Plastics are a wide range of synthetic or semi-synthetic materials, mainly consisting of polymers. The use of plastics has increased to over 300 million metric tonnes in recent years, and by 2050, it is expected to grow to 800 million. Presently, a mere 10% of plastic waste is recycled, with approximately 75% ended up in landfills. Inappropriate disposal of plastic waste into the environment poses a threat to human lives and marine species. Therefore, this review article highlights potential routes for converting plastic/microplastic waste into valuable resources to promote a greener and more sustainable environment. The literature review revealed that plastics/microplastics (P/MP) could be recycled or upcycled into various products or materials via several innovative processes. For example, P/MP are recycled and utilized as anodes in lithium-ion (Li-ion) and sodium-ion (Na-ion) batteries. The anode in Na-ion batteries comprising PP carbon powder exhibits a high reversible capacity of ∼340 mAh/g at 0.01 A/g current state. In contrast, integrating Fe3O4 and PE into a Li-ion battery yielded an excellent capacity of 1123 mAh/g at 0.5 A/g current state. Additionally, recycled Nylon displayed high physical and mechanical properties necessary for excellent application as 3D printing material. Induction heating is considered a revolutionary pyrolysis technique with improved yield, efficiency, and lower energy utilization. Overall, P/MPs are highlighted as abundant resources for the sustainable production of valuable products and materials such as batteries, nanomaterials, graphene, and membranes for future applications.
Based on the panel data of 22 inland provinces in China from 2010 to 2020, this study constructs and measures the level of rural ecological environment in China. The impact of the financial performance of green-listed companies on the rural ecological environment and its moderating and threshold effects are analyzed. The following conclusions are drawn: (1) During 2010-2020, China's rural ecological environment shows a trend of "fluctuating-decreasing-rising" with significant regional non-equilibrium characteristics. (2) The financial performance of green-listed companies has a significantly negative impact on rural ecology. This negative impact has a crucial heterogeneous feature, with a more significant negative impact in areas with a higher rural ecological environment index and less substantial performance in regions with a lower rural ecological environment index. (3) There is a significant positive moderating effect of education level and digitalization on the relationship between the financial performance of green-listed companies on the level of rural ecological development. As moderating variables, the digitalization and education level weakens the negative impact of green-listed companies' performance on the ecological environment. The positive impact of the financial performance of green-listed companies on the development level of the rural ecological environment is more vital in areas with higher per capita education levels and digitalization in rural areas. (4) There is a significant threshold effect on the financial performance of green-listed companies on the level of rural ecological development. When the financial performance of green-listed companies exceeds a particular threshold value, the impact of the financial performance of green-listed companies on the development level of the rural ecological environment is significantly positive. Based on the above findings, this paper puts forward corresponding countermeasure suggestions.
The recent progress report of Sustainable Development Goals (SDG) 2023 highlighted the extreme reactions of environmental degradation. This report also shows that the current efforts for achieving environmental sustainability (SDG 13) are inadequate and a comprehensive policy agenda is needed. However, the present literature has highlighted several determinants of environmental degradation but the influence of geopolitical risk on environmental quality (EQ) is relatively ignored. To fill this research gap and propose a inclusive policy structure for achieving the sustainable development goals. This study is the earliest attempt that delve into the effects o of geopolitical risk (GPR), financial development (FD), and renewable energy consumption (REC) on load capacity factor (LCF) under the framework of load capacity curve (LCC) hypothesis for selected Asian countries during 1990-2020. In this regard, we use several preliminary sensitivity tests to check the features and reliability of the dataset. Similarly, we use panel quantile regression for investigating long-run relationships. The factual results affirm the existence of the LCC hypothesis in selected Asian countries. Our findings also show that geopolitical risk reduces environmental quality whereas financial development and REC increase environmental quality. Drawing from the empirical findings, this study suggests a holistic policy approach for achieving the targets of SDG 13 (climate change).
