Insights into the adsorption of pharmaceuticals and personal care products (PPCPs) on biochar and activated carbon with the aid of machine learning

Coupling machine learning and theoretical models to compare key properties of biochar in adsorption kinetics rate and maximum adsorption capacity for emerging contaminants

Insights into key properties of biochar with a fast adsorption rate and high adsorption capacity are urgent to design biochar as an adsorbent in pollution emergency treatment. Machine learning (ML) incorporating classical theoretical adsorption models was applied to build prediction models for adsorption kinetics rate (i.e., K) and maximum adsorption capacity (i.e., Qm) of emerging contaminants (ECs) on biochar. Results demonstrated that the prediction performance of adaptive boosting algorithm significantly improved after data preprocessing (i.e., log-transformation) in the small unbalanced datasets with R2 of 0.865 and 0.874 for K and Qm, respectively. The surface chemistry, primarily led by ash content of biochar significantly influenced the K, while surface porous structure of biochar showed a dominant role in predicting Qm. An interactive platform was deployed for relevant scientists to predict K and Qm of new biochar for ECs. The research provided practical references for future engineered biochar design for ECs removal.

Machine learning-based exploration of biochar for environmental management and remediation

Biochar has a wide range of applications, including environmental management, such as preventing soil and water pollution, removing heavy metals from water sources, and reducing air pollution. However, there are several challenges associated with the usage of biochar for these purposes, resulting in an abundance of experimental data in the literature. Accordingly, the purpose of this study is to examine the use of machine learning in biochar processes with an eye toward the potential of biochar in environmental remediation. First, recent developments in biochar utilization for the environment are summarized. Then, a bibliometric analysis is carried out to illustrate the major trends (demonstrating that the top three keywords are heavy metal, wastewater, and adsorption) and construct a comprehensive perspective for future studies. This is followed by a detailed review of machine learning applications, which reveals that adsorption efficiency and capacity are the primary utilization targets in biochar utilization. Finally, a comprehensive perspective is provided for the future. It is then concluded that machine learning can help to detect hidden patterns and make accurate predictions for determining the combination of variables that results in the desired properties which can be later used for decision-making, resource allocation, and environmental management.

Machine learning application for predicting key properties of activated carbon produced from lignocellulosic biomass waste with chemical activation

The successful application of gradient boosting regression (GBR) in machine learning to forecast surface area, pore volume, and yield in biomass-derived activated carbon (AC) production underscores its potential for enhancing manufacturing processes. The GBR model, collecting 17 independent variables for two-step activation (2-SA) and 14 for one-step activation (1-SA), demonstrates effectiveness across three datasets-1-SA, 2-SA, and a combined dataset. Notably, in 1-SA, the GBR model yields R2 values of 0.76, 0.90, and 0.83 for TPV, yield, and SSA respectively, and records R2 of 0.90 and 0.91 for yield in 2-SA and combined datasets. The model highlights the significance of the soaking procedure alongside activation temperature in shaping AC properties with 1-SA or 2-SA, illustrating machine learning's potential in optimizing AC production processes.

Active Learning-Based Guided Synthesis of Engineered Biochar for CO2 Capture

Biomass waste-derived engineered biochar for CO2 capture presents a viable route for climate change mitigation and sustainable waste management. However, optimally synthesizing them for enhanced performance is time- and labor-intensive. To address these issues, we devise an active learning strategy to guide and expedite their synthesis with improved CO2 adsorption capacities. Our framework learns from experimental data and recommends optimal synthesis parameters, aiming to maximize the narrow micropore volume of engineered biochar, which exhibits a linear correlation with its CO2 adsorption capacity. We experimentally validate the active learning predictions, and these data are iteratively leveraged for subsequent model training and revalidation, thereby establishing a closed loop. Over three active learning cycles, we synthesized 16 property-specific engineered biochar samples such that the CO2 uptake nearly doubled by the final round. We demonstrate a data-driven workflow to accelerate the development of high-performance engineered biochar with enhanced CO2 uptake and broader applications as a functional material.

Feature engineering for improved machine-learning-aided studying heavy metal adsorption on biochar

Due to the broad interest in using biochar from biomass pyrolysis for the adsorption of heavy metals (HMs) in wastewater, machine learning (ML) has recently been adopted by many researchers to predict the adsorption capacity (η) of HMs on biochar. However, previous studies focused mainly on developing different ML algorithms to increase predictive performance, and no study shed light on engineering features to enhance predictive performance and improve model interpretability and generalizability. Here, based on a dataset widely used in previous ML studies, features of biochar were engineered-elemental compositions of biochar were calculated on mole basis-to improve predictive performance, achieving test R2 of 0.997 for the gradient boosting regression (GBR) model. The elemental ratio feature (H-O-2N)/C, representing the H site links to C (non-active site to HMs), was proposed for the first time to help interpret the GBR model. The (H-O-2N)/C and pH of biochar played essential roles in replacing cation exchange capacity (CEC) for predicting η. Moreover, expanding the coverages of variables by adding cases from references improved the generalizability of the model, and further validation using cases without CEC and specific surface area (R2 0.78) and adsorption experimental results (R2 0.72) proved the ML model desirable. Future studies in this area may take into account algorithm innovation, better description of variables, and higher coverage of variables to further increase the model's generalizability.

Promotion of anaerobic biodegradation of azo dye RR2 by different biowaste-derived biochars: Characteristics and mechanism study by machine learning

The addition of biochar resulted in a 31.5 % to 44.6 % increase in decolorization efficiency and favorable decolorization stability. Biochar promoted extracellular polymeric substances (EPS) secretion, especially humic-like and fulvic-like substances. Additionally, biochar enhanced the electron transfer capacity of anaerobic sludge and facilitated surface attachment of microbial cells. 16S rRNA gene sequencing analysis indicated that biochar reduced microbial species diversity, enriching fermentative bacteria such as Trichococcus. Finally, a machine learning model was employed to establish a predictive model for biochar characteristics and decolorization efficiency. Biochar electrical conductivity, H/C ratio, and O/C ratio had the most significant impact on RR2 anaerobic decolorization efficiency. According to the results, the possible mechanism of RR2 anaerobic decolorization enhanced by different types of biochar was proposed.

