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. [Display omitted] •Size and shape of aggregation zones effect location and magnitude of emissions.•If the aggregation zones are too small, this may result in false emission “hot spots”.•If the aggregation zones are too large, detail may be lost and emissions smoothed out. Capsule: The aggregation level, i.e. the size and shape of the aggregation zones of point data, has a significant effect on the location and local magnitude of emissions in spatial inventories where ag