Program Overview

An Approach to Describe Design Pattern Structures for Sustainable Software Products

Software affects society and environment in a variety of ways. In addition to the energy and resource requirements driven by software products, social factors are to be mentioned as sustainability-relevant aspects. In order to support software developers in creating products that can be positively assessed from a sustainability point of view, the question of practically usable assistance arises in this context. With this aim, in this paper the methodological approach of design patterns is examined. It is discussed how sustainability patterns can be described in a structured way and how they can be incorporated into practical software engineering processes. Particular attention is paid to aspects of completeness and consistency of the descriptions as well as the explicit inclusion of human action. It is recommended to provide pattern descriptions with a high formalization degree in order to enable verifiability for logical plausibility and completeness and to allow further machine-assisted processing. Prospectively appropriate pattern repositories could be set up to support practical software product development processes in the future.

A Literature Review to analyze the State of the Art of Virtual Power Plants in Context of Information Security

Compared to conventional power plants, Virtual Power Plant is a new electrotechnical and information technology concept that is used for the interdisciplinary merging of electrical engineering and information technology for the central control and monitoring of decentralized renewable energy systems and components. A formal presentation of definitions and consensus building is seen as an instrument to specify the practice-oriented design of Virtual Power Plants. The present paper focuses on the Virtual Power Plants as key factor for sustainable energy - especially in its common understanding of terms. This compilation serves as a mutual knowledge base and function because of the “Shared Conceptualization” for the specialist user within the knowledge domain “Information technology” and “Electrical engineering.” An Event-driven Process Chain is created for this purpose. In order to obtain such an Event-driven Process Chain, a qualitative analysis based on semi – systematic literature review led to a selection of suitable term definitions that are contained in the scientific databases. This investigation finally leads to the compilation of entities and their mutual relations, which are required to create the model. The conception of an Event-driven Process Chain is seen in this context as a contribution to the specification and structuring of the discussion about Virtual Power Plants. A potential contribution is, the ability to map a field of research, synthesize the state of knowledge, and create an agenda for further research in context of information systems and information security research.

Erfan Koza

Asiye Öztürk

Microservicebased Architecture for the Integration of Data Backends and Dashboard Applications in the Energy and Environment Domains

This article presents a software architecture based on the onion architecture that uses the concept of application microservices inorder to integrate data backends with dashboard applications. Its main goal is to reduce the complexity in the architecture’s frontend and therefore to increase the performance of the application for the user. The concept of the added application layer as well as its interaction with the other parts of the architecture is described in detail. Then an evaluation of its advantages is presented which shows the benefits of the conceptregarding performance and simplicity using a real-world use case in theenergy and environmental domains.

Rebound Effects in Methods of Artificial Intelligence

Artificial intelligence (AI) is one of the pioneering driving forces of the digital revolution in terms of the areas of application that already exist and those that are emerging as potential. On the technical side, this paper deals in particular with the energy requirements of artificial intelligence processes. It also identifies efficiency approaches in this sector. Increases in productivity often lead to an increased demand for energy, which is contrary to sustainability in terms of reducing CO2 emissions. Therefore, it will be examined to what extent rebound effects can reduce the savings potential for energy in relation to methods of artificial intelligence and what the main factors of CO2 emissions are.

Disaster Risk Information Management Deficits  - Stakeholders in and after the Central European Flood Disaster of July 2021 -

According to the text of the UN SENDAI Framework on Disaster Risk Reduction, the ability for all to access and contribute information, ideas and knowledge is essential in an inclusive Disaster Risk Reduction informed society.One of the major goals to achieve is to enhance / give all stakeholders (citizens, global, national, regional and local authorities, as well as civil society /NGOs, private sector and other organizations) an active voice on issues they experience and believe most relevant to the current and future demands in all phases of Disaster Risk Reduction – in conjunction with other UN Instruments (Transnational Declarations, Conventions, Treaties, Frameworks and Directives).The Central European Flood Disaster of July 2021 showed a level of public awareness of stakeholders far beyond traditional first aid organizations in an unprecedented comprehensiveness, manner and way.The sharing and strengthening of local, national and global knowledge for Disaster Risk Reduction DRR can be enhanced by removing barriers to equitable access to information for economic, social, political, health, cultural, educational, and scientific activities and by facilitating access to public domain knowledge, information and data.This presentation willgive a methodological framework that covers Risk Information Management from a holistic point of view on local, regional, national and international levels of operationindicate on information management deficits consequences, especially when considering effects of decision support, decisions, and actions, based on examples of public media content (Media, Broadcasting and Journalists)exemplify on the special needs of the most vulnerable stakeholdersemphasize on considerably growing “all-of-society” interest in disaster aftermathstrengthen the need to document disaster information management facts and consequences to stakeholderscompile a central function Risk Information Management set of methods, techniques and tasks needed in all socio-economic aspects in the Disaster Management Phases (from preparedness through all facets of disaster aftermath) for massive improvement of physical, environmental, economic and social resiliencecollect proposals for selected programs and projects that are needed to overcome current deficits

Fighting climate risk through gaming by an energy transition game, utopia or reality?

The implementation of a new game engine for physical computing serious games aims to change behavior in terms of energy production and consumption. The proposal is to develop a non-coercive process, which offers a reward to residents, employees or officials as well as to businesses and institutions in the form of a reduction in their electricity bill and CO2 emissions. The aim is to develop a specific research and analysis methodology, a framework of operations adapted to groups of populations, a collaborative game and implementation procedures with, ultimately, a results verification strategy. The necessity and urgency of communication about the issues and challenges of climate change and possible solutions to advert or at least reduce this risk by setting up an energy transition and reorganize cities within the framework of the smart-city model, is leading the way to new educational projects. Gamification is an effective way to communicate complex ideas and concepts and increase stakeholder engagement. Smart City Game and Transition Today are two electronic physical board games that share a common software engine that is briefly described in this paper.   

Alberto Susini

CITADINE: A platform to provide the experiences of survivors of natural disaster as open educational resources for risk communication

This paper presents the concept, and the prototype of a platform that enables citizens from local communities to share their experiences with historic dis-asters online as open educational resources for teachers, community builders, urban planners, or volunteers. Key focus of the platform is to highlight the impact of disasters on citizens’ everyday lives. Based upon an overview of previous research, a requirements analysis, personas and related use cases, we present insights into the prototypical implementation of the platform. The design and development process of the platform was based upon the principles outlined in the Sendai framework of disaster risk reduction. We also provide a brief outlook upon possible practical uses of the platform, and its contents.   

A Validation of the Attack on the Power Grid as described in the Novel "Blackout"

In his best-selling novel “Blackout: Tomorrow Will Be Too Late”, the author Marc Elsberg describes a cyber-attack on the power grid of (mainly) Europe, leading to a prolonged power outage with catastrophic social and environmental impacts. Hackers attack the grid on three different attack fronts through vulnerabilities in the process of generation, distribution, and consumption of electricity. The first front is an attack on smart meters that causes them to disconnect from the power grid. The second front is an attack on the supervisory control and data acquisition (SCADA) software of the power generation industry by a manipulated standard library. The third front is an attack on European grid operators where the attackers spread malware by the means of social engineering on the operators’ infrastructure. In this paper, we examine these three attack fronts and validate the attack from a technical perspective. The real-world smart meter standard we examined turned out to be protected with weak and recurring passwords and remotely disconnectable. Social engineering is empirically proven to be a promising method for spreading malware. Finally, SCADA software indeed contains banned and insecure C-APIs and other vulnerabilities such as a lack of authentication/authorization. Even though our results indicate serious security issues in every attack front, we conclude that a blackout of the magnitude described in Blackout through the immediate effect of an attack is unlikely due to the large system size and the numerous players that would have to be targeted, such as the various SCADA software providers and power network operators.   

