Uncertainties and implications of applying aggregated data for spatial modelling of atmospheric ammonia emissions

Ammonia emissions vary greatly at a local scale, and effects (eutrophication, acidification) occur primarily close to sources. Therefore it is important that spatially distributed emission estimates are located as accurately as possible. The main source of ammonia emissions is agriculture, and therefore agricultural survey statistics are the most important input data to an ammonia emission inventory alongside per activity estimates of emission potential. In the UK, agricultural statistics are collected at farm level, but are aggregated to parish level, NUTS-3 level or regular grid resolution for distribution to users. In this study, the Modifiable Areal Unit Problem (MAUP), associated with such amalgamation, is investigated in the context of assessing the spatial distribution of ammonia sources for emission inventories. England was used as a test area to study the effects of the MAUP. Agricultural survey data at farm level (point data) were obtained under license and amalgamated to different areal units or zones: regular 1-km, 5-km, 10-km grids and parish level, before they were imported into the emission model. The results of using the survey data at different levels of amalgamation were assessed to estimate the effects of the MAUP on the spatial inventory. The analysis showed that the size and shape of aggregation zones applied to the farm-level agricultural statistics strongly affect the location of the emissions estimated by the model. If the zones are too small, this may result in false emission “hot spots”, i.e., artificially high emission values that are in reality not confined to the zone to which they are allocated. Conversely, if the zones are too large, detail may be lost and emissions smoothed out, which may give a false impression of the spatial patterns and magnitude of emissions in those zones. The results of the study indicate that the MAUP has a significant effect on the location and local magnitude of emissions in spatial inventories where amalgamated, zonal data are used

Modelling the spatial distribution of agricultural ammonia emissions in the UK.

Accurate models of the spatial distribution of ammonia (NH3) emissions are an essential input to models of atmospheric transport and deposition. This is especially important when resulting deposition maps are used to calculate patterns of critical loads exceedance or to determine suitable abatement measures. A new methodology has been developed to model the distribution of agricultural ammonia emissions and is applied here for the UK. The model employs a specific spatial weighted redistribution of NH3 emission sources onto suitable landcover types at a 1-km grid level. Key input data to the model are agricultural census data, a satellite-based landcover map and estimates of NH3 emission source strength. The model provides more realistic spatial NH3 emission estimates than previous models, especially for semi-natural/natural areas by relocating emission sources from extensively used upland areas to the more intensively farmed lowland areas within each parish. At present the model results are summarised as maps at a 5-km grid resolution to reduce uncertainty in the spatial location of NH3 sources. Compared with coarser resolution estimates this also provides a more accurate link to critical load exceedances. The more accurate redistribution also reduces the apparent critical loads exceedance on upland areas. Results are presented and compared for 1988 and 1996. These show broadly similar patterns between the years, although substantial local changes have occurred, particularly for intensive livestock farming. The model has been used to generate initial spatially resolved abatement scenarios and provides a general tool for locating NH3 emissions that could be applied to other regions

Assessment of the magnitude of ammonia emissions in the United Kingdom

Estimates of ammonia emission in the U.K. have been critically reviewed with the aim of establishing the magnitude and uncertainty of each of the sources. European studies are also reviewed, with the U.K. providing a useful case study to highlight the uncertainties common to all ammonia emission inventories.This analysis of the emission factors and their application to U.K. sources supports an emission of 450 (231-715) Gg NH3 yr-1. Agricultural activities are confirmed as the major source, providing 406 (215-630) Gg NH3 yr-1 (90% of the total), and therefore dominate uncertainties. Non-agricultural sources include sewage, pets, horses, humans, combustion and wild animals, though these contribute only 44 (16-85) Gg yr-1. Cattle represent the largest single uncertainty, accounting for 245 (119-389) Gg yr-1.The major uncertainties for cattle derive from estimation of the amount of nitrogen (N) excreted, the % N volatilized from land spreading of wastes, and the % N volatilized from stored farm-yard manure. Similar relative uncertainties apply to each of sheep, pigs and poultry, as well as fertilized crops, though these are quantitatively less important.Accounting for regional differences in livestock demography, emission of 347, 63 and 40 Gg yr-1 are estimated for England & Wales, Scotland, and Northern Ireland, respectively. Though very uncertain, the total is in good agreement with estimates required to balance the U.K. atmospheric NHx budget

Modelling seasonal dynamics from temporal variation in agricultural practices in the UK Ammonia Emission Inventory

Most ammonia (NH3) emission inventories have been calculated on an annual basis and do not take into account the seasonal variability of emissions that occur as a consequence of climate and agricultural practices that change throughout the year. When used as input to atmospheric transport models to simulate concentration fields, these models therefore fail to capture seasonal variations in ammonia concentration and dry and wet deposition. In this study, seasonal NH3 emissions from agriculture were modelled on a monthly basis for the year 2000, by incorporating temporal aspects of farming practice. These monthly emissions were then spatially distributed using the AENEID model (Atmospheric Emissions for National Environmental Impacts Determination). The monthly model took the temporal variation in the magnitude of the ammonia emissions, as well as the fine scale (1-km) spatial variation of those temporal changes into account to provide improved outputs at 5-km resolution. The resulting NH3 emission maps showed a strong seasonal emission pattern, with the highest emissions during springtime (March and April) and the lowest emissions during summer (May to July). This emission pattern was mainly influenced by whether cattle were outside grazing or housed and by the application of manures and fertilizers to the land. When the modelled emissions were compared with measured NH3 concentrations, the comparison suggested that the modelled emission trend corresponds fairly well with the seasonal trend in the measurements. The remaining discrepancies point to the need to develop functional parametrisations of the interactions with climatic seasonal variation

GeosMeta: a prototype metadata and provenance service

1. School of GeoSciences 2. EPCCGeosMeta is the name of a new service being developed for use in the University's School of GeoSciences, in a collaboration with EPCC. The objective of GeosMeta is to gather metadata during a project: • to assist the research activity by improving management, discovery and re-use of data and computation • to record provenance of files • to contribute to end-project archiving into data centres

The importance of modelling fine-scale spatial variability and local deposition in the assessment of ammonia emissions

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