This study explores the impact of fiscal policy on environmental pollution, employing the vector autoregressive (VAR) model on annual data from 1976 to 2018 in Pakistan. We estimate the effect of total expenditure, total revenue, education expenditures, health expenditures, and other dynamic determinants such as gross domestic product (GDP), private investment, market rate, and crude oil price on carbon dioxide (CO2) emissions in particular. Further, this study creates impulse response functions to check the fiscal shocks, coordinating with five scenarios of public expenditures, segregated into government revenue, and education and health expenditures. The outcomes indicate that government spending in the public sectors (education and health) had a diminishing effect on CO2 emissions, whereas government revenue that was collected from taxes improved economic growth but at a cost of environmental pollution. In Pakistan, a fiscal policy scenario has been implemented that increases government expenditures to alleviate the effects of CO2 emissions. Therefore, policymakers should provide the right direction for the feasible distribution of resources in every public sector through a powerful structure, which will ultimately reduce the overall level of environmental deficit.
The agricultural sector is one of the most important sources of CO2 emissions. Thus, the current study predicted CO2 emissions based on data from the agricultural sectors of 25 provinces in Iran. The gross domestic product (GDP), the square of the GDP (GDP2), energy use, and income inequality (Gini index) were used as the inputs. The study used support vector machine (SVM) models to predict CO2 emissions. Multiobjective algorithms (MOAs), such as the seagull optimization algorithm (MOSOA), salp swarm algorithm (MOSSA), bat algorithm (MOBA), and particle swarm optimization (MOPSO) algorithm, were used to perform three important tasks for improving the SVM models. Additionally, an inclusive multiple model (IMM) used the outputs of the MOSOA, MOSSA, MOBA, and MOPSO algorithms as the inputs for predicting CO2 emissions. It was observed that the best kernel function based on the SVM-MOSOA was the radial function. Additionally, the best input combination used all the gross domestic product (GDP), squared GDP (GDP2), energy use, and income inequality (Gini index) inputs. The results indicated that the quality of the obtained Pareto front based on the MOSOA was better than those of the other algorithms. Regarding the obtained results, the IMM model decreased the mean absolute errors of the SVM-MOSOA, SVM-MOSSA, SVM-MOBA, and SVM-PSO models by 24, 31, 69, and 76%, respectively, during the training stage. The current study showed that the IMM model was the best model for predicting CO2 emissions.
Dye wastewater has raised a prevalent environmental concern due to its ability to prevent the penetration of sunlight through water, thereby causing a disruption to the aquatic ecosystem. Carbon quantum dots (CQDs) are particularly sought after for their highly tailorable photoelectrochemical and optical properties. Simultaneously, graphitic carbon nitride (g-C3N4) has gained widespread attention due to its suitable band gap energy as well as excellent chemical and thermal stabilities. Herein, a novel boron-doped CQD (BCQD)-hybridized g-C3N4 homojunction (CN) nanocomposite was fabricated via a facile hydrothermal route. The optimal photocatalyst sample, 1-BCQD/CN (with a 1:3 mass ratio of boron to CQD) accomplished a Rhodamine B (RhB, 10 mg/L) degradation efficiency of 96.8% within 4 h under an 18 W LED light irradiation. The kinetic rate constant of 1.39 × 10-2 min-1 achieved by the optimum sample was found to be 3.6- and 2.8-folds higher than that of pristine CN and un-doped CQD/CN, respectively. The surface morphology, crystalline structure, chemical composition and optical properties of photocatalyst samples were characterized via TEM, FESEM-EDX, XRD, FTIR, UV-Vis DRS and FL spectrometer. Based on the scavenging tests, it was revealed that the photogenerated holes (h+), superoxide anions (∙O2-) and hydroxyl radicals (∙OH) were the primary reactive species responsible for the photodegradation process. Overall, the highly efficient 1-BCQD/CN composite with excellent photocatalytic activity could provide a cost-effective and robust means to address the increasing concerns over global environmental pollution.