11:00 – 11:05 Uhr
Begrüßung / Einführung in den Workshop UISDigi-Trans / Moderationshinweise/ Einführung BMU-Vortrag

11:05 – 11:25 Uhr

Das BMU als Motor und Umweltinformationen im Mittelpunkt der digitalen Transformation

Markus Meinert (BMU)

11:25 – 11:55 Uhr
(inkl 10 Min. Diskussion)

Umweltinformationssysteme für den Naturschutz – Potenziale, Risiken, Anwendungsfelder und Entwicklungsperspektiven incl. Postervorstellung

Poster zum Vortrag

Christian Schneider (BfN)

11:55 – 12:25 Uhr
(inkl 10 Min. Diskussion)

Einschätzung des Lebensraumangebots für ausgestorbene Arten aus Fernerkundungs-und Forsteinrichtungsdaten am Beispiel des Haselhuhns (Tetrastes bonasia rupestris) im Nationalpark Schwarzwald

Markus Handschuh

12:25 – 12:55 Uhr
(inkl 10 Min. Diskussion)

Der UBA-Umweltatlas als Werkzeug des Umweltbundesamtes für die Öffentlichkeitsinformation

Das Beispiel „Reaktiver Stickstoff“

Stefan von Andrian-Werburg

12:55 – 13:25 Uhr
(inkl 10 Min. Diskussion)

envVisio: Universelle Bereitstellung von Umweltdaten

Heino Rudolf

ZEUS - A novel tool for the management and coordination of large-scale evacuations

This paper introduces the newly developed software ZEUS: a management and coordination tool for large scale evacuation situations. ZEUS was developed at hand of official German blueprints for the planning of large-scale evacuations and enables authorities to create and manage emergency accommodations, collecting points and civil protection contact points. Furthermore, during an evacuation situation, ZEUS allows first responders at civil protection contact points to make reservations for evacuees in an emergency accommodation. Managers in emergency accommodations can track their allocation numbers, which are processed by ZEUS: this allows officials to monitor the ongoing situation. Thus, situational awareness is enhanced, accommodation overloading is prevented and authorities can effectively recap their response to a given evacuation situation. ZEUS establishes an information exchange point for both the authorities executing the evacuation and the authorities receiving the evacuees. Crossborder collaboration is taken into respect: ZEUS allows the management of evacuation flows over several (federal) states. The implemented features complement the efforts of the federal state Baden-Württemberg to implement the Sendai-Framework.

Use of Community Data in Crisis and Disaster Management Using the Example of Forest Fires in Germany

Preventing forest fires in the context of climate change is an important measure to protect the environment from destruction. Most forest fires do not have a natural origin, but are directly or indirectly caused by humans. In the field of forest fire detection and suppression, there are already many application systems that support different measures. In recent years, information on environmental data has been collected in communities and made freely available. Using the example of forest fire, preventive measures, reactive measures and follow-up measures using community data are described. Existing work in the field of state of the art and state of the science will be examined and further requirements for the use of community data will be derived.

Towards digital twin generation of collapsed buildings - use of new sensors and digital methods to support search and rescue efforts

After severe natural disasters such as earthquakes, tornadoes or tsunamis, which often result in the partial or total collapse of buildings, the search for survivors under rubble piles is a top priority. Specific and fast intervention by first responders can potentially increase victims' chances of survival. In order to make search and rescue operations more efficient and safer for both the victims and emergency forces, an advanced modular system for digital twinning, structural analysis and monitoring of collapsed infrastructures is being developed as part of the research joint project "Sensor Systems for Localization of Trapped Victims in Collapsed Infrastructures” (SORTIE). In this short paper, the current methodology on digital twinning of collapsed buildings is presented where 3D point clouds are taken as input and processed into 3D models using a semi-automatic procedure. The digital twin of the destroyed infrastructure represents a semantic 3D model based on Building Information Model (BIM) technology which will be used as a data exchange platform by search and rescue teams. The performance of the developed approach is demonstrated by a case study.

SnR Project Emerging technologies for the Early location of Entrapped victims under Collapsed Structures and Advanced Wearables for risk assessment and First Responders Safety in SAR operations

Now more than ever, we face a challenge as a civil, professional, institutional and global society to address current and future emerging challenges. Emergency Professionals need to advance multidisciplinary work and support the development of technologies to ensure the safety of emergency interventions and rescue operations. "Search and Rescue" project is funded by the European Commission with the aim of developing new technologies to improve the impact of a disaster (natural, chemical, and biological) in order to make society as resilient as possible to disasters both impacting in management risk and decreasing number of victims affected and gravity. This is the aim of technicians and emergency professionals from different European countries within USAR Teams and Medical Units
Objective: To increase the effectiveness of emergency professionals improving communication technologies, chemical sensors, smart watches, smart uniforms, rescue systems specific to children, drones with interoperability in Concorde platform, and ground robots.

Material and methods:
These technologies are validated in pilots: a simulation of a Multi-Victim Incident is carried out, consisting of an earthquake with collapsed structures plus and ammonia spill. The last one takes place at the National School of Civil Protection of Rivas (Madrid).

Results:
Technologies within advanced uniforms, with improved communications for first responders, as well as drones interconnected and health monitoring devices for emergency response, with the teams communicate by a common platform produce a benefit impact assessment to improve management risk in natural disasters.

Conclusion:
There is a dynamic work where new technologies are developed to deal with natural and man-made disasters to reduce the risk for both professionals and victims, achieving min the face of disasters.
search-and-rescue.eu

STAMINA - Disaster risk and crises management methodological approach

Currently we all are facing a challenging time around the globe regarding pandemics, natural as well as manmade disasters. First to mention the current Covid-19 situation in Europe but also worldwide, which forces ministers, politicians, responsible from health and environmental organisation on regional, national level, first responders and many more to (re)act under extreme conditions to manage this pandemic crisis.
Additionally, or in parallel other disaster are happening like earth quakes, fires (e.g. in a Covid-19 hospital, like in Bagdad, Iraq, April 25th, 2021) that needs to be handled immediately, mostly with the same existing resources available that causes real stress as they are often already occupied by the parallel ongoing event.
Thus, it is unavoidable to have a framework (incl. models, workflows, processes) as well as already tested (or let’s say “trialed”) solutions at hand, so that under extreme circumstances you just need to select and execute your plans and follow dedicated processes for a specific risk.
The EU funded project STAMINA (Demonstration of Intelligent Decision Support for Pandemic Crisis Prediction and Management within and across European Borders) is going to take up these challenges to manage pandemic crisis and is following the Driver+ (Driving Innovation for European Resilience) project modelling approach. In this article we first briefly explain the Driver+ approach and secondly, it’s adaptation for STAMINA.

