Choosing a suitable gridded climate dataset is a significant challenge in hydro-climatic research, particularly in areas lacking long-term, reliable, and dense records. This study used the most common method (Perkins skill score (PSS)) with two advanced time series similarity algorithms, short time series distance (STS), and cross-correlation distance (CCD), for the first time to evaluate, compare, and rank five gridded climate datasets, namely, Climate Research Unit (CRU), TERRA Climate (TERRA), Climate Prediction Center (CPC), European Reanalysis V.5 (ERA5), and Climatologies at high resolution for Earth's land surface areas (CHELSA), according to their ability to replicate the in situ rainfall and temperature data in Nigeria. The performance of the methods was evaluated by comparing the ranking obtained using compromise programming (CP) based on four statistical criteria in replicating in situ rainfall, maximum temperature, and minimum temperature at 26 locations distributed over Nigeria. Both methods identified CRU as Nigeria's best-gridded climate dataset, followed by CHELSA, TERRA, ERA5, and CPC. The integrated STS values using the group decision-making method for CRU rainfall, maximum and minimum temperatures were 17, 10.1, and 20.8, respectively, while CDD values for those variables were 17.7, 11, and 12.2, respectively. The CP based on conventional statistical metrics supported the results obtained using STS and CCD. CRU's Pbias was between 0.5 and 1; KGE ranged from 0.5 to 0.9; NSE ranged from 0.3 to 0.8; and NRMSE between - 30 and 68.2, which were much better than the other products. The findings establish STS and CCD's ability to evaluate the performance of climate data by avoiding the complex and time-consuming multi-criteria decision algorithms based on multiple statistical metrics.
Biogas plant operators often face huge challenges in the monitoring, controlling and optimisation of the anaerobic digestion (AD) process, as it is very sensitive to surrounding changes, which often leads to process failure and adversely affects biogas production. Conventional implemented methods and mechanistic models are impractical and find it difficult to model the nonlinear and intricate interactions of the AD process. Thus, the development of machine learning (ML) algorithms has attracted considerable interest in the areas of process optimization, real-time monitoring, perturbation detection and parameter prediction. This paper provides a comprehensive and up-to-date overview of different machine learning algorithms, including artificial neural network (ANN), fuzzy logic (FL), adaptive network-based fuzzy inference system (ANFIS), support vector machine (SVM), genetic algorithm (GA) and particle swarm optimization (PSO) in terms of working mechanism, structure, advantages and disadvantages, as well as their prediction performances in modelling the biogas production. A few recent case studies of their applications and limitations are also critically reviewed and compared, providing useful information and recommendation in the selection and application of different ML algorithms. This review shows that the prediction efficiency of different ML algorithms is greatly impacted by variations in the reactor configurations, operating conditions, influent characteristics, selection of input parameters and network architectures. It is recommended to incorporate mixed liquor volatile suspended solids (MLVSS) concentration of the anaerobic digester (ranging from 16,500 to 46,700 mg/L) as one of the input parameters to improve the prediction efficiency of ML modelling. This review also shows that the combination of different ML algorithms (i.e. hybrid GA-ANN model) could yield better accuracy with higher R2 (0.9986) than conventional algorithms and could improve the optimization model of AD. Besides, future works could be focused on the incorporation of an integrated digital twin system coupled with ML techniques into the existing Supervisory Control and Data Acquisition (SCADA) system of any biogas plant to detect any operational abnormalities and prevent digester upsets.
The fungi-based technology, wild-Serbian Ganoderma lucidum (WSGL) as myco-alternative to existing conventional microbial-based wastewater treatment is introduced in this study as a potential alternative treatment. The mycoremediation is highly persistent for its capability to oxidatively breakdown pollutant substrates and widely researched for its medicinal properties. Utilizing the nonhazardous properties and high degradation performance of WSGL, this research aims to optimize mycoremediation treatment design for chemical oxygen demand (COD) and ammonia nitrogen (AN) removal in domestic wastewater based on proposed Model 1 (temperature and treatment time) and Model 2 (volume of pellet and treatment time) via response surface methodology (RSM). Combined process variables were temperature (0C) (Model 1) and the volume of mycelial pellets (%) (Model 2) against treatment time (hour). Response variables for these two sets of central composite design (CCD) were the removal efficiencies of COD (%) and AN (%). The regression line fitted well with the data with R2 values of 0.9840 (Model 1-COD), 0.9477 (Model 1-AN), 0.9988 (Model 2-COD), and 0.9990 (Model 2-AN). The lack of fit test gives the highest value of sum of squares equal to 9494.91 (Model 1-COD), 9701.68 (Model 1-AN), 23786.55 (Model 2-COD), and 13357.02 (Model 2-AN), with probability F values less than 0.05 showing significant models. The optimized temperature for Model 1 was at 25 °C within 24 h of treatment time with 95.1% COD and 96.3% AN removals. The optimized condition (temperature) in Model 1 was further studied in Model 2. The optimized volume of pellet for Model 2 was 0.25% in 24-h treatment time with 76.0% COD and 78.4% AN removals. Overall, the ascended sequence of high volume of pellet considered in Model 2 will slow down the degradation process. The best fit volume of pellet with maximum degradation of COD and AN is equivalent to 0.1% at 25 °C in 24 h. The high performance achieved demonstrates that the mycoremediation of G. lucidum is highly potential as part of the wastewater treatment system in treating domestic wastewater of high organic loadings.
