Meningitis is an inflammation of the protective membranes called meninges and fluid adjacent the brain and spinal cord. The inflammatory progression expands all through subarachnoid space of the brain and spinal cord and occupies the ventricles. The pathogens like bacteria, fungi, viruses, or parasites are main sources of infection causing meningitis. Bacterial meningitis is a life-threatening health problem that which needs instantaneous apprehension and treatment. Nesseria meningitidis, Streptococcus pneumoniae, and Haemophilus flu are major widespread factors causing bacterial meningitis. The conventional drug delivery approaches encounter difficulty in crossing this blood-brain barrier (BBB) and therefore are insufficient to elicit the desired pharmacological effect as required for treatment of meningitis. Therefore, application of nanoparticle-based drug delivery systems has become imperative for successful dealing with this deadly disease. The nanoparticles have ability to across BBB via four important transport mechanisms, i.e., paracellular transport, transcellular (transcytosis), endocytosis (adsorptive transcytosis), and receptor-mediated transcytosis. In this review, we reminisce distinctive symptoms of meningitis, and provide an overview of various types of bacterial meningitis, with a focus on its epidemiology, pathogenesis, and pathophysiology. This review describes conventional therapeutic approaches for treatment of meningitis and the problems encountered by them while transmitting across tight junctions of BBB. The nanotechnology approaches like functionalized polymeric nanoparticles, solid lipid nanoparticles, nanostructured lipid carrier, nanoemulsion, liposomes, transferosomes, and carbon nanotubes which have been recently evaluated for treatment or detection of bacterial meningitis have been focused. This review has also briefly summarized the recent patents and clinical status of therapeutic modalities for meningitis.
The United Nation's Sustainable Development Goals include the target of ensuring access to water and sanitation and hygiene (WASH) for all; however, very few studies have assessed comprehensive school WASH service in Pakistan. The purpose of this study was to identify WASH services in primary schools of Pakistan, and to assess how recent WASH interventions and policies are associated with the school's academic performance. A representative cross-sectional study was conducted in primary schools in the Sindh province of Pakistan. Structured observations and interviews were done to ascertain the schools' WASH conditions. The primary exposures of interest were the implementation of previous WASH interventions and National WASH policy in the school and the WASH coverage. Outcomes of interest included WASH conditions and school performance. The structural equation modeling (SEM) using a bootstrap resampling procedure was employed to characterize how WASH exposures were associated with WASH conditions and school performance. Data were collected from 425 schools. The Basic WASH facilities coverage in the primary schools of Sindh remains overall low according to WHO WASH service ladder criteria. Also, inconsistency in all three inclusive domains of WASH (availability, accessibility, and functionality) facilities were found. The school performance was significantly associated (P<0.001) with the presence of WASH interventions and/or WASH policy, while WASH policy and/or recent WASH intervention at the school were not associated with overall water quality. Our assessment unveiled several WASH gaps that exist, including high heavy metal and fecal contamination. Adoption of national WASH policy and financing of evidence-based WASH interventions are recommended in primary schools to improve educational outcomes.
The processing of rice (Oryza sativa L.) generates large quantities of lignocellulosic wastes termed rice husks (RH). Numerous researchers have proposed biomass gasification as the panacea to the waste disposal and management challenges posed by RH. However, a comprehensive analysis of RH gasification is required to examine the research landscape and future directions on the area. The research landscape and global developments on RH gasification from 1995 to 2019 are examined through bibliometric analysis of 228 publications extracted from the Web of Science. Bioresource Technology is considered the most influential journal on the topic, whereas China is the most productive nation due to government policies and research funding. The most productive organization is the Harbin Institute of Technology, which is due to the significant contributions of Zhao YiJun and co-workers. Keyword analysis revealed three crucial research themes: gasification, biomass, and rice husks. The literature revealed that the syngas yield, distribution, and performance of RH gasification are significantly influenced by temperature, equivalence ratio, selected reactor, and gasifying medium. The techno-economic analysis of RH gasification revealed that government interventions such as high sales rates and low investment costs could enhance the commercial viability of the technology. Furthermore, the integration of RH gasification with carbon capture utilization and storage could promote the decarbonization of power plants, negative emissions, and net-zero climate goals. Overall, the paper provides valuable information for future researchers to identify strategic collaborators, journal publications, and research frontiers yet unexplored.
