Integrated fixed-film activated sludge (IFAS) process is considered as one of the leading-edge processes that provides a sustainable solution for wastewater treatment. IFAS was introduced as an advancement of the moving bed biofilm reactor by integrating the attached and the suspended growth systems. IFAS offers advantages over the conventional activated sludge process such as reduced footprint, enhanced nutrient removal, complete nitrification, longer solids retention time and better removal of anthropogenic composites. IFAS has been recognized as an attractive option as stated from the results of many pilot and full scales studies. Generally, IFAS achieves >90% removals for combined chemical oxygen demand and ammonia, improves sludge settling properties and enhances operational stability. Recently developed IFAS reactors incorporate frameworks for either methane production, energy generation through algae, or microbial fuel cells. This review details the recent development in IFAS with the focus on the pilot and full-scale applications. The microbial community analyses of IFAS biofilm and floc are underlined along with the special emphasis on organics and nitrogen removals, as well as the future research perspectives.
Pilot-scale constructed wetlands planted with Scirpus grossus, were used to investigate the effects of applying a three-rhizobacterial consortium (Bacillus cereus strain NII, Bacillus subtilis strain NII and Brevibacterium sp. strain NII) on the growth of S. grossus and also on the accumulation of iron (Fe) and aluminium (Al) in S. grossus. The experiment includes constructed wetlands with the addition of 2% of the consortium rhizobacteria and without the consortium rhizobacteria addition (acting as control). During each sampling day (0, 5, 10, 15, 20, 25, 30, 42, 72 and 102), plant height, concentration of Fe and Al and sand microbial community were investigated. The results for the constructed wetland with the addition of consortium rhizobacteria showed the growth of S. grossus increased significantly at 26% and 29% for plant height and dry weight, respectively. While the accumulation of Fe and Al in S. grossus were enhanced about 48% and 19% respectively. To conclude, the addition of the rhizobacteria consortium has enhanced both the growth of S. grossus and the metal accumulation. These results suggesting that rhizobacteria has good potential to restore Fe and Al contaminated water in general and particularly for mining wastewater.
This paper empirically investigates the effect of carbon emissions on sovereign risk? To answer this question, we use fixed effects model by using annual data from G7 advanced economies, which includes Canada, France, Germany, Italy, Japan, UK and USA, for the period from 1996 to 2014. We employ a novel extreme value theory to measure sovereign risk. The results indicate that climate change (carbon emissions) are likely to increase sovereign risk significantly. We also expand our analysis to some specific sectors, as some of the sectors emit more carbon than others. Specifically, we take top three polluting sectors namely: transportation, electricity and industry and show that they are more likely to increase the sovereign risk. Our results are robust to change in risk measures, estimation in differences and dynamic version of econometric models. Therefore, we have robust consideration that the carbon emissions significantly explain the sovereign risk.
The concentration of soluble salts in surface water and rivers such as sodium, sulfate, chloride, magnesium ions, etc., plays an important role in the water salinity. Therefore, accurate determination of the distribution pattern of these ions can improve better management of drinking water resources and human health. The main goal of this research is to establish two novel wavelet-complementary intelligence paradigms so-called wavelet least square support vector machine coupled with improved simulated annealing (W-LSSVM-ISA) and the wavelet extended Kalman filter integrated with artificial neural network (W-EKF- ANN) for accurate forecasting of the monthly), magnesium (Mg+2), and sulfate (SO4-2) indices at Maroon River, in Southwest of Iran. The monthly River flow (Q), electrical conductivity (EC), Mg+2, and SO4-2 data recorded at Tange-Takab station for the period 1980-2016. Some preprocessing procedures consisting of specifying the number of lag times and decomposition of the existing original signals into multi-resolution sub-series using three mother wavelets were performed to develop predictive models. In addition, the best subset regression analysis was designed to separately assess the best selective combinations for Mg+2 and SO4-2. The statistical metrics and authoritative validation approaches showed that both complementary paradigms yielded promising accuracy compared with standalone artificial intelligence (AI) models. Furthermore, the results demonstrated that W-LSSVM-ISA-C1 (correlation coefficient (R) = 0.9521, root mean square error (RMSE) = 0.2637 mg/l, and Kling-Gupta efficiency (KGE) = 0.9361) and W-LSSVM-ISA-C4 (R = 0.9673, RMSE = 0.5534 mg/l and KGE = 0.9437), using Dmey mother that outperformed the W-EKF-ANN for predicting Mg+2 and SO4-2, respectively.