Dyeing sludge-derived biochar for efficient removal of antibiotic from water

Adsorption is one of the most effective methods for ecotoxic antibiotics removal, while developing high-performance adsorbents with excellent adsorption capacity is indispensable. As the unavoidable by-product of wastewater, sewage sludge has dual properties of pollution and resources. In this study, dyeing sludge waste was converted to biochar by KOH activation and pyrolysis, and used as an efficient adsorbent for aqueous antibiotics removal. The optimized dyeing sludge-derived biochar (KSC-8) has excellent specific surface area (1178.4 m2/g) and the adsorption capacity for tetracycline (TC) could reach up to 1081.3 mg/g, which is four and five times higher than those without activation, respectively. The PSO (pseudo-second-order) kinetic model and the Langmuir isotherm model fitted better to the experimental data. The obtained KSC-8 has stabilized adsorption capacity for long-term fixed-bed experiments, and maintained 86.35% TC removal efficiency after five adsorption-regeneration cycles. The adsorption mechanism involves electrostatic attraction, hydrogen bonding, π-π interactions and pore filling. This work is a green and eco-friendly way as converting the waste to treat waste in aiming of simultaneous removal of antibiotics and resource recovery of dyeing sludge.

Ultrahigh-porosity Ranunculus-like MgO adsorbent coupled with predictive deep belief networks: A transformative method for phosphorus treatment

Phosphorus is a nonrenewable material with a finite supply on Earth; however, due to the rapid growth of the manufacturing industry, phosphorus contamination has become a global concern. Therefore, this study highlights the remarkable potential of ranunculus-like MgO (MO4-MO6) as superior adsorbents for phosphate removal and recovery. Furthermore, MO6 stands out with an impressive adsorption capacity of 596.88 mg/g and a high efficacy across a wide pH range (2-10) under varying coexisting ion concentrations. MO6 outperforms the top current adsorbents for phosphate removal. The process follows Pseudo-second-order and Langmuir models, indicating chemical interactions between the phosphate species and homogeneous MO6 monolayer. MO6 maintains 80 % removal and 96 % recovery after five cycles and adheres to the WHO and EUWFD regulations for residual elements in water. FT-IR and XPS analyses further reveal the underlying mechanisms, including ion exchange, electrostatic, and acid-base interactions. Ten machine learning (ML) models were applied to simultaneously predict multi-criteria (sorption capacity, removal efficiency, final pH, and Mg leakage) affected by 15 diverse environmental conditions. Traditional ML models and deep neural networks have poor accuracy, particularly for removal efficiency. However, a breakthrough was achieved by the developed deep belief network (DBN) with unparalleled performance (MAE = 1.3289, RMSE = 5.2552, R2 = 0.9926) across all output features, surpassing all current studies using thousands of data points for only one output factor. These captivating MO6 and DBN models also have immense potential for effectively applying in the real water test with error < 5 %, opening immense horizons for transformative methods, particularly in phosphate removal and recovery.

Machine learning applications for biochar studies: A mini-review

Biochar is a promising carbon sink whose application can assist in reducing carbon emissions. Development of this technology currently relies on experimental trials, which are time-consuming and labor-intensive. Machine learning (ML) technology presents a potential solution for streamlining this process. This review summarizes the current research on ML's applications in biochar production, characterization, and applications. It briefly explains commonly used machine learning algorithms and discusses prospects and challenges. A hybrid model that combines ML with mechanism-based analysis could be a future trend, addressing the ML's black-box nature. While biochar studies have adopted ML technology, current works mostly use lab-scale data for model training. Further work is needed to develop ML models based on pilot or industrial-scale data to realize the use of ML techniques for the field application of biochar.

Predicting apparent adsorption capacity of sediment-amended activated carbon for hydrophobic organic contaminants using machine learning

In-situ stabilization of hydrophobic organic compounds (HOCs) using activated carbon (AC) is a promising sediment remediation approach. However, predicting HOC adsorption capacity of sediment-amended AC remains a challenge because a prediction model is currently unavailable. Thus, the objective of this study was to develop machine learning models that could predict the apparent adsorption capacity of sediment-amended AC (KAC,apparent) for HOCs. These models were trained using 186 sets of experimental data obtained from the literature. The best-performing model among those employing various model frameworks, machine learning algorithms, and combination of candidate input features excellently predicted logKAC,apparent with a coefficient of determination of 0.94 on the test dataset. Its prediction results and experimental data for KAC,apparent agreed within 0.5 log units with few exceptions. Analysis of feature importance for the machine learning model revealed that KAC,apparent was strongly correlated with the hydrophobicity of HOCs and the particle size of AC, which agreed well with the current knowledge obtained from experimental and mechanistic assessments. On the other hand, correlation of KAC,apparent to sediment characteristics, duration of AC-sediment contact, and AC dose identified in the model disagreed with relevant arguments made in the literature, calling for further assessment in this subject. This study highlights the promising capability of machine learning in predicting adsorption capacity of AC in complex systems. It offers unique insights into the influence of model parameters on KAC,apparent.

Inhibiting release of phenanthrene from rice-crab coculture sediments to overlying water with rice stalk biochar: Performance and mechanisms

Rice crab coculture is a new ecological agriculture model combining rice cultivation and crab farming. Current research related to rice crab coculture only focuses on production theory and technical system establishment, while ignoring the potential ecological risk of Polycyclic aromatic hydrocarbon(PAHs) in rice crab coculture sediment. In this study, rice straw was used to make rice straw biochar to explore the performance and mechanism of inhibiting release of phenanthrene(PHE) from rice-crab coculture sediments to overlying water with rice stalk biochar. The kinetic and isotherm adsorption data were best represented by the Langmuir model and pseudo-second-order model with a maximum adsorption capacity of 53.35 mg/g at 12 h contact time. The results showed that PHE was released from the rice-crab substrate to the overlying water in dissolved and particle forms as a result of bioturbation, and the PHE concentrations in dissolved and particle forms were 20.9 μg/L and 14.22 μg/L, respectively. This leads to secondary ecological risks in rice-crab co-culture systems. This is related to dissolved organic carbon(DOC) carrying the dissolved PHE and total suspended solids(TSS) carrying the particle PHE in the overlying water. Due to its large specific surface area, rice straw biochar is rich in functional groups, providing multiple hydrophobic adsorption sites. After adding rice straw biochar at 0.5 % w/w (dry weight) dose, the removal efficiency of dissolved and particulate PHE in the overlying water were 78.99 % and 42.11 %, respectively. Rice straw biochar is more competitively adsorbed PHE in the overlying water than TSS and DOC. The removal efficiency of PHE from the sediment was 52.75 %. This study confirmed that rice stalk biochar could effectively inhibit PHE migration and release in paddy sediment. It provides an environment- friendly in situ remediation method for the management of PAHs pollution from crab crops in rice fields.

