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Institution

Technical University of Crete

EducationChania, Greece
About: Technical University of Crete is a education organization based out in Chania, Greece. It is known for research contribution in the topics: Population & Traffic flow. The organization has 2433 authors who have published 6039 publications receiving 160730 citations.


Papers
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Journal ArticleDOI
TL;DR: The effectiveness of various AOPs for pharmaceutical removal from aqueous systems is assessed, including water and wastewater treatment, air pollution abatement and soil remediation.

1,583 citations

Journal ArticleDOI
Marielle Saunois1, Ann R. Stavert2, Ben Poulter3, Philippe Bousquet1, Josep G. Canadell2, Robert B. Jackson4, Peter A. Raymond5, Edward J. Dlugokencky6, Sander Houweling7, Sander Houweling8, Prabir K. Patra9, Prabir K. Patra10, Philippe Ciais1, Vivek K. Arora, David Bastviken11, Peter Bergamaschi, Donald R. Blake12, Gordon Brailsford13, Lori Bruhwiler6, Kimberly M. Carlson14, Mark Carrol3, Simona Castaldi15, Naveen Chandra9, Cyril Crevoisier16, Patrick M. Crill17, Kristofer R. Covey18, Charles L. Curry19, Giuseppe Etiope20, Giuseppe Etiope21, Christian Frankenberg22, Nicola Gedney23, Michaela I. Hegglin24, Lena Höglund-Isaksson25, Gustaf Hugelius17, Misa Ishizawa26, Akihiko Ito26, Greet Janssens-Maenhout, Katherine M. Jensen27, Fortunat Joos28, Thomas Kleinen29, Paul B. Krummel2, Ray L. Langenfelds2, Goulven Gildas Laruelle, Licheng Liu30, Toshinobu Machida26, Shamil Maksyutov26, Kyle C. McDonald27, Joe McNorton31, Paul A. Miller32, Joe R. Melton, Isamu Morino26, Jurek Müller28, Fabiola Murguia-Flores33, Vaishali Naik34, Yosuke Niwa26, Sergio Noce, Simon O'Doherty33, Robert J. Parker35, Changhui Peng36, Shushi Peng37, Glen P. Peters, Catherine Prigent, Ronald G. Prinn38, Michel Ramonet1, Pierre Regnier, William J. Riley39, Judith A. Rosentreter40, Arjo Segers, Isobel J. Simpson12, Hao Shi41, Steven J. Smith42, L. Paul Steele2, Brett F. Thornton17, Hanqin Tian41, Yasunori Tohjima26, Francesco N. Tubiello43, Aki Tsuruta44, Nicolas Viovy1, Apostolos Voulgarakis45, Apostolos Voulgarakis46, Thomas Weber47, Michiel van Weele48, Guido R. van der Werf7, Ray F. Weiss49, Doug Worthy, Debra Wunch50, Yi Yin1, Yi Yin22, Yukio Yoshida26, Weiya Zhang32, Zhen Zhang51, Yuanhong Zhao1, Bo Zheng1, Qing Zhu39, Qiuan Zhu52, Qianlai Zhuang30 
Université Paris-Saclay1, Commonwealth Scientific and Industrial Research Organisation2, Goddard Space Flight Center3, Stanford University4, Yale University5, National Oceanic and Atmospheric Administration6, VU University Amsterdam7, Netherlands Institute for Space Research8, Japan Agency for Marine-Earth Science and Technology9, Chiba University10, Linköping University11, University of California, Irvine12, National Institute of Water and Atmospheric Research13, New York University14, Seconda Università degli Studi di Napoli15, École Polytechnique16, Stockholm University17, Skidmore College18, University of Victoria19, Babeș-Bolyai University20, National Institute of Geophysics and Volcanology21, California Institute of Technology22, Met Office23, University of Reading24, International Institute for Applied Systems Analysis25, National Institute for Environmental Studies26, City University of New York27, University of Bern28, Max Planck Society29, Purdue University30, European Centre for Medium-Range Weather Forecasts31, Lund University32, University of Bristol33, Geophysical Fluid Dynamics Laboratory34, University of Leicester35, Université du Québec à Montréal36, Peking University37, Massachusetts Institute of Technology38, Lawrence Berkeley National Laboratory39, Southern Cross University40, Auburn University41, Joint Global Change Research Institute42, Food and Agriculture Organization43, Finnish Meteorological Institute44, Imperial College London45, Technical University of Crete46, University of Rochester47, Royal Netherlands Meteorological Institute48, Scripps Institution of Oceanography49, University of Toronto50, University of Maryland, College Park51, Hohai University52
TL;DR: The second version of the living review paper dedicated to the decadal methane budget, integrating results of top-down studies (atmospheric observations within an atmospheric inverse-modeling framework) and bottom-up estimates (including process-based models for estimating land surface emissions and atmospheric chemistry, inventories of anthropogenic emissions, and data-driven extrapolations) as discussed by the authors.
