- freely available
Int. J. Environ. Res. Public Health 2014, 11(2), 1725-1746; https://doi.org/10.3390/ijerph110201725
2. Specific Climate Change and Human Health Data Challenges
2.1. Statistics and Analysis
3. Climate-Environment-Health Data Mashups
- Facilitating novel research into environmental exposures and health (including “natural experiments”) using integrated models to detect and attribute changes in health with changes in climate and other environmental variables;
- Rapidly identifying “hot spots” (locations and points in time with convergent increased environmental and human health risks to vulnerable populations;
- Providing healthcare practitioners, public health planners, and environmental managers with relevant surveillance and other information for improving services for locations and populations identified as being at risk;
- Initiating and evaluating interventions to promote adaptation (and unintended adverse consequences) by reducing the exposures, and thereby the health effects at both the individual and population levels;
- Disseminating and providing access to data as part of outreach and engagement with the research community, policymakers and civil society;
- Providing novel perspectives, allowing a greater understanding of the effect of climate change on human health within the context of ecosystem health;
- Fostering resilience and adaptive capacities for individuals, households, communities, and regions to the health and wellbeing impacts of climate change by scaling up adaptation strategies of proven effectiveness.
3.1. Examples of Existing Programmes with Focus on Linking up Different Types of Data
|Institution/Project||Brief Description and Links|
|EVO||The Environmental Virtual Observatory (EVO) is a proof of concept project with NERC funding that has been created to demonstrate that linking data, models and expert knowledge will provide cost effective answers to vital wide-ranging environmental issues, initially in the soil-water system. The project exploits cloud computing to develop new applications for accessing, filtering and synthesising data to develop new knowledge and evaluation tools. It investigates possible structures for the cloud environment and develops exemplars at a local and national scale to demonstrate how the EVO could make environmental monitoring and decision making more efficient, effective and transparent to the whole community. http://www.nerc.ac.uk/research/programmes/virtualobservatory/|
|ECDC E3 Geoportal||The objective of European Centres for Disease Control (ECDC) E3 Geoportal is to promote geospatial infectious disease modelling in Europe and its integration in public health. There are many different determinants of infectious disease transmission but they are often highly dispersed and/or difficult to obtain. The E3 Geoportal will facilitate the collection and exchange of these datasets in a user-friendly manner. It is an inventory of information and resources which are collected, maintained, and managed by a collaborative effort under the European Environment and Epidemiology Network. https://e3geoportal.ecdc.europa.eu/SitePages/Home.aspx|
|SAIL (Wales)||The Secure Anonymised Information Linkage (SAIL) Databank is a large scale data warehouse technology. The SAIL system links together the widest possible range of person-based data using robust anonymisation techniques on the College of Medicine’s IBM supercomputer and bespoke data transportation fabric to a wide range of NHS systems in Wales, allowing for future data mashups. SAIL is continually expanding, both in types of dataset and in geographical coverage, and many additional organisations have since provided, or agreed to provide, their datasets. Through the robust processes that have been developed and implemented, this growing databank represents a valuable resource for health-related research and service development, whilst complying with the requirements of data protection legislation and confidentiality guidelines. http://www.ehi2.swansea.ac.uk/en/sail-databank.htm|
|URGENCHE Project||Urban Reduction of GHG Emissions in China and Europe (URGENCHE) is a FP7 funded project bringing together a team of internationally recognised scientists to develop and apply a methodological framework for the assessment of the overall risks and benefits of alternative greenhouse gas (GHG) emission reduction policies for health and well-being in China and Europe. http://www.urgenche.eu|
|NOAA MATCH||NOAA Metadata Access Tool for Climate Change and Health (MATCH) is a publicly accessible, online tool for researchers that offers centralized access to metadata (standardized contextual information) about thousands of government-held datasets related to health, the environment, and climate-science. http://match.globalchange.gov/geoportal/catalog/main/home.page|
|PULSE-Brazil||NERC-funded project involving the University of Exeter, the Met Office and Brazilian partners. PULSE-Brazil brings together health data (especially respiratory health) and environmental data. It uses different kinds of data (e.g. satellite records on fires in the Amazon) and it has a different main output (a tool to support decision makers, rather than a platform to aid researchers). Both projects can learn from each other across a range of technical, methodological and theoretical issues. http://gtr.rcuk.ac.uk/project/E994D2D9-6A89-4F14-9C70-28076CCFBBBE|
