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Sun HZ, Zhao J, Liu X, Qiu M, Shen H, Guillas S, Giorio C, Staniaszek Z, Yu P, Wan MW, Chim MM, van Daalen KR, Li Y, Liu Z, Xia M, Ke S, Zhao H, Wang H, He K, Liu H, Guo Y, Archibald AT. Antagonism between ambient ozone increase and urbanization-oriented population migration on Chinese cardiopulmonary mortality. Innovation (N Y) 2023; 4:100517. [PMID: 37822762 PMCID: PMC10562756 DOI: 10.1016/j.xinn.2023.100517] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Accepted: 09/17/2023] [Indexed: 10/13/2023] Open
Abstract
Ever-increasing ambient ozone (O3) pollution in China has been exacerbating cardiopulmonary premature deaths. However, the urban-rural exposure inequity has seldom been explored. Here, we assess population-scale O3 exposure and mortality burdens between 1990 and 2019 based on integrated pollution tracking and epidemiological evidence. We find Chinese population have been suffering from climbing O3 exposure by 4.3 ± 2.8 ppb per decade as a result of rapid urbanization and growing prosperity of socioeconomic activities. Rural residents are broadly exposed to 9.8 ± 4.1 ppb higher ambient O3 than the adjacent urban citizens, and thus urbanization-oriented migration compromises the exposure-associated mortality on total population. Cardiopulmonary excess premature deaths attributable to long-term O3 exposure, 373,500 (95% uncertainty interval [UI]: 240,600-510,900) in 2019, is underestimated in previous studies due to ignorance of cardiovascular causes. Future O3 pollution policy should focus more on rural population who are facing an aggravating threat of mortality risks to ameliorate environmental health injustice.
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Affiliation(s)
- Haitong Zhe Sun
- Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, UK
- Department of Earth Sciences, University of Cambridge, Cambridge CB2 3EQ, UK
- Department of Environmental Health and Engineering, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA
| | - Junchao Zhao
- State Key Joint Laboratory of ESPC, State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, School of Environment, Tsinghua University, Beijing 100084, China
| | - Xiang Liu
- School of Atmospheric Sciences, Nanjing University, Nanjing 210023, China
| | - Minghao Qiu
- Department of Earth System Science, Stanford University, Stanford, CA 94305, USA
| | - Huizhong Shen
- School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
| | - Serge Guillas
- Department of Statistical Science, University College London, London WC1E 6BT, UK
- The Alan Turing Institute, London NW1 2DB, UK
| | - Chiara Giorio
- Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, UK
| | - Zosia Staniaszek
- Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, UK
| | - Pei Yu
- School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC 3004, Australia
| | - Michelle W.L. Wan
- Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, UK
| | - Man Mei Chim
- Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, UK
| | - Kim Robin van Daalen
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK
- Heart and Lung Research Institute, University of Cambridge, Cambridge CB2 0BD, UK
- Barcelona Supercomputing Center, Department of Earth Sciences, 08034 Barcelona, Spain
| | - Yilin Li
- Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, UK
| | - Zhenze Liu
- School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China
| | - Mingtao Xia
- Department of Mathematics, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Shengxian Ke
- State Key Laboratory of New Ceramics and Fine Processing, Key Laboratory of Advanced Materials of Ministry of Education, School of Materials Science and Engineering, Tsinghua University, Beijing 100084, China
| | - Haifan Zhao
- Department of Engineering, University of Cambridge, Cambridge CB2 1PZ, UK
| | - Haikun Wang
- School of Atmospheric Sciences, Nanjing University, Nanjing 210023, China
| | - Kebin He
- State Key Joint Laboratory of ESPC, State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, School of Environment, Tsinghua University, Beijing 100084, China
| | - Huan Liu
- State Key Joint Laboratory of ESPC, State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, School of Environment, Tsinghua University, Beijing 100084, China
| | - Yuming Guo
- School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC 3004, Australia
| | - Alexander T. Archibald
- Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, UK
- National Centre for Atmospheric Science, Cambridge CB2 1EW, UK
