Improved Modelling of Ammonia by Using Manure Transport Data

Accurate representation of ammonia emission patterns from agriculture in chemistry transport models (CTMs) is important for the evaluation and prediction of particulate matter episodes. The temporal variability of ammonia emissions from manure application is currently not well represented in CTMs. In this study we examine the use of Flemish manure transportation data to model the temporal variability in ammonia emissions from manure application and assess the impact on the LOTOS-EUROS model performance for ammonia and secondary inorganic aerosols (SIA). Manure transport data reflect national regulations as well as meteorological conditions influencing temporal manure application patterns. We used manure transport data from Flanders (Belgium) as a proxy to derive the emission variability of emissions from manure application. The temporal variability for livestock housing and mineral fertilizer is improved based on Skjoth et al. (2011). With the improved emission variabilities air quality simulations for north-western Europe for the period 2007–2011 were performed with the CTM LOTOS-EUROS at 7 × 7 km2 resolution. Model performance was evaluated using two-weekly passive sampler data from 20 locations in Flanders. Model performance for ammonia improved by using meteorologically dependent temporal variability for ammonia, mainly by a better representation of the spring maximum. The improved performance is reflected in a smaller bias and 15–20% higher temporal correlation for all stations. Both improvements in temporal variability (livestock housing/fertilizer, and manure application) are important to increase the agreement between model and measurements. The impact on model performance for secondary inorganic aerosols (SIA) is negligible. Although the use of manure transport data as proxy for emissions from manure application comes with quite large uncertainties and simplifications, the developments provide a good starting point to improve representation of temporal variability of this source.