Advances and New Trends in Environmental Informatics

The increasing awareness of environmental protection, e.g. in the course of climate change, also affects products of the information and communication technology industry along their lifecycle. Companies have to consider how their processes and products can be designed correspondingly the expectations of their stakeholders as well as the public. Therefore they have to measure and analyze environmentally concerning data to improve their service provision. Using the Apple iPhone as an example this paper will execute such an analysis to evaluate its environmental impact. The data investigation is performed by reconditioning, analyzing and interpreting the data as well as giving potential causes and correlations with other datasets. Basing on the performed analysis a generalized model for the assessment of the environmental impact of ICT products can be enabled.

Very small organizations rarely use software to organize their business processes, because there are comparatively few known solutions for their particular problems. On the one hand, these software solutions are often associated with high costs and on the other hand, particularly very small organizations do not have enough resources to find suitable solutions. These organizations miss a lot of opportunities: Through the use of the Information and Communication Technique (ICT) business processes can be processed not only resource-efficient due to reduced paper consumption. The ICT also helps to make business processes more efficient. To find such a sustainable software solution for very small organizations, requirements are identified. To determine a structure for such software to document business processes, it is important to apply design aspects for the implementation of the software. A definition of such design aspects can be achieved by the properties of a sheet of paper, which help to organize business processes in very small organizations. By analogy of the dominant metaphor of paper we specify requirements of sustainable software design for very small organizations.

Energy efficiency and -awareness are of growing importance in the field of information and communication technology. On the one hand, computing center aim for reducing their energy consumption in order to save money and improve their carbon footprint. On the other hand, mobile devices that are typically battery powered, have to be aware of their energy consumption in order to prolong their up-time while at the same time keeping a specific level of quality-of-service. Research has shown that especially software, running on a (mobile) device has a large impact onto the energy consumption of that device. Software developers should be aware of a software systems’ energy related cost and about means for optimizing them. Best practices, community-believed guidelines for improving the quality (e.g., performance, energy efficiency, usability, etc.) of software, are one way to do so. This paper evaluates the effects of selected guidelines by a small experiment series and within a larger, commercial product. Results show that by systematically applying selected programming guidelines, energy consumption can be reduced by 9 % and performance by 1 % in average.

Sustainability is something that unites humankind and the important 2015 UN Climate Change Conference manifested this and was described by many as our last chance. A shifting towards sustainability through design is a challenge for managers and policymakers of organizations since the existing system or product could be complex and may have difficulty to adopt such a shift. This paper explores how organizations and their designers and developers could benefit from having a predictable process to follow for conducting such a shift, since numerous challenges are associated with costs and revenues. While universal design (UD) is a design philosophy closely associated with the sustainable design, an advanced perspective of UD could be implied as a redesign strategy for existing design and may be used as a radical design and innovation strategy for sustainability. In this paper, some of the aspects of UD as a redesign strategy for sustainability are addressed. Based on the previous theoretical frameworks, a UD approach for redesigning towards sustainability was formulated and discussed.

In the event of a devastating earthquake, a large number of streets will be damaged and/or blocked by collapsed buildings, and the use of emergency vehicles is expected to be paralyzed and unavailable. It is, therefore, important to quickly collect and utilize disaster-information for mitigation. We develop a Web application for collecting and sharing disaster-information collected by users in real time. Furthermore, we demonstrate that the system can provide effective information in real time for reducing the damage of disaster, by performing a demonstration experiment and a simulation carried out by assuming a devastating earthquake in densely built-up wooden residential area in Tokyo.

Social media and instant messaging services are nowadays considered as important communication infrastructures on which people rely on. However, the exchange of content during breakdowns of the underlying technical infrastructures, which sometimes happens based on environmental occurrences, is challenging. Hence, with this paper, we examine the resilience of social media during breakdowns. We discuss communication options and examine ad-hoc functionality for the exchange of social media data between different actors in such cases. To address this, we have developed a concept, which makes use of mobile ad-hoc networks (MANETs) for the spontaneous exchange of information with smartphones. We implemented our concept as the mobile application Social Offline Map (SOMAP) and evaluated it within two iterations (1.0 and 2.0). Finally, we discuss our contribution within the context of related work and the limitations of our approach.

Public health emergencies, whether natural or manmade, require a timely and well planned response from the local health authorities to mitigate the economic and human loss. Creation of well-defined service areas with Points of Dispensing (POD) facilities for providing medical or other care to the population within these areas is a widely accepted approach. However, not every individual may have equal access to the POD or the resources available at the POD due to various social, behavioral, cultural, and economic or health disparities. Therefore, a realistic and working response plan must provide ways to address these access disparities associated with each POD. The creation of such a response plan is a data intensive problem and requires demographic, spatial, transportation and resource data at different geographic levels. This paper demonstrates the collection of these data from disparate sources and their integration to analyze vulnerabilities in emergency response plans. The variety and volume of the required data and the manipulation process faced challenges of big data.

