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Global wind resources greatly exceed current electricity demand and the levelized cost of energy from wind turbines has shown precipitous declines. Accordingly, the installed capacity of wind turbines grew at an annualized rate of about 14% during the last two decades and wind turbines now provide ~6–7% of the global electricity supply. This renewable electricity generation source is thus already playing a role in reducing greenhouse gas emissions from the energy sector. Here we document trends within the industry, examine projections of future installed capacity increases and compute the associated climate change mitigation potential at the global and regional levels. Key countries (the USA, UK and China) and regions (e.g., EU27) have developed ambitious plans to expand wind energy penetration as core aspects of their net-zero emissions strategies. The projected climate change mitigation from wind energy by 2100 ranges from 0.3–0.8 °C depending on the precise socio-economic pathway and wind energy expansion scenario followed. The rapid expansion of annual increments to wind energy installed capacity by approximately two times current rates can greatly delay the passing of the 2 °C warming threshold relative to pre-industrial levels. To achieve the required expansion of this cost-effective, low-carbon energy source, there is a need for electrification of the energy system and for expansion of manufacturing and installation capacity.

We assessed the physical, chemical and toxicological characteristics of particulate emissions from four light-duty gasoline direct injection vehicles when operated over the LA92 driving cycle. Our results showed that particle mass and number emissions increased markedly during accelerations. For three of the four vehicles tested, particulate matter (PM) mass and particle number emissions were markedly higher during cold-start and the first few accelerations following the cold-start period than during the hot running and hot-start segments of the LA92 cycle. For one vehicle (which had the highest emissions overall) the hot-start and cold-start PM emissions were similar. Black carbon emissions were also much higher during the cold-start conditions, indicating severe fuel wetting leading to slow evaporation and pool burning, and subsequent soot formation. Particle number concentrations and black carbon emissions showed large reductions during the urban and hot-start phases of the test cycle. The oxidative potential of PM was quantified with both a chemical and a biological assay, and the gene expression impacts of the PM in a macrophage model with PCR (polymerase chain reaction) and ELISA (enzyme-linked immunosorbent assay) analyses. Inter- and intra-vehicle variability in oxidative potential per milligram of PM emitted was relatively low for both oxidative assays, suggesting that real-world emissions and exposure can be estimated with distance-normalized emission factors. The PCR response from signaling markers for oxidative stress (e.g., NOX1) was greater than from inflammatory, AhR (aryl hydrocarbon receptor), or MAPK (mitogen-activated protein kinase) signaling. Protein production associated with inflammation (tumor necrosis factor alpha-TNFα) and oxidative stress (HMOX-1) were quantified and displayed relatively high inter-vehicle variability, suggesting that these pathways may be activated by different PM components. Correlation of trace metal concentrations and oxidative potential suggests a role for small, insoluble particles in inducing oxidative stress.

A statement in this recently published paper makes a point that is largely at odds with the main point of the paper that is cited. Stating that higher air temperatures lead to greater evapotranspiration is an oversimplification; the true story is more complex. Although this is by no means central to the conclusions of the paper being commented on, we have demonstrated the danger in taking too literally the idea that air temperature determines potential evapotranspiration.

Climate change models are predicting increased frequency and severity of droughts in arid and semiarid environments, and these areas are responsible for much of the world's livestock production. Because cattle (Bos Taurus) grazing can impact the abundance, distribution, and ecological function of native plant and animal communities, it is important to understand how cattle might respond to increasingly arid conditions. Here, we evaluate changes in habitat selection by cattle across an 8-yr period as a function of rainfall and other environmental covariates. Using resource selection functions, we evaluated habitat selection based on 2 behaviors, stationary and mobile. Models revealed similarity in cattle habitat selection across years, with only modest changes in selection as a function of precipitation, despite marked seasonal and interannual differences in rainfall. Cattle preferred gentle slopes, forest edges, wet meadows, and areas near water as well as areas far from water on plateaus. Cattle avoided areas at intermediate distances from water, typically associated with steep slopes. As conditions became drier during the late season, cattle did not switch selection patterns but instead contracted their selection around water. Cattle also selected similar habitats whether they were mobile or stationary, possibly making microsite decisions therein. This consistent pattern of selection across years could be particularly problematic for riparian communities as climates become drier; however, it may also simplify cattle management, as range managers can focus vegetation monitoring efforts on riparian areas. Due to the uncertainty surrounding future climatic conditions, it is imperative that both range and wildlife managers develop long-term plans to continue managing these multiuse landscapes in an ecologically sustainable manner based on expected patterns of livestock grazing.

The developmental and behavioral effects of prenatal exposure to cocaine and/or ethanol were examined in rats. Pregnant rats received ethanol (E; 2 g/kg, b.i.d.) orally, cocaine (C; 6 mg/kg/day, IV), or both (C/E) on gestational days 8-20. Controls consisted of pair-fed (PF) and untreated (UNT) groups. Offspring were weighed and examined for developmental markers beginning postnatal day one (PD1). On PD21 pups were individually observed in an open-field following either an injection of cocaine (10 mg/kg, IP), an injection of saline, or no treatment. Drug-treated and PF dams ate less food and gained less weight than the UNT dams. C and E litters had slightly increased mortality rates. Pups from both the C and E groups appeared less sensitive to the locomotor stimulant effect of cocaine. Pups from the E group engaged in significantly less spontaneous stereotypic locomotion than UNT and PF pups, while male pups from the C group exhibited a decrease in spontaneous exploratory behavior. Thus, prenatal exposure to C or E altered spontaneous and/or cocaine-induced behavior in weanling-aged rats, while the C/E combination did not augment either effect.

