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Multiannual satellite measurements of tropospheric NO2 columns are used for evaluation of CO2 emission changes in China in the period from 1996 to 2008. Indirect top-down annual estimates of CO2 emissions are derived from the satellite NO2 column measurements by means of a simple inverse modeling procedure involving simulations performed with the CHIMERE mesoscale chemistry- transport model and the CO2-to-NOx emission ratios from the Emission Database for Global Atmospheric Research (EDGAR) global anthropogenic emission inventory and Regional Emission Inventory in Asia (REAS). Exponential trends in the normalized time series of annual emissions are evaluated separately for the periods from 1996 to 2001 and from 2001 to 2008. The results indicate that the both periods manifest strong positive trends in the CO2 emissions, and that the trend in the second period was significantly larger than the trend in the first period. Specifically, the trends in the first and second periods are best estimated to be in the range from 3.7 to 8.3 and from 11.0 to 13.2% per year, respectively, taking into account statistical uncertainties and differences between the CO2-to-NOx emission ratios from the EDGAR and REAS inventories. Comparison of our indirect top-down estimates of the CO2 emission changes with the corresponding bottom-up estimates provided by the EDGAR (version 4.2) and Global Carbon Project (GCP) glomal emission inventories reveals that while acceleration of the CO2 emission growth in the considered period is a common feature of both kinds of estimates, nonlinearity in the CO2 emission changes may be strongly exaggerated in the global emission inventories. Specifically, the atmospheric NO2 observations do not confirm the existence of a sharp bend in the emission inventory data time series in the period from 2000 to 2002. A significant quantitative difference is revealed between the bottom-up and indirect top-down estimates of the CO2 emission trend in the period from 1996 to 2001 (specifically, the trend was not positive according to the global emission inventories, but is strongly positive in our estimates). These results confirm the findings of earlier studies that indicated probable large uncertainties in the energy production and other activity data for China from international energy statistics used as the input information in the global emission inventories. For the period from 2001 to 2008, some quantitative differences between the different kinds of estimates are found to be in the range of possible systematic uncertainties associated with our estimation method. In general, satellite measurements of tropospheric NO2 are shown to be a useful source of information on CO2 sources collocated with sources of nitrogen oxides; the corresponding potential of these measurements should be exploited further in future studies. [ABSTRACT FROM AUTHOR]

National and European legislation over the past 20 years, and the modernisation or removal of industrial sources, have significantly reduced European ozone precursor emissions. This study quantifies observed and modelled European ozone annual and seasonal linear trends from 158 harmonised rural background monitoring stations over a constant time period of a decade (1996-2005). Mean ozone concentrations are investigated, in addition to the ozone 5th percentiles as a measure of the baseline or background conditions, and the 95th percentiles that are representative of the peak concentration levels. This study aims to characterise and quantify surface European ozone concentrations and trends and assess the impact of the changing anthropogenic emission tracers on the observed and modelled trends. Significant (p < 0.1) positive annual trends in ozone mean, 5th and 95th percentiles are observed at 54 %, 52% and 45% of sites respectively (85 sites, 82 sites and 71 sites). Spatially, sites in Central and Northwestern Europe tend to display positive annual ozone trends in mean, 5th and 95th percentiles. Significant negative annual trends in ozone mean 5th and 95th percentiles are observed at 11 %, 12% and 12% of sites respectively (18 sites, 19 sites and 19 sites) which tend to be located in the eastern and south-western extremities of Europe. European-averaged annual trends have been calculated from the 158 sites in this study. Overall there is a net positive annual trend in observed ozone mean (0.16 ± 0.02 ppbv yr-1 (2σ error)), 5th (0.13 ± 0.02 ppbv yr-1) and 95th (0.16 ± 0.03 ppbv yr-1) percentiles, representative of positive trends in mean, baseline and peak ozone. Assessing the sensitivity of the derived overall trends to the constituent years shows that the European heatwave year of 2003 has significant positive influence and 1998 the converse effect; demonstrating the masking effect of inter-annual variability on decadal based ozone trends. The European scale 3-D CTM CHIMERE was used to simulate hourly O3 concentrations for the period 1996-2005. Comparisons between the 158 observed ozone trends to those equivalent sites extracted from regional simulations by CHIMERE better match the observed increasing annual ozone (predominantly in Central and Northwestern Europe) for 5th percentiles, than for mean or 95th ozone percentiles. The European-averaged annual ozone trend in CHIMERE 5th percentiles (0.13 ± 0.01 ppbv yr-1) matches the corresponding observed trend extremely well, but displays a negative trend for the 95th percentile (-0.03 ± 0.02 ppbv yr-1) where a positive ozone trend is observed. Inspection of the EU-averaged monthly means of ozone shows that the CHIMERE model is overestimating the summer month O3 levels. In comparison to trends in EMEP emissions inventories, with the exception of Austria-Hungary, we find anthropogenic NOx and VOC reductions do not appear to have a substantial effect on observed annual mean O3 trends in the rest of Europe. [ABSTRACT FROM AUTHOR]

