干旱减少了沿海海湾的氮供应和一氧化二氮排放

IF 11.4 1区 环境科学与生态学 Q1 ENGINEERING, ENVIRONMENTAL Water Research Pub Date : 2024-09-10 DOI:10.1016/j.watres.2024.122362
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引用次数: 0

摘要

在全球气候变化的影响下,严重干旱越来越普遍,破坏了流域水文和沿岸氮循环。然而,人们对干旱对氮从陆地向海洋传输的具体影响以及随后的氮动态变化仍缺乏足够的了解。本研究评估了 2020-2022 年干旱对中国东南部厦门湾氮素供应和氮氧化物排放的影响。结果表明,与干旱前的2019年相比,干旱使厦门湾年NHN、NON和NON浓度分别下降了49.4%、32.1%和40.3%。NHN 浓度下降的主要原因是各季地表径流减少。NON和NON浓度仅在春季和夏季有所下降,主要原因是潜在蒸散量(PET)的增加阻碍了通过地下水的氮供应,同时加剧了土地反硝化作用。干旱期间,厦门湾的年氮排放量减少了 40.0%∼72.7%,与 NON、DIN 和 DTN 浓度的下降高度相关(<0.001)。对比分析表明,2010-2022年期间,随着厦门湾、三沙湾和切萨皮克湾蒸发需求干旱条件的恶化,NON浓度与PET呈一致的负线性回归,并呈下降趋势。在厦门湾,NHN 浓度与河流排放量呈正回归关系,但在其他两个海湾则呈负回归关系。我们的研究结果表明,干旱减少氮氧化物排放的主要原因是海湾中硝酸盐底质的减少。这项研究为预测全球环境变化下的沿岸氮动态和温室气体排放提供了新的见解。
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Drought reduces nitrogen supply and N2O emission in coastal bays

Severe droughts are increasingly prevalent under global climate change, disrupting watershed hydrology and coastal nitrogen cycling. However, the specific effects of drought on nitrogen transport from land to sea and subsequent nitrogen dynamics remain inadequately understood. In this study, we evaluated the consequences of the 2020–2022 drought on nitrogen supply and N2O emissions in Xiamen Bay, Southeast China. The results showed that drought significantly reduced annual NH4N, NO2N, and NO3N concentrations in Xiamen Bay by 49.4 %, 32.1 %, and 40.3 %, respectively, compared with the pre-drought year of 2019. The decline in NH4N concentration was mainly attributed to reduced surface runoff across all seasons. NO3N and NO2N concentrations declined only during spring and summer, primarily due to increased potential evapotranspiration (PET) hindering nitrogen supply via groundwater and concurrently enhancing land denitrification. Annual N2O emission from Xiamen Bay decreased by 40.0∼72.7 % during the drought, highly correlated with the decline in the concentrations of NO3N, DIN, and DTN (p < 0.001). Comparative analysis revealed that NO3N concentration exhibited consistent negative linear regressions with PET and declined as evaporative demand drought conditions worsened across Xiamen Bay, Sansha Bay, and Chesapeake Bay throughout 2010–2022. NH4N concentration showed a positive regression with river discharge in Xiamen Bay, but negative regressions in the other two bays. Our results indicates that drought reduces N2O emission primarily driven by nitrate substrate reduction in the bay. This study provides new insights for predicting coastal nitrogen dynamics and greenhouse gas emissions under global environmental change.

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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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