List of existing DSS on coastal waters and related inland watersheds.
\\n\\n
Dr. Pletser’s experience includes 30 years of working with the European Space Agency as a Senior Physicist/Engineer and coordinating their parabolic flight campaigns, and he is the Guinness World Record holder for the most number of aircraft flown (12) in parabolas, personally logging more than 7,300 parabolas.
\\n\\nSeeing the 5,000th book published makes us at the same time proud, happy, humble, and grateful. This is a great opportunity to stop and celebrate what we have done so far, but is also an opportunity to engage even more, grow, and succeed. It wouldn't be possible to get here without the synergy of team members’ hard work and authors and editors who devote time and their expertise into Open Access book publishing with us.
\\n\\nOver these years, we have gone from pioneering the scientific Open Access book publishing field to being the world’s largest Open Access book publisher. Nonetheless, our vision has remained the same: to meet the challenges of making relevant knowledge available to the worldwide community under the Open Access model.
\\n\\nWe are excited about the present, and we look forward to sharing many more successes in the future.
\\n\\nThank you all for being part of the journey. 5,000 times thank you!
\\n\\nNow with 5,000 titles available Open Access, which one will you read next?
\\n\\nRead, share and download for free: https://www.intechopen.com/books
\\n\\n\\n\\n
\\n"}]',published:!0,mainMedia:null},components:[{type:"htmlEditorComponent",content:'
Preparation of Space Experiments edited by international leading expert Dr. Vladimir Pletser, Director of Space Training Operations at Blue Abyss is the 5,000th Open Access book published by IntechOpen and our milestone publication!
\n\n"This book presents some of the current trends in space microgravity research. The eleven chapters introduce various facets of space research in physical sciences, human physiology and technology developed using the microgravity environment not only to improve our fundamental understanding in these domains but also to adapt this new knowledge for application on earth." says the editor. Listen what else Dr. Pletser has to say...
\n\n\n\nDr. Pletser’s experience includes 30 years of working with the European Space Agency as a Senior Physicist/Engineer and coordinating their parabolic flight campaigns, and he is the Guinness World Record holder for the most number of aircraft flown (12) in parabolas, personally logging more than 7,300 parabolas.
\n\nSeeing the 5,000th book published makes us at the same time proud, happy, humble, and grateful. This is a great opportunity to stop and celebrate what we have done so far, but is also an opportunity to engage even more, grow, and succeed. It wouldn't be possible to get here without the synergy of team members’ hard work and authors and editors who devote time and their expertise into Open Access book publishing with us.
\n\nOver these years, we have gone from pioneering the scientific Open Access book publishing field to being the world’s largest Open Access book publisher. Nonetheless, our vision has remained the same: to meet the challenges of making relevant knowledge available to the worldwide community under the Open Access model.
\n\nWe are excited about the present, and we look forward to sharing many more successes in the future.
\n\nThank you all for being part of the journey. 5,000 times thank you!
\n\nNow with 5,000 titles available Open Access, which one will you read next?
\n\nRead, share and download for free: https://www.intechopen.com/books
\n\n\n\n
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Learning",subtitle:null,reviewType:"peer-reviewed",abstract:"
\r\n\tStatistical machine learning merges statistics with machine learning which falls in the field of computer science, systems science and optimization. Much of the agenda in statistical machine learning is driven by applied problems in science and technology, where data streams are increasingly large-scale, dynamical and heterogeneous, and where mathematical and algorithmic creativity are required to bring statistical methodology to bear. Fields such as bioinformatics, artificial intelligence, signal processing, communications, networking, and information are all intervened here.
\r\n\tStatistical machine learning specifically poses some of the most challenging theoretical problems in modern statistics, the crucial among them being the general problem of understanding the link between inference and computation. This book intends to provide the reader with a comprehensive overview of linear method for regression, non linear method for regression, deep learning, unsupervised learning, artificial neural network, and support vector machine (SVM).
A Decision Support System (DSS) is a computer-based software that can assist decision makers in their decision process, supporting rather than replacing their judgment and, at length, improving effectiveness over efficiency [1]. Environmental DSS are models based tools that cope with environmental issues and support decision makers in the sustainable management of natural resources and in the definition of possible adaptation and mitigation measures [2]. DSS have been developed and used to address complex decision-based problems in varying fields of research. For instance, in environmental resource management, DSS are generally classified into two main categories: Spatial Decision Support Systems (SDSS) and Environmental Decision Supports Systems (EDSS) [3-5]. SDSS provide the necessary platform for decision makers to analyse geographical information in a flexible manner, while EDSS integrate the relevant environmental models, database and assessment tools – coupled within a Graphic User Interface (GUI) – for functionality within a Geographical Information System (GIS) [1,4-6]. In some detail, GIS is a set of computer tools that can capture, manipulate, process and display spatial or geo-referenced data [7] in which the enhancement of spatial data integration, analysis and visualization can be conducted [8-9]. These functionalities make GIS-tools useful for efficient development and effective implementation of DSS within the management process. For this purpose they are used either as data managers (i.e. as a spatial geo-database tool) or as an end in itself (i.e. media to communicate information to decision makers) [8].
At present the increasing trends of industrialisation, urbanisation and population growth has not only resulted in numerous environmental problems but has increased the complexity in terms of uncertainty and multiplicity of scales. Accordingly, there is a consensus on the consideration of several perspectives in order to tackle environmental problems, particularly, climate change related impacts in coastal zones which are characterised by the dynamics and interactions of socio-economic and biogeophysical phenomena. There is the need to develop and apply relevant tools and techniques capable of processing not only the numerical aspects of these problems but also knowledge from experts, to assure stakeholder participation which is essential in the decision making process [5] and to guarantee the overall effectiveness of assessment and management of coastal environments – including related inland watersheds (i.e. surface and groundwaters affected by, and affecting, coastal waters).
The scientific community projected that climate change would further exacerbate environmental problems due to natural and anthropogenic impacts – with specific emphasis in coastal areas [10]. This data, nevertheless, depends on global and regional policy measures especially in sectors such as energy, economy and agriculture which seem to be a major threat to global sustainable development. As a response to this, mitigation and adaptation measures are already identified through intense research activities, yet these may not limit the projected effects of climate change over the next few decades On one side there is the influence of socio-economic development and environmental response while on the other there is the significant uncertainty still associated with present climatic predictive models. Thus, model inputs need to take into account scenarios highly affected by present and future policy measures in order to further reduce uncertainty in their predictions and thereby guarantee robust adaptation strategies.
In addition, climate change effects have been linked to the increase in global average temperature according to the IPCC emission scenarios [11]. Resulting ocean thermal expansion is expected to generate significant impacts via sea level rise, seawater intrusion into coastal aquifers, enhanced coastal erosion and storm surge flooding, while increasing population in coastal cities, especially megacities on islands and deltas, further aggravates major impacts of climate change on marine coastal regions. The latter include transitional environments such as estuaries, lagoons, low lying lands, lakes, which are particularly vulnerable because of their geographical location and intensive socio-economic activities [12-13].
Accordingly, several environmental resource regulations have already included the need to assess and manage negative impacts derived from climate change through their implementation. For instance, the European Commission approved the Green and White papers [14-15], the Water Framework Directive (WFD) [16], which represent an integrated and sound approach for the protection and management of water-related resources in both inland and coastal zones and signed the protocol for Integrated Coastal Zone Management (ICZM) [17], useful in the promotion of the integrated management of coastal areas in relation to local, regional, national and international goals. Moreover, the principles of Integrated Water Resources Management (IWRM) aimed to address typical water quality and quantity concerns with the optimisation of water management and sustainability in collaboration with WFD policy declarations [18]. Likewise, relevant national legislations like Shoreline Management Planning (SMP) in the United Kingdom [19], Hazard Emergency Management (HEM) in the United States [20] and Groundwater Resources Management (GRM) in Bangladesh and India [21] were ratified and further endorse the assessment and management of coastal communities in relation to climate change impacts.
Within this context, the development of innovative tools is needed to implement regulatory frameworks and the decision making process required to cope with climate related impacts and risks. To this end, DSS are advocated as one of the principal tools for the described purposes.
This work will attempt to examine GIS-based DSS resulting from an open literature survey. It will highlight major features and applicability of each DSS in order to help the reader in the selection of DSS tailored on his specific application needs.
