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This study sought to identify, by means of several analytical methods (GC-MS, HPLC-DAD, CE-DAD, FTIR, and NMR), 4-bromo-2,5-dimethoxyphenethylamine (2C-B), which was found in two sets of tablets obtained from the Swiss black market. Unequivocal identification of 2C-B was only achieved by a combination of mass spectrometric and NMR analysis. Quantitation of 2C-B was performed by HPLC-DAD and CE-DAD. The amounts of 2C-B found in the tablets (3-8 mg) were in the range of the minimum quantity required to induce the effects characteristic of this drug

Our ability to predict future climate change relies on our understanding of current and future CO2 fluxes, particularly on a regional scale (100–1000 km). CO2 regional sources and sinks are still poorly understood. Inverse transport modeling, a method often used to quantify these fluxes, relies on atmospheric CO2 measurements. One of the main challenges for the transport models used in the inversions is to properly reproduce CO2 vertical gradients between the boundary layer and the free troposphere, as these gradients impact on the partitioning of the calculated fluxes between the different model regions. Vertical CO2 profiles are very well suited to assess the performances of the models. In this paper, we conduct a comparison between observed and modeled CO2 profiles recorded during two CAATER campaigns that occurred in May 2001 and October 2002 over Western Europe, as described in a companion paper. We test different combinations between a global transport model (LMDZt), a mesoscale transport model (CHIMERE), and different sets of biospheric fluxes, all chosen with a diurnal cycle (CASA, SiB2 and ORCHIDEE). The vertical profile comparison shows that: 1) in most cases the influence of the biospheric flux is small but sometimes not negligible, ORCHIDEE giving the best results in the present study; 2) LMDZt is most of the time too diffuse, as it simulates a too high boundary layer height; 3) CHIMERE better reproduces the observed gradients between the boundary layer and the free troposphere, but is sometimes too variable and gives rise to incoherent structures. We conclude there is a need for more vertical profiles to conduct further studies to improve the parameterization of vertical transport in the models used for CO2 flux inversions. Furthermore, we use a modeling method to quantify CO2 fluxes at the regional scale from a chosen observing point, coupling influence functions from the transport model LMDZt (that works quite well at the synoptic scale) with information on the space-time distribution of fluxes. This modeling method is compared to a dual tracer method (the so-called Radon method) for a case study on 25 May 2001 during which simultaneous well-correlated in situ CO2 and Radon 222 measurements have been collected. Both methods give a similar result: a flux within the Radon 222 method uncertainty (35%), that is an atmospheric CO2 sink of −4.2 to −4.4 gC m−2 day−1. We have estimated the uncertainty of the modeling method to be at least 33% on average, and even more for specific individual events. This method allows the determination of the area that contributed to the CO2 observed concentration. In our case, the observation point located at 1700 m a.s.l. in the north of France, is influenced by an area of 1500×700 km2 that covers the Benelux region, part of Germany and western Poland. Furthermore, this method allows deconvolution between the different contributing fluxes. In this case study, the biospheric sink contributes 73% of the total flux, fossil fuel emissions for 27%, the oceanic flux being negligible. However, the uncertainties of the influence function method need to be better assessed. This could be possible by applying it to other cases where the calculated fluxes can be checked independently, for example at tall towers where simultaneous CO2 and Radon 222 measurements can be conducted. The use of optimized fluxes (from atmospheric inversions) and of mesoscale models for atmospheric transport may also significantly reduce the uncertainties.

