• Energy Research
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Abstract This paper proposes optimum green energy systems for electricity generation of island resorts in Malaysia. A combination of solar energy and wind energy as intermittent renewable energy sources with a fuel cell (FC) system and a battery storage energy system as a backup to the green energy system is introduced for this study. This system is eco-friendly, economical over the long-term, highly sustainable and reliable. In addition, a diesel-based energy system as a non-green system is compared with the proposed green system. National Renewable Energy Laboratory's (NREL) HOMER software is used to determine and compare the optimal configuration green energy system with the diesel-based energy system in terms of net present cost, sensitivity analysis and pollutant gas emission. The feasibility and assessment of the proposed system is evaluated by utilizing the load profile and considering the geographical condition of a village (Juara village in Tioman Island) with approximately 30 chalets located in southern Malaysia. HOMER uses two types of load profiles for each given year. The first load profile is used during the presence of tourists, and the second profile is used when the locals are the main inhabitants of the island. The optimization management for the green energy system is performed through unit sizing to find the optimum power management analysis and to perform cost analysis of the system. The potential of renewable energy as well as a diesel-based energy system to meet the power demand for such a stand-alone system is considered. Technically feasible and economically viable green energy systems as prospective plans are explored in this study.

Abstract In response to changing international recommendations and national requirements, a number of assessment approaches, and associated tools and models, have been developed over the last circa 20 years to assess radiological risk to wildlife. In this paper, we summarise international intercomparison exercises and scenario applications of available radiological assessment models for wildlife to aid future model users and those such as regulators who interpret assessments. Through our studies, we have assessed the fitness for purpose of various models and tools, identified the major sources of uncertainty and made recommendations on how the models and tools can best be applied to suit the purposes of an assessment. We conclude that the commonly used tiered or graded assessment tools are generally fit for purpose for conducting screening-level assessments of radiological impacts to wildlife. Radiological protection of the environment (or wildlife) is still a relatively new development within the overall system of radiation protection and environmental assessment approaches are continuing to develop. Given that some new/developing approaches differ considerably from the more established models/tools and there is an increasing international interest in developing approaches that support the effective regulation of multiple stressors (including radiation), we recommend the continuation of coordinated international programmes for model development, intercomparison and scenario testing.

The current and projected warming of the earth is unequivocal with humans playing a strong role as both perpetrators and victims. The warming on the African continent is projected to be greater than the global average with an increased average temperature of 3–6°C by the end of the century under a high Representative Concentration Pathway. In Africa, the Sub-Saharan region is identified as the most vulnerable to the changing climate due to its very low capacity to adapt to or mitigate climate change. While it is common to identify studies conducted to assess how climate change independently impacts water, land, or food resources, very limited studies have sought to address the interlinkages, synergies, and trade-offs existing between climate change, water, land, and food (WLF) resources as a system in Sub-Saharan Africa (SSA). The climate change and WLF security nexus, therefore, seeks to address this shortfall in literature and subsequently serve as a relevant source of information for decision-making and policy implementation concerning climate change mitigation and adaptation. In this study, 41 relevant studies were selected from Web of Science, Google Scholar, ResearchGate, and institutional websites. We provide information on the independent relationships between climate change and WLF resources, and further discuss the existing inter-linkages between climate change and the WLF security in SSA using the nexus approach, with recommendations on how decision making and policy implementations should be done using the climate change and WLF security nexus approach.

Food insecurity is a growing concern due to man-made conflicts, climate change, and economic downturns. Forecasting the state of food insecurity is essential to be able to trigger early actions, for example, by humanitarian actors. To measure the actual state of food insecurity, expert and consensus-based approaches and surveys are currently used. Both require substantial manpower, time, and budget. This paper introduces an extreme gradient-boosting machine learning model to forecast monthly transitions in the state of food security in Ethiopia, at a spatial granularity of livelihood zones, and for lead times of one to 12 months, using open-source data. The transition in the state of food security, hereafter referred to as predictand, is represented by the Integrated Food Security Phase Classification Data. From 19 categories of datasets, 130 variables were derived and used as predictors of the transition in the state of food security. The predictors represent changes in climate and land, market, conflict, infrastructure, demographics and livelihood zone characteristics. The most relevant predictors are found to be food security history and surface soil moisture. Overall, the model performs best for forecasting Deteriorations and Improvements in the state of food security compared to the baselines. The proposed method performs (F1 macro score) at least twice as well as the best baseline (a dummy classifier) for a Deterioration. The model performs better when forecasting long-term (7 months; F1 macro average = 0.61) compared to short-term (3 months; F1 macro average = 0.51). Combining machine learning, Integrated Phase Classification (IPC) ratings from monitoring systems, and open data can add value to existing consensus-based forecasting approaches as this combination provides longer lead times and more regular updates. Our approach can also be transferred to other countries as most of the data on the predictors are openly available from global data repositories.

A number of studies have suggested that mangrove forests and their faunal components may be pre-adapted to the impact of organic waste discharge, making them possible natural wastewater treatment wetlands. However, the results from recent research are contradictory. Some studies have shown that negative effects, sometimes subtle and difficult to observe, can be detected on specific biotic components of forests subjected to organic pollution. Therefore, the aim of the present study was to investigate possible alterations in the ecosystem engineering activities of a fiddler crab community dominating the landward belts of Kenyan mangrove forests. The total processed sediment produced by burrowing and foraging activities in a population from a peri-urban mangrove area receiving untreated domestic sewage was compared with that from a forest not affected by urban wastewater. The results showed how the peri-urban site hosted a higher biomass of crabs, which produced a significantly lower amount of processed sediment compared with the pristine site, resulting in a lower total top sediment mixing activity of the crabs. Thus, the present study showed a link between sewage exposure and top sediment reworking by crabs, which is potentially beneficial for mangrove growth and ecosystem functioning. This represents a possible example of cryptic ecological degradation in mangal systems.

Underground Pumped Storage Hydropower (UPSH) is an alternative to manage the electricity production in flat regions. UPSH plants consist of two reservoirs of which at least one is underground. For this last reservoir, abandoned mines could be considered. UPSH related activities may induce hydrochemical variations, such as the increase of the oxygen (O2) partial pressure (pO2), which may entail negative consequences in terms of environment and efficiency, especially in coal mined areas where the presence of sulfide minerals is common. This work assesses the main expected environmental impacts that UPSH using abandoned coal mines may induce. © 2017 The Authors. Published by Elsevier Ltd. E. Pujades and A. Jurado gratefully acknowledge the financial support from the University of Liège and the EU through the Marie Curie BeIPD-COFUND postdoctoral fellowship programme (2014/16 and 2015/17 fellows from “FP7-MSCA-COFUND, 600405”). This research has been supported by the Public Service of Wallonia – Department of Energy and Sustainable Building. Peer reviewed