The adsorption behavior of basic, methylene blue (MB), and reactive, remazol brilliant violet 5R (RBV), dyes from aqueous solution onto Intsia bijuga sawdust-based activated carbon (IBSAC) was executed via batch and column studies. The produced activated carbon was characterized through Brunauer-Emmett-Teller (BET) surface area and pore structural analysis, proximate and ultimate, scanning electron microscopy (SEM), and Fourier transform infrared spectroscopy (FTIR). Batch studies were performed to investigate the effects of contact time, initial concentration, and solution pH. The equilibrium data for both MB and RBV adsorption better fits Langmuir model with maximum adsorption capacity of 434.78 and 212.77 mg/g, respectively. Kinetic studies for both MB and RBV dyes showed that the adsorption process followed a pseudo-second-order and intraparticle diffusion kinetic models. For column mode, the breakthrough curves were plotted by varying the flow rate, bed height, and initial concentration and the breakthrough data were best correlated with the Yoon-Nelson model compared to Thomas and Adams-Bohart model. The adsorption activity of IBSAC shows good stability even after four consecutive cycles.
Adsorption capacity and percentage removal efficiency of Cu(II) and Ni(II) ions were studied and compared between raw kaolinite and acid-activated kaolinite. Acid-activated kaolin was prepared by refluxing raw kaolinite with concentrated sulphuric acid followed by calcination to enhance its surface properties and adsorption ability. Both raw and acid-activated kaolinite samples were characterized by Fourier transform infrared spectroscopy, energy dispersive X-ray, scanning electron micrograph and zeta potential analysis. Upon acid treatment, acid-activated kaolinite was discovered to have altered chemical composition and larger BET surface area as compared with raw kaolinite. The batch adsorption studies on aqueous solution were performed under different factors such as contact time, pH condition, adsorbent dosage, initial metal ion concentration and temperature. The optimum condition was selected for each factor including a contact time of 60 min, pH of 7.0, adsorbent dosage of 0.1 g, initial metal ion concentration of 100 mg/L and temperature of 25 °C. Then, the adsorption studies on wastewater samples were carried out at the selected optimum conditions. Acid-activated kaolinite always had better adsorption capacity and percentage removal efficiency than raw kaolinite due to the increasing amount of negative charges on the adsorbent surface and the number of metal ion binding sites upon acid treatment. The adsorption kinetic obtained was well described by the pseudo-second-order model, whereas the adsorption isotherms obtained were well described by either the Freundlich or the Langmuir adsorption model. The results showed that acid-activated kaolinite adsorbent is a better option as a favourable and feasible commercial low-cost adsorbent for wastewater treatment.
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.
A small proportion of the thousands of pesticides on the market today are associated with a disproportionately high incidence of severe acute pesticide poisoning and suicide. Paraquat stands out as one of the most lethal pesticides in common use, frequently involved in fatal incidents due to suicides or accidental exposure. Even though paraquat has been banned in over 67 countries, it is still widely used in many others, particularly in Asia and Latin America. Based on a literature review and consultations, this paper identifies options for replacing paraquat and distils practical lessons from numerous successes around the world. Our aim is to support regulators, policymakers, agronomists and the supply chain sector with practical information related to phasing out paraquat. Production data consistently failed to show any negative effects of banning paraquat on agricultural productivity. A wide range of alternative approaches to weed management and crop defoliation are available, many of which do not rely on herbicides. Over 1.25 million farmers in low- and middle-income countries (LMICs) successfully produce a range of crops for private voluntary standards (PVS) in food and fiber supply chains which prohibit paraquat use. We conclude from the findings of this study that eliminating paraquat will save lives without reducing agricultural productivity. Less hazardous and more sustainable alternatives exist. To enhance successful adoption and uptake of these methods on a wide scale, farmers require training and support within an enabling policy environment.
