• Energy Research

Existing descriptions of bi-directional ammonia (NH 3 ) land–atmosphere exchange incorporate temperature and moisture controls, and are beginning to be used in regional chemical transport models. However, such models have typically applied simpler emission factors to upscale the main NH 3 emission terms. While this approach has successfully simulated the main spatial patterns on local to global scales, it fails to address the environment- and climate-dependence of emissions. To handle these issues, we outline the basis for a new modelling paradigm where both NH 3 emissions and deposition are calculated online according to diurnal, seasonal and spatial differences in meteorology. We show how measurements reveal a strong, but complex pattern of climatic dependence, which is increasingly being characterized using ground-based NH 3 monitoring and satellite observations, while advances in process-based modelling are illustrated for agricultural and natural sources, including a global application for seabird colonies. A future architecture for NH 3 emission–deposition modelling is proposed that integrates the spatio-temporal interactions, and provides the necessary foundation to assess the consequences of climate change. Based on available measurements, a first empirical estimate suggests that 5°C warming would increase emissions by 42 per cent (28–67%). Together with increased anthropogenic activity, global NH 3 emissions may increase from 65 (45–85) Tg N in 2008 to reach 132 (89–179) Tg by 2100.

In this study, we analysed datasets of N2O emission factors (EFs) from 21 separate studies carried out on arable and managed grasslands across the UK and Ireland over the past 20 years. A total of 641 separate events were collated from 40 experimental field sites. Individual EFs ranged over an order of magnitude (0-12% of applied N) for each fertiliser type, following a log-normal distribution in all cases. Our study shows that a Bayesian approach can provide a robust statistical method that is capable of performing uncertainty analysis on log-normal distributed data in a more defensible manner than conventional statistical methods allow. This method allowed for a national scale comparison of EFs between the most commonly applied mineral fertilisers based solely on previously published data (UK and Ireland in this case). The study shows that ammonium nitrate (AN) and Calcium ammonium nitrate (CAN) are the largest emitting fertiliser types by mass across the British Isles (temperate climate zone), with EFs of 1.1 (1.0-1.2) % and 1.0 (0.7-1.3) % for all recorded events, respectively; however, emissions from AN applications were significantly lower for applications to arable fields (0.6%) than to grasslands (1.3%). EFs associated with urea (CO(NH₂)₂) were significantly lower than AN for grasslands with an EF of 0.6 (0.5-0.7) %, but slightly higher for arable fields with an EF of 0.7 (0.4-1.4) %. The study highlights the potential effectiveness of microbial inhibitors at reducing emissions of N2O from mineral fertilisers, with Dicyandiamide (DCD) treated AN reducing emissions by approximately 28% and urea treated with either DCD or N-(n)-butyl) thiophosphorictriamide (NBTP) reducing emissions by approximately 40%. Although limited by a relatively small sample size (n = 11), urea treated with both DCD and NBPT appeared to have the lowest EF of all treatments at 0.13 (0.08-0.21) %, highlighting the potential to significantly reduce N2O emissions at regional scales if applied instead of conventional nitrogen fertilisers.

Seabird colonies represent a significant source of atmospheric ammonia (NH3) in remote maritime systems, producing a source of nitrogen that may encourage plant growth, alter terrestrial plant community composition and affect the surrounding marine ecosystem. To investigate seabird NH3 emissions on a global scale, we developed a contemporary seabird database including a total seabird population of 261 million breeding pairs. We used this in conjunction with a bioenergetics model to estimate the mass of nitrogen excreted by all seabirds at each breeding colony. The results combined with the findings of mid-latitude field studies of volatilization rates estimate the global distribution of NH3 emissions from seabird colonies on an annual basis. The largest uncertainty in our emission estimate concerns the potential temperature dependence of NH3 emission. To investigate this we calculated and compared temperature independent emission estimates with a maximum feasible temperature dependent emission, based on the thermodynamic dissociation and solubility equilibria. Using the temperature independent approach, we estimate global NH3 emissions from seabird colonies at 404 Gg NH3 per year. By comparison, since most seabirds are located in relatively cold circumpolar locations, the thermodynamically dependent estimate is 136 Gg NH3 per year. Actual global emissions are expected to be within these bounds, as other factors, such as non-linear interactions with water availability and surface infiltration, moderate the theoretical temperature response. Combining sources of error from temperature (±49%), seabird population estimates (±36%), variation in diet composition (±23%) and non-breeder attendance (±13%), gives a mid estimate with an overall uncertainty range of NH3 emission from seabird colonies of 270 [97–442] Gg NH3 per year. These emissions are environmentally relevant as they primarily occur as “hot-spots” in otherwise pristine environments with low anthropogenic emissions.

Land use change has impacts upon many natural processes, and is one of the key measures of anthropogenic disturbance on ecosystems. Agricultural land covers 70% of Great Britain's (GB) land surface and annually undergoes disturbance and change through farming practices such as crop rotation, ploughing and the planting and subsequent logging of forestry. It is important to quantify how much of GB's agricultural land undergoes such changes and what those changes are at an annual temporal resolution. Integrated Administration and Control System (IACS) data give annual snapshots of agricultural land use at the field level, allowing for high resolution spatiotemporal land use change studies at the national scale. Crucially, not only do the data allow for simple net change studies (total area change of a land use, in a specific areal unit) but also for gross change calculations (summation of all changes to and from a land use), meaning that both gains and losses to and from each land use category can be defined. In this study we analysed IACS data for GB from 2005 to 2013, and quantified gross change for over 90% of the agricultural area in GB for the first time. It was found that gross change totalled 63,500 km2 in GB compared to 20,600 km2 of net change, i.e. the real year-on-year change is, on average, three times larger than net change. This detailed information on nature of land use change allows for increased accuracy in modelling the impact of land use change on ecosystem processes and is directly applicable across EU member states, where collection of such survey data is a requirement. The modelled carbon flux associated with gross land use change was at times >100 Gg C y-1 larger than that based on net land use change for some land use transitions.

The aim of this study is to illustrate the importance of farm scale heterogeneity on nitrogen (N) losses in agricultural landscapes. Results are exemplified with a chain of N models calculating farm-N balances and distributing the N-surplus to N-losses (volatilisation, denitrification, leaching) and soil-N accumulation/release in a Danish landscape. Possible non-linearities in upscaling are assessed by comparing average model results based on (i) individual farm level calculations and (ii) averaged inputs at landscape level. Effects of the non-linearities that appear when scaling up from farm to landscape are demonstrated. Especially in relation to ammonia losses the non-linearity between livestock density and N-loss is significant (p > 0.999), with around 20-30% difference compared to a scaling procedure not taking this non-linearity into account. A significant effect of farm type on soil N accumulation (p > 0.95) was also identified and needs to be included when modelling landscape level N-fluxes and greenhouse gas emissions.