Palms are iconic plants. Oil palms are very important economically and originate in Africa where they can act as a model for palms in general. The effect of future climate on the growth of oil palm will be very detrimental. Latitudinal migration of tropical crops to climate refuges may be impossible, and longitudinal migration has only been confirmed for oil palm, of all the tropical crops. The previous method to determine the longitudinal trend for oil palm used the longitudes of various countries in Africa and plotted these against the percentage suitable climate for growing oil palms in each country. An increasing longitudinal trend was observed from west to east. However, the longitudes of the countries were randomly distributed which may have introduced bias and the procedure was time consuming. The present report presents an optimised and systematic procedure that divided the regions, as presented on a map derived from a CLIMEX model, into ten equal sectors and the percentage suitable climates for growing oil palm were determined for each sector. This approach was quicker, systematic and straight forward and will be useful for management of oil palm plantations under climate change. The method confirmed and validated the trends reported in the original method although the suitability values were often lower and there was less spread of values around the trend. The values for the CSIRO MK3.0 and MIROC H models demonstrated considerable similarities to each other, contributing to validation of the method. The procedure of dividing maps equally into sectors derived from models, could be used for other crops, regions, or systems more generally, where the alternative may be a more superficial visual examination of the maps. Methods are required to mitigate the effects of climate change and stakeholders need to contribute more actively to the current climate debate with tangible actions.
Electrocoagulation (ECoag) technique has shown considerable potential as an effective method in separating different types of pollutants (including inorganic pollutants) from various sources of water at a lower cost, and that is environmentally friendly. The EC method's performance depends on several significant parameters, including current density, reactor geometry, pH, operation time, the gap between electrodes, and agitation speed. There are some challenges related to the ECoag technique, for example, energy consumption, and electrode passivation as well as its implementation at a larger scale. This review highlights the recent studies published about ECoag capacity to remove inorganic pollutants (including salts), the emerging reactors, and the effect of reactor geometry designs. In addition, this paper highlights the integration of the ECoag technique with other advanced technologies such as microwave and ultrasonic to achieve higher removal efficiencies. This paper also presents a critical discussion of the major and minor reactions of the electrocoagulation technique with several significant operational parameters, emerging designs of the ECoag cell, operating conditions, and techno-economic analysis. Our review concluded that optimizing the operating parameters significantly enhanced the efficiency of the ECoag technique and reduced overall operating costs. Electrodes geometry has been recommended to minimize the passivation phenomenon, promote the conductivity of the cell, and reduce energy consumption. In this review, several challenges and gaps were identified, and insights for future development were discussed. We recommend that future studies investigate the effect of other emerging parameters like perforated and ball electrodes on the ECoag technique.
The central composite rotatable design (CCD) of response surface methodology (RSM) was used to optimize aluminum dispersed bamboo activated carbon preparation. The independent variables selected for optimization are activating agent (AlCl3) concentration (mol/L), activation temperature (°C), and activation time (min.). The independent variable's response change was observed through the percentage adsorption efficiency of Ciprofloxacin hydrochloride (CIP) antibiotics. The maximum CIP adsorption efficiency was found to be 93.6 ± 0.36% (13.36 mg/g) for the adsorbent prepared at AlCl3 concentration 2.0 mol/L, activation temperature 900 °C, and activation time 120 min. The adsorption efficiency was recorded at the natural pH (7.9) of the adsorbent (3 g/L)-adsorbate (50 mL solution of 50 ppm) mixture. The Al-dispersed bamboo activated carbon was characterized for its surface morphology, surface elemental compositions, molecular crystallinity, surface area, pore morphology, and surface functional groups. The mechanism of adsorbent surface formation and CIP adsorption sites were explored. The characterization data and mechanism study will help in deciding possible future applications in other fields of study.
