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

Bai, Miao; Zhang, Can; Bai, Yuchao; Wang, Tianyi; Qu, Shaojuan; Qi, Hongjuan; Zhang, Minglu; Zhang, Chuanfu

doi:10.3390/ijerph191912517

Cited by 12 publications

(4 citation statements)

References 47 publications

(73 reference statements)

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“…TCR is commonly used to calculate the lifetime probability of developing any type of cancer in an individual due to exposure to carcinogens [44][45][46][47], and in this paper, TCR is used to assess the carcinogenic risk of HMs in rice as the product of contaminant exposure dose and carcinogenic intensity indicator to reflect the likelihood of long-term carcinogenic risk occurrence. The expression of the carcinogenic risk assessment equation is as follows:…”

Section: Total Carcinogenic Riskmentioning

confidence: 99%

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

Dong

Jiang

et al. 2023

Foods

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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.

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Section: Total Carcinogenic Riskmentioning

confidence: 99%

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

Dong

Jiang

et al. 2023

Foods

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“…Carcinogenic risk (CR) is commonly used to calculate the probability that an individual will develop a certain cancer as a result of exposure to a chemical [36][37][38][39], and the sum of the carcinogenic risks of various chemicals is the total carcinogenic risk (TCR) when multiple chemicals act on the body at the same time. In this paper, the total carcinogenic risk was used to assess the carcinogenic risk of heavy metals in wheat to humans through dietary intake, considering that wheat contains several heavy metal elements that can combine to cause carcinogenic effects in humans.…”

Section: Total Carcinogenic Riskmentioning

confidence: 99%

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

Dong

Zhang

et al. 2023

Foods

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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.

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“…Commonly high concentrations of nutrients like nitrogen compounds, phosphates, and pesticides have been found coming from agricultural applications of agrochemicals in agricultural areas, but microbiological contamination has been found from multiple human or animal faecal sources influenced by on-site specific factors (Khatri et al, 2015;O'Dwyer et al, 2018). Another major group of substances are of natural origin, such as inorganic substances, but also trace elements, such as heavy metals or arsenic seems to be a general problem affecting water quality in dug wells as well as boreholes (Wongsasuluk et al, 2014;Bai et al, 2022). Considering the vulnerability of water quality in wells, it is important to continue the search for new water quality characterisation tools to better understand drinking water contamination sources and processes affecting water quality.…”

Section: Introductionmentioning

confidence: 99%

Research for Rural Development 2023 : annual 29th international scientific conference proceedings

2023

Research for Rural Development

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

Cited by 12 publications

References 47 publications

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

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

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

Research for Rural Development 2023 : annual 29th international scientific conference proceedings

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