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Journal articleRiley AI, Blangiardo M, Piel FB, et al., 2026,
A Bayesian multisource fusion model for spatiotemporal PM₂.₅ in an urban setting
, Environmetrics, Vol: 37, ISSN: 1180-4009Airborne particulate matter (PM2.5) is a major public health concern in urban environments, where population density and emission sources exacerbate exposure risks. We present a novel Bayesian spatiotemporal fusion model to estimate monthly PM2.5 concentrations over Greater London (2014–2019) at 1 km resolution. The model integrates multiple PM2.5 data sources, including outputs from two atmospheric air quality dispersion models, and predictive variables, such as vegetation and satellite aerosol optical depth, while explicitly modeling a latent spatiotemporal field. Spatial misalignment of the data is addressed through a hierarchical fusion and spatial interpolation approach to predict across the entire area. Building on stochastic partial differential equations (SPDE) within the integrated nested Laplace approximations (INLA) framework, our method introduces spatially- and temporally-varying coefficients to flexibly calibrate datasets and capture fine-scale variability. Model performance and complexityare balanced using predictive metrics such as the predictive model choice criterion and thorough cross-validation. The best performing model shows excellent fit and robust predictive performance, enabling reliable high-resolution spatiotemporal mapping of PM2.5 concentrations with the associated uncertainty. Furthermore, the model outputs, including full posterior predictive distributions, can be used to map exceedance probabilities of regulatory thresholds, supporting air quality management and targetedinterventions in vulnerable urban areas, as well as providing refined exposure estimates of PM2.5 for epidemiological applications.
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Journal articleBose I, Hadida G, Green R, et al., 2026,
Rainfall and water-related diseases, malnutrition and mortality in Low- and Middle- Income Countries: a systematic review of the epidemiological evidence
, Heliyon, Vol: 12Background Climate change is altering rainfall patterns. Rainfall has been linked to numerous health outcomes, through the impacts on water quality and quantity, but the coherence and strength of evidence across outcomes remain unclear. Objectives Understand and evaluate the strength of evidence on associations between rainfall (both low and heavy events) and health outcomes in Low- and Middle- Income Countries (LMICs). Methods A systematic review of peer-reviewed epidemiological studies quantifying associations between rainfall and human health outcomes in LMIC populations was conducted. Seven databases were searched including MEDLINE and EMBASE. Study quality was evaluated using 9 modified criteria that were previously used to assess environmental epidemiology studies. The strength of evidence for each health outcome was assessed across rainfall exposures. Results Of 23,579 papers identified, 177 met the inclusion criteria. Health outcomes included diarrheal diseases (n = 119); malnutrition (n = 35); mortality (n = 21); helminth infections (n = 6), and eye infections (n = 4). There was moderately strong evidence for positive associations between both heavy and low rainfall and all-cause diarrhea. Evidence for undernutrition was mixed, with moderate evidence of a positive association with low rainfall. Despite sharing causal pathways, diarrheal disease and nutrition studies found contrasting results for heavy rainfall, likely due to differing study designs. Studies were heterogenous in design, rainfall exposure definitions, and lag times. Studies also often lacked a clear hypothesis. Discussion There is substantial evidence that rainfall affects health in LMICs through multiple pathways. Limitations in the data (often from cross-sectional surveys) and study designs, limit the strength of evidence for several health outcomes. Specifically, studies frequently used inappropriate exposures or lags to reflect the causal pathways. In future studies, efforts should be dir
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Journal articleKeeping TR, Shepherd TG, Prentice IC, et al., 2026,
Influence of global climate modes on wildfire occurrence in the contiguous United States under recent and future climates.
, Clim Dyn, Vol: 64, ISSN: 0930-7575UNLABELLED: Predictable modes of climate variability, such as the El Niño Southern Oscillation (ENSO), have a major influence on regional weather patterns, an important control on wildfire occurrence. Although these global climate modes have been associated with historical variability in wildfire occurrence in the United States and are used to forecast seasonal wildfire risk, precise information about the spatial pattern and magnitude of their influence is lacking and the satellite record of wildfires is too short to address these issues. Here we use wildfire occurrence model with a large ensemble of 1600 simulated years from EC-Earth3 in a recent climate (2000-2009) and a future climate corresponding to + 2 °C global warming, to characterise the impact of specific climate modes on wildfire occurrence in the contiguous US. We show that ENSO, the Indian Ocean Dipole (IOD), and the 1-year lagged Tropical North Atlantic (TNA+1) have the greatest effect on annual fire occurrence-strongly contributed by the effect of these modes on hot, dry conditions in the Great Plains and precipitation in the southwestern US. El Niño is not significantly associated with wildfire occurrence in the northwestern US, contrary to expectation, but is associated with a later (earlier) wildfire season peak in the southwestern (southeastern) US. Under future warming, the AMO and PNA become a significant influence over most of the US, and the magnitude of impact of ENSO and TNA+1 increase strongly. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00382-025-07998-w.