With the growth of the number of old buildings in urban cities, there is an imperative demand for retrofitting those buildings to minimize their energy consumption and maximize their sustainability. This article seeks to provide a multi-criteria assessment of different retrofitting scenarios in the Malaysian context, focusing replacement of windows. Four different criteria assessed operation energy usage, global warming potential (GWP) emission, embodied energy, and the cost of each alternative. Life cycle analysis is used for each scenario using the Energy Plus software program to estimate the energy demand. The preliminary result showed that a louvered window is unsuitable for operational energy usage compared to other options. In embodied energy and GWP, double-glazing shows an optimal choice by 532 MJ kg/m2 and 101 kg/M2 CO2 between the other two alternatives for retrofitting. However, in the operational energy category, triple glazing has the best performance by 1.06 kW/a day. Finally, comparing the cost of each other options, plenum windows have the lowest rate by 825 kg/M2 MYR. Thus, multi-criteria decision-making (MCDM) is used to select the most sustainable window for buildings. The result shows that the best option is a double-glazing window, followed by a plenum window. This study revealed the requirement for utilization of MCDM handles to guarantee the correct choice of design strategies for the best decision.
The major breakthroughs in our knowledge of how biology plays a role in Parkinson's disease (PD) have opened up fresh avenues designed to know the pathogenesis of disease and identify possible therapeutic targets. Mitochondrial abnormal functioning is a key cellular feature in the pathogenesis of PD. An enzyme, leucine-rich repeat kinase 2 (LRRK2), involved in both the idiopathic and familial PD risk, is a therapeutic target. LRRK2 has a link to the endolysosomal activity. Enhanced activity of the LRRK2 kinase, endolysosomal abnormalities and aggregation of autophagic vesicles with imperfectly depleted substrates, such as α-synuclein, are all seen in the substantia nigra dopaminergic neurons in PD. Despite the fact that LRRK2 is involved in endolysosomal and autophagic activity, it is undefined if inhibiting LRRK2 kinase activity will prevent endolysosomal dysfunction or minimise the degeneration of dopaminergic neurons. The inhibitor's capability of LRRK2 kinase to inhibit endolysosomal and neuropathological alterations in human PD indicates that LRRK2 inhibitors could have significant therapeutic usefulness in PD. G2019S is perhaps the maximum common mutation in PD subjects. Even though LRRK2's well-defined structure has still not been established, numerous LRRK2 inhibitors have been discovered. This review summarises the role of LRRK2 kinase in Parkinson's disease.
The coral health of Pulau Anak Datai (PAD), located off the northwest of Langkawi, Malaysia, was assessed using the Coral Health Index (CHI) method. Three ecological parameters, namely, benthic cover, fish biomass, and microbes (Vibrio) were determined at four sites around the island in 2019. In addition, community parameters such as coral mortality index, coral richness, relative abundance, diversity index, Evenness tests, and reef morphology were measured for each site. The results revealed that the benthic cover consists of less than 40% of scleractinian corals at all sites. A total of 25 genera of hard corals comprising of 11 families and 1 scleractinian Incertae sedis were observed, with the most dominant corals belong to the genera Porites, Favites, and Diploastrea. The average fish biomass of PAD was low (16.76 g/m2), with only 19 non-cryptic fish species observed. The abundance of Vibrio around the island was within the average range of 29.58 cfu/ml. Based on the benthos, fish, and Vibrio values, the Coral Health Index (CHI) of PAD was classified on the low side of the fair status. All sites tended toward high values of the mortality index (MI > 0.33). Reef morphology was strongly influenced by stress-tolerant corals, dominated by massive and sub-massive corals. The data presented here suggested that the reefs of PAD could be rated as stressed and becoming unhealthy and disturbed. However, in view of the rarity of coral reef ecosystems in the Straits of Malacca, this island deserves increased attention for conservation planning and coral reef protection.
