Important conclusions on a country's greenhouse gas emissions can be derived from the official submission of emission data to the United Nations Framework Convention on Climate Change (UNFCCC) as part of the national reporting obligations of the developed countries of the world (as listed in Annex I to the Kyoto Protocol). These data are available via UNFCCC's web pages, together with background information. Not only are they prepared in a standardized manner for all countries (based on IPCC guidelines: IPCC, 2006) but they also represent the emissions to which a country is officially committed. According to this information, soil N2O is generally not of very great importance to the overall greenhouse gas emissions of a country. As an example, in the European Union, about 4 per cent of total greenhouse gas emissions derive from this source. However, in a country where agriculture is the dominant industry, for example New Zealand, N2O is much more important.
Most countries reporting emissions of soil N2O apply the default IPCC methodology (IPCC, 1996, 2006), as described above, which has a very large associated uncertainty range. This uncertainty also affects the overall uncertainty of greenhouse gas inventories. A number of studies (Rypdal and Winiwarter, 2001; Winiwarter and Rypdal, 2001; Monni et al, 2004; Ramirez et al, 2008) demonstrate that for national inventories of different developed countries, soil N2O is the single most important contributor to overall uncertainty. The extent of its contribution varies and may depend on the relative importance of agriculture in the economy of a country as well as on some subjective choices of parameters by the national experts. Despite such variations, uncertainty associated with soil N2O emissions dominates the overall inventory uncertainty in all countries investigated. Therefore, if one wishes to improve the overall reliability of a national greenhouse gas balance, the first improvement to be made is to the estimate of soil nitrous oxide emissions. For reasons related to the mathematical procedure involved in error propagation, the dominance of N2O emissions in the overall uncertainty applies only to those assessed for a specific year. For emission trends (the difference between two years) the influence of soil N2O is considerably smaller, as long as the quoted uncertainty in EFs applies similarly to both years of the trend.
The large uncertainty associated with N2O emissions from soils leads to repercussions with respect to interpretations of emissions reported, not only regarding reported annual emissions, but also regarding trends. Because of the way in which the EF for direct soil N2O emissions is defined (i.e. as a percentage of the N applied to the land), a decreasing trend in reported N2O emissions derives not from observations but from the decrease in fertilizer application, which in reality may or may not have led to actual decreases of nitrous oxide release. In general, the relationship between emissions and nitrogen availability is well established (see Stehfest and Bouwman, 2006, and citations therein, and Chapters 5 and 6). Nevertheless, potentially changes in weather patterns as well as changes in the practice of N application may lead to deviations from this relationship that are very difficult to trace. Consequently, a global assessment of N2O emissions is an essential tool for validation, where the source term can be estimated fully independently on the basis of the atmospheric concentration increase (Kroeze et al, 1999; Crutzen et al, 2008).
While it may be difficult, from such an independent 'top-down' estimate, to identify an individual (national) source and to conclude what useful abatement measures might be employed, at least the uncertainty margin is considerably smaller than for estimates based on plot-scale measurements. Approaches like the one of IPCC, which are aimed at source attribution and assigning responsibility, will have to continue to rely on source-based assessments. Thus alternative approaches need to be identified which make it possible to draw better connections between the release processes and the input parameters. One promising option is biophysical modelling, but more still needs to be done before models are fully adequate for the purpose (see Chapter 5).
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