scholarly journals Inverse modelling of the spatial distribution of NO<sub>x</sub> emissions on a continental scale using satellite data

2005 ◽  
Vol 5 (6) ◽  
pp. 12641-12695 ◽  
Author(s):  
I. B. Konovalov ◽  
M. Beekmann ◽  
A. Richter ◽  
J. P. Burrows
Keyword(s):  
Spatial Distribution ◽  
Satellite Data ◽  
A Priori ◽  
Inverse Modelling ◽  
Geometric Standard Deviation ◽  
Nox Emissions ◽  
A Posteriori ◽  
Near Surface ◽  
Continental Scale ◽  
Tropospheric No2

Abstract. 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.

scholarly journals Inverse modelling of the spatial distribution of NO<sub>x</sub> emissions on a continental scale using satellite data

2006 ◽  
Vol 6 (7) ◽  
pp. 1747-1770 ◽  
Author(s):  
I. B. Konovalov ◽  
M. Beekmann ◽  
A. Richter ◽  
J. P. Burrows
Keyword(s):  
Spatial Distribution ◽  
Satellite Data ◽  
A Priori ◽  
Inverse Modelling ◽  
Geometric Standard Deviation ◽  
Nox Emissions ◽  
A Posteriori ◽  
Near Surface ◽  
Continental Scale ◽  
Tropospheric No2

Abstract. 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.

scholarly journals Changes in Power Plant NOx Emissions over Northwest Greece Using a Data Assimilation Technique

Atmosphere ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 900
Author(s):  
Ioanna Skoulidou ◽  
Maria-Elissavet Koukouli ◽  
Arjo Segers ◽  
Astrid Manders ◽  
Dimitris Balis ◽  
...  
Keyword(s):  
Power Plant ◽  
Data Assimilation ◽  
Energy Production ◽  
Power Plants ◽  
Transport Model ◽  
A Priori ◽  
Filter Method ◽  
Nox Emissions ◽  
A Posteriori ◽  
Data Assimilation Technique

In this work, we investigate the ability of a data assimilation technique and space-borne observations to quantify and monitor changes in nitrogen oxides (NOx) emissions over Northwestern Greece for the summers of 2018 and 2019. In this region, four lignite-burning power plants are located. The data assimilation technique, based on the Ensemble Kalman Filter method, is employed to combine space-borne atmospheric observations from the high spatial resolution Sentinel-5 Precursor (S5P) Tropospheric Monitoring Instrument (TROPOMI) and simulations using the LOTOS-EUROS Chemical Transport model. The Copernicus Atmosphere Monitoring Service-Regional European emissions (CAMS-REG, version 4.2) inventory based on the year 2015 is used as the a priori emissions in the simulations. Surface measurements of nitrogen dioxide (NO2) from air quality stations operating in the region are compared with the model surface NO2 output using either the a priori (base run) or the a posteriori (assimilated run) NOx emissions. Relative to the a priori emissions, the assimilation suggests a strong decrease in concentrations for the station located near the largest power plant, by 80% in 2019 and by 67% in 2018. Concerning the estimated annual a posteriori NOx emissions, it was found that, for the pixels hosting the two largest power plants, the assimilated run results in emissions decreased by ~40–50% for 2018 compared to 2015, whereas a larger decrease, of ~70% for both power plants, was found for 2019, after assimilating the space-born observations. For the same power plants, the European Pollutant Release and Transfer Register (E-PRTR) reports decreased emissions in 2018 and 2019 compared to 2015 (−35% and −38% in 2018, −62% and −72% in 2019), in good agreement with the estimated emissions. We further compare the a posteriori emissions to the reported energy production of the power plants during the summer of 2018 and 2019. Mean decreases of about −35% and−63% in NOx emissions are estimated for the two larger power plants in summer of 2018 and 2019, respectively, which are supported by similar decreases in the reported energy production of the power plants (~−30% and −70%, respectively).

scholarly journals Global NO<sub>x</sub> emission estimates derived from an assimilation of OMI tropospheric NO<sub>2</sub> columns

2011 ◽  
Vol 11 (12) ◽  
pp. 31523-31583 ◽  
Author(s):  
K. Miyazaki ◽  
H. J. Eskes ◽  
K. Sudo
Keyword(s):  
Data Assimilation ◽  
Transport Model ◽  
A Priori ◽  
Eastern China ◽  
Nox Emissions ◽  
Chemical Transport Model ◽  
Background Error ◽  
Tropospheric No2 ◽  
Data Assimilation System ◽  
Assimilation System

