Occurrence and Health Risks of Heavy Metals in Drinking Water of Self-Supplied Wells in Northern China

Electrochemical sensor based on gum Arabic nanoparticles for rapid and in-situ detection of different heavy metals in real samples

The key solution to combat trace metal pollution and keep the environment, ecosystem, animals, and humans safe is earlier and rapid trace metal detection. For all these reasons, we propose in this work the design of a simple electrochemical sensor functionalized with green nanoparticles for electrochemical detection of the fourth most dangerous heavy metal ions namely copper, zinc, lead, and mercury. The green nanoparticles are fabricated by a one-step, consisting of reducing platinum nanoparticles by a natural gum Arabic polymer. To guarantee the success of these nanoparticles' design, the nanoparticles have been characterized by Fourier-transform infrared spectroscopy FTIR, and thermogravimetric TGA techniques. While, for the electrochemical characterization, we have adopted cyclic voltammetry CV and electrochemical impedance spectroscopy EIS to control different steps of surface modification, and the differential pulse anodic stripping DPAS was monitored to follow up the electrochemical detection of different heavy metals.

Results

have confirmed the good chemical and physical properties of the elaborated nanoparticles. As, the developed sensor showed a specific electrochemical response toward the heavy metal ions separately, with a lower limit of detection lower LOD than that recommended by the World Health Organization, in order of 9.6 ppb for Cu2+, 1.9 ppb for Zn2+, 0.9 ppb for Hg2+, and 4.2 ppb for Pb2+. Impressively, the elaborated sensor has demonstrated also high stability, outstanding sensitivity, and excellent analytical performance.In addition, the elaborated analytical tool has been successfully applied to the determination of various heavy metal ions in real samples, reflecting then its promising prospect in practical application.

Prediction of Food Safety Risk Level of Wheat in China Based on Pyraformer Neural Network Model for Heavy Metal Contamination

Heavy metal contamination in wheat not only endangers human health, but also causes crop quality degradation, leads to economic losses and affects social stability. Therefore, this paper proposes a Pyraformer-based model to predict the safety risk level of Chinese wheat contaminated with heavy metals. First, based on the heavy metal sampling data of wheat and the dietary consumption data of residents, a wheat risk level dataset was constructed using the risk evaluation method; a data-driven approach was used to classify the dataset into risk levels using the K-Means++ clustering algorithm; and, finally, on the constructed dataset, Pyraformer was used to predict the risk assessment indicator and, thus, the risk level. In this paper, the proposed model was compared to the constructed dataset, and for the dataset with the lowest risk level, the precision and recall of this model still reached more than 90%, which was 25.38-4.15% and 18.42-5.26% higher, respectively. The model proposed in this paper provides a technical means for hierarchical management and early warning of heavy metal contamination of wheat in China, and also provides a scientific basis for dynamic monitoring and integrated prevention of heavy metal contamination of wheat in farmland.

Informer-Based Safety Risk Prediction of Heavy Metals in Rice in China

Focused supervision and early warning of heavy metal (HM)-contaminated rice areas can effectively protect people's livelihood security and maintain social stability. To improve the accuracy of risk prediction, an Informer-based safety risk prediction model for HMs in rice is constructed in this paper. First, based on the national sampling data and residential consumption statistics of rice, we construct a dataset of evaluation indicators that can characterize the level of rice safety risk so as to form a safety risk space. Second, based on the K-medoids clustering algorithm, we classify the rice safety risk space into levels. Finally, we use the Informer neural network model to predict the safety risk indicators of rice in each province so as to predict the safety risk level. This study compares the prediction accuracy of a self-constructed dataset of rice safety risk assessment indicators. The experimental results show that the prediction precision of the method proposed in this paper reaches 99.17%, 91.77%, and 91.33% for low, medium, and high risk levels, respectively. The model provides technical support and a scientific basis for screening the time and area of HM contamination of rice, which needs focus.