Precise monitoring of cyanobacteria concentration in water resources is a daunting task. The development of reliable tools to monitor this contamination is an important research topic in water resources management. Indirect methods such as chlorophyll-a determination, cell counting, and toxin measurement of the cyanobacteria are tedious, cumbersome, and often lead to inaccurate results. The quantity of phycocyanin (PC) pigment is considered more appropriate for cyanobacteria monitoring. Traditional approaches for PC estimation are time-consuming, expensive, and require high expertise. Recently, some studies have proposed the application of artificial intelligence (AI) techniques to predict the amount of PC concentration. Nonetheless, most of these researches are limited to standalone modeling schemas such as artificial neural network (ANN), multilayer perceptron (MLP), and support vector machine (SVM). The independent schema provides imprecise results when faced with highly nonlinear systems and data uncertainties resulting from environmental disturbances. To alleviate the limitations of the existing models, this study proposes the first application of a hybrid AI model that integrates the potentials of relevance vector machine (RVM) and flower pollination algorithm (RVM-FPA) to predict the PC concentration in water resources. The performance of the hybrid model is compared with the standalone RVM model. The prediction performance of the proposed models was evaluated at two stations (stations 508 and 478) using different statistical and graphical performance evaluation methods. The results showed that the hybrid models exhibited higher performance at both stations compared to the standalone RVM model. The proposed hybrid RVM-FPA can therefore serve as a reliable predictive tool for PC concentration in water resources.
There has been increasing concern over the toxic effects of microplastics (MP), nanoplastics (NP), and copper (Cu) on microalgae. However, the combined toxicity of the metal in the presence of polystyrene (PS) MP/NP on microalgae has not been well studied, particularly after long-term exposure (i.e., longer than 4 days). The primary aim of the present study was to investigate the effect of PS MP and NP on Cu toxicity on two freshwater microalgae, namely Chlorella sp. TJ6-5 and Pseudokirchneriella subcapitata NIES-35 after acute exposure for 4 days and up to 16 days. The results showed that both microalgae were sensitive to Cu, but tolerant to MP/NP. However, MP/NP increased the toxicity of Cu at EC50 in both microalgae, which was only noticeable in chronic exposure. Single and combined treatment of MP/NP and Cu induced higher oxidative stress and caused morphological and ultrastructural changes in both microalgae. The adsorption of Cu to MP and NP was low (0.23-14.9%), with most of the Cu present in free ionic form (81.6-105.8%). The findings on different sensitivity of microalgae to Cu in the presence of MP/NP may have significant implication as microalgae are likely to be exposed to a mixture of both MP/NP and Cu in the environment. For example, in air-blasting technology, MP and NP are used as abrasive medium to remove Cu-containing antifouling paints on hulls of ship and submerged surfaces. Wastewater treatment plants receive household wastes containing MP and NP, as well as stormwater runoffs and industrial wastes contaminated with heavy metals.
Extant studies address water, food, and health security issues considerably separately and within narrow disciplinary confines. This study investigates the links among these three issues from an ecological viewpoint with a multidisciplinary approach in a modified Millennium Ecosystem Assessment framework developed by the United Nations. The modified framework includes water, food, and health security considerations as the three constituents of human well-being from an ecological (more specifically, ecosystem services) viewpoint. This study examines the links through published data associated with the Minamata incident, which was a historic and horrific methylmercury-induced water, food, and health poisoning crisis in Japan. The results show that when heavy metal pollution changes one component (marine water) of the provisioning ecosystem services, this change subsequently affects another component (seafood) of the services. This then defines the linkages among water, food, and health security as the three constituents of human well-being within the modified framework. The links can have immediate and far-reaching economic, social, legal, ethical, and justice implications within and across generations. This study provides important evidence for emerging economies that ignore the water-food-health security nexus.
