learning multi-layer feed-forward ANN was set up and trained. After that, it was then examined and assessed for its suitability. For the substitution of a hydrodynamic numerical model for calculation of flooding risks (flood areas or surface runoff) by an ANN, the ANN must 'learn' the hydrological processes of the conventional model. For this learning process, the ANN requires input values that essentially determine the hydrological processes (e.g. precipitation, digital elevation model, type of sealing, barriers and culverts, etc.). Moreover, the AAN requires target values. For the ANN, the target values are areas with a certain height water level caused by a precipitation event (flood areas with flood level). These target values were first calculated using a hydrodynamic numerical model.The developed model shows that ANNs are more suitable than conventional hydrodynamic numerical models for certain applications due to their computing speed. Even if the generalizability of the trained ANN to other model areas could not be successfully demonstrated, the ANN however was able to predict good results for a sufficiently known range of values.The biggest advantage of the ANN is the low computing time for predicting flood areas. In contrast to hydrodynamic numerical models, the ANN predicts flood areas with sufficient accuracy in a few seconds. The application possibilities of the developed model thus specialize in cases in which these strengths are indispensable. One indispensability is the prediction of flood area caused by spontaneously and spatially limited heavy rain. This indispensability is also a demand of the municipal flood protection. Dissertation, Rheinisch-Westfälische Technische Hochschule Aachen, 2021; Aachen : RWTH Aachen University 1 Online-Ressource : Illustrationen, Diagramme (2021). = Dissertation, Rheinisch-Westfälische Technische Hochschule Aachen, 2021 Published by RWTH Aachen University, Aachen

Urban drainage infrastructure has to adapt to challenges such as climate change, but also to trends such as population and settlement development. At the same time, the opportunities offered by digitization can be used to meet these challenges. In this thesis, therefore, two geoinformation-based models are developed, which use the advancing digitization to contribute to the adaptation of urban drainage to demographic and climate change. An important aspect of municipal flood protection are nature-oriented measures for the rainwater management as part of water-sensitive urban development. According to the recognized rules of technology, the overriding goal of rainwater management is to approximate the natural water balance. The estimate of this target value is difficult to determine. The potential offered by decentralized rainwater management measures to achieve or approach this target as well. One geoinformation-based model pursues these two goals. The first goal is the estimation of the water balance deficit. The second goal is a limitation of the potential measures for the best compensation of this deficit. For this purpose, structural, geological and hydrogeological restrictions are taken into account. The model works exclusively with geo-referenced data and thus offers direct localization of the results. Furthermore, the model combines the approach of the empirically determined water balance distribution functions of the DWA-A 102 and a GIS-based calculation. The advantages of this combination are the scaling of a selectable study area and the on the fly consideration of restrictions for the measures of decentralized rainwater management. The second geoinformation-based model is an artificial neural network (ANN) for calculating flood areas caused by heavy rain. The trained ANN is suitable for known areas, works in real time and substitute a conventional hydrodynamic model. Therefore, a supervised learning multi-layer feed-forward ANN was set up and trained. After that, it was then examined and assessed for its suitability. For the substitution of a hydrodynamic numerical model for calculation of flooding risks (flood areas or surface runoff) by an ANN, the ANN must 'learn' the hydrological processes of the conventional model. For this learning process, the ANN requires input values that essentially determine the hydrological processes (e.g. precipitation, digital elevation model, type of sealing, barriers and culverts, etc.). Moreover, the AAN requires target values. For the ANN, the target values are areas with a certain height water level caused by a precipitation event (flood areas with flood level). These target values were first calculated using a hydrodynamic numerical model.The developed model shows that ANNs are more suitable than conventional hydrodynamic numerical models for certain applications due to their computing speed. Even if the generalizability of the trained ANN to other model areas could not be successfully demonstrated, the ANN however was able to predict good results for a sufficiently known range of values.The biggest advantage of the ANN is the low computing time for predicting flood areas. In contrast to hydrodynamic numerical models, the ANN predicts flood areas with sufficient accuracy in a few seconds. The application possibilities of the developed model thus specialize in cases in which these strengths are indispensable. One indispensability is the prediction of flood area caused by spontaneously and spatially limited heavy rain. This indispensability is also a demand of the municipal flood protection. Dissertation, Rheinisch-Westfälische Technische Hochschule Aachen, 2021; Aachen : RWTH Aachen University 1 Online-Ressource : Illustrationen, Diagramme (2021). = Dissertation, Rheinisch-Westfälische Technische Hochschule Aachen, 2021 Published by RWTH Aachen University, Aachen