This paper provides a summary of the EPISECC consortium decisions taken when developing a common information space with respect to data sharing in the field of public protection and disaster relief. After explaining why defining data flows and design process in the EPISECC is important, the paper introduces a high-level overview of the EPISECC Common Information Space (CIS). Additionally, the paper explores the CIS user requirements and software architecture. To attain the main objective of this paper and to provide an operational overview of the EPISECC CIS, the paper defines a methodology to map data flows within the CIS and examines the basic functionality of the proposed system.

The aggregation of distributed energy resources in virtual power plants (VPPs) is a feasible approach to overcome entry barriers for energy markets like, e.g., the European Power Exchange SE (EPEX SPOT SE). An increasing number of energy supply companies offer the integration of decentralized units in VPPs aiming to achieve the maximum profit by trading the power produced by the VPP at energy markets. However, the coordination of the generation units’ operational modes (operation schedule) within a VPP as well as the selection of offered market products (product portfolio) are optimization problems that are mutually dependent. In this contribution a method is proposed that allows automating both the optimized composition of the product portfolio and the determination of the matching operation schedule for the VPP, in terms of profit maximization. Application example of the method is the EPEX SPOT SE day-ahead market. The concept of the approach can be roughly described as follows: First of all, machine learning techniques are used to predict the market prices for the trading day. Then, the market forecast is used in combination with feasible schedule samples from the generation units as input for the optimization process. During the optimization a hybrid approach comprising heuristic algorithms, such as simulated annealing or tabu search, and linear optimization is applied. The approach is evaluated using historical market data and intricate simulation models of generation units.

To increase the share of distributed energy resources (DER), they need to provide grid supporting ancillary services. In order to fulfill large energy products, virtual power plants (VPP) aggregate many small DER. In this paper we present an agent-based approach to the optimization problem of scheduling the DER of a VPP. We compare our approach which uses a evolutionary algorithm to one that uses a mathematical solver. When comparing the two approaches we found that our approach approximates the optimal solution well. The central benefit of our approach is that it scales better wrt. the VPP size. The increased scalability opens the ancillary services market to VPPs that aggregate more and smaller DERs.

Demand Response (DR) is expected to play a crucial role in balancing supply and demand in future smart grids with increased proportion of electricity from renewable sources. However, previous studies on price-based DR programs have shown that there is a substantial need to strengthen the incentive models in order to achieve sufficient end-user response. In addition, recent studies are starting to explore alternative incentives based on environmental performance as a support to dynamic pricing tariffs. In this paper, we investigate in the potential of using a dynamic wind signal, reflecting the hourly variations in wind power generation, as an environmental incentive for load shift in DR programs. A wind signal is constructed based on Swedish electricity generation data for 2014, and intraday and seasonally patterns of wind power generation are analyzed with respect to hourly electricity spot prices. The results show that a wind signal is supportive to the economic incentive of a dynamic price signal to stimulate intraday load shift by end-use customers; shifting electricity consumption from hours of high price and low wind power generation to hours of low price and high wind power generation, leading to both consumer cost-savings and reduced climate impact in the long term.

The development of transparent and reproducible electricity grid models poses a scientific challenge, mainly due to a lack of data transparency in grid economy. This paper presents a modelling method based on Open Data and Open Source. It is focused on a model of the high voltage grid of the German federal state Schleswig-Holstein. The developed model enables to perform static AC power flow simulations. Topological OpenStreetMap information is combined with literature-based assumptions of typical line data. Generation capacities were assigned to the grid buses using freely available registers. The demand was allocated to these buses using the correlating distribution of population. The output of the AC power flow simulations respectively bus voltages and line currents for different worst-case scenarios are presented, analysed and discussed. The results show the potential of scientific electricity grid modelling using Open Data and Open Source. Realistic grid congestion within the high voltage grid of Schleswig-Holstein was identified, providing a basis for further discussion. For a comprehensive validation of the simulated results relevant data need to be opened for the public.

The renewable energy is becoming an integral part for the electric vehicle charging station to reduce greenhouse gases. The combination of this clean technology with appropriate business models reinforce the positive impact on the environment. In this paper we present empirical study of photovoltaic charging system used in a new car-sharing business model “in Tandem” implemented at the University of Hildesheim under the project name e2work. The data analyzed from photovoltaic (PV) over the period of one year, exhibits an interesting insight into the renewable energies supply of electric vehicles under preset conditions of the business model and thus helps to dimension an appropriate size and storage capacity of a photovoltaic system considering different weather conditions and mobility demands. It ensures the quick transferability and easy implementation of both the business model and the appropriate infrastructure to any place.