|ESCAPE||EU funded project on long-term health risks to air pollution exposure. ESCAPE concentrates on respiratory, cardiovascular, cancer and pregnancy-related risks. The project’s communications strategy concentrates on producing material for use with patient groups. http://www.escapeproject.eu|
|AVOID||The Met Office Hadley Centre has datasets produced under the DECC/Defra funded Avoiding Danger Climate Change (AVOID) Programme. Includes observations programme to measure salinity, current velocity and temperature in the upper oceans. http://www.metoffice.gov.uk/avoid/|
|EO2HEAVEN||EO2HEAVEN (Earth Observation and Environmental Modelling for the Mitigation of Health Risks) was a research project co-funded by the European Commission as part of the 7th Framework Programme (FP7) Environmental theme. EO2HEAVEN contributed to a better understanding of the complex relationships between environmental changes and their impact on human health. The project monitored changes induced by human activities, with emphasis on atmospheric, river, lake and coastal marine pollution. The result of this collaboration was the design and development of a GIS-based system upon an open and standards-based Spatial Information Infrastructure (SII) envisaged as a helpful tool for research of human exposure and early detection of infections. http://www.eo2heaven.org|
|EXPOSOMICS/HELIX||EXPOSOMICS is an EU funded project, led by Imperial College, and involving institutions from six other countries. It aims to predict individual disease risk from examining drinking water and air-borne contaminants; health data (long-term cohorts) and environmental data will be analysed together. HELIX project is an EU funded project, led by the Centre for Research in Environmental Epidemiology (CREAL) involving institutions from eight other countries. It is focused on the early life exposome since pregnancy and the early years of life are well recognized to be periods of high susceptibility to environmental damage with lifetime consequences. http://www.projecthelix.eu/en/news/item/4-ec-fp7-exposome-programme-launch-at-who-iarc|
BOX 1. MED MI: Linking Human Health and Wellbeing with Weather, Climate, and the Environment
3.2. Potential Future Uses of Linkages between and among Health, Environmental, and Climatic Data
3.3. Potential uses for Public Health Professionals and Policymakers
|Creating and Maintaining the Mashup|
|Using the Mashup|
|Health||CPRD||UK Biobank||ONS Mortality||Million Women||DSSS||RCGP WRS||LABBASE PHE||ELS PHE||Vec S PHE|
|From||2012||2010||1836||1996||1999||1967||1975 *||1980||1900 **|
|Cohort||20M patients registered with general practitioners (projected)||503,316||n/a||1.36 m||n/a||n/a||n/a||n/a||n/a|
|Area||England||UK||England + Wales||UK||England + Wales||England + Wales||England||England||England|
|Info.||Underpins a comprehensive interventional research service. Extremely comprehensive and vital to this kind of linking research.||Age 40–69 Very broad range of genetic variables, phenotypic and exposure data.||All causes of death||Women 50–64. Special focus on HRT and breast health.||Self-reported, including cold, flu, fever, rash, heat etc. |
Provides early warning of infectious diseases
Gold standard of sentinel GP networks
Very large dataset, including known seasonal diseases e.g., giardia
Additional demography and other context
|Vector distr. |
Species host and number
|Geo-ref||Postcode||Postcode||Postcode||Postcode||SHA ***||SHA ***||Postcode||Postcode||Grid Ref|
|Health (Possible Future Collaborations)||ARS||1958 Birth Cohort||ALSPAC||ELSA||CFAS I, II||White-Hall II|
|Information||Response times to 999 calls (weather-related)||Single week, with follow-ups||Strong environmental and genetic data||Age > 50. Health and social. Ongoing study with new recruits.||Age 65+. Genetic and other data.|
Focus on dementia
|Age 35–55 in 1985–1988 Civil Service staff|
(Fixed Station Observations)
|Daily Land |
Gridded 5 km
|Monthly Land |
Gridded 5 km
|Daily Sea Surface Temperature Gridded 5 km||Marine Biotoxins |
|From||1961 (a few further back to 1850)||<1950||1961||1961||1985||2001: England & Wales; 2005: Scotland|
|Area||UK and coastal ships||UK land||UK land||UK land||Global Ocean||UK coastal locations|
|Availability||Research License via BADC or from Met Office||Owned by MAARA/Pollen UK (see letter of support)||Research License from Met Office||Research License from Met Office||Freely available through MyOcean.||Owned by CEFAS on behalf of Food Standards Authority|
|Information||450 stations supply daily: mean, maximum & minimum temperatures; sunshine amount; snow depth at 09:00 UTC|
250 UK stations supply hourly: temperature; wind; cloud base & cover; visibility; weather type
10 marine stations supply sea surface temperature
3,000 UK stations supply daily precipitation data
Boundary layer stability (for pollution dispersion) can be estimated for 250 UK stations.
