School of Water Resources, North China University of Water Resources and Electric Power, Zhengzhou 450045, PR China; Henan Provincial Key Laboratory of Hydrosphere and Watershed Water Security, North China University of Water Resources and Electric Power, Zhengzhou, 450046, PR China.
School of Water Conservancy, North China University of Water Resources and Electric Power, Zhengzhou, 450046, PR China.
Sci Total Environ. 2023 Sep 15;891:164342. doi: 10.1016/j.scitotenv.2023.164342. Epub 2023 May 25.
Shallow groundwater nitrate nitrogen (NO-N) concentrations in agricultural areas usually show high spatial and intra-annual variability. It is hard to predict such concentrations due to the complexity of influencing factors (e.g., different forms of N in soil, vadose zone characteristics, and groundwater physiochemical conditions). Here, a large number of groundwater and soil samples were collected monthly over two years at 14 sites to analyze the soil and groundwater physiochemical properties and the stable isotopes of δN and δO of groundwater NO-N in agricultural areas. Based on field observations, a random forest (RF) model was used to predict the groundwater NO-N concentrations and reveal the importance of effect factors. The results show that there are large spatiotemporal variations in NO-N, δN-NO, and δO-NO in groundwater. NO-N is the major dominant specie of inorganic N in groundwater, and the groundwater NO-N concentration in 24 % of the samples failed to meet the drinking water standard of the WHO (10 mg L). The RF model satisfactorily predicted groundwater NO-N concentrations with R of 0.90-0.94, RMSE of 4.54-5.07, and MAE of 2.17-3.38. Groundwater nitrite and ammonium are the most important factors related to NO-N consumption and production, respectively, in groundwater. Denitrification and nitrification were further identified by the relationships among δN-NO, δO-NO, and NO-N, and by the ranges of δN-NO, δO-NO, temperature, pH, DO, and ORP in groundwater. Soil-soluble organic nitrogen (S-SON) and the depth of groundwater table were identified as vital factors related to N sourcing and leaching. Overall, as a first approach to adopting a RF model for high spatiotemporal-resolution prediction of groundwater NO-N variations, the findings of this study enable a better understanding of groundwater N pollution in agricultural areas. Optimizing management of irrigation and N inputs is anticipated to reduce S-SON accumulation and mitigate the threat to groundwater quality in agricultural areas.
农业区浅层地下水中硝酸盐氮 (NO-N) 浓度通常表现出高的空间和年内变异性。由于影响因素的复杂性（例如，土壤中不同形式的 N、包气带特征和地下水物理化学条件），很难预测这些浓度。在这里，在两年内，每月在 14 个地点采集大量地下水和土壤样本，以分析农业区地下水和土壤的物理化学性质以及地下水 NO-N 的稳定同位素 δN 和 δO。基于现场观测，使用随机森林 (RF) 模型预测地下水 NO-N 浓度并揭示影响因素的重要性。结果表明，NO-N、δN-NO 和 δO-NO 在地下水中存在较大的时空变化。NO-N 是地下水无机 N 的主要优势种，24%的样本中地下水 NO-N 浓度不符合世界卫生组织（WHO）的饮用水标准（10 mg L）。RF 模型对地下水 NO-N 浓度的预测效果良好，R 为 0.90-0.94，RMSE 为 4.54-5.07，MAE 为 2.17-3.38。地下水中的亚硝酸盐和铵盐分别是与地下水 NO-N 消耗和产生最相关的最重要因素。通过 δN-NO、δO-NO 和 NO-N 之间的关系，以及地下水 δN-NO、δO-NO、温度、pH、DO 和 ORP 的范围，进一步确定了反硝化和硝化作用。土壤可溶性有机氮 (S-SON) 和地下水水位埋深被确定为与氮源和淋溶有关的重要因素。总的来说，作为采用 RF 模型对地下水 NO-N 变化进行高时空分辨率预测的初步方法，本研究的结果使人们更好地了解了农业区地下水氮污染。优化灌溉和 N 投入的管理有望减少 S-SON 的积累，减轻农业区地下水质量受到的威胁。