This research explicitly investigates the utilization of Chlorella Vulgaris sp. microalgae as a renewable source for lipid production, focusing on its application in bioplastic manufacturing. This study employed the supercritical fluid extraction technique employing supercritical CO2 (sCO2) as a green technology to selectively extract and produce PHA's precursor utilizing CO2 solvent as a cleaner solvent compared to conventional extraction method. The study assessed the effects of three extraction parameters, namely temperature (40-60 °C), pressure (15-35 MPa), and solvent flow rate (4-8 ml/min). The pressure, flowrate, and temperature were found to be the most significant parameters affecting the sCO2 extraction. Through Taguchi optimization, the optimal parameters were determined as 60 °C, 35 MPa, and 4 ml/min with the highest lipid yield of 46.74 wt%; above-average findings were reported. Furthermore, the pretreatment process involved significant effects such as crumpled and exhaustive structure, facilitating the efficient extraction of total lipids from the microalgae matrix. This study investigated the microstructure of microalgae biomatrix before and after extraction using scanning electron microscopy (SEM) and thermogravimetric analysis (TGA). Fourier-transform infrared spectroscopy (FTIR) was utilized to assess the potential of the extracted material as a precursor for biodegradable plastic production, with a focus on reduced heavy metal content through inductively coupled plasma-optical emission spectrometry (ICP-OES) analysis. The lipid extracted from Chlorella Vulgaris sp. microalgae was analysed using gas chromatography-mass spectrometry (GC-MS), identifying key constituents, including oleic acid (C18H34O2), n-Hexadecanoic acid (C16H32O2), and octadecanoic acid (C18H36O2), essential for polyhydroxyalkanoate (PHA) formation.
The stress of pharmaceutical and personal care products (PPCPs) discharging to water bodies and the environment due to increased industrialization has reduced the availability of clean water. This poses a potential health hazard to animals and human life because water contamination is a great issue to the climate, plants, humans, and aquatic habitats. Pharmaceutical compounds are quantified in concentrations ranging from ng/Lto μg/L in aquatic environments worldwide. According to (Alsubih et al., 2022), the concentrations of carbamazepine, sulfamethoxazole, Lutvastatin, ciprofloxacin, and lorazepam were 616-906 ng/L, 16,532-21635 ng/L, 694-2068 ng/L, 734-1178 ng/L, and 2742-3775 ng/L respectively. Protecting and preserving our environment must be well-driven by all sectors to sustain development. Various methods have been utilized to eliminate the emerging pollutants, such as adsorption and biological and advanced oxidation processes. These methods have their benefits and drawbacks in the removal of pharmaceuticals. Successful wastewater treatment can save the water bodies; integrating green initiatives into the main purposes of actor firms, combined with continually periodic awareness of the current and potential implications of environmental/water pollution, will play a major role in water conservation. This article reviews key publications on the adsorption, biological, and advanced oxidation processes used to remove pharmaceutical products from the aquatic environment. It also sheds light on the pharmaceutical adsorption capability of adsorption, biological and advanced oxidation methods, and their efficacy in pharmaceutical concentration removal. A research gap has been identified for researchers to explore in order to eliminate the problem associated with pharmaceutical wastes. Therefore, future study should focus on combining advanced oxidation and adsorption processes for an excellent way to eliminate pharmaceutical products, even at low concentrations. Biological processes should focus on ideal circumstances and microbial processes that enable the simultaneous removal of pharmaceutical compounds and the effects of diverse environments on removal efficiency.
Textile wastewater laden with dyes has emerged as a source of water pollution. This possesses a challenge in its effective treatment using a single functional material. In respond to this technological constraint, this work presents multifunctional cotton fabrics (CFs) within a single, streamlined preparation process. This approach utilizes the adherence of Ag NPs (nanoparticles) using Si binder on the surface of CFs, resulting in Ag-coated CFs through a pad dry method. The prepared samples were characterized using scanning electron microscope-energy dispersive X-ray electroscopy (SEM-EDS), thermal gravimetric analysis (TGA), Fourier transformation infrared (FT-IR). It was found that the FT-IR spectra of Ag NPs-coated CFs had peaks appear at 3400, 2900, and 1200 cm-1, implying the stretching vibrations of O-H, C-H, and C-O, respectively. Based on the EDX analysis, the presence of C, O, and Ag related to the coated CFs were detected. After coating the CFs with varying concentrations of Ag NPs (1%, 2% and 3% (w/w)), they were used to remove dyes. Under the same concentration of 10 mg/L and optimized pH 7.5 and 2 h of reaction time, 3% (w/w) Ag-coated CFs exhibited a substantial MB degradation of 98 %, while removing 95% of methyl orange, 85% of rhodamine B, and 96% of Congo red, respectively, following 2 h of Vis exposure. Ag NPs had a strong absorption at 420 nm with 2.51 eV of energy band gap. Under UV irradiation, electrons excited and produced free radicals that promoted dyes photodegradation. The oxidation by-products included p-dihydroxybenzene and succinic acid. Spent Ag-coated CFs attained 98% of regeneration efficiency. The utilization of Ag-coated CFs as a photocatalyst facilitated treated effluents to meet the required discharge standard of lower than 1 mg/L mandated by national legislation. The integration of multifunctional CFs in the treatment system presents a new option for tackling water pollution due to dyes.
