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An appropriate representation of point source emissions in atmospheric transport models is very challenging. In the Stochastic Time-Inverted Lagrangian Transport model (STILT), all point source emissions are typically released from the surface, meaning that the actual emission stack height plus subsequent plume rise is not considered. This can lead to erroneous predictions of trace gas concentrations, especially during nighttime when vertical atmospheric mixing is minimal. In this study we use two Weather Research and Forecasting (WRF)–STILT model approaches to simulate fossil fuel CO2 (ffCO2) concentrations: (1) the standard “surface source influence (SSI)” approach and (2) an alternative “volume source influence (VSI)” approach where nearby point sources release CO2 according to their effective emission height profiles. The comparison with 14C-based measured ffCO2 data from 2-week integrated afternoon and nighttime samples collected at Heidelberg, 30 m above ground level shows that the root-mean-square deviation (RMSD) between modelled and measured ffCO2 is indeed almost twice as high during the night (RMSD =6.3 ppm) compared to the afternoon (RMSD =3.7 ppm) when using the standard SSI approach. In contrast, the VSI approach leads to a much better performance at nighttime (RMSD =3.4 ppm), which is similar to its performance during afternoon (RMSD =3.7 ppm). Representing nearby point source emissions with the VSI approach could thus be a first step towards exploiting nocturnal observations in STILT. The ability to use nighttime observations in atmospheric inversions would dramatically increase the observational data and allow for the investigation of different source mixtures or diurnal cycles. To further investigate the differences between these two approaches, we conducted a model experiment in which we simulated the ffCO2 contributions from 12 artificial power plants with typical annual emissions of 1 million tonnes of CO2 and with distances between 5 and 200 km from the Heidelberg observation site. We find that such a power plant must be more than 50 km away from the observation site in order for the mean modelled ffCO2 concentration difference between the SSI and VSI approach to fall below 0.1 ppm during situations with low mixing heights smaller than 500 m.

This paper presents a state-of-the-art anthropogenic emission inventory developed for the European domain for an 18-year time series (2000–2017) at a 0.05∘ × 0.1∘ grid resolution, specifically designed to support air quality modelling. The main air pollutants are included: NOx, SO2, non-methane volatile organic compounds (NMVOCs), NH3, CO, PM10 and PM2.5, and also CH4. To stay as close as possible to the emissions as officially reported and used in policy assessment, the inventory uses the officially reported emission data by European countries to the UN Framework Convention on Climate Change, the Convention on Long-Range Transboundary Air Pollution and the EU National Emission Ceilings Directive as the basis where possible. Where deemed necessary because of errors, incompleteness or inconsistencies, these are replaced with or complemented by other emission data, most notably the estimates included in the Greenhouse gas Air pollution Interaction and Synergies (GAINS) model. Emissions are collected at the high sectoral level, distinguishing around 250 different sector–fuel combinations, whereafter a consistent spatial distribution is applied for Europe. A specific proxy is selected for each of the sector–fuel combinations, pollutants and years. Point source emissions are largely based on reported facility-level emissions, complemented by other sources of point source data for power plants. For specific sources, the resulting emission data were replaced with other datasets. Emissions from shipping (both inland and at sea) are based on the results from a separate shipping emission model where emissions are based on actual ship movement data, and agricultural waste burning emissions are based on satellite observations. The resulting spatially distributed emissions are evaluated against earlier versions of the dataset as well as against alternative emission estimates, which reveals specific discrepancies in some cases. Along with the resulting annual emission maps, profiles for splitting particulate matter (PM) and NMVOCs into individual components are provided, as well as information on the height profile by sector and temporal disaggregation down to the hourly level to support modelling activities. Annual grid maps are available in csv and NetCDF format (https://doi.org/10.24380/0vzb-a387, Kuenen et al., 2021).

