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One of the key principles of the Ottawa Charta is participation. Community health interventions should involve the community, i.e., residents and local actors. In Hamburg, we tried to put this principle into practice during a project initiated by the municipal health service in a disadvantaged quarter with about 3000 inhabitants. The contribution starts with an account of the meaning of participation in health promotion (1) and gives an overview of participation methods in general (2). The next part contains a short account of our project (3). Five examples are presented in part four: a survey with key actors and two approaches to listening to residents’ perceptions of capacity building in the neighborhood. Another survey of residents was meant to explore the use of health promotion offers and the preparedness to get involved. The last example is the round table of local actors as a sort of steering committee for health promotion interventions (4). Key findings of the examples are: Surveys do result in valuable information for the local actors. The expressed willingness to participate is larger than the actual participation. Opportunities to participate (in surveys and at the round table) are welcome by local actors. In the last section we discuss the advantages, problems and some illusions concerning participation in health promotion (5).

We study a new variant of the classical lot sizing problem with uncertain demand where neither the planning horizon nor demands are known exactly. This situation arises in practice when customer demands arriving over time are confirmed rather lately during the transportation process. In terms of planning, this setting necessitates a rolling horizon procedure where the overall multistage problem is dissolved into a series of coupled snapshot problems under uncertainty. Depending on the available data and risk disposition, different approaches from online optimization, stochastic programming, and robust optimization are viable to model and solve the snapshot problems. We evaluate the impact of the selected methodology on the overall solution quality using a methodology-agnostic framework for multistage decision-making under uncertainty. We provide computational results on lot sizing within a rolling horizon regarding different types of uncertainty, solution approaches, and the value of available information about upcoming demands.

Course timetables are the organizational foundation of a university’s educational program. While students and lecturers perceive timetable quality individually according to their preferences, there are also collective criteria derived normatively such as balanced workloads or idle time avoidance. A recent challenge and opportunity in curriculum-based timetabling consists of customizing timetables with respect to individual student preferences and with respect to integrating online courses as part of modern course programs or in reaction to flexibility requirements as posed in pandemic situations. Curricula consisting of (large) lectures and (small) tutorials further open the possibility for optimizing not only the lecture and tutorial plan for all students but also the assignments of individual students to tutorial slots. In this paper, we develop a multi-level planning process for university timetabling: On the tactical level, a lecture and tutorial plan is determined for a set of study programs; on the operational level, individual timetables are generated for each student interlacing the lecture plan through a selection of tutorials from the tutorial plan favoring individual preferences. We utilize this mathematical-programming-based planning process as part of a matheuristic which implements a genetic algorithm in order to improve lecture plans, tutorial plans, and individual timetables so as to find an overall university program with well-balanced timetable performance criteria. Since the evaluation of the fitness function amounts to invoking the entire planning process, we additionally provide a proxy in the form of an artificial neural network metamodel. Computational results exhibit the procedure’s capability of generating high quality schedules.

Operating room (OR) resources are limited, and for this reason there is usually a competition among surgeons to win them. However, the methods developed for allocating OR sessions are mostly based on optimisation methods which compensate the preferences of surgeons or surgical specialities in favour of the productivity of the entire OR department. This leads to conflict and dissatisfaction among surgeons. To overcome this problem, a methodology based on game theoretic solutions is presented in this paper that formulates the allocation problem as a simple game. The surgeons or specialities as players then jointly pursue the goal of achieving overall stability. Stability is defined and measured using a method called Power Index. The proposed method is then combined with the Monte-Carlo technique to deal with uncertainties. To demonstrate the capability of the suggested procedures, they are applied to a case study from the literature and a set of hypothetical scenarios.

This article focuses on balancing supply and demand for physicians and panel patients on a tactical level to ensure a manageable workload for the physician and access to care for patients. Patients are part of the physician’s panel if they visit the physician somewhat regularly. For the first time, we propose deterministic integer linear programs that decide on the intake of new patients into panels over time, taking into account the future panel development. The main objective is to minimize the deviation between the expected panel workload and the physician’s capacity over time. We classify panel patients with respect to age and the number of visits in a period and assume a transition probability from one visit category to another from one period to the next. We can include stationary patient attributes and consider several physicians together. The programs work with aggregation levels for the new patients’ demand concerning the patient attributes. We conduct experiments with parameters based on real-world data. We consider the transition between visit categories and the new patients’ demand to be stochastic in a discrete-event simulation. We define upper bounds on the number of patients in a patient class to be accepted in a period through solving the programs several times with different demand inputs. Even in this uncertain environment, we can significantly reduce the expected differences between workload and capacity over time, taking into account several future periods instead of one. Using a detailed classification of new patients decreases the expected differences further.

