The depletion of fossil fuel sources and increase in energy demands have increased the need for a sustainable alternative energy source. The ability to produce hydrogen from microalgae is generating a lot of attention in both academia and industry. Due to complex production procedures, the commercial production of microalgal biohydrogen is not yet practical. Developing the most optimum microalgal hydrogen production process is also very laborious and expensive as proven from the experimental measurement. Therefore, this research project intended to analyse the random time series dataset collected during microalgal hydrogen productions while using various low thermally pre-treated palm kernel expeller (PKE) waste via machine learning (ML) approach. The analysis of collected dataset allowed the derivation of an enhanced kinetic model based on the Gompertz model amidst the dark fermentative hydrogen production that integrated thermal pre-treatment duration as a function within the model. The optimum microalgal hydrogen production attained with the enhanced kinetic model was 387.1 mL/g microalgae after 6 days with 1 h thermally pre-treated PKE waste at 90 °C. The enhanced model also had better accuracy (R2 = 0.9556) and net energy ratio (NER) value (0.71) than previous studies. Finally, the NER could be further improved to 0.91 when the microalgal culture was reused, heralding the potential application of ML in optimizing the microalgal hydrogen production process.
Wind is a renewable energy source. Overall, using wind to produce energy has fewer effects on the environment than many other energy sources. Wind and solar energy provide public health and environmental benefits to the social. Wind turbines may also reduce the amount of electricity generation from fossil fuels, which results in lower total air pollution and carbon dioxide emissions. In order to better optimize the effect of social energy economic management and facilitate the multiobjective decision making of coordinated development of energy and socioeconomic environment, a modeling and analysis method of economic benefits of wind power generation based on deep learning is proposed. In this paper, based on the principle of deep learning, the evaluation indicators of wind power economic benefits are excavated, a scientific and reasonable economic benefit evaluation system is constructed, a wind power economic benefit analysis model supported by public management is constructed, and the steps of wind power economic benefit analysis are simplified. It is concluded that the modeling and analysis method of wind power economic benefits based on deep learning has high practicability in the actual application process, which is convenient for the prediction and analysis of energy demand for social and economic development.
Life cycle assessment was used to evaluate the environmental impacts of phytoplanktonic biofuels as possible sustainable alternatives to fossil fuels. Three scenarios were examined for converting planktonic biomass into higher-value commodities and energy streams using the alga Scenedesmus sp. and the cyanobacterium Arthrospira sp. as the species of interest. The first scenario (Sc-1) involved the production of biodiesel and glycerol from the planktonic biomass. In the second scenario (Sc-2), biodiesel and glycerol were generated from the planktonic biomass, and biogas was produced from the residual biomass. The process also involved using a catalyst derived from snail shells for biodiesel production. The third scenario (Sc-3) was similar to Sc-2 but converted CO2 from the biogas upgrading to methanol, which was then used in synthesizing biodiesel. The results indicated that Sc-2 and Sc-3 had a reduced potential (up to 60 % less) for damaging human health compared to Sc-1. Sc-2 and Sc-3 had up to 61 % less environmental impact than Sc-1. Sc-2 and Sc-3 reduced the total cumulative exergy demand by up to 44 % compared to Sc-1. In conclusion, producing chemicals and utilities within the biorefinery could significantly improve environmental sustainability, reduce waste, and diversify revenue streams.
The fate of humankind and all other life forms on earth is threatened by a foe, known as climate change. All parts of the world are affected directly or indirectly by this phenomenon. The rivers are drying up in some places and in other places, it is flooding. The global temperature is rising every year and the heat waves are taking many souls. The cloud of "extinction" is upon the majority of flora and fauna; even humans are prone to various fatal and life-shortening diseases from pollution. This is all caused by us. The so-called "development" by deforestation, releasing toxic chemicals into air and water, burning of fossil fuels in the name of industrialisation, and many others have made an irreversible cut in the heart of the environment. However, it is not too late; all of this could be healed back with the help of technology and our efforts together. As per the international climate reports, the average global temperature has increased by a little more than 1 °C since 1880s. The research is primarily focused on the use of machine learning and its algorithm to train a model that predicts the ice meltdown of a glacier, given the features using the Multivariate Linear Regression. The research strongly encourages the use of features by manipulating them to determine the feature with a major impact on the cause. The burning of coal and fossil fuels is the main source of pollution as per the study. The research focuses on the challenges to gather data that would be faced by the researchers and the requirement of the system for the development of the model. The study is aimed to spread awareness in society about the destruction we have caused and urges everyone to come forward and save the planet.