Numerous wildfires provoked by an unprecedented intensive heat wave caused continuous episodes of extreme air pollution in several Russian cities and densely pullulated regions, including the Moscow megacity region. This paper analyzes the chemical evolution of the atmosphere over the Moscow region during the 2010 heat wave by integrating available ground based and satellite measurements with results of mesoscale modeling. The state-of-the-art CHIMERE CTM is used, which is modified to take into account air pollutant emissions from wildfires and the shielding effect of smoke aerosols. The wild fire emissions are derived from satellite measurements of the fire radiative power and are optimized by assimilating data of ground measurements of carbon monoxide (CO) and particulate matter (PM10) into the model. It is demonstrated that the optimized simulations reproduce independent observations, which were withheld during the optimisation procedure, quite adequately (specifically, the correlation coefficient of daily time series of CO and PM10 exceeds 0.8) and that inclusion of the fire emissions into the model significantly improves its performance. The results of the analysis show that wildfires were a principal factor causing the observed air pollution episodes associated with the extremely high level of daily mean CO and PM10 concentrations (up to 10mgm-3 and 700 µgm-3 in the averages over available monitoring sites, respectively) in the Moscow region, although accumulation of anthropogenic pollution was also favoured by a stagnant meteorological situation. In contrast, diagnostic model runs indicate that ozone concentrations could reach very high values even without fire emissions which provide "fuel" for ozone formation, but, at the same time, inhibit it as a result of absorption and scattering of solar radiation by smoke aerosols. The analysis of MOPITT CO measurements and of corresponding simulations indicates that the observed episodes of extreme air pollution in Moscow were only a part of a very strong perturbation of the atmospheric composition, caused by wildfires, over the largest part of European Russia. It is estimated that 2010 fires in the European part of Russia emitted ~9.7 TgCO, that is more than 85% of the total annual anthropogenic CO emissions in this region. About 30% of total CO fire emissions in European Russia are identified as emissions from peat fires. [ABSTRACT FROM AUTHOR]

Hazardous impact of air pollutant emissions from megacities on atmospheric composition on regional and global scales is currently an important issue in atmospheric research. However, the quantification of emissions and related effects is frequently a difficult task, especially in the case of developing countries, due to the lack of reliable data and information. This study examines possibilities to retrieve multi-annual NOx emissions changes in megacity regions from satellite measurements of nitrogen dioxide and to quantify them in terms of linear and nonlinear trends. By combining the retrievals of the GOME and SCIAMACHY satellite instrument data with simulations performed by the CHIMERE chemistry transport model, we obtain the time series of NOx emission estimates for the 12 largest urban agglomerations in Europe and the Middle East in the period from 1996 to 2008. We employ then a novel method allowing estimation of a nonlinear trend in a noisy time series of an observed variable. The method is based on the probabilistic approach and the use of artificial neural networks; it does not involve any quantitative a priori assumptions. As a result, statistically significant nonlinearities in the estimated NOx emission trends are detected in 5 megacities (Bagdad, Madrid, Milan, Moscow and Paris). Statistically significant upward linear trends are detected in Istanbul and Tehran, while downward linear trends are revealed in Berlin, London and the Ruhr agglomeration. The presence of nonlinearities in NOx emission changes in Milan, Paris and Madrid is confirmed by comparison of simulated NOx concentrations with independent air quality monitoring data. A good quantitative agreement between the linear trends in the simulated and measured near surface NOx concentrations is found in London. [ABSTRACT FROM AUTHOR]

