Sources and health risks of nitrate pollution in surface water in the Weihe River watershed, China

Shi, Wen-min; Zhang, Yan; Zhang, Cheng-qian; Zhang, Wen-rui

doi:10.1007/s11629-021-7301-6

Cited by 7 publications

(4 citation statements)

References 57 publications

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“…In the study of predecessors, atmospheric deposition (AD), nitrogen from soil (SN), nitrogen fertilizer (NF), and manure and sewage (M&S) these four sources are generally used to analyze NO 3 − (Chen et al, 2020b;Chang et al, 2022;Shi et al, 2022). And Ji et al (2017) used these four sources to analyze NO 3 − in Changle river which was one of the main tributaries of the Cao-E River (Ji et al, 2017).…”

Section: Mixsiar Modelmentioning

confidence: 99%

Using dual stable isotopes method for nitrate sources identification in Cao-E River Basin, Eastern China

Sun

Lei

et al. 2023

Front. Environ. Sci.

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Excess nitrate (NO3−) of water is a worldwide environmental problem. Therefore, identifying the sources and analyzing respective contribution rates are of great importance for improving water quality. The current study was carried out to identify the potential sources of NO3− pollution in Cao-E River basin, in Eastern China. Surface water samples were collected during the dry season and wet season. Multiple hydrochemical indices, dual NO3− isotopes (δ15N–NO3− and δ18O–NO3−) and a Bayesian model (stable isotope analysis in R, MixSIAR) were applied to identify NO3− sources and estimate the proportional contributions of multiple NO3− sources. During the sampling period, nitrification was a dominant nitrogen transformation process in the study area. The results of the NO3− isotopes suggested that manure and sewage (M&S), soil nitrogen (SN) and nitrogen fertilizer (NF) were the major contributors to NO3−. Moreover, the results obtained from the MixSIAR model showed that the proportional contributions of atmospheric deposition (AD), NF, M&S and SN to NO3− were 2.82, 15.45, 44.25, 37.47% and 3.14, 23.39, 31.78, 41.69% in the dry and wet season, respectively. This study provided evidence to further understand the sources, transport, and transformation of N in Cao-E River basin, which deepens the understanding of the management of N contaminant.

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Section: Mixsiar Modelmentioning

confidence: 99%

Using dual stable isotopes method for nitrate sources identification in Cao-E River Basin, Eastern China

Sun

Lei

et al. 2023

Front. Environ. Sci.

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“…Studies of environmental factors on lake water quality have also attracted the attention of many researchers. Many nonlinear methods were used, such as the artificial network method, random forest models, and a generalized additive model (Recknagel et al, 1997;Quan et al, 2020;Shi et al, 2021). Cao et al (2006) have developed the hybrid evolutionary algorithm (HEA) to determine the evolution of long time-series data through evolutionary simulation and genetic selection, and to make inductive inferences and predictions for multivariate nonlinear data.…”

Section: Introductionmentioning

confidence: 99%

How the land use/cover changes and environmental factors at different scales affect lake water quality in arid and semi-arid regions

Liu

Jin³

et al. 2023

Front. Ecol. Evol.

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The quantitative relationship between land use/cover change (LUCC) within basins, lake water environmental factors, and water quality has long been a popular research area. In this study, we investigated three typical basins (Ulansuhai Basin, Daihai Basin, and Dalinor Basin) in arid and semi-arid areas of China. The assessment was based on the China Land Cover Dataset (CLCD), which was used to calculate the land use dynamic degree index and Markov transfer matrix. Moreover, an econometric analysis model and a hybrid evolutionary algorithm (HEA) model were used to explore the quantitative relationship between LUCC and environmental factors on the lake water quality. The results showed that the LUCC of the three lake basins was dominated by cultivated land and grassland over the past 20 years. In all of the basins, grassland and water area were converted into cultivated land, water area decreased to varying degrees, and impervious surface area increased continuously. Moreover, the concentration of TN and TP were generally proportional to chemical oxygen demand concentration (COD), biochemical oxygen demand concentration (BOD5), and dissolved oxygen concentration (DO), but inversely proportional to water depth and transparency; the relationship between Nutrient concentration and pH was usually related to the ecological threshold. On the basin scale, the grassland and forestland played positive roles in improving water quality, while cultivated land and impervious surface were the main factors contributing to water pollution. Overall, this study provides a scientific theoretical basis for the rational utilization of land resources, improvement of lake water quality, and ecological protection of typical lake basins in arid and semi-arid regions.

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“…For the development of human, agriculture, and industry, rivers are vital sources of fresh water [10][11][12]. However, they are facing multiple threats including natural factors (precipitation, erosion, and weathering) and anthropogenic activities (agriculture, industry, and urban activities) [13][14][15][16][17][18][19]. Over the last century, river water quality has gradually changed as a result of human activities, especially for riverine nitrogen [14,[20][21][22].…”

Section: Introductionmentioning

confidence: 99%

“…Presently, more than half of severe environmental problems are related to nitrogen cycles, which is caused by excessive nitrogen from intensified anthropogenic activities [17,23]. Generally, the temporal variations of nitrogen in river water are driven by some key processes including nitrogen Water 2022, 14, 3124 2 of 16 sources, mobilization, transformation, and delivery [16,24,25]. Specifically, the key influencing factors include topography [26], precipitation or runoff [27,28], land use [26,27,29], the oxidation-reduction environment of water/sediment [30][31][32][33], and so on.…”

Section: Introductionmentioning

confidence: 99%

Seasonal Variation and Driving Factors of Nitrate in Rivers of Miyun Reservoir Watershed, North China

Qing-suo

Sun

et al. 2022

Water

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In order to identify the seasonal variations and dominant driving factors of NO3-N in rivers, investigations of five consecutive years were conducted in seven rivers of the Miyun Reservoir Watershed. Significant seasonal variation of NO3-N in rivers was separately found in the dormant season (non-growing season) and the growing season. Furtherly, the V-shaped, W-shaped, and indistinct seasonal patterns of NO3-N accounted for 53.0%, 38.7%, and 8.3%, respectively. They were remarkably affected by stream flow, and their significant quadratic function was discovered. The annual maxima and minima of NO3-N corresponded to medium flow in the dormant season and low flow or flood in the growing season, respectively. On one hand, flood mainly played a role in the diluent for the Chao River with high NO3-N, and on the other hand, it acted as a nitrogen source for the Bai River with low NO3-N. The NO3-N was closely correlated with human activities, and this correlation had obvious seasonal change trend. In the dormant season, significant and mostly extremely significant high correlation coefficient (R) values were determined, while partly non-significant with low R values were found in July, August, September, and October. Increasing seasonal variation index of NO3-N from upstream to downstream was found that was gentle for large rivers and sharp for small tributaries. The seasonality of NO3-N was more affected by natural factors, especially flood, than human factors.

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Sources and health risks of nitrate pollution in surface water in the Weihe River watershed, China

Cited by 7 publications

References 57 publications

Using dual stable isotopes method for nitrate sources identification in Cao-E River Basin, Eastern China

Using dual stable isotopes method for nitrate sources identification in Cao-E River Basin, Eastern China

How the land use/cover changes and environmental factors at different scales affect lake water quality in arid and semi-arid regions

Seasonal Variation and Driving Factors of Nitrate in Rivers of Miyun Reservoir Watershed, North China

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