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    Rethinking urban forests as essential infrastructure for resilience, equity, and biodiversity in the current climate emergency
    (Public Library of Science (PLOS), 2026-07-01) Esperon-Rodriguez, Manuel; Gallagher, Rachael V.; Arndt, Stefan; Asibey, Michael Osei; Augustinus, Benno Andreas; Ballinas, Monica; Barradas, Victor L.; Barton, David N.; Bauhus, Jurgen; Beaumont, Linda J.; Bissonnette, Jean-Francois; Brookhouse, Matthew; Calaza-Martinez, Pedro; Calfapietra, Carlo; Lourenco, Tiago Capela; Carinanos, Paloma; Clemente, Matteo; Conway, Tenley; Hoogh, Kees de; Pauw, Karen De; Dendoncker, Morgane; Dobbs, Cynnamon; Duinker, Peter; Ejaz, Ujala; Eleuterio, Ana Alice; Endreny, Theodore; Esperon-Rodriguez, Diego; Farrell, Claire; Guo, Zhengfei; Gwedla, Nanamhla; Hauer, Richard; Hermy, Martin; Jeffries, Peta; Juno, Edith; Lee, Gervais; Litvak, Elizaveta; Love, Natalie; Manoli, Gabriele; Marchin, Renee M.; Martin, Alexander J. F.; Martin, John M.; McPhearson, Timon; Messier, Christian; Messier, Julie; Morgain, Rachel; Nagendra, Harini; Nieuwenhuijsen, Mark; Olson, Lukas G.; Ostberg, Johan; Pataki, Diane; Pfautsch, Sebastian; Power, Sally A.; Rahman, Mohammad A.; Randrup, Thomas; Reich, Peter B.; Russo, Alessio; Rymer, Paul D.; Schifanella, Rossana; Shackleton, Charlie; Sharmin, Mahmuda; Siclari, Davide; Sivarajah, Sivajanani; Sjoman, Johanna Deak; Sjoman, Henrik; Solfjeld, Ingjerd; St-Denis, Annick; Svenning, Jens-Christian; Szota, Christopher; Toledo-Garibaldi, Maria; Villemaire-Cote, Olivier; Vogt, Jess; Wan, Mingxuan; Wen, Linsheng; Williams, Geoffrey; Williams, Nicholas S. G.; Wistrom, Bjorn; Zoungrana, Benewinde Jean-Bosco; Tjoelker, Mark G.
    Urban forests are not merely green amenities; they support critical ecosystem functioning and services vital for healthy, resilient cities. Recognising urban forests as core infrastructure is essential to reversing the loss of mature trees, preserving biodiversity, and maintaining liveability amid increasing climate and environmental pressures. Although the benefits of urban forests for climate resilience, biodiversity, and public health are broadly acknowledged, policies to protect and enhance these vital ecosystems are often limited, underfunded, and inadequately enforced. As mature canopy loss today takes decades to be replaced (if ever), immediate and sustained investment is crucial to safeguard urban forests. This urgency reveals four interconnected gaps in current urban forest management and stewardship. First, urban forests require recognition, investment, and maintenance as essential infrastructure contributing to urban resilience, including biodiversity support, and to maximise the delivery of key ecosystem services such as cooling and carbon sequestration. Second, equitable access to greenspaces across all communities must be ensured to redress long-standing social and environmental injustices. Third, integrating urban forest into broader climate and biodiversity governance frameworks is critical to mainstreaming their management and protection. Lastly, resilience must be strengthened through evidence-based management practices responsive to evolving environmental changes and social contexts. These priorities must be complemented with strong legal protections, rigorous enforcement of legislation against illegal tree removal, and robust community engagement supported by integrated urban planning and improved monitoring. Without these, the ecological, social, and economic benefits provided by urban forests will remain threatened. By reframing urban forests as essential living infrastructure embedded in legal, financial, and planning frameworks, cities can become cooler, healthier, more biodiverse, and socially just. This framework offers timely guidance for policymakers to prioritise urban forests within climate resilience and sustainability strategies, securring benefits for current and future generations.