Natural soil minerals often contain numerous impurities, resulting in comparatively lower catalytic activity. Tropical soils are viewed as poor from soil organic matter, cations, and anions, which are considered the main impurities in the soil that are restricted to utilizing natural minerals as a catalyst. In this regard, the dissolved iron and hematite crystals that presented naturally in tropical soil were evaluated to activate oxidants and degrade pyrene. The optimum results obtained in this study were 73 %, and the rate constant was 0.0553 h-1 under experimental conditions [pyrene] = 300 mg/50 g, pH = 7, T = 55 °C, airflow = 260 mL/min, [Persulfate (PS)] = 1.0 g/L, and humic acid (HA) ( % w/w) = 0.5 %. The soil characterization analysis after the remediation process showed an increase in moieties and cracks of the soil aggregate, and a decline in the iron and aluminium contents. The scavengers test revealed that both SO4•- and O2•- were responsible for the pyrene degradation, while HO• had a minor role in the degradation process. In addition, the monitoring of by-products, degradation pathways, and toxicity assessment were also investigated. This system is considered an efficient, green method, and could provide a step forward to develop low-cost soil remediation for full-scale implementation.
Randomly collected food waste results in inaccurate experimental data with poor reproducibility for composting. This study investigated standard food waste samples as replacements for randomly collected food waste. A response surface methodology was utilised to analyse data from a 28-day compost process optimisation experiment using collected food waste, and the optimal combination of composting parameters was derived. Experiments using different standard food waste samples (high oil and salt, high oil and sugar, balanced diet, and vegetarian) were conducted for 28 days under optimal conditions. The ranking of differences between the standard samples and collected food waste was vegetarian > balanced diet > high oil and sugar > high oil and salt. Statistical analysis indicated t-tests for increased oil and salt samples and collected food waste were not significant, and Cohen's d effect values were minimal. High oil and salt samples can be used as replacements for collected food waste in composting experiments.
This study investigates the relationship between economic growth, renewable energy consumption, financial openness, and environmental degradation in selected ASEAN (Association of Southeast Asian Nations) countries (Cambodia, Indonesia, Malaysia, Philippines, Singapore, Thailand, and Vietnam) from 1996 to 2018. We aim to analyze how macroeconomic situation, energy-related factors, and financial determinants contribute to environmental deprivation in selected countries whose growth has recently been substantial. To address this issue, we employ second-generation panel data regression models and quantiles with fixed-effects estimators. Initially, the cointegration analysis supports a long-run association between the variables of our interest. Empirical findings confirm the environmental Kuznets curve hypothesis, but it seems valid only for Singapore. Moreover, results highlight the ecological role of renewable energy for ASEAN countries to achieve Sustainable Development Goals, such as transitioning to a low-carbon economy and reducing air pollution. On the contrary, financial openness is a cause that positively influences CO2 emissions. This research offers practical policy recommendations for many countries, including the ASEAN economies, to attain sustainable development.
The effective reduction of hazardous organic pollutants in wastewater is a pressing global concern, necessitating the development of advanced treatment technologies. Pollutants such as nitrophenols and dyes, which pose significant risks to both human and aquatic health, making their reduction particularly crucial. Despite the existence of various methods to eliminate these pollutants, they are not without limitations. The utilization of nanomaterials as catalysts for chemical reduction exhibits a promising alternative owing to their distinguished catalytic activity and substantial surface area. For catalytically reducing the pollutants NaBH4 has been utilized as a useful source for it because it reduces the pollutants quiet efficiently and it also releases hydrogen gas as well which can be used as a source of energy. This paper provides a comprehensive review of recent research on different types of nanomaterials that function as catalysts to reduce organic pollutants and also generating hydrogen from NaBH4 methanolysis while also evaluating the positive and negative aspects of nanocatalyst. Additionally, this paper examines the features effecting the process and the mechanism of catalysis. The comparison of different catalysts is based on size of catalyst, reaction time, rate of reaction, hydrogen generation rate, activation energy, and durability. The information obtained from this paper can be used to steer the development of new catalysts for reducing organic pollutants and generation hydrogen by NaBH4 methanolysis.