Application of machine learning algorithms and feature selection methods for better prediction of sludge production in a real advanced biological wastewater treatment plant

Although the management of sewage sludge is an important and challenging task of wastewater treatment, there is a scarcity of studies on the prediction of waste sludge. To overcome this deficiency, the present work aims to develop an appropriate model providing accurate and fast prediction of sewage sludge. With this aim, different machine learning (ML) algorithms were tested by data obtained from a real advanced biological wastewater treatment plant located in Kocaeli, Turkey. In modelling studies, a data set from January 2022 to December 2022 composed of 208 daily measurements was considered. The flow rate of the plant (Q), polyelectrolyte dosage (PD) and removed amounts of total suspended solids (TSS), chemical oxygen demand (COD), biological oxygen demand (BOD), total phosphorous (TP), total nitrogen (TN) were assigned as input parameters to predict sludge production (SP). The precision of the models was evaluated in terms of Mean Square Error (MSE), Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and correlation coefficient (R2). Among the various tested models Kernel Ridge Regression provided the best accuracy with R2 value of 0.94 and MAE value of 3.25. Mutual information-based feature selection (MIFS) and correlation-based feature selection (CFS) algorithms were also used in the study in order to enhance the model performance. Thus, higher prediction accuracies were achieved using the selected subset of features. Furthermore, importance contribution of features were calculated and visualized by SHapley Additive exPlanations (SHAP) technique. The overall results of the work indicate the feasibility of ML models for describing the dynamic and complex nature of SP. The process operators may benefit from this modelling approach since it enables accurate and fast estimation of sewage sludge by using fewer and easily measurable parameters.

On network construction and module detection for molecular graph of titanium dioxide

Titanium dioxide is the most common and valuable oxide among four types of oxides of titanium. Its physicochemical properties make it a very valuable compound. The main objective of this article is to initially detect the modules based on highly connected links of the network of the degree-based topological indices. This information is lately integrated with different physical properties of the chemical compound of titanium dioxide to develop different mathematical frameworks based on master regulatory indices of the modules. This connection can be helpful in studying the physical measures at a deeper level in the form of different degree based topological indices.Communicated by Ramaswamy H. Sarma.

A new attempt to control volatile organic compounds (VOCs) pollution - Modification technology of biomass for adsorption of VOCs gas

The detrimental impact of volatile organic compounds on the surroundings is widely acknowledged, and effective solutions must be sought to mitigate their pollution. Adsorption treatment is a cost-effective, energy-saving, and flexible solution that has gained popularity. Biomass is an inexpensive, naturally porous material with exceptional adsorbent properties. This article examines current research on volatile organic compounds adsorption using biomass, including the composition of these compounds and the physical (van der Waals) and chemical mechanisms (Chemical bonding) by which porous materials adsorb them. Specifically, the strategic modification of the surface chemical functional groups and pore structure is explored to facilitate optimal adsorption, including pyrolysis, activation, heteroatom doping and other methods. It is worth noting that biomass adsorbents are emerging as a highly promising strategy for green treatment of volatile organic compounds pollution in the future. Overall, the findings signify that biomass modification represents a viable and competent approach for eliminating volatile organic compounds from the environment.

Emerging applications of biochar: A review on techno-environmental-economic aspects

Biomass fast pyrolysis produces bio-oil and biochar achieving circular economy. This review explored the emerging applications of biochar. Biochar possesses the unique properties for removing emerging contaminants and for mine remediation, owing to its negative charge surface, high specific surface area, large pore size distribution and surface functional groups. Additionally, biochar could adsorb impurities such as CO2, moisture, and H2S to upgrade the biogas. Customizing pyrolysis treatments, optimizing the feedstock and pyrolysis operating conditions enhance biochar production and improve its surface properties for the emerging applications. Life cycle assessment and techno-economic assessment indicated the benefits of replacing conventional activated carbon with biochar.

The influence of various microplastics on PBDEs contaminated soil remediation by nZVI and sulfide-nZVI: Impedance, electron-accepting/-donating capacity and aging

The microplastics (MPs) existed in the environment widely has resulted in novel thinking about in-situ remediation techniques, such as nano-zero-valent iron (nZVI) and sulfided nZVI (S-nZVI), which were often compromised by various environmental factors. In this study, three common MPs such as polyvinyl chloride (PVC), polystyrene (PS), and polypropylene (PP) in soil were found to inhibit the degradation rate of decabromodiphenyl ether (BDE209) by nZVI and S-nZVI to different degrees due to MPs inhibiting of electron transfer which is the main way to degrade BDE209. The inhibition strength was related to its impedance (Z) and electron-accepting (EAC)/-donating capacity (EDC). Based on the explanation of the inhibition mechanism, the reason for different aging degrees of nZVI and S-nZVI in different MPs was illustrated, especially in PVC systems. Furthermore, the aging of reacted MPs, functionalization and fragmentation in particular, indicated that they were involved in the degradation process. Moreover, this work provided new insights into the field application of nZVI-based materials for removing persistent organic pollutants (POPs).

A new approach to prediction riboflavin absorbance using imprinted polymer and ensemble machine learning algorithms

The molecularly imprinted polymer (MIP) is useful for measuring the amount of riboflavin (vitamin B2), in various samples using UV/Vis instruments. The practical optimization of the MIP synthesis conditions has a number of drawbacks, like the need to spend money, the need to spend time, the use of the compounds that cause contamination, needing laboratory equipment and tools. Using machine learning (ML) to predict the amount of riboflavin absorbance is a creative solution to overcome the problems and shortcomings of optimizing polymer synthesis conditions. In fact, by using the model without needing real work in the laboratory, the optimum laboratory conditions are determined, and as a result the maximized absorption of the riboflavin is obtained. In this paper, MIP was synthesized for selective extraction of the riboflavin, and UV/Vis spectrophotometry was used to quantitatively measure riboflavin absorbance. Various factors affect the performance of the polymer. The effect of six important factors, including the molar ratio of the template, the molar ratio of monomer, the molar ratio of cross-linker, loading time, stirring rate, and pH, were investigated. Then, using ensemble ML algorithms, like gradient boosting (GB), extra trees (ET), random forest (RF), and Ada boost (Ada) algorithms, an accurate model was created to predict the riboflavin absorption. Also, the mutual information feature selection method was used to determine the important features. The results of using feature selection method was shown that variables such as the molar ratio of the template, the molar ratio of the monomer, and the molar ratio of the cross-linker had a high effect on riboflavin absorbance. The GB and Ada boost algorithms performed better than ET and RF algorithms. After tuning the n-estimator hyper parameter (n-estimator = 300), the GB algorithm was shown an excellent performance in predicting the absorbance of riboflavin and the maximum R²-scoring of the model was obtained at 0.965995, the minimum of the mean absolute error (MAE), and mean square error (MSE) of the model respectively were obtained -0.003711 and -0.000078. Therefore, by using the proposed model, it is possible to predict riboflavin absorbance theoretically, and with high accuracy by changing the inputs of model, and using the model instead of working in the lab saves time, money, chemical compounds, and lab ware.