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Ming D, Williamson D, Guillas S. Deep Gaussian Process Emulation using Stochastic Imputation. Technometrics 2022. [DOI: 10.1080/00401706.2022.2124311] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
Affiliation(s)
- Deyu Ming
- School of Management, University College London, London, UK
| | - Daniel Williamson
- Department of Mathematics, College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, UK
| | - Serge Guillas
- Department of Statistical Science, University College London, London, UK
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Gopinathan D, Heidarzadeh M, Guillas S. Probabilistic quantification of tsunami current hazard using statistical emulation. Proc Math Phys Eng Sci 2021; 477:20210180. [PMID: 35153568 PMCID: PMC8364761 DOI: 10.1098/rspa.2021.0180] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2021] [Accepted: 05/10/2021] [Indexed: 12/02/2022] Open
Abstract
In this paper, statistical emulation is shown to be an essential tool for the end-to-end physical and numerical modelling of local tsunami impact, i.e. from the earthquake source to tsunami velocities and heights. In order to surmount the prohibitive computational cost of running a large number of simulations, the emulator, constructed using 300 training simulations from a validated tsunami code, yields 1 million predictions. This constitutes a record for any realistic tsunami code to date, and is a leap in tsunami science since high risk but low probability hazard thresholds can be quantified. For illustrating the efficacy of emulation, we map probabilistic representations of maximum tsunami velocities and heights at around 200 locations about Karachi port. The 1 million predictions comprehensively sweep through a range of possible future tsunamis originating from the Makran Subduction Zone (MSZ). We rigorously model each step in the tsunami life cycle: first use of the three-dimensional subduction geometry Slab2 in MSZ, most refined fault segmentation in MSZ, first sediment enhancements of seabed deformation (up to 60% locally) and bespoke unstructured meshing algorithm. Owing to the synthesis of emulation and meticulous numerical modelling, we also discover substantial local variations of currents and heights.
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Affiliation(s)
- Devaraj Gopinathan
- Department of Statistical Science, University College London, Gower Street, London WC1E 6BT, UK
| | - Mohammad Heidarzadeh
- Department of Civil and Environmental Engineering, Brunel University London, Uxbridge UB8 3PH, UK
| | - Serge Guillas
- Department of Statistical Science, University College London, Gower Street, London WC1E 6BT, UK
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Chang K, Guillas S. Computer model calibration with large non‐stationary spatial outputs: application to the calibration of a climate model. J R Stat Soc Ser C Appl Stat 2018. [DOI: 10.1111/rssc.12309] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Gopinathan D, Venugopal M, Roy D, Rajendran K, Guillas S, Dias F. Uncertainties in the 2004 Sumatra-Andaman source through nonlinear stochastic inversion of tsunami waves. Proc Math Phys Eng Sci 2017; 473:20170353. [PMID: 28989311 PMCID: PMC5627378 DOI: 10.1098/rspa.2017.0353] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2017] [Accepted: 08/17/2017] [Indexed: 12/01/2022] Open
Abstract
Numerical inversions for earthquake source parameters from tsunami wave data usually incorporate subjective elements to stabilize the search. In addition, noisy and possibly insufficient data result in instability and non-uniqueness in most deterministic inversions, which are barely acknowledged. Here, we employ the satellite altimetry data for the 2004 Sumatra–Andaman tsunami event to invert the source parameters. We also include kinematic parameters that improve the description of tsunami generation and propagation, especially near the source. Using a finite fault model that represents the extent of rupture and the geometry of the trench, we perform a new type of nonlinear joint inversion of the slips, rupture velocities and rise times with minimal a priori constraints. Despite persistently good waveform fits, large uncertainties in the joint parameter distribution constitute a remarkable feature of the inversion. These uncertainties suggest that objective inversion strategies should incorporate more sophisticated physical models of seabed deformation in order to significantly improve the performance of early warning systems.
