The current study examines sustainable electricity consumption for economic growth in a small open and tourist economy. The energy-tourism nexus is evaluated for the relationship between sustainable electricity consumption and the international tourist arrival for the South African economy. The present study leverages on annual frequency data for South Africa from 1995 to 2019 for empirical analysis using the ARDL technique. Accordingly, empirical findings indicate a significant direct connection between the sustainable electricity consumption and the international tourism arrival; the study affirms that tourism-induced energy hypothesis is valid in South Africa. However, from a policy standpoint, alternative energy efficiency mechanisms such as renewable energy systems and emancipation of current energy management capabilities are recommended in South Africa. This is necessary for sustainable eco-friendly tourism that engenders clean energy consumption for the study area. More insights into policy caveats are presented in the concluding section.
Electrodialysis desalination uses ion exchange membranes, membrane spacers, and conductors to remove salt from water. Membrane spacers, made of polymeric strands, reduce concentration polarization. These spacers have properties such as porosity and filament shape that affect their performance. One important property is the spacer-bulk attack angle. This study systematically reviews the characteristics of a 45° attack angle of spacers and its effects on concentration polarization and fluid dynamics. Membrane spacers in a channel create distinct flow fields and concentration profiles. When set at a 45° attack angle, spacers provide greater turbulence and mass-heat transfer than traditional spacers. This is because both the transverse and longitudinal filaments become diagonal in relation to the bulk flow direction. A lower attack angle (<45°) results in a lower pressure drop coupled with a decline in wakes and stream disruption because when the filaments are more parallel to the primary fluid direction, the poorer their affect. This research concludes that membrane spacers with a 45° spacer-bulk attack angle function optimally compared to other angles.
Several parts of the world have been facing the problem of nitrite and nitrate contamination in ground and surface water. The acute toxicity of nitrite has been shown to be 10-fold higher than that of nitrate. In the present study, aminated silica carbon nanotube (ASCNT) was synthesised and tested for nitrite removal. The synergistic effects rendered by both amine and silica in ASCNT have significantly improved the nitrite removal efficiency. The IEP increased from 2.91 for pristine carbon nanotube (CNT) to 8.15 for ASCNT, and the surface area also increased from 178.86 to 548.21 m2 g-1. These properties have promoted ASCNT a novel adsorbent to remove nitrite. At optimum conditions of 700 ppm of nitrite concentration at pH 7 and 5 h of contact with 15 mg of adsorbent, the ASCNT achieved the maximal loading capacity of 396 mg/g (85% nitrite removal). The removal data of nitrite onto ASCNT fitted the Langmuir isotherm model better than the Freundlich isotherm model with the highest regression value of 0.98415, and also, the nonlinear analysis of kinetics data showed that the removal of nitrite followed pseudo-second-order kinetic. The positive values of both ΔS° and ΔH° suggested an endothermic reaction and an increase in randomness at the solid-liquid interface. The negative ΔG° values indicated a spontaneous adsorption process. The ASCNT was characterised using FESEM-EDX and FTIR, and the results obtained confirmed the removal of nitrite. Based on the findings, ASCNT can be considered as a novel and promising candidate for the removal of nitrite ions from wastewater.
The rapid increase in urbanization has given rise to the need of proper waste management. Within municipal waste, the plastic waste is a growing concern which is causing severe harm to our ecosystem. If ignored, this problem will have harmful effects on both human and wildlife. Therefore, this study aims to find out the factors that influence the recycling behavior patterns of consumers regarding plastic waste. The variables from the theory of planned behavior were adopted to study the behavior of consumers toward recycling plastic waste. The data was collected from 243 residents of Karachi-metropolitan city of Pakistan. The partial least square-structural equation modelling was applied to analyze the data. The findings of the current study reveal that different consumers' attributes and attitudes trigger different types of recycling behavior when it comes to waste disposal. Pressure from family and friends and perceived behavioral control trigger the behavior of reselling the waste plastic products while consumer's awareness of consequences and personal attitude toward proper waste disposal leads to reuse or donating that product to someone who can use that plastic product. The understanding of these consumer attributes may help to shape the behavioral outcomes in order to manage waste disposal. This study will be beneficial for business managers looking to improve reverse logistics as well as government/municipal policy makers and academics/researchers who are interested in a solution-oriented study.