Carbon-based single-atom catalysts in advanced oxidation reactions for water remediation: From materials to reaction pathways

Single-atom catalysts (SACs) have been widely recognized as state-of-the-art catalysts in environment remediation because of their exceptional performance, 100% metal atomic utilization, almost no secondary pollution, and robust structures. Most recently, the activation of persulfate with carbon-based SACs in advanced oxidation processes (AOPs) raises tremendous interest in the degradation of emerging contaminants in wastewater, owning to its efficient and versatile reactive oxidant species (ROS) generation. However, the comprehensive and critical review unraveling the underlying relationship between structures of carbon-based SACs and the corresponding generated ROS is still rare. Herein, we systematically summarize the fundamental understandings and intrinsic mechanisms between single metal atom active sites and produced ROS during AOPs. The types of emerging contaminants are firstly elaborated, presenting the prior pollutants that need to be degraded. Then, the preparation and characterization methods of carbon-based SACs are overviewed. The underlying material structure-ROS type relationship in persulfate-based AOPs is discussed in depth to expound the catalytic mechanisms. Finally, we briefly conclude the current development of carbon-based SACs in AOPs and propose the prospects for rational design and synthesis of carbon-based SACs with on-demand catalytic performances in AOPs in future research.

Bio-inspired sustainable synthesis of novel SnS2/biochar nanocomposite for adsorption coupled photodegradation of amoxicillin and congo red: Effects of reaction parameters, and water matrices

This study aims to fabricate a novel integrated photocatalytic adsorbent (IPA) via a green solvothermal process employing tea (Camellia sinensis var. assamica) leaf extract as a stabilizing and capping agent for the removal of organic pollutants from wastewater. An n-type semiconductor photocatalyst, SnS_2, was chosen as a photocatalyst due to its remarkable photocatalytic activity supported over areca nut (Areca catechu) biochar for the adsorption of pollutants. The adsorption and photocatalytic properties of fabricated IPA were examined by taking amoxicillin (AM) and congo red (CR) as two emerging pollutants found in wastewater. Investigating synergistic adsorption and photocatalytic properties under varying reaction conditions mimicking actual wastewater conditions marks the novelty of the present research. The support of biochar for the SnS₂ thin films induced a reduction in charge recombination rate, which enhanced the photocatalytic activity of the material. The adsorption data were in accordance with the Langmuir nonlinear isotherm model, indicating monolayer chemosorption with the pseudo-second-order rate kinetics. The photodegradation process follows pseudo-first-order kinetics with the highest rate constant of 0.0450 min^-1 for AM and 0.0454 min^-1 for CR. The overall removal efficiency of 93.72 ± 1.19% and 98.43 ± 1.53% could be achieved within 90 min for AM and CR via simultaneous adsorption and photodegradation model. A plausible mechanism of synergistic adsorption and photodegradation of pollutants is also presented. The effect of pH, Humic acid (HA) concentration, inorganic salts and water matrices have also been included.The photodegradation activity of SnS₂ under visible light coupled with the adsorption capability of the biochar results in the excellent removal of the contaminants from the liquid phase.

"Quasi-In Situ Synthesis of Oxygen Vacancy-Enriched Strontium Iron Oxide Supported on Boron-Doped Reduced Graphene Oxide to Elevate the Photocatalytic Destruction of Tetracycline"

The efficient use of visible light is necessary to take advantage of photocatalytic processes in both indoor and outdoor circumstances. Precisely manipulating the in situ growth method of heterojunctions is an effective way to promote photogenerated charge separation. Herein, the SrFeO₃@B-rGO catalyst was prepared by an in situ growth method. At a loading of 10 wt % B-rGO, the nanocomposites revealed an excellent morphology and thermal, optical, electrochemical, and mechanical properties. X-ray diffraction analysis revealed the cubic spinel structure and a space group of Pm̅3m for SrFeO₃. High-resolution scanning electron microscopy and high-resolution transmission electron microscopy show the core-shell formation between SrFeO₃ and B-rGO. Furthermore, density functional theory of SrFeO₃ was performed to find its band structure and density of states. The SrFeO₃@B-rGO nanocomposite shows the degradation rate of tetracycline (TC) reaching 92% in 75 min and the highest rate constant of 0.0211 min^-1. To improve the catalytic removal rate of antibiotics, the efficiency of e^- and h ⁺ separation must be improved, as well as the generation of additional radicals. Radical trapping tests and the electron paramagnetic resonance method indicated that the combination of Fe²⁺ and Fe³⁺ in SrFeO₃ effectively separated e^- and h⁺ while also promoting the development of the superoxide anion (^•O₂^-) to accelerate TC degradation. The entire TC degradation pathway using high-performance liquid chromatography and its mechanism were discussed. As a whole, this study delineates that photocatalysis is a viable strategy for the treatment of environmental antibiotic wastewater.

Exploration of Reactive Black 5 Dye Desorption from Composite Hydrogel Beads—Adsorbent Reusability, Kinetic and Equilibrium Isotherms

A low-cost adsorbent was prepared by using cherry stones powder and chitosan and used to retain Reactive Black 5 dye from aqueous solution. Then, the spent material was submitted to a regeneration process. Five different eluents (water, sodium hydroxide, hydrochloric acid, sodium chloride and ethanol) were tested. Among them, sodium hydroxide was selected for an advanced investigation. Values of three working conditions, namely the eluent volume, its concentration and the desorption temperature, were optimized by Response Surface Methodology-Box–Behnken Design. In the established settings (NaOH volume: 30 mL, NaOH concentration: 1.5 M, working temperature: 40 °C), three successive cycles of adsorption/desorption were conducted. The analysis performed by Scanning Electron Microscopy and by Fourier Transform Infrared Spectroscopy revealed the evolution of the adsorbent throughout the dye elution from the material. Pseudo-second-order kinetic model and Freundlich equilibrium isotherm were able to accurately describe the desorption process. Based on the acquired results, our outcomes sustain the suitability of the synthesized material as dye adsorbent and the possibility of efficaciously recycling and reusing it.