We present a performance assessment of the European Integrated Carbon Observing System (ICOS) atmospheric network for constraining European biogenic CO2 fluxes (hereafter net ecosystem exchange, NEE). The performance of the network is assessed in terms of uncertainty in the fluxes, using a state-of-the-art mesoscale variational atmospheric inversion system assimilating hourly averages of atmospheric data to solve for NEE at 6 h and 0.5° resolution. The performance of the ICOS atmospheric network is also assessed in terms of uncertainty reduction compared to typical uncertainties in the flux estimates from ecosystem models, which are used as prior information by the inversion. The uncertainty in inverted fluxes is computed for two typical periods representative of northern summer and winter conditions in July and in December 2007, respectively. These computations are based on a observing system simulation experiment (OSSE) framework. We analyzed the uncertainty in a 2-week-mean NEE as a function of the spatial scale with a focus on the model native grid scale (0.5°), the country scale and the European scale (including western Russia and Turkey). Several network configurations, going from 23 to 66 sites, and different configurations of the prior uncertainties and atmospheric model transport errors are tested in order to assess and compare the improvements that can be expected in the future from the extension of the network, from improved prior information or transport models. Assimilating data from 23 sites (a network comparable to present-day capability) with errors estimated from the present prior information and transport models, the uncertainty reduction on a 2-week-mean NEE should range between 20 and 50 % for 0.5° resolution grid cells in the best sampled area encompassing eastern France and western Germany. At the European scale, the prior uncertainty in a 2-week-mean NEE is reduced by 50 % (66 %), down to ~ 43 Tg C month−1 (26 Tg C month−1) in July (December). Using a larger network of 66 stations, the prior uncertainty of NEE is reduced by the inversion by 64 % (down to ~ 33 Tg C month−1) in July and by 79 % (down to ~ 15 Tg C month−1) in December. When the results are integrated over the well-observed western European domain, the uncertainty reduction shows no seasonal variability. The effect of decreasing the correlation length of the prior uncertainty, or of reducing the transport model errors compared to their present configuration (when conducting real-data inversion cases) can be larger than that of the extension of the measurement network in areas where the 23 station observation network is the densest. We show that with a configuration of the ICOS atmospheric network containing 66 sites that can be expected on the long-term, the uncertainties in a 2-week-mean NEE will be reduced by up to 50–80 % for countries like Finland, Germany, France and Spain, which could significantly improvement (and at least a high complementarity to) our knowledge of NEE derived from biomass and soil carbon inventories at multi-annual scales.

We present a performance assessment of the European Integrated Carbon Observing System (ICOS) atmospheric network for constraining European biogenic CO2 fluxes (hereafter net ecosystem exchange, NEE). The performance of the network is assessed in terms of uncertainty in the fluxes, using a state-of-the-art mesoscale variational atmospheric inversion system assimilating hourly averages of atmospheric data to solve for NEE at 6 h and 0.5° resolution. The performance of the ICOS atmospheric network is also assessed in terms of uncertainty reduction compared to typical uncertainties in the flux estimates from ecosystem models, which are used as prior information by the inversion. The uncertainty in inverted fluxes is computed for two typical periods representative of northern summer and winter conditions in July and in December 2007, respectively. These computations are based on a observing system simulation experiment (OSSE) framework. We analyzed the uncertainty in a 2-week-mean NEE as a function of the spatial scale with a focus on the model native grid scale (0.5°), the country scale and the European scale (including western Russia and Turkey). Several network configurations, going from 23 to 66 sites, and different configurations of the prior uncertainties and atmospheric model transport errors are tested in order to assess and compare the improvements that can be expected in the future from the extension of the network, from improved prior information or transport models. Assimilating data from 23 sites (a network comparable to present-day capability) with errors estimated from the present prior information and transport models, the uncertainty reduction on a 2-week-mean NEE should range between 20 and 50 % for 0.5° resolution grid cells in the best sampled area encompassing eastern France and western Germany. At the European scale, the prior uncertainty in a 2-week-mean NEE is reduced by 50 % (66 %), down to ~ 43 Tg C month−1 (26 Tg C month−1) in July (December). Using a larger network of 66 stations, the prior uncertainty of NEE is reduced by the inversion by 64 % (down to ~ 33 Tg C month−1) in July and by 79 % (down to ~ 15 Tg C month−1) in December. When the results are integrated over the well-observed western European domain, the uncertainty reduction shows no seasonal variability. The effect of decreasing the correlation length of the prior uncertainty, or of reducing the transport model errors compared to their present configuration (when conducting real-data inversion cases) can be larger than that of the extension of the measurement network in areas where the 23 station observation network is the densest. We show that with a configuration of the ICOS atmospheric network containing 66 sites that can be expected on the long-term, the uncertainties in a 2-week-mean NEE will be reduced by up to 50–80 % for countries like Finland, Germany, France and Spain, which could significantly improvement (and at least a high complementarity to) our knowledge of NEE derived from biomass and soil carbon inventories at multi-annual scales.

In this issue, Ramonet et al. revealed a positive trend in European, atmospheric CO2 concentrations relative to a marine, North Atlantic reference baseline, for the years 2001–2006. The observed build up mainly occurred during the cold season where it reaches a 0.8 ppm yr-1 at low-altitude stations to a 0.3 ppm yr-1 at mid-altitude stations. We explore the cause of this build-up using the mesoscale model CHIMERE. We first model the observed trends, using interannually varying fluxes and transport, then suppress the interannual variability in fluxes or aspects of transport to elucidate the cause. The run with no interannual variability in fluxes still matches observed trends suggesting that transport is the major cause. Separate runs varying either boundary layer height or winds show that changes in boundary layer height explain the trends at low-altitude stations within the continents while changes in wind regimes drive changes elsewhere.DOI: 10.1111/j.1600-0889.2009.00443.x