The recent important developments in satellite measurements of the composition of the lower atmosphere open the challenging perspective to use such measurements as independent information on sources and sinks of atmospheric pollutants. This study explores the possibility to improve estimates of gridded NOx emissions used in a continental scale chemistry transport model (CTM), CHIMERE, by employing measurements performed by the GOME and SCIAMACHY instruments. We set-up an original inverse modelling scheme that not only enables a computationally efficient optimisation of the spatial distribution of seasonally averaged NOx emissions (during summertime), but also allows estimating uncertainties in input data and a priori emissions. The key features of our method are (i) replacement of the CTM by a set of empirical models describing the relationships between tropospheric NO2 columns and NOx emissions with sufficient accuracy, (ii) combination of satellite data for tropospheric NO2 columns with ground based measurements of near surface NO2 concentrations, and (iii) evaluation of uncertainties in a posteriori emissions by means of a special Bayesian Monte-Carlo experiment which is based on random sampling of errors of both NO2 columns and emission rates. We have estimated the uncertainty in a priori emissions based on the EMEP emission inventory to be about 1.9 (in terms of geometric standard deviation) and found the uncertainty in a posteriori emissions obtained from our inverse modelling scheme to be significantly lower (about 1.4). It is found also that a priori NOx emission estimates are probable to be persistently biased in many regions of Western Europe, and that the use of a posteriori emissions in the CTM improves the agreement between the modelled and measured data. [ABSTRACT FROM AUTHOR]

The recent important developments in satellite measurements of the composition of the lower atmosphere open the challenging perspective to use such measurements as independent information on sources and sinks of atmospheric pollutants. This study explores the possibility to improve estimates of gridded NOx emissions used in a continental scale chemistry transport model (CTM), CHIMERE, by employing measurements performed by the GOME and SCIAMACHY instruments. We set-up an original inverse modelling scheme that not only enables a computationally efficient optimisation of the spatial distribution of seasonally averaged NOx emissions (during summertime), but also allows estimating uncertainties of input data and a priori emissions. The key features of our method are (i) replacement of the CTM by a set of empirical models describing the relationships between tropospheric NO2 columns and NOx emissions with sufficient accuracy, (ii) combination of satellite data for tropospheric NO2 columns with ground based measurements of near surface NO2 concentrations, and (iii) evaluation of uncertainties of the a posteriori emissions by means of a special Bayesian Monte-Carlo experiment which is based on random sampling of errors of both NO2 columns and emission rates. We have estimated the uncertainty in a priori emissions based on the EMEP emission inventory to be about 1.9 (in terms of the geometric standard deviation) and found the uncertainty in a posteriori emissions obtained from our inverse modelling scheme to be significantly lower (about 1.4). It is found also that a priori NOx emission estimates are probable to be persistently biased in many regions of Western Europe, and that the use of a posteriori emissions in the CTM improves the agreement between the modelled and measured data. [ABSTRACT FROM AUTHOR]

We present the results of a first comparison of the tropospheric NO2 column amounts derived from the measurements of the Global Ozone Monitoring Experiment (GOME) with the simulated data from a European scale chemistry transport model (CTM) which is distinct from existing global scale CTMs in higher horizontal resolution and more detailed description of the boundary layer processes and emissions. We employ, on the one hand, the newly developed extended version of the CHIMERE CTM, which covers both Western and Eastern Europe, and, on the other hand, the most recent version (Version 2) of GOME measurement based data-products, developed at the University of Bremen. We evaluate our model with the data from ground based monitoring of ozone and verify that it has a sufficiently high level of performance, which is expected for a state-of-the-art continental scale CTM. The major focus of the study is on a systematic statistical analysis and a comparison of spatial variability of the tropospheric NO2 columns simulated with CHIMERE and derived from GOME measurements. The analysis is performed separately for Western and Eastern Europe using the data for summer months of 1997 and 2001. In this way, we obtain useful information on the nature and magnitudes of uncertainties of spatial distributions of the considered data. Specifically, for Western Europe, it is found that the uncertainties of NO2 columns from GOME and CHIMERE are predominantly of the multiplicative character, and that the mean relative random (multiplicative) errors of the GOME measurement derived and simulated data averaged over the summer seasons considered do not exceed 23% and 32%, respectively. The mean absolute (additive) errors of both kinds of the data are estimated to be less than 3 x 1014 mol/cm². In Eastern Europe, the uncertainties have more complex character, and the separation between their multiplicative and additive parts is not sufficiently unambiguous. It is found, however, that the total random errors of NO2 columns from both GOME and CHIMERE over Eastern Europe are not, on the average, larger than the errors of the NO2 columns with similar magnitudes over Western Europe. [ABSTRACT FROM AUTHOR]