Hysteresis analysis of nitrate dynamics in the Neuse River, NC.

Anthropogenic activities have caused N saturation in many terrestrial ecosystems. The transfer of nutrients and sediments to freshwater environments has resulted in water quality impairments including eutrophication, increased turbidity, ecosystem acidification, and loss of biodiversity. Storm events account for the transport of a large proportion of nutrients and sediments found in watersheds on an annual basis. To implement effective water-quality management strategies, the importance of surface and subsurface flow paths during storm events and low flow conditions need to be quantified. The increased availability of optical in-situ sensors makes high-frequency monitoring of catchment fluxes practical. In this study, we present a high-resolution nitrate monitoring record over a 10-year period in the Neuse River Basin near Clayton, North Carolina. The relationship between discharge and nitrate concentration for 365 storm events are categorized into hysteresis classes that indicate different transport mechanisms into the river. Storm events over the entire period of this study are divided between clockwise, counter-clockwise, and complex hysteresis patterns, indicating multiple nitrate flow paths during different seasons and years. Logistic regression of a suite of environmental variables demonstrates that antecedent soil moisture is a significant factor in determining the storm hysteresis class, with the odds of counter-clockwise hysteresis increasing by 10.3% for every 1 percentage point increase in the soil moisture. There is also an overlying seasonal effect, which indicates that dry soil conditions and frequent small storms during summer leads to greater nitrate transport on the rising limb, in contrast to slower, groundwater-driven inputs during the rest of the year.

Key metabolites in tissue extracts of Elliptio complanata identified using (1)H nuclear magnetic resonance spectroscopy.

We used (1)H nuclear magnetic resonance spectroscopy to describe key metabolites of the polar metabolome of the freshwater mussel, Elliptio complanata. Principal components analysis documented variability across tissue types and river of origin in mussels collected from two rivers in North Carolina (USA). Muscle, digestive gland, mantle and gill tissues yielded identifiable but overlapping metabolic profiles. Variation in digestive gland metabolic profiles between the two mussel collection sites was characterized by differences in mono- and disaccharides. Variation in mantle tissue metabolomes appeared to be associated with sex. Nuclear magnetic resonance spectroscopy is a sensitive means to detect metabolites in the tissues of E. complanata and holds promise as a tool for the investigation of freshwater mussel health and physiology.

Nitrate contamination in groundwater on an urbanized dairy farm.

Urbanization of rural farmland is a pervasive trend around the globe, and maintaining and protecting adequate water supplies in suburban areas is a growing problem. Identification of the sources of groundwater contamination in urbanized areas is problematic, but will become important in areas of rapid population growth and development. The isotopic composition of NO3 (delta15N(NO3) and delta18O NO3), NH4 (delta15N(NH4)), groundwater (delta2H(wt) and delta18O(wt)) and chloride/bromide ratios were used to determine the source of nitrate contamination in drinking water wells in a housing development that was built on the site of a dairy farm in the North Carolina Piedmont, U.S. The delta15N(NO3) and delta18O NO3 compositions imply that elevated nitrate levels at this site in drinking well water are the result of waste contamination, and that denitrification has not significantly attenuated the groundwater nitrate concentrations. delta15N(NO3) and delta18O(NO3) compositions in groundwater could not differentiate between septic effluent and animal waste contamination. Chloride/ bromide ratios in the most contaminated drinking water wells were similar to ratios found in animal waste application fields, and were higher than Cl/Br ratios observed in septic drain fields in the area. delta18O(wt) was depleted near the site of a buried waste lagoon without an accompanying shift in delta2H(wt) suggesting water oxygen exchange with CO2. This water-CO2 exchange resulted from the reduction of buried lagoon organic matter, and oxidation of the released gases in aerobic soils. delta18O(wt) is not depleted in the contaminated drinking water wells, indicating that the buried dairy lagoon is not a source of waste contamination. The isotope and Cl/Br ratios indicate that nitrate contamination in these drinking wells are not from septic systems, but are the result of animal waste leached from pastures into groundwater during 35 years of dairy operations which did not violate any existing regulations. Statutes need to be enacted to protect the health of the homeowners that require well water to be tested prior to the sale of homes built on urbanized farmland.

Nitrate movement in shallow ground water from swine-lagoon-effluent spray fields managed under current application regulations.

Rapid increases in the swine (Sus scrofa domestica) population in the 1990s and associated potential for nitrate N pollution of surface waters led the state of North Carolina to adopt stringent waste management regulations in 1993. Our objectives were to characterize (i) nitrate N movement from waste application fields (WAFs) in shallow ground water, and (ii) soil, hydrologic, and biological factors influencing the amount of nitrate N in the adjacent stream. A ground water monitoring study was conducted for 36 mo on a swine farm managed under new regulations. Water table contours and lack of vertical gradients indicated horizontal flow over most of the site. Nitrate N concentrations in water from shallow wells in WAFs averaged 30 +/- 19 mg L(-1) and delta15N ratios for nitrate N were between +20 and +25 per mil. Nitrate N concentration decreased from field-edge to streamside wells by 22 to 99%. Measurement of delta18O and delta15N enrichment of nitrate in ground water throughout the WAF-riparian system indicated that denitrification has not caused significant 15N enrichment of nitrate. Over a 24-mo period, delta15N ratios for nitrate N in the stream approached delta15N ratios for nitrate N in ground water beneath WAFs indicating delivery of some waste-derived nitrate N to the stream in shallow ground water. Nitrate N concentrations in the stream were relatively low, averaging 1 mg L(-1). Dilution of high nitrate N water in shallow horizontal flow paths with low nitrate N water from deeper horizontal flow paths at or near the stream, some denitrification as ground water discharges through the stream bottom, and some denitrification in riparian zone contributed to this low nitrate N concentration.

Application of classification-tree methods to identify nitrate sources in ground water.

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 < 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 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.