Greenhouse gas emitted by the transport sector around the world is a serious issue of concern. To minimize such emission the automobile engineers have been working relentlessly. Researchers have been trying hard to switch fossil fuel to alternative fuels and attempting to various driving strategies to make traffic flow smooth and to reduce traffic congestion and emission of greenhouse gas. Automobile emits a massive amount of pollutants such as Carbon Monoxide (CO), hydrocarbons (HC), carbon dioxide (CO2), particulate matter (PM), and oxides of nitrogen (NO x ). Intelligent transport system (ITS) technologies can be implemented to lower pollutant emissions and reduction of fuel consumption. This paper investigates the ITS techniques and technologies for the reduction of fuel consumption and minimization of the exhaust pollutant. It highlights the environmental impact of the ITS application to provide the state-of-art green solution. A case study also advocates that ITS technology reduces fuel consumption and exhaust pollutant in the urban environment.
Theory-based evaluation (TBE) is an effectiveness assessment technique that critically analyses the theory underlying an intervention. Whilst its use has been widely reported in the area of social programmes, it is less applied in the field of energy and climate change policy evaluations. This paper reports a recent study that has evaluated the effectiveness of the national biofuel policy (NBP) for the transport sector in Malaysia by adapting a TBE approach. Three evaluation criteria were derived from the official goals of the NBP, those are (i) improve sustainability and environmental friendliness, (ii) reduce fossil fuel dependency, and (iii) enhance stakeholders' welfare. The policy theory underlying the NBP has been reconstructed through critical examination of the policy and regulatory documents followed by a rigorous appraisal of the causal link within the policy theory through the application of scientific knowledge. This study has identified several weaknesses in the policy framework that may engender the policy to be ineffective. Experiences with the use of a TBE approach for policy evaluations are also shared in this report.
Inventory Routing Problem (IRP) has been continuously developed and improved due to pressure from global warming issue particularly related to greenhouse gases (GHGs) emission. The burning of fossil fuel for transportations such as cars, trucks, ships, trains, and planes primarily emits GHGs. Carbon dioxide (CO2) from burning of fossil fuel to power transportation and industrial process is the largest contributor to global GHGs emission. Therefore, the focus of this study is on solving a multi-period inventory routing problem (MIRP) involving carbon emission consideration based on carbon cap and offset policy. Hybrid genetic algorithm (HGA) based on allocation first and routing second is used to compute a solution for the MIRP in this study. The objective of this study is to solve the proposed MIRP model with HGA then validate the effectiveness of the proposed HGA on data of different sizes. Upon validation, the proposed MIRP model and HGA is applied on real-world data. The HGA is found to be able to solve small size and large size instances effectively by providing near optimal solution in relatively short CPU execution time.
Black Carbon (BC) deteriorates air quality and contributes to climate warming, yet its regionally- and seasonally-varying emission sources are poorly constrained. Here we employ natural abundance radiocarbon (14C) measurements of BC intercepted at a northern Malaysia regional receptor site, Bachok, to quantify the relative biomass vs. fossil source contributions of atmospheric BC, in a first year-round study for SE Asia (December 2015-December 2016). The annual average 14C signature suggests as large contributions from biomass burning as from fossil fuel combustion. This is similar to findings from analogous measurements at S Asian receptors sites (~50% biomass burning), while E Asia sites are dominated by fossil emission (~20% biomass burning). The 14C-based source fingerprinting of BC in the dry spring season in SE Asia signals an even more elevated biomass burning contribution (~70% or even higher), presumably from forest, shrub and agricultural fires. This is consistent with this period showing also elevated ratio of organic carbon to BC (up from ~5 to 30) and estimates of BC emissions from satellite fire data. Hence, the present study emphasizes the importance of mitigating dry season vegetation fires in SE Asia.
Dwindling fossil fuels and improper waste management are major challenges in the context of increasing population and industrialization, calling for new waste-to-energy sources. For instance, refuse-derived fuels can be produced from transformation of municipal solid waste, which is forecasted to reach 2.6 billion metric tonnes in 2030. Gasification is a thermal-induced chemical reaction that produces gaseous fuel such as hydrogen and syngas. Here, we review refuse-derived fuel gasification with focus on practices in various countries, recent progress in gasification, gasification modelling and economic analysis. We found that some countries that replace coal by refuse-derived fuel reduce CO2 emission by 40%, and decrease the amount municipal solid waste being sent to landfill by more than 50%. The production cost of energy via refuse-derived fuel gasification is estimated at 0.05 USD/kWh. Co-gasification by using two feedstocks appears more beneficial over conventional gasification in terms of minimum tar formation and improved process efficiency.