The literature survey led to identify twenty DSS designed to support the decision making-process related to climate change and environmental issues in coastal environments – including inland watersheds. The identified DSS are listed in Table 1 with the indication of the developer, development years, and literature reference. In order to provide a description of major features and an evaluation of the applicability of the 20 examined DSS, the work adopted the sets of criteria reported in Table 2 and grouped them within three different categories: general technical criteria, specific technical criteria, and availability and applicability criteria. The general technical criteria underline relevant general features related to each DSS, which include: the target coastal regions and ecosystems domain; the regulatory frameworks and specific legislations supported by each DSS; the considered climate change impacts and related scenarios, as well as the objectives of the examined systems. The specific technical aspects include the main functionalities, analytical methodologies and inference engine (i.e.structural elements) of the systems. A final set of criteria concerned applicability, i.e. scale and study areas, flexibility, status and availability of the examined systems. Within the following sections the identified DSS, listed in Table 1, will be presented discussed according to these criteria.
Helsinki University of Technology, Finland | 1998-2003 | [22] http://clime.tkk.fi | |
Within a World Bank funded Project :LA3EU | 1994-1995 | [23] | |
Coastal Zone Management Centre, Hague | 1992 | [24] | |
Tyndall Centre for Climate Change Research, UK. | 2000-2009 | [25] | |
National Oceanic and Atmospheric Administration, US. | 1999 | [20] www.csc.noaa.gov/products/nchaz/startup.htm | |
Euro-Mediterranean Centre for Climate Change, (CMCC) Italy. | 2005-2010 | [2] | |
Within the European region project: DITTY | 2002- 2005 | [26] | |
Potsdam Institute for Climate Impact Research, Germany | 2003-2004 | [27] http://www.dinas-coast.net. | |
Research Institute of Knowledge System- RIKS, Netherland | 2000-2006 | [28] www.riks.nl/projects/Elbe-DSS | |
University of Thrace and Water Resource Management Authority, Greece. | 2003-2004 | [29] | |
Institute of Water Modelling, Bangladesh | 2002-2010 | [21] www.iwmbd.org | |
Within the KRIM Project in Germany. | 2001-2004 | [30] www.krim.uni-bremen.de | |
Labadie of Colorado State University, US | 1970 | [31-32] www.modsim.engr.colostate.edu | |
Cranfield University, UK | 2003-2010 | [33] http://www.cranfield.ac.uk/sas/naturalresources/research/projects/regis2.html | |
Research Institute of Knowledge System- RIKS, Netherland | 1996-1999 | [34-35] http://www.riks.nl/projects/RAMCO | |
Research Institute of Knowledge System- RIKS within the UNEP Project, Netherland | 1988-1996 | [36] http://www.riks.nl/projects/SimLUCIA | |
University of Waikato and CLIMsystem limited, New Zealand. | 2005 | [37] www.climsystems.com | |
Vrije Universiteit Amsterdam and Coastal Zone Management Centre, Hague | 1999 | [38] http://www.geo.vu.nl/users/ivmstream/ | |
National Taiwan University, Taiwan | 2008 | [39] | |
Research Institute of Knowledge System- RIKS, Netherland | 1996-2002 | [40-41] www.riks.nl/projects/WADBOS |
List of existing DSS on coastal waters and related inland watersheds.
Coping with regulatory framework. This indicates the particular legislation or policy, the DSS refers to and which phase of the decision-making process is supported at the National, Regional and Local level (e.g., EU WFD, ICZM, IWRM, SMP, GRM, and HEM). Study/ field of application area. The coastal zones where this DSS has been applied and tested (e.g., coastal zone, lakes, river basin, lagoon, groundwater aquifer etc.) Objective. It specifies the main aims of the DSS. Climate change impacts. This refers to relevant impacts due to climate change on the system (e.g., sea-level rise, coastal flooding, erosion, water quality). Climate Change Scenarios. The kind of scenarios considered by the DSS, which are relevant to the system analysis and connected to climate change (e.g., emission, sea level rise, climatic scenarios). | |
Functionalities. These indicate relevant functionalities (key outcomes) of the system useful to the decision process: environmental status evaluation, scenarios import (climate change and socio-economic scenarios) and analysis, measure identification and/or evaluation, relevant pressure identification and indicators production. Analytical methodologies. These indicate the methodologies included in the system such as risks analysis, scenarios construction and/or analysis, integrated vulnerability analysis, Multi-Criteria Decision Analysis (MCDA), socio-economic analysis, uncertainty analysis, ecosystem-based approach etc. Structural elements. The three major components of the DSS: dataset (i.e., the typology of data), models (e.g., economic, ecological, hydrological and morphological), interface (i.e., addressing if it’s user-friendly and desktop or web-based). | |
Scale and area of application. This specifies the spatiality of the system (e.g., local, regional, national, supra-national and global) within the case study areas. Flexibility. The characteristics of the system to be flexible, in terms of change of input parameters, additional modules or models and functionalities. It is also linked to the fact that it can be apply on different coastal regions or case study areas. Status and Availability. This specifies if the system is under development or already developed and ready for use, and if it is restricted to the developer and case study areas only or the public can access it too and the website where information about the DSS can be found. |
List of criteria used for the description of existing DSS.
As far the application domain, the considered DSS focus on coastal zones and related ecosystems (e.g. lagoons, groundwaters, river basins, estuaries, and lakes), specifically thirteen DSS are on coastal zones, seven concern coastal associated ecosystems and four focus on both (Table 3).
As far as regulatory frameworks (i.e. ICZM, WFD, IWRM) and national legislations are concerned, the examined DSS reflect the assessment and management aspects of the related decision making process. Within the coastal, marine and river basin environments, the assessment phase of these frameworks consists of the analysis of environmental, social, economic and regulatory conditions, while the management phase looks at the definition and implementation of management plans. Accordingly, support is provided by each DSS to the implementation of one or two frameworks in the assessment and/or management phase in relation to specific objectives and application domain. Specifically, the investigated DSS can provide the evaluation of ecosystem pressures, the assessment of climate change hazard, vulnerability and risks, the development and analysis of relevant policies, and the definition and evaluation of different management options. Eight out of the twenty examined DSS provide support for the ICZM implementation through an integrated assessment involving regional climatic, ecological and socio-economic aspects (Table 3, second column). With respect to the WFD (i.e. six DSS) and IWRM (i.e. seven DSS), the main focus is on the assessment of environmental or ecological status of coastal regions and related ecosystems and on the consideration of anthropogenic impacts and risks on coastal resources. These two groups of DSS consider also the river basins management via evaluation of adaptation options, which is essential for the management phase of the WFD and IWRM implementation. Particularly interesting are the approaches adopted by three DSS: CLIME, STREAM and COSMO. CLIME supports both the assessment and management phases of WFD through the analysis of present and future climate change impacts on ecosystems and the socio-economic influence on water quality of the European lakes. STREAM evaluates climate change and land use effects on the hydrology of a specific river basin, in order to support the management phase of IWRM and WFD via the identification of water resources management measures. Lastly, COSMO provides support for the ICZM through the identification and evaluation of feasible management strategies for climate change and anthropogenic impacts relevant for coastal areas. Moreover, RegIS, Coastal Simulator, CVAT and GVT specifically support the implementation of national legislations through the consideration of socio-economic and technological issues relevant for identifying suitable mitigation actions. To this purpose, these DSS promote the involvement of stakeholders through participatory processes.
The main objective of the examined DSS is the analysis of vulnerability, impacts and risks, and the identification and evaluation of related management options, in order to guarantee robust decisions required for sustainable management of coastal and inland water resources. Specifically, the objectives of the examined DSS are concerned with three major issues: (1) the assessment of vulnerability to natural hazards and climate change (four DSS: CVAT, GVT, SimLUCIA, TaiWAP); (2) the evaluation of present and potential climate change impacts and risks on coastal zones and linked ecosystems, in order to predict how coastal regions will respond to climate change (nine DSS); (3) the evaluation or analysis of management options for the optimal utilisation of coastal resources and ecosystems through the identification of feasible measures and adequate coordination of all relevant users/stakeholders (seven DSS: WADBOS, COSMO CORAL, DITTY, ELBE, MODSIM, RAMCO).