The disastrous consequences of climate change for human life and environmental sustainability have drawn worldwide attention. Increased global warming is attributed to anthropogenic greenhouse gas (GHG) emissions, biodiversity loss, and deforestation due to industrial output and huge consumption of fossil fuels. Financial inclusion can be acted as an adaptation or a mitigation measure for environmental degradation. This study analyzed the impact of financial inclusion on environmental degradation in OIC countries for the period 2004-2018. A novel approach, "Dynamic Common Correlated Effects (DCCE)" is used to tackle the problem of heterogeneity and cross-sectional dependence (CSD). Various GHG emissions along with deforestation and ecological footprint are used as indicators of environmental degradation. Long-run estimation confirms that financial inclusion is positively and significantly linked with CO2 emission, CH4 emission, and deforestation while negatively correlated with ecological footprint and N2O emission in overall and higher-income OIC economies. An inverted U-shaped environmental Kuznets curve (EKC) is validated when ecological footprint, CO2, and CH4 are used in all panels of OIC countries. An inverted U-shaped EKC is also observed for deforestation in lower-income and overall OIC countries. In the case of N2O emission, however, a U-shaped EKC appears in lower-income and overall OIC countries. It is suggested that the governments of OIC countries should continue to have easy access to financial services and maintain sustainable use of forests and biocapacity management to address environmental challenges.
In recent years, the research direction is shifted toward introducing new supplementary cementitious materials (SCM) in lieu of in place of Portland cement (PC) in concrete as its production emits a lot of toxic gases in the atmosphere which causes environmental pollution and greenhouse gases. SCM such as sugarcane bagasse ash (SCBA), metakaolin (MK), and millet husk ash (MHA) are available in abundant quantities and considered as waste products. The primary aim of this experimental study is to investigate the effect of SCBA, MK, and MHA on the fresh and mechanical properties of concrete mixed which contributes to sustainable development. A total of 228 concrete specimens were prepared with targeted strength of 25MPa at 0.52 water-cement ratio and cured at 28 days. It is found that the compressive strength and split tensile strength were enhanced by 17% and 14.28%, respectively, at SCBA4MK4MHA4 (88% PC, 4% SCBA, 4% MK, and 4% MHA) as ternary cementitious material (TCM) in concrete after 28 days. Moreover, the permeability and density of concrete are found to be reduced when SCBA, MK, and MHA are used separately and combined as TCM increases in concrete at 28 days, respectively. The results showed that the workability of the fresh concrete was decreased with the increase of the percentage of SCBA, MK, and MHA separately and together as TCM in concrete.
This paper investigates the non-linear impacts of the agricultural, industrial, financial, and service sectors on environmental pollution in Malaysia during the 1980-2018 period. It employs the extended STIRPAT model and two indicators of environmental pollution (carbon dioxide emissions and ecological footprints). It uses the autoregressive distributed lag (ARDL) technique to estimate the parameters. Evidence from the study indicate that the agricultural, industrial, and service sectors have inverted U-shaped non-linear impacts on carbon dioxide emissions and ecological footprints, while the financial sector has a U-shaped non-linear relationship with carbon dioxide emissions and ecological footprint. These empirical outcomes are robust to diagnostic tests, structural breaks, and alternative estimation technique and proxies. The economic implication of this paper is that, at the early stage of sectoral growth, the pollution intensity of sectoral output increases, but after a certain turning point, a further increase in sectoral output will reduce environmental pollution. Precisely, environmental pollution will reduce if the agricultural, industrial, and service sectors exceed threshold levels of 11%, 44%, and 49% of GDP, respectively, while environmental pollution will be aggravated if financial sector exceeds a threshold level of 94%. Therefore, efforts to mitigate environmental pollution in Malaysia should integrate sectoral growth to attain sustainable development.
To boost the stability of economic and financial aspects along with the apprehensions for sustainability, it is important to promote the development of clean energy stocks around the globe. In the current research, the researchers have examined the impact of oil prices, coal prices, natural gas prices, and gold prices on clean energy stock using the autoregressive distribution lag (ARDL) approach from the year 2011 to the year 2020. The result of daily data analysis specifies that in the long as well as in the short run, gold prices, oil prices, and coal prices have a positive and significant effect on clean energy stock. On the other side, natural gas prices in the long as well as in the short run have a negative and significant effect on clean energy stock. So, the empirical analysis of our study is of interest to investors at an institutional level who aim at detecting the risk associated with the clean energy market through proper financial modeling. Besides, this study opens up a new domain to sustain financial as well as economic prospects by protecting the environment through clean energy stock as the investment in clean energy stocks results in producing a substantial effect on the economy and the environment as well.
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.
