• Energy Research
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Abstract Recharging infrastructure (RI) deployment plays a vital role in improving the public recharging availability for transport electrification. Decarbonizing transportation using low-emission electricity requires massive RI network. Even though the consumers are reluctant to purchase electric vehicles (EVs) until RIs are sufficiently placed, the investors are not willing to invest in RIs due to recharging demand uncertainties. Therefore, a scientific planning framework is needed to ensure the sustainable deployment of EV-RIs in complex networks. In this study, a lifecycle thinking-based multi-period infrastructure-planning framework is proposed to develop sustainable public EV-RIs in an urban context. This framework consists of a temporal model to find the dynamic EV-RI demands, a stochastic model to obtain travel distances, and a multi-objective optimization model to select the best desirable capacities and locations for potential EV-RIs. A case study of a typical medium-scale municipality in Canada was assessed using the proposed framework and validated using conventional infrastructure planning scenarios. The geo-processing data, regional travel behaviors, and recharging characteristics were used as model inputs. The results of the case study showed that the proposed framework can be used to estimate multi-period public recharging demands, minimize lifecycle costs, maximize service coverage and infrastructure utilization, and ensure reasonable paybacks compared to conventional planning approaches. Moreover, this framework can be used to compare different investment assistances, which are required in the early stages of the RI deployment process to encourage investors. Furthermore, government and private institutions can use this framework to identify recharging demands, permitting, and developing RIs in the long-run.

Abstract The residential sector accounts for most of energy-consumption in developing countries in the form of traditional energy. The use of commercial energy is nominal and confined mostly to urban areas where fuelwood is already monetized. A model, based on an end-use/process analysis approach, is developed on a spreadsheet, which is capable of simulating scenarios to address issues of increasing traditional energy-demand caused by population growth, sustainable supply capacity of the existing energy resources, potential for development of new and renewable energy resources, technology. This paper is divided into two parts: general energy issues and the modelling approach, and the application of this approach to Nepal in the context of fuelwood-supply sustainability.

Abstract Communication between vehicles and road infrastructure can enable more efficient use of the road network and hence reduce congestion in urban areas. This improvement can be enhanced by distributed control due to its lighter computational load and higher reliability. Despite favourable impacts on traffic, little is known about the effects of such systems on near-road air quality. In this study, an End-To-End (E2E) dynamic distributed routing algorithm in Connected and Automated Vehicles (CAVs) was applied in downtown Toronto, to identify whether benefits to network throughput were associated with lower near-road NO2 concentrations. We observe significant reductions in the emissions of Greenhouse Gases (GHGs) with increased penetration of CAVs. Nonetheless, at times, the emissions of nitrogen oxides (NOx) increased with higher CAVs. Besides, a higher frequency and severity of NO2 hot-spots were observed under a 100% CAV scenario. Impacts of the proposed system on electric energy consumption in a full electric vehicle network were also investigated, indicating that the addition of CAVs that are electric did not contribute to high energy savings. We propose that such new transformative technologies in transportation should be designed with air pollution and public health goals.

Abstract Conversion of carbon contained in the solid residues (tars + biochar) derived from urban biomass gasification named herein TC would allow enhancing the yield of carbon species (CO/CO2) in synthetic gas. For this purpose, three low cost materials have been tested as possible catalysts: iron species (reduced Fe), bone meal (BM), and ashes (ash) recovered from biochar complete oxidation. The parametric study used the following as variables: air GHSV, onset of reaction temperature, reaction time to optimize CO/CO2 molar ratio and tar content in the produced gas. Results showed an autocatalytic effect of biochar leading to the catalytic conversion of approximately 78% of tars by the native metals contained in TC. The catalytic effect was further enhanced by adding Fe, BM, and extra ash. Addition of Fe catalyst resulted in significant heat generation (temperature increase of ca. 500 °C) and a twofold decrease in reaction time to consume all the carbon. Use of ash and BM as catalysts exhibit heat generation comparable to Fe, along with an improved reaction time, complete tars conversion and a CO/CO2 molar ratio to above 1.3 in the produced gas.

The transportation impact on pollution and global climate change, has forced the automotive sector to search for more ecological solutions. Owing to the different properties of Fuel Cell (FC), real potential for reducing vehicles’ emissions has been witnessed. The optimization of FC integration within Electric Vehicles (EVs) is one of the original solutions. This paper presents an innovating solution of multi-stack Fuel Cell Electrical Vehicle (FCEV) in terms of efficiency, durability and ecological impact on environment. The main objective is to illustrate the interest of using the multi-stack FC system on the global autonomy, cycling, and efficiency enhancement, besides optimizing its operation performance.