Regulatory framework of reference | |||||
CLIME | Lakes. | WFD for environmental assessment. | To explore the potential impacts of climate change on European lakes dynamics linked coast. | Water quality. | Emission scenarios. Temperature scenarios. |
CORAL | Coral reef | IWRM and ICZM both for environmental assessment and management. | Sustainable management of coastal ecosystems in particular, coral reef. | ND | ND |
COSMO | Coastal zones. | ICZM for environmental management. | To evaluate coastal management options considering anthropic (human) forcing and climate change impacts. | Sea-level rise. | Sea-level rise scenarios. |
Coastal Simulator | Coastal zones. | National legislation for environmental assessment and management. | Effects of climate change /management decisions on the future dynamics of the coast. | Storm surge flooding. Coastal erosion. | Emission scenarios. Sea-level rise scenarios. |
CVAT | Coastal zones. | National legislation for environmental assessment and management. | To assess hazards, vulnerability and risks related to climate change and support hazard mitigation options. | Storm surge flooding. Coastal erosion. Cyclone. Typhoon. Extreme events | Past observations. |
DESYCO | Coastal zones. Coastal Lagoons | ICZM for environmental assessment and management. | To assess risks and impacts related to climate change and support the definition of adaptation measures. | Sea-level rise. Relative sea-level rise Storm surge flooding. Coastal erosion. Water quality | Emission scenarios. Sea level rise scenarios. |
DITTY | Coastal Lagoons. | IWRM and WFD for environmental management. | To achieve sustainable and rational utilization of resources in the southern European lagoons by taking into account major anthropogenic impacts. | ND | ND |
DIVA | Coastal zones. | ICZM for environmental assessment and management. | To explore the effects of climate change impacts on coastal regions. | Sea-level rise. Coastal erosion. Storm surge flooding. | Emission scenarios. Sea-level rise scenarios. |
ELBE | River basin. Catchment. | WFD for environmental management. | To improve the general status of the river basin usage and provide sustainable protection measure within coast. | Precipitation and temperature variation. | Emission scenarios. |
GVT | Coastal zones. | National legislation for environmental assessment. | To describe the vulnerability of groundwater resources to pollution in a particular coastal region. | Groundwater quality. Saltwater intrusion. | Sea-level rise scenarios. |
IWRM | Coastal zones. River basin | IWRM for environmental assessment and management. | To explore potential risks on coastal resources due to climate and water management policies. | Sea-level rise. Coastal erosion. | Sea-level rise scenarios. Emission scenarios. |
KRIM | Coastal zones. | ICZM for environmental assessment. | To determine how coastal systems reacts to climate change in order to develop modern coastal management strategies. | Sea-level rise. Extreme events. Coastal erosion. | Sea-level rise scenarios. Extreme events scenarios. |
MODSIM | River basin. | IWRM for environmental management. | To improve coordination and management of water resources in a typical river basin. | ND | ND |
RegIS | Coastal zones. | SMP and Habitats regulation (UK) for environmental assessment and management. | To evaluate the impacts of climate change, and adaptation options. | Coastal and river flooding. Sea level rise | Emission scenarios Socio-economic scenarios Sea level rise scenarios |
RAMCO | River basin. Coastal zones. | WFD and ICZM for environmental assessment and management. | For effective and sustainable management of coastal resources at the regional and local scales. | ND | ND |
SimLUCIA | Coastal zones. | National legislation for environmental assessment. | To assess the vulnerability of low lying areas in the coastal zones and island to sea-level rise due to climate change. | Sea-level rise. Coastal erosion. Storm surge flooding. | Sea-level rise scenarios. |
SimCLIM | Coastal zones. | ICZM for environmental assessment and management. | To explore present and potential risks related to climate change and natural hazards (e.g. erosion, flood). | Sea-level rise. Coastal flooding. Coastal erosion. | Sea-level rise scenarios. |
STREAM | River basin. Estuaries. | IWRM and WFD for environmental management. | To integrate the impacts of climate change and land-use on water resources management. | Water quality variation. Saltwater intrusion. | Emission scenarios. |
TaiWAP | River basin. | IWRM for environmental assessment. | To assess vulnerability of water supply systems to impacts of climate change and water demand. | Water quality variations. | Emission scenarios. |
WADBOS | River basin. Coastal zones. | WFD and ICZM for environmental assessment and management. | To support the design and analysis of policy measures in order to achieve an integrated and sustainable management. | ND | ND |
List of the examined DSS according to the general technical criteria (ND: Not Defined).
According to the climate change impacts considered by the examined DSS, the review highlights that fifteen out of the 20 DSS applications regard the assessment of climate change impacts and related risks (CC-DSS). These DSS consider climate change impacts relative to sea level rise, coastal erosion, storm surge flooding and water quality. In particular, DESYCO also consider relative sea level rise in coastal regions where there are records of land subsidence, whereas KRIM and CVAT assess impacts related to extreme events and natural hazards (e.g. typhoon, cyclone, etc.) respectively. Moreover, GVT is specifically devoted to groundwater quality variations.
The relevant climate change related scenarios considered by the examined DSS refer to emission of greenhouse gases, temperature increase, sea level rise and occurrence of extreme events. In addition, CVAT used previous observations as baseline scenarios for the assessment of natural hazards; while RegIS considered scenarios related to coastal and river flooding along with socio-economic scenarios in order to estimate their potential feedback on climate change impacts. Although most of these CC-DSS applications used sea level rise scenarios, only DIVA used global sea level rise scenarios to estimate related impacts like coastal erosion and storm surge flooding. KRIM is the only DSS considering extreme events scenarios in its analysis to support the development of robust coastal management strategies.
The criteria related to the specific technical aspects are reported in Table 4. As far as the functionalities are concerned (Table 4, first column), the ones implemented by DESYCO, COSMO, SimCLIM, KRIM and RegIS include the identification and prioritisation of impacts, targets and areas at risk from climate change, sectorial evaluation of impacts or integrated assessment approach, and vulnerability evaluation and problem characterisation, in order to effectively differentiate and quantify impacts and risks at the regional scale. Moreover, they also support the definition and evaluation of management options through GIS-based spatial analysis. Other DSS, i.e. DIVA, SimCLIM and KRIM, implement scenarios import and generation, environmental status evaluation, impacts and vulnerability analysis and evaluation of adaptation strategies to adequately achieve a sustainable state of coastal resources and ecosystems.
Identification of pressure generated by climatic variables. Environmental status evaluation. Water quality evaluation related to climate change. Socio-economic evaluation. Spatial analysis (GIS). | Scenarios construction and analysis. Probabilistic Bayesian network. Uncertainty analysis. | Climatic, hydrological, chemical, geomorphological data. Climate, ecological and hydrological models. Web-based user interface | |
Evaluation of management strategies Spatial analysis (GIS). | Scenarios construction and analysis. Cost-effectiveness analysis. Ecosystem-based. | Environmental, socio-economic, ecological, biological data. Economic and ecological models. Desktop user interface. | |
Problem characterization (e.g. water quality variation, coastal erosion etc.) Impact evaluation of different development and protection plans. Indicator production. Spatial analysis (GIS). | Scenarios construction and analysis. MCDA. Ecosystem-based | Socio-economic, climatic, environmental, hydrological data. Ecological, economic and hydrological models. Desktop user friendly interface | |
Environmental status evaluation. Management strategies identification and evaluation. Indicator production. Spatial analysis (GIS). | Scenarios construction and analysis. Uncertainty analysis. Risk analysis. Ecosystem-based. | Climatic, socio-economic, environmental, hydrological, geomorphological data. Ecological, morphological climatic and hydrological models. Desktop user interface. | |
Environmental status evaluation. Hazard identification. Indicators production. Mitigation options identification and evaluation. Spatial analysis (GIS). | Hazard analysis. Critical facilities analysis. Society analysis. Economic analysis. Environmental analysis. Mitigation options analysis. | Environmental and socio-economic data. Hydrological model. Desktop user friendly interface | |
Prioritization of impacts, targets and areas at risk from climate change. Impacts, vulnerability and risks identification. Indicators production. Adaptation options definition Spatial analysis (GIS). | Regional Risk Assessment methodology. Scenarios construction and analysis. MCDA. Risk analysis. | Climatic, biophysical, socio-economic, geomorphological, hydrological data. Desktop automated user interface. | |
Management options evaluation Indicator production. Spatial analysis (GIS). | Scenarios construction and analysis. Uncertainty analysis. MCDA. Social cost and benefits analysis. DPSIR. | Morphological, social, hydrological, ecological data. Hydrodynamics, biogeochemical, socio-economic models. Desktop user interface. | |