Thermoplastic starch composites have attracted significant attention due to the rise of environmental pollutions induced by the use of synthetic petroleum-based polymer materials. The degradation of traditional plastics requires an unusually long time, which may lead to high cost and secondary pollution. To solve these difficulties, more petroleum-based plastics should be substituted with sustainable bio-based plastics. Renewable and natural materials that are abundant in nature are potential candidates for a wide range of polymers, which can be used to replace their synthetic counterparts. This paper focuses on some aspects of biopolymers and their classes, providing a description of starch as a main component of biopolymers, composites, and potential applications of thermoplastics starch-based in packaging application. Currently, biopolymer composites blended with other components have exhibited several enhanced qualities. The same behavior is also observed when natural fibre is incorporated with biopolymers. However, it should be noted that the degree of compatibility between starch and other biopolymers extensively varies depending on the specific biopolymer. Although their efficacy is yet to reach the level of their fossil fuel counterparts, biopolymers have made a distinguishing mark, which will continue to inspire the creation of novel substances for many years to come.
Demand for diesel continues to increase due to rapid population growth, which could contribute to fossil fuel exhaustion. Biodiesel has been widely developed as a replacement for conventional diesel to resolve the issue. Biodiesel production from waste cooking oil (WCO) was carried out via the transesterification process using two types of bentonite catalysts, which are raw bentonite and NaOH/bentonite. By using the impregnation method, the NaOH/bentonite catalyst was synthesized at 60°C for 12 hours. The transesterification was conducted with 0.5wt% of catalyst, at 15:1 (methanol- to-oil), for 2 hours at different reaction temperatures. The characterization of both raw bentonite and NaOH/bentonite was done using X-ray Diffraction (XRD) and Brunauer, Emmett, Teller (BET) surface characterization. A high yield of FAMEs (72%) was found to be obtained in continuous stirring at 55ºC for 2 hours and 15:1 methanol/oil molar ratio with 0.5wt.% (0.15g) of NaOH/bentonite catalyst.
China is the world's largest fossil fuel consumer and carbon emitter country. In September 2020, China pledged to reduce carbon emissions, and achieve carbon neutrality by 2060. Therefore, this study aimed to contribute to the literature and show the pictorial nexus of bioenergy and fossil fuel consumption, carbon emission, and agricultural bioeconomic growth, a new pathway towards carbon neutrality. For this study, time-series data from 1971 to 2019 were used to analyze the autoregressive distributed lag (ARDL) bound testing and novel dynamic autoregressive distributed lag (DYNARDL) simulation models. Initially, the unit root tests results showed that all variables were stationarity at the level and first difference. The presence of cointegration between selected variables was confirmed by the results from ARDL bound test. In addition, the results of long-run and short-run nexus show an increase in bioenergy consumption that caused an increase in agricultural bioeconomic growth both in the long and short-run nexus. A decrease in fossil fuel consumption was shown to result in increased agricultural bioeconomic growth with respect to both long- and short-term effects. Furthermore, the results of the novel dynamic ARDL simulation model demonstrated that a 10% positive shock from bioenergy consumption caused an increase in agricultural bioeconomic growth, while at the same time, a 10% negative shock in bioenergy consumption led to a decrease. A 10% negative shock from fossil fuels caused an increase in agricultural bioeconomic growth, whereas a 10% positive shock from fossil fuels led to a decrease. Therefore, this study suggests that China needs to switch from fossil fuel and other non-renewable energy consumption to sources of bioenergy and other renewable energy consumption to achieve carbon neutrality by 2060.
The oil palm seed production unit that generates germinated oil palm seeds is the first link in the palm oil supply chain, followed by the nursery to produce seedling, the plantation to produce fresh fruit bunches (FFB), the mill to produce crude palm oil (CPO) and palm kernel, the kernel crushers to produce crude palm kernel oil (CPKO), the refinery to produce refined palm oil (RPO) and finally the palm biodiesel plant to produce palm biodiesel. This assessment aims to investigate the life cycle assessment (LCA) of germinated oil palm seeds and the use of LCA to identify the stage/s in the production of germinated oil palm seeds that could contribute to the environmental load. The method for the life cycle impact assessment (LCIA) is modelled using SimaPro version 7, (System for Integrated environMental Assessment of PROducts), an internationally established tool used by LCA practitioners. This software contains European and US databases on a number of materials in addition to a variety of European- and US-developed impact assessment methodologies. LCA was successfully conducted for five seed production units and it was found that the environmental impact for the production of germinated oil palm was not significant. The characterised results of the LCIA for the production of 1000 germinated oil palm seeds showed that fossil fuel was the major impact category followed by respiratory inorganics and climate change.
