张妍, 张秋英, 李发东, 张鑫, 毕直磊, 张强. 基于稳定同位素和贝叶斯模型的引黄灌区地下水硝酸盐污染源解析[J]. 中国生态农业学报(中英文), 2019, 27(3): 484-493. DOI: 10.13930/j.cnki.cjea.180887
引用本文: 张妍, 张秋英, 李发东, 张鑫, 毕直磊, 张强. 基于稳定同位素和贝叶斯模型的引黄灌区地下水硝酸盐污染源解析[J]. 中国生态农业学报(中英文), 2019, 27(3): 484-493. DOI: 10.13930/j.cnki.cjea.180887
ZHANG Yan, ZHANG Qiuying, LI Fadong, ZHANG Xin, BI Zhilei, ZHANG Qiang. Source identification of nitrate contamination of groundwater in Yellow River Irrigation Districts using stable isotopes and Bayesian model[J]. Chinese Journal of Eco-Agriculture, 2019, 27(3): 484-493. DOI: 10.13930/j.cnki.cjea.180887
Citation: ZHANG Yan, ZHANG Qiuying, LI Fadong, ZHANG Xin, BI Zhilei, ZHANG Qiang. Source identification of nitrate contamination of groundwater in Yellow River Irrigation Districts using stable isotopes and Bayesian model[J]. Chinese Journal of Eco-Agriculture, 2019, 27(3): 484-493. DOI: 10.13930/j.cnki.cjea.180887

基于稳定同位素和贝叶斯模型的引黄灌区地下水硝酸盐污染源解析

Source identification of nitrate contamination of groundwater in Yellow River Irrigation Districts using stable isotopes and Bayesian model

  • 摘要: 地下水硝酸盐(NO3-)污染已经成为全球严重的水环境问题之一,由于饮用水中高含量NO3-会转化成亚硝酸盐而增加各种疾病和癌症风险,其来源的确定对于NO3-污染的预防和控制非常重要。本文以黄河下游第二大灌区——潘庄灌区为例,首次采用NO3-的氮氧稳定同位素结合贝叶斯模型追溯地下水NO3-的来源并量化各种来源的贡献比例。结果表明,地下水NO3-含量分布在0.1~197.0 mg·L-1,平均值为34.2 mg·L-1。与《生活饮用水卫生标准》中规定的地下水NO3-最大含量20 mg(N)·L-1,相当于NO3-含量90 mg·L-1相比,有10%的样品NO3-含量超标。井深 < 30 m、30~60 m和>60 m的地下水NO3-平均含量分别为25.9 mg·L-1、39.7 mg·L-1和20.1 mg·L-1。空间上,宁津县、武城县、平原县和禹城市有大片区域地下水NO3-含量较高。地下水NO3-的δ15N组成范围为0.72‰~23.93‰,平均值为11.62‰;δ18O组成范围为0.49‰~22.50‰,平均值为8.46‰。同位素结果表明粪便和污水、农业化肥是地下水中NO3-的主要污染来源。这反映了人类活动是引起地下水NO3-污染的主要原因。贝叶斯模型结果显示,粪便和污水对潘庄灌区地下水中NO3-平均贡献率高达56.2%,化肥的平均贡献率为19.3%,大气降水和土壤的平均贡献率分别为6.2%和12.3%。由于污水、粪便和化肥是地下水中NO3-的主要来源,为保护和改善研究区地下水水质,建议加强污水管道建设,强化畜禽粪便的管理以及提高化肥利用效率。

     

    Abstract: Nitrate (NO3-) pollution in groundwater has become a serious environmental problem across the world. It is very important to determine the sources of nitrogen contamination in order to prevent and control NO3- pollution in groundwater. This is because the intake of polluted water can increase health risk of methemoglobinemia and cancer in both aquatic lives and humans. There has been an increasing trend in NO3- pollution in groundwater in the Lower Yellow River Irrigation Districts. Once groundwater is polluted by NO3-, recovery efforts can be very daunting. The effective control and management of NO3- pollution require accurate identification of the actual sources of pollution. In this paper, the sources of NO3- in groundwater in the Lower Yellow River Irrigation District (Panzhuang Irrigation District) were identified using stable isotopes (δ15N and δ18O) and the Bayesian model. The results showed that the range of NO3- concentrations in groundwater in the study area was 0.1-197.0 mg·L-1, with a mean of 34.2 mg·L-1. About 10% of the groundwater samples had NO3- concentration in excess of the maximal standard of nitrate level in drinking water in China (90 mg·L-1). Samples were divided into three depths, including 0-30 m (shallow layer), 30-60 m (middle layer) and >60 m (deep layer). The average NO3- concentrations in shallow groundwater layer, middle layer and deep layer were 25.9 mg·L-1, 39.7 mg·L-1 and 20.1 mg·L-1, respectively. There were high NO3- concentrations in groundwater across Ningjin County, Wucheng County, Pingyuan County and Yucheng City. The composition of δ15N was in the range of 0.72‰-23.93‰, with an average of 11.62‰. That of δ18O was 0.49‰-22.50‰, with an average of 8.46‰. The values of δ15N and δ18O indicated that NO3- in groundwater in the study area mainly originated from chemical fertilizers, manure and sewage. The contributions of the four sources of NO3- (precipitation, chemical fertilizer, soil, manure and sewage) were quantified and estimated using the Bayesian model. The results showed that manure and sewage contributed the most to the overall NO3- level, with a mean NO3- contribution ratio of 56.2%. Chemical fertilizer was the second contributor, with a mean NO3- contribution ratio of 19.3%. The mean NO3- contribution ratio of precipitation and soil was 6.2% and 12.3%, respectively. After identification of NO3- pollution levels and sources, measures were required to reduce NO3- pollution in groundwater. Based on this study, the necessary measures included the construction of sewage pipeline and improving the utilization rate of chemical fertilizers in order to reduce NO3- pollution and improve water quality.

     

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