Daily shortwave radiation and daily erythemic UV radiation can be estimated for 450 UK stations.
|Over three decades of data on airborne pollen and fungal spores.|
Longest running aerobiology datasets with strong links to Leicester Institute for Lung Health.
Derby/Leicester data available for free; other data series from London and Wales may be negotiable.
|Daily mean temperature, daily max temperature, daily minimum temperature, precipitation amount all provided for each 5 km grid square.|
Temperature data available free. License charge for precipitation data.
|Precipitation amount, weather type, sunshine amount, provided for each month for each 5 km grid square.|
Daily shortwave radiation and daily erythemic UV radiation can be estimated.
|Sea Surface Temperature retrieved from a combination of remote and in situ measurement at a resolution of 1/20 degree (~5 km).||Sampling records for a variety of sites around the UK coastline. Changes in sampling practice make year-to-year trends difficult to extract, but case study comparison with simulated results should be possible.|
Access to data by negotiation with CEFAS.
Homogenization of the series will require resources beyond the scope of MED MI.
|Geo-ref||Latitude & longitude; height above mean sea level||Latitude & longitude; height above mean sea level||Latitude & Longitude||Latitude & Longitude|
Conflicts of Interest
- Climate Change 2007: Synthesis Report; Pachauri, R.K.; Reisinger, A. (Eds.) IPCC Core Writing Team: Geneva, Switzerland, 2007.
- The Interagency Working Group on Climate Change and Health (IWGCCH). A Human Health Perspective On Climate Change: A Report Outlining the Research Needs on the Human Health Effects of Climate Change. Available online: http://www.niehs.nih.gov/health/assets/docs_a_e/climatereport2010.pdf (accessed on 21 January 2014).
- Costello, A.; Abbas, M.; Allen, A.; Ball, S.; Bell, S.; Bellamy, R.; Friel, S.; Groce, N.; Johnson, A.; Kett, M.; et al. Managing the health effects of climate change. Lancet 2009, 373, 1693–1733. [Google Scholar] [CrossRef]
- Willox, A.C.; Harper, S.L.; Ford, J.D.; Edge, V.L.; Landman, K.; Houle, K.; Blake, S.; Wolfrey, C. Climate change and mental health: An exploratory case study from Rigolet, Nunatsiavut, Canada. Climatic Change 2013, 121, 255–270. [Google Scholar] [CrossRef]
- Berry, H. Pearl in the oyster: Climate change as a mental health opportunity. Australas. Psychiatry 2009, 17, 453–456. [Google Scholar] [CrossRef]
- United Nations Environmental Programme (UNEP). Research Priorities on Vulnerability, Impacts and Adaptation: Responding to the Climate Change Challenge. UNEP 2013. Available online: http://www.unep.org/pdf/DEW1631NA.pdf (accessed on 21 January 2014).
- Haines, A.; McMichael, A.J.; Smith, K.R.; Roberts, I.; Woodcock, J.; Markandya, A.; Armstrong, B.G.; Campbell-Lendrum, D.; Dangour, A.D.; Davies, M.; et al. Public health benefits of strategies to reduce greenhouse-gas emissions: Overview and implications for policy makers. Lancet 2009, 374, 2104–2114. [Google Scholar] [CrossRef]
- Bruckner, T.; Petschel-Held, G.; Tóth, F.L.; Füssel, H.-M.; Helm, C.; Leimbach, M.; Schellnhuber, H.-J. Climate change decision-support and the tolerable windows approach. Environ. Model. Assess. 1999, 4, 217–234. [Google Scholar] [CrossRef]
- Wilby, R.L.; Dawson, C.W. A Decision Support Tool for the Assessment of Regional Climate Change Impacts; Lancaster University: Lancaster, UK, 2007. [Google Scholar]
- Weitzman, M.L. On modeling and interpreting the economics of catastrophic climate change. Rev. Econ. Stat. 2009, 91, 1–19. [Google Scholar] [CrossRef]
- Dickinson, T. The Compendium of Adaptation Models for Climate Change. 2007. ISBN No.: 978-0-662-47510-1. Available online: http://www.preventionweb.net/files/2287_CompendiumofAdaptationModelsforCC.pdf (accessed on 21 January 2014).
