This study aimed to assess the dynamic simulation models provided by Aspen adsorption (ASPAD) and artificial neural network (ANN) in understanding the adsorption behavior of atenolol (ATN) on gasified Glyricidia sepium woodchips activated carbon (GGSWAC) within fixed bed columns for wastewater treatment. The findings demonstrated that increasing the bed height from 1 to 3 cm extended breakthrough and exhaustion times while enhancing adsorption capacity. Conversely, higher initial ATN concentrations resulted in shorter breakthrough and exhaustion times but increased adsorption capacity. Elevated influent flow rates reduced breakthrough and exhaustion times while maintaining constant adsorption capacity. The ASPAD software demonstrated competence in accurately modeling the crucial exhaustion points. However, there is room for enhancement in forecasting breakthrough times, as it exhibited deviations ranging from 6.52 to 239.53% when compared to the actual experimental data. ANN models in both MATLAB and Python demonstrated precise predictive abilities, with the Python model (R2 = 0.985) outperforming the MATLAB model (R2 = 0.9691). The Python ANN also exhibited superior fitting performance with lower MSE and MAE. The most influential factor was the initial ATN concentration (28.96%), followed by bed height (26.39%), influent flow rate (22.43%), and total effluent time (22.22%). The findings of this study offer an extensive comprehension of breakthrough patterns and enable accurate forecasts of column performance.
Nitrogen (N) fertilizer is an important yield limiting factor for sunflower production. The correlation between yield components and growth parameters of three sunflower hybrids (Hysun-33, Hysun-38, Pioneer-64A93) were studied with five N rates (0, 60, 120, 180, 240 kg ha(-1)) at three different experimental sites during the two consecutive growing seasons 2008 and 2009. The results revealed that total dry matter (TDM) production and grain yield were positively and linearly associated with leaf area index (LAI), leaf area duration (LAD), and crop growth rate (CGR) at all three sites of the experiments. The significant association of yield with growth components indicated that the humid climate was most suitable for sunflower production. Furthermore, the association of these components can be successfully used to predict the grain yield under diverse climatic conditions. The application of N at increased rate of 180 kg ha(-1) resulted in maximum yield as compared to standard rate (120 kg ha(-1)) at all the experimental sites. In this way, N application rate was significantly correlated with growth and development of sunflower under a variety of climatic conditions. Keeping in view such relationship, the N dose can be optimized for sunflower crop in a particular region to maximize the productivity. Multilocation trails help to predict the input rates precisely while taking climatic variations into account also. In the long run, results of this study provides basis for sustainable sunflower production under changing climate.
Violacein, violet pigment produced by Chromobacterium violaceum, has attracted much attention recently due to its pharmacological properties including antibacterial activity. The present study investigated possible antibacterial mode of action of violacein from C. violaceum UTM5 against Staphylococcus aureus and methicillin-resistant S. aureus (MRSA) strains. Violet fraction was obtained by cultivating C. violaceum UTM5 in liquid pineapple waste medium, extracted, and fractionated using ethyl acetate and vacuum liquid chromatography technique. Violacein was quantified as major compound in violet fraction using HPLC analysis. Violet fraction displayed bacteriostatic activity against S. aureus ATCC 29213 and methicillin-resistant S. aureus ATCC 43300 with minimum inhibitory concentration (MIC) of 3.9 μg/mL. Fluorescence dyes for membrane damage and scanning electron microscopic analysis confirmed the inhibitory effect by disruption on membrane integrity, morphological alternations, and rupture of the cell membranes of both strains. Transmission electron microscopic analysis showed membrane damage, mesosome formation, and leakage of intracellular constituents of both bacterial strains. Mode of action of violet fraction on the cell membrane integrity of both strains was shown by release of protein, K+, and extracellular adenosine 5'-triphosphate (ATP) with 110.5 μg/mL, 2.34 μg/mL, and 87.24 ng/μL, respectively, at 48 h of incubation. Violet fraction was toxic to human embryonic kidney (HEK293) and human fetal lung fibroblast (IMR90) cell lines with LC50 value of 0.998 ± 0.058 and 0.387 ± 0.002 μg/mL, respectively. Thus, violet fraction showed a strong antibacterial property by disrupting the membrane integrity of S. aureus and MRSA strains. This is the first report on the possible mode of antibacterial action of violet fraction from C. violaceum UTM5 on S. aureus and MRSA strains.