The palm oil industry has played an important role in the economic development of Malaysia and has enhanced the economic welfare of its people. To determine the environmental impact of the oil palm seedling at the nursery stage, information on inputs and outputs need to be assessed. The oil palm nursery is the first link in the palm oil supply chain. A gate-to-gate study was carried out whereby the system boundary was set to only include the process of the oil palm seedling. The starting point was a germinated seed in a small polyethylene bag (6 in × 9 in) in which it remained until the seedling was approximately 3 to 4 months old. The seedling was then transferred into a larger polyethylene bag (12 in × 15 in), where it remained until it was 10-12 months old, when it was planted in the field (plantation). The functional unit for this life cycle inventory (LCI) is based on the production of one seedling. Generally, within the system boundary, the production of an oil palm seedling has only two major environmental impact points, the polybags used to grow the seedling and the fungicide (dithiocarbamate) used to control pathogenic fungi, as both the polybags and the dithiocarbamate are derived from fossil fuel.
Anthropogenic airborne depositions of 210Po,
210Pb and 210Po/210Pb in the mosses and surface soils
collected at the vicinity of a coal-fired power plant were studied. The purpose of the study was to
determine activity concentrations of 210Po,
210Pb and 210Po/210Pb for assessing their variation
accumulation in the mosses and surface soils collected at the vicinity of a coal-fired power plant.
Other purposes were to determine their concentration factor (CF) in relation to track the potential
source of those radionuclides and to identify most suitable moss species as a biological indicator
for atmospheric deposition contaminants. In this study, different species of moss Leucobryum
aduncum, Campylopus serratus, Syrrhopodon ciliates and Vesicularia montagnei were collected in
May 2011 at the area around 15 km radius from Tanjung Bin coal-fired power plant located in
Pontian, Johor. The activity concentrations of 210Po,
210Pb and 210Po/210Pb in mosses were in the
range of 76.81 ± 4.94 – 251.33 ± 16.33 Bq/kg dry wt., 54.37 ± 3.38 – 164.63 ± 11.64 Bq/kg dry wt.
and 1.10 – 2.00, respectively. Meanwhile the ranges for those radionuclides in the surface soil
were 33.53 ± 2.10 – 179.67 ± 12.15 Bq/kg dry wt., 20.55 ± 1.33 – 106.62 ± 6.64 Bq/kg dry wt. and
1.61 – 2.44, respectively. Corresponding high ability of Leucobryum aduncum to accumulate more
210Po and 210Pb, wide geographical distribution, most abundant and high CF, therefore, the
findings can be concluded this species was the most suitable as a biological indicator for
atmospheric deposition contaminants such as 210Po and 210Pb. Furthermore, it is clear the
accumulation of 210Po and 210Pb in mosses might be supplied from various sources of atmospheric
deposition such as coal-fired power plant operation, industrial, agriculture and fertilizer activities,
burned fuel fossil and forest; and other potential sources. Meanwhile, the
Thermoelectric nanostructures hold great promise for capturing and directly converting into electricity some vast amount of low-grade waste heats now being lost to the environment (e.g. from nuclear power plant, fossil fuel burning, automotives and household appliances). In this study, large-area vertically-aligned silicon nanowire (SiNW) arrays were synthesized in an aqueous solution containing AgN•i and HF on p-type Si (100) substrate by self-selective electroless etching process. The etching conditions were systematically varied in order to achieve different stages of nanowire formation. Diameters of the SiNWs obtained varied from approximately 50 to 200 nm and their lengths ranged from several to a few tens of um. Te/Bi2Tex.Si thermoelectric core-shell nanostructures were subsequently obtained via galvanic displacement of SiNWs in acidic HF electrolytes containing HTe02+ and 139' /HTe02+ ions. The reactions were basically a nano-electrochemical process due to the difference in redox potentials between the materials. The surface-modified SiNWs of core-shell structures had roughened surface morphologies and, therefore, higher surface-to-bulk ratios compared to unmodified SiNWs. They have potential applications in sensors, photovoltaic and thermoelectric nanodevices. Growth study on the SiNWs and core-shell nanostructures produced is presented using various microscopy, diffraction and probe-based techniques for microstructural, morphological and chemical characterizations.