Air pollution has a serious and adverse effect on human health, and it has become a risk to human welfare and health throughout the globe. One of the major effects of air pollution on health is hospitalizations associated with air pollution. Recently, the estimation and prediction of air pollution-based hospitalization is carried out using artificial intelligence (AI) and machine learning (ML) techniques, i.e., deep learning and long short-term memory (LSTM). However, there is ample room for improvement in the available applied methodologies to estimate and predict air pollution-based hospital admissions. In this paper, we present the modeling and analysis of air pollution and cardiorespiratory hospitalization. This study aims to investigate the association between cardiorespiratory hospitalization and air pollution, and predict cardiorespiratory hospitalization based on air pollution using the artificial intelligence (AI) techniques. We propose the enhanced long short-term memory (ELSTM) model and provide a comparison with other AI techniques, i.e., LSTM, DL, and vector autoregressive (VAR). This study was conducted at seven study locations in Klang Valley, Malaysia. The utilized dataset contains the data from January 2006 to December 2016 for five study locations, i.e., Klang (KLN), Shah Alam (SA), Putrajaya (PUJ), Petaling Jaya (PJ), and Cheras, Kuala Lumpur (CKL). The dataset for Banting contains data from April 2010 to December 2016, and the data for Batu Muda, Kuala Lumpur, contains data from January 2009 to December 2016. The prediction results show that the ELSTM model performed significantly better than other models in all study locations, with the best RMSE scores in Klang study location (ELSTM: 0.002, LSTM: 0.013, DL: 0.006, VAR: 0.066). The results also indicated that the proposed ELSTM model was able to detect and predict the trends of monthly hospitalization significantly better than the LSTM and other models in the study. Hence, we can conclude that we can utilize AI techniques to accurately predict cardiorespiratory hospitalization based on air pollution in Klang Valley, Malaysia.
Although industrialisation is a crucial aspect of economic growth across developing nations, through the release of air contaminants, industrial activities may also create adverse environmental health consequences. Noting that continuous production and other economic activities are crucial for continued survival, this study explores this issue by including the role of governance that is deemed essential but the literature is relatively sparse particularly in the context of developing countries. This research empirically analyses the relationship between air pollution and adult mortality rates from 72 developing countries from the period of 2010 until 2017. Particulate matter (PM2.5) and carbon dioxide (CO2) are used as indicators of air pollution. From the generalized method of moments (GMM) estimations, the results reveal that air pollution negatively affects adult mortality rate. The result reveals that a 10% increase in the PM2.5 level induces the adult mortality rates to increase between 0.04% and 0.06%. In addition, the government significantly moderates the negative effect of air pollution on adult mortality, whereby a one-unit enhancement in governance quality index reduces mortality among the adults in the developing countries by 0.01%. On the other hand, CO2 emission also appears to be positive, but not statistically significant. The results suggest that governance and public health interplay in the sense of a transition towards economic development for improved living and health states can be achievable with improved governance quality.
While studies have demonstrated that air pollution can be catastrophic to the population's health, few empirical studies are found in the economic literature because a considerable proportion of the evidence comes from epidemiological studies. Because of the crucial role of governance in the health community, good governance has been a contentious issue in public sector management in recent years. Therefore, the aim of this study is to examine the effects of air pollution and the role of governance on health outcomes. This study employed the generalized method of moment (GMM) estimation techniques to analyse panel data for 72 developing countries from 2010 to 2017. The empirical results confirm that higher PM2.5 and CO2 levels have a detrimental influence on life expectancy and healthy life expectancy, whereas the role of governance has a positive impact on life expectancy and healthy life expectancy. Furthermore, the findings show governance quality plays a role in moderating the negative effect of PM2.5 on health outcomes. The ongoing rise in air pollution has had a significant impact on the health of developing countries. It appears that governance quality has improved health outcomes. The findings have important policy implications, such that strengthening governance can reduce air pollution emissions in developing countries. However, to reduce the health effects of air pollution, developing countries must implement effective environmental development policies and track the implementation and enforcement of such policies.
