Terrestrial anionic surfactants (AS) enter the marine environment through coastal region. Despite that, in general limited knowledge is available on the coastal AS transfer pathway. This paper aims to assess the distributions and exchange of AS in the Peninsular Malaysia coastal environments, adjacent to the southern waters of South China Sea and Strait of Malacca. An assessment case study was conducted by a review on the available data from the workgroup that span between the year 2008 and 2019. The findings showed that AS dominated in the sea surface microlayer (SML, 57%) compared to subsurface water (SSW, 43 %). AS were also found to have dominated in fine mode (FM, 71 %) compared to coarse mode (CM, 29 %) atmospheric aerosols. SML AS correspond to the SSW AS (p < 0.01); however, highest enrichment factor (EF) of the SML AS was not consistent with highest SSW AS. Direct AS exchange between SML and FM and CM was not observed. Furthermore, the paper concludes AS mainly located in the SML and FM and could potentially be the main transfer pathway in the coastal environment.
This study is a scholarly effort to broaden the existing literature on the impact of transportation services, urbanization, and financial development on ecological footprints in Pakistan. Data used in this study covers the period of 39 years from 1980 to 2018. This study adopted the QARDL model to tackle the non-linear association of variables and test their long-run stability across the different quantiles. The findings of this study indicated a significant negative association of transportation services and financial development with ecological footprints in Pakistan at almost all quantiles whereas, the urban population was found to be positively associated with the ecological footprint in Pakistan. Results also justify the existence of the EKC hypothesis in the scenario of Pakistan. Policymakers are advised to frame strategies for investors to invest more in eco-friendly projects to curtail the ecological footprints in Pakistan. Minimizing the dependency of the transportation sector on fossil fuel, and increased use of energy-efficient appliances in the urban population would be beneficial to control the negative influence on ecological footprints in Pakistan.
The study estimates the long-run dynamics of a cleaner environment in promoting the gross domestic product of E7 and G7 countries. The recent study intends to estimate the climate change mitigation factor for a cleaner environment with the GDP of E7 countries and G7 countries from 2010 to 2018. For long-run estimation, second-generation panel data techniques including augmented Dickey-Fuller (ADF), Phillip-Peron technique and fully modified ordinary least square (FMOLS) techniques are applied to draw the long-run inference. The results of the study are robust with VECM technique. The outcomes of the study revealed that climate change mitigation indicators significantly affect the GDP of G7 countries than that of E7 countries. The GDP of both E7 and G7 countries is found depleting due to less clean environment. However, green financing techniques helps to clean the environment and reinforce the confidence of policymakers on the elevation of green economic growth in G7 and E7 countries. Furthermore, study results shown that a 1% rise in green financing index improves the environmental quality by 0.375% in G7 countries, while it purifies 0.3920% environment in E7 countries. There is a need to reduce environmental pollution, shift energy generation sources towards alternative, innovative and green sources.The study also provides different policy implications for the stakeholders guiding to actively promote financial hedging for green financing. So that climate change and envoirnmental pollution reduction could be achieved effectively. The novelty of the study lies in study framework.
Globally, the rising concentration of anthropogenic greenhouse gases emission in the atmosphere is extremely detrimental to the environment. The high concentration among all greenhouse gases is carbon dioxide emission. Therefore, this study explores the linkages between energy consumption, trade openness, globalization, urbanization, and carbon dioxide emission for Malaysia over the spans from 1978 to 2018. ARDL bound testing model was employed to investigate involvement in the elevation of carbon dioxide emissions in the economy. The study illustrates that a 1% growth in energy consumption, trade openness, and urbanization will deteriorate the environment by 0.18%, 0.03%, and 2.51% respectively. Further, variance decomposition analysis predicts that all the determinants in the study have significantly caused carbon dioxide emission in Malaysia. The paper presents scientific support for further studies and argues for the use of innovation shocks as a policy instrument for a prosperous future by formulating more successful environmental policies.