Climate change continues to pose a threat to the agricultural sectors worldwide, jeopardizing food and nutritional security, which is a critical component of the sustainable development agenda. Consequently, this study attempts to examine the impact of climatic variables (CO2 emissions, energy resources, rainfall, temperature, fossil fuel consumption, and humidity) on agricultural production of rice, cereals, vegetables, coffee, and agriculture value added (as a percentage of GDP) in the Malaysian context. To this end, this study applied a generalized method of moments (GMM) estimator on the data obtained from the metrological station Malaysia, Department of Statistics Malaysia and World Development Indicators (WDI) spanning the period 1985-2016. The results revealed that temperature and energy consumption negatively and significantly affect rice and vegetable production, while the negative effect of rainfall, temperature, fossil fuel consumption, and humidity on cereal production is insignificant. The results also confirmed that CO2 emissions have a negative and significant impact on coffee production. Likewise, temperature, energy consumption, and fossil fuel consumption exhibit a negative and significant influence on agriculture value added. These observations evidenced the adverse effect of climate change on various agricultural products in Malaysia. Therefore, in order to ensure robust and sustainable agricultural output in Malaysia, policymakers as well as environmentalists should work together to formulate appropriate adaptation strategies.
Morocco is an energy-deficient country depending on almost 94% of energy imports to fuel its growing economy. Due to its fast-growing population, Morocco's energy consumption is projected to increase significantly, adding more pressure on the energy system. On the other hand, the rising tension of scarcity of resources, energy price fluctuations, and environmental issues have all made energy security one of its top priorities. Therefore, Morocco launched the National Energy Strategy (NES) in 2009 to reach 42% renewable generation by 2020, which was renewed to up to 52% by 2050. This study analyzes Morocco's energy security under the 4-As framework from 2000 to 2016.The 4-As methodology aims to assess and graphically illustrate the changes in Morocco's energy security by mapping these changes into four key dimensions: the availability of energy resources, the applicability of technology, the acceptability by the environment and society, and the affordability of energy resources. The quantitative analysis shows that Morocco's energy security performance was at its optimum during the first period of study (2000-2004) but then regressed for the remainder of the study period, as energy imports and prices increased, in addition to the low performance in applicability characterized by low energy efficiency. To improve Morocco's energy security status and move toward a sustainable energy transition, this study suggests integrating a higher share of renewable energy into the energy mix and boosting efficient technologies through a large scale of green finance and green investment projects.
Environmental degradation is frequently cited as one of the eminent issues in the modern era. To limit environmental degradation, prior literature discerns several macroeconomic, socio-economic, and institutional factors that affect environmental degradation. However, the relationship between geopolitical risk and environmental degradation is understudied in the previous literature. To fill this gap, the inquiry at hand aims to scrutinize the influence of geopolitical risk on environmental degradation for E7 countries while controlling the effect of renewable energy, non-renewable energy, and GDP. Further, we utilize both the ecological footprint and CO2 emissions as proxies of environmental degradation and employ second-generation panel methods for robust findings. In addition to this, the present study uses augmented mean group (AMG) estimator to provide long-run relationship among the selected variables. The findings from the AMG estimator expound that there exists environmental Kuznets curve (EKC) for E7 countries. Moreover, renewable energy ameliorates environmental quality because it plunges both ecological footprint and CO2 emissions. On the contrary, non-renewable energy consumption escalates both ecological footprint and CO2 emissions. Finally, geopolitical risk tends to decrease CO2 emissions as well as ecological footprint. Our findings deduce a few policy implications to replenish environmental quality. For instance, the share of renewables in the energy mix should be surged to ameliorate the environmental quality. Further, to control both the geopolitical risk and environmental degradation at the same time, policymakers should put forward reforms and initiatives (e.g., policies to escalate R&D, technological innovations, and tax exemptions on imports of renewables) that can help to improve environmental quality without affecting geopolitical risk. At times of low geopolitical risk, environmental degradation will surge; therefore, the rate of environmental control taxes should be increased by the policymakers.
