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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]

Examines the relationship of nitrogen emissions to specific components of swine production systems. Accurate ammonia emission factors appropriate for the regional climate geography and production systems; Relation of ammonia emissions to lagoon ammonium concentration, acidity, temperature and wind turbulence; Nitrogen input and output in the production system.

Ammonium (NH4+) inhibits the microbial oxidation of methane (CH4), and gives rise to the formation of nitrous oxide (N2O) in aerobic soils. However, the NH4+ concentration in the soil water is not necessarily proportional to the total NH4+ concentration inthe soil, due to cation exchange or fixation phenomena. It was foundthat introducing the concept of ion exchange or fixation in kinetic models on CH4 consumption and N2O formation canincrease our knowledge on these processes in soils. A fit of these kinetic models with experimental data showed a standard deviation of slightly more than 10%. The effects of NH4+ on CH4 oxidation and N2O formation can only be compared in soils with the same cation exchange capacity (CEC). [ABSTRACT FROM AUTHOR]