This study aimed to realize Sustainable Development Goals (SDGs), i.e., no poverty, zero hunger, and sustainable cities and communities through the implementation of an intelligent cattle-monitoring system to enhance dairy production. Livestock industries in developing countries lack the technology that can directly impact meat and dairy products, where human resources are a major factor. This study proposed a novel, cost-effective, smart dairy-monitoring system by implementing intelligent wireless sensor nodes, the Internet of Things (IoT), and a Node-Micro controller Unit (Node-MCU). The proposed system comprises three modules, including an intelligent environmental parameter regularization system, a cow collar (equipped with a temperature sensor, a GPS module to locate the animal, and a stethoscope to update the heart rate), and an automatic water-filling unit for drinking water. Furthermore, a novel IoT-based front end has been developed to take data from prescribed modules and maintain a separate database for further analysis. The presented Wireless Sensor Nodes (WSNs) can intelligently determine the case of any instability in environmental parameters. Moreover, the cow collar is designed to obtain precise values of the temperature, heart rate, and accurate location of the animal. Additionally, auto-notification to the concerned party is a valuable addition developed in the cow collar design. It employed a plug-and-play design to provide ease in implementation. Moreover, automation reduces human intervention, hence labor costs are decreased when a farm has hundreds of animals. The proposed system also increases the production of dairy and meat products by improving animal health via the regularization of the environment and automated food and watering. The current study represents a comprehensive comparative analysis of the proposed implementation with the existing systems that validate the novelty of this work. This implementation can be further stretched for other applications, i.e., smart monitoring of zoo animals and poultry.

AbstractAutotrophs play an essential role in the cycling of carbon and nutrients, yet disease‐ecosystem relationships are often overlooked in these dynamics. Importantly, the availability of elemental nutrients like nitrogen and phosphorus impacts infectious disease in autotrophs, and disease can induce reciprocal effects on ecosystem nutrient dynamics. Relationships linking infectious disease with ecosystem nutrient dynamics are bidirectional, though the interdependence of these processes has received little attention. We introduce disease‐mediated nutrient dynamics (DND) as a framework to describe the multiple, concurrent pathways linking elemental cycles with infectious disease. We illustrate the impact of disease–ecosystem feedback loops on both disease and ecosystem nutrient dynamics using a simple mathematical model, combining approaches from classical ecological (logistic and Droop growth) and epidemiological (susceptible and infected compartments) theory. Our model incorporates the effects of nutrient availability on the growth rates of susceptible and infected autotroph hosts and tracks the return of nutrients to the environment following host death. While focused on autotroph hosts here, the DND framework is generalizable to higher trophic levels. Our results illustrate the surprisingly complex dynamics of host populations, infection patterns, and ecosystem nutrient cycling that can arise from even a relatively simple feedback between disease and nutrients. Feedback loops in disease‐mediated nutrient dynamics arise via effects of infection and nutrient supply on host stoichiometry and population size. Our model illustrates how host growth rate, defense, and tissue chemistry can impact the dynamics of disease–ecosystem relationships. We use the model to motivate a review of empirical examples from autotroph–pathogen systems in aquatic and terrestrial environments, demonstrating the key role of nutrient–disease and disease–nutrient relationships in real systems. By assessing existing evidence and uncovering data gaps and apparent mismatches between model predictions and the dynamics of empirical systems, we highlight priorities for future research intended to narrow the persistent disciplinary gap between disease and ecosystem ecology. Future empirical and theoretical work explicitly examining the dynamic linkages between disease and ecosystem ecology will inform fundamental understanding for each discipline and will better position the field of ecology to predict the dynamics of disease and elemental cycles in the context of global change.

Abstract The increased environmental awareness coupled with the recent changes in the oil prices triggered the necessity of focusing on effective management of energy systems. Global climate change has caused many people to consider ways of reducing greenhouse gases Renewable energy has become an essential feature in curtailing emission of Green House Gases, while meeting the demand for energy. This paper presents an innovation system framework for development and diffusion of renewable energy technologies. The framework is used to identify opportunities for small and medium enterprises in the renewable energy sector. A case study on a successful development, installation and implementation of solar thermal systems households in Calgary, Alberta, by an entrepreneurial firm, is also presented.