There is a growing interest in developing bio-based biodegradable plastics to reduce the dependence on depleting fossil fuels and provide a sustainable alternative. Bio-based plastics can usually be produced from lipids, proteins or carbohydrates, which are major components of microalgae. Despite its potential for algal plastics, little information is available on strain selection, culture optimization and bioplastics fabrication mechanism. In this review, we summarized the recent developments in understanding the utilization of seaweed polysaccharides, such as alginate and carrageenan for bio-based plastics. In addition, a conceptual biorefinery framework for algal plastics through promising components (e.g., lipids, carbohydrates and proteins) from microalgae is comprehensively presented. Moreover, the reasons for variations in bioplastics performance and underlying mechanism of various algal biocomposites have been critically discussed. We believe this review can provide valuable information to accelerate the development of innovative green technologies for improving the commercial viability of algal plastics.
The rapid depletion of fossil fuels and ever-increasing environmental pollution have forced humankind to look for a renewable energy source. Microalgae, a renewable biomass source, has been proposed as a promising feedstock to generate biofuels due to their fast growth rate with high lipid content. However, literatures have indicated that sustainable production of microalgae biofuels are only viable with a highly optimized production system. In the present study, a cradle-to-gate approach was used to provide expedient insights on the effect of different cultivation systems and biomass productivity toward life cycle energy (LCEA), carbon balance (LCCO2) and economic (LCC) of microalgae biodiesel production pathways. In addition, a co-production of bioethanol from microalgae residue was proposed in order to improve the economic sustainability of the overall system. The results attained in the present work indicated that traditional microalgae biofuels processing pathways resulted to several shortcomings, such as dehydration and lipid extraction of microalgae biomass required high energy input and contributed nearly 21 to 30% and 39 to 57% of the total energy requirement, respectively. Besides, the microalgae biofuels production system also required a high capital investment, which accounted for 47 to 86% of total production costs that subsequently resulted to poor techno-economic performances. Moreover, current analysis of environmental aspects of microalgae biorefinery had revealed negative CO2 balance in producing microalgae biofuels.
This study was performed to observe the variation in the distribution of 210Po,210Pb and 210Po/210Pb activity ratio throughtheir vertical profile of the sediment cores takenat surrounding Sungai Linggi estuary. Five sediment cores were takenin February 2011 and were cutto an intervalof 2 cm layer. Activity concentrations of 210Po and 210Pb were determined using alpha radiochemical analysis and gamma direct measurement, respectively. Generally, the measured activity of 210Po, 210Pb and 210Po/210Pbwere in the ranges of 22.73 –139.06 Bqkg-1dw., 37.88 –176.24 Bqkg-1dw.and 0.23 –1.34, respectively. The variation in the distribution profile for the radionuclides are believed to be influencedby human activities such as agriculture, fertilizer, vehicles, burned fuel fossil and forest, industrialand others via river input from land-base.Other factor is due to organic mattercontent played importantrole as the geochemical carrier to transportthose radionuclides at study area. It was provedthat hasa strong correlation between the radionuclide distribution and the sedimentcomposition of organic matter.Furthermore, in those rangesreflectedthat 210Pb activities were higher than210Po with an activity ratio average of 0.79. This is probably due to dramatic increase of excess 210Pb supplied from atmospheric deposition, in situ decay of 226Ra and as a result of diagenetic remolibilazationof 210Pbin deeper layesof the sediment column. Thus, thosefactors are majorcontributions on thevariation of 210Po and 210Pb in the sediment core at surrounding Sungai Linggi estuary.
This study is a scholarly effort to broaden the existing literature on the impact of transportation services, urbanization, and financial development on ecological footprints in Pakistan. Data used in this study covers the period of 39 years from 1980 to 2018. This study adopted the QARDL model to tackle the non-linear association of variables and test their long-run stability across the different quantiles. The findings of this study indicated a significant negative association of transportation services and financial development with ecological footprints in Pakistan at almost all quantiles whereas, the urban population was found to be positively associated with the ecological footprint in Pakistan. Results also justify the existence of the EKC hypothesis in the scenario of Pakistan. Policymakers are advised to frame strategies for investors to invest more in eco-friendly projects to curtail the ecological footprints in Pakistan. Minimizing the dependency of the transportation sector on fossil fuel, and increased use of energy-efficient appliances in the urban population would be beneficial to control the negative influence on ecological footprints in Pakistan.