Background The EU Biodiversity Strategy to 2020 foresees that Member States assess conditions and potential developments of ecosystems under climate change and atmospheric nitrogen deposition. This combination of environmental impacts has never been modelled for the German territory before. Therefore, the aim of the presented dynamic modelling of soil parameters under the influence of changing atmospheric nitrogen deposition with simultaneous climate change at representative sites in Germany was to derive knowledge about the expected development of ecosystem conditions up to a possible change of the respective site-specific current ecosystem type. The dynamic modelling was performed with the Very Simple Dynamic soil model. The selection of 15 modelling sites regarded the availability of data from environmental monitoring programmes routinely operated by public institutions and the aptitude of data for parametrising the soil model. The most important input data are time series of nitrogen and acid deposition as well as time series of the relevant climatic-ecological parameters. The simulation period covered the years 1920–2070. Results There are no continuous linear correlations between the level of acidifying or eutrophying inputs and the course of soil parameter values. The step-like courses result from the resilience of the ecosystems within certain parameter ranges. Atmospheric nitrogen deposition has led to nitrogen saturation at 14 of 15 sites selected for modelling. Currently, no linear (negative) correlation between nitrogen deposition and carbon/nitrogen ratio could be established at these sites any more. An increase in the N-content in the soil was only slight, if at all. On the other hand, the nitrate concentration in the leachate increases in correlation to the N deposition. A clear (negative) correlation was found for the dependence of the C/N ratio on the temperature development in connection with climate change. The predicted air temperature rise until 2070 will also cause a decrease of the carbon content in the future, caused by the increasing activity of decomposing soil organisms. Thus, the drastic decrease of the C/N ratio at all of the study sites is due to the significant decrease in the C content. The validation shows that the dynamic modelling of abiotic site parameters has delivered plausible results at the investigated sites. The applicability of the results could be demonstrated. Thus, the evaluation of the time series of soil and climate parameters resulted in forest ecosystem types that are capable of self-regeneration in the future under the conditions of air pollutant inputs and climate change. Conclusions The dynamic modelling of soil parameters under the influence of atmospheric nitrogen deposition and of climate change enables to transparently rank the potential development of ecosystem conditions up to a possible extinction of the current ecosystem type. Thus, the soil modelling approach presented contributes to the implementation of the European Biodiversity Strategy.

In today's industrial world, depletion of fossil resources and the adverse environmental effects of consuming fossil fuels have become one of the serious challenges in sustainable development of the societies. In recent years, substantial attention has been paid to using biomass for producing bioenergy in order to increase economic performance, reduce environmental effects, and providing new opportunities in different societies in pursuit of sustainable development. The complexities related to procurement, logistics, technology selection, raw material management (biomass), and product distribution planning are the main causes of using the optimization models to design the bioenergy supply chains. On the other hand, the globalization of economy and industry increased the significance of the subjects related to global logistics and the ecological and social objectives of the countries have undeniable influences on each other. Hence, in this paper, a mathematical model has been developed to redesign a global bioenergy supply network. This model has simultaneously studied the economic, environmental, and social objectives and the environmental coefficients of the model were calculated using SimaPro software. The multi-objective model was solved by augmented ɛ-constraint method and the decision makers were informed of the obtained Pareto solutions. Data taken from the study on Iran and Armenia was used to validate the model and the Geographic information system (GIS) software was used with the goal of studying the geographical map of each country more accurately.

This work describes the operational load planning problems of air cargo carriers. In the literature the term air cargo loading is used ambiguously for different subproblems that appear during the load planning process for a flight. In its entirety the problem has not yet been the subject of published research. This work introduces the overall problem, the planning decisions, the objectives of the involved stakeholders, and the restrictions occurring in practice. It reviews and categorizes related literature dealing with similar problems or subproblems. The typical problem setting in practice is analyzed to get insights into the real world problem and a large benchmark dataset is presented. Finally, challenges of the current planning practice and research gaps are highlighted.

Biomass, as a renewable material and a clean alternative fuel in generating electricity and heat, is of particular importance in the transportation industry for the production of biofuel instead of fossil fuels. So far, many models have been proposed for the Bioenergy Supply network design, all of which are local and focus on designing new configurations that conform to a specific country's economic policies. But since globalization of the economy and industry is unavoidable today, effort has been made in this paper to propose a global Bioenergy Supply network management model. Contrary to the present literature on the Bioenergy Supply network, the proposed model addresses the redesign of an existing biomass supply network through logistic decisions, financial problems, and related merits. The model objectives can help managers to make decisions using the best transfer prices to maximize the global after tax profit of the biomass company even under uncertain conditions. To validate the model, use has been made of the data of a case study in Iran and Armenia. At last, it is concluded when transfer price is considered at the model, the global after tax profit can increase more than 8% compared with the case of without transfer price.

