Daniel Neumann

Marine litter ensemble transport simulations in the southern North Sea

The drift of marine litter in the southern North Sea was simulated with the offline Lagrangian transport model PELETS-2D. Assuming different source regions, passive tracer particles were released every 28 h within a nine-year period. Based on pre-calculated hourly wind and ocean current data, drift simulations were carried out forward and backward in time with and without the assumption of extra wind forces influencing particle movement. Due to strong variability of currents, backward simulations did not allow for the identification of particular source regions influencing given monitoring sites. Neither accumulation regions at open sea could be identified by forward simulations. A seasonal signal, however, could be identified in the number of tracer particles that reached the coastal areas. Both particle drift velocity and variability of drift paths further increased when an extra wind drift was assumed.

A multi-criteria evaluation system for marine litter pollution based on statistical analyses of OSPAR beach litter monitoring time series

During the last decades, marine pollution with anthropogenic litter has become a worldwide major environmental concern. Standardized monitoring of litter since 2001 on 78 beaches selected within the framework of the Convention for the Protection of the Marine Environment of the North-East Atlantic (OSPAR) has been used to identify temporal trends of marine litter. Based on statistical analyses of this dataset a two-part multi-criteria evaluation system for beach litter pollution of the North-East Atlantic and the North Sea is proposed. Canonical correlation analyses, linear regression analyses, and non-parametric analyses of variance were used to identify different temporal trends. A classification of beaches was derived from cluster analyses and served to define different states of beach quality according to abundances of 17 input variables. The evaluation system is easily applicable and relies on the above-mentioned classification and on significant temporal trends implied by significant rank correlations.

Modeling emissions for three-dimensional atmospheric chemistry transport models

ABSTRACT Poor air quality is still a threat for human health in many parts of the world. In order to assess measures for emission reductions and improved air quality, three-dimensional atmospheric chemistry transport modeling systems are used in numerous research institutions and public authorities. These models need accurate emission data in appropriate spatial and temporal resolution as input. This paper reviews the most widely used emission inventories on global and regional scales and looks into the methods used to make the inventory data model ready. Shortcomings of using standard temporal profiles for each emission sector are discussed, and new methods to improve the spatiotemporal distribution of the emissions are presented. These methods are often neither top-down nor bottom-up approaches but can be seen as hybrid methods that use detailed information about the emission process to derive spatially varying temporal emission profiles. These profiles are subsequently used to distribute bulk emissions such as national totals on appropriate grids. The wide area of natural emissions is also summarized, and the calculation methods are described. Almost all types of natural emissions depend on meteorological information, which is why they are highly variable in time and space and frequently calculated within the chemistry transport models themselves. The paper closes with an outlook for new ways to improve model ready emission data, for example, by using external databases about road traffic flow or satellite data to determine actual land use or leaf area. In a world where emission patterns change rapidly, it seems appropriate to use new types of statistical and observational data to create detailed emission data sets and keep emission inventories up-to-date. Implications: Emission data are probably the most important input for chemistry transport model (CTM) systems. They need to be provided in high spatial and temporal resolution and on a grid that is in agreement with the CTM grid. Simple methods to distribute the emissions in time and space need to be replaced by sophisticated emission models in order to improve the CTM results. New methods, e.g., for ammonia emissions, provide grid cell–dependent temporal profiles. In the future, large data fields from traffic observations or satellite observations could be used for more detailed emission data.

Effect of Sea Salt Emissions on Anthropogenic Air Pollution and Nitrogen Deposition in Northwestern Europe

The North Sea region is characterised by several anthropogenic activities such as shipping, agriculture, industry and tourism. These activities go along with emissions of air pollutants such as NO X , NH3, and SO2 leading to the formation of HNO3, H2SO4, and particulate matter. Gaseous bases and acids (mainly HNO3, H2SO4 and NH3) tend to form new particles or to condense on existing ones. Meteorological conditions and size distribution of existing particles affect partitioning of these substances between gas and particle phase and between particle modes. In the marine troposphere, sea salt particles (mainly Cl–, Na+ and \({\text{SO}}_{4}^{2 - }\)) account for a considerable amount of fine and coarse particles providing surface for condensation of above mentioned substances. The presence of sea salt may also affect N deposition because dry deposition velocities of gaseous substances and different particle modes vary considerably. In the presented study, the effect of sea salt emissions on atmospheric air pollution in the North Sea region was analysed by the means of the Community Multiscale Air Quality (CMAQ) Model. We simulated on a \(24 \times 24\;\text{km}^{2}\) grid including the North and Baltic Sea. It was found, that the presence of sea salt increases coarse mode \({\text{NH}}_{4}^{ + }\) and \({\text{NO}}_{3}^{ - }\) concentrations considerably while fine mode concentrations are decreased. This leads to increased total N deposition in coastal regions. At the same time, the deposition distant to the shore on the land as well as into the ocean decreases. However, this study shows that on spatial average only about 5 % of N deposition into the North Sea is caused by sea salt particles. Locally, the effect of sea salt on N deposition is partly higher. Therefore, sea salt emissions in regional air quality models are important for predicting the partitioning of anthropogenic pollutants between gas and particle phase and their deposition patterns correctly.