Agricultural production is sensitive to climate variability, so climate change-agriculture sector nexus is topical in developing countries. To this end, this study examines the impact of climate change variables-rainfall and temperature-and non-climatic factors on maize production in Somalia for the period between 1980 and 2018 using the autoregressive distributed lag (ARDL) bound test, dynamic ordinary least square (DOLS), variance decomposition(VD), and impulse response function (IRF). The empirical results of the ARDL bound test confirmed the presence of long-run cointegration between the dependent variable and the explanatory variables. Furthermore, the long-run results revealed that average temperature, average rainfall, and political instability significantly inhibit maize production in the long and short runs, but rainfall has a favorable effect on maize production in the short run. Furthermore, rural population and land area under maize cultivation have negative and positive effects on maize production in the long run, respectively-albeit they are statistically insignificant. The empirical results of the study are robust to different econometric methods. Based on these findings, the study emphasizes the importance of the de-escalation of conflicts and the implementation of irrigation facilities which will enhance the productivity of maize crop production.
Middle-income countries are currently undergoing massive structural changes towards more industrialized economies. In this paper, we carefully examine the impact of these transformations on the environmental quality of middle-income countries. Specifically, we examine the role of sector value addition to GDP on CO2 emission nexus for middle-income economies controlling for the effects of population growth, energy use, and trade openness. Using recently developed panel methods that consider cross-sectional dependence and allow for heterogeneous slope coefficients, we show that energy use and growth of industrial and service sectors positively explain CO2 emissions in middle-income economies. We also find that population growth is insignificantly associated with CO2 emission. Hence, our paper provides a solid ground for developing a sustainable and pro-growth policy for middle-income countries.
Reliable and accurate prediction model capturing the changes in solar radiation is essential in the power generation and renewable carbon-free energy industry. Malaysia has immense potential to develop such an industry due to its location in the equatorial zone and its climatic characteristics with high solar energy resources. However, solar energy accounts for only 2-4.6% of total energy utilization. Recently, in developed countries, various prediction models based on artificial intelligence (AI) techniques have been applied to predict solar radiation. In this study, one of the most recent AI algorithms, namely, boosted decision tree regression (BDTR) model, was applied to predict the changes in solar radiation based on collected data in Malaysia. The proposed model then compared with other conventional regression algorithms, such as linear regression and neural network. Two different normalization techniques (Gaussian normalizer binning normalizer), splitting size, and different input parameters were investigated to enhance the accuracy of the models. Sensitivity analysis and uncertainty analysis were introduced to validate the accuracy of the proposed model. The results revealed that BDTR outperformed other algorithms with a high level of accuracy. The funding of this study could be used as a reliable tool by engineers to improve the renewable energy sector in Malaysia and provide alternative sustainable energy resources.
Sustainable water demand management has become a necessity to the world since the immensely growing population and development have caused water deficit and groundwater depletion. This study aims to overcome water deficit by analyzing water demand at Kenyir Lake, Terengganu, using a fuzzy inference system (FIS). The analysis is widened by comparing FIS with the multiple linear regression (MLR) method. FIS applied as an analysis tool provides good generalization capability for optimum solutions and utilizes human behavior influenced by expert knowledge in water resources management for fuzzy rules specified in the system, whereas MLR can simultaneously adjust and compare several variables as per the needs of the study. The water demand dataset of Kenyir Lake was analyzed using FIS and MLR, resulting in total forecasted water consumptions at Kenyir Lake of 2314.38 m3 and 1358.22 m3, respectively. It is confirmed that both techniques converge close to the actual water consumption of 1249.98 m3. MLR showed the accuracy of the water demand values with smaller forecasted errors to be higher than FIS did. To attain sustainable water demand management, the techniques used can be examined extensively by researchers, educators, and learners by adding more variables, which will provide more anticipated outcomes.