Due to its increasing demands for fossil fuels, Indonesia needs an alternative energy to diversify its energy supply. Landfill gas (LFG), which key component is methane (CH4), has become one of the most attractive options to sustain its continued economic development. This exploratory study seeks to demonstrate the added value of landfilled municipal solid waste (MSW) in generating sustainable energy, resulting from CH4 emissions in the Bantargebang landfill (Jakarta). The power generation capacity of a waste-to-energy (WTE) plant based on a mathematical modeling was investigated. This article critically evaluated the production of electricity and potential income from its sale in the market. The project's environmental impact assessment and its socio-economic and environmental benefits in terms of quantitative and qualitative aspects were discussed. It was found that the emitted CH4 from the landfill could be reduced by 25,000 Mt annually, while its electricity generation could reach one million kW ⋅h annually, savings on equivalent electricity charge worth US$ 112 million/year (based on US' 8/kW ⋅ h). An equivalent CO2 mitigation of 3.4 × 106 Mt/year was obtained. The income from its power sale were US$ 1.2 ×106 in the 1st year and 7.7 ×107US$ in the 15th year, respectively, based on the projected CH4 and power generation. The modeling study on the Bantargebang landfill using the LFG extraction data indicated that the LFG production ranged from 0.05 to 0.40 m3 per kg of the landfilled MSW. The LFG could generate electricity as low as US' 8 per kW ⋅ h. With respect to the implications of this study, the revenue not only defrays the cost of landfill's operations and maintenance (O&M), but also provides an incentive and means to further improve its design and operations. Overall, this work not only leads to a diversification of primary energy, but also improves environmental protection and the living standard of the people surrounding the plant.
Effluent originating from cheese production puts pressure onto environment due to its high organic load. Therefore, the main objective of this work was to compare the influence of different process variables (transmembrane pressure (TMP), Reynolds number and feed pH) on whey protein recovery from synthetic and industrial cheese whey using polyethersulfone (PES 30 kDa) membrane in dead-end and cross-flow modes. Analysis on the fouling mechanistic model indicates that cake layer formation is dominant as compared to other pore blocking phenomena evaluated. Among the input variables, pH of whey protein solution has the biggest influence towards membrane flux and protein rejection performances. At pH 4, electrostatic attraction experienced by whey protein molecules prompted a decline in flux. Cross-flow filtration system exhibited a whey rejection value of 0.97 with an average flux of 69.40 L/m2h and at an experimental condition of 250 kPa and 8 for TMP and pH, respectively. The dynamic behavior of whey effluent flux was modeled using machine learning (ML) tool convolutional neural networks (CNN) and recursive one-step prediction scheme was utilized. Linear and non-linear correlation indicated that CNN model (R2 - 0.99) correlated well with the dynamic flux experimental data. PES 30 kDa membrane displayed a total protein rejection coefficient of 0.96 with 55% of water recovery for the industrial cheese whey effluent. Overall, these filtration studies revealed that this dynamic whey flux data studies using the CNN modeling also has a wider scope as it can be applied in sensor tuning to monitor flux online by means of enhancing whey recovery efficiency.
Oilfield produced water (OPW) is one of the most important by-products, resulting from oil and gas exploration. The water contains a complex mixture of organic and inorganic compounds such as grease, dissolved salt, heavy metals as well as dissolved and dispersed oils, which can be toxic to the environment and public health. This article critically reviews the complex properties of OPW and various technologies for its treatment. They include the physico-chemical treatment process, biological treatment process, and physical treatment process. Their technological strengths and bottlenecks as well as strategies to mitigate their bottlenecks are elaborated. A particular focus is placed on membrane technologies. Finally, further research direction, challenges, and perspectives of treatment technologies for OPW are discussed. It is conclusively evident from 262 published studies (1965-2021) that no single treatment method is highly effective for OPW treatment as a stand-alone process however, conventional membrane-based technologies are frequently used for the treatment of OPW with the ultrafiltration (UF) process being the most used for oil rejection form OPW and oily waste water. After membrane treatment, treated effluents of the OPW could be reused for irrigation, habitant and wildlife watering, microalgae production, and livestock watering. Overall, this implies that target pollutants in the OPW samples could be removed efficiently for subsequent use, despite its complex properties. In general, it is however important to note that feed quality, desired quality of effluent, cost-effectiveness, simplicity of process are key determinants in choosing the most suitable treatment process for OPW treatment.
