Escuela de Ingeniería y Ciencias, Tecnologico de Monterrey, Campus Monterey, Eugenio Garza Sada 2501, Monterrey, 64149, Nuevo León, Mexico.
Escuela de Ingeniería y Ciencias, Tecnologico de Monterrey, Campus Puebla, Atlixcáyotl 5718, Puebla de Zaragoza, 72453, Puebla, Mexico.
Environ Pollut. 2021 Jan 15;269:115445. doi: 10.1016/j.envpol.2020.115445. Epub 2020 Aug 24.
The identification of nitrate (NO) sources and biogeochemical transformations is critical for understanding the different nitrogen (N) pathways, and thus, for controlling diffuse pollution in groundwater affected by livestock and agricultural activities. This study combines chemical data, including environmental isotopes (δH, δO, δN and δO), with land use/land cover data and a Bayesian isotope mixing model, with the aim of reducing the uncertainty when estimating the contributions of different pollution sources. Sampling was taken from 53 groundwater sites in Comarca Lagunera, northern Mexico, during 2018. The results revealed that the NO (as N) concentration ranged from 0.01 to 109 mg/L, with more than 32% of the sites exceeding the safe limit for drinking water quality established by the World Health Organization (10 mg/L). Moreover, according to the groundwater flow path, different biogeochemical transformations were observed throughout the study area: microbial nitrification was dominant in the groundwater recharge areas with elevated NO concentrations; in the transition zones a mixing of different transformations, such as nitrification, denitrification, and/or volatilization, were identified, associated to moderate NO concentrations; whereas in the discharge area the main process affecting NO concentrations was denitrification, resulting in low NO concentrations. The results of the MixSIAR isotope mixing model revealed that the application of manure from concentrated animal-feeding operations (∼48%) and urban sewage (∼43%) were the primary contributors of NO pollution, whereas synthetic fertilizers (∼5%), soil organic nitrogen (∼4%), and atmospheric deposition played a less important role. Finally, an estimation of an uncertainty index (UI90) of the isotope mixing results indicated that the uncertainties associated with atmospheric deposition and NO-fertilizers were the lowest (0.05 and 0.07, respectively), while those associated with manure and sewage were the highest (0.24 and 0.20, respectively).
硝酸盐（NO）来源和生物地球化学转化的识别对于理解不同的氮（N）途径至关重要，因此对于控制受牲畜和农业活动影响的地下水的扩散污染至关重要。本研究结合了化学数据，包括环境同位素（δH、δO、δN 和 δO）、土地利用/土地覆盖数据和贝叶斯同位素混合模型，旨在减少估计不同污染源贡献时的不确定性。采样于 2018 年在墨西哥北部拉古纳地区的 53 个地下水点进行。结果表明，NO（以 N 计）浓度范围为 0.01 至 109mg/L，超过 32%的地点超过了世界卫生组织（10mg/L）规定的饮用水质量安全限值。此外，根据地下水流动路径，在整个研究区域观察到不同的生物地球化学转化：在硝酸盐浓度较高的地下水补给区，微生物硝化作用占主导地位；在过渡区，确定了不同转化的混合，如硝化、反硝化和/或挥发，与中等硝酸盐浓度有关；而在排泄区，影响硝酸盐浓度的主要过程是反硝化，导致硝酸盐浓度较低。MixSIAR 同位素混合模型的结果表明，集中饲养场（约 48%）和城市污水（约 43%）的粪便应用是硝酸盐污染的主要来源，而合成肥料（约 5%）、土壤有机氮（约 4%）和大气沉降的作用较小。最后，同位素混合结果不确定性指数（UI90）的估计表明，与大气沉降和硝酸盐肥料相关的不确定性最低（分别为 0.05 和 0.07），而与粪便和污水相关的不确定性最高（分别为 0.24 和 0.20）。