This paper presents the landscape of research on airborne microplastics and nanoplastics (MPs/NPs) according to the bibliometric analysis of 147 documents issued between 2015 and 2021, extracted from the Web of Science database. The publications on airborne MPs/NPs have increased rapidly from 2015 onwards, which is largely due to the existence of funding support. Science of the Total Environment is one of the prominent journals in publishing related papers. China, England, the USA, and European Countries have produced a significant output of airborne MP/NP research works, which is associated with the availability of funding agencies regionally or nationally. The research hotspot on the topic ranges from the transport of airborne MPs/NPs to their deposition in the terrestrial or aquatic environments, along with the contamination of samples by indoor MPs/NPs. Most of the publications are either research or review papers related to MPs/NPs. It is crucial to share the understanding of global plastic pollution and its unfavorable effects on humankind by promoting awareness of the existence and impact of MPs/NPs. Funding agencies are vital in boosting the research development of airborne MPs/NPs. Some countries that are lacking funding support were able to publish research findings related to the field of interest, however, with lesser research output. Without sufficient fundings, some impactful publications may not be able to carry a substantial impact in sharing the findings and discoveries with the mass public.
The feasibility of using dried attached-growth biomass from the polyurethane (PU) foam cubes as a solid carbon source to enhance the denitrification process in the intermittently aerated moving bed sequencing batch reactor (IA-MBSBR) during the treatment of low COD/N containing wastewater was investigated. By packing the IA-MBSBR with 8% (v/v) of 8-mL PU foam cubes saturated with dried attached-growth biomass, total nitrogen removal efficiency of 80% could be achieved for 10 consecutive cycles of operation when the intermittent aeration strategy of consecutive 1 h of aeration followed by 2 h of non-aeration period during the REACT period of the IA-MBSBR was adopted. Negligible release of ammonium nitrogen (NH4(+)-N) and slow-release of COD from the dried biomass would ensure that the use of this solid carbon source would not further burden the treatment system. The slow-releasing COD was found to have no effect in promoting the assimilation process and would also allow the carbon source to be used for many cycles of operation. The 'carbon-spent' PU foam cubes could be reused by merely drying at 60 °C at the end of the operational mode. Thus, the dried attached-growth biomass formed on the PU foam cubes could be exploited as an alternative solid carbon source for the enhancement of denitrification process in the IA-MBSBR.
In this work, a sunlight-sensitive photocatalyst of nanocubic-like titanium dioxide (TiO2) and N-doped graphene quantum dots (N-GQDs) is developed through a simple hydrothermal and physical mixing method. The successful amalgamation composite photocatalyst characteristics were comprehensively scrutinized through various physical and chemical analyses. A complete removal of bisphenol A (BPA) is attained by a synthesized composite after 30 min of sunlight irradiation as compared to pure TiO2. This clearly proved the unique contribution of N-GQDs that enhanced the ability of light harvesting especially under visible light and near-infrared region. This superior characteristic enables it to maximize the absorbance in the entire solar spectrum. However, the increase of N-GQDs weight percentage has created massive oxygen vacancies that suppress the generation of active radicals. This resulted in a longer duration for a complete removal of BPA as compared to lower weight percentage of N-GQDs. Hence, this finding can offer a new insight in developing effective sunlight-sensitive photocatalysts for various complex organic pollutants degradation.
