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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

View larger version (35K): [in a new window]   Fig. 1. Locations of sites sampled in North Carolina and nitrate contamination sources.

View larger version (19K): [in a new window]   Fig. 2. Diagram of hypothetical classification tree showing node types, split variables, and associated split values.

View larger version (32K): [in a new window]   Fig. 3. Classification tree for Model 1 using the predictor variables potassium plus 15N of nitrate (KNO315), nitrate to ammonia ratio (NO3NH4), sodium to potassium ratio (NAK), and dissolved zinc (ZN), in micrograms per liter.

View larger version (33K): [in a new window]   Fig. 4. Classification tree for Model 2 using the predictor variables sum of sodium plus potassium (NAKSUM), nitrate to ammonia ratio (NO3NH4), calcium to magnesium ratio (CMR), and sodium to potassium ratio (NAK).

View larger version (17K): [in a new window]   Fig. 5. Distributions of (A) N15 (15N in per mil) and (B) potassium (mg/L) plus 15N of nitrate () (KNO315) in five source categories demonstrating the effect of adding potassium to increase separation of the animal and fertilizer groups and particularly the Poultry and Septic categories.

View larger version (14K): [in a new window]   Fig. 6. Distributions of (A) NA (sodium, in milligrams per liter) and (B) NAK (sodium to potassium ratio, unitless) in five source categories showing increase of separation between septic and the other two animal source categories when NAK is used.