scholarly journals The CarboCount CH sites: characterization of a dense greenhouse gas observation network

2015 ◽  
Vol 15 (9) ◽  
pp. 12911-12956 ◽  
Author(s):  
B. Oney ◽  
S. Henne ◽  
N. Gruber ◽  
M. Leuenberger ◽  
I. Bamberger ◽  
...  
Keyword(s):  
Carbon Flux ◽  
Temporal Dynamics ◽  
Transport Model ◽  
Atmospheric Transport ◽  
Regional Scale ◽  
Observation Network ◽  
Simulated Surface ◽  
Swiss Plateau ◽  
The Impact ◽  
Atmospheric Transport Model

Abstract. We describe a new rural network of four densely placed (< 100 km apart), continuous atmospheric carbon (CO2, CH4, and CO) measurement sites in north-central Switzerland and analyze their suitability for regional-scale (~ 100 to 500 km) carbon flux studies. We characterize each site by analyzing surrounding land cover, observed local meteorology, and sensitivity to surface fluxes, as simulated with the Lagrangian particle dispersion model FLEXPART-COSMO. The Beromünster measurements are made on a tall tower (212 m) located on a gentle hill. At Beromünster, regional CO2 signals (measurement minus background) vary diurnally from −4 to +4 ppmv on average, and are simulated to come from nearly the entire Swiss Plateau, where 50% of surface influence is simulated to be within 130 to 260 km distance. The Früebüel site measurements are made 4 m above ground on the flank of a gently sloping mountain. Nearby (< 50 km) pasture and forest fluxes exert the most simulated surface influence, except during convective summertime days when the site is mainly influenced by the eastern Swiss Plateau, which results in summertime regional CO2 signals varying diurnally from −5 to +12 ppmv and elevated summer daytime CH4 signals (+30 ppbv above other sites). The Gimmiz site measurements are made on a small tower (32 m) in flat terrain. Here, strong summertime regional signals (−5 to +60 ppmv CO2) stem from large, nearby (< 50 km) crop and anthropogenic fluxes of the Seeland region, except during warm or windy days when simulated surface influence is of regional scale (< 250 km). The Lägern-Hochwacht measurements are made on a small tower (32 m) on top of the steep Lägern crest, where simulated surface influence is typically of regional scale (130 to 300 km) causing summertime regional signals to vary from −5 to +8 ppmv CO2. Here, considerable anthropogenic influence from the nearby industrialized region near Zurich cause the average wintertime regional CO2 signals to be 5 ppmv above the regional signals simultaneously measured at Früebüel site. We find that the suitability of the datasets from our current observation network for regional carbon budgeting studies largely depends on the ability of the high-resolution (2 km) atmospheric transport model to correctly capture the temporal dynamics of the stratification of the lower atmosphere at the different sites. The current version of the atmospheric transport model captures these dynamics well, but it clearly reaches its limits at the sites in steep topography, and at the sites that generally remain in the surface layer. Trace gas transport and inverse modeling studies will be necessary to determine the impact of these limitations on our ability to derive reliable regional-scale carbon flux estimates in the complex Swiss landscape.

scholarly journals The CarboCount CH sites: characterization of a dense greenhouse gas observation network

2015 ◽  
Vol 15 (19) ◽  
pp. 11147-11164 ◽  
Author(s):  
B. Oney ◽  
S. Henne ◽  
N. Gruber ◽  
M. Leuenberger ◽  
I. Bamberger ◽  
...  
Keyword(s):  
Carbon Flux ◽  
Transport Model ◽  
Atmospheric Transport ◽  
Dispersion Model ◽  
Regional Scale ◽  
Particle Dispersion ◽  
Observation Network ◽  
Simulated Surface ◽  
Swiss Plateau ◽  
Atmospheric Transport Model