The carbon dioxide emissions from Portland cement production have increased significantly, and Portland cement is the main binder used in self-compacting concrete, so there is an urgent need to find environmentally friendly materials as alternative resources. In most developing countries, the availability of huge amounts of agricultural waste has paved the way for studying how these materials can be processed into self-compacting concrete as binders and aggregate compositions. Therefore, this experimental program was carried out to study the properties of self-compacting concrete (SCC) made with local metakaolin and coal bottom ash separately and combined. Total 25 mixes were prepared with four mixes as 5, 10, 15, and 20% replacement of cement with metakaolin; four mixes as 10, 20, 30, and 40% of coal bottom ash as partial replacement of fine aggregates separately; and 16 mixes prepared combined with metakaolin and coal bottom ash. The fresh properties were explored by slump flow, T50 flow, V-funnel, L-box, and J-ring sieve segregation test. Moreover, the hardened properties of concrete were performed for compressive, splitting tensile and flexural strength and permeability of SCC mixtures. Fresh concrete test results show that even if no viscosity modifier is required, satisfactory fresh concrete properties of SCC can be obtained by replacing the fine aggregate with coal bottom ash content. At 15% replacement of cement with local metakaolin is optimum and gave better results as compared to control SCC. At 30% replacement of fine aggregate is optimum and gave better results as compared to control SCC. In the combined mix, 10% replacement of cement with metakaolin combined with 30% replacement of fine aggregate with coal bottom ash is optimum and gave better results as compared to control SCC.
The role of risk assessment and capital structure is vital for the sustainable growth of firms and increasing the shareholders' wealth. This research explores the correlation between firm risk and capital structure using datasets from the sugar and cement sectors of Pakistan as a developing economy. This study is unique as it involved two firms of different nature (sugar firms operate seasonally while cement firms operate yearly) to view the real picture on the impact of risk and structure assessment on firms' credibility and shareholders' wealth. For this purpose, 15-year data (2000-2014) containing the financial statements of the target sectors were collected and the ANOVA analysis was applied with credit risk, liquidity risk, systematic risk, and firm size were used as the regressor variables, firm growth and dividend payout ratio as the control variables, and leverage as the regression variable. The findings showed that credit risk and liquidity risk are significantly correlated with leverage. This suggests that decision-makers pertaining to firms' risk and efficiency must focus more on risk to pursue a stronger and sustainable increase in shareholder wealth.
Excessive accumulation of waste materials has presented a serious environmental problem on a global scale. This has prompted many researchers to utilise agricultural, industrial, and by-product waste materials as the replacement of aggregate in the concrete matrix. In this present study, the prediction and optimisation of coconut shell (CA) content as the replacement of fine aggregate were evaluated based on the mechanical properties of the concrete (M30). Based on the suggested design array from the response surface methodology (RSM) model, experimental tests were carried out to achieve the goal of this study. The collected data was used to develop mathematical predictive equations using both GEP and RSM models. Analysis of variance (ANOVA) was also taken into account to appraise and verify the performance of the proposed models. Based on the results, the optimum content of replacing CA was 50%. In particular, the compressive, tensile, and flexural strength obtained after 28 days of curing were 46.2, 3.74, and 8.06 MPa, respectively, from the RSM model and 46.18, 3.85, and 7.99 MPa, respectively, from the GEP model. The obtained values were superior to those of the control concrete sample (43.12, 3.51 and 7.14 MPa, respectively). Beyond the optimum content, a loss in strength was observed. It was also found that both the GEP and RSM models exhibited high prediction accuracy with strong correlations (R2 = 0.97 and 0.95, respectively). In addition, minimum prediction error (RMSE
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.
Emerging contaminants (ECs) originated from different agricultural, biological, chemical, and pharmaceutical sectors have been detected in our water sources for many years. Several technologies are employed to minimise EC content in the aqueous phase, including solvent extraction processes, but there is not a solution commonly accepted yet. One of the studied alternatives is based on separation processes of emulsion liquid membrane (ELM) that benefit low solvent inventory and energy needs. However, a better understanding of the process and factors influencing the operating conditions and the emulsion stability of the extraction/stripping process is crucial to enhancing ELM's performance. This article aims to describe the applications of this technique for the EC removal and to comprehensively review the ELM properties and characteristics, phase compositions, and process parameters.