Some regions of Argentina are affected by high concentrations of molybdenum, arsenic and vanadium from natural sources in their groundwater. In particular, Mo levels in groundwater from Eduardo Castex (La Pampa, Argentina) typically exceed the guidelines for drinking water formerly established by WHO at 70 μg/L. Therefore, this study investigated the uptake of Mo in plants, using cress (Lepidium sativum L.) as a model using hydroponic experiments with synthetic solutions and groundwater from La Pampa. Cress grown from control experiments (150 μg/L Mo, pH 7) presented an average Mo concentration of 35.2 mg/kg (dry weight, d.w.), higher than the typical total plant range (0.7-2.5 mg/kg d.w.) in the literature. Using pooled groundwater samples (65.0-92.5 μg/L Mo) from wells of La Pampa (Argentina) as growth solutions resulted in significantly lower cress Mo levels (1.89-4.59 mg/kg d.w.) than were obtained for synthetic solutions of equivalent Mo concentration. This may be due to the high levels in these groundwater samples of As, V, Fe and Mn which are known to be associated with volcanic deposits. This research addressed the hitherto scarcity of data about the effect of various physicochemical parameters on the uptake of Mo in plants.
Tin oxide (SnO2) with versatile properties is of substantial standing for practical application, and improved features of the material are demonstrated in the current issue through the integration of nanotechnology with bio-resources leading to what is termed as biosynthesis of SnO2 nanoparticles (NPs). This review reveals the recent advances in biosynthesis of SnO2 NPs by chemical precipitation method focused on distinct methodologies, characterization, and reaction mechanism along with a photocatalytic application for dye degradation. According to available literature reviews, numerous bio-based precursors selectively extracted from biological substrates have effectively been applied as capping or reducing agents to achieve the metal oxide NPs. The major precursor obtained from the aqueous extract of root barks of Catunaregam spinosa is found to be 7-hydroxy-6-methoxy-2H-chromen-2-one that has been proposed as a model compound for the reduction of metal ions into nanoparticles due to having highly active functional groups, being abundant in plants (67.475 wt%), easy to extract, and eco benign. In addition, the photocatalytic activity of SnO2 NPs for the degradation of organic dyes, pharmaceuticals, and agricultural contaminants has been discussed in the context of a promising bio-reduction mechanism of the synthesis. The final properties are supposed to depend exclusively upon a number of factors, e.g., particle size (
The enforcement of the Movement Control Order to curtail the spread of COVID-19 has affected home energy consumption, especially HVAC systems. Occupancy detection and estimation have been recognized as key contributors to improving building energy efficiency. Several solutions have been proposed for the past decade to improve the precision performance of occupancy detection and estimation in the building. Environmental sensing is one of the practical solutions to detect and estimate occupants in the building during uncertain behavior. However, the literature reveals that the performance of environmental sensing is relatively poor due to the poor quality of the training dataset used in the model. This study proposed a smart sensing framework that combined camera-based and environmental sensing approaches using supervised learning to gather standard and robust datasets related to indoor occupancy that can be used for cross-validation of different machine learning algorithms in formal research. The proposed solution is tested in the living room with a prototype system integrated with various sensors using a random forest regressor, although other techniques could be easily integrated within the proposed framework. The primary implication of this study is to predict the room occupation through the use of sensors providing inputs into a model to lower energy consumption. The results indicate that the proposed solution can obtain data, process, and predict occupant presence and number with 99.3% accuracy. Additionally, to demonstrate the impact of occupant number in energy saving, one room with two zones is modeled each zone with air condition with different thermostat controller. The first zone uses IoFClime and the second zone uses modified IoFClime using a design-builder. The simulation is conducted using EnergyPlus software with the random simulation of 10 occupants and local climate data under three scenarios. The Fanger model's thermal comfort analysis shows that up to 50% and 25% energy can be saved under the first and third scenarios.
Researchers in recent years have utilized a broad spectrum of treatment technologies in treating bakers' yeast production wastewater. This paper aims to review the treatment technologies for the wastewater, compare the process technologies, discuss recent innovations, and propose future perspectives in the research area. The review observed that nanofiltration was the most effective membrane process for the treatment of the effluent (at >95% pollutant rejection). Other separation processes like adsorption and distillation had technical challenges of desorption, a poor fit for high pollutant load and cost limitations. Chemical treatment processes have varying levels of success but they are expensive and produce toxic sludge. Sludge production would be a hurdle when product recovery and reuse are targeted. It is difficult to make an outright choice of the best process for treating the effluent because each has its merits and demerits and an appropriate choice can be made when all factors are duly considered. The process intensification of the industrial-scale production of the bakers' yeast process will be a very direct approach, where the process optimisation, zero effluent discharge, and enhanced recovery of value-added product from the waste streams are important approaches that need to be taken into account.
