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Global climate change alters the dynamic of natural ecosystems and directly affects species distributions, persistence and diversity. The impacts of climate change may lead to dramatic changes in biotic interactions, such as pollination and seed dispersal. Life history traits are extremely important to consider the vulnerability of a species to climate change, producing more robust models than those based primarily on species distributions. Here, we hypothesized that rising temperatures and aridity will reduce suitable habitats for the endemic flora of the Caatinga, the most diverse dry tropical forest on Earth. Specifically, species with specialized reproductive traits (e.g. vertebrate pollination, biotic dispersal, obligatory cross-pollination) should be more affected by climate change than those with generalist traits. We performed two ecological niche models (current and future) to simulate the effects of climate change on the distribution area of endemic species in relation to life-history traits. We used the MIROC-ESM and CCSM4 models for both intermediate (RCP4.5) and highest predicted (RCP8.5) GHG emission scenarios, with a resolution of 30' (~1 km2). Habitat with high occurrence probability (>80%) of endemic species will be reduced (up to ~10% for trees, ~13% for non-arboreous, 10-28% for species with any pollination/reproductive system), with the greatest reductions for species with specialized reproductive traits. In addition, the likely concentration of endemic plants in the extreme northeastern portion of the Caatinga, in more mesic areas, coincides with the currently most human-modified areas of the ecosystem, which combined with climate change will further contract suitable habitats of endemic species. In conclusion, plant species endemic to the Caatinga are highly vulnerable to even conservative scenarios of future climate change and may lose much of their climatic envelopes. New protected areas should be located in the northeastern portion of the Caatinga, which hosts a more favorable climate, but is currently exposed to escalating agricultural intensification.

Abstract In this study, a polystyrene sulphonic acid resin catalysed hydrolysis of low grade lipids from meat processing waste, via an in-situ hydrolysis pathway was evaluated for enhanced fatty acid yield. Process evaluation was achieved using a response surface methodology based on a central composite design for four process variables (temperature, catalyst load, moisture mass fraction of the meat processing waste, and reaction time). This study enabled the development of a model that was sufficient to predict fatty acid yield as a function of the four process variables investigated. The optimum values of the temperature, catalyst loading, moisture mass fraction, and reaction time for enhanced fatty acid yield were determined to be 92.5 °C, a mass ratio of 0.09 resin to wet meat processing waste, 92%, and 1 h respectively. Catalyst recovery and regeneration were also demonstrated, with up to 88% mass fraction of the catalyst recoverable, while retaining an activity of 96% relative to the fresh catalyst. The study therefore showed that it is possible to produce high yields of fatty acids under moderate temperature conditions from high moisture low grade lipid sources. It was therefore shown that a highly efficient hydrolysis process can be achieved without the energetic penalty of preliminary drying operations that are associated with lipid extractive processes characterising existing lipid hydrolysis systems.

Urbanization towards the expansion of the city area causes urban sprawl and changes in space use. Furthermore, urban agglomeration towards urban spatial integration causes a decrease in environmental quality. This study aims to analyze (1) land-use change and urban sprawl work as determinants of environmental quality degradation in suburban areas. (2) The effect of urban sprawl, urban agglomeration, land-use change, urban activity systems, and transportation systems on environmental quality degradation in suburban areas. A combination of quantitative and qualitative approaches is used sequentially in this study. Data obtained through observation, surveys, and documentation. The results showed that the expansion of the Makassar City area to the suburbs had an impact on spatial dynamics, spatial segregation, and environmental degradation. Furthermore, urban sprawl, land-use change, urban agglomeration, activity systems, and transportation systems have a positive correlation to environmental quality degradation with a determination coefficient of 85.9%. This study recommends the handling of urban sprawl, land-use change, and urban agglomeration to be considered in the formulation of development policies towards the sustainability of natural resources and the environment of Makassar City, Indonesia.

Abstract This paper is the first comprehensive report about the renewable fuels produced from the pyrolytic conversion of anaerobic and aerobic Brazilian high-ash sewage sludges. The study was divided into three steps: (1) an in-deep evaluation of physicochemical characteristics of both sewage sludges; (2) thermogravimetric analysis under inert conditions for determining bench-scale pyrolysis conditions; (3) characterization of volatile pyrolytic products from fixed bed reactor using GC/MS analysis. The anaerobic and aerobic sewage sludges present substantial high heating values and high contents of ash and sulfur, which are adverse properties for solid fuels. Regarding the pyrolytic behavior, the sewage sludge samples presented a two-step thermal decomposition process. The bio-oil produced from pyrolysis indicates that the high-ash sewage sludges are a source of high value-added chemicals, such as aliphatic and monoaromatic hydrocarbons. In addition, the pyrolysis can promote the production of a hydrogen-rich syngas for thermal boilers or engines use. In this research, it was demonstrated that the pyrolysis process of Brazilian high-ash sewage sludge can be applied for the production of useful biofuels - as bio-oil and bio-syngas - also adding value to this waste, thus contributing to the establishment of renewable source of bioenergy.

