Global Lagrangian atmospheric dispersion model

The Global Lagrangian Atmospheric Dispersion Model (GLADIM) is described. GLADIM is based on the global trajectory model, which had been developed earlier and uses fields of weather parameters from different atmospheric reanalysis centers for calculations of trajectories of air mass that include trace gases. GLADIM includes the parameterization of turbulent diffusion and allows the forward calculation of concentrations of atmospheric tracers at nodes of a global regular grid when a source is specified. Thus, GLADIM can be used for the forward simulation of pollutant propagation (volcanic ash, radionuclides, and so on). Working in the reverse direction, GLADIM allows the detection of remote sources that mainly contribute to the tracer concentration at an observation point. This property of Lagrangian models is widely used for data analysis and the reverse modeling of emission sources of a pollutant specified. In this work we describe the model and some results of its validation through a comparison with results of a similar model and observation data

Top-down assessment of the Asian carbon budget since the mid 1990s

Increasing atmospheric carbon dioxide (CO2) is the principal driver of anthropogenic climate change. Asia is an important region for the global carbon budget, with 4 of the world’s 10 largest national emitters of CO2. Using an ensemble of seven atmospheric inverse systems, we estimated land biosphere fluxes (natural, land-use change and fires) based on atmospheric observations of CO2 concentration. The Asian land biosphere was a net sink of −0.46 (−0.70–0.24) PgC per year (median and range) for 1996–2012 and was mostly located in East Asia, while in South and Southeast Asia the land biosphere was close to carbon neutral. In East Asia, the annual CO2 sink increased between 1996–2001 and 2008–2012 by 0.56 (0.30–0.81) PgC, accounting for ∼35% of the increase in the global land biosphere sink. Uncertainty in the fossil fuel emissions contributes significantly (32%) to the uncertainty in land biosphere sink change

Global Lagrangian atmospheric dispersion model

The Global Lagrangian Atmospheric Dispersion Model (GLADIM) is described. GLADIM is based on the global trajectory model, which had been developed earlier and uses fields of weather parameters from different atmospheric reanalysis centers for calculations of trajectories of air mass that include trace gases. GLADIM includes the parameterization of turbulent diffusion and allows the forward calculation of concentrations of atmospheric tracers at nodes of a global regular grid when a source is specified. Thus, GLADIM can be used for the forward simulation of pollutant propagation (volcanic ash, radionuclides, and so on). Working in the reverse direction, GLADIM allows the detection of remote sources that mainly contribute to the tracer concentration at an observation point. This property of Lagrangian models is widely used for data analysis and the reverse modeling of emission sources of a pollutant specified. In this work we describe the model and some results of its validation through a comparison with results of a similar model and observation data

Estimation of fossil-fuel CO2 emissions using satellite measurements of "proxy" species

International audienceFossil-fuel (FF) burning releases carbon dioxide (CO 2) together with many other chemical species, some of which, such as nitrogen dioxide (NO 2) and carbon monoxide (CO), are routinely monitored from space. This study examines the feasibility of estimation of FF CO 2 emissions from large industrial regions by using NO 2 and CO column retrievals from satellite measurements in combination with simulations by a mesoscale chemistry transport model (CTM). To this end, an inverse modeling method is developed that allows estimating FF CO 2 emissions from different sectors of the economy, as well as the total CO 2 emissions, in a given region. The key steps of the method are (1) inferring "top-down" estimates of the regional budget of anthropogenic NO x and CO emissions from satellite measurements of proxy species (NO 2 and CO in the case considered) without using formal a priori constraints on these budgets, (2) the application of emission factors (the NO x-to-CO 2 and CO-to-CO 2 emission ratios in each sector) that relate FF CO 2 emissions to the proxy species emissions and are evaluated by using data of "bottom-up" emission inventories , and (3) cross-validation and optimal combination of the estimates of CO 2 emission budgets derived from measurements of the different proxy species. Uncertainties in the top-down estimates of the NO x and CO emissions are evaluated and systematic differences between the measured and simulated data are taken into account by using original robust techniques validated with synthetic data. To examine the potential of the method, it was applied to the budget of emissions for a western European region including 12 countries by using NO 2 and CO column amounts retrieved from, respectively, the OMI and IASI satellite measurements and simulated by the CHIMERE mesoscale CTM, along with the emission conversion factors based on the EDGAR v4.2 emission inventory. The analysis was focused on evaluation of the uncertainty levels for the top-down NO x and CO emission estimates and "hybrid" estimates (that is, those based on both atmospheric measurements of a given proxy species and respective bottom-up emission inventory data) of FF CO 2 emissions, as well as on examining consistency between the FF NO 2 emission estimates derived from measurements of the different proxy species. It is found that NO 2 measurements can provide much stronger constraints to the total annual FF CO 2 emissions in the study region than CO measurements , the accuracy of the NO 2-measurement-based CO 2 emission estimate being mostly limited by the uncertainty in the top-down NO x emission estimate. Nonetheless, CO measurements are also found to be useful as they provide additional constraints to CO 2 emissions and enable evaluation of the hybrid FF CO 2 emission estimates obtained from NO 2 measurements. Our most reliable estimate for the total annual FF CO 2 emissions in the study region in 2008 (2.71 ± 0.30 Pg CO 2) is found to be about 11 and 5 % lower than the respective estimates based on the EDGAR v.4.2 (3.03 Pg CO 2) and CDIAC (2.86 Pg CO 2) emission inventories , with the difference between our estimate and the CDIAC inventory data not being statistically significant. In general, the results of this study indicate that the proposed method has the potential to become a useful tool for identification of pos-Published by Copernicus Publications on behalf of the European Geosciences Union. 13510 I. B. Konovalov et al.: Estimation of fossil-fuel CO 2 emissions sible biases and/or inconsistencies in the bottom-up emission inventory data regarding CO 2 , NO x , and CO emissions from fossil-fuel burning in different regions of the world