Abstract. We describe a new rural network of four densely placed (< 100 km apart), continuous atmospheric carbon (CO2, CH4, and CO) measurement sites in north-central Switzerland and analyze its suitability for regional-scale (~ 100–500 km) carbon flux studies. We characterize each site for the period from March 2013 to February 2014 by analyzing surrounding land cover, observed local meteorology, and sensitivity to surface fluxes, as simulated with the Lagrangian particle dispersion model FLEXPART-COSMO (FLEXible PARTicle dispersion model-Consortium for Small-Scale Modeling). The Beromünster measurements are made on a tall tower (212 m) located on a gentle hill. At Beromünster, regional CO2 signals (measurement minus background) vary diurnally from −4 to +4 ppmv, on average, and are simulated to come from nearly the entire Swiss Plateau, where 50 % of surface influence is simulated to be within 130–260 km distance. The Früebüel site measurements are made 4 m above ground on the flank of a gently sloping mountain. Nearby (< 50 km) pasture and forest fluxes exert the most simulated surface influence, except during convective summertime days when the site is mainly influenced by the eastern Swiss Plateau, which results in summertime regional CO2 signals varying diurnally from −5 to +12 ppmv and elevated summer daytime CH4 signals (+30 ppbv above other sites). The Gimmiz site measurements are made on a small tower (32 m) in flat terrain. Here, strong summertime regional signals (−5 to +60 ppmv CO2) stem from large, nearby (< 50 km) crop and anthropogenic fluxes of the Seeland region, except during warm or windy days when simulated surface influence is of regional scale (< 250 km). The Lägern-Hochwacht measurements are made on a small tower (32 m) on top of the steep Lägern crest, where simulated surface influence is typically of regional scale (130–300 km) causing summertime regional signals to vary from −5 to +8 ppmv CO2. Here, considerable anthropogenic influence from the nearby industrialized region near Zurich causes the average wintertime regional CO2 signals to be 5 ppmv above the regional signals simultaneously measured at the Früebüel site. We find that the suitability of the data sets from our current observation network for regional carbon budgeting studies largely depends on the ability of the high-resolution (2 km) atmospheric transport model to correctly capture the temporal dynamics of the stratification of the lower atmosphere at the different sites. The current version of the atmospheric transport model captures these dynamics well, but it clearly reaches its limits at the sites in steep topography and at the sites that generally remain in the surface layer. Trace gas transport and inverse modeling studies will be necessary to determine the impact of these limitations on our ability to derive reliable regional-scale carbon flux estimates in the complex Swiss landscape.

scholarly journals The Canadian atmospheric transport model for simulating greenhouse gas evolution on regional scales: GEM-MACH-GHG v.137-reg

2019 ◽  
Author(s):  
Jinwoong Kim ◽  
Saroja Polavarapu ◽  
Douglas Chan ◽  
Michael Neish
Keyword(s):  
Greenhouse Gas ◽  
Transport Model ◽  
Atmospheric Transport ◽  
Forecast Error ◽  
Regional Scale ◽  
The United States ◽  
Regional Model ◽  
Boreal Summer ◽  
Chemical Transport Model ◽  
Atmospheric Transport Model

Abstract. In this study, we present the development of a regional atmospheric transport model for greenhouse gas (GHG) simulation based on an operational weather forecast model and a chemical transport model at Environment and Climate Change Canada (ECCC), with the goal of improving our understanding of the high spatio-temporal resolution interaction between the atmosphere and surface GHG fluxes over Canada and the United States. The regional model uses 10 km × 10 km horizontal grid spacing and 80 vertical levels spanning the ground to 0.1 hPa. The lateral boundary conditions of meteorology and tracers are provided by the global transport model used for GHG simulation at ECCC. The performance of the regional model and added benefit of the regional model over our lower resolution global models is investigated in terms of modelled CO2 concentration and meteorological forecast quality for multiple seasons in 2015. We find that our regional model has the capability to simulate high spatial (horizontal and vertical) and temporal scales of atmospheric CO2 concentrations, based on comparisons to surface and aircraft observations. In addition, reduced bias and standard deviation of forecast error in boreal summer are obtained by the regional model. Better representation of model topography in the regional model reduces transport and representation errors significantly compared to the global model, especially in regions of complex topography, as revealed by the more precise and detailed structure of the CO2 diurnal cycle produced at observation sites and in model space. The new regional model will form the basis of a flux inversion system that estimates regional scale fluxes of GHGs over Canada.