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Journal articleDean TR, Abbott TH, Engberg Z, et al., 2025,
Impact of forecast stability on navigational contrail avoidance
, ENVIRONMENTAL RESEARCH: INFRASTRUCTURE AND SUSTAINABILITY, Vol: 5 -
Journal articleGavasso-Rita YL, Zaerpour M, Abdelmoaty H, et al., 2025,
Rainfed spring canola yield response to changing heat and water stress in the Canadian Prairie region
, Agricultural Water Management, Vol: 322, ISSN: 0378-3774Canola is a significant crop in Canadian agriculture and the economy. However, Canada's average temperatures have risen rapidly over the past eight decades, changing temperature patterns and water availability for canola production. This study aims to explore the impacts of air temperature and soil water availability on spring canola production from 2025 to 2050. Accordingly, this study introduces DSSAT calibration and simulation of the current hybrid InVigor®L340PC, integrating the Shared Socioeconomic Pathways. Leveraging DSSAT-Pythia, gridded simulations capture spatial variability in water and temperature stress interactions, driven by a large ensemble of climate models. The analysis reveals how precipitation and temperature changes jointly influence spring canola development. Yield projections under these conditions provide critical insights into the future viability of rainfed spring canola and inform adaptation strategies for growers and policymakers. Findings demonstrate negative impacts on exclusively rainfed spring canola production in the Canadian Prairie Region under diverse climate scenarios from 2025 to 2050. The main canola growing ecozone (Aspen Parkland) is expected to have higher air temperatures and lower soil water content if greenhouse gas emissions keep rising. An average increase of 1.5°C in air temperature and 0.025 in the water stress factor indices may result in annual yield reductions of 203 ± 4.3 and 121 ± 13.6 kg ha<sup>−1</sup>, in Lake Manitoba Plain and Aspen Parkland ecoregions, respectively. Given that future canola production is expected to continue in the same ecoregions it is recommended that adaptation and mitigation strategies are developed and adopted to improve canola production conditions in these ecoregions.
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Journal articlePonsonby J, Teoh R, Kärcher B, et al., 2025,
An updated microphysical model for particle activation in contrails: the role of volatile plume particles
, Atmospheric Chemistry and Physics, Vol: 25, Pages: 18617-18637<jats:p>Abstract. Global simulations suggest the mean annual contrail cirrus net radiative forcing is comparable to that of aviation's accumulated CO2 emissions. Currently, these simulations assume non-volatile particulate matter (nvPM) and ambient particles are the only source of condensation nuclei, omitting activation of volatile particulate matter (vPM) formed in the nascent plume. Here, we extend a microphysical model to include vPM and benchmark this against a more advanced parcel model (pyrcel) modified to treat contrail formation. We explore how the apparent emission index (EI) of contrail ice crystals (AEIice) scales with EInvPM, vPM properties, ambient temperature, and aircraft/fuel characteristics. We find model agreement within 10 %–30 % in the previously defined “soot-poor” regime. However, discrepancies increase non-linearly (up to 60 %) in the “soot-rich” regime, due to differing treatment of droplet growth. Both models predict that, in the “soot-poor” regime, AEIice approaches 1016 kg−1 for low ambient temperatures (< 210 K) and sulfur-rich vPM, which is comparable to estimates in the “soot-rich” regime. Moreover, our sensitivity analyses suggest that the point of transition between the “soot-poor” and “soot-rich” regimes is a dynamic threshold that ranges from 1013–1016 kg−1 and depends sensitively on ambient temperature and vPM properties, underlining the need for vPM emission characterisation measurements. We suggest that existing contrail simulations omitting vPM activation may underestimate AEIice, especially for flights powered by lean-burn engines. Furthermore, our results imply that, under these conditions, AEIice might be reduced by (i) reducing fuel sulfur content, (ii) minimising organic emissions, and/or (iii) avoiding cooler regions of the atmosphere.</jats:p>
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Journal articleKatsiferis A, Joensen A, Petersen LV, et al., 2025,
"Developing machine learning models of self-reported and register-based data to predict eating disorders in adolescence".