Nanomaterials are threatening the environment and human health, but there has been little discussion about the stability and mobility of nanoparticles (NPs) in saturated porous media at environmentally relevant concentrations of surfactants, which is a knowledge gap in exploring the fate of engineered NPs in groundwater. Therefore, the influences of the anionic surfactant (sodium dodecylbenzene sulfonate, SDBS), the cationic surfactant (cetyltrimethylammonium bromide, CTAB), and the nonionic surfactant (Tween-80) with environmentally relevant concentrations of 0, 5, 10, and 20 mg/L on nano-TiO2 (nTiO2, negatively charged) and nano-CeO2 (nCeO2, positively charged) transport through saturated porous media were examined by column experiments. On the whole, with increasing SDBS concentration from 0 to 20 mg/L, the concentration peak of nTiO2 and nCeO2 in effluents increased by approximately 0.2 and 0.3 (dimensionless concentration, C/C0), respectively, because of enhanced stability and reduced aggregate size resulting from enhanced electrostatic and steric repulsions. By contrast, the transportability of NPs significantly decreased with increasing CTAB concentration due to the attachment of positive charges, which was opposite to the charge on the medium surface and facilitated the NP deposition. On the other hand, the addition of Tween-80 had no significant influence on the stability and mobility of nTiO2 and nCeO2. The results were also demonstrated by the colloid filtration theory (CFT) modeling and the Derjaguin-Landau-Verwey-Overbeek (DLVO) interaction calculations; it might promote the assessment and remediation of NP pollution in subsurface environments.
Surface water quality deterioration is commonly associated with environmental changes and human activities. Although some research has been carried out to evaluate the relationship between various influencing factors and water quality, there is still very little scientific understanding on how to accurately define the key factors of water quality deterioration. This study aims to quantify the impact of environmental factors and land use land cover (LULC) changes on water quality in the Ebinur Lake Watershed, Xinjiang, China. A total of 20 water parameters were used to calculate the Environment Water Quality Index (CWQI). Meanwhile, the partial least squares-structural equation model (PLS-SEM) was used to quantify the impact of eleven factors influencing water quality in the watershed. About 33.3% of the monitoring points that located mostly in the downstream region with dominant anthropogenic activities were detected as poor quality. There were no obvious temporal changes in water quality from 2016 to 2019. The PLS-SEM simulation shows that the latent variable "land use/cover types" (path coefficient = - 0.600) and "Environmental factor" (path coefficient = - 0.313) are two major factors affected water quality in the Ebinur Lake Watershed, with a strong explanatory power to water quality change (R2 = 0.727). In the latent variable "Environmental factors", the "NDVI" and "night light brightness value" have a great influence on water quality, with the weights of 0.451 and 0.427, respectively. Correspondingly, the "farmland" and "forest land" within the latent variable of "Land use/cover type" have a considerable impact water quality, with the weights of 0.361 and - 0.340, respectively. In conclusion, the influence of anthropogenic activities on surface water quality of the Ebinur Lake Watershed is greater than that of environmental factors. Compared with the traditional multivariate statistical method, PLS-SEM provides a new insight for quantifying the complex relationship between different influencing factors and water quality.
With the recent increase in demand for high-strength concrete, higher cement content is utilized, which has increased the need for cement. The cement industry is one of the most energy-consuming sectors globally, contributing to 10% of global carbon dioxide (CO2) gas emissions and global warming. Similarly, with rapid urbanization and industrialization, a vast number of by-products and waste materials are being generated in abundance, which causes environmental and health issues. Focusing on these two issues, this study aimed to develop an M50-grade eco-friendly high-strength concrete incorporating waste materials like marble dust powder (MDP) and fly ash (FA) as partial cement replacement. 2.5%, 5%, 7.5%, and 10% MDP and FA by weight of total binder was utilized combinedly, such that the 5%, 10%, 15%, and 20% cement content was replaced, respectively. The fresh state properties in terms of workability and hardened state properties in terms of compressive and flexural strengths were evaluated at 7, 14, 28, 56, and 90 days. Furthermore, to assess the environmental impact of MDP and FA, the embodied carbon and eco-strength efficiency were calculated. Based upon the results, it was observed that a combined 10% (5% MDP and 5% FA) achieved the highest strength; however, 15% (7.5% MDP and 7.5% FA) substitution could be optimal. Furthermore, the combined utilization of FA and MDP also enabled a reduction in the total embodied carbon. It decreased the cost of concrete, resulting in an eco-friendly, high-strength concrete.