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Affiliation(s)
- D Gopinathan
- Department of Statistical Science, University College London, London WC1E 6BT, UK
| | - M Venugopal
- Department of Instrumentation and Applied Physics, Indian Institute of Science, Bangalore 560012, India
| | - D Roy
- Computational Mechanics Laboratory, Department of Civil Engineering, Indian Institute of Science, Bangalore 560012, India
| | - K Rajendran
- Centre for Earth Sciences, Indian Institute of Science, Bangalore 560012, India
| | - S Guillas
- Department of Statistical Science, University College London, London WC1E 6BT, UK
| | - F Dias
- School of Mathematics and Statistics, University College Dublin, Dublin 4, Ireland
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Salmanidou DM, Guillas S, Georgiopoulou A, Dias F. Statistical emulation of landslide-induced tsunamis at the Rockall Bank, NE Atlantic. Proc Math Phys Eng Sci 2017; 473:20170026. [PMID: 28484339 PMCID: PMC5415699 DOI: 10.1098/rspa.2017.0026] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2017] [Accepted: 03/14/2017] [Indexed: 11/12/2022] Open
Abstract
Statistical methods constitute a useful approach to understand and quantify the uncertainty that governs complex tsunami mechanisms. Numerical experiments may often have a high computational cost. This forms a limiting factor for performing uncertainty and sensitivity analyses, where numerous simulations are required. Statistical emulators, as surrogates of these simulators, can provide predictions of the physical process in a much faster and computationally inexpensive way. They can form a prominent solution to explore thousands of scenarios that would be otherwise numerically expensive and difficult to achieve. In this work, we build a statistical emulator of the deterministic codes used to simulate submarine sliding and tsunami generation at the Rockall Bank, NE Atlantic Ocean, in two stages. First we calibrate, against observations of the landslide deposits, the parameters used in the landslide simulations. This calibration is performed under a Bayesian framework using Gaussian Process (GP) emulators to approximate the landslide model, and the discrepancy function between model and observations. Distributions of the calibrated input parameters are obtained as a result of the calibration. In a second step, a GP emulator is built to mimic the coupled landslide-tsunami numerical process. The emulator propagates the uncertainties in the distributions of the calibrated input parameters inferred from the first step to the outputs. As a result, a quantification of the uncertainty of the maximum free surface elevation at specified locations is obtained.
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Affiliation(s)
- D. M. Salmanidou
- School of Mathematics and Statistics, University College Dublin, Dublin, Ireland
- Earth Institute, University College Dublin, Dublin, Ireland
| | - S. Guillas
- Department of Statistical Science, University College London, London, UK
| | - A. Georgiopoulou
- Earth Institute, University College Dublin, Dublin, Ireland
- School of Earth Sciences, University College Dublin, Dublin, Ireland
| | - F. Dias
- School of Mathematics and Statistics, University College Dublin, Dublin, Ireland
- Earth Institute, University College Dublin, Dublin, Ireland
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Abstract
In this paper, we consider a Hilbert-space-valued autoregressive stochastic sequence (Xn) with several regimes. We suppose that the underlying process (In) which drives the evolution of (Xn) is stationary. Under some dependence assumptions on (In), we prove the existence of a unique stationary solution, and with a symmetric compact autocorrelation operator, we can state a law of large numbers with rates and the consistency of the covariance estimator. An overall hypothesis states that the regimes where the autocorrelation operator's norm is greater than 1 should be rarely visited.
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Dabo-Niang S, Guillas S, Ternynck C. Efficiency in multivariate functional nonparametric models with autoregressive errors. J MULTIVARIATE ANAL 2016. [DOI: 10.1016/j.jmva.2016.01.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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Cloutman-Green E, Kalaycioglu O, Wojani H, Hartley JC, Guillas S, Malone D, Gant V, Grey C, Klein N. The important role of sink location in handwashing compliance and microbial sink contamination. Am J Infect Control 2014; 42:554-5. [PMID: 24773795 DOI: 10.1016/j.ajic.2013.12.020] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2013] [Revised: 12/20/2013] [Accepted: 12/20/2013] [Indexed: 11/30/2022]
Abstract
Handwashing is one of the most important means of reducing the spread of infection. In this study, we investigated how sink location and visibility influences handwashing and microbial contamination detected on clinical sinks in 3 pediatric intensive care units. We conclude that the visibility of sinks directly impacts on handwashing frequency and duration and also impacts on levels of bacterial contamination on and around the sink area.