This study intends to deal with the environmental consequences of the COVID-19 pandemic in Malaysia, by providing a summary of the effects of COVID-19 on municipal solid waste (MSW). In this analysis, the data on domestic waste collection were collected from the Solid Waste Management and Public Cleaning Corporation (SWCorp) from 1 January 2020 to 31 December 2020 to evaluate the relative changes in MSW percentage via a waste weighing method. The data consisted of the cumulative tonnage of MSW for every local authority in Peninsular Malaysia and was classified according to MCO phases; before the MCO, during the MCO, during the conditional MCO (CMCO) and during the recovery MCO (RMCO) phases. The results indicated that the enforcement of the early MCO showed a positive effect by decreasing the volume of MSW. This decrease was noted across 41 local authorities, which accounts for 87.23% of Peninsular Malaysia. However, the amount of MSW began to increase again when the MCO reached the conditional and recovery stages. From this, it can be concluded that the implementation of the MCO, in its various incarnations, has shown us that our lifestyles can have a harmful impact on our environment. While the pandemic was still spreading and limitations were still in place in Malaysia, local governments and waste management companies had to quickly alter their waste management systems and procedures. The current circumstance allows us to rethink our social and economic structures while improving environmental and social inclusion.
Present climate change consists of global warming that is caused by the emission of greenhouse gases, generally carbon dioxide. The study examines the pollution haven, pollution halo, and environmental Kuznets curve for a number of Asian countries during the period of 1985 to 2020. Outcomes suggest that urbanization, gross domestic product per capita, energy consumption, and foreign direct investment inflow have positive effects, while gross domestic product square, foreign direct investment square, and tourism have negative effects on emissions of carbon dioxide. Furthermore, findings support the validity of the environmental Kuznets curve, pollution haven, and pollution halo hypothesis for the selected Asian countries. We also find robust results of rationality of the environmental Kuznets curve hypothesis for Pakistan, Bangladesh, India, China, Indonesia, Korea, Japan, Malaysia, Vietnam, and Singapore; of pollution haven hypothesis for Bangladesh, China, Indonesia, Japan, Pakistan, and Singapore; and of pollution halo hypothesis for Bangladesh, China, Indonesia, Japan, Pakistan, and Singapore.
The interrelationships between air quality, land cover change, and road networks in the Lagos megacity have not been explored. Globally, there are knowledge gaps in understanding these dynamics, especially using remote sensing data. This study used multi-temporal and multi-spectral Landsat imageries at four epochs (2002, 2013, 2015, and 2020) to evaluate the aerosol optical thickness (AOT) levels in relation to land cover and road networks in the Lagos megacity. A look-up table (LUT) was generated using Py6S, a python-based 6S module, to simulate the AOT using land surface reflectance and top of atmosphere reflectance. A comparative assessment of the method against in situ measurements of particulate matter (PM) at different locations shows a strong positive correlation between the imagery-derived AOT values and the PMs. The AOT concentration across the land cover and road networks showed an increasing trend from 2002 to 2020, which could be explained by urbanization in the megacity. The higher concentration of AOT along the major roads is attributed to the high air pollutants released from vehicles, including home/office generators and industries along the road corridors. The continuous rise in pollutant values requires urgent intervention and mitigation efforts. Remote sensing-based AOT monitoring is a possible solution.
Most human activities that use water produced sewage. As urbanization grows, the overall demand for water grows. Correspondingly, the amount of produced sewage and pollution-induced water shortage is continuously increasing worldwide. Ensuring there are sufficient and safe water supplies for everyone is becoming increasingly challenging. Sewage treatment is an essential prerequisite for water reclamation and reuse. Sewage treatment plants' (STPs) performance in terms of economic and environmental perspective is known as a critical indicator for this purpose. Here, the window-based data envelopment analysis model was applied to dynamically assess the relative annual efficiency of STPs under different window widths. A total of five STPs across Malaysia were analyzed during 2015-2019. The labor cost, utility cost, operation cost, chemical consumption cost, and removal rate of pollution, as well as greenhouse gases' (GHGs) emissions, all were integrated to interpret the eco-environmental efficiency. Moreover, the ordinary least square as a supplementary method was used to regress the efficiency drivers. The results indicated the particular window width significantly affects the average of overall efficiencies; however, it shows no influence on the ranking of STP efficiency. The labor cost was determined as the most influential parameter, involving almost 40% of the total cost incurred. Hence, higher efficiency was observed with the larger-scale plants. Meanwhile, the statistical regression analysis illustrates the significance of plant scale, inflow cBOD concentrations, and inflow total phosphorus concentrations at [Formula: see text] on the performance. Lastly, some applicable techniques were suggested in terms of GHG emission mitigation.
