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To identify different NO(3)(-) sources in surface water and to estimate their proportional contribution to the nitrate mixture in surface water, a dual isotope and a Bayesian isotope mixing model have been applied for six different surface waters affected by agriculture, greenhouses in an agricultural area, and households. Annual mean δ(15)N-NO(3)(-) were between 8.0 and 19.4‰, while annual mean δ(18)O-NO(3)(-) were given by 4.5-30.7‰. SIAR was used to estimate the proportional contribution of five potential NO(3)(-) sources (NO(3)(-) in precipitation, NO(3)(-) fertilizer, NH(4)(+) in fertilizer and rain, soil N, and manure and sewage). SIAR showed that "manure and sewage" contributed highest, "soil N", "NO(3)(-) fertilizer" and "NH(4)(+) in fertilizer and rain" contributed middle, and "NO(3)(-) in precipitation" contributed least. The SIAR output can be considered as a "fingerprint" for the NO(3)(-) source contributions. However, the wide range of isotope values observed in surface water and of the NO(3)(-) sources limit its applicability., (Copyright © 2011 Elsevier Ltd. All rights reserved.)

Currently, bacterial denitrification is becoming the accepted method for delta(15)N- and delta(18)O-NO(3)(-) determination. However, proper correction methods with international references (USGS32, USGS34 and USGS35) are needed. As a consequence, it is important to realize that the corrected isotope values are derived from a combination of several other measurements with associated uncertainties. Therefore, it is necessary to consider the propagated uncertainty on the final isotope value. This study demonstrates how to correctly estimate the uncertainty on corrected delta(15)N- and delta(18)O-NO(3)(-) values using a first-order Taylor series approximation. The bacterial denitrification method errors from 33 batches of 561 surface water samples varied from 0.2 to 2.1 per thousand for delta(15)N-NO(3)(-) and from 0.7 to 2.3 per thousand for delta(18)O-NO(3)(-), which is slightly wider than the machine error, which varied from 0.2 to 0.6 per thousand for delta(15)N-N(2)O and from 0.4 to 1.0 per thousand for delta(18)O-N(2)O. The overall uncertainties, which are composed of the machine error and the method error, for the 33 batches ranged from 0.3 to 2.2 per thousand for delta(15)N-NO(3)(-) and from 0.8 to 2.5 per thousand for delta(18)O-NO(3)(-). In addition, the mean corrected delta(15)N and delta(18)O values of 132 KNO(3)-IWS (internal working standard) measurements were computed as 8.4 +/- 1.0 per thousand and 25.1 +/- 2.0 per thousand, which is a slight underestimation for delta(15)N and overestimation for delta(18)O compared with the accepted values (delta(15)N = 9.9 +/- 0.3 per thousand and delta(18)O = 24.0 +/- 0.3 per thousand). The overall uncertainty of the bacterial denitrification method allows the use of this method for source identification of NO(3)(-)., (Copyright 2010 John Wiley & Sons, Ltd.)

Nitrogen (N) and oxygen (O) isotope ratios of NO(3) (-) are often used to trace dominant NO(3) (-) pollution sources in water. Both the silver nitrate (AgNO(3)) method and the bacterial denitrification method are frequently used analytical techniques to determine delta(15)N- and delta(18)O-NO(3) (-) in aqueous samples. The AgNO(3) method is applicable for freshwater and requires a concentration of 100-200 micromol of NO(3) (-) for isotope determination. The bacterial denitrification method is applicable for seawater and freshwater and for KCl extracts of soils with a NO(3) (-) concentration as low as 1 micromol. We have carried out a thorough method comparison using 42 real surface water samples having a wide range of delta(15)N- and delta(18)O-NO(3) (-) values and NO(3) (-) concentrations. Various correction pairs using three international references and blanks were used to correct raw delta(15)N- and delta(18)O-NO(3) (-) values. No significant difference between the corrected data was observed when using various correction pairs for each analytical method. Both methods also showed excellent repeatability with high intraclass correlation coefficients (ICC). The ICC of the AgNO(3) method was 0.992 for delta(15)N and 0.970 for delta(18)O. The ICC of the bacterial denitrification method was 0.995 for delta(15)N and 0.954 for delta(18)O. Moreover, a positive linear relationship with a high correlation coefficient (r >or= 0.88) between the two methods was found for delta(15)N- and delta(18)O-NO(3) (-). The comparability of the methods was assessed by the Bland-Altman technique using 95% limits of agreement. The average difference between results obtained by the bacterial denitrification and the AgNO(3) method for delta(15)N was -1.5 per thousand with 95% limits of agreement -3.6 and +0.5 per thousand. For delta(18)O this was +2.0 per thousand, with 95% limits of agreement -3.3 and +7.3 per thousand. We found that for delta(15)N and for delta(18)O, 97% of the differences fell within these 95% limits of agreement. In conclusion, the AgNO(3) and the bacterial denitrification methods are highly correlated and statistically interchangeable., (Copyright (c) 2010 John Wiley & Sons, Ltd.)

