ALBERT

Description: We present a modelling framework for fossil fuel CO2 emissions in an urban environment, which allows constraints from emission inventories to be combined with atmospheric observations of CO2 and its co-emitted species CO, NOx, and SO2. Rather than a static assignment of average emission rates to each unit area of the urban domain, the fossil fuel emissions we use are dynamic: they vary in time and space in relation to data that describe or approximate the activity within a sector, such as traffic density, power demand, 2 m temperature (as proxy for heating demand), and sunlight and wind speed (as proxies for renewable energy supply). Through inverse modelling, we optimize the relationships between these activity data and the resulting emissions of all species within the dynamic fossil fuel emission model, based on atmospheric mole fraction observations. The advantage of this novel approach is that the optimized parameters (emission factors and emission ratios, N=44) in this dynamic emission model (a) vary much less over space and time, (b) allow for a physical interpretation of mean and uncertainty, and (c) have better defined uncertainties and covariance structure. This makes them more suited to extrapolate, optimize, and interpret than the gridded emissions themselves. The merits of this approach are investigated using a pseudo-observation-based ensemble Kalman filter inversion set-up for the Dutch Rijnmond area at 1 km×1 km resolution. We find that the fossil fuel emission model approximates the gridded emissions well (annual mean differences

Description: Monitoring urban–industrial emissions is often challenging because observations are scarce and regional atmospheric transport models are too coarse to represent the high spatiotemporal variability in the resulting concentrations. In this paper we apply a new combination of an Eulerian model (Weather Research and Forecast, WRF, with chemistry) and a Gaussian plume model (Operational Priority Substances – OPS). The modelled mixing ratios are compared to observed CO2 and CO mole fractions at four sites along a transect from an urban–industrial complex (Rotterdam, the Netherlands) towards rural conditions for October–December 2014. Urban plumes are well-mixed at our semi-urban location, making this location suited for an integrated emission estimate over the whole study area. The signals at our urban measurement site (with average enhancements of 11 ppm CO2 and 40 ppb CO over the baseline) are highly variable due to the presence of distinct source areas dominated by road traffic/residential heating emissions or industrial activities. This causes different emission signatures that are translated into a large variability in observed ΔCO : ΔCO2 ratios, which can be used to identify dominant source types. We find that WRF-Chem is able to represent synoptic variability in CO2 and CO (e.g. the median CO2 mixing ratio is 9.7 ppm, observed, against 8.8 ppm, modelled), but it fails to reproduce the hourly variability of daytime urban plumes at the urban site (R2 up to 0.05). For the urban site, adding a plume model to the model framework is beneficial to adequately represent plume transport especially from stack emissions. The explained variance in hourly, daytime CO2 enhancements from point source emissions increases from 30 % with WRF-Chem to 52 % with WRF-Chem in combination with the most detailed OPS simulation. The simulated variability in ΔCO :  ΔCO2 ratios decreases drastically from 1.5 to 0.6 ppb ppm−1, which agrees better with the observed standard deviation of 0.4 ppb ppm−1. This is partly due to improved wind fields (increase in R2 of 0.10) but also due to improved point source representation (increase in R2 of 0.05) and dilution (increase in R2 of 0.07). Based on our analysis we conclude that a plume model with detailed and accurate dispersion parameters adds substantially to top–down monitoring of greenhouse gas emissions in urban environments with large point source contributions within a  ∼  10 km radius from the observation sites.

Description: Monitoring urban-industrial emissions is often challenging, because observations are scarce and regional atmospheric transport models are too coarse to represent the high spatiotemporal variability in the resulting concentrations. In this paper we apply a new combination of a Eulerian model (WRF with chemistry) and a Gaussian plume model (OPS). The modelled mixing ratios are compared to observed CO2 and CO mole fractions at four sites along a transect from an urban-industrial complex (Rotterdam, Netherlands) towards rural conditions for October–December 2014. Urban plumes are well-mixed at our semi-urban location, making this location suited for an integrated emission estimate over the whole study area. The signals at our urban measurement site (with average enhancements of 11 ppm CO2 and 40 ppb CO over the baseline) are highly variable due to the presence of distinct source areas dominated by road traffic/residential heating emissions or industrial activities. This causes different emission signatures that are translated into a large variability in observed ΔCO : ΔCO2 ratios, which can be used to identify dominant source types. We find that WRF-Chem is able to represent synoptic variability in CO2 and CO (e.g. the median CO2 mixing ratio is 9.7 ppm (observed) against 8.7 ppm (modelled)) , but it fails to reproduce the hourly variability of daytime urban plumes at the urban site (R2 up to 0.05). For the urban site, a plume model should be added to the model framework to adequately represent plume transport especially from stack emissions. The explained variance in hourly, daytime CO2 enhancements from point source emissions increases from 30 % with WRF-Chem to 52 % with WRF-Chem in combination with the most detailed OPS simulation. The simulated variability in ΔCO : ΔCO2 ratios decreases drastically from 1.5 to 0.6 ppb ppm−1 which agrees better with the observed standard deviation of 0.4 ppb ppm−1. This is partly due to improved wind fields (increase in R2 of 0.10), but also due to improved point source representation (increase in R2 of 0.05) and dilution (increase in R2 of 0.07). Based on our analysis we conclude that a plume model with detailed and accurate dispersion parameters is inevitable for top-down monitoring of greenhouse gas emissions in urban environments with large point source contributions within a ~ 10 km radius from the observation sites.

Description: Monitoring urban-industrial emissions is often challenging, because observations are scarce and regional atmospheric transport models are too coarse to represent the high spatiotemporal variability in the resulting concentrations. In this paper we present a new combination of a Eulerian model (WRF-Chem with an urban parameterisation) and a Lagrangian transport-deposition model (OPS), demonstrating that a plume model strongly improves our ability to capture urban plume transport. This follows from a comparison to observed CO2 and CO mole fractions at four sites along a transect from an urban-industrial complex (Rotterdam, Netherlands) towards rural conditions. At the urban measurement site we find strong enhancements of up to 33.1 ppm CO2 and 84 ppb CO over the rural background concentrations. These signals are highly variable due to the presence of distinct source areas dominated by road traffic/residential heating emissions or industrial activities. This causes different emission signatures that are observed in the CO : CO2 ratios and can be well-reproduced with our framework, suggesting that top-down emission monitoring within this urban-industrial complex is feasible. Further downwind from the city, the urban plume is less frequently observed and its concentration becomes smaller and less variable, making these locations more suited for an integrated emission estimate over the whole study area. We find that WRF-Chem, although able to represent mesoscale patterns, lacks spatiotemporal detail to reproduce the timing, magnitude and variability of urban plumes at the regional background and urban sites. The implementation of the OPS plume model improves the simulation of the CO2 and CO enhancements. The bias for extreme CO2 pollution events is reduced with almost 80 % from 15.4 to 3.4 ppm, while the reproducible fraction of observed variability over 750 measurements more than doubles (to 38 %) with the use of a plume model. Therefore, we argue that a plume model with detailed and accurate dispersion parameters is crucial for top-down monitoring of greenhouse gas emissions in urban environments. Future research could benefit from assimilating observed wind fields to improve the plume representation at urban scales.

Description: 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 % for CO2 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.