This paper presents a fresh understanding of the vigorous connection between inward FDI, renewable energy consumption, economic growth and carbon emission in the Chinese economy employing novel Morlet wavelet analysis. Wavelet correlation, continuous wavelet transform and partial and the multiple wavelet coherence analyses are applied on variables under study for data acquired during the period 1979 to 2017. The outcome of these analyses reveals that the connections among the variables progress over frequency and time. From the frequency domain point of view, the current study discovers noteworthy wavelet coherence and robust lead and lag linkages, although time domain reveals inconsistent associations among the considered variables. The wavelet analysis according to economic point of view supports that inward foreign direct investment (FDI) and renewable energy consumption help to enhance economic condition in Chinese economy. The results also suggested that inward FDI enhances the environmental degradation in medium and long run in China. The results emphasize the significance of having organized strategies by the policymakers to cope with huge environmental degradation occurred for a couple of decades in China.
The BRICS nations-Brazil, Russia, India, China, and South Africa-have grown significantly in importance over the past few decades, playing a vital role in the development and growth of the global economy. This expansion has not been without cost, either, since these countries' concern over environmental deterioration has risen sharply. Both researchers and decision-makers have focused a lot of attention on the connection between economic growth and ecological sustainability. By using nonlinear autoregressive distributed lag (NARDL) approach, the complex relationships were analyzed between important economic indicators-such as gross domestic product (GDP), ecological innovations (EI), energy consumption (ENC), institutional performance (IP), and trade openness (TOP)-and their effect on carbon emissions and nitrous oxide emissions in the BRICS countries from 1990 to 2021, this study seeks to contribute to this important dialog. Principal component analysis is formed for technological innovations and institutional performance using six (ICT service exports as a percentage of service exports, computer communications as a percentage of commercial service exports, fixed telephone subscriptions per 100 people, internet users as a percentage of the population, number of patent applications, and R&D expenditures as a percentage of GDP) and twelve (government stability, investment profile, socioeconomic conditions, internal conflict, external conflict, military in politics, control of corruption, religious tensions, ethnic tensions, law and order, bureaucracy quality, and democratic accountability) distinct indicators, respectively. The results of nonlinear autoregressive distributed lag estimation show that increase in economic growth would increase carbon dioxide and nitrous oxide emissions. The positive and negative shocks in trade openness have positive and significant impact on carbon dioxide and nitrous oxide emissions in BRICS countries. Furthermore, the positive shock energy consumptions have positive and significant effect on Brazil and India when carbon dioxide and nitrous oxide emissions are used. However, EKC exists in BRICS countries when carbon dioxide and nitrous oxide emissions are used. According to long-term estimation, energy consumption and technological innovations in the BRICS countries show a strong and adverse link with nitrous oxide and a favorable relationship with carbon dioxide emissions. In the long run, environmental indicators are seen to have a major and unfavorable impact in BRICS nations. Finally, it is proposed that BRICS nations can assure environmental sustainability if they support creative activities, enhance their institutions, and support free trade policies.
This study provides insight into the decolorization strategy for crude glycerol obtained from biodiesel production using waste cooking oil as raw material. A sequential procedure that includes physico-chemical treatment and adsorption using activated carbon from oil palm biomass was investigated. The results evidenced decolorization and enrichment of glycerol go hand in hand during the treatment, achieving >89% color removal and > 98% increase in glycerol content, turning the glycerol into a clear (colorless) solution. This is attributed to the complete removal of methanol, free fatty acids, and triglycerides, as well as 85% removal of water, and 93% removal of potassium. Properties of the resultant glycerol met the quality standard of BS 2621:1979. The economic aspects of the proposed methods are examined to fully construct a predesign budgetary estimation according to chemical engineering principles. The starting capital is proportionate to the number of physical assets to acquire where both entail a considerable cost at USD 13,200. Having the benefit of sizeable scale production, it reasonably reduces the operating cost per unit product. As productivity sets at 33 m3 per annum, the annual operating costs amount to USD 79,902 in glycerol decolorization. This is translatable to USD 5.38 per liter glycerol, which is ~69% lower compared to using commercial activated carbon.