International audience; The use of renewable energies has become very attractive, because it protects the environment and boosts regional development. In this paper, a sustainable two-phase sequential approach is proposed for the design of bioenergy SCs under uncertainties. The first phase, integrating geographical information, social aspects, and multi-criteria decision-making techniques, helps find appropriate locations for the bio-refineries. High rates of unemployment, and high vulnerability to the variation in the markets in an economic crisis are considered as social concerns. Integrating these factors filters the areas for the second phase. The first phase reduces complexity in the computation of the problem, and helps set up sustainable development in the supply chain. In the second phase, to cope with the uncertainties in the bioenergy supply network, a robust model is introduced. It reduces the sensitivity to inaccurate input data, and the obtained solutions stay optimal when the parameters change slightly. In order to validate this two-phase sequential approach, a case study is investigated. The results of tests show that integrating the concepts of uncertainties and sustainability with geographical information of the area in a two-phase sequential approach outperforms the traditional models, and leads to the creation of 262 jobs. The jobs have a high impact on the surrounding area.

Background Atmospheric deposition of nitrogen and climate change can have impacts on ecological structures and functions, and thus on the integrity of ecosystems and their services. Operationalization of ecosystem integrity is still an important desideratum. Results A methodology for classifying the ecosystem integrity of forests in Germany under the influence of climate change and atmospheric nitrogen deposition is presented. The methodology was based on 14 indicators for six ecosystem functions: habitat function, net primary function, carbon sequestration, nutrient and water flux, resilience. It allows assessments of ecosystem integrity changes by comparing current or prospective ecosystem states with ecosystem-type-specific reference states as described by quantitative indicators for 61 forest ecosystem types based on data before 1990. Conclusion The method developed enables site-specific classifications of ecosystem integrity as well as classifications with complete coverage and determinations of temporal trends as shown using examples from the Thuringian Forest and the “Kellerwald-Edersee” National Park (Germany).

Highlights • Both migrants and natives scored higher in mental than in physical Health-related Quality of Life. • Mental health scores between migrants and natives significantly differ. • School education was found to be a significant predictor of mental health in the migrant group: mental health increases with an increase of school education., Purpose The aim of this observational study was firstly, to assess the Health-related Quality of Life (HrQoL) among migrants and German natives in Hamburg, Germany, using the SF-12 mental and physical summary scores and secondly, to evaluate the contribution of selected sociodemographic and socioeconomic variables to explain the variance in mental and physical HrQoL separately for migrants and natives. Methods Face-to-face interviews were conducted with n=809 participants between May 2018 and July 2019 in six randomly selected statistical districts of Hamburg grouped into four levels of socioeconomic status (SES). The SF-12 questionnaire was used to measure the HrQoL. Socioeconomic (school education, income) and sociodemographic (age, gender, marital status, children) data was recorded, too. Results Migrants and natives scored higher in mental (migrants: M=45.77, SD=7.66; natives: M=47.60, SD=6.14) than in physical HrQoL (migrants: M=42.55, SD=5.55; natives: M=42.03, SD=4.71). Natives had a significantly higher (p

Background Structures and functions of ecosystems and, subsequently, their services for human societies may be influenced by climate change and atmospheric deposition. Jenssen et al. (UBA Texte 87/2013: 1–381, 2013) developed a spatially explicit evaluation system enabling the evaluation of ecosystems’ integrity. This methodology is based on a spatially explicit ecosystem classification of forests. Based on the six ecological functions, the methodology enables to compare the ecosystem type-specific integrity at different levels of ecological hierarchy for a reference state (1961–1990) with the further development of the forest ecosystem types as measured for the years 1991–2010 and as modelled for the period 2011–2070. The present study aimed at deepening the methodology and developing it into a practical system for assessing and mapping forest ecosystem integrity and services. The objectives of this advanced investigation were: (1) to quantify the reference conditions for a total of 61 forest ecosystem types; (2) to test the possibility of supplementing the quantification of ecosystem integrity by information on soil biocenoses as yielded by soil monitoring; (3) to model chemical soil indicators and to compare the respective results with those derived by Ellenberg’s indicator values for nutrient state; and (4) to verify the indicator modelling. Results Reference states related to the time prior to 1991 have been quantified for a total of 61 forest ecosystem types covering 85% (81,577 km2) of the mapped forest area of Germany. The reference states comprise statistical indicators for the plant-species diversity (habitat function), for nutrient and water balances and further ecological information as net-primary production and carbon storage. The assignment of lumbricide communities as soil biocenosis indicators was attempted but not succeeded because of insufficient data availability. The nutrient cycle types of the elaborated reference states were characterized by humus form, C/N ratio in topsoil and N indicator values according to Ellenberg et al. (Scr Geobot 18:1–262, 2001). Applying the developed methodology, for 83 out of 105 study plots the reference states prior to 1991 could be determined. Conclusions For complementing forest ecosystem reference states by soil biocenosis indicators it is necessary to further evaluate the primary literature looking for missing observation data. The W.I.E. indicator value applied in this paper to determine topsoil C/N ratios in forests is well suited for area-covering mapping of both near-natural forest–soil states and deposition-induced disharmonic state changes, in which C/N value and base saturation are no longer correlated.