Abstract. A data assimilation system has been developed to estimate global nitrogen oxides (NOx) emissions using OMI tropospheric NO2 columns (DOMINO product) and a global chemical transport model (CTM), CHASER. The data assimilation system, based on an ensemble Kalman filter approach, was applied to optimize daily NOx emissions with a horizontal resolution of 2.8° during the years 2005 and 2006. The background error covariance estimated from the ensemble CTM forecasts explicitly represents non-direct relationships between the emissions and tropospheric columns caused by atmospheric transport and chemical processes. In comparison to the a priori emissions based on bottom-up inventories, the optimized emissions were higher over Eastern China, the Eastern United States, Southern Africa, and Central-Western Europe, suggesting that the anthropogenic emissions are mostly underestimated in the inventories. In addition, the seasonality of the estimated emissions differed from that of the a priori emission over several biomass burning regions, with a large increase over Southeast Asia in April and over South America in October. The data assimilation results were validated against independent data: SCIAMACHY tropospheric NO2 columns and vertical NO2 profiles obtained from aircraft and lidar measurements. The emission correction greatly improved the agreement between the simulated and observed NO2 fields; this implies that the data assimilation system efficiently derives NOx emissions from concentration observations. We also demonstrated that biases in the satellite retrieval and model settings used in the data assimilation largely affect the magnitude of estimated emissions. These dependences should be carefully considered for better understanding NOx sources from top-down approaches.

scholarly journals Estimation of NO<sub><b>x</b></sub> emissions from Delhi using car MAX-DOAS observations and comparison with OMI satellite data

2011 ◽  
Vol 11 (7) ◽  
pp. 19179-19212 ◽  
Author(s):  
R. Shaiganfar ◽  
S. Beirle ◽  
M. Sharma ◽  
A. Chauhan ◽  
R. P. Singh ◽  
...  
Keyword(s):  
Wind Speed ◽  
Spatial Patterns ◽  
Satellite Data ◽  
Satellite Observations ◽  
Nox Emissions ◽  
Pollution Levels ◽  
Satellite Retrieval ◽  
Tropospheric No2 ◽  
High Pollution ◽  
Good Agreement

Abstract. We present the first Multi-Axis- (MAX-) DOAS observations in India performed during April 2010 and January 2011 in Delhi and nearby regions. The MAX-DOAS instrument was mounted on a car roof, which allowed us to perform measurements along individual driving routes. From car MAX-DOAS observations along closed circles around Delhi, together with information on wind speed and direction, the NOx emissions from the greater Delhi area were determined: our estimate of 3.7 × 1025 molec s−1 is found to be slightly lower than the corresponding emission estimates using the EDGAR data base and substantially smaller compared to a recent study by Gurjar et al. (2004). We have also used the MAX-DOAS observations of the tropospheric NO2 VCD for validation of simultaneous satellite observations from the OMI instrument and found a good agreement of the spatial patterns. The absolute values show a reasonably good agreement. However, OMI data tends to underestimate the tropospheric NO2 VCDs in regions with high pollution levels, and tends to overestimate the tropospheric NO2 VCDs in more clean areas. These findings indicate possible discrepancies between the true vertical NO2 profiles and the profile assumptions in the OMI satellite retrieval.

scholarly journals Uncertainty assessment and applicability of an inversion method for volcanic ash forecasting

2017 ◽  
Author(s):  
Birthe Marie Steensen ◽  
Arve Kylling ◽  
Nina Iren Kristiansen ◽  
Michael Schulz
Keyword(s):  
Satellite Data ◽  
Volcanic Ash ◽  
A Priori ◽  
A Priori Estimate ◽  
Satellite Observations ◽  
Inversion Method ◽  
A Posteriori ◽  
Priori Estimate ◽  
Satellite Retrieval ◽  
Emission Estimate