scholarly journals The Canadian atmospheric transport model for simulating greenhouse gas evolution on regional scales: GEM–MACH–GHG v.137-reg

2020 ◽  
Vol 13 (1) ◽  
pp. 269-295
Author(s):  
Jinwoong Kim ◽  
Saroja M. Polavarapu ◽  
Douglas Chan ◽  
Michael Neish
Keyword(s):  
Greenhouse Gas ◽  
Transport Model ◽  
Atmospheric Transport ◽  
Forecast Error ◽  
Regional Scale ◽  
The United States ◽  
Regional Model ◽  
Boreal Summer ◽  
Spatiotemporal Resolution ◽  
Atmospheric Transport Model

Abstract. In this study, we present the development of a regional atmospheric transport model for greenhouse gas (GHG) simulation based on an operational weather forecast model and a chemical transport model at Environment and Climate Change Canada (ECCC), with the goal of improving our understanding of the high-spatiotemporal-resolution interaction between the atmosphere and surface GHG fluxes over Canada and the United States. The regional model uses 10 km×10 km horizontal grid spacing and 80 vertical levels spanning the ground to 0.1 hPa. The lateral boundary conditions of meteorology and tracers are provided by the global transport model used for GHG simulation at ECCC. The performance of the regional model and added benefit of the regional model over our lower-resolution global models is investigated in terms of modelled CO2 concentration and meteorological forecast quality for multiple seasons in 2015. We find that our regional model has the capability to simulate the high spatial (horizontal and vertical) and temporal scales of atmospheric CO2 concentrations based on comparisons to surface and aircraft observations. In addition, the bias and standard deviation of forecast error in boreal summer are reduced by the regional model. Better representation of model topography in the regional model results in improved simulation of the CO2 diurnal cycle compared to the global model at Walnut Grove, California. The new regional model will form the basis of a flux inversion system that estimates regional-scale fluxes of GHGs over Canada.

scholarly journals The potential of satellite spectro-imagery for monitoring CO<sub>2</sub> emissions from large cities

2017 ◽  
Author(s):  
Grégoire Broquet ◽  
François-Marie Bréon ◽  
Emmanuel Renault ◽  
Michael Buchwitz ◽  
Maximilian Reuter ◽  
...  
Keyword(s):  
Co2 Emissions ◽  
Measurement Errors ◽  
Transport Model ◽  
Atmospheric Transport ◽  
European Space Agency ◽  
Image Resolution ◽  
Spatial Coverage ◽  
Paris Area ◽  
The Impact ◽  
Atmospheric Transport Model

Abstract. This study assesses the potential of 2 to 10 km resolution imagery of CO2 concentrations retrieved from the Short Wave Infra Red measurements of a space borne passive spectrometer for monitoring the spatially integrated emissions from the Paris area. Such imagery could be provided by missions similar to CarbonSat, which was studied as a candidate Earth Explorer 8 mission by the European Space Agency (ESA). This assessment is based on Observing System Simulation Experiments (OSSEs) with an atmospheric inversion approach at city scale. The inversion system solves for hourly city CO2 emissions and natural fluxes, or for these fluxes per main anthropogenic sector or ecosystem, during the 6 hours before a given satellite overpass. These 6 hours correspond to the period during which emissions produce CO2 plumes that can be identified on the image from this overpass. The statistical framework of the inversion accounts for the existence of some prior knowledge about the hourly emissions from an inventory based on energy use and carbon fuel consumption statistics. The link between the hourly or sectorial emissions and the vertically-integrated column of CO2 observed by the satellite is simulated using a coupled flux and atmospheric transport model. This coupled model is built with the information on the spatial and temporal distribution of emissions from the emission inventory produced by the local air-quality agency (Airparif) and a 2 km horizontal resolution atmospheric transport model. Tests are conducted for different realistic simulations of the spatial coverage, resolution, precision and accuracy of the imagery from sun-synchronous polar-orbing missions, corresponding to the specifications of CarbonSat and Sentinel-5 or extrapolated from these specifications. First, OSSEs are conducted with a rather optimistic configuration in which the inversion system is perfectly informed about the statistics of the limited number of error sources. These OSSEs indicate that the image resolution has to be finer than 4 km to decrease the uncertainty in the 6-hour mean emissions by more than 50 %. More complex experiments assess the impact of more realistic error estimates that current inversion methods do not properly account for, in particular the systematic measurement errors with spatially correlated patterns. These experiments highlight the difficulty to improve current knowledge on CO2 emissions for urban areas like Paris with CO2 observations from satellites, and call for more technological innovations in the remote sensing of vertically integrated columns of CO2 and in the inversion systems that exploit it.