Deposition of N and heavy metals can impact ecological and human health. This state-of-the-art review addresses spatial and temporal trends of atmospheric deposition as monitored by element accumulation in moss and compares heavy metals Critical Loads for protecting human health and ecosystem’s integrity with modelled deposition. The element accumulation due to deposition was measured at up to 1026 sites collected across Germany 1990–2015. The deposition data were derived from chemical transport modelling and evaluated with regard to Critical Loads published in relevant legal regulations. The moss data indicate declining nitrogen and HM deposition. Ecosystem and human health Critical Loads for As, Ni, Zn, and Cr were not exceeded in Germany 2009–2011. Respective Critical Loads were exceeded by Hg and Pb inputs, especially in the low rainfall regions with forest coverage. The Critical Load for Cu was exceeded by atmospheric deposition in 2010 in two regions. Human health Critical Loads for Cd were not exceeded by atmospheric deposition in 2010. However, the maximum deposition in 2010 exceeded the lowest human health Critical Load. This impact assessment was based only on deposition but not on inputs from other sources such as fertilizers. Therefore, the assessment should be expanded with regard to other HM sources and specified for different ecosystem types.

Whenever a system needs to be operated by a central decision making authority in the presence of two or more conflicting goals, methods from multi-criteria decision making can help to resolve the trade-offs between these goals. In this work, we devise an interactive simulation-based methodology for planning and deciding in complex dynamic systems subject to multiple objectives and parameter uncertainty. The outline intermittently employs simulation models and global sensitivity analysis methods in order to facilitate the acquisition of system-related knowledge throughout the iterations. Moreover, the decision maker participates in the decision making process by interactively adjusting control variables and system parameters according to a guiding analysis question posed for each iteration. As a result, the overall decision making process is backed up by sensitivity analysis results providing increased confidence in terms of reliability of considered decision alternatives. Using the efficiency concept of Pareto optimality and the sensitivity analysis method of Sobol’ sensitivity indices, the methodology is then instantiated in a case study on planning and deciding in an infectious disease epidemic situation similar to the 2020 coronavirus pandemic. Results show that the presented simulation-based methodology is capable of successfully addressing issues such as system dynamics, parameter uncertainty, and multi-criteria decision making. Hence, it represents a viable tool for supporting decision makers in situations characterized by time dynamics, uncertainty, and multiple objectives.

Wet, dry, and occult atmospheric deposition may be modified by vegetation canopies. The aim of this study was to verify canopy drip effect studies conducted in 2012, in 2013, and in 2015/2016. For this purpose, 26 moss samples were taken at each of eight monitoring sites of the European Moss Survey 2020/2021 in Germany from a corresponding number of subplots, each representing the site categories “under tree canopy” and adjacent “open land”. The sampling, as well as the chemical analyses, of 12 metals (Al, As, Cd, Cr, Cu, Fe, Hg, Pb, Ni, Sb, V, Zn) and nitrogen (N) and the recording of sample- and site-describing metadata were conducted according to the ICP Vegetation experimental protocol. The results demonstrate an overall higher metal and nitrogen accumulation in moss samples of “canopy” sites compared to neighboring “open land sites” (grassland, heath). The ratios between the “canopy” and “open land” sites of 1.18 to 1.69 and significant correlations of r > 0.8 in case of five elements agree well with corresponding values from samplings in 2012, 2013, and 2015/2016. These results should be used for modeling atmospheric deposition aiming at more realistic results. With regard to the question of whether, and to what extent, moss samples should preferably be taken from “open land” or “canopy” sites, the following can be concluded: The recommendations of ICP Vegetation with regard to the minimum distance to be maintained from trees and shrubs should not be interpreted to mean that “open” sites are fundamentally more suitable for moss sampling in Germany than, for example, clearings in forests. The mostly higher variability of the measured values compared to the “canopy” sites rather suggests that in the open country a much higher number of influencing factors could be significant for the element accumulation in mosses in addition to the background pollution through atmospheric deposition. This is also supported by the fact that the metal contents in the moss samples of the “open” sites can clearly exceed those of the neighboring “canopy” sites in individual cases. With regard to “open” land, grassland sites seem to be less suitable for moss sampling than bog and heathland sites. In grassland, moss occurrences are often sparser and/or cut short by meadow mowing, so that the removal of three-year shoots on grassland, as recommended by ICP Vegetation, must be replaced in places by one-year shoots. The comparatively higher state dynamics of grassland sites also make the resampling of moss at previously sampled sites more difficult.