The effective treatment of waste to be used as a resource in future has a major role in achieving environmental sustainability and moving towards circular economy. The current research is aimed to provide in-depth detail regarding prominent trends and research themes, evolution, future research orientation, main characteristics, and mapping of research publications on waste management, technological innovation in circular economy domain from the year 2000 to 2021. Different analyses including text mining and bibliometric and content analyses were applied to answer the research question and provide the details on aforementioned variables. From the bibliometric analyses, a total of 1118 articles were drawn out from the Scopus database to conceptualize the core body of research. As a result, the following themes were identified: electronic waste, circular economy transition, plastic waste, bio-based waste management, lifecycle assessment, and ecological impacts, and construction and demolition waste management. The highlighted features, future research orientation, and prominent research perspective can provide guideline for future research to enrich the literature through conducting studies on provided research directions and help lead waste management and technological innovation policymakers, professionals, and practitioners in moving towards circular transition.
Metal remediation is important considering the environmental pressure due to soil pollution from landfill leachate. Hence, identifying potential plant-based option for remediation, especially the use of bio-/hyper-accumulators, is inevitable. Contamination of soil with heavy metals has been a decades-long concern. This study is therefore aimed to evaluating the metal-remediation potentials of four ornamental plant species-Cordyline fruticosa, Duranta variegated, Tradescantia spathacea, and Chlorophylum comosum-on leachate-contaminated soil. Details of the study involved leachate analysis, soil characterization, and metal-accumulation test on selected plants. Characterization of both landfill soil and leachate has indicated that Pb, Cu, As, Mn, Cr, Zn, Fe, and Ni were higher than the prescribed limits. The high metal reduction efficiency of C. fruticosa on all the studied metals was about 63%, 85%, 77.88%, 77.55%, and 75% for Pb, As, Mn, Zn, and Cr concentrations. The metal removal by the plants was significantly higher as compared to control soil (P
Consumption of natural resources and waste generation continues to rise as the human population increases. Ever since the industrial revolution, consumers have been adopting a linear economy model based on the 'take-make-dispose' approach. Raw materials are extracted to be converted into products and finally discarded as wastes. Consequently, this practice is unsustainable because it causes a massive increase in waste production. The root problems of the linear system can be addressed by transitioning to a circular economy. Circular economy is an economic model in which wastes from one product are recycled and used as resources for other processes. This literature review discovers the potential of vermicompost as a sustainable strategy in circular economy and highlights the benefits of vermicompost in ensuring food security, particularly in improving agricultural yield and quality, as well as boosting crop's nutritional quality. Vermicompost has the potential to be used in a variety of ways in the circular economy, including for agricultural sustainability, managing waste, pollutant remediation, biogas production and animal feed production. The recycling of organic wastes to produce vermicompost can benefit both the consumers and environment, thus paving the way towards a more sustainable agriculture for the future.
The production of renewable materials from alternative sources is becoming increasingly important to reduce the detrimental environmental effects of their non-renewable counterparts and natural resources, while making them more economical and sustainable. Chemical surfactants, which are highly toxic and non-biodegradable, are used in a wide range of industrial and environmental applications harming humans, animals, plants, and other entities. Chemical surfactants can be substituted with biosurfactants (BS), which are produced by microorganisms like bacteria, fungi, and yeast. They have excellent emulsifying, foaming, and dispersing properties, as well as excellent biodegradability, lower toxicity, and the ability to remain stable under severe conditions, making them useful for a variety of industrial and environmental applications. Despite these advantages, BS derived from conventional resources and precursors (such as edible oils and carbohydrates) are expensive, limiting large-scale production of BS. In addition, the use of unconventional substrates such as agro-industrial wastes lowers the BS productivity and drives up production costs. However, overcoming the barriers to commercial-scale production is critical to the widespread adoption of these products. Overcoming these challenges would not only promote the use of environmentally friendly surfactants but also contribute to sustainable waste management and reduce dependence on non-renewable resources. This study explores the efficient use of wastes and other low-cost substrates to produce glycolipids BS, identifies efficient substrates for commercial production, and recommends strategies to improve productivity and use BS in environmental remediation.
