Methane mitigation is often evaluated by sector or by reactor performance, although deployment changes energy, food, waste, and wastewater systems beyond the point of treatment. This critical review evaluates prevention, recovery, destruction, and atmospheric removal within a common life-cycle framework. Evidence is screened by methane concentration, source controllability, recovery value, auxiliary demand, burden shifting, evidence maturity, governance fit, and net cost and incentives. Across sectors, prevention is considered first when methane generation or release can be avoided before dilution. Recovery is credible for concentrated streams only when collection losses are low and recovered products displace existing supply. Destruction is justified for controlled residual streams, but conversion efficiency alone does not establish net benefit. Atmospheric methane removal remains an early stage option. At a concentration of approximately 1.9 ppm, methane removal requires large volumes of air and may impose substantial energy, land, and material demands. Reported technology rankings are conditional on the counterfactual, system boundary, operating context, and evidence scale. The resulting stage-gated framework links production and consumption choices to intervention timing, life-cycle performance, and verification. Atmospheric removal is treated as a research contingency, not a substitute for source mitigation. The final gates specify the evidence required before an emerging approach can progress beyond controlled research.
This study applies an instrumented membrane electrode assembly (iMEA) approach to enable multi-location, in-plane mapping of oxygen concentration at the cathode catalyst layer/proton exchange membrane (CL/PEM) interface in a 250 cm2 PEM fuel cell. Three sensors positioned at 63, 163, and 203 mm from the cathode inlet were used to evaluate local oxygen concentration under controlled inlet oxygen concentration steps and air stoichiometry sweeps. Sensor calibration was based on an extended oxygen flux-interrupt approach combined with simultaneous DC current and high-frequency impedance measurements, and a Gaussian Process Regression model was applied to correct for extraneous effects of temperature and humidity to determine oxygen concentration. For all sensors, the measured oxygen concentration decreased with decreasing air stoichiometry and with increasing distance from the cathode inlet. The upstream sensor consistently recorded the highest oxygen concentration, while the downstream sensor showed the lowest values, confirming an oxygen gradient along the cathode flow direction. At the lowest stoichiometry, the most downstream location approached complete oxygen depletion, while the cell voltage remained within a range that could still appear acceptable. These results demonstrate the capability of the developed approach for operando, in-plane mapping of oxygen concentration at the cathode CL/PEM interface.
The continuous rating of a multi-cell proton-exchange-membrane (PEM) fuel cell stack is normally inferred from mean polarisation behaviour, yet sustained operation is limited by the weakest cell. Here, cell voltages measured on a 20-cell, 250 cm2 short stack over 27 operating points (0-1.8 A cm−2) are used to infer cell-to-cell variation in ohmic resistance and limiting current density, from which a virtual 300-cell, 60 kW-class stack is sampled. Voltage dispersion grows from below 1 mV near open circuit to ∼36 mV at the 62.9 kW peak, and the minimum cell voltage falls below 0.45 V above ∼1.5 A cm−2, so cell spread, not mean performance, sets the continuous limit. The limit falls at the same set-point in 91% of 10,000 sampled stacks, but a 20-cell stack from the same population overstates it by about 6%. Published degradation behaviour reduces it to 1.10-1.30 A cm−2 at end of life. For a modular marine auxiliary power unit, levelised cost is £0.42-0.82 kWh−1 at £4-£10 kg−1 H2 and operational carbon intensity is ∼85% below diesel. The method gives stack manufacturers and marine integrators a practical route to rating a full-size stack on its weakest cell before it is built.
This study explores intergenerational vehicle comfort using the Etic-Emic framework, translating a psychological concept into automotive guidelines. A scoping review identified 34 parameters, and trend analysis of 1789 Scopus articles (1953–2025) tracing comfort across vehicle generations. Expert interviews with 15 specialists classified them as Etic (relevant to L0 and L5) or Emic (emerging specifically in L5), suggesting additional factors. Group brainstorming refined classifications, yielding 44 parameters, including factors such as brand identity and cargo space. Safety, vibration control, and fuel efficiency were stable Etic parameters, whereas non-driving tasks, confidence to sleep or fully recline, legal liability transparency, and cybersecurity were Emic. The novel “Intergenerational Car Comfort” concept provides OEMs an actionable decision tool: its Etic-Emic matrix guides trade-offs, including safety constraints versus expectations to recline and sleep. Ultimately, it identifies legacy architectures to retain or those requiring new interior packaging and HMI solutions, providing a roadmap for next-generation automated vehicles.
West Africa faces devastating flood hazards that affect more than 400 million people. Yet flood risk assessment is hindered by sparse and often unreliable hydrological data. Regional flood frequency analysis (RFFA) is widely used to estimate design values at ungauged catchments and there is a need for a systematic intercomparison of RFFA approaches in this region. With an unprecedented dataset of 211 near-natural catchments, we compared a Direct Regression Approach (DRA) and three homogeneous region delineation methods using the index-flood methods based on spatial proximity, Principal Component Analysis (PCA), and Canonical Correlation Analysis (CCA) with catchment attributes. Each regional approach was paired with two regression models: (i) Stepwise Regression and (ii) Least Absolute Shrinkage and Selection Operator (LASSO), and four machine learning algorithms: (i) Random Forest (RF), (ii) eXtreme Gradient Boosting (XGB), (iii) Support Vector Regression (SVR), and (iv) a hybrid linear-tree ensemble (LinRF). Results show that index-flood methods consistently outperformed DRA, with the CCA-based framework achieving the highest accuracy. CCA-SVR combination is the best-performing regional model, yielding the lowest estimation errors (mean absolute relative error = 0.21 and relative bias = −0.03) for 20- or 50-year flood quantiles. Feature importance analysis revealed that subsurface properties, catchment area, and topographic attributes have stronger influence on regional flood estimation than surface features or land use patterns. The methodology and findings of this study offer practical tools for infrastructure design and climate adaptation, supporting more resilient flood risk management across vulnerable West African communities.