Towards an automated classification of Hess & Brezowsky’s atmospheric circulation patterns Tief and Trog Mitteleuropa using Deep Learning Methods

Climate Change alters the atmospheric circulation over Europe and increases the risk of heavy precipitation. 'Tief Mitteleuropa' and 'Trog Mitteleuropa' are two atmospheric circulation patterns that are associated with heavy precipitation over Central Europe. Thus the research question of how climate change influences their occurrence is of high relevance. However, the spatio-temporal data structure and the imbalanced classes demand for sophisticated modelling architectures in order to detect these circulation patterns in climate models. This paper introduces and compares deep learning algorithms that are able to tackle  the problem of detecting the two atmospheric circulation classes. A ResNet18 and a Convolutional LSTM are set up and fitted to the data. The best model achieves a Matthews correlation coefficient of 0.33. Overall, both model types are generally able to detect the atmospheric circulation patterns and, moreover, carry immense potential in the way they can be set up and fitted to the data. These promising results open the gate for further research in the future.

Resilient Smart Farming (RSF) as an opportunity for sustainable, environmentally friendly and resilient digital agriculture

Since the COVID-19 pandemic, the word “resilience” is more common than ever. However, resilience can be found in many other sectors. This present work revolves around Resilient Smart Farming (RSF) as a possibility for sustainable, environmentally friendly, and resilient digital agriculture. The main focus is on the reliability and security against attacks of agricultural systems as a central component of the Critical Infrastructure Protection (KRITIS) of food. The question is not whether smart farming makes sense in agricultural practices, but whether its infrastructure meets the requirements of a fail-safe (resilient) infrastructure.

Initial results from the project GeoBox show that edge computing offers the possibility of realizing RSF. Edge computing is structured in such a way that it also functions decentrally for the end users, so that the dependency on the internet infrastructure is reduced, in comparison to other solutions. By setting up decentralized edge data centers for regional IoT sensor networks, a digital ecosystem for regional and resilient networking of smart farming is supported. Resilient Edge Computing (REC) focuses on the resilience and reliability of agricultural production, a requirement reinforced by the Agricultural Ministers' Conference. The GeoBox-Infrastructure (GBI) serves as a foundation and can be extended by features of resilient edge computing. With the open source software "Open Horizon", managed by the Linux Foundation, the GBI has an edge framework at its disposal that enables the automation of software installation and data synchronization on thousands of edge devices (e.B miniservers – also called “HofBoxes”).
Such a resilient, digital infrastructure for decentralization and regionalization should be coordinated and introduced nationwide, starting with KRITIS "Food and Agriculture" for reasons based on the Food Security and Preparedness Act (ESVG).

Resource and Energy Efficiency Analysis in Bottle-to-Bottle Recycling Plant – Case Study

This research presents a practical approach towards resources and energy efficiency in production systems in small and medium enterprises (SMEs). The approach is based on mapping the production line using the simulation tool UMBERTO® Efficiency+ for the representation of the material and energy flows. A case study from a bottle-to-bottle recycling plant is selected. Material flow bottle-necks are spotted and energy hotspots are identified. Optimization is implemented in the model and the new values for savings in material, energy and carbon emissions are calculated.  The simulated optimized model estimates yearly savings by around 8% in materials and 10% in electricity consumption and consequently in costs and CO2 emissions. The simulation tool UMBERTO Efficiency+ facilitated the simple and time efficient assessment of the production sustainability as well as the prediction of the expected savings upon implementation of the modifications which is a practical and convenient approach for the industrial field.

Vision Open Data Farm - Opportunities and Possibilities in Precision Farming

In order to utilize the full potential of smart/precision farming and sustainable agricultural practices, which lie at the heart of the EU Sustainable Development Goals, the transition from (prevalent) analog to digital methods requires supportive platforms. The German Federal Ministry of Food and Agriculture has created 14 “digital trial fields” to showcase how this transition can be reinforced, guiding the implementation and acceptance of innovative digital practices among German farmers (https://www.bmel.de/EN/topics/digitalisation/digital-trial-fields.html).  

A major focus of the digital trial field “EF-Südwest” is implementing digital practices across various agricultural production areas by creating a standardized data management platform (living lab) that can be used to demonstrate, educate, and coach. This digital platform is based on data collected from various digital agricultural practices on the Hofgut Neumühle, a teaching and research institute for animal husbandry in Rhineland-Palatinate, Germany. The pilot project is to visually depict the Hofgut Neumühle as a 3D-Model, as well as visualize the data flow surrounding their daily work. This work will be performed in cooperation with additional digital trial fields as well as research groups in Rhineland-Palatinate.

The first step in implementing digital practices is displaying the value and benefits from data as well as how to integrate them seamlessly into daily operations. A main goal of the living lab Open Data Farm is demonstrating how to do this using data collected on the Hofgut Neumühle. In addition, educational material explaining the value of the data as it relates to daily agricultural operations is provided. The overall goal is developing an interactive educational platform that can integrate existing resilient smart farming infrastructures such as the GeoBox-Infrastructure, as well as serves as test platform for new, innovative ideas.

Charging heuristics of electric automated guided vehicles (e-AGVs) in the last-mile delivery

Substituting fuel-based and manual delivery for the last miles with electric automated guided vehicles (e-AGVs) promises to substantially reduce CO2 and noise emissions, and traffic congestion in urban areas. As they are battery-based vehicles, charging of these vehicles needs to be adjusted into their operation planning, in particular, that their batteries are too small for a full day of operations.  However, when, where, and how many parcels should be transported is uncertain and so is the energy demand of e-AGVs. To avoid computationally expensive calculation of online optimization, we develop three recharging heuristics to ensure the energy supply for e-AGVs dealing with demand uncertainties. We propose a simulation framework that enables a grid search for the best combination of heuristics’ parameters (charging goal, state of charge, lower limit, etc.). The simulation is applied on a real-world data set to test and evaluate charging strategies regarding different key performance indicators (KPIs). Our work provides a systemic multicriteria comparison of charging heuristics for decision-makers planning to employ e-AGVs in the last-mile delivery.

14:00 – 14:20 Uhr
Impulse für die Weiterentwicklung des Blauen Engels aus einer verhaltenswissenschaftlichen Perspektive

Jan Tomaschek

Malte Räuchle

Felix Meyer

Klaus Schlender

14:00 – 14:30 Uhr
(inkl. 10 Min. Diskussion)

lebensmittelwarnung.de - Rückrufe und öffentliche Warnungen in Deutschland

Ein Einblick in die Arbeit in der Lebensmittelüberwachung.