The standard deviation of the Horizontal Wind Speed as a proxy of wind turbulence is used to compare the apparent wind turbulence measured by an off-shore floating Doppler lidar to the one measured by a fixed lidar on a metmast. We use statistical analysis based on clustering the horizontal wind speed measured by the floating lidar as well as buoy angular amplitude and period under the approximation of harmonic motion. Three scenarios with different wave and wind conditions are discussed from the IJmuiden's test campaign (North Sea.). This work was supported via Spanish Government–European Regional Development Funds project PGC2018-094132-B-I00 and EU H2020 ACTRIS-2 (GA 654109). The European Institute of Innovation and Technology (EIT), KIC InnoEnergy project NEPTUNE (Offshore Metocean Data Measuring Equipment and Wind, Wave and Current Analysis and Forecasting Software, call FP7) supported measurements campaigns. CommSensLab is a María-de-Maeztu Unit of Excellence funded by the Agencia Estatal de Investigacion (Spanish National Science Foundation). Peer Reviewed

Abstract Organocell lignin has been subjected to catalytic hydrocracking in a semicontinuous reactor system using four petroleum-derived slurry oils and a lignin-derived one. The objective was to maximize the yield of phenols which can be further converted to methylarylethers for the use as gasoline octane number improvement. Experiments were carried out by varying temperature (375°–450°C), hydrogen pressure (75–180 bar), and catalyst (sulfided NiMo, zeolite A, and none). The most complete conversion (coke formation only 0.3%) was obtained at 375°C and 180 bar when lignin was mixed with a lignin-derived slurry oil. The formation of phenols was monitored by quantitative capillary gas chromatography. Up to 12.8wt% (based on lignin) of a mixture of mono-phenols was obtained. The yields of single products (in wt% based on lignin) are as follows: phenol 2.3, cresols 5.0, xylenols 4.2, guaiacol 1.3.

Adaptability is defined as the ability of a crop (or variety) to respond positively to changes in agricultural conditions. The trait is genetically controlled and provides an ability to exploit environmental attributes, both natural and agronomic. Values of relative adaptability can be determined by the regression of the yield of the tested crop over the average yield of compared crops from several environments. We evaluated relative adaptability of 12 staple crops in 12 European countries and compared the yield data over a 43-year period from 1961 to 2003. An additional set of average yield data was also available for the 15 European Union (EU15) member countries. A wider range of 26 crop species was investigated that allowed comparisons between Europe and the USA between 1961 and 2003. Adaptability was closely related to the annual yield increases of the crops studied (r2 = 0.999 both in the EU15 and the USA). However, the adaptability of certain crops differed between the two regions. Pulse, maize, millet, wheat and sorghum showed the highest adaptability in the EU15 region, whereas strawberry, pear, tomato, walnut and maize were highest in the USA. The lowest adaptability was found for walnut, pear, apple, cauliflower and hop in the EU15 and for mustard, hop, sugar beet, millet and oat in the USA. In European countries, crops with similar biology, environment and agronomical practices (like the amount of fertilizers and pesticides applied) tended to have similar adaptability values. The data indicate that high adaptability is an important prerequisite for continued yield gains in the best environments.

In recent years, liquid biofuels for transport have benefited from significant political support due to their potential role in curbing climate change and reducing our dependence on fossil fuels. They may also participate to rural development by providing new markets for agricultural production. However, the growth of energy crops has raised concerns due to their high consumption of conventional fuels, fertilizers and pesticides, their impacts on ecosystems and their competition for arable land with food crops. Low-input species such as Jatropha curcas, a perennial, inedible crop well adapted to semiarid regions, has received much interest as a new alternative for biofuel production, minimizing adverse effects on the environment and food supply. Here, we used life-cycle assessment to quantify the benefits of J. curcas biofuel production in West Africa in terms of greenhouse gas emissions and fossil energy use, compared with fossil diesel fuel and other biofuels. Biodiesel from J. curcas has a much higher performance than current biofuels, relative to oil-derived diesel fuels. Under West Africa conditions, J. curcas biodiesel allows a 72% saving in greenhouse gas emissions compared with conventional diesel fuel, and its energy yield (the ratio of biodiesel energy output to fossil energy input) is 4.7. J. curcas production studied is eco-compatible for the impacts under consideration and fits into the context of sustainable development.

Low temperature energy sources for protected cropping include geothermal waters, waste heat from Industry, and trapped sunshine; application depends on the recovery of heat from circulating warmed water, either via the soil in which the plants are growing or via the air in the greenhouse. Soil warming pipes and ‘water-curtain’ systems of space-heating have shown promise, but heat storage, either for short periods or longer, remains a problem common to all such schemes.

More than 60 percent of the global surface is covered by clouds, and they play a vital role in the hydrological circle, climate change, and radiation budgets by modifying shortwaves and longwave. Weather forecast reports are critical to areas such as air and sea transport, energy, agriculture, and the environment. The time has come for artificial intelligence-powered devices to take the place of the current method by which decision-making experts determine cloud types. Convolutional neural network models (CNNs) are starting to be utilized for identifying the types of clouds that are caused by meteorological occurrences. This study uses the publicly available Cirrus Cumulus Stratus Nimbus (CCSN) dataset, which consists of 2543 ground-based cloud images altogether. We propose a model called Cloud-MobiNet for the classification of ground-based clouds. The model is an abridged convolutional neural network based on MobileNet. The architecture of Cloud-MobiNet is divided into two blocks, namely the MobileNet building block and the support MobileNet block (SM block). The MobileNet building block consists of the weights of the depthwise separable convolutions and pointwise separable convolutions of the MobileNet model. The SM block is made up of three dense network layers for feature extraction. This makes the Cloud-MobiNet model very lightweight to be implemented on a smartphone. An overall accuracy success of 97.45% was obtained for the CCSN dataset used for cloud-type classification. Cloud-MobiNet promises to be a significant model in the short term, since automated ground-based cloud classification is anticipated to be a preferred means of cloud observation, not only in meteorological analysis and forecasting but also in the aeronautical and aviation industries.