Abstract: Understanding and quantifying the global methane (CH4) budget is important for assessing realistic pathways to mitigate climate change. Atmospheric emissions and concentrations of CH4 continue to increase, making CH4 the second most important human-influenced greenhouse gas in terms of climate forcing, after carbon dioxide (CO2). The relative importance of CH4 compared to CO2 depends on its shorter atmospheric lifetime, stronger warming potential, and variations in atmospheric growth rate over the past decade, the causes of which are still debated. Two major challenges in reducing uncertainties in the atmospheric growth rate arise from the variety of geographically overlapping CH4 sources and from the destruction of CH4 by short-lived hydroxyl radicals (OH). To address these challenges, we have established a consortium of multidisciplinary scientists under the umbrella of the Global Carbon Project to synthesize and stimulate new research aimed at improving and regularly updating the global methane budget. Following Saunois et al. (2016), we present here the second version of the living review paper dedicated to the decadal methane budget, integrating results of top-down studies (atmospheric observations within an atmospheric inverse-modelling framework) and bottom-up estimates (including process-based models for estimating land surface emissions and atmospheric chemistry, inventories of anthropogenic emissions, and data-driven extrapolations). For the 2008–2017 decade, global methane emissions are estimated by atmospheric inversions (a top-down approach) to be 576 Tg CH4 yr−1 (range 550–594, corresponding to the minimum and maximum estimates of the model ensemble). Of this total, 359 Tg CH4 yr−1 or ∼ 60 % is attributed to anthropogenic sources, that is emissions caused by direct human activity (i.e. anthropogenic emissions; range 336–376 Tg CH4 yr−1 or 50 %–65 %). The mean annual total emission for the new decade (2008–2017) is 29 Tg CH4 yr−1 larger than our estimate for the previous decade (2000–2009), and 24 Tg CH4 yr−1 larger than the one reported in the previous budget for 2003–2012 (Saunois et al., 2016). Since 2012, global CH4 emissions have been tracking the warmest scenarios assessed by the Intergovernmental Panel on Climate Change. Bottom-up methods suggest almost 30 % larger global emissions (737 Tg CH4 yr−1, range 594–881) than top-down inversion methods. Indeed, bottom-up estimates for natural sources such as natural wetlands, other inland water systems, and geological sources are higher than top-down estimates. The atmospheric constraints on the top-down budget suggest that at least some of these bottom-up emissions are overestimated. The latitudinal distribution of atmospheric observation-based emissions indicates a predominance of tropical emissions (∼ 65 % of the global budget, < 30∘ N) compared to mid-latitudes (∼ 30 %, 30–60∘ N) and high northern latitudes (∼ 4 %, 60–90∘ N). The most important source of uncertainty in the methane budget is attributable to natural emissions, especially those from wetlands and other inland waters. Some of our global source estimates are smaller than those in previously published budgets (Saunois et al., 2016; Kirschke et al., 2013). In particular wetland emissions are about 35 Tg CH4 yr−1 lower due to improved partition wetlands and other inland waters. Emissions from geological sources and wild animals are also found to be smaller by 7 Tg CH4 yr−1 by 8 Tg CH4 yr−1, respectively. However, the overall discrepancy between bottom-up and top-down estimates has been reduced by only 5 % compared to Saunois et al. (2016), due to a higher estimate of emissions from inland waters, highlighting the need for more detailed research on emissions factors. Priorities for improving the methane budget include (i) a global, high-resolution map of water-saturated soils and inundated areas emitting methane based on a robust classification of different types of emitting habitats; (ii) further development of process-based models for inland-water emissions; (iii) intensification of methane observations at local scales (e.g., FLUXNET-CH4 measurements) and urban-scale monitoring to constrain bottom-up land surface models, and at regional scales (surface networks and satellites) to constrain atmospheric inversions; (iv) improvements of transport models and the representation of photochemical sinks in top-down inversions; and (v) development of a 3D variational inversion system using isotopic and/or co-emitted species such as ethane to improve source partitioning.