Silvia Raschke (BVL)

14:30 – 15:00 Uhr
(inkl. 10 Min. Diskussion)

Die umweltgerechte Stadt -Entwicklung und Umsetzung einer GIS-gestützten quartiersbezogenen Umweltbelastungsanalyse im Land Berlin

H.-Josef Klimeczek

15:00 – 15:30 Uhr
(inkl. 10 Min. Diskussion)

Konzeption und Entwicklung einer Weboberfläche zur Visualisierung umweltfreundlich erzeugter Energie in Industrienetzwerken, am Beispiel Motzener Strasse in Berlin-Marienfelde

Volker Wohlgemuth

15:30 – 16:00 Uhr
(inkl. 10 Min. Diskussion)

Nutzerzentrierte Referenzmodellierung für Nachhaltigkeitskennzahlen - Datenstrukturierung für ein transparentes und Stakeholder-orientiertes Informationsangebot von Unternehmen an Investoren als Datennutzer

Raphaela Helbig

16:00 – 16:15 Uhr

Resümee

BMU/UBA

Human Behavior Model in Public Pedestrian-only Space Estimated using High-precision Trajectory Data

A human behavior model describing the actions of pedestrians in public pedestrian-only space was constructed on the basis of high-precision trajectory data gathered using a laser scanner sensor system. First, a route selection model that could be used to describe macroscopic trajectories was construct-ed. Next, a walking model that includes the psychological stress imposed by the presence of walls, columns, and other hindrances to motion. These models were then combined to create the human behavior model. Next, using laser scanner sensors, highly precise measurements were taken of the trajecto-ries of pedestrians in the reception area of a hospital. The observational data were employed to estimate unknown parameters in the human behavior model, and to note the characteristics (sex, patient or staff, mobility aid us-age) of the pedestrians as they varied with pedestrian attributes. Finally, we proposed a procedure for evaluating the comfort and efficiency of public pedestrian-only space. Using simulation analysis, we demonstrated that it is possible to uniquely estimate the spatial distribution of comfort and efficiency at any given location from the frequency of passages at that location.

A Taxonomy about Information Systems Complexity and Sustainability

With the increasing digitalization of all industry sectors, information systems are becoming more and more complex. At the same time, also thanks to their crucial societal role, they have the potential to help (or hinder) organizations in their ambition to contribute to sustainability goals. In our work, we aim at helping them by making explicit the important concerns that shape their information systems and that are found to influence complexity in some sustainability aspects. To this aim, we perform a study in a large bank, and build a taxonomy of concerns that emerge from real projects and experience, and blend both complexity and sustainability.    

Approaches to assessing the vulnerability of large city population to natural and man-made hazards using mobile operators data (case study of Moscow, Russia)

The complex nature of threats to large cities residents requires a rethinking of the existing methods for the vulnerability assessment of the population to various kinds of them. Moscow concentrates about 9% of the Russian population, and with the Moscow oblast - about 12%. The high level of spatial concentration of the population and its active dynamics determine the increased level of natural and man-made risk in the city. The purpose of this study is to assess the vulnerability of the Moscow population to natural and man-made hazards, taking into account the actual population size and its movement in the city within different time cycles (daily and weekly-seasonal). Trying to find an appropriate solution, authors used alternative sources of statistical information – mobile operators data. The use of these data made it possible to obtain more detailed information on the state of socio-geographical systems, to overcome barriers associated with the incompleteness and ‘static’ nature of official statistical information (data provided by Rosstat). Mobile operators data allow obtaining more reliable depictions of the localization of its users at a certain point in time, which made it possible to adjust and clarify the current ideas about the distribution of the population in Moscow. As a result of the study, it was shown that for the center of Moscow and for New Moscow, the population vulnerability level is much higher than reflected in official documents. On the contrary, in the peripheral areas of Old Moscow, the potential risks are reduced because the real population density is significantly lower than it is estimated in the calculations provided by Rosstat.

Assisting PV experts in on-site condition evaluation of PV modules using weather-independent dark IV string curves, artificial intelligence and a web-database

Photovoltaic (PV) modules can make a huge contribution to achieve the Sustainable Development Goals of the United Nations. To be able to make that contribution, regular check-ups and evaluation of installed PV modules are necessary as they can develop faults and degenerate over time. In this project, we improve the *dark IV string curve method* used for on-site fault detection and module evaluation. We do so by training artificial intelligence (AI) models to predict the maximum power point and the bright IV curve of PV modules given the weather-independent dark IV string curve. We present some background on this topic, describe the data used for training and the developed models. The results are illustrated graphically. To make the models available for PV experts in practice and to support their decision-making process, we also developed the web-database-application *iPVModule* for storing historical PV Module data and integrated the AI-models.

Air pollution due to central heating of a city-centered university campus

The aim of this study was to determine the gaseous pollutant concentrations re-sulting from the natural gas-powered central heating at the Aristotle University of Thessaloniki main campus, on the basis of limited emission and meteorological data. For this reason, a methodology was compiled addressing emissions and concentration levels as a function of a set of meteorological scenarios: Emissions were estimated based on campus operational conditions and concentration levels were calculated by employing the Gaussian plume model approach via an in-house implementation in Python. The necessary climatic conditions were used to compile a total of 1080 different weather – dispersion model simulation scenari-os. The obtained results allowed the adequate analysis of the geospatial distribu-tion of pollutants. Concentration levels were estimated to be below relevant limit values but dependent on the prevailing meteorological conditions.

EpiDesktop - A Spatial Decision Support System for Simulating Epidemic Spread and Human Mobility Trends Under Different Scenarios

Human mobility has been recognized as one of the critical factors of contagious diseases spread. SARS-CoV-2 as a highly contagious and eluding virus is not an exception affecting the normal lives of more than half of the global population in a way or another and claiming the lives of hundreds of thousands. As a response to such a situation, mobility should be managed by imposing certain policies. In light of this, this proposed study presents a newly developed GIS platform aiming at simulating and mapping the spread of infectious diseases and the mobility patterns under different scenarios based on different epidemiological models. In addition to the "business as usual" scenario, other response scenarios can be defined to reflect real-world situations taking into consideration various parameters including the daily rise of infected and deaths, among others. The developed system might offer a useful tool for decision-makers for insights about strategies to be implemented and measures to control the spread of the virus.

Evaluation of a Sustainable Crowd Logistics Concept for the Last Mile Based on Electric Cargo Bikes

During to the recent growth of e-commerce and as the number of shipments are rising last mile services are facing many challenges and hurdles especially in rela-tion to sustainable action. Within the project NaCl measures were tested to ad-dress and to positively impact the three dimensions of sustainability on the last mile. Based on the usage of electric cargo bikes and a crowd logistics approach shipments of a regional logistics service provider and beyond of regional station-ary retailers were realized during a three-month pilot phase. The project's activi-ties and objects were evaluated regarding positive effects on social and ecological sustainability while including the economic aspects of the transportation system. The aim of the evaluation was to be able to make a fundamental statement regard-ing the usability of the crowd logistics approach and the sustainable Customer Relationship Management approach and, if necessary, to identify optimization po-tential and concrete suggestions for improvement. Another objective of the evalu-ation was to determine the needs of different participants such as retailers and shippers as well as of receivers and deliverers while considering sustainable goals. Various data collection and analysis methods were adopted to evaluate the project objectives in quantitative and qualitative ways. The evaluation audited the approach’s ability to have positive impacts on different dimensions of sustainability and showed further possibilities for improvement.

Mobility as a Service and the Avoid-Shift-Improve approach

During the last few years, “Mobility as a Service” (MaaS) has been concep-tualized and researched as a platform for integrated, mixed-mode mobility. While some hope it will lead to environmental benefits, its real effects are still unclear. Here, we explore how MaaS is related to, and can be combined with, the established “Avoid-Shift-Improve” transport planning approach (ASI). We see that the MaaS concept described in research does not support “Avoid”-ing unnecessary transport. We combine learnings from MaaS re-search with learnings from a living lab, where mobility services can be booked in combination with a local co-working hub for commuters. In both literature and living lab, we especially examine the role of public authori-ties for ASI in MaaS. We conclude that more research is needed on how MaaS can be guided by ASI, and suggest that non-travel accessibility ser-vices, such as co-working hubs, could be part of the MaaS concept to sup-port “Avoid”-ing unnecessary transport. Furthermore, we suggest that urban form needs to be considered in MaaS research. We also see that public au-thorities have an important role to play in ensuring that MaaS serves ASI and sustainable mobility.