, Npj Ment Health Res, Vol: 4Early detection and prevention of eating disorders (EDs) in adolescence are crucial yet challenging. We developed and validated diagnostic and prognostic models to predict EDs using data from 44,357 Danish National Birth Cohort participants. Models were trained to identify ED presence in early and late adolescence (11- and 18-year follow-up), utilizing approximately 100 predictors from self-reported and registry-based data. The machine learning model demonstrated strong discrimination for both tasks (diagnostic Area Under the receiver operating characteristic Curve = 81.3; prognostic AUC = 76.9), while a logistic regression model using the top 10 predictors achieved comparable performance. Sex, emotional symptoms, peer relationship and conduct problems, stress levels, parental BMI values, body dissatisfaction, and BMI at the 7-year follow-up emerged as key predictors. Our models showed potential utility in supporting clinical risk assessment, particularly for low-risk preventive interventions, though further validation studies are needed to evaluate their effectiveness in real-world clinical settings.
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Journal articleXu H, Wang H, Prentice IC, et al., 2025,
Global variation in the ratio of sapwood to leaf area explained by optimality principles
, New Phytologist, ISSN: 0028-646X• The sapwood area supporting a given leaf area (Huber value, vH) reflects the coupling between carbon uptake and water transport and loss at a whole-plant level. Geographic variation in vH presumably reflect plant strategic adaptations but the lack of a general explanation for such variation hinders its representation in vegetation models and assessment of how its impact on the global carbon and water cycles. • Here we develop a simple hydraulic trait model to predict optimal vH by matching stem water supply and leaf water loss, and test its performance against two extensive plant hydraulic datasets. • We show that our eco-evolutionary optimality-based model explains nearly 60% of global vH variation in response to light, vapour pressure deficit, temperature and sapwood conductivity. Enhanced hydraulic efficiency with warmer temperatures reduces the sapwood area required to support a given leaf area, whereas high irradiance (supporting increased photosynthetic capacity) and drier air increase it. • This study thus provides a route to modelling variation in functional traits through the coordination of carbon uptake and water transport processes.
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Journal articleAl Khalili U, Karmpadakis I, 2025,
Breaking occurrence and dissipation in shortcrested waves in finite water
, Coastal Engineering, Vol: 202, ISSN: 0378-3839The understanding of wave breaking has long been a critical concern for engineers and scientists. However, accurately identifying the onset of breaking and quantifying the associated energy dissipation remain significant challenges. To address this, the present study develops a novel methodology to identify breaking wave events in shortcrested seas in finite water depths. This is achieved through a unique dataset which couples laboratory and numerically-generated waves. The data reflect realistic sea-states used in engineering design and cover a wide range of conditions from mild to extreme. Using the proposed algorithm, key physical properties of breaking waves are examined. In particular, the probability of wave breaking and the associated wave energy dissipation are quantified to provide a statistical description of their behaviour. Complementarily, waves exhibiting significant nonlinear amplifications are also identified and modelled in a similar manner. This enables traditional wave distributions to be decomposed into more detailed distributions of breaking and non-breaking waves. These insights are combined to define a new model that predicts crest height statistics in intermediate water depths. This new mixture model is shown to reproduce experimental measurements with high accuracy, while also providing critical additional information about wave breaking.
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Journal articleJones G, Zhang Z, Clayton K, et al., 2025,
Utilizing Soil Centrifugation for Accurate Estimates of Carbon Dioxide Removal via Enhanced Rock Weathering.
, Environ Sci TechnolEnhanced rock weathering (ERW) is a promising CO2 removal (CDR) strategy that aims to accelerate the natural process of silicate weathering to increase soil pore water alkalinity and sequester CO2. However, the measurement, reporting, and verification (MRV) of ERW remains challenging due to existing limitations of aqueous-phase sampling methodologies, such as passive and tension lysimeters, which may not fully capture weathering fluxes across varying soil moisture conditions. This study assesses the potential of a centrifugation-based pore water extraction method to improve the accuracy and reliability of ERW measurements. Using a forest ERW trial in Wales, UK, we compared the chemistry of soil pore waters obtained via lysimeters and centrifugation from feedstock-amended and control plots. The centrifugation method detected elevated total alkalinity and Ca concentrations in soil pore waters from feedstock-amended soils, whereas the effect of feedstock amendment was not detectable in pore waters extracted with lysimeters. The high tensions applied during centrifugation likely capture weathering products dissolved in meso- and micropore water, which lysimeters cannot extract. These findings suggest that centrifugation provides a scalable, low-cost approach for ERW monitoring, with implications for improving existing MRV protocols.
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