Photocatalytic activity of TiO2 films grown on different substrates

Titanium dioxide films were prepared on glass, indium-tin oxide (ITO) glass and p-type monocrystalline silicon and studied for the photocatalytic degradation of rhodamine B in an aqueous medium. Raman, AFM, and XPS spectroscopic investigations of these films indicated that microstructure of titanium oxide films were greatly affected by the substrate materials. Rutile was confirmed to be easily formed on the surface of ITO glass, and TiO2 tended to grow as closely packed particles that were elongated strips with an average size of 20 nm, and had lovely contrast with the perfectly round particles grown on p-type monocrystalline silicon. Charge transfer between the film and silicon substrate was verified by surface photovoltage spectra. This may be the real reason why the films grown on ITO glass and silicon substrates exhibit higher photocatalytic reactivity than the film on glass substrate. Moreover, the different surface properties also seem to be responsible for the different activity.     

Temperature adaptation of the heart in the grass snake (Natrix natrix L.), common European viper (Vipera berus L.), and steppe viper (Vipera renardi Christoph) (Reptilia: Squamata: Serpentes)

Changes in the heart rate (HR) in more and less thermophilic reptiles (Natrix natrix L., Vipera berus L., and V. renardi Christoph) exposed to heating from 0 to 40°C under anesthesia have been found to depend on the previous adaptation of the animals to the temperature parameters characteristic of the natural habitats of their populations.     

Modelling with uncertainty: Introducing a probabilistic framework to predict animal population dynamics

Predictive population models designed to assist managers and policy makers require an explicit treatment of inherent uncertainty and variability. These are particular concerns when modelling non-native and reintroduced species, when data have been collected within one geographical or ecological context but predictions are required for another, or when extending models to predict the consequences of environmental change (e.g., climate or land-use). We present an aspatial, probabilistic framework of hierarchical process models for predicting population growth even when data are sparse or of poor quality. Insight into the factors affecting population dynamics in real landscapes can be provided and Kullback–Leibler distances are used to compare the relative output of models. This flexible yet robust framework gives easily interpretable results, allowing managers as well as modellers to invalidate anomalous models and apply others to real-life scenarios.We illustrate the framework’s power with a meta-analysis of European wild boar (Sus scrofa) data. We test hypotheses about the effect of geographic region, hunting and mast years on wild boar population growth, to build models of wild boar dynamics for the UK. The framework quantifies the importance of hunting pressure as a driver of population growth, and confirms that reproductive success is greatly decreased in poor mast years, suggesting that the key to predicting wild boar dynamics is to ascertain local hunting pressure and to better understand changing food availability. Geography had no significant effect, indicating that it is not a good proxy for modelling the impact of change in climate or land-use on wild boar populations at the European scale. We use the framework to predict population abundance 9 years after an isolated population of wild boar established in the UK; in a comparison with the only field data and two independent modelling exercises, our framework provides the most robust and informative results.     

Modelling plant species richness using functional groups

Conservation biologists increasingly rely on spatial predictive models of biodiversity to support decision-making. Therefore, highly accurate and ecologically meaningful models are required at relatively broad spatial scales. While statistical techniques have been optimized to improve model accuracy, less focus has been given to the question: How does the autecology of a single species affect model quality? We compare a direct modelling approach versus a cumulative modelling approach for predicting plant species richness, where the latter gives more weight to the ecology of functional species groups. In the direct modelling approach, species richness is predicted by a single model calibrated for all species. In the cumulative modelling approach, the species were partitioned into functional groups, with each group calibrated separately and species richness of each group was cumulated to predict total species richness. We hypothesized that model accuracy depends on the ecology of individual species and that the cumulative modelling approach would predict species richness more accurately. The predictors explained plant species richness by ca. 25%. However, depending on the functional group the deviance explained varied from 3 to 67%. While both modelling approaches performed equally well, the models of the different functional groups highly varied in their quality and their spatial richness pattern. This variability helps to improve our understanding on how plant functional groups respond to ecological gradients.     

A database for integrated assessment of European agricultural systems

A major bottleneck for data-based policy making is that data sources are collected, managed, and distributed by different institutions, residing in different locations, resulting in conceptual and practical problems. The use of dispersed data for agricultural systems research requires the integration of data sources, which means to ensure consistency in data interpretations, units, spatial and temporal scales, to respect legal regulations of privacy, ownership and copyright, and to enable easy dissemination of data. This paper describes the SEAMLESS integrated database on European agricultural systems. It contains data on cropping patterns, production, farm structural data, soil and climate conditions, current agricultural management and policy information. To arrive at one integrated database, a shared ontology was developed according to a collaborative process, which facilitates interdisciplinary research. The paper details this process, which can be re-used in other research projects for integrating data sources.     

Quantitative chemical composition and characteristics of aerosols over western India: One-year record of temporal variability

One-year quantitative chemical data set consisting of water-soluble constituents (NH₄⁺, Na⁺, K⁺, Mg²⁺, Ca²⁺, Cl^?, NO₃^?, SO₄^2? and HCO₃^?), crustal and trace elements (Al, Fe, Ca, Mg, K, Mn, Zn, Pb) and carbonaceous species (OC, EC) in ambient aerosols, collected over an urban site located in a high-dust semi-arid region of western India, reveals excellent linear relationship (r² = 0.92; slope = 0.96 ± 0.05) between gravimetrically assessed TSP (total suspended particulates) and chemically analyzed aerosol mass. The TSP abundance ranging from 60 to 250 μg m^?3, over a period of 12 months (January–December), is dominated by mineral dust (～70%); whereas contribution from sea-salts, anthropogenic and carbonaceous species exhibits significant temporal variability depending upon the wind regimes. The mineral dust is enriched in Ca, Mg and Fe with respect to upper continental crust (UCC); whereas Zn and Pb exhibit a characteristic anthropogenic source and high enrichment factors. The carbonaceous species show significant seasonality; with dominance of OC (range: 4.6–28 μg m^?3; average: 12.8 μg m^?3; SD: 6.8) and minor contribution from EC (range: 0.3–4.4 μg m^?3; average: 2.4 μg m^?3; SD: 1.4). The observed concentrations are significantly lower than those reported for the metro cities in South Asia but the OC/EC ratios (range: 4.3–35; average: 8.3; SD: 5.7) are significantly higher than the characteristic ratio (～2–4) reported for the urban atmosphere. Such quantitative chemical characterization of aerosols is essential in assessing their role in atmospheric chemistry and climate change. This study could also be useful in understanding the physical and optical aerosol properties documented from the same site and thus, in validating regional climate models.     

Future risk of flooding: an analysis of changes in potential loss of life in South Holland (The Netherlands)

Potential loss of life is considered an important indicator of flood risk. We examine the future development of potential loss of life due to flooding for a major flood prone area in The Netherlands. The analysis is based on projections and spatial distribution of population under a high economic growth scenario and a loss of life model. Results show that the projected population growth in flood prone areas is higher than average in the Netherlands between 2000 and 2040. Due to this effect the potential number of fatalities is projected to increase by 68% on average for 10 different flood scenarios, not including impacts from climate change and sea level rise. Just sea level rise of 0.30 m leads to an average 20% increase in the number of fatalities. The combined impact of sea level rise and population growth leads to an estimated doubling in the potential number of fatalities. Taking into account increasing probability of flooding due to sea level rise and extreme river discharges, the expected number of fatalities could quadruple by 2040. The presented results give a conservative and upper bound estimate of the increase of the risk level when no preventive measures are undertaken. It is found that the consideration of the exact spatial distribution of population growth is essential for arriving at reliable estimates of future risk of flooding.