Application of classification-tree methods to identify nitrate sources in ground water.
Application of classification-tree methods to identify nitrate sources in ground water.
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DOI：
10.2134/jeq2002.1538
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发表时间：
2002-09
影响因子：
2.4
通讯作者：
T. B. Spruill;W. Showers;Stephen S. Howe
中科院分区：
文献类型：
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作者：
T. B. Spruill;W. Showers;Stephen S. Howe
进行了一项研究，以确定地下水中的硝酸盐来源（作物肥料、高尔夫球场肥料、猪（Sus scrofa）废物的灌溉喷雾以及家禽垃圾和化粪池系统的渗滤液）是否可以分类，成功率达到80%或更高。从5个来源的48个含硝酸盐水样中设计了2个统计分类树模型。模型1通过评估32个变量，并选择4个主要预测变量（δ 15N、硝氨比、钠钾比和锌）来确定硝酸盐来源。硝酸盐+钾δ 15N值> 18.2表明动物源；值575表示无机肥料对农作物的影响；比率3.2表示化粪池系统废物；比值为2.8表示来自猪湖的喷雾废弃物；值< 2.8表示禽粪。模型2采用除δ 15N外的所有变量设计。该模型还包括四个变量（钠加钾、硝氨比、钙镁比和钠钾比）来区分类别。两个模型都能够区分所有五个源类别，总体成功率超过80%，使用学习样本在单个类别上的成功率为71%至100%。17个未用于模型开发的水样，使用模型2对3个类别进行检验，全部分类正确。分类树模型在识别污染源和污染源识别过程中重要的变量方面显示出巨大的潜力。
A study was conducted to determine if nitrate sources in ground water (fertilizer on crops, fertilizer on golf courses, irrigation spray from hog (Sus scrofa) wastes, and leachate from poultry litter and septic systems) could be classified with 80% or greater success. Two statistical classification-tree models were devised from 48 water samples containing nitrate from five source categories. Model 1 was constructed by evaluating 32 variables and selecting four primary predictor variables (delta 15N, nitrate to ammonia ratio, sodium to potassium ratio, and zinc) to identify nitrate sources. A delta 15N value of nitrate plus potassium > 18.2 indicated animal sources; a value 575 indicated inorganic fertilizer on agricultural crops; a ratio 3.2 indicated septic-system wastes; a ratio 2.8 indicated spray wastes from hog lagoons; a value < 2.8 indicated poultry wastes. Model 2 was devised by using all variables except delta 15N. This model also included four variables (sodium plus potassium, nitrate to ammonia ratio, calcium to magnesium ratio, and sodium to potassium ratio) to distinguish categories. Both models were able to distinguish all five source categories with better than 80% overall success and with 71 to 100% success in individual categories using the learning samples. Seventeen water samples that were not used in model development were tested using Model 2 for three categories, and all were correctly classified. Classification-tree models show great potential in identifying sources of contamination and variables important in the source-identification process.