scholarly journals The potential of satellite spectro-imagery for monitoring CO<sub>2</sub> emissions from large cities

2018 ◽  
Vol 11 (2) ◽  
pp. 681-708 ◽  
Author(s):  
Grégoire Broquet ◽  
François-Marie Bréon ◽  
Emmanuel Renault ◽  
Michael Buchwitz ◽  
Maximilian Reuter ◽  
...  
Keyword(s):  
Co2 Emissions ◽  
Measurement Errors ◽  
Transport Model ◽  
Atmospheric Transport ◽  
Image Resolution ◽  
Spatial Coverage ◽  
Paris Area ◽  
Infrared Measurements ◽  
The Impact ◽  
Atmospheric Transport Model

Abstract. This study assesses the potential of 2 to 10 km resolution imagery of CO2 concentrations retrieved from the shortwave infrared measurements of a space-borne passive spectrometer for monitoring the spatially integrated emissions from the Paris area. Such imagery could be provided by missions similar to CarbonSat, which was studied as a candidate Earth Explorer 8 mission by the European Space Agency (ESA). This assessment is based on observing system simulation experiments (OSSEs) with an atmospheric inversion approach at city scale. The inversion system solves for hourly city CO2 emissions and natural fluxes, or for these fluxes per main anthropogenic sector or ecosystem, during the 6 h before a given satellite overpass. These 6 h correspond to the period during which emissions produce CO2 plumes that can be identified on the image from this overpass. The statistical framework of the inversion accounts for the existence of some prior knowledge with 50 % uncertainty on the hourly or sectorial emissions, and with ∼ 25 % uncertainty on the 6 h mean emissions, from an inventory based on energy use and carbon fuel consumption statistics. The link between the hourly or sectorial emissions and the vertically integrated column of CO2 observed by the satellite is simulated using a coupled flux and atmospheric transport model. This coupled model is built with the information on the spatial and temporal distribution of emissions from the emission inventory produced by the local air-quality agency (Airparif) and a 2 km horizontal resolution atmospheric transport model. Tests are conducted for different realistic simulations of the spatial coverage, resolution, precision and accuracy of the imagery from sun-synchronous polar-orbiting missions, corresponding to the specifications of CarbonSat and Sentinel-5 or extrapolated from these specifications. First, OSSEs are conducted with a rather optimistic configuration in which the inversion system is perfectly informed about the statistics of the limited number of error sources. These OSSEs indicate that the image resolution has to be finer than 4 km to decrease the uncertainty in the 6 h mean emissions by more than 50 %. More complex experiments assess the impact of more realistic error estimates that current inversion methods do not properly account for, in particular, the systematic measurement errors with spatially correlated patterns. These experiments highlight the difficulty to improve current knowledge on CO2 emissions for urban areas like Paris with CO2 observations from satellites, and call for more technological innovations in the remote sensing of vertically integrated columns of CO2 and in the inversion systems that exploit it.

scholarly journals Potential of European <sup>14</sup>CO<sub>2</sub> observation network to estimate the fossil fuel CO<sub>2</sub> emissions via atmospheric inversions