Background Do we measure enough to calculate statistically valid characteristic values from random sample measurements, or do we measure too much—without any further increase in knowledge? This question is actually one of the key issues of every empirical measurement design, but is rarely investigated in environmental monitoring. Results In this study, the methodology used for the design of the German Moss Survey 2015 network to determine statistically valid minimum sample numbers (MSN) for the calculation of the arithmetic mean value in compliance with certain accuracy requirements was further developed for data that are neither normally nor lognormally distributed. The core element of the procedure for estimating MSN without prerequisite to the distribution of data is an iterative Monte Carlo simulation. The methodological principle consists of using reference data (values measured in Moss Surveys preceding that in 2015) for a series of MSN candidate values to determine what accuracy would be achieved with these, and then calculating the MSN with which the specified accuracy requirement is met from a quadratic function between MSN candidates and their accuracy. The program Sample Size for Arbitrary Distributions (SSAD) was developed for the calculation of the MSN in the open programming language R. Conclusions The SSAD procedure closes a gap in the existing methodology for calculating statistically valid minimum sample numbers.

Development of heavy metals and nitrogen concentrations in moss throughout Germany Under the Convention on Long-range Transboundary Air Pollution, moss samples have been collected in Europe every five years since 1990 and evaluated for the concentration of heavy metals (since 1990) and nitrogen (since 2005). This article uses the data of this European Moss Survey to geostatistically evaluate and map the spatial distribution and temporal development of the heavy metals cadmium (Cd), lead (Pb) and mercury (Hg) as well as nitrogen (N) for Germany. Since the year of first sampling in Germany (Cd, Pb: 1990, Hg: 1995), heavy metal concentrations in moss tissue have decreased, while nitrogen concentrations have been remaining at almost the same level since 2005. Since 1990, Cd and Pb have shown a consistent hotspot of bioaccumulation within a broad band from North Rhine-Westphalia to Saxony, while for the other regions and with regard to Hg for most regions of Germany the geostatistical analyses corroborate that the spatial patterns of element concentrations in moss are changing across time. With regard to N, North Rhine-Westphalia and Mecklenburg-West Pomerania (2005–2015), as well as northwestern Germany (2015), proved to be a hotspot of nitrogen accumulation in moss. By contrast, the N concentration in mosses has decreased significantly in the southern German states, especially in Bavaria and Baden-Württemberg.

This paper introduces a viable two-objective model for the design of algae biofuel production and distribution network with integrated formulation of location, inventory and routing decisions. The model consists of an algae biomass production facility, several distribution facilities, multiple extraction sites, and transport vehicles. In this model, the system implementation cost and its pollution impacts via CO2 emission are considered simultaneously. Our model allows the decision maker to determine the number and location of distribution facilities, the allocation of extraction sites to these facilities, the routes by which extraction sites need to be served, and the minimum inventory level that should be maintained at each distribution facility to achieve the mentioned objectives. In an attempt to bridge the gaps in the literature, the paper tries to incorporate the uncertainty in parameters and also a queueing system into integrated formulation of location, inventory and routing decisions and develop a viable model with economic and environmental objectives considered simultaneously. Since the proposed model belongs to the class of NP-hard problems, two multi-objective metaheuristic algorithms, namely MOPSO and NSGA-II, are tested for solving the formulated model. For this purpose, Taguchi method is used to set the parameters of these algorithms, and then their results are compared in terms of five evaluation criteria. These comparisons show that, for the formulated model, NSGA-II outperforms MOPSO in the majority of evaluation criteria. Finally, the proposed model is evaluated with the real data related to the implementation of an algae biofuel production and distribution network in Iran and the results are further examined. The results demonstrate the good performance of the proposed model.

Monitoring and mapping of atmospheric deposition can be achieved by use of chemical transport models, technical sampling devices and/or bio-accumulators such as mosses, lichens, etc. Within the European Moss Survey programme, since 1990 every 5 years mos specimens have been collected and analysed for heavy metals, nitrogen and persistent organic pollutants according a harmonised methodology. Calculation and geostatistical mapping of percentile statistics of element concentration in moss is in the focus of this paper. Thereby, element- and survey-specific as well as heavy metals and surveys integrating statistical evaluations and GIS-mapping were performed. Cr, Sb and Zn show contrary to Fe and Pb, no constant decrease of element concentrations, but an intermediate increase between 2000 and 2005, which did not continue until 2015. Cd, Hg, Pb and N which are of priority according to the Convention on Long-Range Transboundary Air Pollution are in focus in this paper. Additionally, Cr is regarded as representing those elements with an intermediate increase between the years 2000 and 2005 as is also true for Sb and Zn. Surveys integrating percentile statistics depict the spatial patterns of, in most cases, declining element concentrations across time. Survey-specific statistical analyses corroborate that the spatial patterns of element concentrations in moss are changing element-specifically across time. The long-term information on the percentile statistics of bioaccumulation of atmospheric deposition in moss is essential for further scientific evaluation as well as for measurements and reporting of nature protection and environmental management.

