Application of grading evaluation method of water radioactivity level in Chongqing section of Yangtze River

Hu, Shijie; Xue-feng, Lin; Yang, Qiang; Du, Hao; Zhao, Xiaoxue; Chen, Rui; Li, Guang

doi:10.1016/j.jenvrad.2022.106843

Cited by 6 publications

(1 citation statement)

References 20 publications

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“…Nemerow's pollution index (NPI) method is one of the water quality index methods that uses statistical values to evaluate the pollution status of water bodies to determine the degree of pollution, qualitatively reflecting the pollution status of water bodies. The evaluation results are intuitive, and the NPI method highlights the impact of evaluation indicators that significantly exceed the standard [11]. At the same time, some studies have concluded that a single water quality evaluation method cannot fully and objectively reflect the water function, showing certain limitations of water quality evaluation.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Water Quality of Collected Rainwater in the Northeastern Loess Plateau

et al. 2022

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Water resources are scarce in the Northeastern Loess Plateau, and water cellar water (WCW) is a vital water resource available in the vast rural areas of the region. The quality of WCW was assessed by principal component analysis (PCA) and Nemerow’s pollution index (NPI) for different rainfall catchment areas, depths, and storage times. Eleven indicators were measured, including pH, electrical conductivity (EC), F−, Cl−, NO3−, SO42−, Na+, NH4+, Ca2+, Mg2+, and K+. The results show that the tap water quality in the rural areas of the Northeastern Loess Plateau is above the second level and meets the drinking water standard (DWS), which is similar to the tap water quality in the region. The main component score of water quality from tile roof + cement ground (I) is 0.32, and the Nemero index is 0.41; the principal component score of water quality from cement ground (I) is 0.45, and the Nemero index is 0.29; the principal component score of water quality from trampled land (I) is 0.59, and the Nemero index is 0.44; the principal component score of water quality from tile roof + trampled land (II) is 1.87, and the Nemero index is 1.10. The rainwater harvesting catchment area of tile roof + cement ground (I) ensured the highest water quality, followed by cement ground (I), trampled ground (I), and tile roof + trampled ground (II). The water quality of the catchment area for artificially collected rainwater (roof tile surface, cement ground, etc.) was better than that of the original soil (trampled ground). The highest water quality was found at a storage time of 1 year (I), followed by 2.5 years (I), and 2 months (II). A depth of 4 m (I) contributed to the highest water quality, followed by 2 m (II), 3 m (II), and 1 m (II). Water quality improved with the increasing depth of WCW. The rainfall and WCW in the area were weakly alkaline, and the groundwater was contaminated with NO3−. PCA’s water quality assessment results were similar to the NPI method, indicating that both methods can be used in combination for unconventional water quality assessment.

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Section: Introductionmentioning

confidence: 99%

Evaluation of Water Quality of Collected Rainwater in the Northeastern Loess Plateau

et al. 2022

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Regional quality analysis of the hydrological environment with an improved random forest model based on the chimpanzee algorithm

Li,

Zhang,

Chen

et al. 2024

J of Env Quality

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High‐precision evaluations of water environment quality are highly important for improving the accuracy of early warning systems of regional water pollution risk and improving the regional water environment. This paper employs the chimp optimization algorithm (ChOA) to enhance the traditional random forest model, resulting in the chimp optimization algorithm‐random forest (ChOA‐RF) water quality assessment model for evaluating the Jiansanjiang area in Heilongjiang Province, China. The results show that the overall water environment in Jiansanjiang has the following characteristics: “The water quality of farms in the northwest is poor, and the quality of groundwater is better than that of surface water.” Total nitrogen (TN) and total phosphorus (TP) in surface water and ammonium nitrogen (NH3‐N), ferrum (Fe), and manganese (Mn) in groundwater are the main pollutants. The TP and TN in surface water and the NH3‐N in groundwater exceeded the relevant standards, likely due to the excessive application of chemical fertilizers, especially nitrogen fertilizers. Additionally, Fe and Mn are harmful native substances. According to these findings, targeted improvement strategies, such as reducing nitrogen fertilizer application, plugging well, and increasing the surface water utilization rate, are proposed. Moreover, the ChOA‐RF model is compared with the traditional empirical value model and the particle swarm optimization‐random forest (PSO‐RF) model. The results show that the ChOA‐RF model can effectively reduce the root mean square error and mean absolute percentage error and improve the coefficient of determination. The running time and convergence ability are also better than those of the PSO‐RF model, which is a more accurate and efficient machine learning model. The model can be used not only for high‐precision evaluation of regional water environment quality but also for other machine learning fields.

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Evaluation of surface water quality in Heilongjiang Province, China: Based on different quantities of water quality indicators

et al. 2023

Ecological Indicators

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Application of grading evaluation method of water radioactivity level in Chongqing section of Yangtze River

Cited by 6 publications

References 20 publications

Evaluation of Water Quality of Collected Rainwater in the Northeastern Loess Plateau

Evaluation of Water Quality of Collected Rainwater in the Northeastern Loess Plateau

Regional quality analysis of the hydrological environment with an improved random forest model based on the chimpanzee algorithm

Evaluation of surface water quality in Heilongjiang Province, China: Based on different quantities of water quality indicators

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