• Energy Research
• 2021-2025close
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• 1. No poverty close
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AbstractLightning is the main precursor of natural wildfires and Long-Continuing-Current (LCC) lightning flashes are proposed to be the main igniters of lightning-ignited wildfires (LIW). Previous studies predict a change of the global occurrence rate and spatial pattern of total lightning. Nevertheless, the sensitivity of lightning-ignited wildfire occurrence to climate change is uncertain. Here, we investigate space-based measurements of LCC lightning associated with lightning ignitions and present LCC lightning projections under the Representative Concentration Pathway RCP6.0 for the 2090s by applying a recent LCC lightning parameterization based on the updraft strength in thunderstorms. We find a 41% global increase of the LCC lightning flash rate. Increases are largest in South America, the western coast of North America, Central America, Australia, Southern and Eastern Asia, and Europe, while only regional variations are found in northern polar forests, where fire risk can affect permafrost soil carbon release. These results show that lightning schemes including LCC lightning are needed to project the occurrence of lightning-ignited wildfires under climate change.

[Abstract] This research work presents an artificial intelligence approach to predicting the hydrogen concentration in the producer gas from biomass gasification. An experimental gasification plant consisting of an air-blown downdraft fixed-bed gasifier fueled with exhausted olive pomace pellets and a producer gas conditioning unit was used to collect the whole dataset. During an extensive experimental campaign, the producer gas volumetric composition was measured and recorded with a portable syngas analyzer at a constant time step of 10 seconds. The resulting dataset comprises nearly 75 hours of plant operation in total. A hybrid intelligent model was developed with the aim of performing fault detection in measuring the hydrogen concentration in the producer gas and still provide reliable values in the event of malfunction. The best performing hybrid model comprises six local internal submodels that combine artificial neural networks and support vector machines for regression. The results are remarkably satisfactory, with a mean absolute prediction error of only 0.134% by volume. Accordingly, the developed model could be used as a virtual sensor to support or even avoid the need for a real sensor that is specific for measuring the hydrogen concentration in the producer gas. Junta de Andalucía; 1381442 Xunta de Galicia; ED431G 2019/01 Ministerio de Universidades; FPU19/00930

Throughout the 21st century, urban reports demand solutions to the obsolescence and aging process suffered by the existing buildings, due to the growth and expansion of cities that took place in the second half of the 20th century [...]

Despite the growing interest in perceived social support as a means of achieving sustainable psychological development and well-being in the various contexts of the adolescent, its definition and composition remain unclear. The aim of this study is to test both the hierarchical order and the types and sources of a theoretical model of the social support perceived by adolescents. The model is based on the theories of Tardy and Lin, as well as on the findings of recent studies on the construct. Three theoretical models are compared using the structural equations method. The participants were 1081 randomly selected secondary school students (aged 12–18 years) from the north of Spain, who completed the APIK Perceived Social Support Questionnaire. The model positing that perceived social support comprises a hierarchical first level formed by sources of support (family, friends and teachers) and a lower second level derived from the first one, formed by types of support (emotional, material and informational), proved to be the first-choice solution. The results establish a solid theoretical base for both future studies on perceived social support during adolescence and possible educational interventions designed to improve social support for teenagers.

High nutrient discharge from groundwater (GW) into surface water (SW) have multiple undesirable effects on river water quality. With the aim to estimate the impact of anthropic pressures and river–aquifer interactions on nitrate status in SW, this study integrates two hydrological simulation and water quality models. PATRICAL models SW–GW interactions and RREA models streamflow changes due to human activity. The models were applied to the Júcar River Basin District (RBD), where 33% of the aquifers have a concentration above 50 mg NO3−/L. As a result, there is a direct linear correlation between the nitrate concentration in rivers and aquifers (Júcar r2 = 0.9, and Turia r2 = 0.8), since in these Mediterranean basins, the main amount of river flows comes from groundwater discharge. The concentration of nitrates in rivers and GW tends to increase downstream of the district, where artificial surfaces and agriculture are concentrated. The total NO3− load to Júcar RBD rivers was estimated at 10,202 tN/year (239 kg/km2/year), from which 99% is generated by diffuse pollution, and 3378 tN/year (79 kg/km2/year) is discharged into the Mediterranean Sea. Changes in nitrate concentration in the RBD rivers are strongly related to the source of irrigation water, river–aquifer interactions, and flow regulation. The models used in this paper allow the identification of pollution sources, the forecasting of nitrate concentration in surface and groundwater, and the evaluation of the efficiency of measures to prevent water degradation, among other applications.

Sustainable development goals (SDGs) adopted in 2015 are geared toward sustainable development through various pathways, one being reducing inequality as covered in SDG 10. Inequalities are a threat to health and wellbeing of populations and a planet Earth in which we live. This rapid review aims to identify key issues that are likely to exacerbate inequalities around the six SDGs directly related to One Health, which are SDG 3, 6, 11, 13, 14 and 15, and suggest some actions that may help to address them using inclusive governance taking into account the coronavirus disease of 2019 (COVID-19) pandemic. Informed by the literature on SDGs and using the “inclusive development concept” by Gupta and Vegelin, literature search was done in Google Scholar, PubMed Central, as well as, searching of references in the relevant articles identified using search terms from the six SDGs that are directly related to One Health. In the context of the SDGs, in order to achieve One Health through inclusive governance, and tackle inequalities, the following needs to be considered and addressed: increasing number of armed conflicts; ongoing COVID-19 pandemic; ensuring availability of water and sanitation facilities; improving city and urban areas planning to cope with climate change; improving governance arrangements for addressing climate change factoring gender and human rights; multisectoral planning for conservation of oceans, seas, and marine resources; balancing trade regulation of wildlife trade with conservation efforts; need for a research collaborative involving experts from environmental sciences, wildlife, agriculture and human health to study and develop scientific evidence on contribution of changes in land use practices to occurrence of zoonotic diseases; and need of a legislation for promoting animal welfare to protect public health. Also, inclusion of people with disabilities in the use of digital technologies is critical.

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.