2018 ◽  
Vol 18 (6) ◽  
pp. 4229-4250 ◽  
Author(s):  
Yilong Wang ◽  
Grégoire Broquet ◽  
Philippe Ciais ◽  
Frédéric Chevallier ◽  
Felix Vogel ◽  
...  
Keyword(s):  
Fossil Fuel ◽  
Transport Model ◽  
Atmospheric Transport ◽  
Regional Scale ◽  
Uncertainty Reduction ◽  
General Estimate ◽  
The Balkans ◽  
Observation Network ◽  
Posterior Estimate ◽  
Atmospheric Inversions

Abstract. Combining measurements of atmospheric CO2 and its radiocarbon (14CO2) fraction and transport modeling in atmospheric inversions offers a way to derive improved estimates of CO2 emitted from fossil fuel (FFCO2). In this study, we solve for the monthly FFCO2 emission budgets at regional scale (i.e., the size of a medium-sized country in Europe) and investigate the performance of different observation networks and sampling strategies across Europe. The inversion system is built on the LMDZv4 global transport model at 3.75∘ × 2.5∘ resolution. We conduct Observing System Simulation Experiments (OSSEs) and use two types of diagnostics to assess the potential of the observation and inverse modeling frameworks. The first one relies on the theoretical computation of the uncertainty in the estimate of emissions from the inversion, known as “posterior uncertainty”, and on the uncertainty reduction compared to the uncertainty in the inventories of these emissions, which are used as a prior knowledge by the inversion (called “prior uncertainty”). The second one is based on comparisons of prior and posterior estimates of the emission to synthetic “true” emissions when these true emissions are used beforehand to generate the synthetic fossil fuel CO2 mixing ratio measurements that are assimilated in the inversion. With 17 stations currently measuring 14CO2 across Europe using 2-week integrated sampling, the uncertainty reduction for monthly FFCO2 emissions in a country where the network is rather dense like Germany, is larger than 30 %. With the 43 14CO2 measurement stations planned in Europe, the uncertainty reduction for monthly FFCO2 emissions is increased for the UK, France, Italy, eastern Europe and the Balkans, depending on the configuration of prior uncertainty. Further increasing the number of stations or the sampling frequency improves the uncertainty reduction (up to 40 to 70 %) in high emitting regions, but the performance of the inversion remains limited over low-emitting regions, even assuming a dense observation network covering the whole of Europe. This study also shows that both the theoretical uncertainty reduction (and resulting posterior uncertainty) from the inversion and the posterior estimate of emissions itself, for a given prior and “true” estimate of the emissions, are highly sensitive to the choice between two configurations of the prior uncertainty derived from the general estimate by inventory compilers or computations on existing inventories. In particular, when the configuration of the prior uncertainty statistics in the inversion system does not match the difference between these prior and true estimates, the posterior estimate of emissions deviates significantly from the truth. This highlights the difficulty of filtering the targeted signal in the model–data misfit for this specific inversion framework, the need to strongly rely on the prior uncertainty characterization for this and, consequently, the need for improved estimates of the uncertainties in current emission inventories for real applications with actual data. We apply the posterior uncertainty in annual emissions to the problem of detecting a trend of FFCO2, showing that increasing the monitoring period (e.g., more than 20 years) is more efficient than reducing uncertainty in annual emissions by adding stations. The coarse spatial resolution of the atmospheric transport model used in this OSSE (typical of models used for global inversions of natural CO2 fluxes) leads to large representation errors (related to the inability of the transport model to capture the spatial variability of the actual fluxes and mixing ratios at subgrid scales), which is a key limitation of our OSSE setup to improve the accuracy of the monitoring of FFCO2 emissions in European regions. Using a high-resolution transport model should improve the potential to retrieve FFCO2 emissions, and this needs to be investigated.

