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Abstract Pyrolysis is currently being considered as an alternative method of treating sewage sludge. It yields residual oils and gases, which can be used as fuels, and a solid which can either be burned or physically activated with air or CO 2 . The aim of this work was to study the influence of different pyrolysis conditions (e.g. temperature and heating rate) on the reactivity in air and in CO 2 of carbonaceous materials obtained from these types of residues. An anaerobic sewage sludge produced in a Spanish urban waste water treatment plant, containing 5 wt.% moisture after air-drying, was pyrolyzed in an electrical laboratory furnace under different pyrolysis conditions. Non-isothermal reactivities (up to 1100°C) in air and in CO 2 of the carbonaceous materials obtained after pyrolysis were performed in a thermobalance. The TG and DTG curves obtained from these experiments are discussed.

From the perspective of the recent orientations of virtual geographies, the idea of smart and novelty villages in the context of renovated material rural worlds is conceptualized. The sum of new virtual and new materiality produces virtual and novelty spaces and places, which acquire a precise territorial dimension in the rural policy and politics of smart villages. Smart villages can not only be framed in global smart contexts, but they can also play a fundamental role in de-global territorial horizons as an instrument of resistance to global processes of rural restructuring. The smart political idea or orientation takes shape in each rural community with a different expression in the form of new local materials. The concept of quality virtuality is developed theoretically along three axes: the encounter between smart, novelty and new materials; the smart in the equitable rural community; and the right to disconnection in remote rural areas.

An ever more environmentally conscious society demands the use of green, sustainable and high-efficiency renewable energy resources. However, large-scale energy storage remains a challenge for a deep penetration of power produced from renewables into the grid. The Calcium-Looping (CaL) process, based on the reversible carbonation/calcination of CaO, is a promising technology for thermochemical energy storage (TCES) in Concentrated Solar Power (CSP) plants. Natural limestone to be used as CaO precursor is cheap, non-toxic and abundant. Nevertheless, recent works have shown that carbonation of CaO derived limestone at optimum conditions for TCES is limited by pore-plugging, which leads to severe deactivation for large enough particles to be employed in practice. In our work, we have synthesized inexpensive CaO/SiO composites by means of a biotemplate method using rice husk as support. The morphological and compositional features of the biomorphic materials synthesized help improve the CaO multicycle activity under optimum CSP storage conditions and for particles sufficiently large to be managed in practical processes. Peer Reviewed

Source apportionment of atmospheric PM1 is important for air quality control, especially in urban areas where high mass concentrations are often observed. Chemical analysis of molecular inorganic and organic tracer compounds and subsequently data analysis with receptor models give insight on the origin of the PM1 sources. In the present study, four source apportionment approaches were compared with an extended database containing inorganic and organic compounds that were measured during an intensive sampling campaign at urban traffic and urban background sites in Barcelona. Source apportionment of the combined database, containing both inorganic and organic compounds, was compared with more conventional approaches using inorganic and organic databases separately. Traffic emission sources were identified in all models for the two sites. The combined inorganic and organic databases provided higher discrimination capacity of emission sources. It identified aerosols generated by regional recirculation of biomass burning, secondary biogenic organic aerosols, harbor emissions, and specific industrial emissions. In this respect, this approach identified a relevant industrial source situated at NE Barcelona in which a waste incinerator plant, a combined-cycle power plant, and an industrial glass complex are located. Models using both inorganic and organic molecular tracer compounds improve the source apportionment of urban PM.