In this paper, we present a modeling framework for hub location problems with a service time limit considering congestion at hubs. Service time is modeled taking the traveling time on the hub network as well as the handling time and the delay caused by congestion at hubs into account. We develop mixed-integer linear programming formulations for the single and multiple allocation versions of this problem. We further extend the multiple allocation model with a possibility of direct shipments. We test our models on the well-known AP data set and analyze the effects of congestion and service time on costs and hub network design. We introduce a measure for the value of modeling congestion and show that not considering the effects of congestion may result in increased costs as well as in building infeasible hub networks.

Operations Researchers support Supply Chain Management and Supply Chain Planning by developing adequate mathematical optimization models and providing suitable solution procedures. In this paper we discuss what adequate could mean. Therefore, we may ask several questions concerning “optimality” in Supply Chain Planning under causal and temporal uncertainty: What is an optimal solution? When is it optimal? For how long is it optimal? How should the design of a supply chain be changed when conditions and requirements ask for new structures? In particular, we discuss new approaches to Supply Chain Planning in order to give an optimal transformation from an initial solution over multiple periods to a desired one rather than just specifying an optimal snapshot solution. Time and uncertainty are the factors triggering the whole discussion. In particular, several flaws often found when dealing with these factors result in so-called “time traps”. We look at the impact of recent technological developments like the Internet of Things or Industry 4.0 on operational supply chain planning and control, and we show how online optimization can help to cope with real-time challenges. Moreover, we re-coin the concept of risk in the realm of Supply Chain Planning. Here the question is how to measure supply chain specific risks and how to incorporate them “adequately” into mathematical models.

This study focuses on the development of a modeling framework for multi-period stochastic capacitated multiple allocation hub location problems. We consider a planning horizon divided into several time periods. Uncertainty is assumed for the demands. The decisions to make concern the location of the hubs, their initial capacity, the capacity expansion of existing hubs and the transportation between origin–destination pairs. The goal is to minimize the total expected cost. For the situation in which uncertainty can be captured by a finite set of scenarios each occurring with some estimated probability we derive the extensive form of the deterministic equivalent. The resulting model is compact. However, it includes a set of binary variables that becomes too large for medium and large instances of the problem and thus hardly can it be tackled by a general optimization solver. For this reason, enhancements are proposed to the model making it possible to solve optimally instances that could not be solved using the initial model. This is confirmed by the computational tests performed using the well-known CAB data.

Metal deposition into terrestrial ecosystems: evaluating data from modelling and biomonitoring In Germany, atmospheric deposition of heavy metals derived by modelling and measurements using biological and technical samplers are used to spatially differentiate respective European data. However, a comparative analysis is still missing. Therefore, this article compares results from deposition modelling derived by LOTOS-EUROS and EMEP with data on heavy metals concentrations in leaves, needles and organic surface soil collected by intensive forest monitoring (ICP Forests Level II) and the Environmental Specimen Bank (ESB). The concentrations in leaves and needles collected during the years 2007–2011 for the specimen bank revealed clear correlations with the modelled values. They were specific for the matrices sampled and, in case of Cd concentrations in one year old spruce needles, for ecosystem types. Thereby, the correlations with LOTOS-EUROS were stronger than those with EMEP. Yet, these differences could not be proved to be significant. Most data retrieved from ICP Forests Level II monitoring showed stronger correlations with EMEP. They are lower than those calculated from specimen bank data. The advantage of IPC Forests Level II data is that it yields concentrations for different age groups of needles. Rather high correlations were found especially between LOTOS-EUROS results and Pb concentrations in two year old needles collected from pine and spruce. Additionally to leaves and needles, Forests Level II data were used to analyze heavy metal concentrations in organic soil layers. Significant correlations were computed for Cd between the modelled total deposition and respective concentrations in OH soil layers and for Pb between modelled total deposition and OL and OH soil layers. The different biomonitoring networks should be connected more tightly to enhance the spatial significance of their common data.

The determination of atmospheric deposition in forests can be accomplished using technical sampling devices (bulk samplers, wet only samplers), biomonitors or modelling. In Europe, since 1990 moss sampled every five years at up to 7300 places in up to 35 countries was used as biomonitor. In the moss specimens, heavy metals (HM), nitrogen (N, since 2005) and persistent organic pollutants (POPs, since 2010) were determined. Germany participated in all surveys with the exception of that in 2010. For the moss survey 2015, the biomonitoring network applied in the 2005 campaign should be reorganized. To this end, a complex statistically based methodology including a decision support system was developed and implemented. Its application yielded a network with a reduction of sample points from 726 to 402. By use of the data collected in 2005 the performance of the reorganized network did not reveal significant loss of statistical validity.