Abstract In developed countries, buildings are involved in almost 50% of total energy use and 30% of global green-house gas emissions. Buildings' operational energy is highly dependent on various building physical, operational, and functional characteristics, as well as meteorological and temporal properties. Besides physics-based building energy modeling, machine learning techniques can provide faster and higher accuracy estimates, given buildings' historic energy consumption data. Looking beyond individual building levels, forecasting buildings’ energy performance helps city and community managers have a better understanding of their future energy needs, and plan for satisfying them more efficiently. Focusing on an urban-scale, this study systematically reviews 70 journal articles, published in the field of building energy performance forecasting between 2015 and 2018. The recent literature have been categorized according to five criteria: 1. Learning Method, 2. Building Type, 3. Energy Type, 4. Input Data, and 5. Time-scale. The scarcity of building energy performance forecasting studies in urban-scale versus individual level is considerable. There is no study incorporating building functionality in terms of space functionality share percentages, nor assessing the effects of climate change on urban buildings energy performance using machine learning approaches and future weather scenarios. There is no optimal criteria combination for achieving the most accurate machine learning-based forecast, as there is no universal measure able to provide such global comparison. Accuracy levels are highly correlated with the characteristics of forecasting problems. The goal is to provide a comprehensive status of machine learning applications in urban building energy performance forecasting, during 2015–2018.

Abstract The growth and development of maize (Zea mays L.) largely depends on its nutrient uptake through the root. Hence, studying its growth, response, and associated metabolic reprogramming to stress conditions is becoming an important research direction. A genome-scale metabolic model (GSM) for the maize root was developed to study its metabolic reprogramming under nitrogen stress conditions. The model was reconstructed based on the available information from KEGG, UniProt, and MaizeCyc. Transcriptomics data derived from the roots of hydroponically grown maize plants were used to incorporate regulatory constraints in the model and simulate nitrogen-non-limiting (N+) and nitrogen-deficient (N−) condition. Model-predicted flux-sum variability analysis achieved 70% accuracy compared with the experimental change of metabolite levels. In addition to predicting important metabolic reprogramming in central carbon, fatty acid, amino acid, and other secondary metabolism, maize root GSM predicted several metabolites (l-methionine, l-asparagine, l-lysine, cholesterol, and l-pipecolate) playing a regulatory role in the root biomass growth. Furthermore, this study revealed eight phosphatidylcholine and phosphatidylglycerol metabolites which, even though not coupled with biomass production, played a key role in the increased biomass production under N-deficient conditions. Overall, the omics-integrated GSM provides a promising tool to facilitate stress condition analysis for maize root and engineer better stress-tolerant maize genotypes.

The agriculture and horticulture sector in the Netherlands is one of the most productive in the world. Although the sector is one of the most advanced and intense agricultural production systems worldwide, it faces challenges, such as climate change and environmental and social unsustainability of industrial production. To overcome these challenges, alternative food production initiatives have emerged, especially in large cities such as Amsterdam. Some initiatives involve producing food in the urban environment, supported by new technologies and practices, so-called high-tech urban agriculture (HTUA). These initiatives make cultivation of plants inside and on top of buildings possible and increase green spaces in urban areas. The emerging agricultural technologies are creating new business environments that are shape d by technology developers (e.g., suppliers of horticultural light emitting diodes (LED) and control environment systems) and developers of alternative food production practices (e.g., HTUA start-ups). However, research shows that the uptake of these technological innovations in urban planning processes is problematic. Therefore, this research analyzes the barriers that local government planners and HTUA developers are facing in the embedding of HTUA in urban planning processes, using the city of Amsterdam as a case study. This study draws on actor-network theory (ANT) to analyze the interactions between planners, technologies, technology developers and developers of alternative food production practices. Several concepts of ANT are integrated into a multi-level perspective on sustainability transitions (MLP) to create a new theoretical framework that can explain how interactions between technologies and planning actors transform the incumbent social–technical regime. The configuration of interactions between social and material entities in technology development and adoption processes in Amsterdam is analyzed through the lens of this theoretical framework. The data in this study were gathered by tracing actors and their connections by using ethnographic research methods. In the course of the integration of new technologies into urban planning practices, gaps between technologies, technology developers, and planning actors have been identified. The results of this study show a lacking connection between planning actors and technology developers, although planning actors do interact with developers of alternative food production practices. These interactions are influenced by agency of artefacts such as visualizations of the future projects. The paper concludes that for the utilization of emerging technologies for sustainability transition of cities, the existing gap between technology developers and planning actors needs to be bridged through the integration of technology development visions in urban agendas and planning processes.