Tropical rainforests of Latin America (LATAM) are one of the world's largest carbon sinks, with substantial future carbon sequestration potential and contributing a major proportion of the global supply of forest carbon credits. LATAM is poised to contribute predominantly towards high-quality forest carbon offset projects designed to reduce emissions from deforestation and forest degradation, halt biodiversity loss, and provide equitable conservation benefits to people. Thus, carbon markets, including compliance carbon markets and voluntary carbon markets continue to expand in LATAM. However, the extent of the growth and status of forest carbon markets, pricing initiatives, stakeholders, amongst others, are yet to be explored and extensively reviewed for the entire LATAM region. Against this backdrop, we reviewed a total of 299 articles, including peer-reviewed and non-scientific gray literature sources, from January 2010 to March 2023. Herein, based on the extensive literature review, we present the results and provide perspectives classified into five categories: (i) the status and recent trends of forest carbon markets (ii) the interested parties and their role in the forest carbon markets, (iii) the measurement, reporting and verification (MRV) approaches and role of remote sensing, (iv) the challenges, and (v) the benefits, opportunities, future directions and recommendations to enhance forest carbon markets in LATAM. Despite the substantial challenges, better governance structures for forest carbon markets can increase the number, quality and integrity of projects and support the carbon sequestration capacity of the rainforests of LATAM. Due to the complex and extensive nature of forest carbon projects in LATAM, emerging technologies like remote sensing can enable scale and reduce technical barriers to MRV, if properly benchmarked. The future directions and recommendations provided are intended to improve upon the existing infrastructure and governance mechanisms, and encourage further participation from the public and private sectors in forest carbon markets in LATAM.
Water is a vital resource supporting a broad spectrum of ecosystems and human activities. The quality of river water has declined in recent years due to the discharge of hazardous materials and toxins. Deep learning and machine learning have gained significant attention for analysing time-series data. However, these methods often suffer from high complexity and significant forecasting errors, primarily due to non-linear datasets and hyperparameter settings. To address these challenges, we have developed an innovative HDTO-DeepAR approach for predicting water quality indicators. This proposed approach is compared with standalone algorithms, including DeepAR, BiLSTM, GRU and XGBoost, using performance metrics such as MAE, MSE, MAPE, and NSE. The NSE of the hybrid approach ranges between 0.8 to 0.96. Given the value's proximity to 1, the model appears to be efficient. The PICP values (ranging from 95% to 98%) indicate that the model is highly reliable in forecasting water quality indicators. Experimental results reveal a close resemblance between the model's predictions and actual values, providing valuable insights for predicting future trends. The comparative study shows that the suggested model surpasses all existing, well-known models.
Cultivation of microalgae in wastewater stream has been extensively reported, especially for simultaneous production of biolipid and wastewater treatment process. This study aimed to derive the research trend and focus on biolipid production from microalgae cultivated in wastewater by using bibliometric approach. The search strategy used in Scopus database resulted in 1339 research articles from 1990 to November 2023. Majority of publications (46%) were affiliated to China and India, showing their predominance in this field. Keywords related to the center of attention included biodiesel, biofuel, biomass and nutrient removal. Meanwhile, keyword with recent publication year, indicating the emerging research trends, revolved around the cultivation techniques and application of the system. Co-culture involving more than one microalgae species, bacteria and yeast showed promising results, while addition of nanoparticles was also found to be beneficial. Increasing exploration on the application of microalgae for treatment of saline wastewater was also reported and the carbon fixation mechanism by microalgae has been widely investigated to promote less environmental impact. Future research on these topics were suggested based on the findings of the bibliometric analyses.
In recent years, food waste has been a global concern that contributes to climate change. To deal with the rising impacts of climate change, in Hong Kong, food waste is converted into electricity in the framework of low-carbon approach. This work provides an overview of the conversion of food waste into electricity to achieve carbon neutrality. The production of methane and electricity from waste-to-energy (WTE) conversion are determined. Potential income from its sale and environmental benefits are also assessed quantitatively and qualitatively. It was found that the electricity generation from the food waste could reach 4.33 × 109 kWh annually, avoiding equivalent electricity charge worth USD 3.46 × 109 annually (based on US' 8/kWh). An equivalent CO2 mitigation of 9.9 × 108 kg annually was attained. The revenue from its electricity sale in market was USD 1.44×109 in the 1st year and USD 4.24 ×109 in the 15th year, respectively, according to the projected CH4 and electricity generation. The modelling study indicated that the electricity production is 0.8 kWh/kg of landfilled waste. The food waste could produce electricity as low as US' 8 per kW ∙ h. In spite of its promising results, there are techno-economic bottlenecks in commercial scale production and its application at comparable costs to conventional fossil fuels. Issues such as high GHG emissions and high production costs have been determined to be resolved later. Overall, this work not only leads to GHG avoidance, but also diversifies energy supply in providing power for homes in the future.
