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This paper explains the fusion of the physics-based material degradation mechanism with the statistics-based data modeling approach for predicting the degradation rate of photovoltaic (PV) modules. The degradation of PV module is mainly associated with the module construction type and climatic condition at its use location. The aim of this paper is to quantify the effect of dynamic environmental stresses (dynamic covariates) on the power degradation of the module over its lifetime. There are various physics-based models, such as Arrhenius model, for understanding the physical or chemical reaction-related root causes of PV degradation. But, to estimate the underlying material properties, such as activation energy (Ea), statistical modeling plays a key role. In addition, instead of being continuously monitored, the performance characteristics of PV modules are often measured only at intervals like quarterly or annually, which makes it difficult to model the complete degradation path of the module. On the other hand, the information on dynamic covariates is recorded more frequently with the development of sophisticated sensors and data acquisition systems. This information can be integrated through physics-based models to study the effects of environmental variables in degradation processes. Hence, in this paper, a cumulative exposure model is used to link the module degradation path and the environmental variables, including module temperature (both static and cyclic), ultraviolet radiation, and relative humidity, which are recorded as multivariate time series.

Background: Concurrent substance use among adolescents has been associated with an increase in physical and mental health problems. These outcomes tend to be exacerbated among adolescents of color in underserved urban settings. The purpose of this study was to understand alcohol and concurrent drug use patterns among adolescents in an underserved urban community to provide targeted prevention and treatment recommendations. Method: This study examined data among adolescents in an underserved urban community (N = 1789; 56.90% female; 70.86% Hispanic/Latino/a; meanage = 15.96 ± 1.56). Using latent class analysis (LCA) and multinomial logistic regression modeling, analyses identified independent correlates of latent class membership. Results: Five latent classes (LC) were identified including LC group 1: Predominant alcohol use and limited to no concurrent-drug use (n = 213; 11.9%); LC group 2: Concurrent drug and alcohol use including methamphetamine, marijuana and synthetic marijuana use, and alcohol use (n = 74; 4.2%); LC group 3: Concurrent drug and alcohol use, with no marijuana use (n = 204; 11.39%); LC group 4: High Concurrent drug use and alcohol use (n = 204; 11.40%); and LC group 5: Concurrent drug use without alcohol use (n = 1101; 61.52%). Significant between group differences were noted between latent class groups and sociodemographic characteristics. Multinomial logistic regression models identified the associations between sociodemographic characteristics and corollary clinical features of substance use on latent class groupings of alcohol and concurrent drug use. Conclusion: Understanding concurrent substance use LC groups among adolescents is essential to providing targeted interventions and treatment programs, as well as early intervention programs that may help reduce substance use during adolescence.

Abstract Fuel poverty, the inability of households to afford adequate energy services, such as heating, is a major energy justice concern. Increasing residential energy efficiency is a strategic fuel poverty intervention. However, the absence of easily accessible household energy data impedes effective targeting of energy efficiency programs. This paper uses publicly available data, bottom-up modeling and small-area estimation techniques to predict the mean census block group residential heating energy use intensity (EUI), an energy efficiency proxy, in Kansas City, Missouri. Results mapped using geographic information systems (GIS) and statistical analysis, show disparities in the relationship between heating EUI and spatial, racial/ethnic, and socioeconomic block group characteristics. Block groups with lower median incomes, a greater percentage of households below poverty, a greater percentage of racial/ethnic minority headed-households, and a larger percentage of adults with less than a high school education were, on average, less energy efficient (higher EUIs). Results also imply that racial segregation, which continues to influence urban housing choices, exposes Black and Hispanic households to increased fuel poverty vulnerability. Lastly, the spatial concentration and demographics of vulnerable block groups suggest proactive, area- and community-based targeting of energy efficiency assistance programs may be more effective than existing self-referral approaches.

The main challenge to energy policy makers in the 21st century is how to develop and manage adequate, affordable and reliable energy services on a sustainable manner to fuel social and economic development. About 60 per cent of the world's two billion population, who do not have access to modern energy services, live in Asia and the Pacific region. The demand for energy is expected to continue growing at a high rate well into the century, often at a greater rate than economic growth. Latest assessment of conventional energy resources shows that their availability is not going to be an immediate threat to the security of supply but the question is can the region afford to allow current patterns of production and consumption of energy to continue in a rapidly deteriorating health of the environment? Changing these unsustainable patterns is the main challenge for the developed and developing countries alike. The paper dwells upon what are the major issues facing the region in promoting sustainable energy development and what are some of the policy options and possible strategies that the countries could consider to attain the objective of sustainable energy development. In this respect, the paper also dwells upon the need for strategic planning and management of energy resources.

Local adaptation and species interactions have been shown to affect geographic ranges; therefore, we need models of climate impact that include both factors. To identify possible dynamics of species when including these factors, we ran simulations of two competing species using an individual‐based, coupled map–lattice model using a linear climatic gradient that varies across latitude and is warmed over time. Reproductive success is governed by an individual's adaptation to local climate as well as its location relative to global constraints. In exploratory experiments varying the strength of adaptation and competition, competition reduces genetic diversity and slows range change, although the two species can coexist in the absence of climate change and shift in the absence of competitors. We also found that one species can drive the other to extinction, sometimes long after climate change ends. Weak selection on local adaptation and poor dispersal ability also caused surfing of cooler‐adapted phenotypes from the expanding margin backwards, causing loss of warmer‐adapted phenotypes. Finally, geographic ranges can become disjointed, losing centrally‐adapted genotypes. These initial results suggest that the interplay between local adaptation and interspecific competition can significantly influence species’ responses to climate change, in a way that demands future research.

