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Experimental research of photocatalytical oxidation (PCO) of aqueous solutions of de‐icing agents (ethylene glycol and ethylene glycol monoethyl ether) and methyl tert‐butyl ether (MTBE) was undertaken. These chemicals are water‐soluble components of jet and motor fuels accidentally disposed to the environment. Titanium dioxide (Degussa P25) under near‐UV irradiation was selected as a photocatalyst. A slightly acidic medium was preferable for the process efficiency for MTBE, whereas a neutral medium was beneficial for de‐icing agents and jet fuel aqueous extracts. TiO2 suspension fractional composition was found to be dependent on pH and the presence of organic admixtures: the minimum size of TiO2 particles at their maximum uniformity was established in an acidic medium, where the efficiency of PCO of de‐icing agents was the poorest. On the other hand, neutral and slightly acidic media, beneficial for PCO efficiency, were favourable for particle agglomeration, which indicates a minimal role for photocatalyst particle size in PCO efficiency. PCO efficiency increased with increasing MTBE and icing inhibitor concentration. The biodegradability of aqueous solutions of oxygenated additives increased as PCO proceeded. The influence of mineral additives—sulphate, calcium, ferric and manganese ions—on the process efficiency was found to be complex. Special attention was paid to energy‐saving PCO with a photocatalyst attached to buoyant glass micro‐spheres and reduced intensity of stirring of the slurry.

Environmental and intrinsic factors interact to determine energy requirements in vertebrates. Glucocorticoid hormones (GCs) are key mediators of this interaction, as they fluctuate with energetic demands and regulate physiological and behavioral responses to environmental challenges. While a great body of research has focused on GC variation among individuals, the mechanisms driving GC variation across species and at broad spatial scales remain largely unexplored. Here, we adopted a macrophysiological approach to investigate the environmental factors and life-history traits driving variation in baseline GCs across lizard species. We tested three hypotheses: (1) If GCs increase with body temperature to meet higher metabolic demand, we expect an association between average baseline GCs and the mean species’ body temperature in the field (GC-temperature dependence hypothesis); (2) If GCs mediate behavioral responses to avoid thermal extremes, we expect that individuals frequently exposed to extreme conditions exhibit higher baseline GC levels (Behavioral thermoregulation hypothesis); (3) If GCs increase to support higher energy demands in active foragers during their period of activity, we expect that active foraging species have higher baseline GCs than sit-and-wait foragers, and that GC levels increase in relation to the duration of daily activity windows (Activity hypothesis). We used biophysical models to calculate operative temperatures and the activity patterns of lizards in sun-exposed and shaded microenvironments. Then, we tested the association between baseline GCs, body temperature, operative temperatures, foraging mode, and activity windows across 37 lizard species, using data from HormoneBase. Our comparative analyses showed that variation in baseline GCs was primarily related to the mean field body temperature and foraging mode, with higher baseline GCs in active foragers with higher body temperatures. Our results suggest that body temperature and foraging mode drive GC variation through their effects on energy requirements across lizard species.

In the amphoteric membrane-less decoupled water electrolysis, hydrogen and oxygen are co-produced in separate cells with higher energy efficiency. Ion exchange is mediated by the auxiliary electrodes – HxWO3 for acid and NiOOH for alkaline cell.

