Evaluation of a new inference method for estimating ammonia volatilisation from multiple agronomic plots

Abstract

Tropospheric ammonia (NH3) is a threat to the environment and human health and is mainly emitted by agriculture. Ammonia volatilisation following application of nitrogen in the field accounts for more than 40% of the total NH3 emissions in France. This represents a major loss of nitrogen use efficiency which needs to be reduced by appropriate agricultural practices. In this study we evaluate a novel method to infer NH3 volatilisation from small agronomic plots consisting of multiple treatments with repetition. The method is based on the combination of a set of NH3 diffusion sensors exposed for durations of 3h to 1 week and a short-range atmospheric dispersion model, used to retrieve the emissions from each plot. The method is evaluated by mimicking NH3 emissions from an ensemble of nine plots with a resistance analogue-compensation point-surface exchange scheme over a yearly meteorological database separated into 28-day periods. A multifactorial simulation scheme is used to test the effects of sensor numbers and heights, plot dimensions, source strengths, and background concentrations on the quality of the inference method. We further demonstrate by theoretical considerations in the case of an isolated plot that inferring emissions with diffusion sensors integrating over daily periods will always lead to underestimations due to correlations between emissions and atmospheric transfer. We evaluated these underestimations as-8%±6% of the emissions for a typical western European climate. For multiple plots, we find that this method would lead to median underestimations of-16% with an interquartile [-8-22%] for two treatments differing by a factor of up to 20 and a control treatment with no emissions. We further evaluate the methodology for varying background concentrations and NH3 emissions patterns and demonstrate the low sensitivity of the method to these factors. The method was also tested in a real case and proved to provide sound evaluations of NH3 losses from surface applied and incorporated slurry. We hence showed that this novel method should be robust and suitable for estimating NH3 emissions from agronomic plots. We believe that the method could be further improved by using Bayesian inference and inferring surface concentrations rather than surface fluxes. Validating against controlled source is also a remaining challenge.