- Haines, A.; McMichael, A.J.; Epstein, P.R. Global health watch: Monitoring impacts of environmental change. Lancet 1993, 342, 1464–1469. [Google Scholar] [CrossRef]
- Last, J.; Guidotti, T.L. Implications for human health of global ecological changes. Public Health Review 1990–1991, 18, 49–67. [Google Scholar]
- McMichael, A.J.; Woodruff, R.E.; Hales, S. Climate change and human health: Present and future risks. Lancet 2006, 367, 859–869. [Google Scholar] [CrossRef]
- McMichael, A.J. Integrated assessment of potential health impact of global environmental change: Prospects and limitations. Environ. Model. Assess. 1997, 2, 129–137. [Google Scholar] [CrossRef]
- McMichael, A.J. Prisoners of the Proximate: Loosening the constraints on epidemiology in an age of change. Am. J. Epidemiol. 1999, 149, 887–897. [Google Scholar] [CrossRef]
- McMichael, A.J.; Campbell-Lendrum, D.H.; Corvalan, C.F.; Ebi, K.L.; Githeko, A.K.; Scheraga, J.D.; Woodward, A. Climate Change and Human Health: Risks and Responses; WHO: Geneva, Switzerland, 2003. [Google Scholar]
- Raynor, G.; Lang, T. Ecological Public Health: Reshaping the Conditions for Good Health; Routledge: New York City, NY, USA, 2012. [Google Scholar]
- Charron, D.F. Ecohealth Research in Practice: Innovative Applications of an Ecosystem Approach to Health; Springer: New York City, NY, USA, 2012. [Google Scholar]
- Reis, S.; Morris, G.; Fleming, L.E.; Beck, S.; Taylor, T.; White, M.; Depledge, M.H.; Steinle, S.; Sabel, C.E.; Hurley, F.; et al. Integrating health and environmental impact analysis. Public Health 2013. [Google Scholar] [CrossRef][Green Version]
- Hajat, S.; Kovats, R.S.; Lachowycz, K. Heat-related and cold-related deaths in England and Wales: who is at risk? Occup. Environ. Med. 2007, 64, 93–100. [Google Scholar]
- Redshaw, C.H.; Stahl-Timmins, W.; Fleming, L.E.; Davidson, I.; Depledge, M.H. Potential changes in disease patterns and pharmaceutical use in response to climate change. J. Toxicol. Environ. Health Part B Crit. Rev. 2013, 16, 285–320. [Google Scholar] [CrossRef]
- Evans, B.; Sabel, C.E. Open-source web-based Geographical Information System for health exposure assessment. Int. J. Health Geogr. 2012, 11, 2. [Google Scholar] [CrossRef]
- Koschmider, A.; Torres, V.; Pelechano, V. Elucidating the Mashup Hype: Definition, Challenges, Methodical Guide and Tools for Mashups. Available online: http://mashup.pubs.dbs.uni-leipzig.de/files/paper14.pdf (accessed on 21 January 2014).
- Hajat, S.; Bird, W.; Haines, A. A Cold weather and GP consultations for respiratory conditions by elderly people in 16 locations in the UK. Eur. J. Epidemiol. 2004, 19, 959–968. [Google Scholar] [CrossRef]
- Hajat, S.; Haines, A.; Atkinson, R.; Anderson, H.R.; Emberlin, J. Association between air pollution and daily consultations with general practitioners for allergic rhinitis in London. Am. J. Epidemiol. 2001, 153, 704–714. [Google Scholar] [CrossRef]
- Hajat, S.; Goubet, S.; Haines, A. Thunderstorm-associated asthma: The effect on GP consultations. Br. J. Gen. Pract. 1997, 47, 639–641. [Google Scholar]
- Lochner, K.; Bartee, S.; Wheatcroft, G.; Cox, C. Privacy in statistical databases: A practical approach to balancing data confidentiality and research needs: The NHIS linked mortality file. Lect. Notes Comput. Sci. 2008, 5262, 90–99. [Google Scholar] [CrossRef]
- Lochner, K.; Hummer, R.A.; Bartee, S.; Wheatcroft, G.; Cox, C. The public-use national health interview survey linked mortality files: Methods of reidentification risk avoidance and comparative analysis. Am. J. Epidemiol. 2008, 168, 336–344. [Google Scholar] [CrossRef]
- Wakefield, J. Tomorrow’s Cities: How Big Data is Changing the World. BBC. 28 August 2013. Available online: http://www.bbc.co.uk/news/technology-23253949 (accessed on 21 January 2014).