Mobility is necessary to participate on day-to-day life, but comes along with negative implications on our environment. A change in our mobility behavior caused by the growing digitalization and arise of new mobility services is essential to reduce the environmental impact of our mobility. To achieve such goal, new services with a strengthened competition has to be established. In addition, sharing of workforce and operating supplies between companies are addressed to adopt new business models. B2B sharing, including occasional workers, has huge potential to generate new services and business models, especially in the service sector. The described approach of a mobility broker provides such a tool for sharing between businesses, but also between businesses and occasional working persons in the field of mobility services.

Life Cycle Assessment (LCA), the most prominent technique for the assessment of environmental impacts of products, typically operates on the basis of average meteorological and ecological conditions of whole countries or large regions. This limits the representativeness and accuracy of LCA, particularly in the field of agriculture. The production processes associated with agricultural commodities are characterized by high spatial sensitivity as both inputs (e.g. mineral and organic fertilizers) and the accompanying release of emissions into soil, air and water (e.g. nitrate, dinitrogen monoxide, or phosphate emissions) are largely determined by micro-spatial environmental parameters (precipitation, soil properties, slope, etc.) and therefore highly context dependent. This spatial variability is vastly ignored under the “unit world” assumption inherent to LCA. In this paper, we present a new calculation framework for regionalized life cycle inventory modeling that aims to overcome this inherent limitation. The framework allows an automated, site-specific generation and assessment of regionalized unit process datasets. We demonstrate the framework in a case study on rapeseed cultivation in Germany. The results from the research are (i) a framework for generating regionalized data structures, and (ii) a first examination of the significance of further use cases.

In EU27 0.2 % of global emissions correspond to rail transport. Infrastructure supposes 28 % of these emissions, half of them caused during construction. This shows the high environmental impact of these activities. Life cycle assessment (LCA) techniques combined with intelligent data analysis improves sustainability of railway infrastructure construction processes as a whole, considering environmental, economic and social aspects. The goal is the development of methodologies and tools to optimize decision making process, reducing carbon and water footprints of railway infrastructure construction projects from their earliest stages, i.e. design and planning processes. Environmental and social impact of most relevant tasks have been reviewed and analyzed in order to set the impact of railway networks construction processes. Multi-objective optimization has been performed to find a trade-off solution for project units scheduling. A tool has been developed from this information compilation, providing selected specific footprint values and environmental indicators as open data to the community.

We employ Computational Intelligence (CI) methods to investigate possible associations between air pollution and Diabetes Mellitus (DM) in Thessaloniki, Greece. Models are developed for describing key DM parameters and for identifying environmental influences to patient status. On this basis new, more accurate models for the estimation of renal function levels are presented while a possible linkage is indicated concerning disease parameters and the quality of the atmospheric environment.

This paper addresses theoretical differences between the perceived content of social sustainability and different socially attributed capital stocks, as for example, rather transaction oriented so called social capital, human capital or societal capital. It does so, by mapping different aspects from existing capital approaches to social sustainability, in order to resolve theoretical misconceptions. In addition, the review is intended to highlight different approaches for the measurement and aggregation of social aspects. Consequently the balance within sustainability approaches is addressed and strengthened by elaborating ways to formalize the social perspective. Furthermore, technical solutions considering the resulting impact assessment of social aspects are discussed and in order to demonstrate the significance of the presented ideas, an own application of a threshold based aggregation method, in a manufacturing simulation software, is presented.

One of the fundamental components in assessing air quality is continuous monitoring. However, all measuring devices are bound to sensing noise. Commonly the noise is assumed to have zero mean and, thus, is removed by averaging data over temporal windows. Generally speaking, the larger the window, the better the noise removal. This operation, however, which corresponds to low pass filtering, might result in loss of real abrupt changes in the signal. Therefore, the need arises to set the window size so it optimally removes noise with minimum corruption of real data. This article presents a mathematical model for finding the optimal averaging window size. The suggested method is based on the assumption that while real measured physical phenomenon affects the measurements of all collocated sensors, sensing noise manifests itself independently in each of the sensors. Hence, the smallest window size which presents the highest correlation between the collocated sensors, is deemed as optimal. The results presented here show the great potential of the method in air quality measurements.

The Web and the growing popularity of Internet of Things show a large need for data visualization. This paper presents both a concept and an implemented prototype for a framework that supports a user in creating data visualizations on the Web. The concept is based on a microservice architecture using lightweight REST based service interfaces for communication. The prototype is evaluated using environmental data gathered by the State Office for the Environment, Measurements and Nature Conservation of the Federal State of Baden-Württemberg (LUBW). The main advantages of the framework are high customizability and no requirements for programming skills for using a large part of the framework.