A Framework for Assessing Impacts of Information and Communication Technology on Passenger Transport and Greenhouse Gas Emissions

Information and communication technology (ICT) provides unprecedented opportunities to reduce greenhouse gas (GHG) emissions from passenger transport by avoiding, shifting or improving transport. Research on climate protection through ICT applications in passenger transport mainly focuses on theoretical potentials, is assuming that digital mobility services replace GHG-intensive transport modes (e.g. car travel), and does not specify the conditions under which decarbonization potentials will materialize. It is known that digital mobility services can also take a complementary (as opposed to substituting) role in travel or replace non-motorized travel, which can increase GHG emissions. Based on existing literature, we develop a conceptual framework to guide qualitative and quantitative assessments of the relationship between ICT use, the transport system and GHG emissions. The framework distinguishes three types of effects: (1) First-order effects, GHG impacts of producing, operating and disposing the ICT hardware and software, (2) second-order effects, impacts of ICT on properties of transport modes, transport mode choice and travel demand, and (3) third-order effects, long-term structural changes due to ICT use (e.g. residential relocation). We qualitatively demonstrate the framework at the example of automated driving and discuss methodological challenges in assessments of ICT impacts on passenger transport such as the definition of system boundaries, selection of indicators to measure ICT use, consideration of socio-demographic characteristics of individuals and the inference of causality. The framework supports researchers in scoping assessments, designing suitable assessment methods and correctly interpreting the results, which is essential to put digitalization in passenger transport at the service of climate protection.

Analysis of the Grid Capacity for Electric Vehicles in Districts with a Major Need for Sustainable Energy Refurbishment: The Case of a District in Lower Saxony

The demand for charging facilities is growing in parallel to the number of electric vehicles (EV). This demand will be predominantly covered by private charging points connected to the low-voltage grid. The increased load resulting from these charging processes may cause grid instabilities depending on operational factors. These high load cases were unknown while planning and building the grid of existing districts. Therefore, critical grid situations resulting from high penetration rates of EV can oc-cur. The goal of this research is to analyze the effects of an increasing EV penetration rate in existing districts with opportunities for different levels of cooperative energy generation and measure the maximum possible grid capacity for EV charging. Identi-fied limiting factors are then considered in further simulations regarding the energy refurbishment of the district trying to enhance the grid’s capacity for EV.

Due to the multidisciplinary nature of the components involved in the simulation of this system, a co-simulation framework is required to conduct the power system analyses. This will allow coupling different modelling tools and will enable orchestra-tion and communication of parameters between components, so that a systems-wide perspective is achieved. The simulation scenario, the existing models and the newly developed or modified models will be accessible under open-source license enabling a transparent research process and improving research quality as well as accessibility.

Emobpy, a tool for calculating the grid electricity demand of EV from empirical data, is coupled with pandapower to perform quasi dynamic load flow calculations and hence determine the current grid capacity for EV. The EV model provides time-dependent power values of the charging processes occurring in the district. The user-specific behaviour (charging frequency, charging time), current and future penetra-tion rates, common charging capacities, coincidence factors and typical path lengths are considered. Photovoltaic and battery storage models are added and coupled to the energy system to increase the grid capacity and prevent critical grid situations with high penetration rates.

Automatic Recording and Analysis of the Quality of Biking Paths

The number of people living in urban environments is continuously increasing. The available space for a living gets more and more expensive and rare. On the other hand, more and more people prefer using bikes for their daily routine.
Especially in cities, the environment is not always bike-friendly: High air pollution, bumpy and noisy streets or blocked paths hinder a relaxed biking trip in city areas.
The system presented in this work consists of two parts: A sensing platform collects information about the current road being used on a bike, like the noise level or the air quality. The second part is a server platform, which collects the data from the sensor platform and can offer optimized route planning for bikers according to the experience of all users.
Using this crowd-sensing approach, the biking community can benefit from navigation to find the nicest route to the destination.

Hydrogen Technology Business Management Modeling: A project to support the ramp-up of the hydrogen infrastructure

The transport and mobility sector is responsible for nearly one quarter of all global energy-related greenhouse gas emissions. The project ‘Hydrogen Technology Business Process Management Modeling’, focuses on the ramp-up of hydrogen infrastructure, which is fundamental to enabling e.g. fuel cell vehicles for a wider variety of use cases. To accelerate the development of hydrogen infrastructure for transport and mobility purposes, it is essential to simplify the processes involved.
There is an inherent complexity which can be seen, for example, in the approval processes required by German law. Here, different legal bases apply, depending on the specific configuration of the infrastructure design. Approval processes, e.g. of hydrogen filling stations, but other processes in mobility and logistics also, are information heavy. Here, the flow of information between different stakeholders and systems is of great importance. The project aims at solving the problems mentioned above by examining the approval process for a hydrogen filling station exemplary for hydrogen related process in order to develop a standardized approach for applicants and approval authorities and to develop a modelling procedure that allows administration processes to be optimized and simplified through information technologies.
The scientific approach of the project involves the use of different methods like morphological analysis, document analysis, standard BPMN Modeling and the Function Analysis System Technique to clarify the complexity and challenges within this use case.

Avoiding Obsolescence – Taking Usability into Account: The Augmented Reality Glasses Example

In the context of this work, it was examined whether the integration of AR glasses in industrial processes is a useful tool to avoid obsolescence. AR is already being used in various areas such as medicine, maintenance and repair, and many other areas. For this purpose, three different models of AR glasses were then tested. When selecting the AR glasses, care was taken to cover as wide a range of properties as possible. For this reason, both lightweight AR glasses and heavier AR glasses were selected, which also differed in terms of performance. In the interest of sustainable use of this in-formation and communication technology, success criteria were defined for the trial, on the basis of which success or failure could be determined after the trial. The three AR glasses were each tested by 10 subjects, all of whom were experienced employees in an industrial environment.
This leads to the following conclusion: If specific information and communication devices are purchased in the context of technological euphoria without thorough testing, there is a risk that such devices will immediately degenerate into electronic waste.

Multi-model Simulation for Serious Games in Sustainability Research

One of the aims of serious role-playing games (SRPG) is to educate people by simulating the impact of their actions in various domains. Climate change is one of these domains. Such SRPGs are usually based on complex simulations of heterogeneous models. However, interfacing a SRPG with heterogeneous simulations is currently a manual and fault-prone process that requires both domain-specific and technical knowledge. This research proposes a conceptual framework that supports game designers in interfacing their SRPGs with pre-existing or custom-tailored simulation models. In addition, a first implementation of such an interface is presented that further facilitates the creation, configuration and execution of such games.

