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Conference paperMorgan O, Elwy L, Oluleye O, 2024,
Assessing the UK’s attempt to establish a zero-carbon hydrogen economy in the industrial sector
, International Conference on Applied Energy, Publisher: Scanditale AB, ISSN: 2004-2965This study determines the cost reducing effect of hydrogen supply-push targets, which will indicate the sufficiency of current UK government policy to initiate a hydrogen economy within the industrial sector. This study will also answer the question “What demand-pull policies can support fuel switching to hydrogen in UK industry?” A novel mixed-methods approach is used, in-depth rapid evidence assessment and a macro market penetration assessment to understand how to best establish an industrial hydrogen economy. Our findings show that without demand-pull policies, 65 GW to 350 GW of hydrogen supply is required to achieve price parity with natural gas.
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Journal articleShi J, Feng X, Toumi R, et al., 2024,
Global increase in tropical cyclone ocean surface waves
, Nature Communications, Vol: 15, ISSN: 2041-1723The long-term changes of ocean surface waves associated with tropical cyclones (TCs) are poorly observed and understood. Here, we present the global trend analysis of TC waves for 1979–2022 based on the ERA5 wave reanalysis. The maximum height and the area of the TC wave footprint in the six h reanalysis have increased globally by about 3%/decade and 6%/decade, respectively. The TC wave energy transferred at the interface from the atmosphere to the ocean has increased globally by about 9%/decade, which is three times larger than that reported for all waves. The global energy changes are mostly driven by the growing area of the wave footprint. Our study shows that the TC-associated wave hazard has increased significantly and these changes are larger than those of the TC maximum wind speed. This suggests that the wave hazard should be a concern in the future.
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Journal articleCao XE, Murguía Burton ZF, Wang M, 2024,
Advancing equitable climate education
, Matter, Vol: 7, Pages: 9-12, ISSN: 2590-2393The climate crisis requires immediate action, including training a sizable climate workforce and involving people of all backgrounds in creating climate solutions. Emerging interdisciplinary, societally engaged climate education programs provide a template for the development of globally accessible, equitable education models.
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Conference paperOluleye O, Hu F, Abu Ali H, et al., 2024,
A novel stochastic market potential optimisation model for clean technology uptake modelling
, International Conference on Applied Energy, Publisher: Scanditale AB, ISSN: 2004-2965The high mitigation cost of clean innovations, warrants policy support for increased uptake. This study applies optimization techniques to investigate the impact of market-based policies in generating sufficient demand pull to trigger cost reduction under uncertainty. A novel Stochastic Market Potential Optimization model (SMPOM) is developed to maximize the cost difference between the initial cost of a technology and the new cost using a market-based policy. The model is applied to a case study of carbon capture and storage (CCS) in 32 integrated steel plants in Europe. Results show policy induced demand pull can reduce the mitigation cost of CCS.
