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Estimating background and threshold nitrate concentrations using probability graphs

Ground Water

By:
, , , and
DOI: 10.1111/j.1745-6584.2006.00240.x

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Abstract

Because of the ubiquitous nature of anthropogenic nitrate (NO 3-) in many parts of the world, determining background concentrations of NO3- in shallow ground water from natural sources is probably impossible in most environments. Present-day background must now include diffuse sources of NO3- such as disruption of soils and oxidation of organic matter, and atmospheric inputs from products of combustion and evaporation of ammonia from fertilizer and livestock waste. Anomalies can be defined as NO3- derived from nitrogen (N) inputs to the environment from anthropogenic activities, including synthetic fertilizers, livestock waste, and septic effluent. Cumulative probability graphs were used to identify threshold concentrations separating background and anomalous NO3-N concentrations and to assist in the determination of sources of N contamination for 232 spring water samples and 200 well water samples from karst aquifers. Thresholds were 0.4, 2.5, and 6.7 mg/L for spring water samples, and 0.1, 2.1, and 17 mg/L for well water samples. The 0.4 and 0.1 mg/L values are assumed to represent thresholds for present-day precipitation. Thresholds at 2.5 and 2.1 mg/L are interpreted to represent present-day background concentrations of NO3-N. The population of spring water samples with concentrations between 2.5 and 6.7 mg/L represents an amalgam of all sources of NO3- in the ground water basins that feed each spring; concentrations >6.7 mg/L were typically samples collected soon after springtime application of synthetic fertilizer. The 17 mg/L threshold (adjusted to 15 mg/L) for well water samples is interpreted as the level above which livestock wastes dominate the N sources. Copyright ?? 2006 The Author(s).

Additional Publication Details

Publication type:
Article
Publication Subtype:
Journal Article
Title:
Estimating background and threshold nitrate concentrations using probability graphs
Series title:
Ground Water
DOI:
10.1111/j.1745-6584.2006.00240.x
Volume
44
Issue:
5
Year Published:
2006
Language:
English
Larger Work Type:
Article
Larger Work Subtype:
Journal Article
Larger Work Title:
Ground Water
First page:
697
Last page:
709
Number of Pages:
13