Abstract. Significant improvements in the way we can observe and model volcanic ash clouds have been obtained since the 2010 Eyjafjallajökull eruption. One major development has been data assimilation techniques, which aim to bring models in closer agreement to satellite observations and reducing the uncertainties for the ash emission estimate. Still, questions remains to which degree the forecasting capabilities are improved by inclusion of such techniques are and how these improvements depend on the data input. This study exploits how different satellite data and different uncertainty assumptions of the satellite and a priori emissions affect the calculated volcanic ash emission estimate, which is computed by an inversion method that couples the satellite and a priori emissions with dispersion model data. Two major ash episodes over four days in April and May of the 2010 Eyjafjallajökull eruption are studied. Specifically, inversion calculations are done for four different satellite data sets with different size distribution assumptions in the retrieval. A reference satellite data set is chosen and the range between the minimum and maximum 4 day average load of hourly retrieved ash is 121 % in April and 148 % in May, compared to the reference. The corresponding a posteriori maximum and minimum emission sum found for these four satellite retrievals range from 26 % and 47 % of the a posteriori reference estimate for the same two periods. Varying the assumptions made in the satellite retrieval therefore translates into uncertainties in the calculated emissions and the modelled ash column loads. By further exploring the weighting of uncertainties connected to a priori emissions and the other-than-size uncertainties in the satellite data, the uncertainty in the a priori estimate is found to have an order of magnitude more impact on the a posteriori solution compared to the other-than-size uncertainties in the satellite. Part of this is explained by a too high a priori estimate used in this study that is reduced by around half in the a posteriori reference estimate. Setting large uncertainties connected to both a priori and satellite input data is shown to compensate each other. Because of this an inversion based emission estimate in a forecasting setting needs well tested and considered assumptions on uncertainties for the a priori emission and satellite data. The quality of using the inversion in a forecasting environment is tested by adding gradually, with time, more observations to improve the estimated height versus time evolution of Eyjafjallajökull ash emissions. We show that the initially too high a priori emissions are reduced effectively when using just 12 hours of satellite observations. More satellite observations (> 12 h), in the Eyjafjallajökull case, place the volcanic injection at higher altitudes. Adding additional satellite observations (> 36 h) changes the a posteriori emissions to only a small extent for May and minimal for the April period, because the ash is dispersed and transported effectively out of the domain after 1–2 days. A best-guess emission estimate for the forecasting period was constructed by averaging the last 12 hours of the a posteriori emission. Using this emission for a forecast simulation performs better especially compared to model simulations with no further emissions over the forecast period in the case of a continued volcanic eruption activity. Because of undetected ash in the satellite retrieval and diffusion in the model, the forecast simulations generally contain more ash than the observed fields and the model ash is more spread out. Overall, using the a posteriori emissions in our model reduces the uncertainties connected to both the satellite observations and the a priori estimate to perform a more confident forecast in both amount of ash released and emission heights.

scholarly journals Inverse modelling of atmospheric tracers: non-Gaussian methods and second-order sensitivity analysis

2008 ◽  
Vol 15 (1) ◽  
pp. 127-143 ◽  
Author(s):  
M. Bocquet
Keyword(s):  
Sensitivity Analysis ◽  
Cost Function ◽  
Information Content ◽  
Degrees Of Freedom ◽  
Second Order ◽  
Inverse Modelling ◽  
Cost Functions ◽  
A Posteriori ◽  
Continental Scale ◽  
Non Gaussian

Abstract. For a start, recent techniques devoted to the reconstruction of sources of an atmospheric tracer at continental scale are introduced. A first method is based on the principle of maximum entropy on the mean and is briefly reviewed here. A second approach, which has not been applied in this field yet, is based on an exact Bayesian approach, through a maximum a posteriori estimator. The methods share common grounds, and both perform equally well in practice. When specific prior hypotheses on the sources are taken into account such as positivity, or boundedness, both methods lead to purposefully devised cost-functions. These cost-functions are not necessarily quadratic because the underlying assumptions are not Gaussian. As a consequence, several mathematical tools developed in data assimilation on the basis of quadratic cost-functions in order to establish a posteriori analysis, need to be extended to this non-Gaussian framework. Concomitantly, the second-order sensitivity analysis needs to be adapted, as well as the computations of the averaging kernels of the source and the errors obtained in the reconstruction. All of these developments are applied to a real case of tracer dispersion: the European Tracer Experiment [ETEX]. Comparisons are made between a least squares cost function (similar to the so-called 4D-Var) approach and a cost-function which is not based on Gaussian hypotheses. Besides, the information content of the observations which is used in the reconstruction is computed and studied on the application case. A connection with the degrees of freedom for signal is also established. As a by-product of these methodological developments, conclusions are drawn on the information content of the ETEX dataset as seen from the inverse modelling point of view.

scholarly journals Changes in Power Plant NOx Emissions over Northwest Greece Using a Data Assimilation Technique

2021 ◽  
Author(s):  
Ioanna Skoulidou ◽  
Maria-Elissavet Koukouli ◽  
Arjo Segers ◽  
Astrid Manders ◽  
Dimitris Balis ◽  
...  
Keyword(s):  
Power Plant ◽  
Data Assimilation ◽  
Energy Production ◽  
Power Plants ◽  
Transport Model ◽  
A Priori ◽  
Filter Method ◽  
Nox Emissions ◽  
A Posteriori ◽  
Data Assimilation Technique