Efficiently photocatalysis activation of peroxydisulfate by Fe-doped g-C3N5 for pharmaceuticals and personal care products degradation

Peroxydisulfate (PDS) based advanced oxidation processes (AOPs) are widely used for the degradation of pharmaceutical and personal care products (PPCP) in wastewater treatment. In this study, a Fe-doped g-C₃N₅ (Fe@g-C₃N₅) was synthesized as a photocatalyst for catalyzing the PDS-based AOPs to degrade tetracycline hydrochloride (TH) at pH 3 and Naproxen (NPX) at pH 7. The photocatalytic performance of Fe@g-C₃N₅ was 19% and 67% higher than g-C₃N₅ and g-C₃N₄ for degradation of TH at pH 3, respectively, while it was 21% and 35% at pH 7. The Fe:N ratio in Fe@g-C₃N₅, was calculated as 1:3.79, indicating that the doped Fe atom formed a FeN₄ structure with an adjacent two-layer graphite structure of g-C₃N₅, which improved the charge separation capacity of g-C₃N₅ and act as a new reaction center that can efficiently combine and catalyze the PDS to radicals. Although the intrinsic photo-degradation performance is weak, the photocatalytic performance of Fe@g-C₃N₅ has great room for the improvement and application in wastewater treatment.

Mechanistic insights into the removal of As(III) and As(V) by iron modified carbon based materials with the aid of machine learning

The machine learning (ML) technique was used to examine the effects of different microscopic material features on the ability of iron modified carbon-based materials (Fe-CBMs) to remove As(V) and As(III). The findings showed that specific CBMs and Fe-CBMs features (such as surface functionality) from sophisticated microscopic and spectroscopic techniques led to models that were more accurate than those constructed using more basic information, such as bulk elemental composition and surface area (the root-mean-square error fell by 44.7% for As(V) and 56.9% for As(III), respectively). The high non-polar carbon (NPC) content of CBMs and Fe-CBMs had a detrimental influence on As(V) and As(III) removal capability, whereas surface oxygen-containing functional groups (SOFGs) contents on CBMs and Fe-CBMs played an essential role in arsenic removal based on ML approaches. The relative importance of CO was greater by 77.8% and 40.6% than that of C-O on the elimination of As(V) and As(III), respectively. The accurate ML models are helpful for the future design of Fe-CBMs and the relative importance and partial dependence plot analysis can direct the use of Fe-CBMs for arsenic removal in a sensible manner under different application situations.

Alkyl chain length of quaternized SBA-15 and solution conditions determine hydrophobic and electrostatic interactions for carbamazepine adsorption

Santa Barbara Amorphous-15 (SBA) is a stable and mesoporous silica material. Quaternized SBA-15 with alkyl chains (Q_SBA) exhibits electrostatic attraction for anionic molecules via the N⁺ moiety of the ammonium group, whereas its alkyl chain length determines its hydrophobic interactions. In this study, Q_SBA with different alkyl chain lengths were synthesized using the trimethyl, dimethyloctyl, and dimethyoctadecyl groups (C1Q_SBA, C8Q_SBA, and C18Q_SBA, respectively). Carbamazepine (CBZ) is a widely prescribed pharmaceutical compound, but is difficult to remove using conventional water treatments. The CBZ adsorption characteristics of Q_SBA were examined to determine its adsorption mechanism by changing the alkyl chain length and solution conditions (pH and ionic strength). A longer alkyl chain resulted in slower adsorption (up to 120 min), while the amount of CBZ adsorbed was higher for longer alkyl chains per unit mass of Q_SBA at equilibrium. The maximum adsorption capacities of C1Q_SBA, C8Q_SBA, and C18Q_SBA, were 3.14, 6.56, and 24.5 mg/g, respectively, as obtained using the Langmuir model. For the tested initial CBZ concentrations (2-100 mg/L), the adsorption capacity increased with increasing alkyl chain length. Because CBZ does not dissociate readily (pK_a = 13.9), stable hydrophobic adsorption was observed despite the changes in pH (0.41-0.92, 1.70-2.24, and 7.56-9.10 mg/g for C1Q_SBA, C8Q_SBA, and C18Q_SBA, respectively); the exception was pH 2. Increasing the ionic strength from 0.1 to 100 mM enhanced the adsorption capacity of C18Q_SBA from 9.27 ± 0.42 to 14.94 ± 0.17 mg/g because the hydrophobic interactions were increased while the electrostatic attraction of the N⁺ was reduced. Thus, the ionic strength was a stronger control factor determining hydrophobic adsorption of CBZ than the solution pH. Based on the changes in hydrophobicity, which depends on the alkyl chain length, it was possible to enhance CBZ adsorption and investigate the adsorption mechanism in detail. Thus, this study aids the development of adsorbents suitable for pharmaceuticals with controlling molecular structure of QSBA and solution conditions.

A novel role of various hydrogen bonds in adsorption, desorption and co-adsorption of PPCPs on corn straw-derived biochars

The effect of various hydrogen bonds with different strength on the environmental behaviors of PPCPs remains unclear. In this study, three pharmaceutical pollutants including clofibric acid (CA), sulfamerazine (SMZ), and acetaminophen (ACT) with different functional groups and pK_a, were selected as representative of PPCPs to investigate the pivotal role of hydrogen bonds in adsorption/desorption and co-adsorption behaviors of PPCPs on two corn straw-derived biochars prepared at 300 °C (BCs-300) and 600 °C (BCs-600), respectively. The results indicated that charge-assisted hydrogen bond (CAHB) and ordinary hydrogen bond (OHB) with different intensities were the pivotal mechanisms responsible for the adsorption of three PPCPs on biochars, which was further confirmed by FTIR, but their immobilization effects of PPCPs on biochars were completely different. Compared with OHB formed between CA and BCs-600, the stronger CAHB (formed between CA and BCs-300, and SMZ/ACT and BCs-300/BCs-600) with covalent bond characteristics that derived from the smaller |ΔpK_a| (<5.0), resulted in the greater adsorption capacity (Q_s) and affinity (K_f) of the three PPCPs on BCs-300 (Q_s ≥ 195 μmol·g^-1, K_f ≥ 1.9956) than that on BCs-600 (Q_s ≤ 92 μmol·g^-1, K_f ≤ 0.5192), thereby making the better immobilization effect of PPCPs by biochar. In addition, in the coexisting systems, either SMZ coexisting with CA/ACT on BCs-300, or ACT coexisting with CA/SMZ on BCs-600, both implied that when the |ΔpK_a| between the target PPCPs and biochar is smaller than that between the coexisting compound and biochar, the target PPCPs can preferentially occupy the shared hydrogen bond sites on the biochar surface, and hard to be replaced by the coexisting compound. This work not only expand the application of designed biochar as engineering adsorbents to control and removal of the specific PPCPs in the environment, but also facilitate accurate assessment of the environmental risk of co-existing PPCPs.