Reconciling the carbon balance of northern Scandinavia through the integration of observations and modelling

2021 ◽  
Author(s):  
Anusha Sathyanadh ◽  
Guillaume Monteil ◽  
Marko Scholze ◽  
Anne Klosterhalfen ◽  
Hjalmar Laudon ◽  
...  
Keyword(s):  
Transport Model ◽  
Atmospheric Transport ◽  
Observation System ◽  
Northern Sweden ◽  
C Cycle ◽  
Flux Measurements ◽  
Vegetation Models ◽  
The Impact ◽  
Atmospheric Transport Model

&lt;p&gt;The boreal biome is an important component of the global carbon (C) cycle. However, current estimates of its sink-source strength at regional scales and its responses to climate change rely primarily on models and thus remain uncertain. We investigated the C balance over a north Scandinavian boreal region by integrating observations of land-atmosphere fluxes and atmospheric CO&lt;sub&gt;2&lt;/sub&gt; concentrations at landscape to regional scales. We also placed a special focus to understand the impact of 2018 drought on the region. Flux estimates can be obtained through various techniques such as in-situ flux measurements, eddy covariance (EC) observations, vegetation modelling and inverse modelling of CO&lt;sub&gt;2&lt;/sub&gt; observations. These techniques are however typically relevant at very different spatial scales ranging from plot scale to country-scale, which makes it difficult to compare them. The -Svartberget site (SVB), an established ICOS (Integrated Carbon Observation System) station in Northern Sweden offers a unique range of observations, from in-situ flux measurements to EC fluxes and tall-tower concentration measurements. Here we used several vegetation models and an atmospheric transport model to connect the different scales for the period 2016-2018. The land-atmosphere carbon fluxes are from four different vegetation models (VPRM, LPJ-GUESS, ORCHIDEE and SiBCASA) and are used in the LUMIA/FLEXPART atmospheric transport model (Lund University Modular Inversion Algorithm) to generate estimates of atmospheric CO&lt;sub&gt;2&lt;/sub&gt; concentration. We found that the northern Sweden region remained as a C sink for the study period with models differed in sink strength. It was also noticed that the site SVB can be taken as a representative for the northern Sweden region. All models indicate similar but small reductions in the net CO&lt;sub&gt;2&lt;/sub&gt; uptake for the drought year 2018 in northern Sweden except LPJ-GUESS that reveal limitations which call for further model improvement. Our work highlights the interest of using combined ecosystem,-atmosphere ICOS sites such as SVB in the Scandinavian region and shows that it is a promising way forward to monitor CO&lt;sub&gt;2&lt;/sub&gt; fluxes at the regional scale.&lt;/p&gt;

scholarly journals Potential of European <sup>14</sup>CO<sub>2</sub> observation network to estimate the fossil fuel CO<sub>2</sub> emissions via atmospheric inversions

2017 ◽  
Author(s):  
Yilong Wang ◽  
Grégoire Broquet ◽  
Philippe Ciais ◽  
Frédéric Chevallier ◽  
Felix Vogel ◽  
...  
Keyword(s):  
Fossil Fuel ◽  
Transport Model ◽  
Atmospheric Transport ◽  
Regional Scale ◽  
Uncertainty Reduction ◽  
General Estimate ◽  
The Balkans ◽  
Observation Network ◽  
Posterior Estimate ◽  
Atmospheric Inversions