Under the Paris Agreement, countries report their anthropogenic greenhouse gas emissions in national inventories, which are used to track progress towards mitigation goals, but they must be independently verified. Atmospheric observations of CO.sub.2, interpreted using inverse methods, can potentially provide that verification. Conventional CO.sub.2 inverse methods infer natural CO.sub.2 fluxes by subtracting a priori estimates of fuel combustion from the a posteriori net CO.sub.2 fluxes, assuming that a priori knowledge for combustion emissions is better than for natural fluxes. We describe an inverse method that uses measurements of CO.sub.2 and carbon monoxide (CO), a trace gas that is co-emitted with CO.sub.2 during combustion, to report self-consistent combustion emissions and natural fluxes of CO.sub.2 . We use an ensemble Kalman filter and the GEOS-Chem atmospheric transport model to explore how satellite observations of CO and CO.sub.2 collected by the TROPOspheric Monitoring Instrument (TROPOMI) and Orbiting Carbon Observatory-2 (OCO-2), respectively, can improve understanding of combustion emissions and natural CO.sub.2 fluxes across the UK and mainland Europe in 2018-2021. We assess the value of using satellite observations of CO.sub.2, with and without CO, above what is already available from the in situ network. Using CO.sub.2 satellite observations leads to small corrections to a priori emissions that are inconsistent with in situ observations, due partly to the insensitivity of the atmospheric CO.sub.2 column to CO.sub.2 emission changes. When we introduce satellite CO observations, we find better agreement with our in situ inversion and a better model fit to atmospheric CO.sub.2 observations. Our regional mean a posteriori combustion CO.sub.2 emission ranges from 4.6-5.0 Gt a.sup.-1 (1.5 %-2.4 % relative standard deviation), with all inversions reporting an overestimate for Germany's wintertime emissions. Our national a posteriori CO.sub.2 combustion emissions are highly dependent on the assumed relationship between CO.sub.2 and CO uncertainties, as expected. Generally, we find better results when we use grid-scale-based a priori CO.sub.2 :CO uncertainty estimates rather than a fixed relationship between the two species.

Under the Paris Agreement, countries report their anthropogenic greenhouse gas emissions in national inventories, which are used to track progress towards mitigation goals, but they must be independently verified. Atmospheric observations of CO2, interpreted using inverse methods, can potentially provide that verification. Conventional CO2 inverse methods infer natural CO2 fluxes by subtracting a priori estimates of fuel combustion from the a posteriori net CO2 fluxes, assuming that a priori knowledge for combustion emissions is better than for natural fluxes. We describe an inverse method that uses measurements of CO2 and carbon monoxide (CO), a trace gas that is co-emitted with CO2 during combustion, to report self-consistent combustion emissions and natural fluxes of CO2. We use an ensemble Kalman filter and the GEOS-Chem atmospheric transport model to explore how satellite observations of CO and CO2 collected by the TROPOspheric Monitoring Instrument (TROPOMI) and Orbiting Carbon Observatory-2 (OCO-2), respectively, can improve understanding of combustion emissions and natural CO2 fluxes across the UK and mainland Europe in 2018–2021. We assess the value of using satellite observations of CO2, with and without CO, above what is already available from the in situ network. Using CO2 satellite observations leads to small corrections to a priori emissions that are inconsistent with in situ observations, due partly to the insensitivity of the atmospheric CO2 column to CO2 emission changes. When we introduce satellite CO observations, we find better agreement with our in situ inversion and a better model fit to atmospheric CO2 observations. Our regional mean a posteriori combustion CO2 emission ranges from 4.6–5.0 Gt a−1 (1.5 %–2.4 % relative standard deviation), with all inversions reporting an overestimate for Germany's wintertime emissions. Our national a posteriori CO2 combustion emissions are highly dependent on the assumed relationship between CO2 and CO uncertainties, as expected. Generally, we find better results when we use grid-scale-based a priori CO2:CO uncertainty estimates rather than a fixed relationship between the two species.

Quantification of greenhouse gas emissions is receiving a lot of attention because of its relevance for climate mitigation. Complementary to official reported bottom-up emission inventories, quantification can be done with an inverse modelling framework, combining atmospheric transport models, prior gridded emission inventories and a network of atmospheric observations to optimize the emission inventories. An important aspect of such a method is a correct quantification of the uncertainties in all aspects of the modelling framework. The uncertainties in gridded emission inventories are, however, not systematically analysed. In this work, a statistically coherent method is used to quantify the uncertainties in a high-resolution gridded emission inventory of CO2 and CO for Europe. We perform a range of Monte Carlo simulations to determine the effect of uncertainties in different inventory components, including the spatial and temporal distribution, on the uncertainty in total emissions and the resulting atmospheric mixing ratios. We find that the uncertainties in the total emissions for the selected domain are 1 % forCO2 and 6 % for CO. Introducing spatial disaggregation causes a significant increase in the uncertainty of up to 40 % for CO2 and 70 % for CO for specific grid cells. Using gridded uncertainties, specific regions can be defined that have the largest uncertainty in emissions and are thus an interesting target for inverse modellers. However, the largest sectors are usually the best-constrained ones (low relative uncertainty), so the absolute uncertainty is the best indicator for this. With this knowledge, areas can be identified that are most sensitive to the largest emission uncertainties, which supports network design.

