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PurposeThis paper seeks to provide an introduction to the special issue of foresight, dedicated to the European Foresight Monitoring Network (EFMN).Design/methodology/approachThe paper sets out the project's rationale and its scope and approach.FindingsThe paper presents some of the achievements of the EFMN, as well as some of its limitations.Originality/valueThe paper considers how the EFMN might be sustained and further developed over the longer term. It also provides a brief description of the main papers in the special issue.

Large-scale radon monitoring is carried out due to the fact that it is directly responsible for public health. European Directive 2013/59/EURATOM has been transposed into the legislation of several countries and provides for the need for long-term monitoring of radon in homes and workplaces by setting the average annual reference level at 300 Bq/m3. At the same time, radon is a precursor factor, its emission being correlated with seismic and volcanic activity. In this case, the protection of the population is ensured by a forecast similar to a meteorological one. The NIEP (National Institute for Earth Physics) is developing a multidisciplinary real-time monitoring network in the most dangerous seismic area in Romania, Vrancea. This is located at the bend of the Carpathian Mountains and is characterized by deep earthquakes (over 80 km), with destructive effects over large distances. Implementing a multidisciplinary monitoring network that includes radon, involves finding the locations and equipment that will give the best results. There is no generic solution for achieving this, because the geological structure depends on the monitoring area, and in most cases the equipment does not offer the ability to transmit data in real time. The positioning of the monitoring stations was based on fault maps of the Vrancea area. Depending on the results, some of the locations were changed in pursuit of a correlation with zonal seismicity. Through repeated tests, we established the optimal sampling rate for minimizing errors, maintaining measurement accuracy, and ensuring the detection of anomalies in real time. The radon 222Rn was determined by the number of counts and ROI1 (region of interest) values, depending on the particularities of the equipment. Finally, we managed to establish a real-time radon monitoring network which transmits data to geophysical platforms and makes correlations with the seismicity in the Vrancea area. The equipment, designed to store data for long periods of time then manually download it with manufacturers' applications, now works in real time, after we implemented software designed specifically for this purpose.

Over the last half of the 20th century air pollution has become a political issue and a severe health problem in most of the developed economies of the world. The concentrations of toxic pollutants reached levels that pose risks to human health, together with overpopulation gathered in megacities and as an aftermath the massive industry activities. The potential for serious consequences of exposure to high levels of ambient air pollution was made clear in the mid-20th century, when cities in Europe and the United States experienced episodes of air pollution, such as the infamous London Fog of 1952 and Donora Smog of 1948 that resulted in large numbers of excess deaths and hospital admissions. In the last years, problems connected with air quality in great urban areas and with the stresses that the atmospheric pollution cause on the human health, have urged the European Community to create a framework to establish criteria for air quality data collection and analytical techniques with particular reference to the location and the minimum number of sampling sites. In particular, it has been proposed that air quality monitoring focused on the protection of human health, must be performed in areas where the exposition to the high levels of pollution is maximum for a significant period of time and that the areas chosen for monitoring should be representative of other similar areas. The re-design/optimization of an existing Air Quality Monitoring Network (AQMN) by excluding or relocating monitoring stations with specified methods and criteria is a complicated procedure. The present work attempts to examine and define some of these criteria together with an evaluation model developed using the method of multiple linear regression. In order to achieve this purpose, two air pollution episodes are going to be studied; the first is a photochemical ozone episode in the city of Athens and the second one is a Particulate Matter smog episode in the city of Istanbul. Both cities are in the east Mediterranean territory with more or less similar climate characteristics, typical for their specific geographic location. Each of these cities has also specific and unique cultural and topographic characteristics that have to be taken in account. Both of these areas are very important for studies which evaluating the relationships between monitoring sites in urban environments and their utility for environmental monitoring programs and epidemiologic studies. Most of the studies are trying to establish uniform criteria for classifying and designating locations for air monitoring stations. Sometimes these criteria are not applicable in such areas with certain particularities. The city of Athens has the unique particularity of the classical Antiquities scattered in the center of the city and the suburbs, facing the significant issue for their protection from various traffic and industrial pollutants. On the other hand the city of Istanbul is the largest city in the European territory with serious environmental problems due to heavy air pollution since the beginning of industrialization of the area. The protection of sensitive receptors in a very densely populated city without methodically arranged spatial planning until recently, is another particularity that has to be considered. The objectives of a new Air Quality Monitoring Station (AQMS) setup are presented here and we are trying to categorize the proposed Macro and Micro scale siting criteria for relocating an AQMS. Simultaneously, these criteria could be useful for a possible exclusion/elimination from the network of an AQMS in operation. An active AQMS should be a candidate for exclusion if it is characterized as "redundant". A redundant AQMS is a less "statistical representative" or a "least informative" one. The measurements of the specific site should not be representative according to its current classification and this is also studied with the assistance of the applicable evaluation model, during the aforementioned air pollution episodes at chapter 7. The initial classification of monitoring sites from the governmental authorities is another subject of the present work and it is discussed further in such cases, as we'll see in Athens AQMN, where the initial classifications of most of the stations had been done without future development predictions in the early 80's. A deficient classification or "a not further updated one" through the years reflects a poor representative site. The classification of those AQMSs from Athens' AQMN was preferred to be analyzed against Istanbul's sites because for the former there is additional information and historical data since 1984 and there is also significant information for the spatial structure and planning of the city of Athens. The necessities and objectives of an AQMN have been reported in literature (Liu et al., 1986; Liu et al., 1977; Modak, and Lohani, 1985; Ludwig et al., 1976) and can be summarized as: (1) Planning and implementing air quality protection and air pollution control strategies; (2) Ensuring that the air quality standard is achieved; (3) Preventing or responding quickly to air quality deterioration; (4) Evaluating the exposure population and other potential receptors; (5) Controlling emissions from significantly important sources. These objectives also cover the minimization of network cost. It is difficult to design an AQMN covering all the objectives stated above. Most of the reported methods applied to specific situations wherein one or two of the previous objectives are considered. The role of AQMNs in early warning systems is also discussed in this study and we will try to recommend a monitoring station as the most representative of the network as a "pilot" indication of a potentially evolving air pollution (PM10) episode. The assessment of how representative (for a specific pollutant) is a part of an active monitoring network (although it is difficult to assess using monitoring data only) in certain areas is also discussed. Finally at Annex I and Annex II of the present work, are referenced some case studies and the implementation of contingency plans. Annex III illustrates the international air quality real time information web-sites, with concentration levels for most of the public related pollutants, maps and in some cases pollution forecasts. The time-series raw data for ozone concentrations are officially provided from the web-site of the Greek Ministry of Productive Reconstruction, Environment and Energy. The Turkish Ministry of Environment and Urban Planning also provided the particulate matter time-series data from its website.