Universal machine-learning algorithm for predicting adsorption performance of organic molecules based on limited data set: Importance of feature description

Adsorption of organic molecules from aqueous solution offers a simple and effective method for their removal. Recently, there have been several attempts to apply machine learning (ML) for this problem. To this end, polyparameter linear free energy relationships (pp-LFERs) were employed, and poor prediction results were observed outside model applicability domain of pp-LFERs. In this study, we improved the applicability of ML methods by adopting a chemical-structure (CS) based approach. We used the prediction of adsorption of organic molecules on carbon-based adsorbents as an example. Our results show that this approach can fully differentiate the structural differences between any organic molecules, while providing significant information that is relevant to their interaction with the adsorbents. We compared two CS feature descriptors: 3D-coordination and simplified molecular-input line-entry system (SMILES). We then built CS-ML models based on neural networks (NN) and extreme gradient boosting (XGB). They all outperformed pp-LFERs based models and are capable to accurately predict adsorption isotherm of isomers with similar physiochemical properties such as chiral molecules, even though they are trained with achiral molecules and racemates. We found for predicting adsorption isotherm, XGB shows better performance than NN, and 3D-coordinations allow effective differentiation between organic molecules.

Current research trends on emerging contaminants pharmaceutical and personal care products (PPCPs): A comprehensive review

Pharmaceutical and personnel care products (PPCPs) from wastewater are a potential hazard to the human health and wildlife, and their occurrence in wastewater has caught the concern of researchers recently. To deal with PPCPs, various treatment technologies have been evolved such as physical, biological, and chemical methods. Nevertheless, modern and efficient techniques such as advance oxidation processes (AOPs) demand expensive chemicals and energy, which ultimately leads to a high treatment cost. Therefore, integration of chemical techniques with biological processes has been recently suggested to decrease the expenses. Furthermore, combining ozonation with activated carbon (AC) can significantly enhance the removal efficiency. There are some other emerging technologies of lower operational cost like photo-Fenton method and solar radiation-based methods as well as constructed wetland, which are promising. However, feasibility and practicality in pilot-scale have not been estimated for most of these advanced treatment technologies. In this context, the present review work explores the treatment of emerging PPCPs in wastewater, via available conventional, non-conventional, and integrated technologies. Furthermore, this work focused on the state-of-art technologies via an extensive literature search, highlights the limitations and challenges of the prevailing commercial technologies. Finally, this work provides a brief discussion and offers future research directions on technologies needed for treatment of wastewater containing PPCPs, accompanied by techno-economic feasibility assessment.

Exposing and understanding synergistic effects in co-pyrolysis of biomass and plastic waste via machine learning

During co-pyrolysis of biomass with plastic waste, bio-oil yields (BOY) could be either induced or reduced significantly via synergistic effects (SE). However, investigating/ interpreting the SE and BOY in multidimensional domains is complicated and limited. This work applied XGBoost machine-learning and Shapley additive explanation (SHAP) to develop interpretable/ explainable models for predicting BOY and SE from co-pyrolysis of biomass and plastic waste using 26 input features. Imbalanced training datasets were improved by synthetic minority over-sampling technique. The prediction accuracy of XGBoost models was nearly 0.90 R² for BOY while greater than 0.85 R² for SE. By SHAP, individual impact and interaction of input features on the XGBoost models can be achieved. Although reaction temperature and biomass-to-plastic ratio were the top two important features, overall contributions of feedstock characteristics were more than 60 % in the system of co-pyrolysis. The finding provides a better understanding of co-pyrolysis and a way of further improvements.

Machine learning-assisted exploration for carbon neutrality potential of municipal sludge recycling via hydrothermal carbonization

In the context of advocating carbon neutrality, there are new requirements for sustainable management of municipal sludge (MS). Hydrothermal carbonization (HTC) is a promising technology to deal with high-moisture MS considering its low energy consumption (without drying pretreatment) and value-added products (i.e., hydrochar). This study applied machine learning (ML) methods to conduct a holistic assessment with higher heating value (HHV) of hydrochar, carbon recovery (CR), and energy recovery (ER) as model targets, yielding accurate prediction models with R² of 0.983, 0.844 and 0.858, respectively. Furthermore, MS properties showed positive (e.g., carbon content, HHV) and negative (e.g., ash content, O/C, and N/C) influences on the hydrochar HHV. By comparison, HTC parameters play a critical role for CR (51.7%) and ER (52.5%) prediction. The primary sludge was an optimal HTC feedstock while anaerobic digestion sludge had the lowest potential. This study provided a comprehensive reference for sustainable MS treatment and industrial application.

Machine learning and statistical analysis for biomass torrefaction: A review

Torrefaction is a remarkable technology in biomass-to-energy. However, biomass has several disadvantages, including hydrophilic properties, higher moisture, lower heating value, and heterogeneous properties. Many conventional approaches, such as kinetic analysis, process modeling, and computational fluid dynamics, have been used to explain torrefaction performance and characteristics. However, they may be insufficient in actual applications because of providing only some specific solutions. Machine learning (ML) and statistical approaches are powerful tools for analyzing and predicting torrefaction outcomes and even optimizing the thermal process for its utilization. This state-of-the-art review aims to present ML-assisted torrefaction. Artificial neural networks, multivariate adaptive regression splines, decision tree, support vector machine, and other methods in the literature are discussed. Statistical approaches (SAs) for torrefaction, including Taguchi, response surface methodology, and analysis of variance, are also reviewed. Overall, this review has provided valuable insights into torrefaction optimization, which is conducive to biomass upgrading for achieving net zero.

Magnetically engineered sulfurized peat-based activated carbon for remediation of emerging pharmaceutical contaminants

Activated carbon derived from peat-based biomass was sulfurized and magnetized forming magnetically-engineered sulfurized peat-based activated carbon (MEPBAC) and used for adsorption of caffeine (CFN) and sulfamethoxazole (SMX) from aqueous media. Modification increased the surface area (724 m²/g) and introduced sulphur-groups and Fe-based nano-structures in MEPBAC. Sulphur-groups enhanced adsorption efficiency, whereas Fe-based nano-structures facilitated easy magnetic separation of MEPBAC after intended use leading to high reusability with consistent removal efficiency (∼95 %). Response surface methodology was employed for design of experiments and process optimization. The results revealed that the maximum removal (SMX 94 %; CFN 97 %) could be achieved at an adsorbent dose of 1.4 and 1.6 g/L, respectively (pH 11, 311 K). Adsorption kinetics was best explained by a pseudo-second-order kinetic model. Adsorption data of SMX was fitted better to Langmuir (linear) and Freundlich (non-linear) isotherms, whereas that of CFN was fitted well with Freundlich (linear) and Langmuir (non-linear) isotherms (R² ≥ 0.99).

