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Prediction Model of River and Groundwater Nitrogen Contamination Caused by Livestock Manure in a River Basin

机译:流域畜禽粪便对河流和地下水氮污染的预测模型

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In this study, we have developed a prediction model of river and groundwater nitrogen contamination caused by livestock manure in a river basin, based on field investigations and a hearing survey used to estimate actual nitrogen loading from farm sites and grass farms located in the basin used in this study. The river basin was divided into meshes, and the model predicted average total nitrogen (T-N) concentration in the groundwater and the river every three months, considering water and nitrogen mass balance in each mesh. The water mass balance in the basin was calibrated using data on the flow rates of the river, the groundwater level, and precipitation in the region. The nitrogen mass balance model was calibrated using total nitrogen (T-N) concentration data in the river and groundwater, based on an estimation of the nitrogen loading from various nitrogen sources, such as the application of manure, urine, and mineral fertilizer, and the inappropriate storage of manure. As a result, our prediction model suggested that 1) leaving piles of manure on the ground had a large effect on the nitrogen groundwater contamination, and that 2) application of too large amount of manure (25 t/10a) to grass farms was responsible for high concentration of T-N in groundwater, especially in the spring because of melting snow. Our prediction model can be used as a tool to develop countermeasures for appropriate livestock manure management in a river basin to decrease groundwater nitrogen contamination.
机译:在这项研究中,我们基于实地调查和听觉调查建立了一个预测流域牲畜粪肥引起的河流和地下水氮污染的预测模型,该听觉调查用于估算所使用流域内农田和草场的实际氮负荷在这个研究中。将流域划分为多个网格,并考虑每个网格中的水和氮质量平衡,该模型每三个月预测一次地下水和河流中的平均总氮(T-N)浓度。使用有关河流流量,地下水位和该地区降水的数据对流域的水质平衡进行了校准。基于河流和地下水中总氮(TN)浓度数据对氮质量平衡模型进行校准,该模型基于对各种氮源(如肥料,尿液和矿物肥料的施用)以及不适当氮源的氮负荷的估算粪肥的储存。结果,我们的预测模型表明:1)将粪肥堆放在地面上对氮地下水污染有很大影响,并且2)对草场施用过多的粪肥(25 t / 10a)是造成这种情况的原因。用于地下水中高浓度的总氮,特别是在春季,因为积雪融化。我们的预测模型可以用作开发对流河流域适当畜禽粪便管理以减少地下水氮污染的对策的工具。

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