Spatially explicit fate factors of waterborne nitrogen emissions at the global scale

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Marine eutrophication impacts due to waterborne nitrogen (N) emissions may vary significantly with their type and location. The environmental fate of dissolved inorganic nitrogen (DIN) forms is essential to understand the impacts they may trigger in receiving coastal waters. Current life cycle impact assessment (LCIA) methods apply fate factors (FFs) with limited specificity of DIN emission routes, and often lack spatial differentiation and global applicability. This paper describes a newly developed method to estimate spatially explicit FFs for marine eutrophication at a global scale and river basin resolution. The FF modelling work includes DIN removal processes in both inland (soil and river) and marine compartments. Model input parameters are the removal coefficients extracted from the Global NEWS 2-DIN model and residence time of receiving coastal waters. The resulting FFs express the persistence of the fraction of the original DIN emission in the receiving coastal large marine ecosystems (LMEs). The method further discriminates three DIN emission routes, i.e., diffuse emission from soils, and direct point emissions to freshwater or marine water. Based on modelling of individual river basins, regionally aggregated FFs are calculated as emission-weighted averages. Among 5772 river basins of the world, the calculated FFs show 5 orders of magnitude variation for the soil-related emission route, 3 for the river-related, and 2 for emissions to marine water. Spatial aggregation of the FFs at the continental level decreases this variation to 1 order of magnitude or less for all routes. Coastal water residence time was found to show inconsistency and scarcity of literature sources. Improvement of data quality for this parameter is suggested. With the proposed method and factors, spatial information of DIN emissions can be used to improve the environmental relevance and the discriminatory power of the assessment of marine eutrophication impacts in a geographically differentiated characterization model at a global scale.