Scenarios generation and analysis. Environmental status evaluation. Indicators production. Adaptation options evaluation. Spatial analysis (GIS). | Scenarios construction and analysis. Cost-benefit analysis. Ecosystem-based. | Climatic, socio-economic, geography, morphological data. Economic, ecological, geomorphological, climate models. Desktop graphical user interface. | |
Environmental status evaluation. Protection measures identification. End-user involvement. Spatial analysis (GIS). | Scenarios construction and analysis. | Hydrological, ecological, socio-economic, morphological data. Economic, Hydrological, models. Desktop complex user interface. | |
Environmental status evaluation. Indicators production Spatial analysis (GIS). Impact and vulnerability evaluation | Risks analysis. Fuzzy logic. MCDA. | Data (environmental, climatic, hydrological, socio-economic). Hydrological, socio-economic and DEM models. Desktop user interface. | |
Environmental status evaluation. Indicators production. Adaptation measures evaluation. Information for non-technical users. Spatial analysis (GIS). | Scenarios construction and analysis. Risk analysis. Cost-benefit analysis. Socio-economic analysis. | Climatic, environmental, socio-economic, geomorphological data. Hydrodynamic, climate, economic models. Desktop user interface. | |
Environmental status evaluation. Adaptation measures evaluation. Information for non-technical users. Spatial analysis (GIS). | Scenarios construction and analysis. Impact and risk analysis. Ecosystem-based. | Climatic, socio-economic, ecological, environmental, hydrological data. Economic, ecological, hydrodynamic, geomorphological models. Desktop user interface. | |
Environmental status evaluation. Management measures evaluation. Spatial analysis (GIS). | Statistical analysis. Analysis of policies. | Administrative, hydrological, socio-economic, environmental data. Socio-economic, hydrological models. Web-based user interface. | |
Indicators production Management measures evaluation. Information for non-technical users. sectoral evaluation Spatial analysis (GIS). | Scenarios construction and analysis. Impact analysis. DPSIR. Integrated assessment. | Climatic, socio-economic, geomorphological, hydrological data. Climate and flood metal-models. Desktop user interface. | |
Environmental status evaluation. Indicators generation. Management measures evaluation. Spatial analysis (GIS). | Scenarios construction and analysis. Cellular automata. Ecosystem-based. | Socio-economic, environmental, climatic data. Biophysical, socio- economic and environmental models. Web-based user interface. | |
Indicators production. Impact and vulnerability evaluation. Management and land-use measures evaluation. Spatial analysis (GIS). | Cellular Automata. Scenarios construction and analysis. Socio-economic analysis. Bayesian probabilistic networks. Ecosystem-based. | Climatic, environmental, socio-economic data. Land use, social and economic, climate models. Web-based user interface. | |
Environmental status evaluation. Impact and vulnerability evaluation. Adaptation strategies evaluation Spatial analysis (GIS). | Scenario construction and analysis. Statistical analysis. Risk analysis. Cost/benefit analysis. Ecosystem-based. | Climatic, hydrological, socio-economic data. Climate, hydrological, economic models. Desktop user interface. | |
Environmental status evaluation. Indicators production. Management measures evaluation spatial analysis (GIS). | Scenarios construction and analysis. | Climatic, socio-economic, ecological, hydrological data. Climate, hydrological models. Web-based user interface. | |
Environmental status evaluation.- Indicators production. Spatial analysis (GIS). | Scenarios construction and analysis. Impact and vulnerability analysis. | Climatic, socio-economic, hydrological data. Climate, hydrological, water system dynamic models. Desktop user interface. | |
Management measures identification and evaluation. Spatial analysis (GIS). | Scenarios construction and analysis. Sensitivity analysis. MCDA. | Socio-economic, hydrological, environmental, ecological data. Socio-economic, ecological, landscape models. Desktop user interface. |
List of the examined DSS according to the specific technical criteria.
In order to effectively support the assessment and management of groundwater resources, GVT and DESYCO estimate indicators in assessing impacts, vulnerability and risks to estimate groundwater quality and coastal environmental quality, respectively. Similarly, STREAM, ELBE, RAMCO and DITTY employ environmental status evaluation, protection measures identification, and spatial analysis to support the management aspects of coastal ecosystems. Moreover, CLIME and CORAL specifically support the assessment and management of lakes and coral reefs via the adoption of management strategies and the evaluation and identification of pressures from climatic variables.
In particular, five out of the 20 examined DSS (i.e. CVAT, GVT, Coastal Simulator, SimLUCIA and RegIS) consider hazards identification, impacts and vulnerability evaluation, mitigation/ management options identification and evaluation and sectoral evaluation to achieve a comprehensive and integrated analysis of coastal issues at the local or regional scale. Among all considered DSS, RegIS is the one most oriented to stakholders.
The second column of table 4 shows the methodologies adopted by each DSS. Seventeen out of 20 examined DSS consider scenarios analysis to enable coastal managers, decision makers and stakeholders to anticipate and visualise coastal problems in the foreseeable future, and to better understand which future scenario is most suitable for consideration in the evaluation process. A useful methodology is represented by the Multi-Criteria Decision Analysis (MCDA) technique that is considered by five DSS (i.e. COSMO, DESYCO, DITTY, GVT and WADBOS) in order to compare, select and rank multiple alternatives that involve several attributes based on several different criteria. Moreover, DITTY and RegIS also consider the DPSIR approach as a causal framework to describe the interactions between the coastal system, society and ecosystems to carry out an integrated assessment with the aim to protect the coastal environment, guarantee its sustainable use, and conserve its biodiversity in accordance to the Convention on Biodiversity (2003). An ecosystemic assessment was developed nine DSS (i.e. CORAL, COSMO, Coastal simulator, DIVA, RegIS, KRIM, RAMCO, SimLUCIA, SimCLIM) to support the analysis of the studied region through the representation of relevant processes and their feedbacks. Furthermore KRIM, IWRM, COSMO, SimCLIM and Coastal Simulator employ the risk analysis approach for impacts and vulnerability evaluation and also for general environmental status evaluation. A more detailed approach to risk analysis, through the regional risk assessment methodology (RRA), was adopted by DESYCO, Coastal Simulator and RegIS with huge emphasis on the local or regional scales. Finally, CLIME and SimLUCIA consider the Bayesian probability network to highlight the causal relationship between ecosystems (e.g. lakes) and climate change effects.
With regard to the structure of examined DSS (Table 4, third column), most of them employ analytical models useful to highlight the basic features and natural processes of the examined territory, such as the landscape and ecological models used by the WADBOS, the environmental model employed by RAMCO, the geomorphological model used within KRIM and the flood meta-model which interface other models considered by the RegIS. Moreover, the majority of these DSS utilise numerical models necessary to simulate relevant circulation and geomorphological processes that may influence climate change and related risks. DSS like CLIME, DESYCO, CVAT and TaiWAP adopt models useful to represent specific climatic processes (e.g. hydrological cycle and fate of sediment). More importantly, ten (i.e. WADBOS, SimLUCIA, RAMCO, MODSIM, GVT, ELBE, DIVA, CORAL, DITTY AND SimCLIM) out of the twenty examined DSS consider relevant socioeconomic models outputs in their analysis to critically support the integrated assessment of coastal zones. Finally, the majority of these DSS consider integrated assessment models in order to emphasise the basic relationship among different categories of environmental processes such as physical, morphological, chemical, ecological and socio-economic – and to provide inclusive information about the environmental and socioeconomic processes.
As far as the software interfaces are concerned, very few of the examined DSS are applied through web-based interfaces, in spite of the fact that web-based facilities enhance easy access to information within a large network of users. Furthermore, all the reviewed DSS consider GIS tools as basic media to express their results or outputs in order to provide fast and intuitive results representation to non-experts (i.e. decision makers and stakeholders) and empower them for robust decisions. In addition to maps, the output produced by each DSS are also graphs, charts, and statistical tables.
Table 5 shows the implementation of the criteria concerning applicability to the examined DSS. Applicability include three aspects: scale/study areas, flexibility and status/availability (Table 2). The spatial scales considered were five: global, supranational, national, regional, and local, in order of decreasing size. The study areas are those reported in the literature cited in Table 1. The flexibility derives from the capability of a given DSS to include new modules and models in its structure, thus new input parameters, and the suitability to be used for regionally different case studies. In order to visualize the estimation of the overall flexibility of a system, highly flexible/flexible/moderately-to-no flexible were indicated as +++/++/+. Status and availability refer to different extent of development (e.g. research prototype, commercial software) and public accessibility/last updated version, respectively.