The large use of renewable sources and plug-in electric vehicles (PEVs) would play a critical part in achieving a low-carbon energy source and reducing greenhouse gas emissions, which are the primary cause of global warming. On the other hand, predicting the instability and intermittent nature of wind and solar power output poses significant challenges. To reduce the unpredictable and random nature of renewable microgrids (MGs) and additional unreliable energy sources, a battery energy storage system (BESS) is connected to an MG system. The uncoordinated charging of PEVs offers further hurdles to the unit commitment (UC) required in contemporary MG management. The UC problem is an exceptionally difficult optimization problem due to the mixed-integer structure, large scale, and nonlinearity. It is further complicated by the multiple uncertainties associated with renewable sources, PEV charging and discharging, and electricity market pricing, in addition to the BESS degradation factor. Therefore, in this study, a new variant of mixed-integer particle swarm optimizer is introduced as a reliable optimization framework to handle the UC problem. This study considers six various case studies of UC problems, including uncertainties and battery degradation to validate the reliability and robustness of the proposed algorithm. Out of which, two case studies defined as a multiobjective problem, and it has been transformed into a single-objective model using different weight factors. The simulation findings demonstrate that the proposed approach and improved methodology for the UC problem are effective than its peers. Based on the average results, the economic consequences of numerous scenarios are thoroughly examined and contrasted, and some significant conclusions are presented.
In the wake of various catastrophic consequences of climate change, Malaysia, a rapidly developing economy, is also inevitably experiencing environmental degradation that merits prompt and serious attention from policymakers and its government. Hence, this study simultaneously highlights the short and long-run dynamic connections between carbon emission in Malaysia and the trio of corruption levels, foreign investment inflow, and trade liberalization. The study also controls for a combination of other factors including energy use, GDP, and urbanization. A robust empirical analysis was conducted on time series observations for the country based on the recent Dynamic ARDL simulation. It was observed that Malaysia's per capita pollution levels significantly reduces based on the corruption perception levels during the sampling period while the economic expansion's effect on emission levels is positive. Additionally, urbanization, trade levels and energy use all aggravate the emission levels. On the other hand, although FDI poses an insignificant environmental damage in the short run, its environmental sustainability enhancement roles were supported by its long-run negative impacts on carbon emission. Lastly, the EKC was established and as such, essential policy directions were provided for stakeholders in the rapidly emerging Malaysian economy.
Predicting sediment transport rate (STR) in the presence of flexible vegetation is a critical task for modelers. Sediment transport modeling methods in the coastal region is equally challenging due to the nonlinearity of the STR-vegetation interaction. In the present study, the kernel extreme learning model (KELM) was integrated with the seagull optimization algorithm (SEOA), the crow optimization algorithm (COA), the firefly algorithm (FFA), and particle swarm optimization (PSO) to estimate the STR in the presence of vegetation cover. The rigidity index, D50/wave height, Newton number, drag coefficient, and cover density were used as inputs to the models. The root mean square error (RMSE), the mean absolute error (MAE), and percentage of bias (PBIAS) were used to evaluate the capability of models. This study applied the novel ensemble model, and the inclusive multiple model (IMM), to assemble the outputs of the KELM models. In addition, the innovations of this study were the introduction of a new IMM model, and the use of new hybrid KELM models for predicting STR and investigating the effects of various parameters on the STR. At the testing level, the MAE of the IMM model was 22, 60, 68, 73, and 76% lower than those of the KELM-SEOA, KELM-COA, KELM-PSO, and KELM models, respectively. The IMM had a PBIAS of 5, whereas the KELM-SEOA, KELM-COA, KELM-PSOA, and KELM had PBIAS of 9, 12, 14, 18, and 21%, respectively. The results indicated that the increasing drag coefficient and D50/wave height had decreased the STR. From the findings, it was revealed that the IMM and KELM-SEOA had higher predictive ability for STR. Since the sediment is one of the most important sources of environmental pollution, therefore, this study is useful for monitoring and controlling environmental pollution.