To replace the obsolete ponding system, palm oil mill effluent (POME) steam reforming (SR) over net-acidic LaNiO3 and net-basic LaCoO3 were proposed as the POME primary treatments, with promising H2-rich syngas production. Herein, the long-term evaluation of POME SR was scrutinized with both catalysts under the optimal conditions (600 °C, 0.09 mL POME/min, 0.3 g catalyst, & 74-105 μm catalyst particle size) to examine the catalyst microstructure changes, transient process stability, and final effluent evaluation. Extensive characterization proved the (i) adsorption of POME vapour on catalysts before SR, (ii) deposition of carbon and minerals on spent SR catalysts, and (iii) dominance of coking deactivation over sintering deactivation at 600 °C. Despite its longer run, spent LaCoO3 (50.54 wt%) had similar carbon deposition with spent LaNiO3 (50.44 wt%), concurring with its excellent coke resistance. Spent LaCoO3 (6.12 wt%; large protruding crystals) suffered a harsher mineral deposition than spent LaNiO3 (3.71 wt%; thin film coating), confirming that lower reactivity increased residence time of reactants. Transient syngas evolution of both SR catalysts was relatively steady up to 4 h but perturbed by coking deactivation thereafter. La2O2CO3 acted as an intermediate species that hastened the coke removal via reverse Boudouard reaction upon its decarbonation. La2O2CO3 decarbonation occurred continuously in LaCoO3 system but intermittently in LaNiO3 system. LaNiO3 system only lasted for 13 h as its compact ash blocked the gas flow. LaCoO3 system lasted longer (17 h) with its porous ash, but it eventually failed because KCl crystallites blocked its active sites. Relatively, LaCoO3 system offered greater net H2 production (72.78%) and POME treatment volume (30.77%) than LaNiO3 system. SR could attain appreciable POME degradation (>97% COD, BOD5, TSS, & colour intensity). Withal, SR-treated POME should be polished to further reduce its incompliant COD and BOD5.
Acid mine drainage (AMD) is recognized as a major environmental challenge in the Western United States, particularly in Colorado, leading to extreme subsurface contamination issue. Given Colorado's arid climate and dependence on groundwater, an accurate assessment of AMD-induced contamination is deemed crucial. While in past, machine learning (ML)-based inversion algorithms were used to reconstruct ground electrical properties (GEP) such as relative dielectric permittivity (RDP) from ground penetrating radar (GPR) data for contamination assessment, their inherent non-linear nature can introduce significant uncertainty and non-uniqueness into the reconstructed models. This is a challenge that traditional ML methods are not explicitly designed to address. In this study, a probabilistic hybrid technique has been introduced that combines the DeepLabv3+ architecture-based deep convolutional neural network (DCNN) with an ensemble prediction-based Monte Carlo (MC) dropout method. Different MC dropout rates (1%, 5%, and 10%) were initially evaluated using 1D and 2D synthetic GPR data for accurate and reliable RDP model prediction. The optimal rate was chosen based on minimal prediction uncertainty and the closest alignment of the mean or median model with the true RDP model. Notably, with the optimal MC dropout rate, prediction accuracy of over 95% for the 1D and 2D cases was achieved. Motivated by these results, the hybrid technique was applied to field GPR data collected over an AMD-impacted wetland near Silverton, Colorado. The field results underscored the hybrid technique's ability to predict an accurate subsurface RDP distribution for estimating the spatial extent of AMD-induced contamination. Notably, this technique not only provides a precise assessment of subsurface contamination but also ensures consistent interpretations of subsurface condition by different environmentalists examining the same GPR data. In conclusion, the hybrid technique presents a promising avenue for future environmental studies in regions affected by AMD or other contaminants that alter the natural distribution of GEP.