Assessing health implications of indoor air quality for energy-efficient manufactured homes

With atmospheric carbon dioxide (CO2) now at the highest level in human history, it is imperative that emissions be reduced – and energy efficiency is considered essential to achieving global climate targets.  In the United States, data from 2020 indicate that the electric power sector accounts for about a third of energy-related CO2 emissions, and the residential sector accounts for about a sixth of U.S. energy consumption. These data illustrate that increasing the energy efficiency of homes could reduce residential electricity demand, thereby reducing emissions of CO2  and other pollutants from carbon-intensive power plants. While making homes more airtight will have a substantial benefit of conserving energy and reducing indoor exposures to ambient (outdoor) pollutants, it is expected to somewhat increase the indoor levels of pollutants that are generated indoors – such as from cooking and cleaning. Therefore, an assessment of potential impacts to indoor air quality and health will provide useful context for energy conservation standards being developed for manufactured homes. Relatively few data are available regarding air quality in homes, particularly manufactured homes. Meanwhile, long-standing toxicity and risk estimators for certain exposures are being updated (including for inhaling acrolein, which is generated during cooking oils or foods with fats).  This presentation describes the approach for assessing health effects from indoor exposures to several pollutants to inform energy conservation standards. Some pollutants are being evaluated quantitatively while others are addressed qualitatively.  The latter include ozone, which can be generated by home air cleaners (increasingly used during the pandemic). The scenarios assessed encompass baseline conditions (per existing energy efficiency standards) and future conditions that might result from new energy conservation standards that would make homes more airtight.  Results can help inform mitigation measures, such as bringing attention to the importance of operating range hoods to reduce exposures to pollutants emitted during cooking.

COVID-19 Impacts: Zoonotic Diseases, Employment, Food Insecurity, Future Challenges

The past year and a half of unforeseen challenges brought on by the COVID-19 outbreak have created many challenges worldwide. While the origin is still being debated, the outbreak has affected all countries. Many countries brought their commerce to a halt. Employees lost their positions due to lack of income on the part of their employers, and many were terminated while others were kept on at a reduced income level. This led to the need for government intervention to provide income necessary for people to survive. While some governments took minor steps and avoided significant impact, others took major steps and held them for lengthy periods making it difficult for the economy to function.

Agricultural economists have utilized existing government and other data or collected their own data to analyze implications of numerous aspects of the COVID-19 outbreaks. This paper provides some insights on the implications of the COVID-19 actions including zoonotic diseases, employment issues, household food insecurity and waste, and future challenges and opportunities.

Environmental implications of single-use plastic wastes from COVID-19 and opportunities to reduce future impacts

A recent study found that illegal dumping and littering of waste plastics are widespread in the United States, despite an infrastructure to readily manage these wastes. A 2010 estimate suggested that from 5 to 13 million metric tons of plastic waste entered U.S. coastal environments that year, and by 2016 that estimate had increased up to five-fold. Single-use plastics are a particular issue, and efforts have been under way for years to move away from linear production, use, and discard toward a circular economy.  During the COVD-19 pandemic, the production and use of single-use plastics rose dramatically in response to urgent demands for personal protective equipment (PPE) to help control exposures, in addition to increased use of other materials such as fast-food containers. Unfortunately, many single-use plastics land in the environment at their end of life, further exacerbating the global challenge of waste plastics.  A recent analysis of the disposition of single-use plastics estimated that a staggering 1.6 million metric tons of waste plastics might have been generated daily due to the pandemic, including an estimated 3.4 billion single-use face shields and masks – many of which were discarded as litter.  Data from studies regarding potential hazards associated with these materials in the environment (including generation of microplastics and potential release of chemical additives) can be combined with estimates of the amounts used in order to assess implications for environmental health.  For this assessment, input has been sought from manufacturers on the composition of common PPE, notably face masks. These data are being integrated to assess opportunities for biobased, biodegradable replacements, with a goal of having more environmentally benign single-use products available in time for the next pandemic, to reduce the environmental impacts of those wastes.

Shareable Goods and Impacts on Consumption; The Case of the Digital Sharing Economy

Digital platforms promote the shared consumption of a variety of material and immaterial goods. As they grow and appeal to more consumers, the new practices and patterns of sharing can have both desirable and undesirable impacts from a sustainability point of view. The present paper takes a closer look at the various impacts of shared consumption practiced in the digital sharing economy (DSE) with the aim to propose a guideline for assessing the DSE’s sustainability impacts. The guideline builds on a typology of shareable goods and a classification of different positive and negative environmental impacts of sharing. By considering different consumption scenarios (based on consumers’ behavioral responses, where applicable), the study develops a conceptual framework for the DSE’s impacts on the environment. This is also an extension of the general impact categories of Information and Communication Technology (ICT) which already exist in the literature. In addition to the impact of consumption, the concepts of the “impact of provision,” “impact of access,” and “impact of maintenance” are introduced as the four main evaluations comprising the sustainability assessment of sharing platforms. The considerations addressed can be helpful in delineating further evaluations of the sustainability of various instances of digital sharing systems.

A review on Key Performance Indicators for Climate Change

Climate change is one of the biggest threats to humanity in the near future. Almost all different scenarios of climate change involve large-scale disasters and hazards. In order to define goals to cities, regions and countries in regards to mitigating climatic change, we first need to understand which the important key performance indicators (KPIs) are, how they can be measured and which values they take. Then, each country can calculate its performance based on these KPIs, setting realistic goals for better performance in the near future. This paper performs a large survey to identify and list 64 relevant KPIs, together with suggested
units and metrics associated with them, divided in eight different thematic areas. It can be considered as an important contribution in the global efforts to understand climatic change, shaping policies and setting goals associated with it.

Developing an Architectural Design for an Environmental Management Information System (EMIS)

Due to climate change and the pressing ecological crisis, companies face the urge to transform their operations in a sustainable way. In this context, environmental management information systems (EMIS) can support companies by systematically collecting, processing, and providing environmentally relevant data and information. Although various approaches for EMIS have been developed, no EMIS is able to address all requirements regarding environmental management accounting yet. Thus, this paper aims to develop an architectural design for EMIS and to create a theoretical basis for further practical analysis and developments. Therefore, this paper identifies and evaluates existing architectures. Subsequently, an architectural design is developed based on a layered architecture and the architecture patterns of service-oriented architecture, data warehouse, business intelligence, and workflow management system. The architectural design can be used as a basic structure for the implementation of EMIS.

A Qualitative Literature Review on Machine Learning Techniques for Predictive Maintenance

The combination of increased cost pressure and increasing digitalization has created the basis for the increased use of predictive maintenance. The enormous amount of data that accumulates in the industrial environment is to be analyzed in order to prevent future failures of production capacities. In recent years, machine learning methods have emerged as a way to address these challenges. This paper shows the current state of research by using a qualitative literature review and answers the question which machine learning techniques are being researched for the use of predictive maintenance. The goal of this paper is to present an overview of the state of the art of the applied and investigated machine learning techniques in the field of predictive maintenance. The results of this study show a disproportionately high level of research activity in the manufacturing industry. It could also be shown that research interest in the public sector is underrepresented, especially in the infrastructure sector.  Furthermore, it could be shown that the focus of applied machine learning techniques can be assigned to supervised learning methods.

Identification of behavior changes in energy consumption behavior with machine learning

Within the research work for the BMBF project ENVIRON we have developed an algorithm in which behavior-relevant phases in correlation with energy consumption can be trained by a machine learning algorithm and evaluated accordingly. The focus is on phases that one or more persons pass through with regard to their energy consumption behavior, the so-called "stage model of self-regulated behavioral change" (SSBC). These phases will be detected by the algorithm in order to better analyze a long-term rebound effect in energy consumption behavior and to support the use of phase specific interventions with the goal of reducing CO2 emissions in apartments.