Soil erosion hazard is one of the prominent climate hazards that negatively impact countries' economies and livelihood. According to the global climate index, Sri Lanka is ranked among the first ten countries most threatened by climate change during the last three years (2018-2020). However, limited studies were conducted to simulate the impact of the soil erosion vulnerability based on climate scenarios. This study aims to assess and predict soil erosion susceptibility using climate change projected scenarios: Representative Concentration Pathways (RCP) in the Central Highlands of Sri Lanka. The potential of soil erosion susceptibility was predicted to 2040, depending on climate change scenarios, RCP 2.6 and RCP 8.5. Five models: revised universal soil loss (RUSLE), frequency ratio (FR), artificial neural networks (ANN), support vector machine (SVM) and adaptive network-based fuzzy inference system (ANFIS) were selected as widely applied for hazards assessments. Eight geo-environmental factors were selected as inputs to model the soil erosion susceptibility. Results of the five models demonstrate that soil erosion vulnerability (soil erosion rates) will increase 4%-22% compared to the current soil erosion rate (2020). The predictions indicate average soil erosion will increase to 10.50 t/ha/yr and 12.4 t/ha/yr under the RCP 2.6 and RCP 8.5 climate scenario in 2040, respectively. The ANFIS and SVM model predictions showed the highest accuracy (89%) on soil erosion susceptibility for this study area. The soil erosion susceptibility maps provide a good understanding of future soil erosion vulnerability (spatial distribution) and can be utilized to develop climate resilience.
This study investigated the effects of biochar-based solid acids (SAs) on carbon conversion, alpha diversity and bacterial community succession during cow manure composting with the goal of providing a new strategy for rapid carbon conversion during composting. The addition of SA prolonged the thermophilic phase and accelerated the degradation of lignocellulose; in particular, the degradation time of cellulose was shortened by 50% and the humus content was increased by 22.56% compared with the control group (CK). In addition, high-throughput sequencing results showed that SA improved the alpha diversity and the relative abundance of thermophilic bacteria, mainly Actinobacteria, increased by 12.955% compared with CK. A redundancy analysis (RDA) showed that Actinobacteria was positively correlated with the transformation of carbon.
Remediation by algae is a very effective strategy for avoiding the use of costly, environmentally harmful chemicals in wastewater treatment. Recently, industries based on biomass, especially the bioenergy sector, are getting increasing attention due to their environmental acceptability. However, their practical application is still limited due to the growing cost of raw materials such as algal biomass, harvesting and processing limitations. Potential use of algal biomass includes nutrients recovery, heavy metals removal, COD, BOD, coliforms, and other disease-causing pathogens reduction and production of bioenergy and valuable products. However, the production of algal biomass using the variable composition of different wastewater streams as a source of growing medium and the application of treated water for subsequent use in agriculture for irrigation has remained a challenging task. The present review highlights and discusses the potential role of algae in removing beneficial nutrients from different wastewater streams with complex chemical compositions as a biorefinery concept and subsequent use of produced algal biomass for bioenergy and bioactive compounds. Moreover, challenges in producing algal biomass using various wastewater streams and ways to alleviate the stress caused by the toxic and high concentrations of nutrients in the wastewater stream have been discussed in detail. The technology will be economically feasible and publicly accepted by reducing the cost of algal biomass production and reducing the loaded or attached concentration of micropollutants and pathogenic microorganisms. Algal strain improvement, consortium development, biofilm formation, building an advanced cultivation reactor system, biorefinery concept development, and life-cycle assessment are all possible options for attaining a sustainable solution for sustainable biofuel production. Furthermore, producing valuable compounds, including pharmaceutical, nutraceutical and pigment contents generated from algal biomass during biofuel production, could also help reduce the cost of wastewater management by microalgae.