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Affiliation(s)
- Elaine Cloutman-Green
- Great Ormond Street Hospital NHS Foundation Trust, Camelia Botnar Laboratories, Department of Microbiology, Virology and Infection Prevention and Control, London, UK.
| | - Oya Kalaycioglu
- University College London, Department of Statistical Science, London, UK
| | - Hedieh Wojani
- University College London, Institute of Child Health, Infectious Diseases and Microbiology Unit, London, UK; HaCIRIC, The University of Reading, Reading, UK
| | - John C Hartley
- Great Ormond Street Hospital NHS Foundation Trust, Camelia Botnar Laboratories, Department of Microbiology, Virology and Infection Prevention and Control, London, UK
| | - Serge Guillas
- University College London, Department of Statistical Science, London, UK
| | - Deirdre Malone
- Great Ormond Street Hospital NHS Foundation Trust, Camelia Botnar Laboratories, Department of Microbiology, Virology and Infection Prevention and Control, London, UK
| | - Vanya Gant
- University College London Hospital, Department of Microbiology, London, UK
| | - Colin Grey
- HaCIRIC, The University of Reading, Reading, UK
| | - Nigel Klein
- University College London, Institute of Child Health, Infectious Diseases and Microbiology Unit, London, UK
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Gaudart J, Ettinger B, Lai M, Dessay N, Coulibaly D, Thera M, Giorgi R, Guillas S. Estimation de surfaces spatiales par des fonctions splines bivariées : application à la densité de population. Rev Epidemiol Sante Publique 2014. [DOI: 10.1016/j.respe.2013.12.038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022] Open
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Gaudart J, Cloutman-Green E, Guillas S, D’Arcy N, Hartley JC, Gant V, Klein N. Healthcare environments and spatial variability of healthcare associated infection risk: cross-sectional surveys. PLoS One 2013; 8:e76249. [PMID: 24069459 PMCID: PMC3777895 DOI: 10.1371/journal.pone.0076249] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2012] [Accepted: 08/22/2013] [Indexed: 11/18/2022] Open
Abstract
Prevalence of healthcare associated infections remains high in patients in intensive care units (ICU), estimated at 23.4% in 2011. It is important to reduce the overall risk while minimizing the cost and disruption to service provision by targeted infection control interventions. The aim of this study was to develop a monitoring tool to analyze the spatial variability of bacteriological contamination within the healthcare environment to assist in the planning of interventions. Within three cross-sectional surveys, in two ICU wards, air and surface samples from different heights and locations were analyzed. Surface sampling was carried out with tryptic Soy Agar contact plates and Total Viable Counts (TVC) were calculated at 48hrs (incubation at 37°C). TVCs were analyzed using Poisson Generalized Additive Mixed Model for surface type analysis, and for spatial analysis. Through three cross-sectional survey, 370 samples were collected. Contamination varied from place-to-place, height-to-height, and by surface type. Hard-to-reach surfaces, such as bed wheels and floor area under beds, were generally more contaminated, but the height level at which maximal TVCs were found changed between cross-sectional surveys. Bedside locations and bed occupation were risk factors for contamination. Air sampling identified clusters of contamination around the nursing station and surface sampling identified contamination clusters at numerous bed locations. By investigating dynamic hospital wards, the methodology employed in this study will be useful to monitor contamination variability within the healthcare environment and should help to assist in the planning of interventions.