One of the major challenges of the supply chain managers is to select the best suppliers among all possible ones for their business. Although the research on the supplier selection with regards to green, sustainability or resiliency criteria has been contributed by many papers, simultaneous consideration of these criteria in a fuzzy environment is rarely studied. Hence, this study proposes a fuzzy decision framework to investigate the sustainable-resilient supplier selection problem for a real case study of palm oil industry in Malaysia. Firstly, the resilient-based sustainable criteria are localized for the suppliers' performance evaluation in palm oil industry of Malaysia. Accordingly, 30 criteria in three different aspects (i.e. general, sustainable and resilient) are determined by statistical tests. Moreover, a hyper-hybrid model with the use of FDEMATEL (fuzzy decision-making trial and evaluation laboratory), FBWM (fuzzy best worst method), FANP (fuzzy analytical network process) and FIS (fuzzy inference system), simultaneously is developed to employ their merits in an efficient way. In this framework, regarding the outset, the relationships among the criteria/sub-criteria are obtained by FDEMATEL method. Then, initial weights of the criteria/sub-criteria are measured by FBWM method. Next, the final weights of criteria/sub-criteria considering the interrelationships are calculated by FANP. Finally, the performance of the suppliers is evaluated by FIS method. To show the applicability of this hybrid decision-making framework, an industrial case of palm oil in Malaysia is presented. The findings indicate the high performance of the proposed framework in this concept and identify the most important criteria including the cost in general aspects, resource consumption as the most crucial sustainable criterion and agility as the most important resilient criterion.
A total of 24 strains of Vibrio alginolyticus were isolated from cockles (Anadara granosa) and identified for VibA and gyrB genes. All V. alginolyticus isolates were then tested against nine different antibiotics. In this study, the highest percentage of antibiotic resistance was obtained against penicillin (37.50%), followed by ampicillin, vancomycin (12.50%) and erythromycin (8.33%). All of V. alginolyticus isolates were susceptible against streptomycin, kanamycin, tetracycline, chloramphenicol and sulfamethoxazole. Polymerase chain reaction (PCR) assay has confirmed the presence of four antibiotic resistance genes of penicillin (pbp2a), ampicillin (blaOXA), erythromycin (ermB) and vancomycin (vanB). Out of 24 V. alginolyticus isolates, 2 isolates possessed the tdh-related hemolysin (trh) (strains VA15 and VA16) and none for the thermostable direct hemolysin (tdh) gene. Both strains of the tdh-related hemolysin (trh) were susceptible to all antibiotics tested. The multiple antibiotic resistance (MAR) index ranging between 0.2 and 0.3 with 5 antibiograms (A1-A5) was observed. Combination of enterobacterial repetitive intergenic consensus-polymerase chain reaction (ERIC-PCR) and antibiotic resistance indicated 18 genome types which showed genetic heterogeneity of those V. alginolyticus isolates. The results demonstrated the presence of V. alginolyticus strain found in cockles can be a potential risk to consumers and can contribute to the deterioration of human health in the study area. Thus, it is essential for local authority to provide the preventive measures in ensuring the cockles are safe for consumption.
Paving block is a widely used pavement material due to its long service life, fast and easy production and easily replaced for maintenance purpose. The huge production volume of paving blocks consumes large amount of natural aggregates such as sand and granite. Therefore, there is a necessity to review the utilization of alternative materials as the aggregate replacement to cut down both the consumption of natural resources and disposal of various waste. This paper thus analyses published works and provides a summary of knowledge on the effect of utilizing selected waste materials such as soda-lime glass, cathode ray tube (CRT) glass, recycled concrete waste, marble waste, crumb rubber (CR) waste and waste foundry sand (WFS) as aggregate replacement in concrete paving blocks fabrication. The influence of each waste material on the properties of paving block is discussed and highlighted in this paper. The adherence of the waste material paving block to the standard requirements is also presented to provide a clear direction on the utilization of these materials for practical application. Soda-lime glass, CRT glass, pre-treated RCA and calcined WFS have the potential to be utilized in high quantities (30-100%), normal RCA and marble waste can be incorporated in moderate amount (30%) while CR waste and WFS is limited to low amount (6-10%). In overall, the usage of waste materials as aggregate replacement has good potential for producing eco-friendly concrete paving block towards the sustainable development of construction material.