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Journal articleKonstantinoudis G, Evangelopoulos D, Katsaounou P, et al., 2024,
Association of high temperatures with cardiovascular and respiratory mortality in a context of high smoking prevalence: The case of Greece
, Tobacco Prevention and Cessation, Vol: 10Introduction Climate change presents a significant threat to human health. Increased temperatures can aggravate COPD and asthma and exacerbate the effect of factors such as air pollution or pollen, which are known to affect these diseases. Older people, people with multiple chronic diseases, and deprived populations are affected the most by high temperatures. Quantifying this variation is crucial to inform adaptation to heat policies and to shed light on how effect modifiers such as deprivation, green space, and smoking modify this effect. Methods Daily data on all-cause mortality at the NUTS3 administrative regions (nomos) during 2000–2019 in Greece by age and sex was retrieved from the Hellenic Statistical Authority. The daily mean temperature at 9kmx9km was retrieved from the ERA-5 reanalysis dataset. We employed a case-crossover study design to examine all-cause mortality, cardiovascular and respiratory mortality by age (75<, 75–84, 85+) and sex (male and female), focusing on the effect of heatwaves (deaths occurring during April to September) using six different definitions (durations = >2 or >3 days and thresholds = 90%, 95% and 99% of the annual space specific temperature percentile). We fitted Bayesian conditional Poisson regression models and examined how the effect varies in time and at the NUTS3 region level. Results We retrieved 216, 758 cardiorespiratory deaths from April and September during 2000–2019. Most of the deaths were among females (52%). Overall, we observed an increasing effect of heatwaves on cardiovascular and respiratory mortality with increasing duration and temperature thresholds. We also observed an overall decreasing trend of the heatwave effect across the study periods in all heatwave definitions, apart from our most extreme definition, where we observed an increasing trend. We observed weak evidence of spatial vulnerabilities. Conclusions Heatwaves are associated with cardiovascular and respiratory mortalit
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Book chapterHolm DD, Hu R, Street OD, 2024,
On the interactions between mean flows and inertial gravity waves in the WKB approximation
, Mathematics of Planet Earth, Publisher: Springer Nature Switzerland, Pages: 111-141, ISBN: 9783031400933We derive a Wentzel–Kramers–Brillouin (WKB) closure of the generalised Lagrangian mean (GLM) theory by using a phase-averaged Hamilton variational principle for the Euler–Boussinesq (EB) equations. Following Gjaja and Holm 1996, we consider 3D inertial gravity waves (IGWs) in the EB approximation. The GLM closure for WKB IGWs expresses EB wave mean flow interaction (WMFI) as WKB wave motion boosted into the reference frame of the EB equations for the Lagrangian mean transport velocity. We provide both deterministic and stochastic closure models for GLM IGWs at leading order in 3D complex vector WKB wave asymptotics. This paper brings the Gjaja and Holm 1996 paper at leading order in wave amplitude asymptotics into an easily understood short form and proposes a stochastic generalisation of the WMFI equations for IGWs.
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Journal articleTsui EYL, Chan PW, Toumi R, 2024,
Boundary layer profile of decaying and non-decaying tropical storms near landfall
, Atmospheric Science Letters, Vol: 25, ISSN: 1530-261XThe vertical profile of the wind structure of translating tropical cyclones, including the associated azimuthal asymmetry, has been the subject of existing theoretical and observational studies using dropsondes. Most of these studies are based on data collected from relatively strong cyclones over the Atlantic. Here we explore the tropical cyclone boundary layer wind profile of mainly relatively weak landfalling cyclones near Hong Kong. We find that decaying tropical storms have a much larger mid- to low-level inflow angle than those that are intensifying or in steady-state. The inflow angles of intensifying, steady-state and decaying tropical storms converge towards the top of the boundary layer. The wind speed reduces through the boundary layer in a similar way in all three cases. The combination of these factors means that decaying tropical storms have stronger inflow than intensifying and steady-state ones. We attribute these local effects to remote enhanced surface friction over land when the storms are weakening.