Obesity is a worldwide concern as it leads to adverse effects on human health. This study uses a panel of 165 countries and annual data from 2000 to 2014 to examine the obesity Kuznets curve (OKC) hypothesis. By using tests and estimators that are robust to cross-section dependence (CSD), our results support the OKC hypothesis. This indicates that obesity increases at the initial stage of economic development and eventually would decrease once the threshold is reached. In addition, we find that the role of global warming on obesity is not significant. Food production is found to be a contributing factor to obesity. Besides, one-way and two-way causalities are identified between the variables. This study provides important insights particularly about the relationship between (i) economic growth and obesity and (ii) environmental degradation and obesity. Implication of the results and policy recommendations are also provided to policymakers and health personnel in finding solutions to the obesity epidemic around the world.
The textile industry consumes a large volume of organic dyes and water. These organic dyes, which remained in the effluents, are usually persistent and difficult to degrade by conventional wastewater treatment techniques. If the wastewater is not treated properly and is discharged into water system, it will cause environmental pollution and risk to living organisms. To mitigate these impacts, the photo-driven catalysis process using semiconductor materials emerges as a promising approach. The semiconductor photocatalysts are able to remove the organic effluent through their mineralization and decolorization abilities. Besides the commonly used titanium dioxide (TiO2), manganese dioxide (MnO2) is a potential photocatalyst for wastewater treatment. MnO2 has a narrow bandgap energy of 1~2 eV. Thus, it possesses high possibility to be driven by visible light and infrared light for dye degradation. This paper reviews the MnO2-based photocatalysts in various aspects, including its fundamental and photocatalytic mechanisms, recent progress in the synthesis of MnO2 nanostructures in particle forms and on supporting systems, and regeneration of photocatalysts for repeated use. In addition, the effect of various factors that could affect the photocatalytic performance of MnO2 nanostructures are discussed, followed by the future prospects of the development of this semiconductor photocatalysts towards commercialization.
The main objective of this paper is to estimate the interfuel substitution elasticities between hydropower and the fossil fuels of coal and natural gas used in the generation of electricity for Malaysia. Due to the violation of the assumption behind the ordinary least squares (OLS) method on account of the correlated error terms in the system of equations, the econometrics techniques of seemingly unrelated regression (SUR) was adopted to obtain the parameter estimates using dataset that covers the period 1988 to 2016. The main finding is that there exists substantial substitution possibility between hydropower and fossil fuels in the generation of electricity for Malaysia. CO2 emissions mitigation scenarios were also conducted to explore the possible effects of substituting fossil fuels for hydropower to generate electricity. The results show that switching from high carbon-emitting fuels to renewable energy such as hydropower will substantially reduce CO2 emission and assist the country towards achieving the carbon emissions reduction targets. Policy recommendations are offered in the body of the manuscript.
Roads play a pivotal role in the overall economic growth of any country. Developed countries allocated sufficient budget to make new roads and to maintain the existing roads. They also have a proper pavement management system (PMS) in practice to manage roads, whereas developing countries suffer from budgetary issues to make new roads and maintain the existing road network. Therefore, this paper explores the awareness of PMS via direct and indirect methods in Pakistan with a proposed framework of the low-cost model and pavement maintenance indicators for developing countries. This paper also performs a scientometric assessment of PMS. A detailed literature review has been carried out for this study, followed by a quantitative study from experienced professionals. The scientometric data is collected from the Scopus database from 1975 to 2020, whereas the data for PMS awareness assessment has been collected using questionnaires from different experts working directly and indirectly in the road management sector. The data has been analyzed using the arithmetic mean because of the nature of the questions and scope of the study. The direct method results show that experts are aware of PMS for a new road, but they have no PMS to rehabilitate roads. The indirect method results show that the authorities are applying various components of PMS, but there is no proper PMS in practice. This paper helps decision-makers to make better decisions and policies for improved road maintenance and rehabilitation. The proposed framework in the study can significantly assist the UN-SDG 9 (Facilitate Sustainable Infrastructure in Developing Countries) and 11 (Affordable and Sustainable Transport System).