The study aims to analyze two objectives: first is to explore the non-linear relationship between tourism development, economic growth, urbanization, and environmental degradation, and also to analyze the threshold level of the contribution of tourism development on environmental degradation in top tourist arrival destinations. We applied the newly proposed econometric method panel smooth transition regression (PSTR) framework with two regimes on yearly panel data from 1995 to 2017. Findings suggest that the relationship between tourism development and environmental degradation is non-linear and regime dependent. Furthermore, the findings indicated that the relationship above the threshold level is negative and significant, while below the threshold, tourism development is positive and significant effect on environmental degradation. Tourism development and environmental degradation also exhibit the inverted U-shape relationship meaning that at a particular point, increase in tourism development increases in environmental degradation but after a particular point, increase in tourism development decreases the environmental degradation. The economic growth and urbanization also portray a non-linear and regime-dependent relationship with environmental degradation. The study assists policies and empirical information.
Financial development is identified as one of the significant factors that affect energy consumption and has been widely discussed in the literature. However, the association between financial development and renewable energy consumption is still at its earlier stage and is limitedly explored. Therefore, the purpose of this study is to examine the non-linear association between financial development and renewable energy consumption in the top renewable energy consumption countries. The study utilized the newly introduced econometric technique panel smooth transition regression (PSTR) model with two regimes on annual panel data consisted of years 1997-2017. The result confirmed that all the financial development indicators increase renewable energy consumption but affect renewable energy consumption differently. Moreover, the economic growth and industrial structure showed a positive and significant association in both regimes, whereas the population showed a negative relationship with renewable energy consumption in a low growth regime but the association becomes positive in high growth regimes. The study suggested several policies for the top renewable consumption countries.
Petroleum, coal, and natural gas reservoir were depleting continuously due to an increase in industrialization, which enforced study to identify alternative sources. The next option is the renewable resources which are most important for energy purpose coupled with environmental problem reduction. Microbial fuel cells (MFCs) have become a promising approach to generate cleaner and more sustainable electrical energy. The involvement of various disciplines had been contributing to enhancing the performance of the MFCs. This review covers the performance of MFC along with different wastewater as a substrate in terms of treatment efficiencies as well as for energy generation. Apart from this, effect of various parameters and use of different nanomaterials for performance of MFC were also studied. From the current study, it proves that the use of microbial fuel cell along with the use of nanomaterials could be the waste and energy-related problem-solving approach. MFC could be better in performances based on optimized process parameters for handling any wastewater from industrial process.
The objective of the study is to examine the relationship between air pollution, fossil fuel energy consumption, water resources, and natural resource rents in the panel of selected Asia-Pacific countries, over a period of 1975-2012. The study includes number of variables in the model for robust analysis. The results of cross-sectional analysis show that there is a significant relationship between air pollution, energy consumption, and water productivity in the individual countries of Asia-Pacific. However, the results of each country vary according to the time invariant shocks. For this purpose, the study employed the panel least square technique which includes the panel least square regression, panel fixed effect regression, and panel two-stage least square regression. In general, all the panel tests indicate that there is a significant and positive relationship between air pollution, energy consumption, and water resources in the region. The fossil fuel energy consumption has a major dominating impact on the changes in the air pollution in the region.
The focus of this study is to analyze the level of knowledge, awareness, and attitude toward plastic waste and to distinguish the key drivers that encourage the households in Kuala Lumpur, Malaysia, to participate in "No plastic campaign," This study used the logistic regression model to explain the factors that may affect the willingness to participate (WTP) of households in the campaign. In this study, it is found that 35 % of households are willing to participate in the campaign. The results of the study also indicate that people who are more informed and more convinced of their knowledge have a more positive attitude toward recycling than their counterparts do. Furthermore, this study provides additional evidence of the level and classification of importance of motivating factors for plastic recycling, using the modified average and coefficient of variation of the models. From the analysis, the factor "helps reduce landfill use" is found as the most important factor and the factor of "raising money for charity" is found as the least important factor that motivates households to participate in recycling. The determinations of the study suggest some strategies that could hold implications for government and households to boost them to participate in the campaign "No Plastic Bag."