International audience; To cite this article: C. Aulagnier, P. Rayner, P. Ciais, R. Vautard, L. Rivier & M. Ramonet (2010) Is the recent build-up of atmospheric CO 2 over Europe reproduced by models. Part 2: an overview with the atmospheric mesoscale transport model CHIMERE, A B S T R A C T In this issue, Ramonet et al. revealed a positive trend in European, atmospheric CO 2 concentrations relative to a marine, North Atlantic reference baseline, for the years 2001-2006. The observed build up mainly occurred during the cold season where it reaches a 0.8 ppm yr −1 at low-altitude stations to a 0.3 ppm yr −1 at mid-altitude stations. We explore the cause of this build-up using the mesoscale model CHIMERE. We first model the observed trends, using interannually varying fluxes and transport, then suppress the interannual variability in fluxes or aspects of transport to elucidate the cause. The run with no interannual variability in fluxes still matches observed trends suggesting that transport is the major cause. Separate runs varying either boundary layer height or winds show that changes in boundary layer height explain the trends at low-altitude stations within the continents while changes in wind regimes drive changes elsewhere.

The ability to reliably estimate CO2 fluxes from current in situ atmospheric CO2 measurements and future satellite CO2 measurements is dependent on transport model performance at synoptic and shorter timescales. The TransCom continuous experiment was designed to evaluate the performance of forward transport model simulations at hourly, daily, and synoptic timescales, and we focus on the latter two in this paper. Twenty-five transport models or model variants submitted hourly time series of nine predetermined tracers (seven for CO2) at 280 locations. We extracted synoptic-scale variability from daily averaged CO2 time series using a digital filter and analyzed the results by comparing them to atmospheric measurements at 35 locations. The correlations between modeled and observed synoptic CO2 variabilities were almost always largest with zero time lag and statistically significant for most models and most locations. Generally, the model results using diurnally varying land fluxes were closer to the observations compared to those obtained using monthly mean or daily average fluxes, and winter was often better simulated than summer. Model results at higher spatial resolution compared better with observations, mostly because these models were able to sample closer to the measurement site location. The amplitude and correlation of model-data variability is strongly model and season dependent. Overall similarity in modeled synoptic CO2 variability suggests that the first-order transport mechanisms are fairly well parameterized in the models, and no clear distinction was found between the meteorological analyses in capturing the synoptic-scale dynamics. [References: 47]

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Methylenedioxy-N-methylamphetamine (MDMA, "Ecstasy") and other related phenylethylamines are nowadays used extensively in Western Switzerland at dance clubs and raves. There is a widely held belief among teenagers and misusers that ecstasy is safe. In the last years however, an increasing number of reports of MDMA-related deaths has been reported. Acute clinical toxicity problems following MDMA ingestion include hyperthermia, convulsions and arrhythmias. There is also growing concern that these phenylethylamines are neurotoxic and cause long-term damage to serotonineric nerve terminals in animal brains. Qualitative analyses by GC-MS of street samples of ecstasy showed that only a part of them contain MDMA or related phenylethylamines (MDA, MDEA, MBDB and 2C-B). Most of them were mixed with caffeine and an excipient (sugars or polyols [e.g. mannitol]). Amphetamine cut with caffeine and other drugs (e.g. testosterone), stimulants (e.g. pseudoephedrine) and other drugs unrelated to stimulants and phenylethylamines (e.g. LSD, chloroquine, vasodilators) were also detected. Quantitative determinations performed by HPLC-DAD or EC-DAD reveal huge fluctuations in the amount of active substance(s) per tablet. MDMA and related compounds display unique psychoactive properties, acting as a stimulant and inducing feelings of empathy. The effects of MDMA intake are very likely the results of the large release of serotonin (5-HT) in the synaptic cleft, of the inhibition of the re-uptake inactivation of 5-HT and of the inhibition of a key-enzyme involved in the biosynthesis of 5-HT. Forensic investigations performed at our institute showed significant blood levels of MDMA, MDEA and MDA in samples drawn from people suspected of driving under the influence of psychoactive drugs. Up to now, no death could be attributed to MDMA intoxication only because our analyses always revealed the additional presence of toxic amounts of other psychoactive drugs (e.g. opiates, cocaine). Our study shows that because of the variable composition of ecstasy tablets, unpredictable types and amounts of drugs may be taken by MDMA misusers. Moreover, there is considerable concern that traffic accidents may be caused by MDMA-abusers. MDMA intake could result in severe intoxication and even death, especially when combined with other types of drugs.

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