Dye-sensitized solar cell (DSSC) is a photovoltaic device that can be produced from natural source pigments or natural dyes. The selection of natural dyes for DSSC application is currently under research. The utilization of natural dye materials that are easy to obtain, cost-effective, and non-toxic can reduce waste during DSSC fabrication. Natural dyes can be extracted from plants through extraction and chromatography methods. The suitability and viability of utilizing natural dyes as photosensitizers in DSSCs can be predicted using appropriate software simulation by varying related parameters to produce high power conversion efficiency. In this context, the purpose of the review is to highlight the evolution of performance improvement in the development of DSSCs with consideration of natural dye extraction and software simulation. This review also focuses on the results of extracting natural dyes from herbal ingredients, which are still very limited in information, and several parts of herbal plants that can be used as natural dye sources in the future of solid-state DSSCs have been identified. Based on the results of this review, the highest efficiency was obtained for the DSSC that used chlorophyll pigments as natural dyes using Peltophorum pterocarpum leaves with 6.07%, followed by anthocyanin pigments as natural dyes using raspberries (black) fruits with 1.5%, flavonoid pigments as natural dyes using Curcuma longa herbs with 0.64%, and flavonoid pigments as natural dyes using Indigofera tinctoria flowers with 0.46%.
The embodied carbon of building materials and the energy consumed during construction have a significant impact on the environmental credentials of buildings. The structural systems of a building present opportunities to reduce environmental emissions and energy. In this regard, mass timber materials have considerable potential as sustainable materials over other alternatives such as steel and concrete. The aim of this investigation was to compare the environment impact, energy consumption, and life cycle cost (LCC) of different wood-based materials in identical single-story residential buildings. The materials compared are laminated veneer lumber (LVL) and glued laminated timber (GLT). GLT has less global warming potential (GWP), ozone layer depletion (OLD), and land use (LU), respectively, by 29%, 37%, and 35% than LVL. Conversely, LVL generally has lower terrestrial acidification potential (TAP), human toxicity potential (HTP), and fossil depletion potential (FDP), respectively, by 30%, 17%, and 27%. The comparative outcomes revealed that using LVL reduces embodied energy by 41%. To identify which of these materials is the best alternative, various environmental categories, embodied energy, and cost criteria require further analysis. Therefore, the multi-criteria decision-making (MCDM) method has been applied to enable robust decision-making. The outcome showed that LVL manufacturing using softwood presents the most sustainable choice. These research findings contribute to the body of knowledge about the use of mass timber in construction.
At the end of 2019, a novel coronavirus COVID-19 emerged in Wuhan, China, and later spread throughout the world, including Iraq. To control the rapid dispersion of the virus, Iraq, like other countries, has imposed national lockdown measures, such as social distancing, restriction of automobile traffic, and industrial enterprises. This has led to reduced human activities and air pollutant emissions, which caused improvement in air quality. This study focused on the analysis of the impact of the six partial, total, and post-lockdown periods (1st partial lockdown from March 1 to16, 2020, 1st total lockdown from March 17 to April 21, 2nd partial lockdown from April 22 to May 23, 2nd total lockdown from May 24 to June 13, 3rd partial lockdown from June 14 to August 19, and end partial lockdown from August 20 to 31) on the average of daily NO2, O3, PM2.5, and PM10 concentrations, as well as air quality index (AQI) in 18 Iraqi provinces during these periods (from March 1st to August 31st, 2020). The analysis showed a decline in the average of daily PM2.5, PM10, and NO2 concentrations by 24%, 15%, and 8%, respectively from March 17 to April 21, 2020 (first phase of total lockdown) in comparison to the 1st phase of partial lockdown (March 1 to March 16, 2020). Furthermore, the O3 increased by 10% over the same period. The 2nd phase of total lockdown, the 3rd partial lockdown, and the post-lockdown periods witnessed declines in PM2.5 by 8%, 11%, and 21%, respectively, while the PM10 increases over the same period. Iraqi also witnessed improvement in the AQI by 8% during the 1st phase of total lockdown compared to the 1st phase of partial lockdown. The level of air pollutants in Iraq declined significantly during the six lockdown periods as a result of reduced human activities. This study gives confidence that when strict measures are implemented, air quality can improve.
