利用古气候学技术将水库年停留时间与氮沉积联系起来

IF 11.4 1区 环境科学与生态学 Q1 ENGINEERING, ENVIRONMENTAL Water Research Pub Date : 2024-08-14 DOI:10.1016/j.watres.2024.122245
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引用次数: 0

摘要

在河流网络中,水库是营养物质转化的热点,为氮处理提供了多种途径。氮沉积是测量较少的途径之一。在这里,我们利用一个包含两个截然不同流域的河流系统中八个干流水库的沉积物岩心,研究了水体停留时间与氮沉积之间的十年关系。其中一个流域的水流量受到管制,城市化程度较高;另一个流域的水流量未受管制,农村土地使用范围较广。我们探讨了沉积物氮浓度在一系列停留时间、土地利用和其他参数之间的关系。结果表明,与水库容积、平均水深、表面积、外流和土地利用相比,年平均停留时间与氮沉积的关系最为密切。色素分析表明,停留时间对氮的影响是允许更长时间的藻类吸收,然后以颗粒有机物的形式沉积。为支持这一机制,沉积物中的碳氮比与年平均停留时间呈强烈的负相关关系,而低值则代表藻类的影响更大;同时,停留时间与蓝藻、硅藻以及绿藻+蓝藻组合的光合色素诊断结果呈正相关关系。此外,我们还研究了干旱条件如何改变水库的停留时间并通过初级生产力加强氮循环。干旱使停留时间增加了 45-60 %。据估计,这种增加会使沉积物中的氮浓度提高约 2.5-4%。
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Linking reservoir annual residence time to nitrogen deposition using paleolimnological techniques

In river networks, reservoirs are hotspots for nutrient transformations, providing multiple pathways for nitrogen processing. One of the less measured pathways is nitrogen deposition. Here, we investigated the decadal relationship between water residence time and nitrogen deposition using sediment cores from eight mainstem reservoirs within a river system containing two contrasting watersheds. One watershed was significantly urbanized with regulated flow and the other watershed was unregulated with extensive rural land use. We explored the relationship of sediment nitrogen concentrations across a range of residence times, land uses, and other parameters throughout this linked river-reservoir system. Results show that average annual residence time had the strongest relationship to nitrogen deposition when compared to reservoir volume, mean depth, surface area, outflow, and land use. Pigment analysis revealed that residence time influences nitrogen by allowing for longer periods of algal uptake, followed by deposition in particulate organic form. Supporting this mechanism, sedimentary C:N, with low values representing greater algal influence, expressed a strong and negative relationship with average annual residence time, as well as a positive relationship between residence time and photosynthetic pigments diagnostic of cyanobacteria, diatoms, and a combination of green algae+cyanobacteria. Furthermore, we investigated how drought conditions could alter residence times and intensify nitrogen cycling through primary productivity in reservoirs. Drought increased residence time by 45–60 %. This increase was estimated to raise sediment nitrogen concentrations by roughly 2.5–4 %.

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来源期刊
Water Research
Water Research 环境科学-工程:环境
CiteScore
20.80
自引率
9.40%
发文量
1307
审稿时长
38 days
期刊介绍: Water Research, along with its open access companion journal Water Research X, serves as a platform for publishing original research papers covering various aspects of the science and technology related to the anthropogenic water cycle, water quality, and its management worldwide. The audience targeted by the journal comprises biologists, chemical engineers, chemists, civil engineers, environmental engineers, limnologists, and microbiologists. The scope of the journal include: •Treatment processes for water and wastewaters (municipal, agricultural, industrial, and on-site treatment), including resource recovery and residuals management; •Urban hydrology including sewer systems, stormwater management, and green infrastructure; •Drinking water treatment and distribution; •Potable and non-potable water reuse; •Sanitation, public health, and risk assessment; •Anaerobic digestion, solid and hazardous waste management, including source characterization and the effects and control of leachates and gaseous emissions; •Contaminants (chemical, microbial, anthropogenic particles such as nanoparticles or microplastics) and related water quality sensing, monitoring, fate, and assessment; •Anthropogenic impacts on inland, tidal, coastal and urban waters, focusing on surface and ground waters, and point and non-point sources of pollution; •Environmental restoration, linked to surface water, groundwater and groundwater remediation; •Analysis of the interfaces between sediments and water, and between water and atmosphere, focusing specifically on anthropogenic impacts; •Mathematical modelling, systems analysis, machine learning, and beneficial use of big data related to the anthropogenic water cycle; •Socio-economic, policy, and regulations studies.
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