Electrocoagulation (EC) is one of the emerging technologies in groundwater and wastewater treatment as it combines the benefits of coagulation, sedimentation, flotation, and electrochemical oxidation processes. Extensive research efforts implementing EC technology have been executed over the last decade to treat chemical oxygen demand (COD)-rich industrial wastewaters with the aim to protect freshwater streams (e.g., rivers, lakes) from pollution. A comprehensive review of the available recent literature utilizing EC to treat wastewater with high COD levels is presented. In addition, recommendations are provided for future studies to improve the EC technology and broaden its range of application. This review paper introduces some technologies which are often adopted for industrial wastewater treatment. Then, the EC process is compared with those techniques as a treatment for COD-rich wastewater. The EC process is considered as the most privileged technology by different research groups owing to its ability to deal with abundant volumes of wastewater. After, the application of EC as a single and combined treatment for COD-rich wastewaters is thoroughly reviewed. Finally, this review attempts to highlight the potentials and limitations of EC. Related to the EC process in batch operation mode, the best operational conditions are found at 10 V and 60 min of voltage and reaction time, respectively. These last values guarantee high COD removal efficiencies of > 90%. This review also concludes that considerably large operation costs of the EC process appears to be the serious drawback and renders it as an unfeasible approach for handling of COD rich wastewaters. In the end, this review has attempted to highlights the potential and limitation of EC and suggests that vast notably research in the field of continuous flow EC system is essential to introduce this technology as a convincing wastewater technology.
The objective of this research is to examine the impact of bioenergy usage on health outcomes, especially adult mortality in both developed and underdeveloped countries in the European Union, where the use of solid biomass is growing to generate bioheat, biocool, and biopower. Over the period studied, findings indicate that increased consumption of bioenergy has increased mortality rates in developed and underdeveloped EU28 countries during the period 1990-2018. This feedback proposes, using generalized least squares (GLS), that the resulting death rate from burning biomass-related cases is higher in the EU15 developed countries compared to EU13 underdeveloped countries. There is a need to lower burning biomass in the entire EU15 countries, more importantly its developed region, by critically evaluating the bioenergy production life cycle before it is available for final consumption. However, there is a continuous need to intensify stringent production procedures in the bioenergy industry in EU15 countries, more importantly the imported biomass crops for energy use. There is also a need to be consistent with the campaign on the usage of bioenergy products, i.e., bioheat, bioelectricity, and biofuels, particularly in the rural areas where the use of wood fuels for cooking, heating, and cooling are significant in EU15 developed countries in comparison to EU13 developing countries.
This study investigates uncertainty in machine learning that can occur when there is significant variance in the prediction importance level of the independent variables, especially when the ROC fails to reflect the unbalanced effect of prediction variables. A variable drop-off loop function, based on the concept of early termination for reduction of model capacity, regularization, and generalization control, was tested. A susceptibility index for airborne particulate matter of less than 10 μm diameter (PM10) was modeled using monthly maximum values and spectral bands and indices from Landsat 8 imagery, and Open Street Maps were used to prepare a range of independent variables. Probability and classification index maps were prepared using extreme-gradient boosting (XGBOOST) and random forest (RF) algorithms. These were assessed against utility criteria such as a confusion matrix of overall accuracy, quantity of variables, processing delay, degree of overfitting, importance distribution, and area under the receiver operating characteristic curve (ROC).
Deforestation and forest degradation are among the leading global concerns, as they could reduce the carbon sink and sequestration potential of the forest. The impoundment of Kenyir River, Hulu Terengganu, Malaysia, in 1985 due to the development of hydropower station has created a large area of water bodies following clearance of forested land. This study assessed the loss of forest carbon due to these activities within the period of 37 years, between 1972 and 2019. The study area consisted of Kenyir Lake catchment area, which consisted mainly of forests and the great Kenyir Lake. Remote sensing datasets have been used in this analysis. Satellite images from Landsat 1-5 MSS and Landsat 8 OLI/TRIS that were acquired between the years 1972 and 2019 were used to classify land uses in the entire landscape of Kenyir Lake catchment. Support vector machine (SVM) was adapted to generate the land-use classification map in the study area. The results show that the total study area includes 278,179 ha and forest covers dominated the area for before and after the impoundment of Kenyir Lake. The assessed loss of carbon between the years 1972 and 2019 was around 8.6 million Mg C with an annual rate of 0.36%. The main single cause attributing to the forest loss was due to clearing of forest for hydro-electric dam construction. However, the remaining forests surrounding the study area are still able to sequester carbon at a considerable rate and thus balance the carbon dynamics within the landscapes. The results highlight that carbon sequestration scenario in Kenyir Lake catchment area shows the potential of the carbon sink in the study area are acceptable with only 17% reduction of sequestration ability. The landscape of the study area is considered as highly vegetated area despite changes due to dam construction.