Status and availability last updated version (year) | |||
Supra-National, National, Local. (Northern, western and central part of Europe). | +++ Flexible in structural modification and study area. | Available to the public. Demo. 2010. | |
Regional, Local. (Coastal areas of Curacao; Jamaica and Maldives). | +++ Flexible in study area. | Not available to the public. Prototype. 1995. | |
National, Local. (Coast of Netherland). | ++ Flexible in study area. | Commercial application. 1998. | |
National, Regional, Local. (Coast of Norfolk in East Anglia, UK). | + | Available only to the Tyndall Research Centre. Prototype. 2009. | |
Regional, Local. (New Hanover County, North Carolina). | ++ Flexible in study area. | Available to public. Prototype. 2002. | |
Regional, Local. (North Adriatic Sea). | ++ Flexible in study area. | Not available to the public. Prototype. 2010. | |
Supranational, National, Regional. (Ria Formosa-Portugal; Mar Menor-Spain; Etang de Thau-France; Sacca di Goro-Italy, Gera-Greece). | +++ Flexible in study area. | Not available to the public. 2006. | |
Global, National. | +++ Flexible in study area. | Available to the public. 2009. | |
Local. (Elbe river basin Germany). | + | Available to the public. 2003. | |
Regional, Local. (Eastern Macedonia and Northern Greece). | + | Not available to the public. 2006. | |
Regional, Local. (Halti-Beel, Bangladesh) | ++ Flexible in study area. | Not available to the public. Prototype. 2009. | |
Regional. (German North sea Coast, Jade-Weser area in Germany). | + | Not available to the public. Prototype. 2003. | |
National, Regional. (San Diego Water County, Geum river basin- Korea). | ++ Flexible in study area. | Available to the public online. 2006. | |
Regional, Local. (North-West, East Anglia). | ++ Flexible in study area. | Available online to stakeholders. Prototype. 2008. | |
Regional, Local. (South-West Sulawesi coastal zone). | ++ Flexible in the used dataset and concepts. | Not available to the public. Prototype. 1999. | |
Local (St Lucia Island, West India) | + | Available online to the public. Demo. 1996. | |
National, Regional, Local. (Rarotonga Island, Southeast Queensland). | ++ Flexible in structural modification and study area. | Available to the public. Demo. 2009. | |
Regional, Local. (Ganges/Brahmaputra river basin, Rhine river basin, Yangtze river basin and Amudarya river basin). | +++ Flexible in structural modification and study area. | Available online to the public. Demo. 1999. | |
Regional, Local. (Touchien river basin). | + | Available to National Taiwan University. Prototype. 2008. | |
Regional, Local. (Dutch Wadden sea). | + | Available online to the public. Demo. 2002. |
List of the examined DSS according to the applicability criteria. (+++, highly flexible; ++, flexible; +: modertly to no-flexible).
As far as the scale of application is concerned, all the examined DSS, except DIVA, have been applied only at the local and regional scales because they were developed for a specific geographical context. Moreover, five out of the 20 examined DSS (i.e. CLIME, CORAL, DITTY, DIVA and STREAM) considered global, supranational, national, regional and local scales during their implementation.
Five of the reported DSS are highly flexible systems because they are used to address several impacts related to different case studies. Although DIVA can be applied to any coastal area around the world, it is sometimes not considered a highly flexible tool in terms of structural modification due to its inability to change its default integrated dataset. Finally, ELBE and WADBOS are identified as moderately-to-no flexible systems because their structure and functionalities were based on the specific needs of particular river basins.
The applicability of DSS reflects their ability to be implemented in several contexts (i.e. case study areas and structural modification), for example to include new models and functionalities ensuring common approaches to decision making and the production of comparable results [42].
Finally, concerning the availability and the status of the development, Table 5 shows that nine DSS are available to the public, three are available with a restricted access (i.e. only to stakeholders or to the developers), one is a commercial software (i.e. COSMO) and seven are not available to the public. Sometimes the restriction of the access is due to the fact that results require special skill for their interpretation, so the public can use them only with the support of the developer team. Among examined DSS, only 11 were developed/updated during the last 5 years, and 4 over the previous five years (for a total of 15 during the last 10 years) with the remaining five DSS showing the last version dating back to the ‘90s.
The overall content of Table 5, together with the main features of each DSS reported in Tables 3 and 4, allow the reader to undertake a screening evaluation of available DSS in relation to the specific impacts from climate change to be addressed.
This work should be regarded as a preliminary attempt to describe and evaluate the main features of available DSS for the assessment and management of climate change impacts on coastal area and related inland watersheds. A further and comprehensive evaluation should be based on comparative application in selected and relevant case studies, in order to evaluate the DSS technical performance, especially in relation to datasets availability, that often represents the real limiting factor. Moreover, sensitivity and uncertainty analyses will provide further evidence of the reliability of the investigated DSS.
This review highlighted the relevance of developing climate change impact assessment and management at the regional scale (i.e. subnational and local scale), according to the requirements of policy and regulatory frameworks and to the methodological and technical features of the described DSS. In fact, most of the available DSS show a regional to local applicability with a moderate to high flexibility. Indeed climate change impacts are very dependent on regional geographical features, climate and socio-economic conditions and regionally-specific information can assist coastal communities in planning adaptation measures to the effects of climate change.
Despite the current situation that shows available DSS mainly focusing on the analysis of specific individual climate change impacts and affected sectors (15 out of the 20 examined DSS), the further developments should aim at the adoption of ecosystem approaches considering the complex dynamics and interactions between coastal systems and other systems closely related to them (e.g. coastal aquifers, surface waters, river basins, estuaries), and at the adoption of multi-risk approaches in order to consider the interaction among different climate change impacts that affect the considered region.
Finally, it is important to remark the need to involve the end users and relevant stakeholders since the initial steps of the development process of these tools, in order to satisfy their actual requirements, especially in the perspective of providing useful climate services, and to avoid the quite often and frustrating situation where time and resource demanding DSS are not used beyond scientific testing exercises.
The authors gratefully acknowledge the Euro-Mediterranean Centre for Climate Change (CMCC; Lecce, Italy), GEMINA project and the European Community\'s Seventh Framework Programme, Marie Curie action,Project EPSEI (under the grant agreement no.269327), for financial support.
Electromagnetic (EM) waves are generated when an electric field comes in contact with a magnetic field. The oscillations of the electric field and the magnetic field are perpendicular to each other and they are also perpendicular to the direction of EM waves propagation. EM waves travel with a constant velocity of 3.0 × 108 m/s in vacuum. Unlike mechanical waves (sound waves) which need a medium to travel, EM waves can travel through anything, such as air, water, a solid material or vacuum. EM radiation refers to the EM waves, propagating through space–time, carrying EM radiant energy [1]. It is a form of energy that is all around us. Human activities like using global positioning system (GPS) device to navigate precise location, heating up a food in a microwave or using X-rays detection by a doctor would be impossible without EM radiation. Figure 1 shows the EM spectrum used to describe different types of EM energy according to their frequencies (or wavelengths). The EM spectrum ranges from lower energy waves (longer wavelength), like radio waves and microwaves, to higher energy waves (shorter wavelength), like X-rays and gamma rays. As for the radiated emission which is focused on in this chapter, the frequency locates in the radio frequency spectrum (3 KHz–300 GHz).
\nA diagram of the EM spectrum showing various properties across the range of frequencies and wavelengths.
Electromagnetic interference (EMI) is a disturbance generated by conduction or external radiation that affects an electrical circuit. The interference emission sources are from the conducted emission (several KHz–30 MHz) to the radiated emission (30 MHz–12 GHz) [2]. The conducted emission is the noise which is internally generated from the poor designed electrical circuit such as electrical cables and power wires. The radiated emission that is externally generated is in the form of transmitting EM waves such as the intended EM radiation from the radio broadcasting antenna and the unintended EM radiation from the high-speed transceivers. While detecting the EMI shielding of the device, it is usually relevant to the radiated emission lonely. The conducted emission is another subject especially for the noise prevention in system level.
\nEMI is encountered by all of us in our daily life and are expected to face exponential rise in future due to the growing numbers of wireless devices and standards, including cell phones, GPS, Bluetooth, Wi-Fi and near-field communication (NFC). Great effort has been dedicated for the development of EMI shielding materials. EMI shielding can be achieved by prevention of EM waves passing through an electric system either by reflection or by absorption of the incident radiation power. In the past, metals were conveniently used in many occasions. Among them, galvanized steel and aluminum are the most widely used. Copper, nickel, pre-tin plated steel, zinc and silver are also used for some purposes. When the trend in today’s electronic devices become faster, smaller and lighter, metals are disadvantageous in weight consideration. Moreover, the EM pollution is not truly eliminated or mitigated since the EM signals are almost completely reflected at the surface of the metal protecting the environment only beyond the shield [3]. Hence, intensive research efforts have been focused on the development of EMI shielding materials that work by tunable reflection and absorption based on novel materials that possess lightness, corrosion resistance, flexibility, easy processing, etc.
\nThis chapter is divided into two sections. In the next section, we will describe the EMI shielding theory in details and the parameters that influence the shielding by reflection and absorption. After that, we introduce three categories of lightweight EMI shielding materials, namely, polymer-based composites, foams and aerogels.
\nThe EMI capability of a material is called shielding effectiveness (SE). It is defined in terms of the ratio between the incoming power (
The unit of EMI SE is given in decibels (dB). According to Eq. (1), how much attenuation is blocked at given SE is given in Table 1.
\nSE (dB) | \n20 | \n30 | \n40 | \n50 | \n60 | \n70 | \n
Attenuation % | \n99 | \n99.9 | \n99.99 | \n99.999 | \n99.9999 | \n99.9999 | \n
Shielding effectiveness and attenuation %.
