Hydrochemistry and stable isotope indication of natural mineral water in Changbai Mountain, China

Li, Yihan; Bian, Jianmin; Li, Jialin; Ma, Yuxi; Anguiano, Jesus Horacio Hernandez

doi:10.1016/j.ejrh.2022.101047

Cited by 7 publications

(4 citation statements)

References 49 publications

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“…According to relevant regulations, when the content of metasilicate is not less than 25 mg/L, it is metasilicate-rich mineral water. As a natural nutritional supplement, metasilicate can help soften blood vessels and promote bone development [ 37 ].…”

Section: Methodsmentioning

confidence: 99%

Predicting Groundwater Indicator Concentration Based on Long Short-Term Memory Neural Network: A Case Study

Liu

et al. 2022

IJERPH

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Prediction of groundwater quality is an essential step for sustainable utilization of water resources. Most of the related research in the study area focuses on water distribution and rational utilization of resources but lacks results on groundwater quality prediction. Therefore, this paper introduces a prediction model of groundwater quality based on a long short-term memory (LSTM) neural network. Based on groundwater monitoring data from October 2000 to October 2014, five indicators were screened as research objects: TDS, fluoride, nitrate, phosphate, and metasilicate. Considering the seasonality of water quality time series data, the LSTM neural network model was used to predict the groundwater index concentrations in the dry and rainy periods. The results suggest the model has high accuracy and can be used to predict groundwater quality. The mean absolute errors (MAEs) of these parameters are, respectively, 0.21, 0.20, 0.17, 0.17, and 0.20. The root mean square errors (RMSEs) are 0.31, 0.29, 0.28, 0.27, and 0.31, respectively. People can be given early warnings and take measures according to the forecast situation. It provides a reference for groundwater management and sustainable utilization in the study area in the future and also provides a new idea for coastal cities with similar hydrogeological conditions.

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Section: Methodsmentioning

confidence: 99%

Predicting Groundwater Indicator Concentration Based on Long Short-Term Memory Neural Network: A Case Study

Liu

et al. 2022

IJERPH

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“…The oxygen isotope values in volcanic areas are obviously biased and are distributed in the upper right of the precipitation line. This suggests that Changbai Mountain snowmelt water may serve as an additional source of recharge, indicating the possibility of other sources besides atmospheric precipitation (Qiao et al, 2019;Li et al, 2022;Strohmenger et al, 2022). With the increase in temperature and evaporation, the δ 18 O of snowmelt water becomes more and more abundant, the recharge elevation is high and the recharge path is long, which leads to the slow runoff process and intensifies the interaction between water and surrounding rock.…”

Section: Stable Isotopesmentioning

confidence: 99%

“…With the increase in temperature and evaporation, the δ 18 O of snowmelt water becomes more and more abundant, the recharge elevation is high and the recharge path is long, which leads to the slow runoff process and intensifies the interaction between water and surrounding rock. In addition, the main source of water vapor in the study area is brought by the Pacific monsoon, and is transported over long distances and continuously depleted, so the hydrogen and oxygen isotope values in the Changbai Mountain volcanic area are heavy (Feng et al, 2022;Li et al, 2022).…”

Section: Stable Isotopesmentioning

confidence: 99%

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Hydrogeochemical characteristics and evaluation of groundwater resources of Jilin Province, China

Zeng,

Cui,

Zhou

et al. 2023

Front. Water

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As one of China's nine border provinces, Jilin Province is an important window for China's “Belt and Road” to open up to the north, and an important channel for China's foreign trade and foreign exchanges. Therefore, understanding hydrogeochemical characteristics is very important for effective water resources management in Jilin Province. A total of 64 groundwater samples were collected from 32 monitoring Wells in Jilin Province. Use of hydrogeochemical analysis techniques, WQI index and human health risk index methods to assess hydrogeochemical processes and groundwater quality, including the determination of hydrogeochemical parameters, conventional ions, trace elements and stable isotopes (δD and δ18O). The results show that the groundwater is slightly alkaline, the cations are mainly Na+, Ca2+, Mg2+, and K+, and the anions are mainly HCO3-, Cl−, and SO42-. Most of the groundwater samples belong to Ca-HCO3 and Na-HCO3 types, which are formed by the dissolution of calcite, dolomite, gypsum, silicates and carbonates. Stable isotope analysis shows that groundwater recharge in Jilin mainly comes from atmospheric precipitation at an altitude of 1,430~2,348 m. The strong correlation between trace elements indicates a common source. Most of the groundwater quality index (WQI) in the study area is Class I to Class III, and hazard quotient (HQ) and hazard index (HI) are both below 1. This indicates excellent water quality and minimal health risks associated with human consumption. It is worth noting that trace elements are enriched in the Tanlu fault zone. It provides scientific guidance for the utilization and allocation of groundwater resources in Jilin Province.

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Hydrochemistry and strontium isotope fingerprints of solute sources and CO2 consumption in Changbai Mountain area, Northeast China

Bian

et al. 2023

Environ Sci Pollut Res

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Hydrochemistry and stable isotope indication of natural mineral water in Changbai Mountain, China

Cited by 7 publications

References 49 publications

Predicting Groundwater Indicator Concentration Based on Long Short-Term Memory Neural Network: A Case Study

Predicting Groundwater Indicator Concentration Based on Long Short-Term Memory Neural Network: A Case Study

Hydrogeochemical characteristics and evaluation of groundwater resources of Jilin Province, China

Hydrochemistry and strontium isotope fingerprints of solute sources and CO2 consumption in Changbai Mountain area, Northeast China

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