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    Diurnal asymmetry in heat stress intensification across Bangladesh, 1985-2024: Accelerated nighttime warming and emerging urban risk
    (Public Library of Science (PLOS), 2026-08-05) Kamruzzaman, Mohammad; Abdullah, Abu Yousuf Md; Islam, A. K. M. Saiful; Talukder, Afifa; Wahid, Shahriar; Shahid, Shamsuddin
    Bangladesh’s rapidly growing cities are becoming hotter, but how heat stress is changing over the day–night cycle has remained unclear. Using 40 years (1985–2024) of hourly Universal Thermal Climate Index (UTCI) data from ERA5-HEAT, we examined long-term changes in physiologically relevant heat stress across Bangladesh and its major cities. Results show a clear day–night imbalance in warming: nighttime heat stress (UTCIₘᵢₙ) is rising faster than daytime extremes. National trends indicate increases of +0.03 °C per decade for UTCIₘᵢₙ, +0.02 °C for daily mean UTCI, and +0.01 °C for UTCIₘₐₓ, with the strongest warming occurring in the early morning hours. These national spatially averaged trends reflect the mean across all 194 grid cells; individual grid cells show local trends of +0.1 to +0.4°C per decade, and the cumulative nighttime warming over the full 40-year period reaches approximately 1.0–1.7°C across most of the country. This signals a steady loss of nighttime cooling that people rely on for physical recovery. The most pronounced nighttime warming occurs in western and southern Bangladesh. Major cities—including Dhaka, Rajshahi, Khulna, Chattogram, and Sylhet—show additional intensification consistent with, but not directly attributed to, urban heat-island dynamics at the spatial scale of this analysis. The number of very strong heat stress days (UTCI > 38 °C) has increased by 4–15 days per decade, and cities such as Rajshahi and Dhaka now experience more than 150 such days annually. Together, these findings indicate a transition from occasional heat extremes to persistent, 24-hour heat stress, increasing risks to health, labor productivity, and urban resilience. By identifying when heat stress is rising fastest and where it is concentrated, this study provides evidence to support city-specific heat-action plans, early-warning systems, and climate-responsive urban design in rapidly warming regions.
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    Design and development of a disease-agnostic remote patient monitoring typology and associated tools
    (Public Lirbary of Science (PLOS), 2026-07-31) Abejirinde, Ibukun-Oluwa Omolade; Kishimoto, Vanessa; Brual, Janette; Pfisterer, Kaylen J.; Uddin, Nishath; Pratt, Taylor; Fleury. Cathleen; Osman, Azza; Bhattacharyya, Onil
    Remote patient monitoring (RPM) expanded dramatically during the COVID-19 pandemic and continues to be implemented. However, no standardized method for classifying them exists which has implication for comparative evaluations. Our goal was to design and develop a disease-agnostic RPM typology tool as a first step towards a standardized approach for understanding and implementing RPM programs for different clinical use-cases. Guided by the Knowledge-To-Action framework, we conducted a rapid review of RPM programs from Canada, the United States, Europe, the United Kingdom, and Australia that were used to manage diabetes, chronic obstructive pulmonary disease, congestive health failure, hypertension, and COVID-19. We identified 87 articles to define common characteristics of real-world RPM interventions to enable comparison across different programs through pattern recognition, information mapping, and sensemaking. We extracted data with a macros-enabled Excel template. Design sessions with key stakeholders (researchers, clinical advisors, provincial RPM managers, and patient partners) provided iterative feedback on the typology and we defined a glossary of characteristics. The 12 most reported characteristics of RPM programs (including size, resources, monitoring team, data flow, alert protocol, workflow, and equity considerations) were clustered into four domains. Integration and equity domains were considered to exist on a spectrum from low-to-high- neither inherently superior to the other. 16 distinct RPM typologies were expressed through a 4x4 matrix (i.e., high or low on each of the four domains). Using this tool can inform insights on program maturity, implementation, and continued investments in RPM. We anticipate this typology will help new initiatives have greater potential to be robustly evaluated and sustained to scale. Future directions include further validity testing and exploring feasibility to systematically categorise real-world RPM programs with this tool.
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    Scale-Up and Design of Bioreactor Process Using Machine Learning Technique
    (University of Waterloo, 2026-10-15) Chang, Shuji
    The increasing demand for biopharmaceutical products has intensified the need for efficient, predictable, and scalable bioprocess development. Stirred-tank bioreactors are central to these processes because they determine the local hydrodynamic and biochemical environments. However, bioreactor process design and scale-up remain challenging because operating conditions optimized at small scale do not necessarily translate to pilot or manufacturing scale. Changes in geometry, agitation, aeration, and liquid volume can generate substantially different local mixing, gas-liquid mass transfer, shear, energy dissipation, and phase distribution patterns. These local environmental variations can affect cell growth, viability, metabolism, and product formation, thereby influencing process productivity and product quality attributes. However, these coupled effects are not accounted for by traditional empirical equation-based scale-up approaches. While computational fluid dynamics (CFD) provides detailed spatially resolved descriptions of these multiphase hydrodynamic phenomena, its high computational cost limits its practical use in design optimization, uncertainty analysis, scale-up studies, and model-based decision-making, where repeated model evaluations are required. This thesis develops physics-informed neural network (PINN) surrogate modeling methods for aerated stirred-tank bioreactors to enable computationally efficient and physically consistent bioprocess prediction, optimization, and scale-up. The central hypothesis is that PINNs can serve as fast and physically consistent surrogates for bioreactor-relevant systems when important challenges such as their ability