School of Water and Environment, Chang'an University, No.126 Yanta Road, Xi'an, 710054, Shaanxi, China; Key Laboratory of Subsurface Hydrology and Ecological Effects in Arid Region of the Ministry of Education, Chang'an University, Xi'an, 710054, Shaanxi, China; Key Laboratory of Eco-hydrology and Water Security in Arid and Semi-arid Regions of the Ministry of Water Resources, Chang'an University, No. 126 Yanta Road, Xi'an, 710054, Shaanxi, China.
School of Water and Environment, Chang'an University, No.126 Yanta Road, Xi'an, 710054, Shaanxi, China; Key Laboratory of Subsurface Hydrology and Ecological Effects in Arid Region of the Ministry of Education, Chang'an University, Xi'an, 710054, Shaanxi, China; Key Laboratory of Eco-hydrology and Water Security in Arid and Semi-arid Regions of the Ministry of Water Resources, Chang'an University, No. 126 Yanta Road, Xi'an, 710054, Shaanxi, China.
Environ Res. 2024 Dec 15;263(Pt 1):120052. doi: 10.1016/j.envres.2024.120052. Epub 2024 Sep 23.
Global water resources affected by excessive nitrate (NO) have caused a series of human health and ecological problems. Therefore, identification of NO sources and transformations is of pivotal significance in the strategic governance of widespread NO contaminant. In this investigation, a combination of statistical analysis, chemical indicators, isotopes, and MixSIAR model approaches was adopted to reveal the hydrochemical factors affecting NO concentrations and quantify the contribution of each source to NO concentrations in surface water and groundwater. The findings revealed that high groundwater NO concentration is concentrated in the southwestern region, peaking at 271 mg/L. NO concentration in the Wei River and Yuxian River exhibited an increase from upstream to downstream, but in the Shidi River and Luowen River, its concentration was highest in the upstream. Groundwater NO has noticeable correlation with Na, Ca, Mg, Cl, HCO, TDS, EC, and ORP. In surface water, NO level is significantly correlated with NH and ORP. Major sources of NO in surface and groundwater comprise manure & sewage and soil nitrogen. Source contribution for surface water was calculated by MixSIAR model to obtain soil nitrogen (57.7%), manure & sewage (23.8%), chemical fertilizer (12%), and atmospheric deposition (6.4%). In groundwater, soil nitrogen and manure & sewage accounted for 19% and 63.8% of nitrate sources, respectively. Both surface water and groundwater exhibited strong oxidation, with nitrification the primary process. It is expected that this study will provide insights into the dynamics of NO and contribute to the development of effective strategies for mitigating NO contaminant, leading to sustainable management of water resources.
全球受过量硝酸盐 (NO) 影响的水资源已引发一系列人类健康和生态问题。因此,识别硝酸盐的来源和转化对于广泛存在的硝酸盐污染物的战略治理至关重要。在这项研究中,采用统计分析、化学指标、同位素和 MixSIAR 模型方法相结合的方法,揭示了影响硝酸盐浓度的水化学因素,并量化了每个来源对地表水和地下水硝酸盐浓度的贡献。研究结果表明,高地下水硝酸盐浓度集中在西南地区,峰值为 271mg/L。渭河流域和洩湖河流域的硝酸盐浓度呈从上游到下游增加的趋势,但在石堤河流域和洛汶河流域,上游的硝酸盐浓度最高。地下水硝酸盐与 Na、Ca、Mg、Cl、HCO、TDS、EC 和 ORP 有明显的相关性。在地表水方面,硝酸盐水平与 NH 和 ORP 呈显著相关。地表水和地下水中硝酸盐的主要来源包括粪肥和污水以及土壤氮。通过 MixSIAR 模型计算得出,地表水中的硝酸盐来源分别为土壤氮(57.7%)、粪肥和污水(23.8%)、化肥(12%)和大气沉降(6.4%)。地下水中,土壤氮和粪肥污水分别占硝酸盐来源的 19%和 63.8%。地表水和地下水均表现出强烈的氧化作用,硝化作用是主要过程。预计本研究将深入了解硝酸盐的动态变化,并为制定有效的硝酸盐污染物缓解策略提供参考,从而实现水资源的可持续管理。