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AbstractAn organic redox couple tetramethylthiourea/tetramethylformaminium disulfide (TMTU/TMFDS2+) is evaluated in dye‐sensitized solar cells in conjunction with a series of indoline and ruthenium‐based dyes. Of these, devices with indoline dye D205 show the best performance, with an optimized power conversion efficiency of 7.6 % under AM 1.5G 1 sun illumination. Charge collection and injection are highly efficient in all TMTU‐based DSCs studied. Regeneration of indoline dyes is highly efficient, whereas regeneration of ruthenium dyes by TMTU is less efficient, accounting for their inferior performance. Impedance spectroscopy results reveal that using an optimized TMTU/TMFDS2+ electrolyte solution, the TiO2 conduction band edge is 300–400 meV lower than when an optimized I3−/I− electrolyte is used. The would‐be loss in open‐circuit voltage caused by the downward conduction band shift is mostly compensated by approximately the 200 meV lower redox level of the TMTU/TMFDS2+ electrolyte and up to 1000 times slower recombination rates. This makes TMTU/TMFDS2+ a promising redox couple in the development of highly efficient solar energy conversion devices.

We previously developed a model for projection of heat-related mortality attributable to climate change. The objective of this paper is to improve the fit and precision of and examine the robustness of the model.We obtained daily data for number of deaths and maximum temperature from respective governmental organizations of Japan, Korea, Taiwan, the USA, and European countries. For future projection, we used the Bergen climate model 2 (BCM2) general circulation model, the Special Report on Emissions Scenarios (SRES) A1B socioeconomic scenario, and the mortality projection for the 65+-year-old age group developed by the World Health Organization (WHO). The heat-related excess mortality was defined as follows: The temperature-mortality relation forms a V-shaped curve, and the temperature at which mortality becomes lowest is called the optimum temperature (OT). The difference in mortality between the OT and a temperature beyond the OT is the excess mortality. To develop the model for projection, we used Japanese 47-prefecture data from 1972 to 2008. Using a distributed lag nonlinear model (two-dimensional nonparametric regression of temperature and its lag effect), we included the lag effect of temperature up to 15 days, and created a risk function curve on which the projection is based. As an example, we perform a future projection using the above-mentioned risk function. In the projection, we used 1961-1990 temperature as the baseline, and temperatures in the 2030s and 2050s were projected using the BCM2 global circulation model, SRES A1B scenario, and WHO-provided annual mortality. Here, we used the "counterfactual method" to evaluate the climate change impact; For example, baseline temperature and 2030 mortality were used to determine the baseline excess, and compared with the 2030 excess, for which we used 2030 temperature and 2030 mortality. In terms of adaptation to warmer climate, we assumed 0 % adaptation when the OT as of the current climate is used and 100 % adaptation when the OT as of the future climate is used. The midpoint of the OTs of the two types of adaptation was set to be the OT for 50 % adaptation.We calculated heat-related excess mortality for 2030 and 2050.Our new model is considered to be better fit, and more precise and robust compared with the previous model.

The sudden increase in the concentration of carbon dioxide (CO2) in the atmosphere due to the high dependency on fossil products has created the need for an urgent solution to mitigate this challenge. Global warming, which is a direct result of excessive CO2 emissions into the atmosphere, is one major issue that the world is trying to curb, especially in the 21st Century where most energy generation mediums operate using fossil products. This investigation considered a number of materials ideal for the capturing of CO2 in the post-combustion process. The application of aqueous ammonia, amine solutions, ionic liquids, and activated carbons is thoroughly discussed. Notable challenges are impeding their advancement, which are clearly expatiated in the report. Some merits and demerits of these technologies are also presented. Future research directions for each of these technologies are also analyzed and explained in detail. Furthermore, the impact of post-combustion CO2 capture on the circular economy is also presented.

AbstractSoil micronutrients are capital for the delivery of ecosystem functioning and food provision worldwide. Yet, despite their importance, the global biogeography and ecological drivers of soil micronutrients remain virtually unknown, limiting our capacity to anticipate abrupt unexpected changes in soil micronutrients in the face of climate change. Here, we analyzed >1300 topsoil samples to examine the global distribution of six metallic micronutrients (Cu, Fe, Mn, Zn, Co and Ni) across all continents, climates and vegetation types. We found that warmer arid and tropical ecosystems, present in the least developed countries, sustain the lowest contents of multiple soil micronutrients. We further provide evidence that temperature increases may potentially result in abrupt and simultaneous reductions in the content of multiple soil micronutrients when a temperature threshold of 12–14°C is crossed, which may be occurring on 3% of the planet over the next century. Altogether, our findings provide fundamental understanding of the global distribution of soil micronutrients, with direct implications for the maintenance of ecosystem functioning, rangeland management and food production in the warmest and poorest regions of the planet.