Coastal areas are more densely populated than inland areas and present faster rates of population increase and urbanization. This trend is expected to continue in the coming decades, and thus, the demand of natural resources in coastal areas, such as water and energy resources, increasing the pressure and impact on the environment, superposed to the effects of climate change. Currently, in Europe, the demand for heating in buildings and businesses outnumbers the demand for cooling. However, the latter is gradually catching up due to rising demand for air cooling or refrigeration for industry such as food, technological and medical supplies. The energy required to cool buildings in Europe is expected to increase by more than 70% by 2030, while energy used to heat buildings may decrease by 30% (UE, 2018). Low Temperature Geothermal Energy (LTGE) is most likely the green energy production method for heating and cooling with the highest potential to provide affordable and clean energy and meet the CO2-emissions reduction goals of the Green Deal. Despite advances on LTGE technologies, the efficiency of these systems remains inherently sensitive to changes in hydrodynamics and the media (e.g., changes in the groundwater thermal regime). Groundwater, on the other hand, is the world's largest freshwater resource, and it is especially important in coastal areas because interactions between aquifer systems and sea water may lead to salinization and resource loss. Because geothermal systems and coastal aquifers interact directly, specially at groundwater discharge areas, it is clear that a better understanding of the potential interactions of geothermal systems with current and prospective coastal aquifer processes is essential for their design and foreseeing potential environmental effects. To address these issues, we model variable-density groundwater coupled with heat transport to simulate the long-term evolution of groundwater salinity and aquifer thermal energy discharge. We find that the heating/cooling-induced water density variations affect the seawater intrusion. Understanding the behavior of the groundwater system is required to ensure sustainable water, energy, and coastal ecosystem management.

Fire danger indices are descriptors of fire potential in a large area, and combine a few variables that affect the initiation, spread and control of forest fires. The Canadian Fire Weather Index (FWI) is one of the most widely used fire danger indices in the world, and it is built upon instantaneous values of temperature, relative humidity and wind velocity at noon, together with 24 hourly accumulated precipitation. However, the scarcity of appropriate data has motivated the use of daily mean values as surrogates of the instantaneous ones in several studies that aimed to assess the impact of global warming on fire. In this paper we test the sensitivity of FWI values to both instantaneous and daily mean values, analyzing their effect on mean seasonal fire danger (seasonal severity rating, SSR) and extreme fire danger conditions (90th percentile, FWI90, and FWI>30, FOT30), with a special focus on its influence in climate change impact studies. To this aim, we analyzed reanalysis and regional climate model (RCM) simulations, and compared the resulting instantaneous and daily mean versions both in the present climate and in a future scenario. In particular, we were interested in determining the effect of these datasets on the projected changes obtained for the mean and extreme seasonal fire danger conditions in future climate scenarios, as given by a RCM. Overall, our results warn against the use of daily mean data for the computation of present and future fire danger conditions. Daily mean data lead to systematic negative biases of fire danger calculations. Although the mean seasonal fire danger indices might be corrected to compensate for this bias, fire danger extremes (FWI90 and specially FOT30) cannot be reliably transformed to accommodate the spatial pattern and magnitude of their respective instantaneous versions, leading to inconsistent results when projected into the future. As a result, we advocate caution when using daily mean data and strongly recommend the application of the standard definition for its calculation as closely as possible. Threshold-dependent indices derived from FWI are not reliably represented by the daily mean version and thus can neither be applied for the estimation of future fire danger season length and severity, nor for the estimation of future extreme events. The research leading to these results has received funding from the European Union's Seventh Framework Programme (FP7/2007-2013) under grant agreement 243888 (FUME Project). J.F. acknowledges nancial support from the Spanish R&D&I programme through grant CGL2010-22158-C02 (CORWES project). The ESCENA project (200800050084265) of the Spanish \Strategic action on energy and climate change" provided the WRF RCM simulation used in this study. We acknowledge three anonymous referees for their useful comments that helped to improve the original manuscript.

AbstractGlobal impact models represent process-level understanding of how natural and human systems may be affected by climate change. Their projections are used in integrated assessments of climate change. Here we test, for the first time, systematically across many important systems, how well such impact models capture the impacts of extreme climate conditions. Using the 2003 European heat wave and drought as a historical analogue for comparable events in the future, we find that a majority of models underestimate the extremeness of impacts in important sectors such as agriculture, terrestrial ecosystems, and heat-related human mortality, while impacts on water resources and hydropower are overestimated in some river basins; and the spread across models is often large. This has important implications for economic assessments of climate change impacts that rely on these models. It also means that societal risks from future extreme events may be greater than previously thought.