Pulsed plasma-chemical modification of SiO2 nanopowder by ZnxOy nanoparticles

This work presents the results of pulsed plasma-chemical modification of silicon dioxide nanopowder with zinc oxide nanoparticles (ZnO@SiO2). The obtained ZnO@SiO2 powders were characterized by transmission electron microscopy (TEM) and X-ray phase analysis. The size of the synthesized particles was in the range of 20-100nm. The photocatalytic characteristics of ZnO@SiO2 were studied. When exposed to ultraviolet radiation, the methylene blue (MB) decomposes efficiently. Two samples characterized by the content of silicon tetrachloride in the initial mixture were synthesized. The band gap estimated from the absorption spectra calculated from the diffuse reflectance spectra for these samples was 2.4eV and 2.95eV for indirect transitions and 3.03eV and 3.24eV for direct allowed transitions

Column-averaged CO2 concentrations in the subarctic from GOSAT retrievals and NIES transport model simulations

AbstractThe distribution of atmospheric carbon dioxide (CO2) in the subarctic was investigated using the National Institute for Environmental Studies (NIES) three-dimensional transport model (TM) and retrievals from the Greenhouse gases Observing SATellite (GOSAT). Column-averaged dry air mole fractions of subarctic atmospheric CO2 (XCO2) from the NIES TM for four flux combinations were analyzed. Two flux datasets were optimized using only surface observations and two others were optimized using both surface and GOSAT Level 2 data. Two inverse modeling approaches using GOSAT data were compared. In the basic approach adopted in the GOSAT Level 4 product, the GOSAT observations are aggregated into monthly means over 5° × 5° grids. In the alternative method, the model–observation misfit is estimated for each observation separately. The XCO2 values simulated with optimized fluxes were validated against Total Carbon Column Observing Network (TCCON) ground-based high-resolution Fourier Transform Spectrometer (FTS) measurements. Optimized fluxes were applied to study XCO2 seasonal variability over the period 2009–2010 in the Arctic and subarctic regions. The impact on CO2 levels of emissions from enhancement of biospheric respiration induced by the high temperature and strong wildfires occurring in the summer of 2010 was analyzed. Use of GOSAT data has a substantial impact on estimates of the level of CO2 interanual variability

Top-down assessment of the Asian carbon budget since the mid 1990s

Increasing atmospheric carbon dioxide (CO2) is the principal driver of anthropogenic climate change. Asia is an important region for the global carbon budget, with 4 of the world’s 10 largest national emitters of CO2. Using an ensemble of seven atmospheric inverse systems, we estimated land biosphere fluxes (natural, land-use change and fires) based on atmospheric observations of CO2 concentration. The Asian land biosphere was a net sink of −0.46 (−0.70–0.24) PgC per year (median and range) for 1996–2012 and was mostly located in East Asia, while in South and Southeast Asia the land biosphere was close to carbon neutral. In East Asia, the annual CO2 sink increased between 1996–2001 and 2008–2012 by 0.56 (0.30–0.81) PgC, accounting for ∼35% of the increase in the global land biosphere sink. Uncertainty in the fossil fuel emissions contributes significantly (32%) to the uncertainty in land biosphere sink change