Long-term riverine nitrogen dynamics reveal the efficacy of water pollution control strategies

Wu, Kaibin; Hu, Minpeng; Zhang, Yufu; Zhou, Jia; Wu, Hao Bin; Wang, Mingfeng; Chen, Dingjiang

doi:10.1016/j.jhydrol.2022.127582

Cited by 19 publications

(7 citation statements)

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“…This leave an estimated 36.4% (range: 13.2%-62.5%) of surplus N (593 kg N ha −1 ) retained within the watershed as legacy N pools in SON (26.2%; range: 12.0%-42.4%) and groundwater (10.2%; range: 1.2%-20.1%; figure 4). These results are supported by a 65% increase of N in the upper 20 cm layer of agricultural soils between 1980 and 2009 (Chen et al 2014b) and a 3.8-fold increase in groundwater N concentrations between 1980 and 2019 in the Yongan watershed (Wu et al 2022). When compared to a previous study within this watershed, the magnitude of legacy N accumulation was much higher than the ∼480 kg N ha −1 estimated by the ReNuMa model (Hu et al 2018).…”

Section: Long-term Legacy N Dynamicssupporting

confidence: 52%

“…Nevertheless, the SON mineralization rate, especially in active pools, often presents significant temporal variations due to changing temperature, soil moisture and land management (Francis et al 2007, Brookshire et al 2011, Moreau et al 2019, Shen et al 2022. For example, the N contribution from SON mineralization increased 4.1-fold during 1980-2019 in the Yongan watershed of eastern China due to increases in temperature and soil drainage systems (Wu et al 2022). Moreover, the N input from SON mineralization varied from 4-12 kg N ha −1 month −1 in two forest watersheds in North Carolina due to changes in temperature (Brookshire et al 2011).…”

Section: Introductionmentioning

confidence: 99%

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Modification of exploration of long‐term nutrient trajectories for nitrogen (ELEMeNT-N) model to quantify legacy nitrogen dynamics in a typical watershed of eastern China

Jia

Wei

et al. 2023

Environ. Res. Lett.

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Legacy nitrogen (N) is recognized as a primary cause for the apparent failure of watershed N management strategies to achieve desired water quality goals. The ELEMeNT-N (Exploration of Long‐Term Nutrient Trajectories for Nitrogen) model, a parsimonious and process-based model, has the potential to effectively distinguish biogeochemical and hydrological legacy effects. However, ELEMeNT-N is limited in its ability to address long-term legacy N dynamics as it ignores temporal changes in soil organic N (SON) mineralization rates. This work represents the first use and modification of ELEMeNT-N to quantify legacy effects and capture spatial heterogeneity of legacy N accumulation in China. An exponential function based on mean annual temperature was employed to estimate yearly changes in SON mineralization rate. Based on a 31-year water quality record (1980-2010), the modified model achieved higher efficiency metrics for riverine N flux in the Yongan watershed in eastern China than the original model (Nash-Sutcliff coefficient: 0.87 vs. 0.72 and R2: 0.80 vs. 0.71). The modified ELEMeNT-N results suggested that the riverine N flux mainly originated from the legacy N pool (88.2%). The mean overall N lag time was 11.9 years (95% CIs: 8.3-21.3), of which biogeochemical lag time was 9.7 years (6.3-18.4) and hydrological lag time was 2.2 years (2.0-3.0). Legacy N accumulation showed considerable spatial heterogeneity, with 219-239 kg N ha-1 accumulated in soil and 143-188 kg N ha-1 accumulated in groundwater. The ELEMeNT-N model was an effective tool for addressing legacy N dynamics, and the modified form proposed here enhanced its ability to capture SON mineralization dynamics, thereby providing managers with critical information to optimize watershed N pollution control strategies.

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Section: Long-term Legacy N Dynamicssupporting

confidence: 52%

Section: Introductionmentioning

confidence: 99%

Modification of exploration of long‐term nutrient trajectories for nitrogen (ELEMeNT-N) model to quantify legacy nitrogen dynamics in a typical watershed of eastern China

Jia

Wei

et al. 2023

Environ. Res. Lett.