Under the Paris Agreement, countries report their anthropogenic greenhouse gas emissions in national inventories, which are used to track progress towards mitigation goals, but they must be independently verified. Atmospheric observations of CO2, interpreted using inverse methods, can potentially provide that verification. Conventional CO2 inverse methods infer natural CO2 fluxes by subtracting a priori estimates of fuel combustion from the a posteriori net CO2 fluxes, assuming that a priori knowledge for combustion emissions is better than for natural fluxes. We describe an inverse method that uses measurements of CO2 and carbon monoxide (CO), a trace gas that is co-emitted with CO2 during combustion, to report self-consistent combustion emissions and natural fluxes of CO2. We use an ensemble Kalman filter and the GEOS-Chem atmospheric transport model to explore how satellite observations of CO and CO2 collected by the TROPOspheric Monitoring Instrument (TROPOMI) and Orbiting Carbon Observatory-2 (OCO-2), respectively, can improve understanding of combustion emissions and natural CO2 fluxes across the UK and mainland Europe in 2018–2021. We assess the value of using satellite observations of CO2, with and without CO, above what is already available from the in situ network. Using CO2 satellite observations leads to small corrections to a priori emissions that are inconsistent with in situ observations, due partly to the insensitivity of the atmospheric CO2 column to CO2 emission changes. When we introduce satellite CO observations, we find better agreement with our in situ inversion and a better model fit to atmospheric CO2 observations. Our regional mean a posteriori combustion CO2 emission ranges from 4.6–5.0 Gta-1 (1.5 %–2.4 % relative standard deviation), with all inversions reporting an overestimate for Germany's wintertime emissions. Our national a posteriori CO2 combustion emissions are highly dependent on the assumed relationship between CO2 and CO uncertainties, as expected. Generally, we find better results when we use grid-scale-based a priori CO2:CO uncertainty estimates rather than a fixed relationship between the two species.

Monitoring, reporting, and verification frameworks for greenhouse gas emissions are being developed by countries across the world to keep track of progress towards national emission reduction targets. Data assimilation plays an important role in monitoring frameworks, combining different sources of information to achieve the best possible estimate of fossil fuel emissions and, as a consequence, better estimates for fluxes from the natural biosphere. Robust estimates for fossil fuel emissions rely on accurate estimates of uncertainties corresponding to different pieces of information. We describe prior uncertainties in CO2 and CO fossil fuel fluxes, paying special attention to spatial error correlations and the covariance structure between CO2 and CO. This represents the first time that prior uncertainties in CO2 and the important co-emitted trace gas CO are defined consistently, with error correlations included, which allows us to make use of the synergy between the two trace gases to better constrain CO2 fossil fuel fluxes. CO:CO2 error correlations differ by sector, depending on the diversity of sub-processes occurring within a sector, and also show a large range of values between pixels within the same sector. For example, for other stationary combustion, pixel correlation values range from 0.1 to 1.0, whereas for road transport, the correlation is mostly larger than 0.6. We illustrate the added value of our definition of prior uncertainties using closed-loop numerical experiments over mainland Europe and the UK, which isolate the influence of using error correlations between CO2 and CO and the influence of prescribing more detailed information about prior emission uncertainties. For the experiments, synthetic in situ observations are used, allowing us to validate the results against a “truth”. The “true” emissions are made by perturbing the prior emissions (from an emission inventory) according to the prescribed prior uncertainties. We find that using our realistic definition of prior uncertainties helps our data assimilation system to differentiate more easily between CO2 fluxes from biogenic and fossil fuel sources. Using improved prior emission uncertainties, we find fewer geographic regions with significant deviations from the prior compared to when using default prior uncertainties (32 vs. 80 grid cells of 0.25°×0.3125°, with an absolute difference of more than 1 kg s-1 between the prior and posterior), but these deviations from the prior almost consistently move closer to the prescribed true values, with 92 % showing an improvement, in contrast to the default prior uncertainties, where 61 % show an improvement. We also find that using CO provides additional information on CO2 fossil fuel fluxes, but this is only the case if the CO:CO2 error covariance structure is defined realistically. Using the default prior uncertainties, the CO2 fossil fuel fluxes move farther away from the truth in many geographical regions (with 50 % showing an improvement compared to 94 % when advanced prior uncertainties are used). With the default uncertainties, the maximum deviation of fossil fuel CO2 from the prescribed truth is about 7 % in both the prior and posterior results. With the advanced uncertainties, this is reduced to 3 % in the posterior results.

