Zhang Jie, Cao Mingda, Jin Menggui, Huang Xin, Zhang Zhixin, Kang Fengxin
State Key Laboratory of Biogeology and Environmental Geology, China University of Geosciences, Wuhan 430078, Hubei, PR China; School of Environmental Studies, China University of Geosciences, Wuhan 430078, Hubei, PR China.
State Key Laboratory of Biogeology and Environmental Geology, China University of Geosciences, Wuhan 430078, Hubei, PR China; School of Environmental Studies, China University of Geosciences, Wuhan 430078, Hubei, PR China.
J Contam Hydrol. 2022 Apr;246:103957. doi: 10.1016/j.jconhyd.2022.103957. Epub 2022 Jan 15.
Nitrate (NO) contamination of surface water is a globally concern, especially in karstic regions affected by intensive agricultural activities. This study combines hydrochemistry, and environmental isotopes (δH, δO, δN, and δO) with a Bayesian isotope mixing model (Simmr) to reduce the uncertainty in estimating the contributions of different pollution sources. Samples were collected from 32 surface water sites in the Yufu River (YFR) watershed, North China, in September and December 2019. The results revealed that NO-N was the predominant form of inorganic nitrogen that caused the deterioration of water quality in the watershed, accounting for approximately 58% of the total nitrogen (TN). The hydrochemical compositions and nitrate isotopes indicated that NO mainly originated from soil nitrogen (SN), ammonium fertilizer (AF), but nitrate fertilizer (NF), manure and sewage (M&S) and atmospheric precipitation (AP) were limited. The isotopic composition of nitrate in the upper reaches of the watershed was mainly affected by microbial nitrification, while the mixture of multiple sources was the dominant nitrogen transformation process in the mid-lower reaches of the watershed. Simmr model outputs revealed that SN (56.5%) and AF (29.5%) were the primary contributor to riverine NO pollution, followed by NF (7.1%), MS (3.6%), and AP (3.4%) sources. Moreover, an uncertainty index (UI) of the isotope mixing showed that SN (0.73) and AF (0.67) had the highest values, followed by NF (0.22), M&S (0.22) and AP (0.10). Chemical fertilizer and SN collectively contributed >50% of nitrate during the two sampling campaigns. These results indicated that reducing the application of nitrogen fertilizers and rational irrigation are the keys to alleviate of NO pollution. The study is helpful in understanding the source and transformation of riverine NO and effectively reducing NO pollution in karst agricultural rivers or watersheds.
地表水的硝酸盐（NO）污染是一个全球关注的问题，特别是在受集约化农业活动影响的岩溶地区。本研究将水化学和环境同位素（δH、δO、δN和δO）与贝叶斯同位素混合模型（Simmr）相结合，以减少估算不同污染源贡献时的不确定性。2019年9月和12月，从中国北方府河（YFR）流域的32个地表水站点采集了样本。结果表明，NO-N是导致该流域水质恶化的无机氮的主要形式，约占总氮（TN）的58%。水化学组成和硝酸盐同位素表明，NO主要来源于土壤氮（SN）、铵肥（AF），但硝态氮肥（NF）、粪便和污水（M&S）以及大气降水（AP）的贡献有限。流域上游硝酸盐的同位素组成主要受微生物硝化作用的影响，而多种来源的混合是流域中下游主要的氮转化过程。Simmr模型输出结果显示，SN（56.5%）和AF（29.5%）是河流NO污染的主要贡献者，其次是NF（7.1%）、MS（3.6%）和AP（3.4%）来源。此外，同位素混合的不确定性指数（UI）表明，SN（0.73）和AF（0.67）的值最高，其次是NF（0.22）、M&S（0.22）和AP（0.10）。在两次采样活动中，化肥和SN共同贡献了超过50%的硝酸盐。这些结果表明，减少氮肥施用量和合理灌溉是缓解NO污染的关键。该研究有助于了解河流NO的来源和转化，并有效减少岩溶农业河流或流域中的NO污染。