Recognition of different PV-module-faults based on LSTM neural network classification with the ground truth of expert labeled data from different PV-fields and modul types

In this work, photovoltaic monitoring data together with weather data are used to classify faults using an LSTM neural network. The label data for this model comes from a previous work where unsupervised models were used to detect anomalies on PV plants for an interpretation by experts.

Clustering Analysis of E-Waste Management in BRICS and G7 Countries

The management of waste electrical electronic equipment (WEEE or e-waste) has motivated the development of regulatory instruments in several countries, as well as specific management practices.
However, the formalization of processes occurs differently according to the strategies and motivations of each nation. This study aimed to analyze the similarities that can indicate the main drivers to the adoption of sustainable practices for e-waste management, using as a case study the BRICS and G7 countries. The methodology was based on multiple regression analysis and k-means clustering algorithm, respectively, considering Gross Domestic Product (GDP) and e-waste generation as indicators. The findings suggested that the economic blocs partially influence e-waste management and socioeconomic indices. Indicators such as Human Development Index (HDI) and e-waste generation have a divergent influence, while GDP uniformly influences the population and
has a significant impact. The clusters formed to show the importance of the e-waste generation potential as a determining factor, an aspect also observed in the correlation analysis. In this way, the analysis tools are complementary and reinforce the possibility of using key indicators in e-waste management.

Preliminary framework for assessment of disaster risk

The challenge of mitigating disaster risk starts with the understanding of the essential concepts and with the recognition of the factors to assess the risk. Preliminary framework for assessment of disaster risk is proposed. Qualitative and quantitative aspects are described in order to make easier the understanding of the proposed planning process. This work is a contribution to The Sendai Framework for Disaster Risk Reduction 2015-2030, adopted by the Member States of the United Nations in 2015 (UNDRR, 2019a). The preliminary framework aims to show the set of concept and factors, to be understand the reduction on the vulnerability and dangerous effects of natural hazards. Resilience is explored to get a fast recovery of the life and activities of the damaged community. Preparation and prevention actions, coordination between government areas, communication with communities and studies on optimal resources allocation are identified as opportunity areas to improve the efficiency of plans to protect the life of people exposed to natural hazards.

LEAN Method applied to USAR Team introducing new technologies for improving the search and rescue procedure, FASTER Project.

Communication during and immediately after a disaster situation is a vital component of response and recovery. Effective communication connects first responders, support systems, and family members with the communities and individuals immersed in the disaster. Reliable communication also plays a key role in a community’s resilience.
FASTER is an H2020 RIA project that develops a set of tools for enhancing the operational capacity of first responders while increasing their safety in the field. It has been developed Mobile and Wearable technologies for better mission management, communication, and information. Moreover, FASTER provide a platform of Autonomous Vehicles aiming to collect valuable information from the disaster scene prior to presence operations, as for example assessment of a possible Covid area. Furthermore, First Responders improve their coordination being in continuous contact within a Portable Common Operational Picture (PCOP). receiving information and analysed it gathering multi-modal data from the field, utilizing an IoT network, and Social Media content to extract meaningful information and to orchestrate an intelligent response to the disaster. The whole system facilitates Resilient Communications Support featuring opportunistic
relay services and emergency communication devices supporting all first responders involved in the disaster area.

Suggested Approaches for the international institutions aiming to reduce the gaps in disaster and climate risk information and to enhance data management and use in sustainable development

This paper provides an overview of the key findings based on an assessment of the current capacities, available services, and needs of the regional and national stakeholders for disaster risk information and risk data management in Asia and the Pacific region. The original report and this paper also provide a set of recommendations for international and regional institutions to support enhancing risk data management and use of risk information in Disaster Risk Reduction (DRR) in the countries. The research confirmed and reiterated the persisting low level of risk information used in public policies and plans due to many factors with the following key factors:

• Disaster risk reduction (DRR) and climate change adaptation (CCA) are not yet integrated effectively into all sectors’ policy design, planning, and operations.
• The connection between science and policy is fragmented and requires better alignment of objectives, approaches, and communications.
• The majority of risk assessments do not diagnose the causes of risk, are not accompanied by risk reduction options, and do not evaluate the performance of those options including the risk reduction opportunities.

The original project, which was completed in October 2020, was commissioned by the Asian and Pacific Center for the Development of Disaster Information Management (APDIM-UNESCAP) and was conducted by Sage On Earth Consulting Ltd.

Governmental Information Systems Supporting Nature Conservation in Schleswig-Holstein

The Ministry of Energy, Agriculture, the Environment, Nature and Digitalization Schleswig-Holstein (MELUND) is responsible for the management of nature conservation information in Schleswig-Holstein supported by the State Agency for Agriculture, the Environment and Rural Areas (LLUR). Both authorities need reliable and up-to-date data in order to fulfil their governmental tasks.
To develop and operate comprehensive information systems supporting – amongst others - the management of nature conservation data the governmental strategy recommends the hosting and service environment of the highly secure central data centre. This paper describes design and architecture of a topical cluster for nature conservation data within the central data centre.

Landfill site selection using GIS and Remote Sensing

Finding and selecting the landfill site for disposing of the solid waste is a serious challenge for a metropolitan like Kathmandu. Due to improper management of waste materials, different health-hazardous diseases are growing. Global warming and methane gas production are also a serious causes of poor disposal which are badly affecting the environment and ecology. There are different methods for selecting the landfill site. The policy 3R viz. reduction, recycle, and reuse adopted by metropolitan should be considered while disposing of the wastes. The use of GIS, Remote sensing and analytic hierarchy become crucial while selecting landfill sites. We obtain the satellite imagery covering
Kathmandu and analyzed using GIS to determine geologically and geographically suitable places. GIS performs some deterministic overlay and buffer operations. Almost 12 criteria were used. Distance from waste generation center, distance from roads, slope, distance from settlements, distance to surface water, distance to groundwater areas, soil permeability, etc. are some of the criteria used. These criteria are given some relative weight according to their importance using analytic hierarchy. The map of a suitable site is prepared using GIS spatial operations, rank the candidate site and the most suitable place is recommended.

A Blueprint for Computer Vision Testing in the Circular Economy

Automating visual inspections of used parts constitutes a challenge impeding wider real-world implementations of ideas from the circular economy (CE). Computer vision (CV) is a promising technology in this regard, but its real-world testing is a technical and economic challenge. This CV testing blueprint is an experimental and practice-proven approach for evaluating whether CV is a technically and economically suitable tool for automating a visual inspection task. The testing of two hypotheses lies at the core of the blueprint: (A) The task is solvable based on digital image data and this data is obtainable, and (B) available CV algorithms can learn the task. While testing Hypothesis A does not require machine learning know-how, testing Hypothesis B uses a new technique to infer the prospective algorithm performance from the shape of the validation accuracy graph when trained on a subset of the required training data.

Garment attribute prediction using camera images and Raman spectroscopy to enhance circular textile economy.