Abstract Hybrid TiO2/SiO2 thin films deposited by material printing technique on flexible substrates were prepared, characterized and tested for solar photocatalytic disinfection. Effect of surface hydrophilicity/hydrophobicity of printed coatings on photocatalytic disinfection was studied by means of (i) drinking water contaminated with natural consortia of fecal bacteria (gram-negative: Escherichia coli and total coliforms; gram-positive: Enterococci), and (ii) seawater containing pathogenic gram-negative bacteria (Vibrio owensii, Vibrio alfacsensis and Vibrio harveyi). Inactivation of gram-negative bacteria in drinking water with fecal contamination by solar photocatalysis was slightly more efficient than solar disinfection, while for gram-positive bacteria similar efficiency was observed. These results, in combination with observed release of titanium from coatings (detected by means of inductively coupled plasma atomic emission spectrometer), indicate that TiO2/SiO2 needs further improvements for solar photocatalytic disinfection of drinking water. Efficiency of seawater disinfection towards gram-negative Vibrio spp. (Vibrio owensii, Vibrio alfacsensis and Vibrio harveyi) was significantly enhanced when TiO2/SiO2 coatings were used under natural solar light. Moreover, hydrophobic thin films led to faster Vibrio spp. inactivation as compared to hydrophilic ones, which was attributed to higher bacteria adhesion on hydrophobic coatings. However, decrease of photocatalytic activity of hydrophobic TiO2/SiO2 coatings was observed after ten experimental cycles mainly due to deposition of salts on the surface of photocatalyst. Generally, results of this study suggest that autochthonous bacteria such as Vibrio spp. in seawater are significantly more resistant to solar disinfection in comparison with not autochthonous bacteria such as Escherichia coli, total coliforms and Enterococci in contaminated drinking water.

This work presents a crude bioethanol fueled integrated power system for backup and off-grid applications. The system is based on ethanol steam reformation to hydrogen that is used in polymer electrolyte fuel cells to produce electricity. We introduce the system design and overall system specifications, and report the experimental process of defining specifications for the produced hydrogen quality, a key variable affecting the final system cost, efficiency and durability. Results from development of individual subsystems are also presented together with discussion on the complete system integration.

In an idealised industrial ecosystem (IE), firms and organisations utilise each other's material and energy flows including wastes and by-products to reduce the system's virgin material and energy input as well as the waste and emission output from the system as a whole, and contribute to sustainable development (SD). IE complements the more conventional individual flow, product, process, organisation, individual actor or sector-focused environmental management approaches and tools with network or systems level approaches. The first research objective of this paper is to construct indicators for IE. The second task is to test the use of these indicators with "what if?" material and energy flow scenarios for the energy and waste system of Satakunta region in Finland including 28 municipalities. Using literature analysis as a source, we arrive at environmental indicators of carbon dioxide (CO2) equivalents, and at economic indicators of fuel, energy and waste management costs and revenues. The social indicators show the employment effects of the waste management system. The scenarios analyse the current situation (0-scenario) against alternative situations in the future. The future scenarios are developed according to the known and anticipated trends in international and national policy and legislation. The indicator application in the scenarios produces social, environmental and economic effects of waste management in four categories: direct negative, direct positive, indirect negative and indirect positive. Industrial ecosystem theory emphasises the utilisation of wastes as a resource with value alongside the objective of reducing waste. Therefore, the indirect positive effects of waste management are important, as well as the conventional focus of waste management, which has usually been on direct positive effects. The main difficulties in our argument are the system boundary definition, the qualitatively different nature of environmental, economic and social effects and indicators as well as the lack of qualitative or interview data on the preferences and interests of the actors involved.

ABSTRACTWind power forecasts are useful tools for power load balancing, energy trading and wind farm operations. Long range monthly‐to‐seasonal forecasting allows the prediction of departures from average weather conditions beyond traditional weather forecast timescales, months in advance. However, it has not yet been demonstrated how these forecasts can be optimally transformed to wind power. The predictable part of a seasonal forecast is for longer monthly averages, not daily averages, but to use monthly averages misses information on variability. To investigate, here a model relating average weather conditions to average wind power output was built, based on the relationship between instantaneous wind speed and power production and incorporating fluctuations in air density due to temperature and wind speed variability. Observed monthly average power output from UK stations was used to validate the model and to investigate the optimal temporal resolution for the data used to drive the model. Multiple simulations of wind power were performed based on reanalysis data, making separate simulations based on monthly, daily and sub‐daily averages, using a distribution defined by the mean across the period to incorporate information on variability. Basing the simulation on monthly averages alone is sub‐optimal: using daily average winds gives the highest correlation against observations. No improvement over this is gained by using sub‐daily averages or including temperature variability. This signifies that to transform seasonal forecasts to wind power a compromise must be made between using the daily averages with debatable skill and the more predictable monthly averages, losing information on day‐to‐day variability.