Solar thermal energy storage (TES) is an outstanding innovation that can help solar technology remain relevant during nighttime and cloudy days. TES using phase change material (PCM) is an avant-garde solution for a clean and renewable energy transition. The present study unveils the unique potential of MXene as a performance enhancer in lauric acid (LA), which functions as a base PCM. The addition of graphene nanoplatelet (GNP) into the LA-MXene composite is prepared to comprehend and evaluate the benefits and detriments of adding carbon-based nanomaterial into the PCM via a two-step homogenizing method. A similar weight percentage of MXene and GNP at 0.75 was used for composite synthesis. The study found that the enthalpy of LA-MXene is comparable to LA at 169.87 J/kg and greater than LA-MXene/GNP, which has 137.53 J/kg. Regarding thermal storage performance, LA-MXene exhibited outstanding performance compared to LA-MXene/GNP in terms of enthalpy efficiency (λ) and relative enthalpy efficiency (η), achieving 95.4% and 96.1%, respectively. This is supported by the XPS spectra, which show that the crosslinking structure acted as a barrier, reinforcing the material and preventing further thermal degradation. This has resulted in robust and denser shells that significantly improved light absorption, enhancing both the photothermal conversion and thermal energy storage efficiency of LA/MXene. The present study reveals that LA-MXene is a promising and optimal candidate for the feasibility and reliability of TES in solar renewable energy applications. It was observed that the incorporation of exclusive MXene may effectively address the limitations of LA as a conventional PCM and surpass the traditional role of GNP. This study offers valuable insights into the superior performance of MXene alone, eliminating the need for doping with various nanomaterials and thereby reducing the complexity in synthesizing the PCM.
Indoor air quality (IAQ) in the built environment is significantly influenced by particulate matter, volatile organic compounds, and air temperature. Recently, the Internet of Things (IoT) has been integrated to improve IAQ and safeguard human health, comfort, and productivity. This review seeks to highlight the potential of IoT integration for monitoring IAQ. Additionally, the paper details progress by researchers in developing IoT/mobile applications for IAQ monitoring, and their transformative impact in smart building, healthcare, predictive maintenance, and real-time data analysis systems. It also outlines the persistent challenges (e.g., data privacy, security, and user acceptability), hampering effective IoT implementation for IAQ monitoring. Lastly, the global developments and research landscape on IoT for IAQ monitoring were examined through bibliometric analysis (BA) of 106 publications indexed in Web of Science from 2015 to 2022. BA revealed the most significant contributing countries are India and Portugal, while the top productive institutions and researchers are Instituto Politecnico da Guarda (10.37% of TP) and Marques Goncalo (15.09% of TP), respectively. Keyword analysis revealed four major research themes: IoT, pollution, monitoring, and health. Overall, this paper provides significant insights for identifying prospective collaborators, benchmark publications, strategic funding, and institutions for future IoT-IAQ researchers.
Renewable energy consumption is a crucial solution to addressing pressing environmental issues, particularly climate change and air pollution. Investigating the factors that drive its adoption is highly significant, as it provides policymakers and stakeholders with valuable insights to accelerate the transition to renewable energy sources. Through this approach, we can minimise the negative consequences of our reliance on fossil fuels, thereby protecting the integrity of the environment. Therefore, the primary goal of this study is to thoroughly investigate the main factors that influence renewable energy consumption and environmental change in six specifically chosen ASEAN countries. The stationarity of the 1990-2019 data was tested using panel data techniques such as Levin, Lin, and Chu (LLC), Im Pesaran (IPS), and the Shin W-stat test. According to the stationarity tests, after the first order, all variables exhibit stationarity. Additionally, Pedroni's co-integration test result confirmed that there was a long-term relationship among the variables. Different methods, such as dynamic ordinary least squares (DOLS), fully modified ordinary least squares (FMOLS), and pooled ordinary least squares (POLS), are used for cointegration estimating. The results suggest that there is a positive co-integration between renewable energy use and GDP in six ASEAN countries, indicating a long-term relationship. The positive relationship between GDP and renewable energy use suggests that economic growth is the primary driving force behind ASEAN's renewable energy adoption. However, factors like carbon emissions, population density, and foreign direct investment (FDI) negatively impact the demand for renewable energy. The limited availability of renewable energy in certain ASEAN countries may discourage foreign direct investment (FDI) due to the inverse relationship between FDI and renewable energy use. The studies also revealed that carbon emissions, which contribute to environmental pollution, do not motivate industries to invest in renewable energy. This finding would challenge the Environmental Kuznets Curve (EKC) hypothesis. According to the EKC, there is a significant transition towards renewable energy as a response to environmental degradation. However, it is worth noting that several ASEAN countries have experienced economic growth while also experiencing higher levels of carbon emissions. Given that economic expansion might not be environmentally beneficial, this research has implications for ASEAN energy policies. The ASEAN region faces a challenge in investing in renewable energy due to the excessive dependence on fossil fuels. Therefore, an in-depth evaluation of the main factor behind ASEAN's environmental concerns, which promotes the adoption of renewable energy, can greatly influence policy decisions, particularly in attaining net zero emissions. Policymakers can utilise this comprehensive analysis to establish informed objectives for policies related to renewable energy and develop strategic plans, i.e. reforming fuel subsidies. The goal is to encourage the development of environmentally friendly and sustainable energy plans for the future in the ASEAN region.
