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中国西北典型集约化农业区硝酸盐的分布、来源及主要控制因素:垂直剖面视角

Distribution, sources and main controlling factors of nitrate in a typical intensive agricultural region, northwestern China: Vertical profile perspectives.

作者信息

Wang Dan, Li Peiyue, Yang Ningning, Yang Chunliu, Zhou Yuhan, Li Jiahui

机构信息

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, No. 126 Yanta Road, 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, No. 126 Yanta Road, 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.

出版信息

Environ Res. 2023 Nov 15;237(Pt 1):116911. doi: 10.1016/j.envres.2023.116911. Epub 2023 Aug 18.

Abstract

Nitrate (NO) pollution of groundwater is a global concern in agricultural areas. To gain a comprehensive understanding of the sources and destiny of nitrate in soil and groundwater within intensive agricultural areas, this study employed a combination of chemical indicators, dual isotopes of nitrate (δN-NO and δO-NO), random forest model, and Bayesian stable isotope mixing model (MixSIAR). These approaches were utilized to examine the spatial distribution of NO in soil profiles and groundwater, identify key variables influencing groundwater nitrate concentration, and quantify the sources contribution at various depths of the vadose zone and groundwater with different nitrate concentrations. The results showed that the nitrate accumulation in the cropland and kiwifruit orchard at depths of 0-400 cm increased, leading to subsequent leaching of nitrate into deeper vadose zones and ultimately groundwater. The mean concentration of nitrate in groundwater was 91.89 mg/L, and 52.94% of the samples exceeded the recommended grade III value (88.57 mg/L) according to national standards. The results of the random forest model suggested that the main variables affecting the nitrate concentration in groundwater were well depth (16.6%), dissolved oxygen (11.6%), and soil nitrate (10.4%). The MixSIAR results revealed that nitrate sources vary at different soil depths, which was caused by the biogeochemical process of nitrate. In addition, the highest contribution of nitrate in groundwater, both with high and low concentrations, was found to be soil nitrogen (SN), accounting for 56.0% and 63.0%, respectively, followed by chemical fertilizer (CF) and manure and sewage (M&S). Through the identification of NO pollution sources, this study can take targeted measures to ensure the safety of groundwater in intensive agricultural areas.

摘要

地下水中的硝酸盐(NO)污染是农业地区的一个全球关注问题。为了全面了解集约化农业地区土壤和地下水中硝酸盐的来源及归宿,本研究采用了化学指标、硝酸盐双同位素(δN-NO和δO-NO)、随机森林模型和贝叶斯稳定同位素混合模型(MixSIAR)相结合的方法。这些方法用于研究土壤剖面和地下水中NO的空间分布,确定影响地下水硝酸盐浓度的关键变量,并量化不同深度包气带和不同硝酸盐浓度地下水中各来源的贡献。结果表明,0-400厘米深度的农田和猕猴桃果园中硝酸盐积累增加,导致硝酸盐随后淋溶到更深的包气带并最终进入地下水。地下水中硝酸盐的平均浓度为91.89毫克/升,52.94%的样品超过了国家标准推荐的Ⅲ类值(88.57毫克/升)。随机森林模型结果表明,影响地下水中硝酸盐浓度的主要变量是井深(16.6%)、溶解氧(11.6%)和土壤硝酸盐(10.4%)。MixSIAR结果显示,不同土壤深度的硝酸盐来源不同,这是由硝酸盐的生物地球化学过程引起的。此外,发现高浓度和低浓度地下水中硝酸盐的最大贡献来源均为土壤氮(SN),分别占56.0%和63.0%,其次是化肥(CF)以及粪肥和污水(M&S)。通过识别NO污染源,本研究可以采取针对性措施,确保集约化农业地区地下水的安全。

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