According to the distance
Wave impedance in far field and near field [
In far field, the EM waves can be regarded as plane waves and EMI should consider both electric field (
where
where
In near field, the wave front is curved instead of planar, so the wave front is not parallel to the surface of the shielding material. In this case, the wave impedance (|
For a magnetic radiation source, the near field is mainly magnetic. The wave impedance is lower than 377 Ω and increases as the distance
In this chapter, all the formulations and results are taken based on far field condition because a distance of 48 cm associated with operating at a frequency of 100 MHz is already considered as far field.
\nFigure 3a illustrates the reflection and transmission of an EM wave when it strikes on a shield material. The uniform EM wave with the electric field
(a) Schematic illustration of EM plane wave is normal incident to a material with thickness
The magnetic field SE can be expressed as:
\nTheoretically, the SE of a material is contributed from three mechanisms including reflection, absorption and multiple-reflections., the materials with mobile charge carriers (electrons or holes) can interact with the incoming EM wave to facilitate reflection. Absorption depends on the thickness of the shield materials. It increases with the increase of the thickness of the shield materials. For significant absorption, the shield materials possess electric and/or magnetic dipoles which could then interact with the EM fields. Multiple-reflections is the third shielding mechanism, which operates via the internal reflections within the shield material. Therefore, the overall SE is the sum of all the three terms:
\nThe EMI SE of the material depends on the distance between radiation source and the shielding material. When the radiation source is far from the shielding material, the SE is called as far field SE. In the case of the short distance between radiation source and the shielding material, the SE is called as near field SE.
\nFigure 3b illustrates three EMI shielding mechanisms in a conductive shield material. When an EM wave strikes the left boundary of the homogenous conductive material, a reflected wave and a transmitted wave will be created at the left external and right external surface, respectively. As the transmitted wave propagates within the shield material, the amplitude of the wave exponentially decreases as a result from absorption, and the energy loss due to the absorption will be dissipated as heat [6]. Once the transmitted wave reaches the internal right surface of the shield (
where
As shown in Figure 3b, in case the shield material is a good conductor,
where \n
where
where
The mechanism of multi-reflections is complicated. For a good conductor material, the multiple-reflection is usually insignificant because most of the incident EM waves are reflected from the external conductive surface of the shield material, and only few penetrated EM waves can be retained for multiple-reflections. The influence is more important for a material that has high permeability and low electrical conductivity. In this case, EM waves can easily penetrate through the external surface of the shield material and most penetrated EM waves are reflected from the second surface of the shield material. The influence is more important in low frequency and is reduced when the frequency gets higher because the ratio between material thickness and skin depth (
Composites are made from fillers and matrices with significantly different physical or chemical properties. Hence, EMI shielding mechanisms are more complicated than those for homogeneous shield materials because of the huge surface area available for reflection and multiple-reflections. The EMI SE of composites can be measured experimentally, and it also can be calculated theoretically. The effective relative permittivity
where
where
where
where
The propagation constant
So, the SE can be calculated in terms of
When modern electronic devices are designed, high performance EMI shielding materials are highly demanded. In addition, lightweight is one additional important technical requirement for potential applications especially in the areas of automobile and aerospace. In the following section, we will briefly review state-of-the-art research work regarding polymer-based composite, foams and aerogels used for EMI shielding.
\nPolymer/conductive fillers composites was seen as a promising advanced EMI shielding materials since the discovery that an insulating polymer would allow the flow of current through the conductive network stablished by conductive fillers above the percolation threshold. The conductive composite materials preserve the advantages of lightness of polymers, low cost, design flexibility and ease of processing, and the incorporation of conductive fillers circumvent intrinsic nature of polymers being transparent to EM waves through interaction between EM wave and the conductive fillers. Metallic fillers, intrinsically conductive polymers and carbon based electrically conductive fillers are discussed in this section with specific examples. Polymer/magnetic particles composites will also be briefly introduced as magnetic portion is an important component in EM waves that should not be ignored. This section aims to provide a general overview on the preparation of polymer-based EMI shielding materials and the advantages and challenges faced by each category and possible strategies towards enhancing the EMI shielding performances.
\nMetals are typical wave-reflection materials used for EMI shielding purpose owing to their abundance in mobile charge carriers that can interact with the incident EM radiation. Metallic fillers of various physical forms, such as fibers or nanoparticles, were dispersed in the polymer matrix to increase the interaction with the incident EM radiation. Injection-molding provides a direct method to disperse metallic fillers into a polymer matrix. Stainless steel fibers (SSF) introduced into polycarbonate matrix through injection molding shown that EMI SE is heavily dependent on the molding parameters which would give an optimum electrical conductivity [11]. Blended textiles of polyester fibers with SSF showed that the EMI SE is more than 50 dB in the frequencies ranging from 30 MHz to 1.5 GHz [12] (see Figure 4a). As shown in Figure 4b and c, comparison of reflectance, absorbance and transmittance, (identified as reflectivity, absorptivity and transmissibility in Figure 4) for SSF and SSF/polyester fiber fabrics as a function of frequency revealed absorption as the dominant EMI shielding mechanism. In the case of SSF/polyester with 10 wt% SSF, EMI shielding by absorption increased from 30 MHz to maximum at 500 MHz and then decreased with the increase in frequency.
\n(a) The EMI SE of the SSF/PET fabric as a function of frequency; (b) reflectivity/absorptivity/transmissibility of SSF fabric and (c) SSF/PET fabric with 10 wt% SSF as a function of frequency [
The challenges in achieving a good dispersion of metallic fillers and the weight increase make polymer/metallic fillers composites a less popular choice. Much attention was switched to intrinsically conductive polymers (including polyaniline, polyacetylene, and polypyrrole), carbon-based materials (including carbon fibers, carbon black, graphite, graphene, carbon nanotubes and mesoporous carbon), and magnetic materials like carbonyl iron and ferrites (including Fe3O4 and α-Fe2O3).
\nBlends of a polymer with an intrinsically conductive polymer results in a composite combining the desired properties of the two components, that is, adequate mechanical properties of the polymer matrix for mechanical support and the electrically conducting component for interaction with the EM radiation. Conducting polymers are conjugated polymers, which on doping exhibit electronic conductivity. Distinctive to metallic fillers, the electrical conductivity of conducting polymers arises from the polymer molecular structure. Alteration of parameters such as chain size, doping level, dopant type and the synthesis route directly affect the molecular structure, hence the EMI shielding properties of the material.
\nAmong the available conducting polymers, polypyrrole (PPY) and polyaniline (PANI) are the most widely used conductive fillers for EMI shielding purposes. PPY is known to possess high conductivity, easy synthesis, good environmental stability and less toxicological problem. Chemical and electrochemical polymerization of PPY on a polyethylene terephthalate (PET) fabric is given as an example for electrically conducting composite. Pyrrole was first dissolved in an aqueous solution containing 10 wt% polyvinyl alcohol (PVA) and sprayed on the PET fabric before subject to electrochemical polymerization at room temperature under a constant current density. The resultant PPY coated PET fabric was shown to exhibit EMI SE about 36 dB over a wide frequency range up to 1.5 GHz [13].
\nPANI was studied extensively for its various structures, unique doping mechanism, excellent physical and chemical properties, stability, and the readily obtainable raw materials. Lakshmi et al. [14] prepared PANI-PU composite film by adding aniline to polyurethane (PU) solution in tetrahydrofuran (THF). Doping of composites was done by adding camphor sulfonic acid to the composite solution. The EMI SE of the PU-PANI film was found to increase with thickness and the frequency specific material is ideal for shielding at 2.2 and 8.8 GHz.
\nOther intrinsically conducting polymers, such as poly(p-phenylene-vinylene) [15, 16] and poly(3-octylthiophene) [17], were also investigated for EMI shielding applications, but too much lesser extent, mainly due to the unsatisfactory performance and complex processing procedures involved.
\nIn general, the EMI shielding performance arises by the addition of conductive polymer consequently dominated by reflection mechanism due to the increase of the level of impedance mismatch with air. One obvious advantage of such polymer-polymer system is the lightweight being preserved, also there is no issue on substrate flexibility as those associated with metallic or carbon-based fillers. However, the main drawbacks of such composites include (1) poor mechanical properties of the most of the intrinsically conducting polymers require a matrix material for structural support; (2) the insoluble and infusible characteristics caused conducting polymers to exhibit poor processability and (3) high filler (conducting polymer) level is usually needed for acceptable performances.