to describe two-phase flow and to address rotating fluid geometries can be tackled. Two related research streams are advanced: i) sensitivity-guided PINN methodologies for improved training efficiency, robustness, and sensor-informed data acquisition; ii) coupled hydrodynamic-biokinetic PINN surrogates for fast bioprocess prediction, optimal design, and scale-up under uncertainty. A sensitivity-based adaptive sampling (SBS) framework was developed to improve PINN training efficiency, accuracy, and robustness by reallocating collocation points according to the sensitivity of the training loss to perturbations in sampling locations. Unlike local residual-, gradient-, or sensitivity-based sampling methods, the proposed global sensitivity formulation accounts for coupled effects among collocation points, loss components, boundary conditions, governing equations, and network-weight updates. This provides a more informative criterion for identifying regions that strongly influence the overall training dynamics, leading to improved prediction accuracy in areas with strong spatial variations. The sensitivity principle was further used to guide sensor placement for PINNs under structural uncertainty. In a channel-flow case with uncertain inlet conditions, sensors were selected at high-sensitivity locations, and both the uncertain operating variable and the selected sensor measurements were incorporated as network inputs. This strategy improved generalization to boundary conditions beyond the training range while adding minimal model complexity. A two-phase PINN surrogate was developed for Euler-Euler gas-liquid hydrodynamics in aerated stirred-tank bioreactors and applied to both two-dimensional rotating-vessel and three-dimensional baffled, impeller-driven geometries. By incorporating agitation speed and aeration rate as operating-condition inputs embedded into the physics-informed residual constraints, the model enabled pointwise prediction of pressure, volume fraction, and gas- and liquid-phase velocity fields across operating conditions. These spatially resolved predictions provide access to local mixing, gas holdup, shear exposure, and interphase transport, which are critical for evaluating oxygen transfer, nutrient distribution, cell stress, and process productivity during process design, optimization, and scale-up. Fourier feature mapping improved the representation of high-gradient structures, and neural tangent kernel analysis motivated a partitioned dual-network architecture to balance the training of coupled hydrodynamic variables. Relative to purely data-driven baselines, the PINN surrogate achieved more accurate and physically consistent predictions, reducing the risk of nonphysical surrogate behavior when predicting new operating conditions or coupling with process-level biokinetic models. Coupling this hydrodynamic surrogate with dynamic transport-reaction biokinetic equations for biomass, substrate, and dissolved oxygen enabled fast process-level prediction at substantially reduced computational cost. The coupled PINN-biokinetic surrogate was embedded into a three-level Optimal Experimental Design for Optimal Process Design (OED-OPD) framework for robust bioreactor optimization under parametric uncertainty. The outer level selects informative experimental conditions for parameter identification, the middle level identifies worst-case parameter scenarios, and the inner level determines optimal process operating conditions. This structure integrates model-based experimental design and robust process optimization into a unified framework, while the PINN surrogate makes the nested optimization computationally tractable compared with direct CFD-based biokinetic simulations. The framework was demonstrated for single-scale optimal design in a 50 L bioreactor and for scale-up from a 250 mL vessel to a 50 L bioreactor. The optimized operating conditions showed close agreement with CFD-based benchmarks, and CFD validation confirmed that the genetic-algorithm solutions improved the objective-function values relative to neighboring candidate designs. The framework also selected different informative experiments at the 250 mL and 50 L scales, showing that optimal experimental designs are scale-dependent. These results demonstrate that the proposed OED-OPD framework integrates model-base experimental design for parameter identification with process optimization, enabling robust and computationally efficient bioreactor design under uncertainty. To summarize, this research establishes a new physics-informed surrogate modeling workflow for bioreactor applications by tackling different challenges related to the development of the model for rotating two-phase fluid conditions. By reducing reliance on repeated high-fidelity CFD simulations while preserving governing-equation structure and physical consistency, the proposed methods provide a foundation for surrogate-assisted bioreactor optimization.
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    Hydrological analysis of the California Basin Model
    (Public Library of Science (PLOS), 2026-06-10) Davison, Jason Hamilton; Hwang, Hyoun-Tae; Sudicky, Edward A.; Mallia, Derek V.; Lin, John C.
    California's water resources are declining and recent droughts have only exaggerated the dire conditions. As rivers and surface water reserves run dry, water managers are forced to drill deeper wells, mining their precious groundwater resources. To better understand California's water, we developed a surface and subsurface model of the California Basin using HydroGeoSphere (HGS), a 3D finite element control-volume surface and variably-saturated subsurface flow model with evapotranspiration processes. Once the integrated HGS model reached steady-state conditions, groundwater and surface water extractions were turned on, and the HGS model was run to 2011. We then implemented the two-way coupled HydroGeoSphere-Weather Research and Forecasting (WRF) model over a 200 day period. This paper investigates feedbacks between groundwater and evapotranspiration and finds an average decrease of 0.029 mm/day in evapotranspiration per meter of water table depth over the period of January 1st to July 20th 2011. The simulation results showed that a minimum net precipitation rate of 0.178 m/year (R^2 = 0.998) is required every year to sustainably maintain current water use and groundwater extractions. The simulated results replicated similar drawdown behavior (49 mm/year simulated) as the GRACE remote sensing satellites (43 mm/year measured).