• Energy Research
• 13. Climate action close
• 7. Clean energy close
• Sustainability close

Petroleum oil pollution is a worldwide problem that results from the continuous exploration, production, and consumption of oil and its products. Petroleum hydrocarbons are produced as a result of natural or anthropogenic practices, and their common source is anthropogenic activities, which impose adverse effects on the ecosystem’s nonliving and living components including humans. Phytoremediation of petroleum hydrocarbon-polluted soils is an evolving, low-cost, and effective alternative technology to most traditional remediation methods. The objective of this study is to evaluate the phytoremediation potentiality of Vinca rosea for crude oil-contaminated soil by understanding its properties and involvement in the enhanced degradation of crude oil. The remediation potentiality was determined by evaluating the total petroleum hydrocarbon degradation percentage (TPH%) and changes in the molecular type composition of saturated and aromatic hydrocarbon fractions. TPH% was estimated gravimetrically, and changes in the molecular type composition of saturated and aromatic fractions were measured using gas chromatography and high-performance liquid chromatography, respectively. Sulfur concentration was measured using X-ray fluorescence. Cadmium and lead quantification was measured using Inductively Coupled Plasma-Atomic Emission Spectrometry (ICP-AES). The results revealed that V. rosea enhanced total petroleum hydrocarbon (TPH) degradation and altered the molecular composition of the crude oil. The saturated hydrocarbons increased and the aromatic hydrocarbons decreased. The saturated hydrocarbon fraction in the crude oil showed a wider spectrum of n-paraffin peaks than the oil extracted from unplanted and V. rosea-planted soils. Polyaromatic hydrocarbon degradation was enhanced in the presence of V. rosea, which was reflected in the increase of monoaromatic and diaromatic constituents. This was parallel to the increased sulfur levels in planted soil. The determination of sulfur and heavy metal content in plant organs indicated that V. rosea can extract and accumulate high amounts from polluted soils. The ability of V. rosea to degrade TPH and alter the composition of crude petroleum oil by decreasing the toxicity of polyaromatic hydrocarbons in soil, as well as its capability to absorb and accumulate sulfur and heavy metals, supports the use of plant species for the phytoremediation of crude oil-polluted sites.

Solar photovoltaic (PV) energy systems are one of the most widely deployed renewable technologies in the world. The efficiency of solar panels has been studied during the last few decades, and, to date, it has not been possible to displace the production of energy using crystalline silicon wafer-based technology whose efficiency has reached values around 26.1%. Moreover, using solar tracking PV systems has become a feasible alternative to increase the electric output of PV silicon technologies instead of using the conventional fixed PV installation on a flat or sloping surface. The following study has compared fixed and dual-axis sun-tracking PV panels in order to quantify the enhancement associated with the amount of energy harvested when using dual-axis tracking PV systems in the city of Manta, located in a coastal region of Ecuador. In order to carry out this study, an IoT monitoring system based on Raspberry Pi3 and Arduino platforms was used. Measurements of solar radiation (W/m2), light intensity (Lux), temperature (°C), short-circuit current (A), and open-circuit voltage (V) were taken every minute for both systems. The results prove that the dual-axis tracking PV system produces, on average, 19.62% more energy than the static PV system. These results present an 8.62% energy increase with respect to a previous study carried out in an equatorial region with similar characteristics to those of the city of Manta, where a one-axis tracking PV system was used.

Water canal networks that are widely used for irrigation are an equally good source of micropower generation to be fed to the nearby areas. A practical example of such a system is the micro-hydro generation at Renala Khurd Pakistan integrated with the national grid known as hydro–grid configuration. Apart from the rare Renala Khurd hydro generation example, solar photovoltaic generation integrated with a mainstream network, i.e., solar PV-Grid configuration, is widely used. The integrated operation of combinations of primary distributed generation sources has different operational attributes in terms of economics and reliability that are needed to be quantified before installation. So far, various combinations of primary distributed generation sources have been simulated and their accumulative impact on project economics and reliability have been reported. A detailed economic and reliability assessment of various configurations is needed for sustainable and cost-effective configuration selection. This study proposes a trigeneration combination of solar–hydro–grid with an optimal sizing scheme to reduce the solar system sizing and grid operational cost. A genetic algorithm based optimal sizing formulation is developed using fixed hydro and variable solar and grid systems with a number of pre-defined constraints. The hydro–grid, solar–grid, and grid–hydro–solar configurations are simulated in HOMER Pro software to analyze the economic impact, and to undertake reliability assessments under various configurations of the project. Finally, optimal values of the genetic algorithm are provided to the HOMER Pro software search space for simulating the grid–hydro–solar configuration. It was revealed that the net present cost (NPC) of hydro-to-grid configuration was 23% lower than the grid–hydro–solar configuration, whereas the NPC of grid–hydro–solar without optimal sizing was 40% lower than the solar–grid configuration, and the NPC of grid–solar–hydro with the genetic algorithm was 36% lower than the hydro–grid configuration, 50.90% lower than solar–grid–hydro without the genetic algorithm, and 17.1% lower than the grid–solar configuration, thus proving utilization of trigeneration sources integration to be a feasible solution for areas where canal hydropower is available.