- Markandya, A.; Armstrong, B.G.; Hales, S.; Chiabai, A.; Criqui, P.; Mima, S.; Tonne, C.; Wilkinson, P. Public health benefits of strategies to reduce greenhouse-gas emissions: Low-carbon electricity generation. Lancet 2009, 374, 2006–2015. [Google Scholar] [CrossRef]
- Wilkinson, P.; Smith, K.R.; Davies, M.; Adair, H.; Armstrong, B.G.; Barrett, M.; Bruce, N.; Haines, A.; Hamilton, I.; Oreszczyn, T.; et al. Public health benefits of strategies to reduce greenhouse-gas emissions: Household energy. Lancet 2009, 374, 1917–1929. [Google Scholar] [CrossRef]
- Woodcock, J.; Edwards, P.; Tonne, C.; Armstrong, B.G.; Ashiru, O.; Banister, D.; Beevers, S.; Chalab, Z.; Chowdhur, Z.; Cohe, A.; et al. Public health benefits of strategies to reduce greenhouse-gas emissions: Urban land transport. Lancet 2009, 374, 1930–1943. [Google Scholar] [CrossRef]
- Friel, S.; Dangour, A.D.; Garnett, T.; Lock, K.; Chalabi, Z.; Roberts, I.; Butler, A.; Butler, C.D.; Waage, J.; McMichael, A.J.; et al. Public health benefits of strategies to reduce greenhouse-gas emissions: Food and agriculture. Lancet 2009, 374, 2016–2025. [Google Scholar] [CrossRef]
- Smith, K.R.; Jerrett, M.; Anderson, H.R.; Burnett, R.T.; Stone, V.; Derwent, R.; Atkinson, R.W.; Cohen, A.; Shonkoff, S.B.; Krewski, D.; et al. Public health benefits of strategies to reduce greenhouse-gas emissions: Health implications of short-lived greenhouse pollutants. Lancet 2009, 374, 2091–2103. [Google Scholar] [CrossRef]
- Hajat, S.; Ebi, K.; Kovats, S.; Menne, B.; Edwards, S.; Haines, A. The human health consequences of flooding in Europe and the implications for public health: A review of the evidence. Appl. Environ. Sci. Public Health 2003, 1, 13–21. [Google Scholar]
- Haines, A.; Patz, J. Health effects of climate change. JAMA 2004, 291, 99–103. [Google Scholar] [CrossRef]
- Stanke, C.; Murray, V.; Amlôt, R.; Nurse, J.; Williams, R. The effects of flooding on mental health: Outcomes and recommendations from a review of the literature. PLOS Curr. Disasters 2012. [Google Scholar] [CrossRef]
- Zaias, J.; Backer, L.C.; Fleming, L.E. Harmful Algal Blooms (HABs). In Human-Animal Medicine: A Clinical Guide to Toxins, Zoonoses, and Other Shared Health Risks; Rabinowitz, P., Conti, L., Eds.; Elsevier Science Publishers: New York, NY, USA, 2010; pp. 91–104. [Google Scholar]
- Lloyd, S.J.; Kovats, R.S.; Chalabi, Z. Climate change, crop yields, and undernutrition: development of a model to quantify the impact of climate scenarios on child undernutrition. Environ. Health Perspect. 2011, 119, 1817–1823. [Google Scholar] [CrossRef]
- Haines, A.; Kovats, R.S.; Campbell-Lendrum, D.; Corvalan, C. Climate change and human health: Impacts, vulnerability, and mitigation. Lancet 2006, 367, 2101–2109. [Google Scholar] [CrossRef]
- Patz, J.A.; Campbell-Lendrum, D.; Holloway, T.; Foley, J.A. Impact of regional climate change on human health. Nature 2005, 438, 310–317. [Google Scholar] [CrossRef]
- Wichmann, J.; Jovanovic Andersen, Z.; Ketzel, M.; Ellermann, T.; Loft, S. Apparent temperature and cause-specific mortality in Copenhagen, Denmark: A case-crossover analysis. Int. J. Environ. Res. Public Health 2011, 8, 3712–3727. [Google Scholar] [CrossRef]