Abstract Two mesocosm experiments were conducted to examine effects of ferric iron (Fe) and mixtures of ferric Fe with aqueous metals (Cu, Zn) on stream benthic communities. Naturally colonized benthic communities were exposed to a gradient of ferric Fe (0, 0.4, 1.0, 2.5, 6.2, and 15.6 mg/L) that bracketed the current US Environmental Protection Agency water quality criterion value (1.0 mg/L). After 10 d of exposure to ferric Fe, total macroinvertebrate abundance, number of taxa, and abundance of all major macroinvertebrate groups (Ephemeroptera, Plecoptera, Trichoptera, and Diptera) were significantly reduced. Heptageniid mayflies and chironomids were especially sensitive to Fe oxide deposition and were significantly reduced at 0.4 and 1.0 mg/L total Fe, respectively. In a second mesocosm experiment, periphyton and macroinvertebrate communities were exposed to ferric Fe (0.60 mg/L) with or without aqueous Cu and Zn at 2 treatment levels: low (0.01 mg/L Cu + 0.1 mg/L Zn) and high (0.05 mg/L Cu + 0.5 mg/L Zn). In contrast to previous research, we observed no evidence of a protective effect of Fe on toxicity of metals. Growth rates and protein content of periphyton were significantly reduced by both ferric Fe and aqueous metals, whereas abundance of heptageniid mayflies (Cinygmula) and whole community metabolism were significantly reduced by ferric Fe alone. We hypothesize that Fe oxides inhibited algal growth and enhanced metal accumulation, leading to a reduction in the quantity and quality of food resources for grazers. Mesocosm experiments conducted using natural benthic communities provide a unique opportunity to quantify the relative importance of indirect physical effects and to develop a better understanding of the relationship between basal food resources and consumers in natural stream ecosystems. Environ Toxicol Chem 2018;37:1320–1329. © 2017 SETAC

Marine organisms are constantly exposed to multiple stressors creating a range of associated environmental and ecotoxicological risks. Several stressors have been identified as key drivers of environmental change that may significantly influence marine near-shore systems. These include increased frequency and duration of extreme rainy events and drought periods, arising from climate change, and the constant discharge of contaminants into aquatic systems. A growing body of evidence demonstrates that climate change can have direct and indirect impacts on marine organisms although the combined effects with other stressors, namely with metals and metalloids, have received very little attention to date. The present study evaluated the biochemical alterations induced in the clam Ruditapes philippinarum, also known as Manila clam, when simultaneously exposed (96 h) to different arsenic concentrations (0, 4 and 17 mg/L) and a range of salinities (14, 21, 28, 35 and 42 g/L). Results obtained revealed that, when acting alone, both stressors induced oxidative stress in clams, with higher LPO levels and lower GSTs activity induced by As contamination, and a stronger inhibition of the antioxidant defenses induced by salinity increase. Furthermore, when exposed to the combination of both stressors, clams experienced stronger biochemical alterations, presenting higher LPO increases and greater decreases of antioxidant enzymes, especially noticed at higher salinities. The present findings may indicate that climate change, including predicted drought periods that will increase salinities in aquatic systems, will seriously affect the clam R. philippinarum, especially those inhabiting contaminated ecosystems.

Abstract This research mainly focuses on the experimental setup of the Hot Box Test Method and comparison of different models for measurement of thermal properties of building envelope systems. Hot Box Test Method has long been used to determine the thermal properties of building envelope systems, however, the steady-state assumption for calculation is not always desired, especially when the environmental conditions cannot be controlled. To utilize models considering the dynamic behavior of buildings for in-situ measurement, it is desired to first validate such models and compare the performances with hot box test. Therefore, the performances of several dynamic models, including Anderlind's Regression Model and R-C Network Model, have been studied. Hot box tests were performed in the Building Enclosure Testing Laboratory (BETL) at Penn State University and the results show the 3R2C model turns out to be the most accurate one among the dynamic models explored in this study. With a temperature difference larger than 20 °C, all dynamic models are validated with a percentage difference lower than 7% compared with the steady-state analysis, giving us alternatives for R-value measurement when in-situ measurement condition are applied.

The great apes-bonobos, chimpanzees, gorillas and orangutans-are critically threatened by human activities. We have destroyed their habitats, hunted them and transmitted fatal diseases to them. Yet we also conduct research on them, try to protect them and live alongside them. They are endangered, and time is running out. Here we outline what must be done to ensure that future generations continue to share this planet with great apes. We urge dialogue with those who live with great apes and interact with them often. We advocate conservation plans that acknowledge the realities of climate change, economic drivers and population growth. We encourage researchers to use technology to minimize risks to great apes. Our proposals will require substantial investment, and we identify ways to generate these funds. We conclude with a discussion of how field researchers might alter their work to protect our closest living relatives more effectively.