Evaluation of the performance of zirconia-multiwalled carbon nanotube nanoheterostructures in adsorbing As(III) from potable water from the perspective of physical chemistry and chemical physics with a special emphasis on approximate site energy distribution

Optimisation led energy-efficient arsenite and arsenate adsorption on various materials with machine learning

The contamination of water by arsenic (As) poses a substantial environmental challenge with far-reaching influence on human health. Accurately predicting adsorption capacities of arsenite (As(III)) and arsenate (As(V)) on different materials is crucial for the remediation and reuse of contaminated water. Nonetheless, predicting the optimal As adsorption on various materials while considering process energy consumption continues to pose a persistent challenge. Literature data regarding the As adsorption on diverse materials were collected and employed to train machine learning models (ML), such as CatBoost, XGBoost, and LGBoost. These models were utilized to predict both As(III) and As(V) adsorption on a variety of materials using their reaction parameters, structural properties, and composition. The CatBoost model exhibited superior accuracy, achieving a coefficient of determination (R²) of 0.99 and a root mean square error (RMSE) of 1.24 for As(III), and an R² of 0.99 and RMSE of 5.50 for As(V). The initial As(III) and As(V) concentrations were proved to be the primary factors influencing adsorption, accounting for 27.9 % and 26.6 % of the variance for As(III) and As(V) individually. The genetic optimization led optimisation process, considering the low energy consumption, determined maximum adsorption capacities of 291.66 mg/g for As(III) and 271.56 mg/g for As(V), using C-Layered Double Hydroxide with reduced graphene oxide and chitosan combined with rice straw biochar, respectively. To further facilitate the process design for different real-life applications, the trained ML models are embedded into a web-app that the user can use to estimate the As(III) and As(V) adsorption under different design conditions. The utilization of ML for the energy-efficient As(III) and As(V) adsorption is deemed essential for advancing the treatment of inorganic As in aquatic settings. This approach facilitates the identification of optimal adsorption conditions for As in various material-amended waters, while also enabling the timely detection of As-contaminated water.

Effects of environmental metal and metalloid pollutants on plants and human health: exploring nano-remediation approach

Metal and metalloid pollutants severely threatens environmental ecosystems and human health, necessitating effective remediation strategies. Nanoparticle (NPs)-based approaches have gained significant attention as promising solutions for efficient removing heavy metals from various environmental matrices. The present review is focused on green synthesized NPs-mediated remediation such as the implementation of iron, carbon-based nanomaterials, metal oxides, and bio-based NPs. The review also explores the mechanisms of NPs interactions with heavy metals, including adsorption, precipitation, and redox reactions. Critical factors influencing the remediation efficiency, such as NPs size, surface charge, and composition, are systematically examined. Furthermore, the environmental fate, transport, and potential risks associated with the application of NPs are critically evaluated. The review also highlights various sources of metal and metalloid pollutants and their impact on human health and translocation in plant tissues. Prospects and challenges in translating NPs-based remediation from laboratory research to real-world applications are proposed. The current work will be helpful to direct future research endeavors and promote the sustainable implementation of metal and metalloid elimination.

Chloride converts lead slag into a bifunctional material to remove heavy metals

Efficient and safe removal of arsenic and lead from industrial wastewater is essential for ecological protection. In this study, we developed a novel method using lead slag as a purifying agent and sodium chloride as a reinforcing agent to remove arsenic and lead from industrial wastewater. Through a combination of experiments and simulations, we elucidated the mechanisms involved in this reaction. The initial concentrations of As and Pb ions in the industrial wastewater were 4333 and 188 mg/L, respectively. After the reaction at 25 °C and a pH ranging from 9.7 to 10, the concentrations of arsenic and lead were reduced to 4.9 mg/L and 0.008 mg/L, respectively, achieving a removal rate of 99.9%. Our experimental results demonstrated that Pb2+ and AsO43- ions released from the lead slag and industrial wastewater reacted with Cl- ions to form Pb5(AsO4)3Cl precipitates, thus effectively eliminating a significant amount of As and Pb species. Simulation studies indicated that Pb5(AsO4)3Cl exhibited exceptional stability below 400 °C and could be directly stored. Additionally, the lead slag, which is rich in silica, played a crucial role in removing and stabilizing As and Pb ions. Under alkaline conditions, silica encapsulated the As and Pb species, adhering to the surface of the Pb-As co-precipitates and forming dense, irregular, small particles with internal and external structures that impeded the efflux of As and Pb ions. This phenomenon was confirmed through scanning electron microscopy (SEM) and transmission electron microscopy (TEM). The kinetics of As and Pb ion removal was consistent with the pseudo-second-order kinetic model, indicating that the removal process was primarily governed by chemical interactions. Lead slag exhibits significant potential and advantages in the removal of As and Pb. This innovative method offers an effective approach to address heavy metal contamination in industrial wastewater, thus contributing to ecological protection.