In the last decade, demand in the fast fashion segment for textile fibers has increased remarkably. The global fiber consumption is expected to reach between 130 and 145 million metric tonnes per year by 2025.
This forecast comes with a huge negative impact on the environment, and it is clear that to minimize the side effects of this industry, a major transformation must occur.
One of the main challenges is to transform the industry from its primarily linear to a circular structure so that it can reuse its raw materials rather than disposing them at the end of use. Closing the loop would mean a significant reduction of greenhouse gas emissions. Recycling garments and turning them into raw material is still a challenging task; among other difficulties, it requires exact information about the composition of the garments to be recycled.
Currently, conventional sorting plants focus on extracting reusable garments to be sold to the second-hand market while paying for the disposal of the non-reusable fraction. However, this task is complex; sorting an enormous amount of mixed bales of worn clothing with up to hundreds of different attributes is nowadays done by hand.
The presented project aims to reliably and objectively sort single garments according to a set of defined attributes, detect the material composition and contaminants, and enable fiber-to-fiber recycling.  In this way, clothing contaminated with harmful substances could be sorted out and sent for hazardous waste recycling, thus preventing contamination in the recycling process or contamination to the environment due to incorrect hazardous disposal.

Distributed Sector Coupled Low Carbon Energy Supply for Logistics Properties –  Concept for the Integration of Heating, Electricity and Transport

In this paper, a concept for a low greenhouse gas emission energy supply is developed, taking a logistics center as an example. Especially sector coupling is considered as a means to achieve a low carbon energy supply.  In  this  regard,  the  modeling  of the energy system and the developed architecture of an energy management system are presented.

RESTful Web Services and gRPC, a comparison of the technologies and the optimization of existing data synchronization and energy consumption

As part of the research work for the BMBF project ENVIRON, we have developed a solution for saving bandwidth in data transmission and are also investigating the theoretically possible savings in power consumption. The Internet is used by the ENVIRON sensor system in over 25 households for data transmission with the research server. gRPC technology was implemented to reduce transfer rates, instead of the previously used technology RESTFul Service[2] In addition to reduced data transfer rates a theoretical reduce of power consumption is also considered upon the basis of the study by Junxian Huang et. al. called "A Close Examination of Performance and Power Characteristics of 4G LTE Networks" in chapter 5 [3]. Several studies have examined the influence of different wireless variants such as 3G, 4G and WiFi, but the choice of communication type may also have an impact on energy costs. This impact was researched by Carolina Luiza Chamas et. al. in the publication "Comparing REST, SOAP, Socket and gRPC in computation offloading of mobile applications: An energy cost analysis"[4]. In this paper, we introduce the data sync process performed by RESTFul services and then compare it with the data sync process using gRPC technologies. The comparison targets the data synchronization process of sensor data to the reserach server.

Digital steering tool for real estate owners in the era of energetic transition

The Fondation communale de Versoix - Samuel May (hereafter the Foundation) is a municipal foundation of public interest who owns several buildings and whose aim is to provide, as a priority, the population of the town of Versoix with comfortable housing at prices corresponding to its needs, as well as professional, commercial, artisanal or general interest premises with sustainability criteria. The Foundation’s attention had been drawn to the eREN project, developed by the HES-SO, proposing a global approach for the building envelope in energetic refurbishment projects. In 2018, the Foundation board contacted HES SO / HEPIA (Engineering and architecture faculty of the HES-SO), in Geneva, to request for the development of a simple and transparent tool to help its board to better plan the renovation interventions of its real estate assets. The project was launched in 2019 and completed in 2020, resulting in a methodology for evaluating the building stock and a steering tool, based on a simple spreadsheet, which makes it possible for the Foundation board to set intervention priorities on the basis of its strategic criteria.

Site Specific Climatic Smart Farming as an important part of Resilient Smart Farming (RSF)

The current situation in Germany as well as in other countries after natural disasters have occurred, reinforces the relevance of resilient and climatic smart farming  as a crucial practice to conserve our environment.
Due to climate change and increasingly varying local weather conditions, there is a need to react earlier to unexpected weather events. Nowadays, technological development enables this just-in-time information management.

The ongoing research project Smart Soil Information for Farmers (SoFI) delivers such geo-data based information services. Through the use of weather data from agrometeorological stations and soil information relating to field capacity and layer thickness, the potential soil moisture is modelled. This is currently being performed at an hourly rate for all agricultural sites and polygons of available soil data throughout Rhineland- Palatinate and Bavaria. The modelling results are classified into different categories, from very dry to very moist. These can then be transfomed into groups which describe the trafficability of the soil, resulting in the avoidance of soil damage through compaction. To derive the susceptibility of soils to compaction and conversely derive trafficability, several approaches have been published. Additionally, microclimate data, recorded specifically for each plot by local sensors, can be used to extend weather forecasting systems, allowing measures to be implemented with greater temporal and spatial precision. Based on LoraWAN enabled communication structures, the sensor data can be easily managed and published, i.e. using the Things Network. Building up such a resilient local network supports the implementation of resilient smart farming (RSF). The so-called GeoBox-Infrastructure (GBI) connects these innovative technologies. The GBI focuses on resilient and decentral data management. Therefore, it is crucial to reduce the strong dependency of operational data from central servers.

The combination of this location-based service and state-wide modelling in the GBI enables, for the first time, the derivation of real-time spatiotemporally high-resolution maps as a service for farmers.

An Automated, Non-Invasive Approach to Determine Bee Pollen Diversity Based on Flora Data

Bees collect pollen from a variety of plant species. This pollen diversity is both an indicator of biodiversity and a balanced diet for bees which has a positive effect on their health. Traditionally, pollen diversity is determined by invasively and manually collecting and analysing the pollen. In this paper, we present a heuristic for assessing the pollen diversity around a hive using data on flora. This data-driven approach is both efficient and non-invasive. We evaluated our approach against the results of a microscope analysis, showing that it delivers useable results, despite the lack of flora data in some places. As a next step, we will incorporate data from our AI-based video monitoring system of hives to match plants based on pollen colours that were detected in the images.

Integrating data on petroplastics to inform new plastics and promote recycling and upcycling

Global production of petroleum-based plastics has topped billions of metric tons and continues to grow. Only a small fraction is recycled or upcycled at the end of a product’s life, with most used plastics being landfilled, incinerated, or released to the environment as litter.  Designed to be durable throughout their useful life, these conventional plastics likewise persist in the environment. For this reason, extensive efforts are under way to stem the environmental accumulation of waste plastics and associated fate products (notably microplastics and nanoplastics) by increasing reuse, recycling, and upcycling. A relational database has been outlined to incorporate linkages among polymer properties, suitability considerations for recycling and upcycling, and biodegradation standards relevant to used plastics that are landfilled or littered. Because literature data vary substantially, including for basic physicochemical and thermomechanical properties, meta data are particularly important in organizing the database to reveal opportunities for advancing the circular economy, with particular attention to recycling. Factors that influence reported data include instrumentation and analytical techniques as well as ambient conditions, such as relative humidity. Of further interest is how field data align with those from laboratory tests designed to mimic environmental conditions, to consider whether standard testing methods might be modified to better represent more real-world conditions.  This presentation highlights how polymer properties and other data can be integrated to inform opportunities for improved recycling, including increasing the number of cycles and evaluating the role of bioplastics. The current database has more than 400 data fields spanning characteristics from molecular weight to environmental degradation for a number of conventional polymers, while limited data have been found for biopolymers – primarily polyethylene and polyethylene terephthalate. The overall aim is to integrate data that can better inform the development of bioplastics that are more biofriendly, toward reducing the environmental impacts of future plastics.