\nSimilar to metallic fillers, carbon-based fillers come in various shapes and aspect ratios. Carbon black (CB), including graphite and CB, is the generic name given to small particle size carbon pigments which are formed in the gas phase by thermal decomposition of hydrocarbons [18]. Carbon fibers (CFs) are 1D carbon structure of diameter generally lies between 50 and 200 nm and aspect ratios around 250 and 2000, largely produced by chemical vaporization of hydrocarbon [19, 20]. Carbon nanotubes (CNTs) can be considered as rolled-up hollow cylinders of graphene sheets of very high aspect ratio due to the small diameter, constituted of a single hollow cylinder, that is, single-walled carbon nanotubes (SWCNTs) or of a collection of graphene concentric cylinders, that is, multi-walled carbon nanotubes (MWCNTs) [21, 22]. Graphene sheet (GS), an atomically thick two-dimensional structure, exhibited excellent mechanical, thermal and electrical properties [23]. Both CNTs and graphene offer substantial advantages over conventional carbon fillers and the percolation threshold can be achieved by both at very low content if properly dispersed.
\nIn general, carbon fillers with high aspect ratio are generally more effective in imparting electrical conductivities to a polymer matrix, hence it is no surprise to observe the highest SE from fillers with the highest aspect ratio, that is, SWCNTs > MWCNTs > CNFs > CB when the volume fraction of the fillers is the same. The different methods of fillers dispersion and various carbon filler surface modification methods were comprehensively reviewed in the published paper and will not be discussed in detail here [3, 24]. The EMI shielding performance of the polymer/carbon-fillers composites can also be found in Ref. [3, 7, 24, 25].
\nA binary or even ternary component consists of two or more types of the fillers provide an effective way to bypass the inherent shortcomings of a single-filler composite. The incorporation of magnetic components will supplement the attenuation properties of a carbon-based EMI shielding material.
\nPhysical blending or deposition of metallic particles within a polymer blend or structure is the most direct way of incorporation a third element, however, such method faces the problem of uniform dispersion and deposition at the bottom layers due to the higher density of metallic particles. Electroless plating of metals on carbon substrates provides a neat way of incorporating metal components uniformly into a system without excessive weight addition. Works by Kim et al. [26] and Yim et al. [27] dispersed nickel coated MWCNT through electroless plating in epoxy and high-density polyethylene, respectively. Figure 5a gives an illustration of the nickel coated MWCNTs. It is apparent that the nickel coated MWCNTs appeared rougher comparing to the pristine ones due to the presence of nickel particles as shown Figure 5b. Yim achieved 140% (at 1 GHz, Figure 5c) in enhancement of the EMI SE compared to the pristine MWCNT/polymer composites. The enhancement was attributed to the increased surface conductivity. Figure 5d shows the proposed shielding mechanism of Ni-MWCNTs/HDPE. EM wave was firstly reflected at the composite surfaces upon reaching the surface of the composite. When the penetrated EM wave meets the nickel layer on the MWCNTs, the metallic layer functioned as EM absorbable or reflective fillers. It is evident that the EMI absorbing nature of the metallic layer can be used as an effective additional shielding material despite the small amount present in the systems.
\n(a) Schematic diagram of the electroless Ni-plating process; (b) SEM images of (1) pristine MWCNTs and (2) Ni-coated MWCNTs, respectively; (c) comparison of the EMI SE of MWCNTs/HDPE and Ni-MWCNTs/HDPE and (d) the proposed shielding mechanism of Ni-MWCNTs/HDPE [
In view of the rigid index of fuel-economy in the applications of automobile and aerospace, lightweight EMI shielding materials with the combination of reduced density and high EMI SE are much preferred. In this section, we aim to provide a general overview on the preparation of foam and aerogel materials used in EMI shielding and the advantages and challenges faced by each category and possible strategies towards enhancing their EMI shielding performances. The specific EMI SE, defined as the ratio of the EMI SE to the density (SSE) or both density and thickness (SSE/t), is a more appropriate criterion to compare the EMI shielding performance with those of other typical materials for the applications where lightweight is required.
\nConductive polymer-based composites foams offer significant reduction in weight, while the pores decrease the real part of the permittivity, accordingly reducing the reflection at the material surface. The porous structure enhances the energy absorption through wave scattering in the walls of the pores. Electrically conductive fillers, including CNFs, CNTs and graphene sheets, are commonly used to form a desirable conducting network within the inherently insulating polymer foam matrix. Yang et al. [28] first reported CNFs reinforced polystyrene (PS) composite foam as a conductive foam for EMI shielding application. The EMI SE of PS/CNFs foam containing 1 wt% CNFs was less than 1 dB, upon increasing CNFs content to 15 wt%, EMI SE increased to 19 dB. Following this work, the authors reported PS/CNTs composite foam with varying CNTs contents from 0 to 7 wt% [4]. The PS/CNTs composite foam achieved a higher EMI SE of above 10 dB compared to 3 dB for the PS/CNFs composite foam at the same filler content of 3 wt%. The difference in the results originated from the remarkable electrical and structural properties of CNTs, such as larger aspect ratio, smaller diameter, higher electrical conductivity and strength, compared to CNFs.
\nSyntactic foam, filling hollow spheres in a matrix, is a kind of lightweight composite materials. The approaches to enhance the EMI SE of syntactic foams include (i) hollow particles made of a conductive material; (ii) coating a conductive layer onto the surface of hollow particles and (iii) adding a second conductive filler in syntactic foam matrix.
\nZhang et al. [29] added a second conductive filler, (CNFs, chopped carbon fiber (CCF), and long carbon fiber (LCF)), into syntactic foams containing conductive hollow carbon microspheres (HCMs). The EMI SE values of used syntactic foams at the same filler content were compared, as shown as Table 2. The results showed that CNFs is more effective in providing EMI shielding compared to CCF and LCF due to the larger aspect ratio of CNFs.
\nFiller content (vol%) | \nCNF Aspect ratio: 500–1700 | \nCCF Aspect ratio: 6–50 | \nLCF Aspect ratio: 150–750 | \n
---|---|---|---|
0.5 | \n5.2 | \n2.2 | \n2.8 | \n
1.0 | \n11.3 | \n3.4 | \n4.4 | \n
1.5 | \n16.4 | \n3.7 | \n6.5 | \n
2.0 | \n24.9 | \n4.3 | \n7.5 | \n
Comparison of the EMI SE (dB) of CNF, CCF, and LCF reinforced syntactic foam.
Zhang et al. [30] also demonstrated the effect of functionalization of HCMs on the EMI SE of the epoxy-HCMs syntactic foam. HCMs were coated with polydopamine (PDA) via the self-polymerization of dopamine. The PDA coating promotes dispersion and served as a reducing agent to deposit silver (Ag) particles on the surface of HCMs as illustrated in Figure 6a. The average EMI SE of the epoxy-HCMs syntactic foam containing Ag-PDA-HCMs with 28.5 and 30.5 wt% of silver in the X-band achieved 49.5 and 60.2 dB, respectively as shown in Figure 6b. The SSE reached up to 46.3 dB cm3/g, demonstrating the prospect of epoxy/Ag-PDA-HCMs syntactic foam as a lightweight high-performance EMI shielding material. The corresponding EMI shielding mechanism of this syntactic foam was analyzed by comparing the values of reflectance (
(a) Schematic illustration of the procedure for preparation of PDA-HCMs and Ag-PDA-HCMs; (b) EMI SE in the frequency range from 8 to 12 GHz for syntactic foam containing pristine HCMs and Ag-PDA-HCMs with different silver contents; and (c) reflectance (
Xu et al. [31] fabricated syntactic foams (“hybridized epoxy composite foams” according to authors) through impregnating expandable epoxy/MWCNT/microsphere blends into a preformed, highly porous, and 3D silver-coated melamine foam (SF) sponge. The highly conductive SF resolved the problem of the foam reduction of high filled epoxy blends and provided channels for rapid electron transport. MWCNTs were used to offset the loss of conductive pathways due to the crystal defects in the silver layer and the insulating epoxy resin. As a result, the EMI SE of 68.1 dB was achieved with only 2 wt% of MWCNTs and 3.7 wt% of silver due to the synergy of the MWCNT and SF.
\nCarbon foam is a class of three-dimensional (3D) architecture consisting of a sponge-like interconnected network of porous carbon. Carbon foams have been wildly used as candidates for realistic EMI shielding applications due to their excellent properties, such as low density, resistance to chemical corrosion, high thermal and electrical conductivity, and high temperature resistance.
\nZhang et al. [32] prepared a novel ultralight (0.15 g/cm3) carbon foam by direct carbonization of phthalonitrile (PN)-based polymer foam, as shown in Figure 7a. High EMI SE of ∼ 51.2 dB (see Figure 7b, C1000 was labeled as the carbonization of 1000°C) was contributed by the high graphitic carbonaceous species and the intrinsic nitrogen-containing structure. The carbon foams showed the best SSE of 341.1 dB cm3/g so far when mechanical property was considered. The carbon foam developed by Zhang provides an excellent low-density and high-performance EMI shielding material for use in areas where mechanical integrity is desired.