Solvent Effects in the Preparation of Catalysts Using Activated Carbon as a Carrier

The role of solvents is crucial in catalyst preparation. With regard to catalysts prepared with activated carbon (AC) as the carrier, when water is used as a solvent it is difficult for the solution to infiltrate the AC. Because AC comprises a large number of C atoms and is a nonpolar material, it is more effective for the adsorption of nonpolar substances. Since the water and active ingredients are polar, they cannot easily infiltrate AC. In this study, the dispersion of the active component was significantly improved by optimizing the solvent, and the particle size of the active component was reduced from 33.08 nm to 15.30 nm. The specific surface area of the catalyst is significantly increased, by 10%, reaching 991.49 m²/g. Under the same reaction conditions, the conversion of acetic acid by the catalyst prepared with the mixed solvent was maintained at approximately 65%, which was 22% higher than that obtained using the catalyst prepared with water as the solvent.

Modeling, optimization and understanding of adsorption process for pollutant removal via machine learning: Recent progress and future perspectives

It is crucial to reduce the concentration of pollutants in water environment to below safe levels. Some cost-effective pollutant removal technologies have been developed, among which adsorption technology is considered as a promising solution. However, the batch experiments and adsorption isotherms widely employed at present are inefficient and time-consuming to some extent, which limits the development of adsorption technology. As a new research paradigm, machine learning (ML) is expected to innovate traditional adsorption models. This reviews summarized the general workflow of ML and commonly employed ML algorithms for pollutant adsorption. Then, the latest progress of ML for pollutant adsorption was reviewed from the perspective of all-round regulation of adsorption process, including adsorption efficiency, operating conditions and adsorption mechanism. General guidelines of ML for pollutant adsorption were presented. Finally, the existing problems and future perspectives of ML for pollutant adsorption were put forward. We highly expect that this review will promote the application of ML in pollutant adsorption and improve the interpretability of ML.

Predictive modeling of pharmaceutical product removal by a managed aquifer recharge system: Comparison and optimization of models using ensemble learners

Pharmaceutical products (PPs) are emerging water pollutants with adverse environmental and health-related impacts, owing to their toxic, persistent, and undetectable microscopic nature. Globally, increasing scientific knowledge and advanced technologies have allowed researchers to study PP-associated problems and their removal for water reuse. Experimental modeling methods require laborious, lengthy, expensive, and environmentally hazardous lab-work to optimize the process. On the other hand, predictive machine learning (ML) models can trace the complex input-output relationship of a process using available datasets. In this study, ensemble ML techniques, including decision tree (DT), random forest (RF), and Xtreme gradient boost (XGB), were used to explore PP (diclofenac, iopromide, propranolol, and trimethoprim) removal by a managed aquifer recharge (MAR) system. The model input parameters included characteristics of reclaimed water and soil used in the columns, pH, dissolved organic carbon, operating time, nitrogen dioxide, sulfate, nitrate, electrical conductivity, manganese, and iron. The selected PP removal was the model output. Datasets were collected through a one-year experimental study of continuous MAR system operation to predict the removal of PPs. DT, RF, and XGB models were then developed for one of the selected compounds and tested for the others to check the reliability of the ML model results. The developed models were assessed using statistical performance matrices. The experimental results showed >80% removal of propranolol and trimethoprim; however, removal of diclofenac and iopromide was only ≈50% by the MAR system. The proposed DT and RF models presented higher coefficients of determination (R² ≥ 0.92) for diclofenac, propranolol, and trimethoprim than for iopromide (R² ≤ 0.63). In contrast, the XGB model showed better results for diclofenac, iopromide, propranolol, and trimethoprim, with R² values of 0.92, 0.72, 0.96, and 0.97, respectively. Therefore, XGB could be the best predictive model to provide insight into the adaptation of ML models to predict PP removal by the MAR system, thereby minimizing experimental work.

Comparative study of machine learning methods integrated with genetic algorithm and particle swarm optimization for bio-char yield prediction

In this study, Machine learning (ML) models integrated with genetic algorithm (GA) and particle swarm optimization (PSO) have been developed to predict, evaluate, and analyze biochar yield using biomass properties and process operating conditions. Comparative study of different ML algorithms integrated with GA and PSO were performed to improve the ML models architecture and parameters selection. The results proposed that Ensembled Learning Tree (ELT-PSO) model outperformed all other models and is favored for biochar yield prediction (R² = 0.99, RMSE = 2.33). The partial dependence plots (PDPs) analysis shows the potential effects of each influencing parameter impact on the biochar yield and as well as shows that how these factors will interact during the pyrolysis process. A user-friendly software was developed based on the ELT-PSO model to avoid extensive and expensive experimentations without requiring considerable ML understanding. Difference recorded by GUI was less than 2% with experimental yield.

Investigation on the evolution of hydrothermal biochar

The objective of this study was to visualize trends and current research status of hydrothermal biochar research through a bibliometric analysis by using CiteSpace software. The original article data were collected from the Web of Science core database published between 2009 and 2020. A visual analysis network of national co-authored, institutional co-authored and author co-authored articles was created, countries, institutions and authors were classified accordingly. By visualizing the cited literature and journal co-citation networks, the main subject distribution and core journals were identified respectively. By visualizing journal co-citations, the main research content was identified. Further the cluster analysis revealed the key research directions of knowledge structure. Keyword co-occurrence analysis and key occurrence analysis demonstrate current research hotspots and new research frontiers. Through the above analysis, the cooperation and contributions of hydrothermal biochar research at different levels, from researchers to institutions to countries to macro levels, were explored, the disciplinary areas of knowledge and major knowledge sources of hydrothermal biochar were discovered, and the development lineage, current status, hotspots and trends of hydrothermal biochar were clarified. The results obtained from the study can provide a reference for scholars to gain a deeper understanding of hydrothermal biochar.