This study empirically estimates farmers' willingness to pay (WTP) for a planned adaptation programme for addressing climate issues in Pakistan's agricultural sectors. The contingent valuation method (CVM) was employed to determine a monetary valuation of farmers' preferences for a planned adaptation programme by ascertaining the value attached to address climatic issues. The survey was conducted by distributing structured questionnaires among Pakistani farmers. The study found that 67 % of respondents were willing to pay for a planned adaptation programme. However, several socioeconomic and motivational factors exert greater influence on their willingness to pay (WTP). This paper specifies the steps needed for all institutional bodies to better address issues in climate change. The outcomes of this paper will support attempts by policy makers to design an efficient adaptation framework for mitigating and adapting to the adverse impacts of climate change.
The tourism industry is vulnerable to a range of economic and political factors, which can have both short-term and long-term impacts on tourist arrivals. The study aims to investigate the temporal dynamics of these factors and their impact on tourist arrivals. The method employed is a panel data regression analysis, using data from BRICS economies over a period of 1980-2020. The dependent variable is the number of tourist arrivals, while the independent variables are geopolitical risk, currency fluctuation, and economic policy. Control variables such as GDP, exchange rate, and distance to major tourist destinations are also included. The results show that geopolitical risk and currency fluctuation have a significant negative impact on tourist arrivals, while economic policy has a positive impact. The study also finds that the impact of geopolitical risk is stronger in the short term, while the impact of economic policy is stronger in the long term. Additionally, the study shows that the effects of these factors on tourist arrivals vary across BRICS countries. The policy implications of this study suggest that BRICS economies need to develop proactive economic policies that promote stability and encourage investment in the tourism industry.
The significance of resistivity-chargeability relationships has been acknowledged and applied in various geologic terrains and different environmental conditions. However, there remains an underexplored opportunity to fully utilize these methods in complex geological terrains with a mixture of granitic and sedimentary rocks where empirical relationships have not been established. Such discoveries are crucial for accurately delineating petrophysical and geomechanical properties, which are essential in addressing urgent environmental concerns like landslides, foundation collapse, groundwater shortages, and pollution. To address this research gap, a novel approach was employed: resistivity-chargeability data with simple linear regression modeling. The study focused on developing resistivity-chargeability relationships specifically tailored for tropical granitic environments, using a typical example from Kedah Langkawi, Malaysia. The regions are characterized by complex geological features, ruggedness, and irregular progressive weathering and fracturing of subsurface strata, making the task challenging. Despite these complexities, the study successfully derived an efficient resistivity-chargeability empirical relation that correlates resistivity and chargeability. The derived empirical relationship exhibited high accuracy, surpassing 87%, in predicting chargeability from resistivity datasets or vice versa. This achievement holds great promise in promptly and accurately addressing environmental issues specific to the target region under study. By utilizing this novel resistivity-chargeability relationship, geoscientists, engineers, and environmental practitioners can make informed decisions and effectively manage environmental challenges in these regions, especially during the pre-development stage.