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Affiliation(s)
- Jean Gaudart
- Aix-Marseille Univ, UMR912 SESSTIM (AMU, INSERM, IRD), Marseille, France
- University College, London, Department of Statistical Science, London, United Kingdom
- * E-mail:
| | - Elaine Cloutman-Green
- Great Ormond Street Hospital NHS Trust, Camelia Botnar Laboratories, Department of Microbiology, London, United Kingdom
| | - Serge Guillas
- University College, London, Department of Statistical Science, London, United Kingdom
| | - Nikki D’Arcy
- Great Ormond Street Hospital NHS Trust, Camelia Botnar Laboratories, Department of Microbiology, London, United Kingdom
| | - John C. Hartley
- Great Ormond Street Hospital NHS Trust, Camelia Botnar Laboratories, Department of Microbiology, London, United Kingdom
| | - Vanya Gant
- University College London Hospital, Department of Microbiology, London, United Kingdom
| | - Nigel Klein
- University College, London, Institute of Child Health, Infectious Diseases and Microbiology Unit, London, United Kingdom
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Beck J, Friedrich D, Brandani S, Guillas S, Fraga ES. Surrogate based Optimisation for Design of Pressure Swing Adsorption Systems. Computer Aided Chemical Engineering 2012. [DOI: 10.1016/b978-0-444-59520-1.50102-0] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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Gaudart J, Cloutman-Green E, Guillas S, Hartley J, Klein N. Variabilité spatiale de l’aérocontamination bactérienne au lit du patient, Londres, UK. Rev Epidemiol Sante Publique 2011. [DOI: 10.1016/j.respe.2011.02.080] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
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Guillas S, Day SJ, McGuire B. Statistical analysis of the El Niño-Southern Oscillation and sea-floor seismicity in the eastern tropical Pacific. Philos Trans A Math Phys Eng Sci 2010; 368:2481-2500. [PMID: 20403838 DOI: 10.1098/rsta.2010.0044] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2023]
Abstract
We present statistical evidence for a temporal link between variations in the El Niño-Southern Oscillation (ENSO) and the occurrence of earthquakes on the East Pacific Rise (EPR). We adopt a zero-inflated Poisson regression model to represent the relationship between the number of earthquakes in the Easter microplate on the EPR and ENSO (expressed using the southern oscillation index (SOI) for east Pacific sea-level pressure anomalies) from February 1973 to February 2009. We also examine the relationship between the numbers of earthquakes and sea levels, as retrieved by Topex/Poseidon from October 1992 to July 2002. We observe a significant (95% confidence level) positive influence of SOI on seismicity: positive SOI values trigger more earthquakes over the following 2 to 6 months than negative SOI values. There is a significant negative influence of absolute sea levels on seismicity (at 6 months lag). We propose that increased seismicity is associated with ENSO-driven sea-surface gradients (rising from east to west) in the equatorial Pacific, leading to a reduction in ocean-bottom pressure over the EPR by a few kilopascal. This relationship is opposite to reservoir-triggered seismicity and suggests that EPR fault activity may be triggered by plate flexure associated with the reduced pressure.
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Affiliation(s)
- Serge Guillas
- Department of Statistical Science, Aon Benfield UCL Hazard Research Centre, University College London, Gower Street, London WC1E 6BT, UK.
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Rougier J, Guillas S, Maute A, Richmond AD. Expert Knowledge and Multivariate Emulation: The Thermosphere–Ionosphere Electrodynamics General Circulation Model (TIE-GCM). Technometrics 2009. [DOI: 10.1198/tech.2009.07123] [Citation(s) in RCA: 23] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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Miller AJ, Cai A, Tiao G, Wuebbles DJ, Flynn LE, Yang SK, Weatherhead EC, Fioletov V, Petropavlovskikh I, Meng XL, Guillas S, Nagatani RM, Reinsel GC. Examination of ozonesonde data for trends and trend changes incorporating solar and Arctic oscillation signals. ACTA ACUST UNITED AC 2006. [DOI: 10.1029/2005jd006684] [Citation(s) in RCA: 26] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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Damon J, Guillas S. Estimation and Simulation of Autoregressive Hilbertian Processes with Exogenous Variables. Stat Infer Stoch Process 2005. [DOI: 10.1007/s11203-004-1031-6] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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