Heavy metal contamination has increased over the globe, causing significant environmental issues owing to direct and indirect releases into water bodies. As a result, metal removal from water entities must be addressed soon. Various adsorbents such as MOFs and chitosan have demonstrated promising results in water treatment. The present study prepared a composite material (chitosan-UiO-66-glycidyl methacrylate MOF) by a microwave-assisted method. The structure and morphology of the chitosan-MOF composite were studied using FE-SEM, EDX, XRD, BET, FT-IR, and TGA techniques. In addition, the adsorption of Pb(II) from aqueous solution onto the chitosan-MOF composite was analyzed in a batch study concerning pH, contact time, initial metal ion concentration, and adsorbent dosage. The composite has a large surface area of 867 m2/g with a total pore volume of 0.51 cm3/g and thermal stability of up to 400 [Formula: see text]. Following an analysis of the adsorption isotherms, kinetics, and thermodynamics, the Langmuir model showed an excellent fit with the adsorption data (R2 = 0.99) and chi-squared (X2 = 3.609). The adsorption process was a spontaneous exothermic reaction and the pseudo-second-order rate equation fitted the kinetic profile well. Moreover, the composite is recyclable, retaining 83.45% of its removal effectiveness after 5 consecutive cycles, demonstrating it as a sustainable adsorbent for metal recovery. This study introduces a novel synthesized composite with enhanced recyclability and a higher potential for eliminating pollutants from industrial wastewater.
Sustainable and alternative energy sources of biofuel and solar power panel have been revolutionizing the lives and economy of many countries. However, these changes mainly occur in the urban areas and the rural population section has long been ignored by policy makers and government in the provision of energy. It is only recently that solar and biofuel are finally making in road to provide cheap and clean energy sources to rural population. As a result, literatures on consumer behavior of rural population towards sustainable energy sources are still very scarce. The present research aims to fulfill this gap by developing a conceptual model to investigate the adoption of solar power and biofuel energy resources in the cross-cultural setting of Malaysia and Pakistan. The data was collected from the rural areas of Pakistan and Malaysia. The two-stage data analysis method of partial least squares structural equation modeling (PLS-SEM) and artificial neural network (ANN) have been applied to satisfy both linear and non-linear regression assumptions, respectively. The results show that consumer in rural areas of Pakistan are willing and possess intention to adopt both biofuel and solar power for commercial and domestic use. Additionally, the results confirm that branding, economic, and altruistic factors are important in yielding intention to use towards biofuel and solar power panel in Pakistan which are validated by the results obtained in Malaysia. Other factors such as climate change awareness, retailer services quality, and ease of use are also important. The results offer wide-ranging theoretical and managerial implications.
An era of rapid changes in the technological and economic aspects of developing and developed countries can have detrimental extortions on the environment around the world. From the perspective of globalization, the rapid development and growth can reroute to enhance the interaction between people, organizations, and countries across the globe including China through the usage of information and communication technology which in turn contributes to the economic growth of one side, whereas on the other side, it affects the environmental quality. Referring to this aspect, this study is focused to inspect the link between information and communication technology, and globalization with the facets of degradation in the environment that as CO2 emission and ecological footprint by keeping the view of economic growth prospects as well via using the EKC hypothesis. In our study, time-series data was employed from 1987 to 2020 for China using the Dynamic ARDL approach. Grounded on the findings of the study, economic growth from the sight of GDP fallouts in rising the emission of CO2 and EFP in the short and long run whereas GDP sqr cause decrease in the CO2 emission and EFP. Thus, this authorizes the presence of inverted U-shaped existence among GDP sqr, CO2 emission, and EFP. Therefore, this provides provision for the EKC hypothesis in China. Furthermore, ICT and globalization cause a decline in the emission of CO2 and EFP in the short and long run respectively. In combatting challenges linked to the environment, globalization, as well as ICT, is seen as a crucial factor based on the pieces of evidence in our study while the policy implications are also proposed in the paper.
