Water environment capacity estimation and pollutant reduction of Yanghe Reservoir Basin in Hebei Province, China, via 0-D water quality model

Li, Daming; Bu, Shilong; Li, Qichen; Chen, Shuo; Zhen, Zhu; Fu, Congsheng

doi:10.1007/s12665-021-09801-5

Cited by 7 publications

(3 citation statements)

References 43 publications

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“…The model suggested that turbidity within the reservoir was not influenced by preceding weekly observations (AR = 0). This trend was possibly related to the large buffering capacity of the reservoir to absorb the pollutant load [54]. The weekly model also suggested a decreasing trend (I = 1, average = −0.070 UNT/year) and variability (MA = 9) of turbidity in the reservoir.…”

Section: Arima Modelsmentioning

confidence: 82%

ARIMA and TFARIMA Analysis of the Main Water Quality Parameters in the Initial Components of a Megacity’s Drinking Water Supply System

Zafra-Mejía,

Rondón-Quintana,

Urazán-Bonells

2024

Hydrology

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The objective of this paper is to use autoregressive, integrated, and moving average (ARIMA) and transfer function ARIMA (TFARIMA) models to analyze the behavior of the main water quality parameters in the initial components of a drinking water supply system (DWSS) of a megacity (Bogota, Colombia). The DWSS considered in this study consisted of the following components: a river, a reservoir, and a drinking water treatment plant (WTP). Water quality information was collected daily and over a period of 8 years. A comparative analysis was made between the components of the DWSS based on the structure of the ARIMA and TFARIMA models developed. The results show that the best water quality indicators are the following: turbidity > color > total iron. Increasing the time window of the ARIMA analysis (daily/weekly/monthly) suggests an increase in the magnitude of the AR term for each DWSS component (WTP > river > reservoir). This trend suggests that the turbidity behavior in the WTP is more influenced by past observations compared to the turbidity behavior in the river and reservoir, respectively. Smoothing of the data series (moving average) as the time window of the ARIMA analysis increases leads to a greater sensitivity of the model for outlier detection. TFARIMA models suggest that there is no significant influence of past river turbidity events on turbidity in the reservoir, and of reservoir turbidity on turbidity at the WTP outlet. Turbidity outlier events between the river and reservoir occur mainly in a single observation (additive outliers), and between the reservoir and WTP also have a permanent effect over time (level shift outliers). The AR term of the models is useful for studying the transfer of effects between DWSS components, and the MA term is useful for studying the influence of external factors on water quality in each DWSS component.

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Section: Arima Modelsmentioning

confidence: 82%

ARIMA and TFARIMA Analysis of the Main Water Quality Parameters in the Initial Components of a Megacity’s Drinking Water Supply System

Zafra-Mejía,

Rondón-Quintana,

Urazán-Bonells

2024

Hydrology

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“…Common water pollution research methods include the empirical formula method, physical model method, and numerical calculation method. The numerical calculation model of water environment has been widely used in the fields of water pollution control and water environment impact assessment due to its unique characteristics of high efficiency and high precision (Li et al 2021a(Li et al , 2021bShahady & Cleary 2021).…”

Section: Introductionmentioning

confidence: 99%

“…Simulation, assessment of environmental effects of non-diffuse source pollution management measures, and release of water environment information. Based on the MIKE SHE, Li et al (2021aLi et al ( , 2021b established the response relationship of the Yanghe water environment to the drainage in the basin, simulated and predicted the temporal and spatial variation of pollutants in the mainstream of the Yanghe River, and compared and analyzed the water quality status of key sections under different water pollution control schemes, to provide technical support for the improvement of water environment in Zhangjiakou City. Zhang et al (2020) artificially solved the problems of poor river water quality and water environment degradation, constructed a coupled model of watershed hydrology, hydrodynamics, and water quality, and further evaluated the improvement efficiency of river water quality under different control measures.…”

Section: Introductionmentioning

confidence: 99%

Simulation of diffuse source polluted water environment based on MIKE21: A case study of the urban section of the Bai river

Zhang

Zhu

et al. 2022

Water Practice and Technology

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Affected by urban construction and industrial discharge, the diffuse source pollution of river water environment in urban sections is becoming more and more serious. The use of numerical models can accurately and efficiently simulate the process of diffuse source pollution and analyze its evolution. Based on MIKE21, the urban river water environment model of the Bai river is constructed. After parameter calibration, it is proved that the model has high accuracy. Based on the discharge of this river section in 2018, the river diffuse source pollution is simulated under three hydrological conditions, and the BP neural network is used to simulate the river diffuse source pollution. Network assessment of water quality in dry years. The results showed that the four water quality indicators showed the spatial distribution characteristics of upstream>urban section>downstream, and the river water quality in the year showed the temporal distribution characteristics of flood season better than a non-flood season. The mean value of the coefficient of certainty R2 reaches 0.94 when comparing the simulated and measured values, which indicates that the model has high applicability and satisfactory fit. If the water quality of the water year does not meet the standard, the diffuse source pollution discharge should be reduced from May to October. This study can provide reference and guidance for urban river diffuse source pollution control.

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Biomarkers for the Detection of Pollutants from the Water Environment

Iyiola,

Setufe,

Ofori

et al. 2024

Biomonitoring of Pollutants in the Global South

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Water environment capacity estimation and pollutant reduction of Yanghe Reservoir Basin in Hebei Province, China, via 0-D water quality model

Cited by 7 publications

References 43 publications

ARIMA and TFARIMA Analysis of the Main Water Quality Parameters in the Initial Components of a Megacity’s Drinking Water Supply System

ARIMA and TFARIMA Analysis of the Main Water Quality Parameters in the Initial Components of a Megacity’s Drinking Water Supply System

Simulation of diffuse source polluted water environment based on MIKE21: A case study of the urban section of the Bai river

Biomarkers for the Detection of Pollutants from the Water Environment

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