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Comment on egusphere-2025-2826

Abstract

Abstract. Ammonia (NH3) is a major component of reactive nitrogen and a key alkaline gas in the atmosphere. With an increase in fertiliser application due to the intensification of agricultural livestock and industrial production, large amounts of NH3 are emitted into the atmosphere at an increasing rate, posing a threat to both human and ecosystem health and contributing to climate change. Although many studies have examined the ammonia budget, large uncertainties remain in its emissions, distribution, and deposition. Top-down, or inversion methods, using measurement data to estimate emissions, can help reduce these uncertainties. In this study, we have applied local-scale Bayesian inversions using ground-based measurements and the LOTOS-EUROS air quality model, with high-resolution emission inventories as prior input. To improve robustness, we estimated observational error by combining reported measurement uncertainties with residual errors and optimized Chi-Square statistics. We applied this approach to the island of Schiermonnikoog in the Netherlands, where GVE (grazing livestock units) on Schiermonnikoog decreased from 639 to 541, with a particularly notable reduction in dairy cattle, corresponding to a 23 % reduction in ammonia emissions between 2019 and 2022. Our inversion captured a similar trend, estimating a 51 % decrease, with associated uncertainty derived from the posterior error covariance. Moreover, we developed a method to assess the usefulness of individual observations and propose that adding a single high-quality continuous measurement in a strategically chosen location can significantly enhance the inversion performance. This strengthens the observational constraint and enhances the system’s ability to resolve temporal variations in emissions.

Authors 1

  1. Leiden University

    Affiliation as printed

    Institute of Environmental Sciences, Universiteit Leiden, Leiden, the Netherlands

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