In this work, we investigate the ability of a data assimilation technique and space-borne observations to quantify and monitor changes in nitrogen oxides (NOx) emissions over North-Western Greece for the summers of 2018 and 2019. In this region, four lignite-burning power plants are located. The data assimilation technique, based on the Ensemble Kalman Filter method, is employed to combine space-borne atmospheric observations from the high spatial resolution Sentinel-5 Precursor (S5P) Tropospheric Monitoring Instrument (TROPOMI) and simulations using the LOTOS-EUROS Chemical Transport model. The Copernicus Atmosphere Monitoring Service-Regional European emissions (CAMS-REG, version 4.2) inventory based on year 2015 is used as the a priori in the simulations. Surface measurements of nitrogen dioxide (NO2) from air quality stations operating in the region are compared with the model surface NO2 output using either the a priori (base run) or the a posteriori (assimilated run) NOx emissions. The high biases found between the in situ NO2 measurements and the base run surface NO2 decrease in the assimilated run in most cases. The bias in the station near the largest power plant decreases to 2.0 &mu;g/m3 (2.83 &mu;g/m3) from 10.5 &mu;g/m3 (8.46 &mu;g/m3) in 2019 (2018 respectively). Concerning the estimated annual a posteriori NOx emissions it was found that, for the pixels hosting the two largest power plants, the assimilated run results in emissions decreased by ~40-50% for 2018 compared to 2015, whereas a larger decrease, of ~70% for both power plants, was found for 2019, after assimilating the space-born observations. For the same power plants, the European Pollutant Release and Transfer Register (E-PRTR) reports decreased emissions in 2018 and 2019 compared to 2015 (-35% and -38% in 2018, -62% and -72% in 2019), in good agreement with the estimated emissions. We further compare the a posteriori emissions to the reported energy production of the power plants during the summer of 2018 and 2019. Mean decreases of about -35% and-63% in NOx emissions are estimated for the two larger power plants in summer of 2018 and 2019, respectively, which are supported by similar decreases in the reported energy production of the power plants (~-30% and -70%, respectively).

scholarly journals Satellite measurement based estimates of decadal changes in European nitrogen oxides emissions

2008 ◽  
Vol 8 (1) ◽  
pp. 2013-2059 ◽  
Author(s):  
I. B. Konovalov ◽  
M. Beekmann ◽  
J. P. Burrows ◽  
A. Richter
Keyword(s):  
Great Britain ◽  
Nitrogen Oxides ◽  
Nox Emissions ◽  
Satellite Measurements ◽  
Data Set ◽  
Bottom Up ◽  
Model Calculations ◽  
Near Surface ◽  
Continental Scale ◽  
General Decline

Abstract. Long-term satellite measurements of nitrogen dioxide in the troposphere are used in combination with a continental scale air quality model in order to verify and improve available estimates of multi-annual changes of emissions of nitrogen oxides (NOx) in Europe and the Mediterranean area between 1996 and 2005. As a result, a measurement-based data set of NOx emissions on a 1° by 1° grid and averaged over summer months is elaborated. The results are compared with emission data based on the EMEP emission inventory. Our data are in agreement with the EMEP estimates suggesting a general decline in the level of NOx emissions in Western and Central European countries (France, Germany, Great Britain and Poland). Over Southern Europe and for shipping emissions, neutral to positive trends are found both for the inverted and bottom-up emissions. In contrast, considerable differences between both data sets are found in some other countries. In particular, significant negative trends instead of the positive ones in the "bottom-up" inventory are found for the Balkan countries, Russia and Turkey. The NOx emission trends derived from satellite measurements demonstrate larger spatial heterogeneity than those calculated with the EMEP data, especially in Russia and Ukraine. The obtained estimates of the decadal trends in NOx emissions for Great Britain are found to be consistent with independent data from the U.K. Automatic Urban and Rural Network (AURN). It is also found that using our emission estimates yields better agreement of model calculations with near-surface ozone measurements of the European EMEP network.

scholarly journals Global NO<sub>x</sub> emission estimates derived from an assimilation of OMI tropospheric NO<sub>2</sub> columns

