J. van Aardenne

Emissions from international shipping: 1. The last 50 years

[1] Seagoing ships emit exhaust gases and particles into the marine boundary layer and significantly contribute to the total budget of anthropogenic emissions. We present an emission inventory for international shipping for the past five decades to be used in global modeling studies with detailed tropospheric chemistry. The inventory is a bottom-up analysis using fuel consumption and fleet numbers for the total civilian and military fleet including auxiliary engines at the end of 2001. Trend estimates for fuel mass, CO 2 , NO x , and other emissions for the time between 1950 and 2001 have been calculated using ship number statistics and average engine statistics. Our estimate for total fuel consumption and global emissions for the year 2001 is similar to previous activity-based studies. However, compared to earlier studies, a detailed speciation of nonmethane hydrocarbons (NMHCs) and particulate matter is given, and carbon monoxide emissions are calculated explicitly. Our results suggest a fuel consumption of approximately 280 million metric tons (Mt) for the year 2001 and 64.5 Mt in 1950. This corresponds to 187 (5.4) Tg CO 2 (NO x ) in 1950, and 813 (21.4) Tg CO 2 (NO x ) in 2001. From 1970 to 2001 the world-merchant fleet increased rapidly in terms of ship numbers, with a corresponding increase in total fuel consumption. The fuel consumption estimates are compared against historical marine bunker fuel statistics, and our emission estimates are related to emission budgets of other transport modes. Global ship emissions are distributed geographically according to global vessel traffic densities of the AMVER (Automated Mutual-assistance Vessel Rescue system) data set for the year 2000. This work also sets the basis to develop future emission scenarios based on average-fleet emission indices in part 2 of this study.

Inverse modeling of European CH4 emissions 2001-2006

European CH4 emissions are estimated for the period 2001-2006 using a four-dimensional variational (4DVAR) inverse modeling system, based on the atmospheric zoom model TM5. Continuous observations are used from various European monitoring stations, complemented by European and global flask samples from the NOAA/ESRL network. The available observations mainly provide information on the emissions from northwest Europe (NWE), including the UK, Ireland, the BENELUX countries, France and Germany. The inverse modeling estimates for the total anthropogenic emissions from NWE are 21% higher compared to the EDGARv4.0 emission inventory and 40% higher than values reported to U.N. Framework Convention on Climate Change. Assuming overall uncertainties on the order of 30% for both bottom-up and top-down estimates, all three estimates can be still considered to be consistent with each other. However, the uncertainties in the uncertainty estimates prevent us from verifying (or falsifying) the bottom-up inventories in a strict sense. Sensitivity studies show some dependence of the derived spatial emission patterns on the set of atmospheric monitoring stations used, but the total emissions for the NWE countries appear to be relatively robust. While the standard inversions include a priori information on the spatial and temporal emission patterns from bottom-up inventories, a further sensitivity inversion without this a priori information results in very similar NWE country totals, demonstrating that the available observations provide significant constraints on the emissions from the NWE countries independent from bottom-up inventories.

Assessing the effect of marine isoprene and ship emissions on ozone, using modelling and measurements from the South Atlantic Ocean

Environmental context. Air over the remote Southern Atlantic Ocean is amongst the cleanest anywhere on the planet. Yet in summer a large-scale natural phytoplankton bloom emits numerous natural reactive compounds into the overlying air. The productive waters also support a large squid fishing fleet, which emits significant amounts of NO and NO2. The combination of these natural and man-made emissions can efficiently produce ozone, an important atmospheric oxidant. Abstract. Ship-borne measurements have been made in air over the remote South Atlantic and Southern Oceans in January–March 2007. This cruise encountered a large-scale natural phytoplankton bloom emitting reactive hydrocarbons (e.g. isoprene); and a high seas squid fishing fleet emitting NOx (NO and NO2). Using an atmospheric chemistry box model constrained by in-situ measurements, it is shown that enhanced ozone production ensues from such juxtaposed marine biogenic and anthropogenic emissions. The relative impact of shipping and phytoplankton emissions on ozone was examined on a global scale using the EMAC model. Ozone in the marine boundary layer was found to be over ten times more sensitive to NOx emissions from ships, than to marine isoprene in the region south of 45°. Although marine isoprene emissions make little impact on the global ozone budget, co-located ship and phytoplankton emissions may explain the increasing ozone reported for the 40–60°S southern Atlantic region.

Using wind-direction-dependent differences between model calculations and field measurements as indicator for the inaccuracy of emission inventories

In forward air quality modelling, an emission inventory is used as input into an atmospheric dispersion model to calculate atmospheric concentrations of the pollutant. Differences between calculated concentrations and concentrations found by atmospheric measurements can be used as an indicator for the inaccuracy of the emission inventory used in the calculations. The problem with comparing calculated and observed concentrations is that it is not easy to pinpoint the emission inventory as a single cause for the differences. One of the reasons for this is that inaccuracies exist in the model, both in measurements and in the inventory. In this paper, we argue that when wind-direction-dependent differences at several measurement stations in different countries point towards a specific region, the emission estimate for that specific region is the likely cause for the differences between modelled and observed concentrations. We have applied this methodology to study the inaccuracies of a European SO2 emissions inventory for 1994, by plotting the calculated SO2 concentrations from a long term ozone simulation model with SO2 concentrations measured in the EMEP network. The results show that we were able to identify inaccuracies in the emission inventory for several areas within Europe. These areas include Sachsen/Brandenburg (Germany), Central England and the Western part of the Russian Federation. Although this type of analysis is accompanied with several limitations, it could provide the emission inventory community with a relatively simple technique to identify inaccuracies in the emission inventory.