\n(a) Schematic representation of the preparation of PN-based carbon foams and (b) EMI SE of carbon foams [
The EMI SE of carbon foams was closely related to the char yield of polymer precursors and the demanding carbonization conditions. Therefore, a new kind of filler-free lightweight EMI shielding material, is in demand, which can be prepared without the stringent processing conditions. In view of the lightweight requirement, assembling one dimensional (1D) CNTs and two-dimensional (2D) graphene sheets into three dimensional (3D) macroscopic porous structures (e.g., sponges, foams and aerogels) emerged as an efficient approach.
\nLu et al. [33] synthesized a flexible CNTs sponge with a density of 10.0 mg/cm3 via chemical vapor deposition (CVD) process, composed of self-assembled and interconnected CNT skeletons. The freestanding CNTs sponge showed the high EMI SE and SSE of 54.8 dB and 5480 dB cm3/g in X-band, respectively. After composited with polydimethylsiloxane (PDMS) by directly infiltrating method, the CNT/PDMS composites still exhibited excellent EMI SE (46.3 dB) at the thickness of 2.0 mm, while the CNT loading content was less than 1.0 wt%.
\nSurface modification is employed to increase the EMI shielding ability of graphene foams. Zhang et al. [34] prepared surfaced modified 3D graphene foams via self-polymerization of dopamine with a subsequent foaming process, as shown in Figure 8a. The polydopamine (PDA) served as a nitrogen doping source and an enhancement tool to achieve higher extent of reduction of the graphene through providing wider pathways and larger accessible surface areas. The enhanced reduction of graphene sheets and the polarization effects introduced by PDA decoration compensated the negative effect of the barrier posed by PDA. As a result, the resultant EMI SE showed 15% improvement compared to PDA-free graphene foam as shown in Figure 8b. Wu et al. [35] also fabricated an ultralight, high performance EMI shielding graphene foam (GF)/poly(3,4-ethylenedioxythiophene):poly(styrene sulfonate) (PEDOT:PSS) composites by drop coating of PEDOT:PSS on the freestanding cellular-structured GFs, as illustrated in Figure 8c. The GF/PEDOT:PSS composites possess an enhanced electrical conductivity from 11.8 to 43.2 S/cm after the incorporation of PEDOT:PSS. The modified grapheme foam with a density of 18.2 × 10−3 g/cm3 provide a remarkable EMI SE of 91.9 dB (identified as
(a) Schematic representation of the preparation of PDA-GO and PDA-rGO; (b) EMI SE of rGO foam and PDA-rGO foam [
Aerogel is a synthetic porous ultralight material derived from a gel, in which the liquid component used in gel are replaced by air. In recent years, the great potential of graphene aerogel (GAs) in EMI shielding applications has been confirmed by several researchers. Song et al. [36] reported that the EMI SE of GA-carbon textile hybrid with a thickness of 2 mm was 27 dB. The 3D scaffold GA greatly enhances the conductive network while maintaining the advantage of light carbon textile. Singh et al. [37] studied the EMI SE of pure GA, which was 20 dB, with a density ∼75 mg /cm3 and a thickness of 2 mm. They discussed the EMI shielding mechanism by correlating the EM wave interaction with the 3D porous structure. Zeng et al. [38] fabricated an ultralight and highly elastic rGO/lignin-derived carbon (LDC) composite aerogel with aligned microspores and cell walls by directional freeze-drying and carbonization method. The EMI SE of rGO/LDC composite aerogels with a thickness of 2 mm could reach up to 49.2 and 21.3 dB under ultralow densities of 8.0 and 2.0 mg/cm3, respectively.
\nThe graphitization of GAs facilitates to improve its electrical conductivity, thus improving the EMI SE. Liu et al. [39] reported an effective method of manufacturing an integrated graphene aerogel (IGA) using a complete bridge between rGO sheets and polyimide macromolecules via graphitization at 2800°C, as shown in Figure 9a. The rGO sheets were efficiently reduced to graphene during graphitization, while the polyimide component was graphitized to turbostratic carbon to connect the graphene sheets, resulting in a high EMI SE of ∼83 dB in X-band at a low density of 18 mg/cm3, as shown in Figure 9b. The EMI shielding mechanism analysis for the porous IGA revealed that most of the incident EM wave was dissipated through absorption, thus forming an absorption-dominant EMI shielding mechanism.
\n(a) Schematic illustration for fabricating IGA and (b) effect of annealing temperature on EMI shielding performance of IGAs [
Different reduction process of graphene oxide (GO), including chemical reduction and thermal reduction would affect the EMI shielding performance of GAs. Bi et al. [40, 41] carried out a comprehensive study of EMI shielding mechanisms of GAs solely consisted of graphene sheets to determine the main parameters of high EMI SE. As shown in Figure 10a, two types of ultralight (4.5–5.5 mg/cm3) 3D GAs were prepared by chemical reduction and thermal reduction of GO aerogels. The EMI SE reached 27.6 and 40.2 dB for chemically reduced graphene aerogel (GAC) and thermally reduced graphene aerogel (GAT), respectively. The distinct graphene surface resulted from different processing pathway led to different EM wave response upon striking the graphene/air interface. Nitrogen-doping and side polar groups induced strong polarization effects in GAC. Higher extent of reduction of the grapheme sheets in GAT left a smaller amount of side polar groups and formed more sp2 graphitic lattice, both favored π-π stacking between the adjacent graphene sheets. The enhanced polarization effects and the increased electrical conductivity of GAT contributed to better EMI shielding performance. Bi further investigated the effect of porosity on EMI shielding mechanisms compressing the aerogel (GA9) into thin film (GA9F), as shown in Figure 10b. The highly connected conducting network resulted in a significant increase in the electrical conductivity of GA9F, while the EMI SE remained unchanged at constant rGO content. The observation was contradictory to the previous outcomes that higher electrical conductivity or better-connected network contributed to higher EMI SE. Hence, the fact can be believed that the EMI SE is highly dependent on the effective amounts of materials response to the EM waves. Despite the similar intrinsic properties of rGO, the amount of absorption of EM waves in GA9 was much higher than that in GA9F when the EM waves penetrated through the porous structure. The cavities within the highly porous GA absorbed the EM waves through multiple internal reflections and eventually depleted the energy. Hence, the tightly connected conducting network within GA9F changed the EMI shielding mechanism from absorption to reflection.
\n(a) Schematic representation of the preparation process of GAC and GAT [
Generally, EMI shielding is defined as the prevention of the propagation of EM waves from one region to another by using shield materials. With the development of electronic industry, weight reduction is an additional technical requirement besides the good EMI shielding performance. Metal as a traditional EMI shielding material has been replacing with lighter materials, such as polymer-based composites, foams and aerogels. This chapter reviewed various types of lightweight materials with their EMI SEs corresponding to their EMI shielding mechanisms. To verify the benefits of using lightweight materials for EMI shielding applications, a comprehensive comparison was performed as shown in Figure 11. All the data in Figure 11 were collected from the reference papers listed in this chapter. Although the data are not involved all the published results, they are representative to the library of lightweight EMI shielding materials. The reported EMI SEs of polymer-based composites containing conductive fillers varied in the range of 20–60 dB corresponding to the densities higher than 0.8 g/cm3. Polymer-based foams reinforced with additional conductive fillers and carbon foams outperform polymer-based composites in terms of EMI SE. They possessed comparable EMI SE of 20–80 dB with the lower density (<0.8 g/cm3). Aerogels with ultralow densities (<100 mg/cm3) exhibited high EMI SEs in the same range of polymer- and carbon-based foams, indicating they can be used as an ideal potential lightweight EMI shielding materials though the mechanical properties of aerogels still remain a big issue.
\nComparison of EMI SEs of lightweight materials as a function of density of materials.
Liying Zhang would like to acknowledge the support by the initial research funds for young teachers of Donghua University. Shuguang Bi would like to acknowledge the financial support of Wuhan Engineering Center for Ecological Dyeing & Finishing and Functional Textiles, Key Laboratory of Textile Fiber & Product (Wuhan Textile University), Ministry of Education, Hubei Biomass Fibers and Eco-dyeing & Finishing Key Laboratory. Zhang and Bi would also thank the funding support by State Key Laboratory for Modification of Chemical Fibers and Polymer Materials, Donghua University (KF1827). Ming Liu would like to acknowledge the support from School of Materials Science and Engineering at Nanyang Technological University for this work.
\nNo conflict of interest.
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\n\nThe first step in obtaining funds for your Open Access publication begins with your institution or library. IntechOpen’s publishing standards align with most institutional funding programs. Our advice is to petition your institution for help in financing your Open Access publication.
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