Trade openness continues to have the potential to influence many parts of today's society, including religion, transportation, lifestyle, language, and international relations; however, its ability to impact environmental quality is the primary issue for environmental policy guidelines. In response to an increasing interest in finding the dynamic association between trade openness and environmental quality, the current study explores the trade openness- environmental quality nexus in the ten most open Organization of Islamic Cooperation (OIC) countries for the years 1991 to 2018. By taking CO2 emissions and ecological footprint as environmental indicators, a novel methodology "quantile-on-quantile (QQ)" is used to indicate how different quantiles of trade openness asymmetrically affect the quantiles of environmental indicators by providing an adequate pattern to comprehend the overall dependence structure. A negative openness-CO2 emissions association is dominant in seven out of ten selected OIC countries (i.e., Suriname, Malaysia, Jordan, UAE, Libya, Brunei, and Qatar). On the other hand, a positive impact of trade openness on ecological footprint is dominant in eight out of ten selected OIC countries (i.e., Oman, Jordan, UAE, Libya, Bahrain, Brunei, Qatar, and Kuwait). The outcomes indicate that the asymmetric strength of openness-induced environmental quality differs with countries at both upper and bottom quantiles of data distribution that need specific attention in contending trade and environment policies in OIC countries.
In response to the growing demand for the global energy supply chain, wind power has become an important research subject among studies in the advancement of renewable energy sources. The major concern is the stochastic volatility of weather conditions that hinder the development of wind power forecasting approaches. To address this issue, the current study proposes a weather prediction method divided into two models for wind speed and atmospheric system forecasting. First, the data-based model incorporated with wavelet transform and recurrent neural networks is employed to predict the wind speed. Second, the physics-informed echo state network was used to learn the chaotic behavior of the atmospheric system. The findings were validated with a case study conducted on wind speed data from Turkmenistan. The results suggest the outperformance of physics-informed model for accurate and reliable forecasting analysis, which indicates the potential for implementation in wind energy analysis.
In this study, nanoporous anodic film was produced by anodization of niobium, Nb in a fluoride ethylene glycol electrolyte. The effect of anodization voltage and electrolyte temperature was studied to find an optimum condition for circular, ordered, and uniform pore formation. The diameter of the pores was found to be larger when the applied voltage was increased from 20 to 80 V. The as-anodized porous film was also observed to comprise of nanocrystallites which formed due to high field-induced crystallization. The nanocrystallites grew into orthorhombic Nb2O5 after post-annealing treatment. The Cr(VI) photoreduction property of both the as-anodized and annealed Nb2O5 samples obtained using an optimized condition (anodization voltage: 60 V, electrolyte temperature: 70 °C) was compared. Interestingly, the as-anodized Nb2O5 film was found to display better photoreduction of Cr(VI) than annealed Nb2O5. However, in terms of stability, the annealed Nb2O5 presented high photocatalytic efficiency for each cycle whereas the as-anodized Nb2O5 showed degradation in photocatalytic performance when used continually.