One of humanity's most significant problems in the twenty-first century revolves around how to balance the mitigation of environmental pollution while achieving sustainable economic development. Despite increased awareness and dedication to climate change, the planet is still seeing a drastic decrease in the volume of pollutant emissions. This study explores the long-run and causal impact of economic growth, financial development, urbanization, and gross capital formation on Malaysia's CO2 emissions based on the STIRPAT framework. The current paper employs recently developed econometric techniques such as Maki co-integration, auto-regressive distribution lag (ARDL), fully modified OLS (FMOLS), dynamic ordinary least square (DOLS), and wavelet coherence and gradual shift causality tests to investigate these interconnections. The advantage of the gradual shift causality test is that it can capture the causality in the presence of a structural break(s). The findings from the Maki co-integration and ARDL bounds tests reveal evidence of cointegration among the variables. The ARDL test reveals that economic growth, gross capital formation, and urbanization exert a positive impact on CO2 emissions. Furthermore, the wavelet coherence test reveals that there is a significant dependency between CO2 emissions and economic growth, gross capital formation, and urbanization. The Toda Yamamoto and Gradual shift causality tests reveal that there is a (a) unidirectional causality from urbanization to CO2 emissions, (b) unidirectional causality from economic growth to CO2 emissions, and (c) unidirectional causality from gross capital formation to CO2 emissions.
The presented work analyzes the energy prices, climate shock, and health deprivation nexus in the BRICS economies for the period 1995-2020. Panel ARDL-PMG technique is used to reveal the underexplored linkages. The long-run estimates of energy prices are observed to be negatively significant to the health expenditure and life expectancy model, whereas, positively significant to the climate change model. These findings suggest that energy prices significantly reduce health expenditures and life expectancy and, thus, increase the death rate in the BRICS economies. The long-run country-wise estimate of energy prices is found negatively significant in case of Brazil, India, China, and South Africa. Alongside, the group-wise significance of CO2 emissions is discovered to be negatively, positively, and insignificant in the cases of life expectancy, death rate, and health expenditure models, respectively. Besides, country-wise long-run estimate of CO2 emissions witnesses negative significance for Russia, India, China, and South Africa.
This study accounts for the Hicks neutral technical change in a calibrated model of climate analysis, to identify the optimum level of technical change for addressing climate changes. It demonstrates the reduction to crop damages, the costs to technical change, and the net gains for the adoption of technical change for a climate-sensitive Pakistan economy. The calibrated model assesses the net gains of technical change for the overall economy and at the agriculture-specific level. The study finds that the gains of technical change are overwhelmingly higher than the costs across the agriculture subsectors. The gains and costs following technical change differ substantially for different crops. More importantly, the study finds a cost-effective optimal level of technical change that potentially reduces crop damages to a minimum possible level. The study therefore contends that the climate policy for Pakistan should consider the role of technical change in addressing climate impacts on the agriculture sector.
This paper gives a projection of the possible damage of climate change on the agriculture sector of Pakistan for the period 2012-2037, based on a dynamic approach, using an environment-related applied computable general equilibrium model (CGE). Climate damage projections depict an upward trend for the period of review and are found to be higher than the global average. Further, the damage to the agricultural sector exceeds that for the overall economy. By sector, climatic damage disproportionately affects the major and minor crops, livestock and fisheries. The largest losses following climate change, relative to the other agricultural sectors, are expected for livestock. The reason for this is the orthodox system of production for livestock, with a low adaptability to negative shocks of climate change. Overall, the findings reveal the high exposure of the agriculture sector to climate damage. In this regard, policymakers in Pakistan should take seriously the effects of climate change on agriculture and consider suitable technology to mitigate those damages.
The increasing level of greenhouse gas carbon emission currently exacerbates the devastating effect of global warming on the Earth's ecosystem. Energy usage is one of the most important determinants that is increasing the amount of carbon gases being released. Simultaneously, the level of energy usage is derived by the price, and therefore, this study examines the contribution of energy price to carbon gas emissions in thirteen African nations for the period spanning 1990 to 2017. It does this by utilising the cross-sectional dependence (CD), augmented mean group (AMG) and pooled mean group (PMG) panel modelling methods. The findings of the AMG model suggest that a 1% increase in energy price leads to a 0.02% decrease in carbon emission. The results further reveal that a 1% increase in energy intensity and technological innovation leads to 0.04% and 3.65% increase in carbon emission, respectively, in the selected African countries. Findings will help policymakers to implement effective energy price policies to reduce carbon emissions and achieve sustainable development goals especially in the emerging economies of Africa.