2012 ◽  
Vol 12 (5) ◽  
pp. 2263-2288 ◽  
Author(s):  
K. Miyazaki ◽  
H. J. Eskes ◽  
K. Sudo
Keyword(s):  
Data Assimilation ◽  
Radiative Forcing ◽  
Transport Model ◽  
A Priori ◽  
Eastern China ◽  
Atmospheric Environment ◽  
Nox Emissions ◽  
Tropospheric No2 ◽  
Data Assimilation System ◽  
Assimilation System

Abstract. A data assimilation system has been developed to estimate global nitrogen oxides (NOx) emissions using OMI tropospheric NO2 columns (DOMINO product) and a global chemical transport model (CTM), the Chemical Atmospheric GCM for Study of Atmospheric Environment and Radiative Forcing (CHASER). The data assimilation system, based on an ensemble Kalman filter approach, was applied to optimize daily NOx emissions with a horizontal resolution of 2.8° during the years 2005 and 2006. The background error covariance estimated from the ensemble CTM forecasts explicitly represents non-direct relationships between the emissions and tropospheric columns caused by atmospheric transport and chemical processes. In comparison to the a priori emissions based on bottom-up inventories, the optimized emissions were higher over eastern China, the eastern United States, southern Africa, and central-western Europe, suggesting that the anthropogenic emissions are mostly underestimated in the inventories. In addition, the seasonality of the estimated emissions differed from that of the a priori emission over several biomass burning regions, with a large increase over Southeast Asia in April and over South America in October. The data assimilation results were validated against independent data: SCIAMACHY tropospheric NO2 columns and vertical NO2 profiles obtained from aircraft and lidar measurements. The emission correction greatly improved the agreement between the simulated and observed NO2 fields; this implies that the data assimilation system efficiently derives NOx emissions from concentration observations. We also demonstrated that biases in the satellite retrieval and model settings used in the data assimilation largely affect the magnitude of estimated emissions. These dependences should be carefully considered for better understanding NOx sources from top-down approaches.

scholarly journals Worldwide biogenic soil NO<sub>x</sub> emissions inferred from OMI NO<sub>2</sub> observations

2014 ◽  
Vol 14 (18) ◽  
pp. 10363-10381 ◽  
Author(s):  
G. C. M. Vinken ◽  
K. F. Boersma ◽  
J. D. Maasakkers ◽  
M. Adon ◽  
R. V. Martin
Keyword(s):  
Emission Inventory ◽  
Transport Model ◽  
A Priori ◽  
Nox Emission ◽  
Nox Emissions ◽  
Ozone Monitoring Instrument ◽  
Top Down ◽  
Tropospheric No2 ◽  
The Usa ◽  
Time Specific

Abstract. Biogenic NOx emissions from soils are a large natural source with substantial uncertainties in global bottom-up estimates (ranging from 4 to 15 Tg N yr−1). We reduce this range in emission estimates, and present a top-down soil NOx emission inventory for 2005 based on retrieved tropospheric NO2 columns from the Ozone Monitoring Instrument (OMI). We use a state-of-science soil NOx emission inventory (Hudman et al., 2012) as a priori in the GEOS-Chem chemistry transport model to identify 11 regions where tropospheric NO2 columns are dominated by soil NOx emissions. Strong correlations between soil NOx emissions and simulated NO2 columns indicate that spatial patterns in simulated NO2 columns in these regions indeed reflect the underlying soil NOx emissions. Subsequently, we use a mass-balance approach to constrain emissions for these 11 regions on all major continents using OMI observed and GEOS-Chem simulated tropospheric NO2 columns. We find that responses of simulated NO2 columns to changing NOx emissions are suppressed over low NOx regions, and account for these non-linearities in our inversion approach. In general, our approach suggests that emissions need to be increased in most regions. Our OMI top-down soil NOx inventory amounts to 10.0 Tg N for 2005 when only constraining the 11 regions, and 12.9 Tg N when extrapolating the constraints globally. Substantial regional differences exist (ranging from −40% to +90%), and globally our top-down inventory is 4–35% higher than the GEOS-Chem a priori (9.6 Tg N yr−1). We evaluate NO2 concentrations simulated with our new OMI top-down inventory against surface NO2 measurements from monitoring stations in Africa, the USA and Europe. Although this comparison is complicated by several factors, we find an encouraging improved agreement when using the OMI top-down inventory compared to using the a priori inventory. To our knowledge, this study provides, for the first time, specific constraints on soil NOx emissions on all major continents using OMI NO2 columns. Our results rule out the low end of reported soil NOx emission estimates, and suggest that global emissions are most likely around 12.9 ± 3.9 Tg N yr−1.

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