In the context of the project KOGGE an Open-Source Spatial data portal based on GeoNetwork is being developed. Firstly, this portal supports the project-internal data exchange in an OGC and INSPIRE conform way. Secondly, it will be used for civic participation. With the integration of thesauri there is a comfortable data search interface available. To animate users to contribute to the portal the effort of data-adding is reduced massively using metadata templates. Furthermore, these templates guarantee the compliance with ISO standards for spatial data. To avoid duplicate datasets from other spatial data portals the GeoNetwork harvesting technology is used. The portal also offers the possibility to view and edit spatial data by making use of web services (WMS, WFS and WPS). The portal is also used to involve the citizens of Rostock in the planning and management process of urban waters. The user will be able to view project results and comment actual hydrological developments. Also an integration of volunteered geographic information with social media is planned.

Since topics such as climate protection come more and more in focus to municipalities, as they are in the role to implement related processes, technical support systems in this area are powerful tools. The most obvious requirement is the implementation of a monitoring infrastructure, which helps to measure the current state of energy consumption in buildings, properties and far beyond, focusing on various sectors such as industry, mobility, waste, etc. Apart, there are requirements and connected technologies and solutions in the fields of Ambient Assisted Living or Smart Home, which can be considered separately from the infrastructure perspective. But, in future, digital services will continuously grow together, simply by the need of various requirements for data exchange in many use cases. In the work at hand we show that in certain areas topics such as IoT in energy management, Ambient Assisted Living or Smart Home solutions should not be considered as separate platforms, but as convergent building blocks of a connected infrastructure landscape. In the context of municipal climate protection, this approach is illustrated in an example case study by presenting a central platform, which allows municipal energy management in conjunction with other services. The development is illustrated based on an innovative software environment that brings the idea in productive application.

The obligation to conform to environmental regulations requires from companies a set of diverse tasks. A classification of these tasks into central tasks, collaborative tasks, and departmental tasks is proposed and corresponding examples are described. Then, an overview is given of state-of-the-art ICT support approaches for central tasks that are usually performed by cross-departmental compliance managers. The overview includes traditional ICT-support approaches but also advanced approaches that attempt to offer smart and active assistance to compliance managers. Furthermore, the future perspective of ICT support approaches for compliance management are described considering recent ICT advancements.

During the last years, the research activities regarding software and its environmental impacts could find their way into the field of “Green IT”. Thus, researchers became aware of the fact that software is one of the drivers of the energy consumption by ICT. However, the awareness for these aspects could be much higher—especially in the non-scientific area. On the one side, software developers should be aware of green strategies of software engineering. On the other side, those using the products need to be responded to the effects of using ICT products onto the environment. One idea to transfer the environmental effects of software into a social issue is to create an eco-label for software products. Next to defining criteria, such a label could be laid on, and methods to evaluate software products, it seems to be helpful to identify aspects influencing the acceptance of a certification for green software products. In this context, acceptance means taking the eco-label into account while searching for new software. Hence, the following paper aims at identifying those aspects by applying the Technology Acceptance Model (TAM 2) to the specific case of labelling green software products. We will present a first version of an acceptance model for a label for green software products. It is still work in-progress and needs to be evaluated as a next step. Generally, the aim is to create a tool that can be used to develop an eco-label for software products that will be strongly accepted.

The paper presents an application of an evolutionary multi-objective optimization method in the estimation a Pareto optimal set of solutions of pipe replacement schedule. An optimization task is a problem with total investment cost, system entropy, hydraulic reliability and water losses as the objective functions. The paper shows that it is possible to optimize a part of a water distribution system without a deterioration of the crucial parameters in the rest of it, which also results in reduction of a computational complexity. Next, the paper presents how to select the pipes that have to be replaced by application of specific coding of decision variables. The results, based on real life example shows that the method is able to identify the pay-off surface characteristic between the proposed objectives.

DPSIR (Driving forces, Pressures, States, Impacts, Responses) is a framework addressing the needs of environmental data reporting and assessment, extensively exploited by the European Union, its Member States and associated countries. The DPSIR framework does not model the environment, however it implies a systems model. Making such model explicit allows users to better understand the semantics of the five DPSIR components, therefore improving the harmonization and quality of the reported data, in particular in order to process them as Big Data. To such purpose, the tools provided by the systems theory are useful, specifically the system-of-systems (SoS) point of view. On such basis, a conceptual model of the relationships between the constituent systems of the socio-ecological system (SES) is proposed. As a result of such analysis some possible evolutions of DPSIR are envisaged, in terms of expanded and refined environmental data flows. Such evolutions aim to build a better integrated environmental knowledge base, as a prerequisite to a wider exploitation of analytics for the relevant decision processes and management activities.