1,047 citations

Journal ArticleDOI
TL;DR: The Monte-Carlo analysis performed, comparing WMN, LORETA, sLorETA and SLF, for different noise levels and different simulated source depths has shown that for single source localization, regularized sLORETA gives the best solution in terms of both localization error and ghost sources.
Abstract: In this primer, we give a review of the inverse problem for EEG source localization. This is intended for the researchers new in the field to get insight in the state-of-the-art techniques used to find approximate solutions of the brain sources giving rise to a scalp potential recording. Furthermore, a review of the performance results of the different techniques is provided to compare these different inverse solutions. The authors also include the results of a Monte-Carlo analysis which they performed to compare four non parametric algorithms and hence contribute to what is presently recorded in the literature. An extensive list of references to the work of other researchers is also provided. This paper starts off with a mathematical description of the inverse problem and proceeds to discuss the two main categories of methods which were developed to solve the EEG inverse problem, mainly the non parametric and parametric methods. The main difference between the two is to whether a fixed number of dipoles is assumed a priori or not. Various techniques falling within these categories are described including minimum norm estimates and their generalizations, LORETA, sLORETA, VARETA, S-MAP, ST-MAP, Backus-Gilbert, LAURA, Shrinking LORETA FOCUSS (SLF), SSLOFO and ALF for non parametric methods and beamforming techniques, BESA, subspace techniques such as MUSIC and methods derived from it, FINES, simulated annealing and computational intelligence algorithms for parametric methods. From a review of the performance of these techniques as documented in the literature, one could conclude that in most cases the LORETA solution gives satisfactory results. In situations involving clusters of dipoles, higher resolution algorithms such as MUSIC or FINES are however preferred. Imposing reliable biophysical and psychological constraints, as done by LAURA has given superior results. The Monte-Carlo analysis performed, comparing WMN, LORETA, sLORETA and SLF, for different noise levels and different simulated source depths has shown that for single source localization, regularized sLORETA gives the best solution in terms of both localization error and ghost sources. Furthermore the computationally intensive solution given by SLF was not found to give any additional benefits under such simulated conditions.

1,013 citations

Journal ArticleDOI
TL;DR: A review of the existing studies on the permeability of gas molecules in nanocomposite materials that consist of inorganic platelet-shaped fillers in polymeric matrices is presented in this paper.

942 citations

Journal ArticleDOI
TL;DR: In this paper, the authors examined how bank's specific characteristics and the overall banking environment affect the profitability of commercial domestic and foreign banks operating in the 15 EU countries over the period 1995-2001.

860 citations


Authors

Showing all 2480 results

NameH-indexPapersCitations
Luc Van Gool1331307107743
Nicholas R. Jennings11680764112
Gerhard Weikum9379634062
Moshe Ben-Akiva8445631805
Zhong-Ping Jiang8159724279
T. E. Simos7745816772
Alex Rogers7348322094
Keshab K. Parhi6874920097
Markos Papageorgiou6743918936
Mark S. Nixon6645616812
Wolfgang Nejdl6550919103
Dionissios Mantzavinos6425413966
Minos Garofalakis6322212638
Frank Marken6056815282
Nicolas Kalogerakis5921111320
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202341
202278
2021332
2020334
2019293
2018333