The main objective of the current work is to investigate the effect of nickel-waste chicken eggshell modified Hydrogen exchanged Zeolite Socony Mobil-5 (Ni-WCE/HZSM-5) on pyrolysis of high-density polyethylene (HDPE). Ni-WCE/HZSM-5 was synthesized via the impregnation incipient wetness (IWI) method with Ni and WCE mass loading of 4 and 12 wt% respectively. HZSM-5, CaO, WCE, WCE/HZSM-5, and Ni/HZSM-5 were prepared for comparison purposes with Ni-WCE/HZSM-5. All the synthesized catalysts were characterized for phase analysis, metal loading, surface morphology, and textural properties. The impregnation of nickel and WCE had significantly affected the original framework of HZSM-5, where the crystallinity percentage and average crystal size of HZSM-5 dropped to 44.97% and increased to 47.90 nm respectively. The surface morphology of HZSM-5 has drastically changed from a cubic-like shape into a spider web-like surface after the impregnation of WCE. The BET surface area of HZSM-5 has been lowered due to the impregnation of nickel and WCE, but the total pore volume has increased greatly from 0.2291 cm3/g to 0.2621 cm3/g. The catalyst performance was investigated in the pyrolysis of HDPE via a fixed bed reactor and the pyrolysis oil was further analysed to evaluate the distribution of C6 to C9> hydrocarbons. Among the tested catalytic samples, the highest pyrolysis oil yield was achieved by WCE (80%) followed by CaO (78%), WCE/HZSM-5 (63%), HZSM-5 (61%), Ni/HZSM-5 (44%) and Ni-WCE/HZSM-5 (50%). For hydrocarbon distribution in pyrolysis oil, the Ni/HZSM-5 produced the highest of total C6 and C7 hydrocarbons at 12% and 27% respectively followed by WCE/HZSM-5 (4% and 20%), non-catalytic (5% and 13%), Ni-WCE/HZSM-5 (0% and 15%), WCE (0% and 10%), HZSM-5 (0% and 6%) and CaO (0% and 0%).
Heavy metals (HMs) such as Lead (Pb) have played a vital role in increasing the sediments of the Australian bay's ecosystem. Several meteorological parameters (i.e., minimum, maximum and average temperature (Tmin, Tmax and TavgoC), rainfall (Rn mm) and their interactions with the other batch HMs, are hypothesized to have high impact for the decision-making strategies to minimize the impacts of Pb. Three feature selection (FS) algorithms namely the Boruta method, genetic algorithm (GA) and extreme gradient boosting (XGBoost) were investigated to select the highly important predictors for Pb concentration in the coastal bay sediments of Australia. These FS algorithms were statistically evaluated using principal component analysis (PCA) Biplot along with the correlation metrics describing the statistical characteristics that exist in the input and output parameter space of the models. To ensure a high accuracy attained by the applied predictive artificial intelligence (AI) models i.e., XGBoost, support vector machine (SVM) and random forest (RF), an auto-hyper-parameter tuning process using a Grid-search approach was also implemented. Cu, Ni, Ce, and Fe were selected by all the three applied FS algorithms whereas the Tavg and Rn inputs remained the essential parameters identified by GA and Boruta. The order of the FS outcome was XGBoost > GA > Boruta based on the applied statistical examination and the PCA Biplot results and the order of applied AI predictive models was XGBoost-SVM > GA-SVM > Boruta-SVM, where the SVM model remained at the top performance among the other statistical metrics. Based on the Taylor diagram for model evaluation, the RF model was reflected only marginally different so overall, the proposed integrative AI model provided an evidence a robust and reliable predictive technique used for coastal sediment Pb prediction.
Despite being directly related to anthropogenic consumption and production, researchers have paid less attention to understanding the dynamics of non-methane volatile organic compounds. The primary objective of this research is to investigate the persistence of potential shocks to non-methane volatile organic compounds in 20 developed from 1820 to 2019 performing traditional unit root approaches and a newly developed Fourier quantile unit root test. Great portion of the empirical results obtained by traditional unit root tests reveal that the sectoral non-methane volatile organic compounds follow a non-stationary process, while the Fourier quantile unit root test indicate quite different results. The Fourier quantile test shows that non-methane volatile organic compounds are stationary in the United Kingdom, Ireland, Germany, France and Austria. In the other 15 countries, government interventions to reduce non-methane volatile organic compounds can have lasting effects and success. The inferences and policy outcomes of the empirical results are discussed in the main body of the paper.