Abstract One of the most significant current discussions in climate change is around the issue of low carbon emission and its effect on human health. It is becoming increasingly difficult to ignore the transport share of world emissions. Recent developments in this field have heightened the need for Driving Cycle establishment in order to understand and reduce vehicle emissions. However, a major problem with this Driving Cycle characteristic is that it varies from one city to another due to the type of principle activities (industrial, agricultural) present. Therefore, individual testing is necessary for each region in order to establish a representative tool for Local Authorities to identify the air quality in terms of traffic emissions. To date, there has been little discussion about Driving Cycle developments, so in this paper, experimental studies, which predict sufficient parameters in driving cycle modelling, were summarized in the literature review and gave comprehensive feedback to the field for further investigations. Subsequently, the author presented a simple method for establishing the driving cycle, and estimating vehicle emission. As COPERT software is one of the most commonly deployed tools in Europe, the methodology involved a formula that is being obtained from COPERT. Later this formula is being used in a program which makes use of bulk traffic movements and average vehicle speeds in order to estimate emissions. The combination of On-board diagnostic data extraction incorporated in all modern passenger cars and program used to allow real world vehicular activities to be recorded, in order to better estimate the contribution of private cars to local emissions inventories. Representative driving cycles reflecting the real-world driving conditions of two cities were proposed and estimated vehicle emissions were compared with measured results.

Abstract Accurate rooftop solar energy potential characterization is critically important for promoting the wide penetration of renewable energy in high-density cities. However, it has been a long-standing challenge due to the complex building shading effects and diversified rooftop availabilities. To overcome the challenge, this study proposed a novel 3D-geographic information system (GIS) and deep learning integrated approach, in which a 3D-GIS-based solar irradiance analyzer was developed to predict dynamic rooftop solar irradiance by taking shading effects of surrounding buildings into account. A deep learning framework was developed to identify the rooftop availabilities. Experimental validations have shown their high accuracies. As a case study, a real urban region of Hong Kong was used. The results showed that the annual solar energy potential of the entire building group was reduced by 35.7% due to the shading effect and the reduced rooftop availability. The reductions of individual buildings varied from 13.4% to 74.5%. In spite of the substantial reductions of the annual solar energy, the shading effect could only slightly reduce the peak solar power. In fact, the annual solar energy reduction could be five times higher than the peak solar power reduction. Further analysis showed that simple addition of the respective solar energy potential reductions, caused by the shading effect and the rooftop availability, tends to highly overestimate the total reduction by up to 26%. For this reason, their impacts cannot be considered separately but as joint effects. The integrated approach provides a viable means to accurately characterize rooftop solar energy potential in urban regions, which can help facilitate solar energy applications in high-density cities.

Management of natural resources is pivotal for sustained economic growth—the increasing ecological footprints causing biocapacity deficit threaten the resource conversation agenda. The study identified the potential causes and consequences of natural resource depletion in a broad cross-section of 138 countries. Ecological footprints, international migrant stocks, industrial value-added, and population growth influenced natural resource capital across countries. The results show that ecological footprints, industrial value-added, and population growth are the detrimental factors of resource capital. In contrast, continued economic growth is helpful to conserve natural resources for future generations. The rise and fall in the natural resource degradation are evident in the wake of international migrants’ stocks to support an inverted U-shaped relationship between them. The Granger causality inferences confirmed the one-way linkages, running from international migrant stocks, economic growth, and population growth to natural resource degradation. It verifies migrants-led, affluence-led, and population-led resource degradation. Ecological footprints Granger causes industrial value-added across countries. The forecasting estimates suggested that economic growth would likely to influenced greater in magnitude to resource degradation by its innovation shocks of 4.791%, followed by international migrant stocks, population growth, ecological footprints, and industrial value added by their innovation shocks of 4.709%, 1.829%, 1.247%, and 0.700%, respectively. The study concludes that international migrant stocks should manage smartly, causing more resource degradation via a channel of increasing biocapacity deficit across countries.