The objective of this investigation was to use multicriteria analysis to analyze and model the main criteria that influence the optimal design of a network to monitor groundwater levels. The multicriteria analysis was performed using a GIS (IDRISI Selva). The Toluca Valley aquifer (Mexico) was chosen as the case study. The definition and importance of the criteria (factors and constraints) that influence the design of the monitoring network were based on available information and consultations with experts in the topic. The factors considered were: rate of decline in groundwater levels, decline in groundwater levels, rise in groundwater levels, cracks, vertical hydraulic gradient, and density of wells. The Analytical Hierarchy Process (AHP) was used to weight the factors, resulting in a consistency ratio of 0.08. The weighted linear combination (WLC) method was then applied which resulted in a map identifying the locations of the priority areas to be monitored. The results show that 1.0 % of the study region corresponds to very high priority monitoring areas, 1.8 % to high priority areas, another 1.8 % to medium priority, 4.4 % to low priority and 91 % to very low priority monitoring areas. The proposed method can be used by government and public and private organizations to determine monitoring strategies that support water resources management.

The objective of this investigation was to use multicriteria analysis to analyze and model the main criteria that influence the optimal design of a network to monitor groundwater levels. The multicriteria analysis was performed using a GIS (IDRISI Selva). The Toluca Valley aquifer (Mexico) was chosen as the case study. The definition and importance of the criteria (factors and constraints) that influence the design of the monitoring network were based on available information and consultations with experts in the topic. The factors considered were: rate of decline in groundwater levels, decline in groundwater levels, rise in groundwater levels, cracks, vertical hydraulic gradient, and density of wells. The Analytical Hierarchy Process (AHP) was used to weight the factors, resulting in a consistency ratio of 0.08. The weighted linear combination (WLC) method was then applied which resulted in a map identifying the locations of the priority areas to be monitored. The results show that 1.0 % of the study region corresponds to very high priority monitoring areas, 1.8 % to high priority areas, another 1.8 % to medium priority, 4.4 % to low priority and 91 % to very low priority monitoring areas. The proposed method can be used by government and public and private organizations to determine monitoring strategies that support water resources management.

Because of the urgent need for robust, long-term information on biodiversity loss and environmental change, we have proposed a Long-Term (>10 years) Environmental Monitoring (LTEM) Network for Australia. The LTEM Network would comprise 25 Nodes distributed throughout Australia, be focused on terrestrial, inland aquatic and coastal estuarine ecosystems, and be established to monitor long-term biodiversity loss and ecological change (patterns and trends). The LTEM Network would be question-problem-process-driven and not infrastructure-driven. Thus, the different Nodes in the LTEM Network would explicitly recognize different biota, different environmental problems, different environmental threats and different kinds of management interventions in different ecosystems. We provide a governance structure for the proposed LTEM Network and envisage that it would be characterized by being: (i) lean, yet powerful, agile and adaptive; (ii) both centralized (through a coordinating role by a Federal Government agency), yet decentralized in terms of where the Nodes are located and how they are operated and managed; and (iii) not overly bureaucratic. We also argue that it would be highly cost-effective; our estimated cost for the establishment of an LTEM Network is $28 million per year or less than 0.25% of the total annual governmental expenditure of $12 billion on environmental management in 2003/2004. The LTEM Network would be a critical part of Australia's environmental infrastructure and provide the Nation, for the first time, with a formal, coordinated, long-term Network to gauge status and change of biodiversity and environmental condition. We argue that this would allow the Nation to begin to address seriously many of the major data-related deficiencies that currently exist in the environment and biodiversity conservation sectors in Australia.

The dynamics of the large outlet glaciers in Greenland is attracting both scientific and political attention due to the possible implications of a rising global sea level. Extensive glaciological and meteorological monitoring programmes have been implemented to quantify and track changes in the ice sheet and local glaciers (Ahlstrøm et al. 2008). The dynamic processes controlling the flow of the outlet glaciers are complex and poorly understood, involving a wealth of parameters such as bed conditions, hydrology and meteorological conditions. It is desirable to obtain as many fundamentally independent data sets as possible to understand and eventually predict the behaviour of the outlet glaciers.