The atmospheric particulate matter (PM) with a diameter of 2.5 μm or less (PM2.5) is one of the key indicators of air pollutants. Accurate prediction of PM2.5 concentration is very important for air pollution monitoring and public health management. However, the presence of noise in PM2.5 data series is a major challenge of its accurate prediction. A novel hybrid PM2.5 concentration prediction model is proposed in this study by combining complete ensemble empirical mode decomposition (CEEMD) method, Pearson's correlation analysis, and a deep long short-term memory (LSTM) method. CEEMD was employed to decompose historical PM2.5 concentration data to different frequencies in order to enhance the timing characteristics of data. Pearson's correlation was used to screen the different frequency intrinsic-mode functions of decomposed data. Finally, the filtered enhancement data were inputted to a deep LSTM network with multiple hidden layers for training and prediction. The results evidenced the potential of the CEEMD-LSTM hybrid model with a prediction accuracy of approximately 80% and model convergence after 700 training epochs. The secondary screening of Pearson's correlation test improved the model (CEEMD-Pearson) accuracy up to 87% but model convergence after 800 epochs. The hybrid model combining CEEMD-Pearson with the deep LSTM neural network showed a prediction accuracy of nearly 90% and model convergence after 650 interactions. The results provide a clear indication of higher prediction accuracy of PM2.5 with less computation time through hybridization of CEEMD-Pearson with deep LSTM models and its potential to be employed for air pollution monitoring.
Urban areas are quickly established, and the overwhelming population pressure is triggering heat stress in the metropolitan cities. Climate change impact is the key aspect for maintaining the urban areas and building proper urban planning because spreading of the urban area destroyed the vegetated land and increased heat variation. Remote sensing-based on Landsat images are used for investigating the vegetation circumstances, thermal variation, urban expansion, and surface urban heat island or SUHI in the three megacities of Iraq like Baghdad, Erbil, and Basrah. Four satellite imageries are used aimed at land use and land cover (LULC) study from 1990 to 2020, which indicate the land transformation of those three major cities in Iraq. The average annually temperature is increased during 30 years like Baghdad (0.16 °C), Basrah (0.44 °C), and Erbil (0.32 °C). The built-up area is increased 147.1 km2 (Erbil), 217.86 km2 (Baghdad), and 294.43 km2 (Erbil), which indicated the SUHI affects the entire area of the three cities. The bare land is increased in Baghdad city, which indicated the local climatic condition and affected the livelihood. Basrah City is affected by anthropogenic activities and most areas of Basrah were converted into built-up land in the last 30 years. In Erbil, agricultural land (295.81 km2) is increased. The SUHI study results indicated the climate change effect in those three cities in Iraq. This study's results are more useful for planning, management, and sustainable development of urban areas.
Accurate prediction of inlet chemical oxygen demand (COD) is vital for better planning and management of wastewater treatment plants. The COD values at the inlet follow a complex nonstationary pattern, making its prediction challenging. This study compared the performance of several novel machine learning models developed through hybridizing kernel-based extreme learning machines (KELMs) with intelligent optimization algorithms for the reliable prediction of real-time COD values. The combined time-series learning method and consumer behaviours, estimated from water-use data (hour/day), were used as the supplementary inputs of the hybrid KELM models. Comparison of model performances for different input combinations revealed the best performance using up to 2-day lag values of COD with the other wastewater properties. The results also showed the best performance of the KELM-salp swarm algorithm (SSA) model among all the hybrid models with a minimum root mean square error of 0.058 and mean absolute error of 0.044.