To maintain high production and growing rates of plants, synthetically obtained fertilizers are commonly used. Excessive amounts of fertilizers damage the natural ecosystem and cause various environmental problems. In relation to the environment and its sustainability, another great environmental, economic, and social issue is food loss and waste. This paper aims to evaluate the impact of spent coffee grounds (SCG) on soil properties, rye growth, and their possibilities to be used as the biodegradable and organic material in the production of organic bulk fertilizer. This study demonstrated that spent coffee grounds contain primary nutrients; moreover, SCG could increase the content of soil organic matter. The addition of 4 wt% to 8 wt% SCG increased the number of spore-forming bacteria from <103 colony forming units/g soil (CFU/g soil) to 3 × 104 CFU/g soil, along with nitrogen assimilating bacteria (plain soil resulted in 5.0 × 105 CFU/g, and addition of SCG increased the value to 5.0 × 107 CFU/g). Since spent coffee grounds have a relatively high porosity and absorbance (25.3 ± 3.4 wt% in a water vapor environment and 4.0 ± 0.6 wt% in the environment of saturated sodium nitrate solution), they could be used to reduce the amount of water required for irrigation. To fully exploit their nutritional value for plants, spent coffee grounds were mixed with green algae biomass along with urea, and, during the research, higher value products (organic bulk fertilizer) were obtained.

Connection between electric power networks is essential to cover any deficit in the generation of power from any of them. The exchange powers of the plants during load disturbance should not be violated beyond their specified values. This can be achieved by installing load frequency control (LFC); therefore, this paper proposes a new metaheuristic-based approach using a skill optimization algorithm (SOA) to design a fractional-order proportional integral derivative (FOPID)-LFC approach with multi-interconnected systems. The target is minimizing the integral time absolute error (ITAE) of frequency and exchange power violations. Two power systems are investigated. The first one has two connected plants of photovoltaic (PV) and thermal units. The second system contains four plants, namely, PV, wind turbine, and two thermal plants, with governor dead-band (GDB) and generation rate constraints (GRC). Different load disturbances are analyzed in both considered systems. Extensive comparisons to the use of chef-based optimization algorithm (CBOA), jumping spider optimization algorithm (JSOA), Bonobo optimization (BO), Tasmanian devil optimization (TDO), and Atomic orbital search (AOS) are conducted. Moreover, statistical tests of Friedman ANOVA table, Wilcoxon rank test, Friedman rank test, and Kruskal Wallis test are implemented. Regarding the two interconnected areas, the proposed SOA achieved the minimum fitness value of 1.8779 pu during 10% disturbance on thermal plant. In addition, it outperformed all other approaches in the case of 1% disturbance on the first area as it achieved ITAE of 0.0327 pu. The obtained results proved the competence and reliability of the proposed SOA in designing an efficient FOPID-LFC in multi-interconnected power systems with multiple sources.

This study analyzes farmers’ perception of grazing restriction policies, grassland environment and ecological management following the implementation of environmental protection policies in northern China. Understanding farmers’ attitudes and their causes will hopefully aid in the creation and execution of future policies. One hundred and thirty-five households were surveyed at three occasions over the course of a decade to explore the causes and processes of farmers’ perception. Farmers’ ecological awareness tends to be short term. In areas with a degraded environment, farmers were eager to implement policies to improve the environment and recognized the positive impact of the grazing ban policy (GBP). However, as conditions improved, farmers’ recognition and acceptance of the GBP became negative. Although farmers recognized the benefits of the GBP, they showed little awareness of the long-term process of environmental governance. As can be seen from the farmers’ ecological awareness and their attitudes toward the GBP, they are more inclined to value short-term economic interest than ecological protection. We suggest that good environmental protection policy must take into account the ecological and economic interests of farmers.

The Internet of Things has recently been a popular topic of study for developing smart homes and smart cities. Most IoT applications are very sensitive to delays, and IoT sensors provide a constant stream of data. The cloud-based IoT services that were first employed suffer from increased latency and inefficient resource use. Fog computing is used to address these issues by moving cloud services closer to the edge in a small-scale, dispersed fashion. Fog computing is quickly gaining popularity as an effective paradigm for providing customers with real-time processing, platforms, and software services. Real-time applications may be supported at a reduced operating cost using an integrated fog-cloud environment that minimizes resources and reduces delays. Load balancing is a critical problem in fog computing because it ensures that the dynamic load is distributed evenly across all fog nodes, avoiding the situation where some nodes are overloaded while others are underloaded. Numerous algorithms have been proposed to accomplish this goal. In this paper, a framework was proposed that contains three subsystems named user subsystem, cloud subsystem, and fog subsystem. The goal of the proposed framework is to decrease bandwidth costs while providing load balancing at the same time. To optimize the use of all the resources in the fog sub-system, a Fog-Cluster-Based Load-Balancing approach along with a refresh period was proposed. The simulation results show that “Fog-Cluster-Based Load Balancing” decreases energy consumption, the number of Virtual Machines (VMs) migrations, and the number of shutdown hosts compared with existing algorithms for the proposed framework.

The energy resource is an essential input of economic growth, which has an important impact on the ecological environment and social welfare. From the perspective of social welfare, considering the radial and non-radial characteristics of different input and output indicators, and the inseparability of the energy input and undesirable output, this study employs the non-separable hybrid DEA (Data Envelopment Analysis) model to evaluate the total energy efficiency of Chinese provinces between 2012 and 2016. Furthermore, this study calculates the energy saving and emission reduction potentials of China. The results reveal that the average total factor energy efficiency in China from 2012 to 2016 is 0.694, which means that there are still 30.6% energy efficiency losses. There is great potential for China to save energy, reduce pollutant emissions, and increase the output of social welfare. There are great differences in the total factor energy efficiency among provinces. The average energy saving potential of the whole country is 60.5%. If the energy efficiency of all provinces can reach the frontier, the whole country can save more than half of the energy consumption. The highest national average emission reduction potential is SO2, followed by dust, CO2, and NOX. The implication of the conclusion is that in the development of regional economy, we cannot sacrifice the social welfare and sustainable development and take the growth rate of GDP as the only objective. Different energy saving and emission reduction policies should be put forward according to the characteristics of different provinces.