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“…The impact of planting on water quality originates from the use of chemical fertilizers, while the impact of aquaculture is mainly due to animal manure. The use of fertilizer and livestock manure leads to changes in water quality, and the excessive use of fertilizer and unreasonable use of livestock manure usually lead to a deterioration of surface water quality [21,29,37]. Therefore, we selected the amount of fertilizer used and the quantities of the main livestock types (including large cattle, pigs, and sheep) to analyze the impacts of social factors on the water quality of the Zuli River Basin.…”

Section: Impact Of Quantity Of Social Factors On Water Qualitymentioning

confidence: 99%

“…Pan et al (2022) found that China's Environmental Protection Interview led to an average reduction in water pollution of 14.5%, and this effect persisted in the long term [28]. However, the research of Wu et al (2022) demonstrated that in the Yongan River watershed of eastern China, although chemical N fertilizer use decreased by 49% and the number of domestic animals decreased by 73% from 2000-2019, flownormalized riverine TN and NO 3 − concentrations progressively increased by 161% and 232%, respectively, from 1980-2019 [29]. Therefore, in general, China's water environment is improving, but there is still a risk of water quality deterioration in some areas.…”

Section: Introductionmentioning

confidence: 99%

Study on Water Quality Change Trend and Its Influencing Factors from 2001 to 2021 in Zuli River Basin in the Northwestern Part of the Loess Plateau, China

Zhang

Zhengzhong

et al. 2023

Sustainability

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The Zuli River is in the northwest of the Loess Plateau. As an important center of production and domestic water source, variations in the water quality of this basin and their influencing factors are important considerations for improving the river water environment. In order to identify and predict changes in the water quality of the watershed, the following water quality indicators, namely, dissolved oxygen content (DO), five-day biological oxygen demand (BOD5), ammonia nitrogen concentration (NH3-N), the high-manganese salt index (CODMn), volatile phenol concentration (VP), total phosphorus (TP), fluoride concentration (F−), and nitrite nitrogen concentration (NO3-N), were studied together with their change trends, influencing factors, and main variation cycles in the basin from 2001 to 2021. The results were as follows: (1) All the water quality indicators except for DO and F- showed an increasing trend before 2011, and DO showed an extreme, significant downward trend. There was an increase in the content of pollutants in the water caused by chemical fertilizer and aquaculture, resulting in a decrease in the DO content. (2) There was an extreme, significant upward trend in DO after 2011, while a significant downward trend was observed in the other water quality indicators except for NO3-N. (3) According to the main variation cycle of the other water quality indicators, the DO will remain in its peak period, while the other water quality indicators except for NO3-N will remain in a trough period (which began in 2021). The increase in precipitation and runoff reduced the content of pollutants in the water. Therefore, the overall water quality of the Zuli River Basin gradually improved after 2011. This may be due to (1) increased precipitation and runoff, thereby diluting the concentration of pollutants in the river, or (2) a decreased concentration of pollutants entering the river with the decrease in soil erosion.

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“…Pollution is also caused by atmospheric precipitation via surface runoff from agricultural land (Akhtar et al, 2021;Sui et al, 2020). Therefore, longterm management and monitoring of watercourse pollution from point/non-point sources are required (Wu et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Surface Water Quality through CCME-WQI and Multivariate Statistical Techniques

Akıner

Chauhan

Singh

2022

Preprint

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The Betwa River Basin, a subbasin of the Yamuna, is the oldest flowing water system in Central India. Factor analysis and principal component analysis (FA/PCA) were multivariate statistical techniques used to extract three and four varimax factors that explained 96.408 and 100.000 percent of the total variance in water quality, respectively. Cluster analysis (CA) categorizes observed items into distinct quality categories based on correlations between stations and years. Point industrial/sewage effluents, non-point runoff from arable land and erosion, and natural source pollution are all factors that contribute to the pollution of the Betwa River, a mineral component of the water. As a result, water quality is threatened or impaired, and conditions often depart from natural or desirable levels at Rajghat, Garrauli, Mohana, and Shahijina stations. According to the Canadian Council of Ministers of the Environment Water Quality Index (CCME-WQI), the water quality ranking at the Rajghat and Mohana stations corresponds to fair ecological status. However, the Garrauli and Shahijina stations' surface water has marginal water quality status. From 1985 to 2018, the Shahijina had the most considerable load of nutrients and organic matter, as established by the CCME-WQI and by comparing the water quality data. A thorough examination revealed a fluctuating trend in the Betwa River pollution, particularly at all stations. Results indicate that between 1985 and 2018, the only defense mechanism of the river was the auto purification mechanism, which is strongly influenced by the drought, point resource of pollution, and extreme meteorological events that probably cause these fluctuations.

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Long-term riverine nitrogen dynamics reveal the efficacy of water pollution control strategies

Cited by 19 publications

References 50 publications

Modification of exploration of long‐term nutrient trajectories for nitrogen (ELEMeNT-N) model to quantify legacy nitrogen dynamics in a typical watershed of eastern China

Modification of exploration of long‐term nutrient trajectories for nitrogen (ELEMeNT-N) model to quantify legacy nitrogen dynamics in a typical watershed of eastern China

Study on Water Quality Change Trend and Its Influencing Factors from 2001 to 2021 in Zuli River Basin in the Northwestern Part of the Loess Plateau, China

Evaluation of Surface Water Quality through CCME-WQI and Multivariate Statistical Techniques

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