Adsorptive removal of perfluorooctanoic acid from aqueous matrices using peanut husk-derived magnetic biochar: Statistical and artificial intelligence approaches, kinetics, isotherm, and thermodynamics.

Removal of perfluorooctanoic acid (PFOA) from water matrices is crucial owing to its pervasiveness and adverse ecological and human health effects. This study investigates the adsorptive removal of PFOA using magnetic biochar (MBC) derived from FeCl3-treated peanut husk at different temperatures (300, 600, and 900 °C). Preliminary experiments demonstrated that MBC600 exhibited superior performance, with its characterization confirming the presence of γ-Fe2O3. However, efficient PFOA removal from water matrices depends on determining the optimum combination of inputs in the treatment approaches. Therefore, optimization and predictive modeling of the PFOA adsorption were investigated using the response surface methodology (RSM) and the artificial intelligence (AI) models, respectively. The central composite design (CCD) of RSM was employed as the design matrix. Further, three AI models, viz. artificial neural network (ANN), support vector machine (SVM), and adaptive neuro-fuzzy inference system (ANFIS) were selected to predict PFOA adsorption. The RSM-CCD model applied to optimize three input process parameters, namely, adsorbent dose (100-400 mg/L), pH (3-10), and contact time (20-60 min), showed a statistically significant (p < 0.05) effect on PFOA removal. Maximum PFOA removal of about 98.3% was attained at the optimized conditions: adsorbent dose: 400 mg/L, pH: 3.4, and contact time: 60 min. Non-linear analysis showed PFOA adsorption was best fitted by pseudo-second-order kinetics (R2 = 0.9997). PFOA adsorption followed Freundlich isotherm (R2 = 0.9951) with a maximum adsorption capacity of ∼307 mg/g. Thermodynamics and spectroscopic analyses revealed that PFOA adsorption is a spontaneous, exothermic, and physical phenomenon, with electrostatic interaction, hydrophobic interaction, and hydrogen bonding governing the process. A comparative analysis of the statistical and AI models for PFOA adsorption demonstrated high R2 (>0.99) for RSM-CCD, ANN, and ANFIS. This research demonstrates the applicability of the statistical and AI models for efficient prediction of PFOA adsorption from water matrices using MBC (MBC600).

Coagulative removal of polystyrene microplastics from aqueous matrices using FeCl3-chitosan system: Experimental and artificial neural network modeling.

Effluent from sewage treatment plants (STPs) is a significant source of microplastics (MPs) re-entry into the environment. Coagulation-flocculation-sedimentation (CFS) process as an initial tertiary treatment step requires investigation for coagulative MPs removal from secondary-treated sewage effluents. In this study, experiments were conducted on synthetic water containing 25 mg/L polystyrene (PS) MPs using varying dosages of FeCl3 (1-10 mg/L) and chitosan (0.25-9 mg/L) to assess the effect of process parameters, such as pH (4-8), stirring speed (0-200 rpm), and settling time (10-40 min). Results revealed that ∼89.3% and 21.4% of PS removal were achieved by FeCl3 and chitosan, respectively. Further, their combination resulted in a maximum of 99.8% removal at favorable conditions: FeCl3: 2 mg/L, chitosan: 7 mg/L, pH: 6.3, stirring speed: 100 rpm, and settling time: 30 min, with a statistically significant (p < 0.05) effect. Artificial neural network (ANN) validated the experimental results with RMSE = 1.0643 and R2 = 0.9997. Charge neutralization, confirmed by zeta potential, and adsorption, ascertained by field-emission scanning electron microscope (FESEM) and Fourier-transform infrared spectroscopy (FTIR), were primary mechanisms for efficient PS removal. For practical considerations, the application of the FeCl3-chitosan system on the effluents from moving bed biofilm reactor (MBBR) and sequencing batch reactor (SBR)-based STPs, spiked with PS microbeads, showed > 98% removal at favorable conditions.

Fungal biotechnology for urban mining of metals from waste printed circuit boards: A review.

Rapid surge in electronic waste (e-waste) and its unscientific handling has an adverse impact on humans and the environment. Waste printed circuit board (WPCB), an integrated component of e-waste, has a high metallic content that includes both toxic and precious metals. Therefore, metal recovery is essential not just to avoid environmental degradation but also for economic growth. The current literature analysis focuses on one such eco-friendly approach, known as fungal biotechnology, for extracting metals from WPCBs. Among diverse bioleaching agents, fungi have shown promising metal extraction efficiency (Al: 65-96%; Co: 45-90%; Cu: 34-100%; Ni: 8-95%; Mn: 70-95%; Pb: 27-95%; Zn: 54-99%) and the ability to work in a wide pH range. However, in terms of metal recovery from WPCBs, fungal bioleaching has been less explored. This review, thus, assesses the fungal biotechnology for metal extraction from WPCBs and discusses the associated mechanism and kinetics involved. Different process parameters affecting the fungal bioleaching have also been discussed briefly. The review highlights that, while this process has enough potential, some associated drawbacks hinder its practical applicability on an industrial scale. Lastly, some suggestions for scaling up and reducing the cost of the process have been made, which need to be addressed.

Sources, occurrence, and treatment techniques of per- and polyfluoroalkyl substances in aqueous matrices: A comprehensive review.

Per- and polyfluoroalkyl substances (PFAS), a class of synthetic organic pollutants, have prompted concerns about their global prevalence and possible health effects. This review consolidates the most recent data on different aspects of PFAS, such as their occurrence, and prominent sources. The current literature analysis of PFAS occurrence suggests significant variation in their concentration ranging from 0.025 to 1.2 × 108 ng/L in wastewater, 0.01 to 8.9 × 105 ng/L in surface water, and <0.01 to 1.3 × 104 ng/L in groundwater globally. Since conventional treatment techniques are inadequate in remediating PFAS, innovative treatment approaches based on their removal or mineralization mechanism have been comprehensively reviewed. Advanced treatment technologies have shown degradation or removal of PFAS to be around 6 and > 99.9% in different aqueous matrices. However, due to significant drawbacks in their applicability in wastewater treatment plants (WWTPs), a novel treatment train approach has emerged as an effective alternative. This approach synergistically integrates multiple remediation techniques while addressing the impediments of individual treatments. Furthermore, nanofiltration (NF270) combined with electrochemical degradation has been demonstrated to be the most efficient (>98%) treatment train approach in PFAS remediation. If implemented in WWTPs, nanofiltration followed by adsorption using activated carbon is also a viable method for PFAS removal.