The advantageous characteristics of aerobic granular sludge (AGS) have led to their increasing popularities among academics and industrial players. However, there has been no bibliometric report on current and future research trends of AGS. This study utilized the available reports of AGS in the Scopus database for comprehensive bibliometric analyses using VOSviewer software. A total of 1203 research articles from 1997 to 2020 were analyzed. The dominance of the Netherlands and China were revealed by the high number of publications and citations. Nevertheless, the Netherlands exhibited higher average citation per article at 76.4. A recent process of AGS involving biochar and algal addition were also identified. Meanwhile, the application of AGS for antibiotic containing wastewater as well as possibility of resource recovery were recently reported and was expected to expand in the future. It was suggested that application of AGS would develop further along with the development of sustainable wastewater treatment process.
The high demand for plastic has led to plastic waste accumulation, improper disposal and environmental pollution. Even though some of this waste is recycled, most ends up in landfills or flows down rivers into the oceans. Therefore, researchers are now exploring better ways to solve the plastic waste management problem. From a socio-economic perspective, there is also a concerted effort to enable energy recovery from plastic waste and convert it into useful products to generate income for targeted segments of the population. In fact, this concept of waste-to-wealth has been adopted by the United Nations as part of its Sustainable Development Goals strategies. The current article begins by reviewing the strengths and weaknesses of plastic recycling before focusing specifically on microwave pyrolysis as an alternative to conventional technologies in plastic waste management, due to its benefit in providing fast and energy-efficient heating. The key parameters that are reviewed in this paper include different types of plastic, types of absorbent, temperatures, microwave power, residence time, and catalysts. The yield of the final product (oil, gaseous and char) varies depending on the main process parameters. Key challenges and limitations of microwave pyrolysis are also discussed in this paper.
Globally, the interaction and vulnerability of tourism and climate change have recently been in focus. This study examines how carbon dioxide emissions respond to changes in the tourism development. Panel data from 2000 to 2017 for 70 countries are analyzed using spatial econometric method to investigate the spatial spillover effect of tourism development on environmental pollution. The direct, indirect, and overall impact of tourism on environmental pollution are estimated after the selection of the most appropriate GNS method. The findings reveal that tourism has a positive direct effect and a negative indirect effect; both are significant at the 1 % level. The negative indirect effect of tourism is greater than its direct positive effect, implying an overall significantly negative impact. Further, the outcome of financial development and carbon emissions have an inverted U-shaped and U-shaped relationship in direct and indirect impacts. Population density, trade openness and economic growth significantly influence on environmental pollution through spatial spill over. In addition, education expenditure and infrastructure play a significant moderating role in the relationship among tourism development and environmental pollution. The results have important policy implications as they establish an inverted-U-shaped relationship among tourism and environmental pollution and indicate that while a country's emissions initially rise with the tourism industry's growth, they begin declining after a limit.
In recent years, the research on human behaviour in relation to waste management has increased at an exponential rate. At the same time, the expanding academic literature on this topic makes it more difficult to understand the main areas of interest, the leading institutions and authors, the possible interconnections among different disciplines, and the gaps. This paper maps knowledge domain on recycling behaviour through bibliometric analysis and text mining in order to identify current trends, research networks and hot topics. 2061 articles between 1975 and 2020 from three different databases are examined with an interdisciplinary approach. The findings reveal that 60% of papers have been published between 2015 and 2020, and this topic is of global interest. Leading countries are mainly located in Europe, North America and Commonwealth; however, China and Malaysia are also assuming a driving role. Bibliometrics and text mining provide the intellectual configuration of the knowledge on recycling behaviour; co-word analysis individuates conceptual sub-domains in food waste, determinants of recycling behaviour, waste management system, waste electrical and electronic equipment (WEEE), higher-level education, plastic bags, and local government. Overall, waste management and related human behaviour represent a universal challenge requiring a structured and interdisciplinary approach at all levels (individual, institutions, industry, academia). Lastly, this paper offers some suggestions for future research such as smart city design, sensor network system, consumer responsibilisation, the adoption of a more comprehensive view of the areas of investigation through the holistic analysis of all stakeholders.