Articles | Volume 10, issue 14
https://doi.org/10.5194/acp-10-6645-2010
https://doi.org/10.5194/acp-10-6645-2010
21 Jul 2010
 | 21 Jul 2010

Modeling the regional impact of ship emissions on NOx and ozone levels over the Eastern Atlantic and Western Europe using ship plume parameterization

P. Huszar, D. Cariolle, R. Paoli, T. Halenka, M. Belda, H. Schlager, J. Miksovsky, and P. Pisoft

Abstract. In general, regional and global chemistry transport models apply instantaneous mixing of emissions into the model's finest resolved scale. In case of a concentrated source, this could result in erroneous calculation of the evolution of both primary and secondary chemical species. Several studies discussed this issue in connection with emissions from ships and aircraft. In this study, we present an approach to deal with the non-linear effects during dispersion of NOx emissions from ships. It represents an adaptation of the original approach developed for aircraft NOx emissions, which uses an exhaust tracer to trace the amount of the emitted species in the plume and applies an effective reaction rate for the ozone production/destruction during the plume's dilution into the background air. In accordance with previous studies examining the impact of international shipping on the composition of the troposphere, we found that the contribution of ship induced surface NOx to the total reaches 90% over remote ocean and makes 10–30% near coastal regions. Due to ship emissions, surface ozone increases by up to 4–6 ppbv making 10% contribution to the surface ozone budget. When applying the ship plume parameterization, we show that the large scale NOx decreases and the ship NOx contribution is reduced by up to 20–25%. A similar decrease was found in the case of O3. The plume parameterization suppressed the ship induced ozone production by 15–30% over large areas of the studied region. To evaluate the presented parameterization, nitrogen monoxide measurements over the English Channel were compared with modeled values and it was found that after activating the parameterization the model accuracy increases.

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