The advanced oxidation process (AOP) is an efficient method to treat recalcitrance pollutants such as pharmaceutical compounds. The essential physicochemical factors in AOP experiments significantly influence the efficiency, speed, cost, and safety of byproducts of the treatment process. In this review, we collected recent articles that investigated the elimination of pharmaceutical compounds by various AOP systems in a water medium, and then we provide an overview of AOP systems, the formation mechanisms of active radicals or reactive oxygen species (ROS), and their detection methods. Then, we discussed the role of the main physicochemical parameters (pH, chemical interference, temperature, catalyst, pollutant concentration, and oxidant concentration) in a critical way. We gained insight into the most frequent scenarios for the proper and improper physicochemical parameters for the degradation of pharmaceutical compounds. Also, we mentioned the main factors that restrict the application of AOP systems in a commercial way. We demonstrated that a proper adjustment of AOP experimental parameters resulted in promoting the treatment performance, decreasing the treatment cost and the treatment operation time, increasing the safeness of the system products, and improving the reaction stoichiometric efficiency. The outcomes of this review will be beneficial for future AOP applicants to improve the pharmaceutical compound treatment by providing a deeper understanding of the role of the parameters. In addition, the proper application of physicochemical parameters in AOP systems acts to track the sustainable development goals (SDGs).
The recent advances in sustainable supply chain management are integrated with Industry 4.0 concepts. This study develops a new integrated model to consider the sustainability and Industry 4.0 criteria for the supplier selection management. The proposed approach consists of the fuzzy best worst method (FBWM) and the two-stage fuzzy inference system (FIS) to assess the selection of suppliers. Firstly, this study determines a comprehensive list of Industry 4.0 and sustainability criteria along with their definitions. Then, the importance weight of each criterion is computed by the FBWM. Subsequently, a two-stage FIS is devoted to nominate the suppliers' performance with regard to the sustainability and Industry 4.0 criteria. To show the applicability of our integrated model, a case study for a textile company in Iran is provided. Finally, some sensitivity analyses are done to assess the efficiency of the proposed integrated approach. One finding is to establish a decision-making framework to evaluate suppliers separately, rather than relatively in a fuzzy environment using Industry 4.0 and sustainability criteria.
In this study, geophysics, geochemistry, and geostatistical techniques were integrated to assess seawater intrusion in Kapas Island due to its geological complexity and multiple contamination sources. Five resistivity profiles were measured using an electric resistivity technique. The results reveal very low resistivity <1 Ωm, suggesting either marine clay deposit or seawater intrusion or both along the majority of the resistivity images. As a result, geochemistry was further employed to verify the resistivity evidence. The Chadha and Stiff diagrams classify the island groundwater into Ca-HCO3, Ca-Na-HCO3, Na-HCO3, and Na-Cl water types, with Ca-HCO3 as the dominant. The Mg(2+)/Mg(2+)+Ca(2+), HCO3 (-)/anion, Cl(-)/HCO3 (-), Na(+)/Cl(-), and SO4 (2-)/Cl(-) ratios show that some sampling sites are affected by seawater intrusion; these sampling sites fall within the same areas that show low-resistivity values. The resulting ratios and resistivity values were then used in the geographical information system (GIS) environment to create the geostatistical map of individual indicators. These maps were then overlaid to create the final map showing seawater-affected areas. The final map successfully delineates the area that is actually undergoing seawater intrusion. The proposed technique is not area specific, and hence, it can work in any place with similar completed characteristics or under the influence of multiple contaminants so as to distinguish the area that is truly affected by any targeted pollutants from the rest. This information would provide managers and policy makers with the knowledge of the current situation and will serve as a guide and standard in water research for sustainable management plan.