Synthesis of co-pyrolyzed biochar using red mud and peanut shell for removing phosphate from pickling wastewater: Performance and mechanism

Iron (Fe)/iron oxide-modified biochar has practicable adsorption capability for phosphorus (P), but it is expensive. In this study, we synthesized novel low-cost and eco-friendly adsorbents co-pyrolyzed biochars using Fe-rich red mud (RM) and peanut shell (PS) wastes via a one-step pyrolysis process for removing P from pickling wastewater. The preparation conditions (heating rate, pyrolysis temperature, and feedstock ratio) and P adsorption behaviors were systematically investigated. In addition, a series of characterization and approximate site energy distribution (ASED) analyses were conducted to understand the P adsorption mechanisms. The magnetic biochar (BR7P3) with m (RM):m (PS) of 7:3 prepared at 900°C and 10 °C/min had a high surface area (164.43 m2/g) and different abundant ions (including Fe3+, and Al3+). In addition, BR7P3 exhibited the best P removal capability (142.6 mg/g). The Fe2O3 from RM was successfully reduced to Fe0, which was easily oxidized as Fe3+ to precipitate with H2PO4-. The electrostatic effect, Fe-O-P bonding, and surface precipitation were the main mechanisms of P removal. ASED analyses revealed that high distribution frequency and solution temperature led to a high P adsorption rate of the adsorbent. Therefore, this study provides new insight into the waste-to-wealth strategy by transforming PS and RM into mineral-biomass biochar with excellent P adsorption capability and environmental adaptability.

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.

A critical review of adsorption isotherm models for aqueous contaminants: Curve characteristics, site energy distribution and common controversies

The quantitative description of the equilibrium data by the isotherm models is an indispensable link in adsorption studies. The previous review papers focus on the underlying assumptions, fitting methods, error functions and practical applications of the isotherm models, usually ignoring their curve characteristics, selection criteria and common controversies. The main contents of this review include: (i) effect of the model parameters on the isotherm curves; (ii) determination of the site energy distribution; (iii) selection criteria of the isotherm models; and (iv) elimination of some common controversies. It is of great significance to reveal the curve characteristics for selecting a proper isotherm model. The site energy distribution is conducive to understanding the physicochemical properties of the adsorbent surface. The complete isotherm is recommended to be correlated with the experimental data. The model parameter q_max should be cautiously adopted for comparison of the adsorbent performance. The residual plot can be used to diagnose the fitting quality of the isotherm models further. This review also addresses some common mistakes and controversies and thereby avoids their propagation in future publications.

Rapid effectual entrapment of arsenic pollutant by Fe2O3 supported on bimodal meso-macroporous silica for cleaning up aquatic system

Arsenic (As) contamination in aqueous media is a major concern due to its adverse impacts on humans and the ecosystem more broadly because of its non-biodegradability. Consequently, an effective and selective sorbent is needed urgently to scavenge As pollutant. Herein, the adsorption behaviors of As(V) by Fe₂O₃ and Fe₂O₃ supported on different silica materials, consisting of unimodal mesoporous silica (Fe₂O₃/U-SiO₂) and dual meso-macroporous silica (Fe₂O₃/B-SiO₂), were compared to examine their structure-efficiency relationships in the elimination of As(V). Fe₂O₃/B-SiO₂ was much faster at As(V) removal and had an impressively higher uptake capability, reaching nearly 50% and 2.5 mg g^-1 within 5 min compared to bare Fe₂O₃ (6% and 0.3 mg g^-1) and Fe₂O₃/U-SiO₂ (11.9% and 0.59 mg g^-1). These better results were because of the highly dispersed Fe₂O₃ nanoparticles on the B-SiO₂ support that provided abundant reactive sites as well as a macropore structure facilitating As(V) diffusion into adsorptive sites. The maximum adsorptive capacity of Fe₂O₃/B-SiO₂ (4.7 mg As per 1 g adsorbent) was 1.3- and 1.7-fold greater than for Fe₂O₃/U-SiO₂ and Fe₂O₃, respectively. The outstanding performance and reusability of Fe₂O₃/B-SiO₂ with its ease of production, economical and environmentally friendly features made it even more attractive for As(V) remediation. The explored relationship between the structure of SiO₂-supported Fe₂O₃ sorbents and their performance in removing As(V) could be informative for the future design of highly efficient adsorbents for the decontamination of water.

The groundwater arsenic contamination in the Bengal Basin-A review in brief

The presence of arsenic in the groundwater of the densely-populated Bengal Basin evolved as a mass-poisoning agent and is a reason for the misery of millions of people living here. High-level arsenic was detected in the shallow aquifer-tube wells of the basin in the late-20th century. The redox conditions and the biogeochemical activities in the shallow aquifers support the existence of arsenic in its most toxic +3 state. The shallow aquifers are constructed by the Holocene reduced grey sands, having a lesser capacity to hold the arsenic brought from the Himalayas by the Ganga-Brahmaputra-Meghna river system. Among several other hypotheses, the reductive dissolution of arsenic bearing Fe-oxyhydroxides coupled with the microbial activities in the organic-matter-rich Holocene grey sands is believed to be the primary reason for releasing arsenic in groundwater of basinal shallow aquifers. The deep aquifers below the late Pleistocene aquifers and the Palaeo-interfluvial aquifers capped by the last glacial maximum Palaeosol generally contain arsenic-free or low-arsenic water. Ingress of arsenic into the deep aquifers from the shallow aquifers was considered to have been caused by extensive non-domestic pumping. However, studies have found that extensive pumping is unlikely to contaminate the deep aquifer water with higher levels of arsenic within decadal time scales. Irrigation-pumping may produce hydraulic barriers between the shallow and deep aquifer-groundwater and distributes arsenic in the topsoil by flushing. Significant disparities have been observed among the Bengal basinal groundwater arsenic concentrations. However, abrupt spatial variation in groundwater arsenic concentrations has been a key feature of the basin.