Accurate SWASV detection of Cd(II) under the interference of Pb(II) by coupling support vector regression and feature stripping currents

Revealing the solid-solution interface interference behaviors between Cu2+ and As(III) via partial peak area analysis of simulations and experiments

The mutual interference in the sensing detection of heavy metal ions (HMIs) is considerably serious and complex. Besides, the co-existed ions may change the stripping peak intensity, shape and position of the target ion, which partly makes peak current analysis inaccurate. Herein, a promising approach of partial peak area analysis was proposed firstly to research the mutual interference. The interference between two species on their electrodeposition processes was investigated by simulating different kinetics parameters, including surface coverage, electro-adsorption, -desorption rate constant, etc. It was proved that the partial peak area is sensitive and regular to these interference kinetics parameters, which is favorable for distinctly identifying different interferences. Moreover, the applicability of the partial peak area analysis was verified on the experiments of Cu2+, As(III) interference at four sensing interfaces: glassy carbon electrode, gold electrode, Co3O4, and Fe2O3 nanoparticles modified electrodes. The interference behaviors between Cu2+ and As(III) relying on solid-solution interfaces were revealed and confirmed by physicochemical characterizations and kinetics simulations. This work proposes a new descriptor (partial peak area) to recognize the interference mechanism and provides a meaningful guidance for accurate detection of HMIs in actual water environment.

Metal-free cysteamine-functionalized graphene alleviates mutual interferences in heavy metal electrochemical detection

Heavy metal pollutants are of great concern to environmental monitoring due to their potent toxicity. Electrochemical detection, one of the main techniques, is hindered by the mutual interferences of various heavy metal ions in practical use. In particular, the sensitivity of carbon electrodes to Cd2+ ions (one of the most toxic heavy metals) is often overshadowed by some heavy metals (e.g. Pb2+ and Cu2+). To mitigate interference, metallic particles/films (e.g. Hg, Au, Bi, and Sn) typically need to be embedded in the carbon electrodes. However, these additional metallic materials may face issues of secondary pollution and unsustainability. In this study, a metal-free and sustainable nanomaterial, namely cysteamine covalently functionalized graphene (GSH), was found to lead to a 6-fold boost in the Cd2+ sensitivity of the screen-printed carbon electrode (SPCE), while the sensitivities to Pb2+ and Cu2+ were not influenced in simultaneous detection. The selective enhancement could be attributed to the grafted thiols on GSH sheets with good affinity to Cd2+ ions based on Pearson's hard and soft acid and base principle. More intriguingly, the GSH-modified SPCE (GSH-SPCE) featured high reusability with extended cycling times (23 times), surpassing the state-of-art SPCEs modified by non-covalently functionalized graphene derivatives. Last, the GSH-SPCE was validated in tap water.

Release of free-state ions from fulvic acid-heavy metal complexes via VUV/H2O2 photolysis: Photodegradation of fulvic acids and recovery of Cd2+ and Pb2+ stripping voltammetry currents

Fulvic acid (FA), a ubiquitous organic matter in the environment, can enhance the mobility and bioavailability of Cd²⁺ and Pb²⁺ through competitive complexation to form FA-heavy metal ions (FA-HMIs) complexes with excellent solubility. Because FA-HMIs are electrochemically inactive, square wave anodic stripping voltammetry (SWASV) cannot accurately detect the content of bioavailable Cd²⁺ and Pb²⁺ in soils and sediments. This study ostensibly aimed to efficiently recover SWASV signals of Cd²⁺ and Pb²⁺ in FA-HMIs by disrupting FA-HMIs complexes using the combined vacuum ultraviolet and H₂O₂ (VUV/H₂O₂) process. Essentially, this study explored the photodegradation behavior and photolysis by-products of FA and their effects on the conversion of FA-HMIs complexes to free-state Cd²⁺ and Pb²⁺ using multiple characterization techniques, as well as revealed the complexation mechanism of FA with Cd²⁺ and Pb²⁺. Results showed that reactive groups such as carboxyl and hydroxyl endowed FA with the ability to complex Cd²⁺ and Pb²⁺. After FA-HMIs underwent VUV/H₂O₂ photolysis for 9 min at 125 mg/L of H₂O₂, FA was decomposed into small molecular organics while removing its functional groups, which released the free-state Cd²⁺ and Pb²⁺ and recovered their SWSAV signals. However, prolonged photolytic mineralization of FA to inorganic anions formed precipitates with Cd²⁺ and Pb²⁺, thereby decreasing their SWSAV signals. Moreover, the VUV/H₂O₂ photolysis significantly improved the SWASV detection accuracy toward the Cd²⁺ and Pb²⁺ in real soil and sediment samples, verifying its practicality.

Spatiotemporal Variability Assessment of Trace Metals Based on Subsurface Water Quality Impact Integrated with Artificial Intelligence-Based Modeling

Increasing anthropogenic emissions due to rapid industrialization have triggered environmental pollution and pose a threat to the well-being of the ecosystem. In this study, the first scenario involved the spatio-temporal assessment of topsoil contamination with trace metals in the Dammam region, and samples were taken from 2 zones: the industrial (ID), and the agricultural (AG) area. For this purpose, more than 130 spatially distributed samples of topsoil were collected from residential, industrial, and agricultural areas. Inductively coupled plasma—optical emission spectroscopy (ICP-OES)—was used to analyze the samples for various trace metals. The second scenario involved the creation of different artificial intelligence (AI) models, namely an artificial neural network (ANN) and a support vector regression (SVR), for the estimation of zinc (Zn), copper (Cu), chromium (Cr), and lead (Pb) using feature-based input selection. The experimental outcomes depicted that the average concentration levels of HMs were as follows: Chromium (Cr) (31.79 ± 37.9 mg/kg), Copper (Cu) (6.76 ± 12.54 mg/kg), Lead (Pb) (6.34 ± 14.55 mg/kg), and Zinc (Zn) (23.44 ± 84.43 mg/kg). The modelling accuracy, based on different evaluation criteria, showed that agricultural and industrial stations showed performance merit with goodness-of-fit ranges of 51–91% and 80–99%, respectively. This study concludes that AI models could be successfully applied for the rapid estimation of soil trace metals and related decision-making.