The 2020 COVID-19 crisis caused an unprecedented drop in anthropogenic emissions of air pollutants and greenhouse gases. Given that emissions estimates from official national inventories for the year 2020 were not reported until 2 years later, new and non-traditional datasets to estimate near-real-time emissions became particularly relevant and widely used in international monitoring and modelling activities during the pandemic. This study investigates the impact of the COVID-19 pandemic on 2020 European (the 27 EU member states and the UK) emissions by comparing a selection of such near-real-time emission estimates, with the official inventories that were subsequently reported in 2022 under the Convention on Long-Range Transboundary Air Pollution (CLRTAP) and the United Nations Framework Convention on Climate Change (UNFCCC). Results indicate that annual changes in total 2020 emissions reported by official and near-real-time estimates are fairly in line for most of the chemical species, with NOx and fossil fuel CO2 being reported as the ones that experienced the largest reduction in Europe in all cases. However, large discrepancies arise between the official and non-official datasets when comparing annual results at the sector and country level, indicating that caution should be exercised when estimating changes in emissions using specific near-real-time activity datasets, such as time mobility data derived from smartphones. The main examples of these differences are observed for the manufacturing industry NOx (relative changes ranging between −21.4 % and −5.4 %) and road transport CO2 (relative changes ranging between −29.3 % and −5.6 %) total European emissions. Additionally, significant discrepancies are observed between the quarterly and monthly distribution of emissions drops reported by the various near-real-time inventories, with differences of up to a factor of 1.5 for total NOx during April 2020, when restrictions were at their maximum. For residential combustion, shipping and the public energy industry, results indicate that changes in emissions that occurred between 2019 and 2020 were mainly dominated by non-COVID-19 factors, including meteorology, the implementation of the Global Sulphur Cap and the shutdown of coal-fired power plants as part of national decarbonization efforts, respectively. The potential increase in NMVOC emissions from the intensive use of personal protective equipment such as hand sanitizer gels is considered in a heterogeneous way across countries in officially reported inventories, indicating the need for some countries to base their calculations on more advanced methods. The findings of this study can be used to better understand the uncertainties in near-real-time emissions and how such emissions could be used in the future to provide timely updates to emission datasets that are critical for modelling and monitoring applications.

Traffic in urban areas is an important source of greenhouse gas (GHG) and air pollutant emissions. Estimating traffic-related emissions is therefore a key component in compiling a city emission inventory. Inventories are fundamental for understanding, monitoring, managing, and mitigating local pollutant emissions. We present DRIVE v1.0, a data-driven framework to calculate road transport emissions based on a multi-modal macroscopic traffic model, vehicle class-specific traffic counting data from more than a hundred counting stations, and HBEFA emission factors. DRIVE introduces a novel approach for estimating traffic emissions with vehicle-specific temporal profiles in hourly resolution. In addition, we use traffic counting data to estimate the uncertainty of traffic activity and the resulting emission estimates at different temporal aggregation levels and with road link resolution. The framework was applied to the City of Munich, covering an area of 311 km2 and accounting for GHGs (CO2, CH4) and air pollutants (PM, CO, NOx). It captures irregular events such as COVID lockdowns and holiday periods well and is suitable for use in near real-time applications. Emission estimates for 2019–2022 are presented and differences in city totals and spatial distribution compared to the official municipal reported and national and European downscaled inventories are examined.

Quantification of greenhouse gas emissions is receiving a lot of attention because of its relevance for climate mitigation. Complementary to official reported bottom-up emission inventories, quantification can be done with an inverse modelling framework, combining atmospheric transport models, prior gridded emission inventories and a network of atmospheric observations to optimize the emission inventories. An important aspect of such a method is a correct quantification of the uncertainties in all aspects of the modelling framework. The uncertainties in gridded emission inventories are, however, not systematically analysed. In this work, a statistically coherent method is used to quantify the uncertainties in a high-resolution gridded emission inventory of CO.sub.2 and CO for Europe. We perform a range of Monte Carlo simulations to determine the effect of uncertainties in different inventory components, including the spatial and temporal distribution, on the uncertainty in total emissions and the resulting atmospheric mixing ratios. We find that the uncertainties in the total emissions for the selected domain are 1 % for CO.sub.2 and 6 % for CO. Introducing spatial disaggregation causes a significant increase in the uncertainty of up to 40 % for CO.sub.2 and 70 % for CO for specific grid cells. Using gridded uncertainties, specific regions can be defined that have the largest uncertainty in emissions and are thus an interesting target for inverse modellers. However, the largest sectors are usually the best-constrained ones (low relative uncertainty), so the absolute uncertainty is the best indicator for this. With this knowledge, areas can be identified that are most sensitive to the largest emission uncertainties, which supports network design.