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Twenty-minute neighbourhoods highlight the importance of well-connected and mixed-used neighbourhoods and communities with proximate access to employment, essential services, public transport, and open spaces. Shorter distances together with re-prioritised public spaces encourage more active transport choices, resulting in public health benefits and reduced environmental pollution. Higher liveability brought about by mixed-use developments enables people to have equitable access to local facilities, amenities, and employment opportunities, promoting vibrancy, social cohesion, and intergenerational connections. The attributes of 20-minute neighbourhoods also combine to create places, that are acknowledged as friendly for all ages, address changing needs across the life course, and provide better support for the ageing population. Furthermore, there are indications that 20-minute neighbourhoods may be more resilient against many of the negative impacts of stringent public health protocols such as those implemented in periods of lockdown during the Covid-19 pandemic. In this article, we evaluate and compare planning policies and practices aimed at establishing 20-minute neighbourhoods in Melbourne (Australia) and Scotland (the UK). Using case studies, we discuss similarities and differences involved in using place-based approaches of 20-minute neighbourhoods to address 21st-century challenges in key areas of health and wellbeing, equity, environmental sustainability, and community resilience.

ABSTRACTA novel design for a high temperature SOFC, based on lamellar electrode-electrolyte segments obtained by solidification of an oxidic eutectic melt on an electrolyte substrate is presented. Such “composite” electrodes contain NiO or MnO - 8Y-ZrO2 lamellae, which after reduction / oxidation yield electrode-electrolyte lamellae with 1–2 μm width and a vertical dimension of> 100 μm, depending upon the amount of eutectic melt solidified on a polycrystalline substrate. The nucleation of the eutectic on a polycrystalline substrate followed by a semi-directional crystallization of the two phases yields a gradient of 3-phase boundaries over the height of such an electrode, with the number of 3-phase boundaries increasing towards the substrate.

In developing an algal treatment system, selenium (Se) removal efficiency by Chlorella vulgaris was evaluated under various conditions such as Se concentration, algal density, temperature and pH. A maximum removal efficiency plateau of ∼90% was observed between 1000-3000 μg Se/L while the tolerance of Se toxicity was found at 6000 μg Se/L. C. vulgaris of 0.75 g DW/L showed the highest removal efficiency (84%), and volatilization was dominant below 1.37 g DW/L. Se volatilization was two times higher at 25 °C than at 20 °C in the first 24 h. Moreover, the highest removal efficiency (77%) was obtained at pH 8.0, compared to 66.5% at pH 6.5 and 40% at pH 10.0. To prevent ecotoxicity, Se laden algae were further burned to ashes or filtered out by Anodonta woodiana. After burning, biomass Se was reduced by 99%, with organo-Se entirely converted into inorganic Se, lowering Se bioavailability. A. woodiana removed 54% of Se in 24 h, leading to Se bioaccumulation in soft tissues, which may serve as dietary Se supplements for human health. Our results suggest the cleanup of Se-contaminated water from either agricultural runoff or industrial discharge could be achieved using an algal treatment system with minimum potential ecotoxicity.

Background: HIV-1 protease inhibitor (PIs) is a good choice for AIDS patients. Nevertheless, for PIs, there are several bugs in clinical application, like drug resistance, the large dose, the high costs and so on, among which, the poor pharmacokinetics property is one of the important reasons that leads to the failure of its clinical application. Objective: We aimed to build computational models for studying the relationship between PIs structure and its pharmacological activities. Methods: We collected experimental values of koff/Ki and structures of 50 PIs through a careful literature and database search. Quantitative structure activity/pharmacokinetics relationship (QSAR/QSPR) models were constructed by support vector machine (SVM), partial-least squares regression (PLSR) and back-propagation neural network (BPNN). Results: For QSAR models, SVM, PLSR and BPNN all generated reliable prediction models with the r2 of 0.688, 0.768 and 0.787, respectively, and r2pred of 0.748, 0.696 and 0.640, respectively. For QSPR models, the optimum models of SVM, PLSR and BPNN obtained the r2 of 0.952, 0.869 and 0.960, respectively, and the r2pred of 0.852, 0.628 and 0.814, respectively. Conclusion: Among these three modelling methods, SVM showed superior ability than PLSR and BPNN both in QSAR/QSPR modelling of PIs, thus, we suspected that SVM was more suitable for predicting activities of PIs. In addition, 3D-MoRSE descriptors may have a tight relationship with the Ki values of PIs, and the GETAWAY descriptors have significant influence on both koff and Ki in PLSR equations.