Nitrate (NO3(-)) contamination of surface- and groundwater is an environmental problem in many regions of the world with intensive agriculture and high population densities. Knowledge of the sources of NO3(-) contamination in water is important for better management of water quality. Stable nitrogen (delta15N) and oxygen (delta18O) isotope data of NO3(-) have been frequently used to identify NO3(-) sources in water. This review summarizes typical delta15N- and delta18O-NO3(-) ranges of known NO3(-) sources, interprets constraints and future outlooks to quantify NO3(-) sources, and describes three analytical techniques ("ion-exchange method", "bacterial denitrification method", and "cadmium reduction method") for delta15N- and delta18)O-NO3(-) determination. Isotopic data can provide evidence for the presence of dominant NO3(-) sources. However, quantification, including uncertainty assessment, is lacking when multiple NO3(-) sources are present. Moreover, fractionation processes are often ignored, but may largely constrain the accuracy of NO3(-) source identification. These problems can be overcome if (1) NO3(-) isotopic data are combined with co-migrating discriminators of NO3(-) sources (e.g. (11)B), which are not affected by transformation processes, (2) contributions of different NO3(-) sources can be quantified via linear mixing models (e.g. SIAR), and (3) precise, accurate and high throughput isotope analytical techniques become available.

The aim of this study was to monitor long-term temporal and spatial groundwater NO(3) (-) removal efficiencies in different riparian zones via a limited number of sampling wells. Groundwater NO(3) (-) concentrations were measured fortnightly or monthly over a period of two years using transects of ground water sampling wells. Depending on the level of the NO(3) (-) load (up to 120mgNL(-1) at the input side of the riparian zone a distance of 10 to 30m was needed to remove NO(3) (-) from the groundwater below 11.3mgNL(-1). Considering all seasons, the mixed vegetation and grass riparian site succeeded to remove groundwater NO(3) (-) efficiently (92-100% within a distance of 30m. The forested riparian zone removed 72-90% of the total NO(3) (-) input within a distance of 30m. Evidence emerged that NO(3) (-) could also be removed actively at depths up to 2m, due to the presence of organically enriched layers of alluvial deposits or roots. Our four dimensional approach (three dimensional space and time), in combination with a limited number of sampling wells, was shown to be a useful monitoring tool to assess the variability of NO(3) (-) removal in riparian zones.

Combination of a partial nitritation process and an anaerobic ammonium oxidation process for the treatment of sludge reject water has some general cost-efficient advantages compared to nitrification-denitrification. The integrated process features two-stage autotrophic conversion of ammonium via nitrite to dinitrogen gas with lower demand for oxygen and no external carbon requirement. A nitrifying membrane-assisted bioreactor (MBR) for the treatment of sludge reject water was operated under continuous aeration at low dissolved oxygen (DO) concentrations with the purpose of generating nitrite accumulation. Microfiltration was applied to allow a high sludge retention time (SRT), resulting in a stable partial nitritation process. During start-up of the MBR, oxygen-limited conditions were induced by increasing the ammonium loading rate and decreasing the oxygen transfer. At a loading rate of 0.9 kg N m(-3) d(-1) and an oxygen concentration below 0.1 mg DO L(-1), conversion to nitrite was close to 50% of the incoming ammonium, thereby yielding an optimal effluent within the stoichiometric requirements for subsequent anaerobic ammonium oxidation. A mathematical model for ammonium oxidation to nitrite and nitrite oxidation to nitrate was developed to describe the oxygen-limited partial nitritation process within the MBR. The model was calibrated with in situ determinations of kinetic parameters for microbial growth, reflecting the intrinsic characteristics of the ammonium oxidizing growth system at limited oxygen availability and high sludge age. The oxygen transfer coefficient (K(L)a) and the ammonium-loading rate were shown to be the appropriate operational variables to describe the experimental data accurately. The validated model was used for further steady state simulation under different operational conditions of hydraulic retention time (HRT), K(L)a, temperature and SRT, with the intention to support optimized process design. Simulation results indicated that stable nitrite production from sludge reject water was feasible with this process even at a relatively low temperature of 20 degrees C with HRT down to 0.25 days., (Copyright 2004 Wiley Periodicals, Inc.)