Stochastic modeling approaches have attracted many researchers to the field. However, fire hotspot detection suffers from not using a Markov chain quasi-Monte Carlo (MCQMC) as a forecasting methodology. This paper proposes improvements to the computational time by combining the strengths of the Markov chain Monte Carlo (MCMC) and quasi-Monte Carlo (QMC) methods. The proposed method can lead to more precise and stable results, particularly in problems with high-dimensional integration or complex probability distributions. The proposed method is applied to a case study of fire hotspot detection in Sarawak, Malaysia. The outcome of this study reveals that the MCQMC method is more computationally efficient, taking only 0.0746 seconds compared to MCMC's 0.0914 seconds and QMC's 0.0994 seconds. It is shown that the best option derived by the proposed method is effective in predicting fire hotspots and providing quick solutions to protect the environment and communities from forest fires.
A novel coronavirus disease (COVID-19) continues to challenge the whole world. The disease has claimed many fatalities as it has transcended from one country to another since it was first discovered in China in late 2019. To prevent further morbidity and mortality associated with COVID-19, most of the countries initiated a countrywide lockdown. While physical distancing and lockdowns helped in curbing the spread of this novel coronavirus, it led to massive economic losses for the nations. Positive impacts have been observed due to lockdown in terms of improved air quality of the nations. In the current research, ten tropical and subtropical countries have been analysed from multiple angles, including air pollution, assessment and valuation of health impacts and economic loss of countries during COVID-19 lockdown. Countries include Brazil, India, Iran, Kenya, Malaysia, Mexico, Pakistan, Peru, Sri Lanka, and Thailand. Validated Simplified Aerosol Retrieval Algorithm (SARA) binning model is used on data collated from moderate resolution imaging spectroradiometer (MODIS) for particulate matters with a diameter of less than 2.5 μm (PM2.5) for all the countries for the month of January to May 2019 and 2020. The concentration results of PM2.5 show that air pollution has drastically reduced in 2020 post lockdown for all countries. The highest average concentration obtained by converting aerosol optical depth (AOD) for 2020 is observed for Thailand as 121.9 μg/m3 and the lowest for Mexico as 36.27 μg/m3. As air pollution is found to decrease in the April and May months of 2020 for nearly all countries, they are compared with respective previous year values for the same duration to calculate the reduced health burden due to lockdown. The present study estimates that cumulative about 100.9 Billion US$ are saved due to reduced air pollution externalities, which are about 25% of the cumulative economic loss of 435.9 Billion US$.
This research aims to look into the effect of COVID-19 on emerging stock markets in seven of the Association of Southeast Asian Nations' (ASEAN-7) member countries from March 21, 2020 to April 31, 2020. This paper uses a ST-HAR-type Bayesian posterior model and it highlights the stock market of this ongoing crisis, such as, COVID-19 outbreak in all countries and related industries. The empirical results shown a clear evidence of a transition during COVID-19 crisis regime, also crisis intensity and timing differences. The most negatively impacted industries were health care and consumer services due to the Covid-19 drug-race and international travel restrictions. More so, study results estimated that only a small number of sectors are affected by COVID-19 fear including health care, consumer services, utilities, and technology, significance at the 1%, 5%, and 10%, that measure current volatility's reliance on weekly and monthly variables. Secondly, it is found that there is almost no chance that the COVID-19 pandemic would positively affect the stock market performance in all the countries, mainly Indonesia and Singapore were the countries most affected. Thirdly, results shown that Thailand's stock market output has dropped by 15%. Results shows that COVID-19 fear causes an eventual reason of public attention towards stock market volatility. The study presented comprehensive way forwards to stabilize movement of ASEAN equity market's volatility index and guided the policy implications to key stakeholders that can better help to mitigate drastic impacts of COVID-19 fear on the performance of equity markets.