Coal will continue to be the main energy source in China for the immediate future, although the environmental pollution and ecological impacts of each stage in the full life cycle of coal mining, transportation, and combustion generate large quantities of external costs. The Late Permian coals in southwestern (SW) China usually contain high amounts of fluorine (F), arsenic (As), and ash, which together with high-F clays cause abnormally high levels of endemic fluorosis, As poisoning, and lung cancer in areas where coal is mined and burned. In this paper, we estimate the external costs of the life cycle of coal. The results show that the externalities of coal in SW China are estimated at USD 73.5 billion or 284.3 USD/t, which would have accounted for 6.5 % of the provincial GDP in this area in 2018. The external cost of human health accounts for 87.2% of the total external costs, of which endemic skeletal fluorosis diseases and related lung cancers have the most important impact. Our study provides a more precise estimate of externalities compared with its counterparts in other provinces in China. Therefore, several policy recommendations would be proposed to internalize the external cost.

Volatile organic compounds (VOCs) can lead to environmental pollution and threaten human health due to their toxic and carcinogenic nature. The emission of VOCs increases dramatically with the accelerated industrialization and economic growth. Adsorption is identified as one of the most promising recovery technologies owing to its cost-effectiveness, flexible operation, and low energy consumption. In particular, adsorption-based technologies have a high potential to recycle both adsorbents and adsorbates, typically to capture valuable aromatic VOCs from industrial exhaust. Porous materials such as carbon-based materials, zeolite-based materials, and organic polymers and their composites have been extensively developed for VOCs adsorption focusing in adsorption capacity, hydrophobic property, thermal stability and regenerability. Among them, porous carbons as VOCs adsorbents have attracted increasingly attention, because they can be regulated by tuning the pore structure for VOCs accessibility during the adsorption process. Moreover, porous carbons can adsorb target VOCs by controlling the pore structure and surface functional groups. Significantly, the pore size distribution of porous carbons mostly controls the VOCs sorption process. Micropores provide the main adsorption sites, while mesopores enhance the diffusion of VOCs. In this review, the adsorption mechanism of VOCs onto porous carbons was generally concluded. Porous carbons can be designed as a specific structure for adsorption of aromatic VOCs by controlling the pore structure, hydrophobic sites, π-electronic structure, and surface functional groups. Since there are limited review literatures on porous carbons derived from renewable resources for VOCs adsorption, this paper will provide an overview on the synthesis of porous carbons from biomass and other organic wastes for VOCs adsorption or integrated oxidation processes (e.g., photocatalysis, non-thermal plasma catalysis, chemical catalysis) under ambient conditions with the objective of guiding ...

Correction for ‘Photocatalyzed redox-neutral decarboxylative alkylation of heteroaryl methanamines’ by Pengfei Niu et al., Green Chem., 2021, 23, 774–779, DOI: 10.1039/D0GC04094K.

AbstractAutotrophs play an essential role in the cycling of carbon and nutrients, yet disease‐ecosystem relationships are often overlooked in these dynamics. Importantly, the availability of elemental nutrients like nitrogen and phosphorus impacts infectious disease in autotrophs, and disease can induce reciprocal effects on ecosystem nutrient dynamics. Relationships linking infectious disease with ecosystem nutrient dynamics are bidirectional, though the interdependence of these processes has received little attention. We introduce disease‐mediated nutrient dynamics (DND) as a framework to describe the multiple, concurrent pathways linking elemental cycles with infectious disease. We illustrate the impact of disease–ecosystem feedback loops on both disease and ecosystem nutrient dynamics using a simple mathematical model, combining approaches from classical ecological (logistic and Droop growth) and epidemiological (susceptible and infected compartments) theory. Our model incorporates the effects of nutrient availability on the growth rates of susceptible and infected autotroph hosts and tracks the return of nutrients to the environment following host death. While focused on autotroph hosts here, the DND framework is generalizable to higher trophic levels. Our results illustrate the surprisingly complex dynamics of host populations, infection patterns, and ecosystem nutrient cycling that can arise from even a relatively simple feedback between disease and nutrients. Feedback loops in disease‐mediated nutrient dynamics arise via effects of infection and nutrient supply on host stoichiometry and population size. Our model illustrates how host growth rate, defense, and tissue chemistry can impact the dynamics of disease–ecosystem relationships. We use the model to motivate a review of empirical examples from autotroph–pathogen systems in aquatic and terrestrial environments, demonstrating the key role of nutrient–disease and disease–nutrient relationships in real systems. By assessing existing evidence and uncovering data gaps and apparent mismatches between model predictions and the dynamics of empirical systems, we highlight priorities for future research intended to narrow the persistent disciplinary gap between disease and ecosystem ecology. Future empirical and theoretical work explicitly examining the dynamic linkages between disease and ecosystem ecology will inform fundamental understanding for each discipline and will better position the field of ecology to predict the dynamics of disease and elemental cycles in the context of global change.