Abstract. Combining measurements of atmospheric CO2 and its radiocarbon (14CO2) fraction and transport modeling in atmospheric inversions offers a way to derive improved estimates of CO2 emitted from fossil fuel (FFCO2). In this study, we solve for the monthly FFCO2 emission budgets at regional scale (i.e. the size of a medium-sized country in Europe) and investigate the performance of different observation networks and sampling strategies across Europe. The inversion system is built on the LMDZv4 global transport model at 3.75° × 2.5° resolution. We conduct Observing System Simulation Experiments (OSSE) and use two types of diagnostics to assess the potential of the observation and inverse modeling frameworks. The first one relies on the theoretical computation of the uncertainty in the estimate of emissions from the inversion, known as “posterior uncertainty”, and on the uncertainty reduction compared to the uncertainty in the inventories of these emissions which are used as a prior knowledge by the inversion (called “prior uncertainty”). The second one is based on comparisons of prior and posterior estimates of the emission to synthetic “true” emissions when these true emissions are used beforehand to generate the synthetic fossil fuel CO2 mixing ratio measurements that are assimilated in the inversion. With 17 stations currently measuring 14CO2 across Europe using 2-week integrated sampling, the uncertainty reduction for monthly FFCO2 emissions in a country where the network is rather dense like Germany, is larger than 30 %. With the 43 14CO2 measurement stations planned in Europe, the uncertainty reduction for monthly FFCO2 emissions is increased for UK, France, Italy, Eastern Europe and the Balkans, depending on the configuration of prior uncertainty. Further increasing the number of stations or the sampling frequency improve the uncertainty reduction (up to 40 % to 70 %) in high emitting regions, but the performance of the inversion remains limited over low-emitting regions, even assuming a dense observation network covering the whole of Europe. This study also shows that both the theoretical uncertainty reduction (and resulting posterior uncertainty) from the inversion and the posterior estimate of emissions itself, for a given prior and “true” estimate of the emissions, are highly sensitive to the choice between two configurations of the prior uncertainty derived from the general estimate by inventory compilers or computations on existing inventories. In particular, when the configuration of the prior uncertainty statistics in the inversion system does not match the difference between these prior and true estimates, the posterior estimate of emissions deviate significantly from the truth. This highlights the difficulty to filter the targeted signal in the model-data misfit for this specific inversion framework, the need to strongly rely on the prior uncertainty characterization for this, and, consequently the need for improved estimates of the uncertainties in current emission inventories for real applications with actual data. We apply the posterior uncertainty in annual emissions to the problem of detecting a trend of FFCO2, showing that increasing the monitoring period (e.g. more than 20 years) is more efficient than reducing uncertainty in annual emissions by adding stations. The coarse spatial resolution of the atmospheric transport model used in this OSSE (typical of models used for global inversions of natural CO2 fluxes) leads to large representation errors (related to the inability of the transport model to capture the spatial variability of the actual fluxes and mixing ratios at sub-grid scales), which is a key limitation of our OSSE setup to improve the accuracy of the monitoring of FFCO2 emissions in European regions. Using a high-resolution transport model should improve the potential to retrieve FFCO2 emissions, and this needs to be investigated.

scholarly journals The Environmental Effects of the April 2020 Wildfires and the Cs-137 Re-Suspension in the Chernobyl Exclusion Zone: A Multi-Hazard Threat

Atmosphere ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 467
Author(s):  
Rocío Baró ◽  
Christian Maurer ◽  
Jerome Brioude ◽  
Delia Arnold ◽  
Marcus Hirtl
Keyword(s):  
Air Quality ◽  
Nuclear Power ◽  
Transport Model ◽  
Atmospheric Transport ◽  
Point Of View ◽  
Plume Dispersion ◽  
Exclusion Zone ◽  
Fire Plume ◽  
Mean Values ◽  
The Impact

This paper demonstrates the environmental impacts of the wildfires occurring at the beginning of April 2020 in and around the highly contaminated Chernobyl Exclusion Zone (CEZ). Due to the critical fire location, concerns arose about secondary radioactive contamination potentially spreading over Europe. The impact of the fire was assessed through the evaluation of fire plume dispersion and re-suspension of the radionuclide Cs-137, whereas, to assess the smoke plume effect, a WRF-Chem simulation was performed and compared to Tropospheric Monitoring Instrument (TROPOMI) satellite columns. The results show agreement of the simulated black carbon and carbon monoxide plumes with the plumes as observed by TROPOMI, where pollutants were also transported to Belarus. From an air quality and health perspective, the wildfires caused extremely bad air quality over Kiev, where the WRF-Chem model simulated mean values of PM2.5 up to 300 µg/m3 (during the first fire outbreak) over CEZ. The re-suspension of Cs-137 was assessed by a Bayesian inverse modelling approach using FLEXPART as the atmospheric transport model and Ukraine observations, yielding a total release of 600 ± 200 GBq. The increase in both smoke and Cs-137 emissions was only well correlated on the 9 April, likely related to a shift of the focus area of the fires. From a radiological point of view even the highest Cs-137 values (average measured or modelled air concentrations and modelled deposition) at the measurement site closest to the Chernobyl Nuclear Power Plant, i.e., Kiev, posed no health risk.

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