The construction industry generates a substantial volume of solid waste, often destinated for landfills, causing significant environmental pollution. Waste recycling is decisive in managing waste yet challenging due to labor-intensive sorting processes and the diverse forms of waste. Deep learning (DL) models have made remarkable strides in automating domestic waste recognition and sorting. However, the application of DL models to recognize the waste derived from construction, renovation, and demolition (CRD) activities remains limited due to the context-specific studies conducted in previous research. This paper aims to realistically capture the complexity of waste streams in the CRD context. The study encompasses collecting and annotating CRD waste images in real-world, uncontrolled environments. It then evaluates the performance of state-of-the-art DL models for automatically recognizing CRD waste in-the-wild. Several pre-trained networks are utilized to perform effectual feature extraction and transfer learning during DL model training. The results demonstrated that DL models, whether integrated with larger or lightweight backbone networks can recognize the composition of CRD waste streams in-the-wild which is useful for automated waste sorting. The outcome of the study emphasized the applicability of DL models in recognizing and sorting solid waste across various industrial domains, thereby contributing to resource recovery and encouraging environmental management efforts.
The practice of aquaculture is associated with the generation of a substantial quantity of effluent. Microalgae must effectively assimilate nitrogen and phosphorus from their surrounding environment for growth. This study modeled the algal biomass film, NO3-N concentration, and pH in the membrane bioreactor using the response surface methodology (RSM) and an artificial neural network (ANN). Furthermore, it was suggested that the optimal condition for each parameter be determined. The results of ANN modeling showed that ANN with a structure of 5-3 and employing the transfer functions tansig-logsig demonstrated the highest level of accuracy. This was evidenced by the obtained values of coefficient (R2) = 0.998, R = 0.999, mean squared error (MAE) = 0.0856, and mean square error (MSE) = 0.143. The ANN model, characterized by a 5-5 structure and employing the tansig-logsig transfer function, demonstrates superior accuracy when predicting the concentration of NO3-N and pH. This is evidenced by the high values of R2 (0.996), R (0.998), MAE (0.00162), and MSE (0.0262). The RSM was afterward employed to maximize the performance of algal film biomass, pH levels, and NO3-N concentrations. The optimal conditions for the algal biomass film were a concentration of 2.884 mg/L and a duration of 6.589 days. Similarly, the most favorable conditions for the NO3-N concentration and pH were 2.984 mg/L and 6.787 days, respectively. Therefore, this research uses non-dominated sorting genetic algorithm II (NSGA II) to find the optimal NO3-N concentration, algal biomass film, and pH for product or process quality. The region has the greatest alkaline pH and lowest NO3-N content.
With climate change and urbanization, flood disasters have significantly affected urban development worldwide. In this study, we developed a paradigm to assess flood economic vulnerability and risk at the urban mesoscale, focusing on urban land use. A hydrological simulation was used to evaluate flood hazards through inundation analyses, and a hazard-vulnerability matrix was applied to assess flood risk, enhancing the economic vulnerability assessment by quantifying the differing economic value and flood losses associated with different land types. The case study of Wangchengpo, Changsha, China, found average total economic losses of 126.94 USD/m2, with the highest risk in the settlement core. Residential areas had the highest flood hazard, vulnerability, and losses (61.10% of the total loss); transportation areas accounted for 27.87% of the total economic losses due to their high flooding depth. Despite low inundation, industrial land showed greater economic vulnerability due to higher overall economic value (10.52% of the total). Our findings highlight the influence of land types and industry differences on flood vulnerability and the effectiveness of land-use inclusion in urban-mesoscale analyses of spatial flood characteristics. We identify critical areas with hazard and economic vulnerability for urban land and disaster prevention management and planning, helping to offer targeted flood control strategies to enhance urban resilience.
Cyclodextrin (CD) and its derivatives are receiving attention as a new-generation adsorbent for water pollution treatment due to their external hydrophilic and internal hydrophobic properties. Among types of CD, β-Cyclodextrin (βCD) has been a material of choice with a proven track record for a range of utilities in distinct domains, owing to its unique cage-like structural conformations and inclusion complex-forming ability, especially to mitigate emerging contaminants (ECs). This article outlines βCD composites in developing approaches of their melds and composites for purposes such as membranes for removal of the ECs in aqueous setups have been explored with emphasis on recent trends. Electrospinning has bestowed an entirely different viewpoint on polymeric materials, comprising βCD, in the framework of diverse functions across a multitude of niches. Besides, this article especially discusses βCD polymer composite membrane-based removal of contaminants such as pharmaceutical substances, endocrine disruptors chemicals, and dyes. Finally, in this article, the challenges and future directions of βCD-based adsorbents are discussed, which may shed light on pragmatic commercial applications of βCD polymer composite membranes.