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A new on-line analytical setup for [15.sub.N] measurements of total dissolved nitrogen (TDN) has been developed through the coupling of a high-temperature catalytic (Ce(IV)[O.sub.2]) oxidation furnace, a Cu reduction furnace, and an isotope ratio mass spectrometer. The detection limit for accurate [sigma] [sub.15]N measurements is 20 mg of N [L.sup.-1]. For N-containing compounds dissolved in water, a standard deviation on N concentration measurements of 0.2 mg of N [L.sup.-1], independent of N concentration, has been found. Reproducibility on [sigma] [sup.15]N increased with increasing N concentration, with standard deviations varying from 0.8 to 0.1%o in the range of 20-100 mg of N [L.sup.-1]. Salt matrixes, in which the N species might be dissolved, could influence the analysis capacity and continuity, mainly at concentrations above 0.1 M. To our knowledge, this system is the first successful on-line setup capable of performing routine [sigma] [sup.15]N and N concentration measurements of the TDN pool. Potential applications are large and are believed to result in an increased insight in N cycling and dissolved organic nitrogen behavior in terrestrial and aquatic ecosystems.

Following mineralization of N-rich crop residues, large amounts of mineral N can be released into the soil. Manipulating N mineralization of crop residues may be an option to reduce N[O.sub.3.sup.-] in soil. The potential to manipulate the N release from vegetable crop residues by using organic wastes was tested under field conditions. At the start of the experiment, cauliflower (Brassica oleracea L. van botrytis L.) residues (~73 Mg flesh matter [ha.sup.-1]) together with an immobilizer waste (~5 Mg C [ha.sup.-1] of straw, green waste compost, sawdust, or paper sludge) were incorporated into a silt loam soil. After 154 d, a remineralizing waste (~1 Mg C [ha.sup.-1] of vinasse or dairy sludge) was incorporated. During the field experiment, the mineral N content in the soil was measured at regular time intervals, and net N release, N[O.sub.3.sup.-] leaching, and denitrification were simulated using a N mineralization-immobilization model coupled to a N[O.sub.3.sup.-] leaching model. Straw, green waste compost, and sawdust were able to immobilize between 54 and 68% of the N released by the cauliflower residues and reduced N[O.sub.3.sup.-] leaching by 56 to 68%. Paper sludge released an extra amount of N due to its low C/N ratio. No consistent remineralization of N could be found in any of the treatments, probably due to an unsuitable composition of the remineralizer wastes. Manipulating N release of N-rich crop residues by using organic wastes may be a suitable method to reduce N[O.sub.3.sup.-] leaching; however, stimulating remineralization of immobilized N by the start of the following spring may not be easy to achieve. Abbreviations: CD, coefficient of determination; CRM, coefficient of residual mass.

Highlights: [•] Litter quality is a major determinant of decomposition. [•] Mixed-species litter can give a negative and positive additive effect. [•] Stand type effect was observed for the forest litter mixture only. [•] Nutrient depletion can occur in long-term in Eucalyptus plantation. [•] Final decay rate is also essential to explain the remaining mass. [Copyright &y& Elsevier]

Apportionment of nitrate (NO3-) sources in surface water and classification of monitoring locations according to NO3- polluting activities may help implementation of water quality control measures. In this study, we (i) evaluated a Bayesian isotopic mixing model (stable isotope analysis in R [SIAR]) for NO3- source apportionment using 2 yr of δ15N-NO3- and δ18O-NO3- data from 29 locations within river basins in Flanders (Belgium) and five expert-defined NO3- polluting activities, (ii) used the NO3- source contributions as input to an unsupervised learning algorithm (k-means clustering) to reclassify sampling locations into NO3- polluting activities, and (iii) assessed if a decision tree model of physicochemical data could retrieve the isotope-based and expert-defined classifications. Based on the SIAR and d11B results, manure/sewage was identified as a major NO3- source, whereas soil N, fertilizer NO3-, and NH4+ in fertilizer and rain were intermediate sources and NO3- in precipitation was a minor source. The k-means clustering algorithm allowed classification of NO3- polluting activities that corresponded well to the expert-defined classifications. A decision tree model of physicochemical parameters allowed us to correctly classify 50 to 100% of the sampling locations as compared with the k-means clustering approach. We suggest that NO3- polluting activities can be identified via clustering of NO3- source contributions from samples representing an entire river basin. Classification of future monitoring locations into these classes could use decision tree models based on physicochemical data. The latter approach holds a substantial degree of uncertainty but provides more inherent information for dedicated abatement strategies than monitoring of NO3- concentrations alone. [ABSTRACT FROM AUTHOR]

To identify different NO3 − 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 δ15N–NO3 − were between 8.0 and 19.4‰, while annual mean δ18O–NO3 − were given by 4.5–30.7‰. SIAR was used to estimate the proportional contribution of five potential NO3 − sources (NO3 − in precipitation, NO3 − fertilizer, NH4 + in fertilizer and rain, soil N, and manure and sewage). SIAR showed that “manure and sewage” contributed highest, “soil N”, “NO3 − fertilizer” and “NH4 + in fertilizer and rain” contributed middle, and “NO3 − in precipitation” contributed least. The SIAR output can be considered as a “fingerprint” for the NO3 − source contributions. However, the wide range of isotope values observed in surface water and of the NO3 − sources limit its applicability. [Copyright &y& Elsevier]