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TECHNICAL REPORTS
Ground Water Quality

Application of Classification-Tree Methods to Identify Nitrate Sources in Ground Water

^a United States Geological Survey, 3916 Sunset Ridge Rd., Raleigh, NC 27607
^b Dep. of Marine Earth and Atmospheric Sciences, North Carolina State University, Raleigh, NC 27695-8208

* Corresponding author (tspruill{at}usgs.gov)

Received for publication August 17, 2001. 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 (¹⁵N, nitrate to ammonia ratio, sodium to potassium ratio, and zinc) to identify nitrate sources. A ¹⁵N value of nitrate plus potassium >18.2 indicated animal sources; a value <18.2 indicated inorganic or soil organic N. A nitrate to ammonia ratio >575 indicated inorganic fertilizer on agricultural crops; a ratio <575 indicated nitrate from golf courses. A sodium to potassium ratio >3.2 indicated septic-system wastes; a ratio <3.2 indicated spray or poultry wastes. A value for zinc >2.8 indicated spray wastes from hog lagoons; a value <2.8 indicated poultry wastes. Model 2 was devised by using all variables except 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.

Abbreviations: CART, classification and regression tree • USGS, United States Geological Survey

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