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Journal articleMurawski J, Scott SB, Rao R, et al., 2024,
Benchmarking Stability of Iridium Oxide in Acidic Media under Oxygen Evolution Conditions: A Review: Part I Probing degradation of iridium-based oxide catalysts
, JOHNSON MATTHEY TECHNOLOGY REVIEW, Vol: 68, Pages: 121-146, ISSN: 2056-5135 -
Journal articleDalder J, Oluleye G, Cannone C, et al., 2024,
Modelling Policy Pathways to Maximise Renewable Energy Growth and Investment in the Democratic Republic of the Congo Using OSeMOSYS (Open Source Energy Modelling System)
, ENERGIES, Vol: 17 -
Journal articlePaiboonsin P, Oluleye G, Howells M, et al., 2024,
Pathways to Clean Energy Transition in Indonesia's Electricity Sector with Open-Source Energy Modelling System Modelling (OSeMOSYS)
, ENERGIES, Vol: 17 -
Journal articleMurawski J, Scott SB, Rao R, et al., 2024,
Benchmarking Stability of Iridium Oxide in Acidic Media under Oxygen Evolution Conditions: A Review: Part II Investigation of catalyst activity and stability via short term testing
, JOHNSON MATTHEY TECHNOLOGY REVIEW, Vol: 68, Pages: 147-160, ISSN: 2056-5135 -
Journal articleSerdeczny O, Andrijevic M, Fyson C, et al., 2024,
Climatic risks to adaptive capacity
, MITIGATION AND ADAPTATION STRATEGIES FOR GLOBAL CHANGE, Vol: 29, ISSN: 1381-2386- Cite
- Citations: 7
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Journal articleOluleye G, Patel D, Matalon P, et al., 2024,
A novel optimisation framework to design market-based policy interventions for the uptake of alternative fuels in the UK chemical industry
, Computer Aided Chemical Engineering, Vol: 53, Pages: 2089-2094, ISSN: 1570-7946Shifting to clean alternatives like biomethane, green hydrogen, and blue hydrogen for industrial heating offers emission reductions, yet their high costs hinder adoption. There is no systematic way to design policy interventions that enable cost reduction at minimum cost to government and industry. This study aims to formulate and apply a novel multiperiod Mixed-Integer Market Penetration Optimization Model to fill this gap and inform decisions about transitioning to alternative fuels for heating in the UK Chemical Industry. The model cost-effectively designs a policy pathway whilst accounting for the fuel cost reduction due to demand-pull induced learning effects in the policy design. The model is applied to 490 boilers in the UK chemical industry, the model designs effective policy mixes to reduce the cost of green hydrogen by 60%, blue hydrogen by 36%, and green gas biomethane by 17%, with revenue from taxes supporting subsidies for cost neutrality.
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Journal articleLefauve A, 2024,
Geophysical stratified turbulence and mixing in the laboratory
, COMPTES RENDUS PHYSIQUE, Vol: 25, ISSN: 1631-0705 -
Journal articleJiang X, Atoufi A, Zhu L, et al., 2023,
Geometry of stratified turbulent mixing: local alignment of the density gradient with rotation, shear and viscous dissipation
, JOURNAL OF FLUID MECHANICS, Vol: 977, ISSN: 0022-1120 -
Journal articleAtoufi A, Zhu L, Lefauve A, et al., 2023,
Stratified inclined duct: two-layer hydraulics and instabilities
, JOURNAL OF FLUID MECHANICS, Vol: 977, ISSN: 0022-1120- Cite
- Citations: 7
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ReportKimutai J, Barnes C, Zachariah M, et al., 2023,
Compounding natural hazards and high vulnerability led to severe impacts from Horn of Africa flooding exacerbated by climate change and Indian Ocean Dipole
, Publisher: Centre for Environmental Policy -
Journal articleHon KK, Ballard R, Blake E, et al., 2023,
Recent advances in operational tropical cyclone genesis forecast
, Tropical Cyclone Research and Review, Vol: 12, Pages: 323-340, ISSN: 2225-6032Tropical cyclone (TC) genesis prediction is a major scientific challenge to the TC operation and research community. This report surveys the current status of TC genesis forecasts by a number of major operational centers covering the key ocean basins across both hemispheres. Since IWTC-9, we see an emergence of probabilistic TC genesis forecast products by operational centers, typically supported by the statistical processing of a combination of ensemble prediction and satellite analysis, covering time periods of couple of days to weeks ahead. The prevalence of multi-center grand ensemble approach highlights the uncertainties involved and the forecast challenges in quantitative genesis prediction. While operational practice might differ across agencies, verification efforts generally report a steady or slightly improving skill level in terms of reliability, which likely results from the continual improvement in global numerical weather prediction capability.