Responsible consumption and production are one of the interlinked global goals in Sustainable Development Goals (SDGs). The environmentally conscious tourists can help planners encourage the sustainable development of recreational areas by assessing their environmental practices. This study aims to examine the role of environmental beliefs and conservation commitment on the environmentally responsible behaviour of tourists in Malaysia. The study used a quantitative approach by distributing the questionnaire to 1000 tourists, and 731 usable questionnaires were utilized for further analysis. The researchers utilized Structural Equation Modelling using Smart PLS version 3.2. The measurement and structural models were assessed and reported in structural equation modelling. The study found that environmental beliefs and conservation commitment significantly influence the environmentally responsible behaviour of tourists in Malaysia. The study posed theoretical and practical implications for future researchers and practitioners.
Sewage contamination is a principal concern in water quality management as pathogens in sewage can cause diseases and lead to detrimental health effects in humans. This study examines the distribution of seven sterol compounds, namely coprostanol, epi-coprostanol, cholesterol, cholestanol, stigmasterol, campesterol, and β-sitosterol in filtered and particulate phases of sewage treatment plants (STPs), groundwater, and river water. For filtered samples, solid-phase extraction (SPE) was employed while for particulate samples were sonicated. Quantification was done by using gas chromatography-mass spectrometer (GC-MS). Faecal stanols (coprostanol and epi-coprostanol) and β-sitosterol were dominant in most STP samples. Groundwater samples were influenced by natural/biogenic sterol, while river water samples were characterized by a mixture of sources. Factor loadings from principal component analysis (PCA) defined fresh input of biogenic sterol and vascular plants (positive varimax factor (VF)1), aged/treated sewage sources (negative VF1), fresh- and less-treated sewage and domestic sources (positive VF2), biological sewage effluents (negative VF2), and fresh-treated sewage sources (VF3) in the samples. Association of VF loadings and factor score values illustrated the correlation of STP effluents and the input of biogenic and plant sterol sources in river and groundwater samples of Linggi. This study focuses on sterol distribution and its potential sources; these findings will aid in sewage assessment in the aquatic environment.
The present paper implements the quantile autoregressive lagged (QARDL) approach of Cho et al. (2015) and the Granger causality in quantiles tests of Troster et al. (2018) to explore the nonlinear effects of US energy consumption, economic growth, and tourist arrivals on carbon dioxide (CO2) emission. Our results unveil the existence of substantial reversion to the long-run equilibrium connectedness between the variables of interest and CO2 emissions. The outcomes show that tourist arrivals decrease CO2 emissions in the long term for each quantile. In addition, we found that the output growth positively influences the carbon emissions at lower quantiles but negatively influences the carbon emissions at upper quantiles. Moreover, our findings of short-term dynamics validate an asymmetric short-run effect of tourist arrivals and economic growth on CO2 emissions in the US economy. Further results and their corresponding policy implications are discussed.
Numerous contaminants in huge amounts are discharged to the environment from various anthropogenic activities. Waterbodies are one of the major receivers of these contaminants. The contaminated water can pose serious threats to humans and animals, by distrubing the ecosystem. In treating the contaminated water, adsorption processes have attained significant maturity due to lower cost, easy operation and environmental friendliness. The adsorption process uses various adsorbent materials and some of emerging adsorbent materials include carbon- and polymer-based magnetic nanocomposites. These hybrid magnetic nanocomposites have attained extensive applications in water treatment technologies due to their magnetic properties as well as combination of unique characteristics of organic and inorganic elements. Carbon- and polymer-related magnetic nanocomposites are more adapted materials for the removal of various kinds of contaminants from waterbodies. These nanocomposites can be produced via different approaches such as filling, pulse-laser irradiation, ball milling, and electro-spinning. This comprehensive review is compiled by reviewing published work of last the latest recent 3 years. The review article extensively focuses on different approaches for producing various carbon- and polymer-based magnetic nanocomposites, their merits and demerits and applications for sustainable water purification. More specifically, use of carbon- and polymer-based magnetic nanocomposites for removal of heavy metal ions and dyes is discussed in detail, critically analyzed and compared with other technologies. In addition, commercial viability in terms of regeneration of adsorbents is also reviewed. Furthermore, the future challenges and prospects in employing magnetic nanocomposites for contaminant removal from various water sources are presented.