Unintentional release of antibiotics associated with nutrients recovery from source-separated human urine by biochar

The use of biochar to recover nitrogen and phosphorus from wastewater especially source-separated human urine is attractive from both economic and environmental standpoints. The widespread use of pharmaceuticals has raised concerns as they are not fully metabolized and ended up in human urine. The objective of this study is to examine adsorption of antibiotics (azithromycin, ciprofloxacin, sulfamethoxazole, trimethoprim, and tetracycline) and nutrients (ammonium and phosphate) in source-separated human urine by biochar and subsequent desorption. Batch adsorption experiments were conducted using biochar prepared from oak wood (OW) and paper mill sludge (PMS) to elucidate the effects of adsorption time, pH, and adsorbent dose. The desorption of adsorbed nutrients and antibiotics was also investigated. While the nutrient adsorption was more favorable by the PMS biochar, antibiotic adsorption was more prolific by the OW biochar. Hydrogen bonding and π-π interaction were identified as potential adsorption mechanisms. Experimental results agree with the Freundlich isotherm and pseudo-second order models (except the OW biochar for the kinetics). The findings suggest that biochar can adsorb both nutrients (43.30-266.67 mg g^-1) and antibiotics (246.70-389.0 μg g^-1) simultaneously. Lower solution pH (<5) was better for antibiotic adsorption, while higher solution pH (≥5) favored nutrient recovery. Also, desorption of the antibiotics (maximum of 92.6% for trimethoprim) was observed and might arise in the environment with the applications of biochar for nutrient recovery from human urine and subsequently for soil quality improvement. The findings serve as a foundation for future research on adsorption-based methods for separating nutrients and antibiotics in aqueous solutions, particularly urine.

Efficiency, by-product valorization, and pollution control of co-pyrolysis of textile dyeing sludge and waste solid adsorbents: Their atmosphere, temperature, and blend ratio dependencies

This study aimed to quantify the co-pyrolytic synergistic effects of textile dyeing sludge (TDS) and waste biochar (WBC) for an optimal utilization of secondary resources and to mitigate environmental pollution and waste volume. TDS and WBC had a strong synergistic effect between 800 and 900 °C in the CO₂-assisted atmosphere. With the increased TDS fraction, NH₃ emission fell significantly regardless of the atmosphere type. The CO₂ atmosphere changed S in TDS char and released SO₂ in the range of 800-1000 °C. With the temperature rise, an unstable N structure turned into a more stable heterocyclic N structure in the CO₂ and N₂ atmospheres. Regardless of the atmosphere type and temperature, the C-containing functional groups in co-pyrolytic biochar existed mainly as C-C/C-H. In the CO₂ atmosphere, inorganic S, aliphatic S, and thiophene S in the co-pyrolytic biochar disappeared and became more stable sulfones. The co-pyrolysis inhibited the formation of S-containing compounds. The retention ability of the co-pyrolytic biochar peaked for most of the heavy metals in the N₂ atmosphere but was better for Pb and Zn in the CO₂ than N₂ atmosphere. Simultaneous optimization showed the co-pyrolysis of 10% TDS and 90% WBC at above 950 °C in the N₂-CO₂ or CO₂ atmosphere as the optimal operational settings combined.

Predicting Carbon Residual in Biomass Wastes Using Soft Computing Techniques

In recent decades, the development of complex materials developed a class of biomass waste-derived porous carbons (BWDPCs), which are used for carbon capture and sustainable waste management. It is difficult in understanding the adsorption mechanism of CO2 in the air as it has a wide range of properties associated with its diverse textures, functional group existence, pressure, and temperature of varying range. These properties influence diversely the adsorption mechanism of CO2 and pose serious challenges in the process. To resolve this multiobjective formulation, we use a machine learning classifier that maps systematically the CO2 adsorption as a function of compositional and textural properties and adsorption parameters. The machine learning classifier helps in the classification of various porous carbon materials during the time of training and testing. The results of the simulation show that the proposed method is more efficient in classifying the porous nature of the CO2 adsorption materials than other methods.

Prediction of Soil Heavy Metal Immobilization by Biochar Using Machine Learning

Biochar application is a promising strategy for the remediation of contaminated soil, while ensuring sustainable waste management. Biochar remediation of heavy metal (HM)-contaminated soil primarily depends on the properties of the soil, biochar, and HM. The optimum conditions for HM immobilization in biochar-amended soils are site-specific and vary among studies. Therefore, a generalized approach to predict HM immobilization efficiency in biochar-amended soils is required. This study employs machine learning (ML) approaches to predict the HM immobilization efficiency of biochar in biochar-amended soils. The nitrogen content in the biochar (0.3-25.9%) and biochar application rate (0.5-10%) were the two most significant features affecting HM immobilization. Causal analysis showed that the empirical categories for HM immobilization efficiency, in the order of importance, were biochar properties > experimental conditions > soil properties > HM properties. Therefore, this study presents new insights into the effects of biochar properties and soil properties on HM immobilization. This approach can help determine the optimum conditions for enhanced HM immobilization in biochar-amended soils.

Sustainable biochar: A facile strategy for soil and environmental restoration, energy generation, mitigation of global climate change and circular bioeconomy

The increasing agro-demands with the burgeoning population lead to the accumulation of lignocellulosic residues. The practice of burning agri-residues has consequences viz. Release of soot and smoke, nutrient depletion, loss of soil microbial diversity, air pollution and hazardous effects on human health. The utilization of agricultural waste as biomass to synthesize biochar and biofuels, is the pertinent approach for attaining sustainable development goals. Biochar contributes in the improvement of soil properties, carbon sequestration, reducing greenhouse gases (GHG) emission, removal of organic and heavy metal pollutants, production of biofuels, synthesis of useful chemicals and building cementitious materials. The biochar characteristics including surface area, porosity and functional groups vary with the type of biomass consumed in pyrolysis and the control of parameters during the process. The major adsorption mechanisms of biochar involve physical-adsorption, ion-exchange interactions, electrostatic attraction, surface complexation and precipitation. The recent trend of engineered biochar can enhance its surface properties, pH buffering capacity and presence of desired functional groups. This review focuses on the contribution of biochar in attaining sustainable development goals. Hence, it provides a thorough understanding of biochar's importance in enhancing soil productivity, bioremediation of environmental pollutants, carbon negative concretes, mitigation of climate change and generation of bioenergy that amplifies circular bioeconomy, and concomitantly facilitates the fulfilment of the United Nation Sustainable Development Goals. The application of biochar as seen is primarily targeting four important SDGs including clean water and sanitation (SGD6), affordable and clean energy (SDG7), responsible consumption and production (SDG12) and climate action (SDG13).