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Working paperOtto F, Kimutai J, Bird J, et al., 2023,
Loss and Damage Fund: the need for climate impact metrics
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Journal articleTheokritoff E, van Maanen N, Andrijevic M, et al., 2023,
Adaptation constraints in scenarios of socio-economic development
, Scientific Reports, Vol: 13, ISSN: 2045-2322Climate change adaptation is paramount, but increasing evidence suggests that adaptation action is subject to a range of constraints. For a realistic assessment of future adaptation prospects, it is crucial to understand the timescales needed to overcome these constraints. Here, we combine data on documented adaptation from the Global Adaptation Mapping Initiative with national macro indicators and assess future changes in adaptation constraints alongside the Shared Socioeconomic Pathways, spanning a wide range of future socio-economic development scenarios. We find that even in the most optimistic scenario, it will take until well after 2050 to overcome key constraints, which will limit adaptation for decades to come particularly in vulnerable countries. The persistence of adaptation constraints calls for stringent mitigation, improved adaptation along with dedicated finance and increasing efforts to address loss and damage. Our approach allows to ground truth indicators that can be further used in climate modelling efforts, improving the representation of adaptation and its risk reduction potential.
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ReportPinto I, Barimalala R, Philip S, et al., 2023,
Extreme poverty renders Madagascar highly vulnerable to underreported extreme heat that would not have occurred without human-induced climate change
, Publisher: Centre for Environmental Policy -
Journal articleHolm DD, Hu R, Street OD, 2023,
Lagrangian reduction and wave mean flow interaction
, Physica D: Nonlinear Phenomena, Vol: 454, ISSN: 0167-2789How does one derive models of dynamical feedback effects in multiscale, multiphysics systems such as wave mean flow interaction (WMFI)? We shall address this question for hybrid dynamical systems, defined as systems whose motion can be expressed as the composition of two or more Lie-group actions. Hybrid systems abound in fluid dynamics. Examples include: the dynamics of complex fluids such as liquid crystals; wind-driven waves propagating with the currents moving on the sea surface; turbulence modelling in fluids and plasmas; and classical–quantum hydrodynamic models in molecular chemistry. From among these examples, the motivating question here is: How do wind-driven waves produce ocean surface currents?The paper first summarises the geometric mechanics approach for deriving hybrid models of multiscale, multiphysics motions in ideal fluid dynamics. It then illustrates this approach for WMFI in the examples of 3D WKB waves and 2D wave amplitudes governed by the nonlinear Schrödinger (NLS) equation propagating in the frame of motion of an ideal incompressible inhomogeneous Euler fluid flow. The results for these examples tell us that the mean flow in WMFI does not create waves, although it does transport the waves. However, feedback in the opposite direction is possible, since the 3D WKB and 2D NLS wave dynamics discussed here do in fact create circulatory mean flow.
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Journal articleChen R, Toumi R, Shi X, et al., 2023,
An adaptive learning approach for tropical cyclone intensity correction
, Remote Sensing, Vol: 15, ISSN: 2072-4292Tropical cyclones (TCs) are dangerous weather events; accurate monitoring and forecasting can provide significant early warning to reduce loss of life and property. However, the study of tropical cyclone intensity remains challenging, both in terms of theory and forecasting. ERA5 reanalysis is a benchmark data set for tropical cyclone studies, yet the maximum wind speed error is very large (68 kts) and is still 19 kts after simple linear correction, even in the better sampled North Atlantic. Here, we develop an adaptive learning approach to correct the intensity in the ERA5 reanalysis, by optimising the inputs to overcome the problems caused by the poor data quality and updating the features to improve the generalisability of the deep learning-based model. Specifically, we use understanding of TC properties to increase the representativeness of the inputs so that the general features can be learned with deep neural networks in the sample space, and then use domain adaptation to update the general features from the known domain with historical storms to the specific features for the unknown domain of new storms. This approach can reduce the error to only 6 kts which is within the uncertainty of the best track data in the international best track archive for climate stewardship (IBTrACS) in the North Atlantic. The method may have wide applicability, such as when extending it to the correction of intensity estimation from satellite imagery and intensity prediction from dynamical models.