We study optimization problems extended by a flexible objective, namely the Ordered Median Function (OMF). The OMF incorporates a large number of objectives into one function. Concrete objectives are given by choosing values for a parameter vector. Using optimization problems extended by the OMF, only one model, solution algorithm and implementation is necessary to analyze many concrete objectives. In this paper, we propose a general framework to model optimization problems extended by the OMF. The approach is based on a formulation of the OMF using partial sums of sorted values. It is problem-independent and can be applied to any optimization problem if a linear or mixed-integer linear representation of the set of feasible solutions is available. Furthermore, it can be applied without any further linearizations. Using the presented modeling framework practioners are able to compare and evaluate different objective functions without changing the model and implementation. This is especially useful in an early stage of problem solving and if the most adequate objective is not yet known. Exemplarily we show how the approach can be applied to optimization problems from the areas of location, routing and scheduling.

Determination of heavy metal deposition in forests by use of modelling and moss biomonitoring Atmospheric deposition of elements such as heavy metals, to which environmental systems are exposed, can be determined by means of technical samplers, biological samplers such as moss and deposition modelling. This article aims at comparing heavy metal deposition as computed by two chemical transport models with empiric measurements of respective concentrations in moss collected in 2005. For Cd and Pb, the correlations (Spearman) were between 0.31 and 0.81 across Europe and across Germany. Thereby, highest correlations were determined for specific land use classes. For As, Cr, Cu, Ni, V and Zn lower correlations were found. It could be shown that heavy metals emission inventories should be complemented by spatial and ecosystem type-specific biomonitoring with moss and modelling at high spatial resolution. This should apply even if emission and atmospheric deposition of heavy metal are low since heavy metals are persistent and, therefore, accumulate in ecosystems.

Background Implementation Action 5 of the EU Biodiversity Strategy to 2020 includes that Member States map and assess the state of ecosystems and their services in their national territory. A fundamental component of the respective methodology developed in Germany is the classification of semi-natural ecosystems. In this context, this study aims to examine the quality and re-usability of the map of current semi-natural ecosystem types (cEsT) in Germany (1: 500,000; Jenssen et al. in Forschungsvorhaben 3710 83 214, UBA-FB 001834. UBA-Texte 87/2013. Dessau, Textband + 9 Anhänge, 381 S, 2013; Schröder et al. in Sci Total Environ 521–522:108–122, 2015, in Abschlussbericht Forschungsvorhaben UFOPLAN 3713 83 254 im Umweltforschungsplan des Bundesministeriums für Umwelt, Naturschutz, Bau und Reaktorsicherheit, Bd. 1:1–493 + 7 Anhänge, Bd. 2:1–343, Bd. 3:1–303. Dessau, 2018]) as well as the cEsT map of the Kellerwald National Park (1: 25,000). Results Based on DIN EN ISO 19113 and (DDGI in Qualitätsmodell für die Beschreibung von Geodaten (PAS 1071:2007-10), Beuth Verlag, Berlin, 14 S. + Anh, 2007), the positional accuracy (absolute positional accuracy) and thematic accuracy (classification correctness) were quantitatively determined. For this purpose, a comparison was made with geometrical data of well-known positional accuracy such as ATKIS-DLM (Hesse), mapping of biotopes and habitat types (Hesse, Germany), current vegetation surveys from the Kellerwald National Park (Hesse; permanent random sample inspection, own survey) as well as vegetation surveys available Germany-wide after 1990 from the database of the Institute of Forestry Eberswalde (Waldkunde-Institut Eberswalde; W.I.E.). The map of cEsT Germany has an absolute positional accuracy of ± 42.29 m (≈ ± 42 m) and has been classified correctly by about 30%. Another approximately 35% are ecologically similar to the existing forest ecosystem types (together 65%). In a further approximately 15%, the ecosystem types were correctly classified, but only occur as accompanying ecosystem types. About 15% occurred as an ecologically related accompanying ecosystem type (together 30%). 5% of the spatial objects were mapped incorrectly. In the Kellerwald National Park (1: 25,000), about 22% of the cEsT were classified correctly. Misclassifications on both scale levels concerned the assignments to the elevation levels (e.g., Eb-5n-C2 instead of D1-5n-C2) and, respectively, to the humus species (e.g., Eb-5n-D1 instead of Eb-5n-D1a). The main reason for misclassifications can be seen in high thematic differentiation of the ecosystem classification according to Jenssen et al. (2013). The biotope and habitat mappings are, due to their generally lower thematic differentiation, more appropriate for a falsification than for a verification of the cEsT approach. However, the spatial information content is valuable for comparisons with regard to the occurrence of cEsT as the main or concomitant ecosystem type. Conclusions The correctness of the classification can best be verified by vegetation samplings, but only at the site level. Any deviations found could then be used to improve the quality of the cEsT mapping, particularly at the regional level (1:5000 to 1:25,000). In principle, the use of the identification key for forest and forest ecosystem types (Schröder et al. 2018, vol. 3) is recommended for mapping on a regional scale.