The quest for eco-sustainable binders like agro-wastes in concrete to reduce the carbon footprint caused by cement production has been ongoing among researchers recently. The application of agro-waste-based cementitious materials in binary concrete has been said to improve concrete performance lately. Coconut and groundnut shells are available in abundant quantities and disposed of as waste in many world regions. Therefore, the use of coconut shell ash (CSA) and groundnut shell ash (GSA) in a ternary blend provides synergistic benefits with Portland cement (PC) and may be sustainably utilized in concrete as ternary cementitious material (TCM). Therefore, this study presents concrete performance with CSA and GSA in a grade 30 ternary concrete. Two hundred ten numbers of standard concrete samples were cast for checking the fresh and mechanical properties of concrete at curing ages of 7, 28, and 90 days. After 28-day curing, the experimental results show an increment in compressive, tensile, and flexural strength by 11.62%, 8.39%, and 9.46% at 10% TCM cement replacement, respectively. The concrete density and permeability coefficient reduce as TCM's content increases. The modulus of elasticity after 90 days improved with the addition of TCM. The concrete's sustainability assessment indicated that the emitted carbon for concrete decreased by around 16% using 20% TCM in concrete. However, the workability of fresh concrete declines as TCM content increases.
Exposing concrete to high temperatures leads to harmful effects in its mechanical and microstructural properties, and ultimately to total failure. In this sense, various types of waste materials are exploited not only to tackle serious environmental issues but also to enhance the thermal stability of concrete exposed to elevated temperatures. Furthermore, nanomaterials have been incorporated in concrete as admixtures to reduce the thermal degradation of concrete due to exposure to high temperatures. In the present study, the effects of nanosilica (NS) incorporation on the properties of concrete subjected to elevated temperature are discussed in several sequential sections. The process mechanism of concrete deterioration due to fire exposure and the important factors that could affect the performance of concrete under fire were evaluated. Moreover, brief highlights on the effect of elevated temperature on concrete containing waste materials are included in this review paper. Reviews and summaries of the available and updated literature regarding concrete containing NS are considered. According to the findings of the studies under review, the addition of nanosilica to concrete contributed in reduced strength loss, minimized internal porosity, and enhanced matrix compactness in concrete.
The present study focuses on the indiscriminate disposal of personal protective equipment (PPEs) and resulting environmental contamination during the 3rd wave of COVID-19-driven global pandemic in the Chittagong metropolitan area, Bangladesh. Because of the very high rate of infection by the delta variant of this virus, the use of PPEs by the public is increased significantly to protect the ingestion/inhalation of respiratory droplets in the air. However, it is a matter of solicitude that general people throw away the PPEs to the dwelling environment unconsciously. With the increase of inappropriate disposal of PPEs (i.e., mostly the disposable face masks made from plastic microfibers), the possibility of transmission of the virus to the general public cannot be neglected completely. This is because this virus can survive for several days on the inanimate matter like plastics and fibers. At the same time, the result of environmental contamination by microplastic/microfiber has been widespread which eventually creates the worst impact on ecosystems and organisms. The present results may help to increase public perception of the use and subsequent disposal of PPEs, especially the face masks.
Pandemics leave their mark quickly. This is true for all pandemics, including COVID-19. Its multifarious presence has wreaked havoc on people's physical, economic, and social life since late 2019. Despite the need for social science to save lives, it is also critical to ensure future generations are protected. COVID-19 appeared as the world grappled with the epidemic of climate change. This study suggests policymakers and practitioners address climate change and COVID-19 together. This article offers a narrative review of both pandemics' impacts. Scopus and Web of Science were sought databases. The findings are reported analytically using important works of contemporary social theorists. The analysis focuses on three interconnected themes: technology advancements have harmed vulnerable people; pandemics have macro- and micro-dimensions; and structural disparities. To conclude, we believe that collaborative effort is the key to combating COVID-19 and climate change, while understanding the lessons learnt from the industrialised world. Finally, policymakers can decrease the impact of global catastrophes by addressing many socioeconomic concerns concurrently.