River water quality management and monitoring are essential responsibilities for communities near rivers. Government decision-makers should monitor important quality factors like temperature, dissolved oxygen (DO), pH, and biochemical oxygen demand (BOD). Among water quality parameters, the BOD throughout 5 days is an important index that must be detected by devoting a significant amount of time and effort, which is a source of significant concern in both academic and commercial settings. The traditional experimental and statistical methods cannot give enough accuracy or solve the problem for a long time to detect something. This study used a unique hybrid model called MVMD-LWLR, which introduced an innovative method for forecasting BOD in the Klang River, Malaysia. The hybrid model combines a locally weighted linear regression (LWLR) model with a wavelet-based kernel function, along with multivariate variational mode decomposition (MVMD) for the decomposition of input variables. In addition, categorical boosting (Catboost) feature selection was used to discover and extract significant input variables. This combination of MVMD-LWLR and Catboost is the first use of such a complete model for predicting BOD levels in the given river environment. In addition, an optimization process was used to improve the performance of the model. This process utilized the gradient-based optimization (GBO) approach to fine-tune the parameters and better the overall accuracy of predicting BOD levels. To assess the robustness of the proposed method, we compared it to other popular models such as kernel ridge (KRidge) regression, LASSO, elastic net, and gaussian process regression (GPR). Several metrics, comprising root-mean-square error (RMSE), R (correlation coefficient), U95% (uncertainty coefficient at 95% level), and NSE (Nash-Sutcliffe efficiency), as well as visual interpretation, were used to evaluate the predictive efficacy of hybrid models. Extensive testing revealed that, in forecasting the BOD parameter, the MVMD-LWLR model outperformed its competitors. Consequently, for BOD forecasting, the suggested MVMD-LWLR optimized with the GBO algorithm yields encouraging and reliable results, with increased forecasting accuracy and minimal error.
Photocatalytic fuel cell (PFC) was employed to provide renewable power sources to photoelectro-Fenton (PEF) process to fabricate a double-chambered hybrid system for the treatment of azo dye, Amaranth. The PFC-PEF hybrid system was interconnected by a circuit attached to the electrodes in PFC and PEF. Circuit connection is the principal channel for the electron transfer and mobility between PFC and PEF. Thus, different circuit connections were evaluated in the hybrid system for their influences on the Amaranth dye degradation. The PFC-PEF system under the complete circuit connection condition attained the highest decolourization efficiency of Amaranth (PFC: 98.85%; PEF: 95.69%), which indicated that the complete circuit connection was crucial for in-situ formation of reactive species in dye degradation. Besides, the pivotal role of ultraviolet (UV) light irradiation in the PFC-PEF system for both dye degradation and electricity generation was revealed through various UV light-illuminating conditions applied for PFC and PEF. A remarkable influence of UV light irradiation on the production of hydrogen peroxide and generation and regeneration of Fe2+ in PEF was demonstrated. This study provided a comprehensive mechanistic insight into the dye degradation and electricity generation by the PFC-PEF system.
The treatment of single and binary azo dyes, as well as the effect of the circuit connection, aeration, and plant on the performance of UFCW-MFC, were explored in this study. The decolorization efficiency of Remazol Yellow FG (RY) (single dye: 98.2 %; binary dye: 92.3 %) was higher than Reactive Black 5 (RB5) (single: 92.3 %; binary: 86.7 %), which could be due to monoazo dye (RY) requiring fewer electrons to break the azo bond compared to the diazo dye (RB5). In contrast, the higher decolorization rate of RB5 in binary dye indicated the removal rate was affected by the electron-withdrawing groups in the dye structure. The closed circuit enhanced about 2% of color and 4% of COD removal. Aeration improved the COD removal by 6%, which could be contributed by the mineralization of intermediates. The toxicity of azo dyes was reduced by 11-26% and the degradation pathways were proposed. The dye removal by the plants was increased with a higher contact time. RB5 was more favorable to be uptook by the plant as RB5 holds a higher partial positive charge. 127.39 (RY), 125.82 (RB5), and 58.66 mW/m3 (binary) of maximum power density were generated. The lower power production in treating the binary dye could be due to more electrons being utilized for the degradation of higher dye concentration. Overall, the UFCW-MFC operated in a closed circuit, aerated, and planted conditions achieved the optimum performance in treating binary azo dyes containing wastewater (dye: 87-92%; COD: 91%) compared to the other conditions (dye: 83-92%; COD: 78-87%).