Simulation of the Transboundary Water Quality Transfer Effect in the Mainstream of the Yellow River

In order to not only solve the technical problems of quantifying the degree and range of the effect that is caused by the water quality of upstream on that of downstream portions of a river, and of dividing the responsibility of transboundary water pollution, but also to tackle the difficulty in adapting to dynamic changes of the traditional water quality model in terms of practical application, pollutant discharge and water consumption were taken as the main influence factors to build the transboundary water quality transfer effect model. Supported by a comprehensive integration platform, the transboundary water quality transfer effect simulation system of the Yellow River mainstream was constructed. The simulation results show that the concentration decreases exponentially along the range. Gansu, Ningxia, and Inner Mongolia had a more significant effect of exceeding standard water consumption on pollution, while Ningxia, Inner Mongolia, Shaanxi, and Shanxi had a more distinct contribution to the over standard pollution discharge effect. The proposed model and simulation system can provide new methods and instruction for quantifying the degree and range of transboundary water pollution, as well as dividing the responsibility for water environment compensation.

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Evaluation of Water Quality and Heavy Metals in Wetlands along the Yellow River in Henan Province

Sustainability ◽

10.3390/su12041300 ◽

2020 ◽

Vol 12 (4) ◽

pp. 1300 ◽

Cited By ~ 3

Author(s):

Hong ◽

Zhao ◽

Chang ◽

Peng ◽

Wang ◽

...

Keyword(s):

Water Pollution ◽

Heavy Metals ◽

Water Quality ◽

Heavy Metal ◽

Yellow River ◽

Spatiotemporal Variation ◽

The Yellow River ◽

Lower Yellow River ◽

Heavy Metal Concentrations ◽

Flood Season

Assessing spatiotemporal variation in water quality and heavy metals concentrations in wetlands and identifying metal contamination source are crucial steps for the protection and sustainable utilization of water resources. Using the water quality identification index (Iwq), heavy metal pollution index (HPI), hierarchical cluster analysis (HCA) and redundancy analysis (RDA), we evaluated spatiotemporal variation in water quality and heavy metals concentrations, and their interrelation in wetlands along the middle and lower Yellow River. The average Iwq was highest during flood season but the average HPI was lowest in the same season. Meanwhile, the trend in mean HPI across three hydrological seasons was the opposite to that of mean Iwq. There was significant variation in wetlands water pollution status across seasons. During the flood season, the wetlands in the affected area with hanging river were seriously polluted. In other seasons, pollution in the artificial wetlands was even more severe. Moreover, serious pollution of wetlands in belt transect #03 (Yuanyang-Zhongmu) was more frequent. Dissolved oxygen and chemical oxygen demand strongly influenced heavy metal concentrations, while other water quality parameters had different influences on heavy metal concentrations in different hydrological seasons. The causes of water pollution were divided into natural factors and human disturbance (with potential relationships between them). The polluted wetlands were greatly affected by the Yellow River during the flood season while they were more impacted by agricultural and domestic sewage discharge in other seasons. However, heavy metal deposition and leaching into riparian wetlands were still affected by diverse channel conditions. If this trend is allowed to continue unabated, wetlands along the middle and lower Yellow River are likely to lose their vital ecological and social functions.

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Water Consumption in Inner Mongolia Reach of the Yellow River since Integrated Regulation

Proceedings of the 2015 International Conference on Automation, Mechanical Control and Computational Engineering ◽

10.2991/amcce-15.2015.190 ◽

2015 ◽

Author(s):

Shuangyan Jin ◽

Suiye Zhang ◽

Zhijin Ma

Keyword(s):

Water Consumption ◽

Inner Mongolia ◽

Yellow River ◽

The Yellow River ◽

Integrated Regulation

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Fuzzy Optimization Neural Network Approach for Ice Forecast in the Inner Mongolia Reach of the Yellow River/Approche d'Optimisation Floue de Réseau de Neurones pour la Prévision de la Glace Dans le Tronçon de Mongolie Intérieure du Fleuve Jaune

Hydrological Sciences Journal ◽

10.1623/hysj.50.2.319.61793 ◽

2005 ◽

Vol 50 (2) ◽

Cited By ~ 22

Author(s):

Chen Shouyu ◽

Ji Honglan

Keyword(s):

Neural Network ◽

Inner Mongolia ◽

Yellow River ◽

Fuzzy Optimization ◽

The Yellow River ◽

Network Approach ◽

Neural Network Approach

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Virtual Water Trade in the Yellow River Economic Belt: A Multi-Regional Input-Output Model

Water ◽

10.3390/w13060748 ◽

2021 ◽

Vol 13 (6) ◽

pp. 748

Author(s):

Ming Li ◽

Qingsong Tian ◽

Yan Yu ◽

Yueyan Xu ◽

Chongguang Li

Keyword(s):