Obesity is a multifaceted disease encompassing deposition of an unnecessary amount of fat which upsurges the possibility of other complications, viz., hypertension and certain type of cancers. Although obesity results from combination of genetic factors, improper diet and inadequate physical exercise also play a major role in its onset. The present study aims at exploring the anti-obesity activity of Crinum latifolia leaf extract in obese rats. The leaves were extracted using hydroalcoholic extraction which was later diluted with water and given to obese rats. The dosing was started from the 4th week (by oral administration of extract of Crinum latifolia (100 mg/kg and 200 mg/kg) and combination of Crinum latifolia leaf extract 200 mg/kg and orlistat 30 mg/kg) till the 10th week. Various angiogenic, antioxidant, biochemical, and inflammatory biomarkers were assessed at the end of the study. The obese symptoms were progressively reduced in treatment groups when compared to disease control groups. The angiogenic parameters and inflammatory parameters were consequently reduced in treatment groups. The oxidative parameters superoxide dismutase (SOD) and catalase were gradually increased, while levels of TBARS were reduced in treatment groups showing antioxidant nature of leaf hydroalcoholic extract. The Crinum latifolia leaf extract possesses anti-obesity properties and therefore can be used as a therapeutic option in the management of obesity.
The study aims to determine the impact of global meteorological parameters on SARS-COV-2, including population density and initiation of lockdown in twelve different countries. The daily trend of these parameters and COVID-19 variables from February 15th to April 25th, 2020, were considered. Asian countries show an increasing trend between infection rate and population density. A direct relationship between the time-lapse of the first infected case and the period of suspension of movement controls the transmissivity of COVID-19 in Asian countries. The increase in temperature has led to an increase in COVID-19 spread, while the decrease in humidity is consistent with the trend in daily deaths during the peak of the pandemic in European countries. Countries with 65°F temperature and 5 mm rainfall have a negative impact on COVID-19 spread. Lower oxygen availability in the atmosphere, fine droplets of submicron size together with infectious aerosols, and low wind speed have contributed to the increase in total cases and mortality in Germany and France. The onset of the D614G mutation and subsequent changes to D614 before March, later G614 in mid-March, and S943P, A831V, D839/Y/N/E in April were observed in Asian and European countries. The results of the correlation and factor analysis show that the COVID-19 cases and the climatic factors are significantly correlated with each other. The optimum meteorological conditions for the prevalence of G614 were identified. It was observed that the complex interaction of global meteorological factors and changes in the mutational form of CoV-2 phase I influenced the daily mortality rate along with other comorbid factors. The results of this study could help the public and policymakers to create awareness of the COVID-19 pandemic.
Tropical peatlands have high potential function as a major source of atmospheric methane (CH4) and can contribute to global warming due to their large soil carbon stock, high groundwater level (GWL), high humidity and high temperature. In this study, a process-based denitrification-decomposition (DNDC) model was used to simulate CH4 fluxes in a pristine tropical peatland in Sarawak. To test the accuracy of the model, eddy covariance tower datasets were compared. The model was validated for the year 2014, which showed the good performance of the model for simulating CH4 emissions. The monthly predictive ability of the model was better than the daily predictive ability, with a determination coefficient (R2) of 0.67, model error (ME) of 2.47, root mean square error (RMSE) of 3.33, mean absolute error (MAE) of 2.92 and mean square error (MSE) of 11.08. The simulated years of 2015 and 2016 showed the good performance of the DNDC model, although under- and overestimations were found during the drier and rainy months. Similarly, the monthly simulations for the year were better than the daily simulations for the year, showing good correlations at R2 at 0.84 (2015) and 0.87 (2016). Better statistical performance in terms of monthly ME, RMSE, MAE and MSE at - 0.11, 3.38, 3.05 and 11.45 for 2015 and - 1.14, 5.28, 4.93 and 27.83 for 2016, respectively, was also observed. Although the statistical performance of the model simulation for daily average CH4 fluxes was lower than that of the monthly average, we found that the results for total fluxes agreed well between the observed and the simulated values (E = 6.79% and difference = 3.3%). Principal component analysis (PCA) showed that CH4, GWL and rainfall were correlated with each other and explained 41.7% of the total variation. GWL was found to be relatively important in determining the CH4 fluxes in the naturally inundated pristine tropical peatland. These results suggest that GWL is an essential input variable for the DNDC model for predicting CH4 fluxes from the pristine tropical peatland in Sarawak on a monthly basis.