Combining the multivariate statistics and dual stable isotopes methods for nitrogen source identification in coastal rivers of Hangzhou Bay, China.
Combining the multivariate statistics and dual stable isotopes methods for nitrogen source identification in coastal rivers of Hangzhou Bay, China.
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结合多元统计与双稳定同位素方法进行杭州湾沿岸河流氮源识别
DOI：
10.1007/s11356-022-21116-x
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发表时间：
2022-11
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沿岸河流将大部分人为氮（N）负荷贡献给沿岸沃茨，经常导致富营养化和缺氧区。准确的氮源识别是优化沿海河流氮污染控制策略的关键。基于杭州湾沿岸两条河流2 a的双稳定同位素（和）和水质参数的季节性记录，采用基于双稳定同位素的MixSIAR模型和绝对主成分得分-多元线性回归（APCS-MLR）模型，研究了杭州湾沿岸两条河流氮素的动态和来源。水质/营养水平指数表明，轻度至中度富营养化状态的研究河流。水质的时空变化与季节性农业，水产养殖和家庭活动，以及季节性降水模式。APCS-MLR模型将土壤+生活污水（69.5%）和养殖尾水（22.2%）确定为主要氮污染源。基于双稳定同位素的MixSIAR模型确定了曹娥江流域土壤、养殖尾水、生活污水和大气沉积物氮的贡献率分别为35.3 ± 21.1%、29.7 ± 17.2%、27.9 ± 14.5%和7.2 ±11.4%，曹娥江流域土壤、养殖尾水、生活污水和大气沉积物氮的贡献率分别为34.4 ± 21.3%、29.5 ± 17.2%、27.4 ± 14.7%，建塘河（JTR）为8.7 ±12.8%。APCS-MLR模型和基于双稳定同位素的MixSIAR模型对河流氮源的识别结果一致。将这两种方法结合起来进行河流氮源解析，可以有效区分APCS-MLR方法中的混合源组分，降低稳定同位素分析的高成本，从而在对水质采样和同位素分析成本要求较低的情况下，提供可靠的氮源解析结果。本研究强调了土壤氮管理和养殖尾水处理在沿海河流氮污染控制中的重要性。
Coastal rivers contributed the majority of anthropogenic nitrogen (N) loads to coastal waters, often resulting in eutrophication and hypoxia zones. Accurate N source identification is critical for optimizing coastal river N pollution control strategies. Based on a 2-year seasonal record of dual stable isotopes ( and ) and water quality parameters, this study combined the dual stable isotope-based MixSIAR model and the absolute principal component score-multiple linear regression (APCS-MLR) model to elucidate N dynamics and sources in two coastal rivers of Hangzhou Bay. Water quality/trophic level indices indicated light-to-moderate eutrophication status for the studied rivers. Spatio-temporal variability of water quality was associated with seasonal agricultural, aquaculture, and domestic activities, as well as the seasonal precipitation pattern. The APCS-MLR model identified soil + domestic wastewater (69.5%) and aquaculture tailwater (22.2%) as the major nitrogen pollution sources. The dual stable isotope-based MixSIAR model identified soil N, aquaculture tailwater, domestic wastewater, and atmospheric deposition N contributions of 35.3 ±21.1%, 29.7 ±17.2%, 27.9 ±14.5%, and 7.2 ±11.4% to riverine in the Cao’e River (CER) and 34.4 ±21.3%, 29.5 ±17.2%, 27.4 ±14.7%, and 8.7 ±12.8% in the Jiantang River (JTR), respectively. The APCS-MLR model and the dual stable isotope-based MixSIAR model showed consistent results for riverine N source identification. Combining these two methods for riverine N source identifications effectively distinguished the mix-source components from the APCS-MLR method and alleviated the high cost of stable isotope analysis, thereby providing reliable N source apportionment results with low requirements for water quality sampling and isotope analysis costs. This study highlights the importance of soil N management and aquaculture tailwater treatment in coastal river N pollution control.