- Milojevic, A.; Armstrong, B.; Kovats, S.; Butler, B.; Hayes, E.; Leonardi, G.; Murray, V.; Wilkinson, P. Long-term effects of flooding on mortality in England and Wales, 1994–2005: controlled interrupted time-series analysis. Environ. Health 2011, 10, 11. [Google Scholar] [CrossRef]
- Nichols, G.L.; Richardson, J.F.; Sheppard, S.K.; Lane, C.; Sarran, C. Campylobacter epidemiology: A descriptive study reviewing 1 million cases in England and Wales between 1989 and 2011. BMJ Open 2012, 2, e001179. [Google Scholar] [CrossRef]
- Brownstein, J.S.; Holford, R.; Fish, D. Effect of climate change on lyme disease risk in North America. Ecohealth 2005, 2, 38–46. [Google Scholar] [CrossRef]
- Armitage, J.M.; Quinn, C.L.; Wania, F. Global climate change and contaminants—An overview of opportunities and priorities for modelling the potential implications for long-term human exposure to organic compounds in the Arctic. J. Environ. Monit. 2011, 13, 1532–1546. [Google Scholar] [CrossRef]
- Wheeler, B.W.; White, M.; Stahl-Timmins, W.; Depledge, M.H. Does living by the coast improve health and wellbeing? Health Place 2012, 18, 1198–1201. [Google Scholar] [CrossRef][Green Version]
- Medlock, J.M.; Vaux, A.G. Colonization of UK coastal realignment sites by mosquitoes: Implications for design, management, and public health. J. Vector Ecol. 2013, 38, 53–62. [Google Scholar] [CrossRef]
- Moore, S.K.; Trainer, V.L.; Mantau, N.J.; Parker, M.S.; Laws, E.A.; Backer, L.C.; Fleming, L.E. Center for oceans and human health: Climate variability, climate change and harmful algal blooms. Mini-monograph: Research in oceans and human health. Environ. Health 2008, 7 (Suppl. 2), S4. [Google Scholar] [CrossRef]
- O’Neil, J.M.; Davis, T.W.; Burford, M.A.; Gobler, C.J. The rise of harmful cyanobacteria blooms: The potential roles of eutrophication and climate change. Harmful Algae 2012, 14, 313–334. [Google Scholar] [CrossRef]
- Hoagland, P.; Jin, D.; Polansky, L.Y.; Kirkpatrick, B.; Kirkpatrick, G.; Fleming, L.E.; Reich, A.; Watkins, S.M.; Ullman, S.G.; Backer, L.C. The costs of respiratory illnesses arising from Florida Gulf Coast Karenia brevis Blooms. Envion. Health Perspect. 2009, 117, 1239–1243. [Google Scholar]
- Carvalho, G.A.; Minnett, P.J.; Fleming, L.E.; Banzon, V.F.; Baringer, W. Satellite remote sensing of harmful algal blooms: A new multi-algorithm method for detecting the Florida Red Tide (Karenia brevis). Harmful Algae 2010, 9, 440–448. [Google Scholar] [CrossRef]
- Exeter, D.; Rodgers, S.E.; Sabel, C.E. Whose data is it anyway? The implications of putting neighbourhood-level health and social data online. Health Policy 2013. [Google Scholar] [CrossRef]
- Batty, M. Smart cities, big data. Environ. Plan. B Plan. Des. 2012, 39, 191–193. [Google Scholar] [CrossRef]
- Mayer-Schönberger, V.; Cukier, K. Big Data: A Revolution that Will Transform How We Live, Work and Think; Houghton Mifflin: New York, NY, USA, 2013. [Google Scholar]
- Silver, N. The Signal and the Noise: the Art and Science of Prediction; Penguin Books: London, UK, 2012. [Google Scholar]
© 2014 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/).