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ReportOtto F, Clarke B, Rahimi M, et al., 2023,
Human-induced climate change compounded by socio-economic water stressors increased severity of drought in Syria, Iraq and Iran
, Publisher: Centre for Environmental Policy -
Journal articleChagot L, Hernandez Gelado S, Quilodran-Casas C, 2023,
Enhancing microdroplets image analysis with deep learning
, Micromachines, Vol: 14, ISSN: 2072-666XMicrofluidics is a highly interdisciplinary field where the integration of deep-learning models has the potential to streamline processes and increase precision and reliability. This study investigates the use of deep-learning methods for the accurate detection and measurement of droplet diameters and the image restoration of low-resolution images. This study demonstrates that the Segment Anything Model (SAM) provides superior detection and reduced droplet diameter error measurement compared to the Circular Hough Transform, which is widely implemented and used in microfluidic imaging. SAM droplet detections prove to be more robust to image quality and microfluidic images with low contrast between the fluid phases. In addition, this work proves that a deep-learning super-resolution network MSRN-BAM can be trained on a dataset comprising of droplets in a flow-focusing microchannel to super-resolve images for scales ×2, ×4, ×6, ×8. Super-resolved images obtain comparable detection and segmentation results to those obtained using high-resolution images. Finally, the potential of deep learning in other computer vision tasks, such as denoising for microfluidic imaging, is shown. The results show that a DnCNN model can denoise effectively microfluidic images with additive Gaussian noise up to
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ReportKew S, Pinto I, Alves L, et al., 2023,
Strong influence of climate change in uncharacteristic early spring heat in South America
, Publisher: Centre for Environmental Policy -
ReportJennings N, Paterson P, 2023,
How do UK citizens perceive the co-benefits of climate action?
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ReportZachariah M, Kotroni V, Kostas L, et al., 2023,
Interplay of climate change-exacerbated rainfall, exposure and vulnerability led to widespread impacts in the Mediterranean region
, Publisher: Centre for Environmental Policy -
Journal articleToumi R, 2023,
John Edward Harries
, Physics Today, Vol: 76, Pages: 53-53, ISSN: 0031-9228 -
Journal articleTong Z, Xin J, Song J, et al., 2023,
A graphics-accelerated deep neural network approach for turbomachinery flows based on large eddy simulation
, Physics of Fluids, Vol: 35, ISSN: 1070-6631In turbomachinery, strongly unsteady rotor-stator interaction triggers complex three-dimensional turbulent flow phenomena such as flow separation and vortex dynamics. Large eddy simulation (LES) is an advanced numerical method that has recently been used to resolve large-scale turbulent motions and model subgrid-scale turbulence in turbomachinery. To largely reduce the computing cost of LES for turbomachinery flow, a graphics processing unit (GPU)-accelerated deep neural network-based flow field prediction approach is explored, which combines convolutional neural network autoencoder (CNN-AE) with long short-term memory (LSTM). CNN-AE extracts spatial features of turbomachinery flow by mapping high-dimensional flow fields into low-dimensional space, while LSTM is used to predict the temporal evolution of fluid dynamics. Automatic mixed precision (AMP) is employed to achieve rapid neural network training using Nvidia GTX 1080 Ti GPU, which shows a significant speedup compared with that without AMP. We evaluated the proposed CNN-AE-LSTM (CAL) method against gated recurrent units (GRU) and simple recurrent network (SRN) on two types of turbomachinery, i.e., centrifugal and axial flow pumps. The results show that the proposed CAL shows better capability of capturing the vortex structure details of turbomachinery. When predicting the temporal vorticity field, the mean square error of CAL results is 0.105%-0.124% for centrifugal pumps and 0.071%-0.072% for axial flow pumps. Meanwhile, the structural similarity index measure of the CAL results is 92.51%-92.77% for centrifugal pumps and 93.81%-94.61% for axial flow pumps. The proposed CAL is noticeably better than GRU and SRN in terms of both mean square error and structural similarity index measure.
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