Water Resources ◽

Water Use ◽

Inner Mongolia ◽

Yellow River ◽

Cumulative Risk ◽

Virtual Water ◽

Hot Water ◽

The Yellow River ◽

Input Output ◽

Virtual Water Trade

The sustainable and efficient use of water resources has gained wide social concern, and the key point is to investigate the virtual water trade of the water-scarcity region and optimize water resources allocation. In this paper, we apply a multi-regional input-output model to analyze patterns and the spillover risks of the interprovincial virtual water trade in the Yellow River Economic Belt, China. The results show that: (1) The agriculture and supply sector as well as electricity and hot water production own the largest total water use coefficient, being high-risk water use sectors in the Yellow River Economic Belt. These two sectors also play a major role in the inflow and outflow of virtual water; (2) The overall situation of the Yellow River Economic Belt is virtual water inflow, but the pattern of virtual water trade between eastern and western provinces is quite different. Shandong, Henan, Shaanxi, and Inner Mongolia belong to the virtual water net inflow area, while the virtual water net outflow regions are concentrated in Shanxi, Gansu, Xinjiang, Ningxia, and Qinghai; (3) Due to higher water resource stress, Shandong and Shanxi suffer a higher cumulative risk through virtual water trade. Also, Shandong, Henan, and Inner Mongolia have a higher spillover risk to other provinces in the Yellow River Economic Belt.

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Hydrogeochemistry and Quality Assessment of Shallow Groundwater in the Southern Part of the Yellow River Alluvial Plain (Zhongwei Section), Northwest China

Earth Sciences Research Journal ◽

10.15446/esrj.v18n1.34048 ◽

2014 ◽

Vol 18 (1) ◽

pp. 27-38 ◽

Cited By ~ 61

Author(s):

Peiyue Li ◽

Jianhua Wu ◽

Hui Qian

Keyword(s):

Water Quality ◽

Yellow River ◽

Saturation Index ◽

Shallow Groundwater ◽

Northwest China ◽

Alluvial Plain ◽

Total Hardness ◽

The Yellow River ◽

Human Consumption ◽

Mineralized Water

Statistical analyses, a Piper diagram, the saturation index and the correlations of chemical parameters were used to reveal the hydrogeochemistry and hydrogeochemical evolution of shallow groundwater in the southern part of the Zhongwei section of the Yellow River alluvial plain. The water quality for agricultural and domestic uses was also assessed in the study. The results suggest that the shallow groundwater in the study area is fresh to moderately mineralized water. Higher Ca2+ and HCO3- are observed in the less mineralized water, whereas Na+ and SO42- are common ions in the highly mineralized water. The major hydrochemical facies for groundwater with total dissolved solids (TDS) <1 g/L are HCO3-Ca·Mg and HCO3-Ca·Na·Mg, and for groundwater with TDS > 1 g/L, SO4·Cl-Na and SO4·Cl-Na·Mg·Ca are the predominant hydrochemical types. The main reactions in the groundwater system are the dissolution/precipitation of gypsum, fluorite, halite, calcite and dolomite. Cation exchange is also important in controlling the groundwater chemistry. The water samples assessed in the paper are of acceptable quality for agricultural use, but most of them are not fit for direct human consumption (drinking). TDS, total hardness (TH), Cl- and SO42- are the main indices that result in the poor drinking water quality. ResumenAnálisis estadísticos, un diagrama de Piper, el índice de saturación y la correlación de los parámetros químicos fueron utilizados para revelar la hidrogeoquímica y la evolución hidrogeoquímica de las aguas subterráneas poco profundas en la parte sur de la sección Zhongwei en la planicie aluvial del río Amarillo. La calidad del agua para el uso doméstico y agrícola también fue evaluada en este estudio. Los resultados sugieren que las aguas subterráneas poco profundas en el área de estudio son entre frescas y moderadamente mineralizadas. Un índice mayor de Ca2+ y HCO3- se observó en las aguas menos mineralizadas, mientras que Na+ y SO42- son iones comunes en las aguas altamente mineralizadas. Los perfiles hidroquímicos predominantes para las aguas subterráneas con Total de Sólidos Disueltos (TDS) <1 g/L son HCO3-Ca·Mg y HCO3-Ca·Na·Mg, y para las aguas subterráneas con TDS >1 g/L, SO4·Cl-Na y SO4·Cl-Na·Mg·Ca. Las mayores reacciones en el sistema de aguas subterráneas son la disolución/ precipitación de yeso, fluorita, halita, calcita y dolomita. El intercambio de cationes también es importante en el control de la química de las aguas subterráneas. Las muestras de agua evaluadas en este manuscrito son de calidad aceptable para el uso agrícola, pero la mayoría no son aptas para el consumo humano. El índice TDS, la dureza total del agua (TH), Cl- y SO42- son las razones principales que influyen en la baja calidad de esta agua.

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