Background The aim of the study was a statistical evaluation of the statistical relevance of potentially explanatory variables (atmospheric deposition, meteorology, geology, soil, topography, sampling, vegetation structure, land-use density, population density, potential emission sources) correlated with the content of 12 heavy metals and nitrogen in mosses collected from 400 sites across Germany in 2015. Beyond correlation analysis, regression analysis was performed using two methods: random forest regression and multiple linear regression in connection with commonality analysis. Results The strongest predictor for the content of Cd, Cu, Ni, Pb, Zn and N in mosses was the sampled species. In 2015, the atmospheric deposition showed a lower predictive power compared to earlier campaigns. The mean precipitation (2013–2015) is a significant factor influencing the content of Cd, Pb and Zn in moss samples. Altitude (Cu, Hg and Ni) and slope (Cd) are the strongest topographical predictors. With regard to 14 vegetation structure measures studied, the distance to adjacent tree stands is the strongest predictor (Cd, Cu, Hg, Zn, N), followed by the tree layer height (Cd, Hg, Pb, N), the leaf area index (Cd, N, Zn), and finally the coverage of the tree layer (Ni, Cd, Hg). For forests, the spatial density in radii 100–300 km predominates as significant predictors for Cu, Hg, Ni and N. For the urban areas, there are element-specific different radii between 25 and 300 km (Cd, Cu, Ni, Pb, N) and for agricultural areas usually radii between 50 and 300 km, in which the respective land use is correlated with the element contents. The population density in the 50 and 100 km radius is a variable with high explanatory power for all elements except Hg and N. Conclusions For Europe-wide analyses, the population density and the proportion of different land-use classes up to 300 km around the moss sampling sites are recommended.

The collection of atmospheric deposition by technical samplers and validated deposition modelling using chemical transport models is spatially complemented by using mosses as bioindicator: since 1990, the European moss survey has been providing data on element concentrations in moss every 5 years at up to 7300 sampling sites. In the moss specimens, heavy metals (since 1990), nitrogen (since 2005) and persistent organic pollutants (since 2010) were determined. Germany participated in all surveys with the exception of that in 2010. In this study, the spatial structures of element concentrations in moss collected in Germany between 1990 and 2015 were comparatively investigated by using Moran’s I statistics and Variogram analysis and mapped by use of Kriging interpolation. This is the precondition to spatially join the moss survey data with data collected at other locations within different environmental networks and to validate spatial patterns of atmospheric deposition as derived by technical sampling and modelling. The calculated maps reveal a clear and statistically significant decrease of most heavy metals, but not of nitrogen, in moss. Due to decreasing element concentrations and the unchanged application of the element concentration classification for the mapping, the heavy metal maps for the survey 2015 do no longer depict much spatial variation. Therefore, in an upcoming study, this analysis needs to be complemented for the heavy metals by calculating maps that depict the spatial structure of survey-specific percentile statistics 1990, 1995, 2000, 2005 and 2015.

We present a modeling and optimization approach for the planning of primaryhealthcare services in order to efficiently direct patient admissions to generalpractitioners (GP) and to leverage the capacity of the healthcare system. We propose a multi-period multi-criteria districting model for the problem of designing GP districts in the presence of six criteria: workload balance, capacity, accessibility, compactness, income equity and district similarity. We combine the last three criteria into a single objective function and formulate the problem as a mixed integer program with binary location decision variables and relaxed allocation decision variables for gradual assignments. To assess the usefulness of the model, we test it on real-case scenarios of Istanbul, Turkey.

A spatial explicit approach coping with potential modifications in ecological structures and functions of forests due to climate change and atmospheric deposition was developed for Germany (Jenssen et al. 2013; Schroder et al. in Sci Total Environ 521–522:108–122, 2015). As a result, two maps of current semi-natural forest ecosystem types of Germany (1991–2010) and the Kellerwald National Park (2001–2010) are presented. The data provided (Nickel et al. 2018) are accessible at https://doi.org/10.1594/PANGAEA.887305 and comprise 120 ecosystem types (89 near-natural ecosystems, 31 cultivated ecosystems) covering 27% of Germany’s territory and, respectively, 17 forest types (12 near-natural, 5 cultivated) covering 86% of the National Park area. The data are in particular of relevance for spatial ecology and conservation purposes. Associated metadata is available at https://agroenvgeo.data.inra.fr/geonetwork/srv/fre/catalog.search#/metadata/b5e5d76f-6176-4d6d-9089-a607bc8a4076