In today's era, the world economy needs to move towards a green transformation. Green total factor productivity provides the judgment about a country or region's ability to achieve long-term sustainable development goals. However, many factors considerably affect green total factor productivity that needs to be explored and clarified. This panel study investigates the link between technological input, environmental policies, governmental involvement, manufacturing and logistics industry cooperation, renewable energy consumption, and green total factor productivity in the context of Chinese's manufacturing and logistics industry. Hypotheses are tested through fully modified ordinary least squares (FMOLS) and dynamic ordinary least squares (DOLS) econometric technique. The study used 12 cities data mainly taken from China Urban Statistical Yearbook (2005-2019) and National Economic and Social Development Statistics Bulletin. The results indicate that technological input, environmental policies, governmental involvement, manufacturing and logistics industry cooperation, and renewable energy consumption are significantly linked to green total factor productivity. The result also implies that the factors mentioned above have a crucial role in the transformation process. Moreover, the current research results will help popularize green total factor productivity and provide a new starting point for reducing non-renewable energy consumption and environmental pollution.
In a time of climate change, critically contributed by the increased global energy consumption, energy efficiency comes out as a critical factor in achieving sustainable growth for the countries. Given the fast economic advancement in the BRICS (Brazil, Russia, India, China, and South Africa) countries that have played a vital role in the global economy, energy usage, and climate governance, this study investigates the role of energy efficiency on the environmental quality of these countries. We proxy environmental quality with CO2 emissions, incorporate renewable energy in our models, and estimate the relationship with a long-panel data of 29 years (1990-2018). Our dynamic heterogeneous panel model findings confirm that energy efficiency significantly reduces CO2 emissions or improves environmental quality in the long run and the short run. Besides, we find that renewable energy has a crucial role in enhancing environmental quality in the long run with the negative impact of economic growth activities. Our findings contribute to the literature in a novel way facilitating the comprehension of the role of energy efficiency using a wide range of sophisticated techniques, thus providing robust results. For the policymakers, we humbly advocate strategies for the clean and sustainable economic transition based on our findings which has notable implications for the BRICS, other developing economies, and the world as a whole.
In the current context of rapid development and urbanization, land use and land cover (LULC) types have undergone unprecedented changes, globally and nationally, leading to significant effects on the surrounding ecological environment quality (EEQ). The urban agglomeration in North Slope of Tianshan (UANST) is in the core area of the Silk Road Economic Belt of China. This area has experienced rapid development and urbanization with equally rapid LULC changes which affect the EEQ. Hence, this study quantified and assessed the spatial-temporal changes of LULC on the UANST from 2001 to 2018 based on remote sensing analysis. Combining five remote sensing ecological factors (WET, NDVI, IBI, TVDI, LST) that met the pressure-state-response(PSR) framework, the spatial-temporal distribution characteristics of EEQ were evaluated by synthesizing a new Remote Sensing Ecological Index (RSEI), with the interaction between land use change and EEQ subsequently analyzed. The results showed that LULC change dominated EEQ change on the UANST: (1) From 2001 to 2018, the temporal and spatial pattern of the landscape on the UANST has undergone tremendous changes. The main types of LULC in the UANST are Barren land and Grassland. (2) During the study period, RSEI values in the study area were all lower than 0.5 and were at the [good] levels, reaching 0.31, 0.213, 0.362, and 0346, respectively. In terms of time and space, the overall EEQ on the UANST experienced three stages of decline-rise-decrease. (3) The estimated changes in RSEI were highly related to the changes of LULC. During the period 2001 to 2018, the RSEI value of cropland showed a trend of gradual increase. However, the rest of the LULC type's RSEI values behave differently at different times. As the UANST is the core area of Xinjiang's urbanization and economic development, understanding and balancing the relationship between LULC and EEQ in the context of urbanization is of practical application in the planning and realization of sustainable ecological, environmental, urban, and social development in the UANST.
This paper examines the effect of climate change and financial development on agricultural production in ASEAN-4, namely Indonesia, Malaysia, the Philippines, and Thailand from 1990 to 2016. Further, we explore the role of renewable energy, institutional quality, and human capital on agricultural production. Since the shocks in one country affect another country, we use second-generation modeling techniques to find out the relationship among the variables. The Westerlund (2007) cointegration tests confirm long-run relationship among the variables. The results from cross-sectionally augmented autoregressive distributed lag (CS-ARDL) model reveal that climate change negatively affects agricultural production; on the other hand, renewable energy, human capital, and institutional quality affect positively agricultural production. Moreover, renewable energy utilization, human capital, and intuitional quality moderates the effect of carbon emission on agricultural production. In addition, a U-shaped relationship exists between financial development and agricultural production, suggesting that financial development improves agricultural production only after reaching a certain threshold. Hence, this study suggests that ASEAN-4 countries must adopt flexible financial and agricultural policies so that farmers would be benefitted and agricultural production can be increased.