Simulating the integrated summertime Δ&amp;lt;sup&amp;gt;14&amp;lt;/sup&amp;gt;CO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt; signature from anthropogenic emissions over Western Europe

Bozhinova, Denica; Molen, M. K. van der; Velde, Ivar R. van der; Krol, Maarten; Laan, S. van der; Meijer, H. A. J.; Peters, Wouter

doi:10.5194/acp-14-7273-2014

Cited by 21 publications

(46 citation statements)

References 86 publications

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“…Table 3 shows that, respectively, 65 and 53 % of the variability in the CO 2 and CO mixing ratios is captured at the Westmaas background site. Although the explained variances are slightly smaller at the urban (Zweth) and semiurban (Cabauw) site, the performance at Cabauw for CO 2 is comparable to previous modelling studies (Bozhinova et al, 2014;Tolk et al, 2009). However, the RMSE (root mean square error) is relatively large for CO and CO 2 at all sites.…”

Section: Wrf-chem Urban Plume Transportsupporting

confidence: 68%

“…The ability of our WRF-Chem framework to rep-resent daytime average mixing ratios is comparable with other model frameworks in the urban environment (Bozhinova et al, 2014;Bréon et al, 2015;Lac et al, 2013;Tolk et al, 2009). However, WRF-Chem has a large wind direction bias that makes it difficult to compare modelled and observed mole fractions.…”

Section: Model Skillmentioning

confidence: 53%

“…Similar to the original study of Bozhinova et al (2014), we omitted the stratosphere-troposphere exchange and ocean fluxes and assume that they are accounted for in the largescale background. With Eq.…”

Section: Eulerian Modelmentioning

confidence: 99%

“…The biogenic (non-biofuel) CO 2 fluxes in WRF-Chem are generated as described by Bozhinova et al (2014). The SiB-CASA model (Schaefer et al, 2008) calculates monthly averaged 1 • × 1 • photosynthetic uptake (A n ) and ecosystem respiration (R) for nine different land use types.…”

Section: Emissionsmentioning

confidence: 99%

“…We have implemented a CO 2 budget based on the methodology used by Bozhinova et al (2014), described in Eq. (1).…”

Section: Eulerian Modelmentioning

confidence: 99%

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A multi-model approach to monitor emissions of CO<sub>2</sub> and CO from an urban–industrial complex

Super

Gon²,

Molen

et al. 2017

Atmos. Chem. Phys.

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Abstract. 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 CO 2 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 CO 2 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 CO : CO 2 ratios, which can be used to identify dominant source types. We find that WRF-Chem is able to represent synoptic variability in CO 2 and CO (e.g. the median CO 2 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 (R 2 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 CO 2 enhancements from point source emissions increases from 30 % with WRF-Chem to 52 % with WRFChem in combination with the most detailed OPS simulation. The simulated variability in CO : CO 2 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 R 2 of 0.10) but also due to improved point source representation (increase in R 2 of 0.05) and dilution (increase in R 2 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.

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Section: Wrf-chem Urban Plume Transportsupporting

confidence: 68%

Section: Model Skillmentioning

confidence: 53%

Section: Eulerian Modelmentioning

confidence: 99%

Section: Emissionsmentioning

confidence: 99%

“…We have implemented a CO 2 budget based on the methodology used by Bozhinova et al (2014), described in Eq. (1).…”

Section: Eulerian Modelmentioning

confidence: 99%

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A multi-model approach to monitor emissions of CO<sub>2</sub> and CO from an urban–industrial complex

Super

Gon²,

Molen

et al. 2017

Atmos. Chem. Phys.

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Interpreting continuous in-situ observations of carbon dioxide and carbon monoxide in the urban port area of Rotterdam

Super

Gon²,

Visschedijk³

et al. 2017

Atmospheric Pollution Research

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Three Years of Δ¹⁴CO₂Observations from Maize Leaves in the Netherlands and Western Europe

et al. 2016

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Atmospheric Δ14CO2 measurements are useful to investigate the regional signals of anthropogenic CO2 emissions, despite the currently scarce observational network for Δ14CO2. Plant samples are an easily attainable alternative, which have been shown to work well as a qualitative measure of the atmospheric Δ14CO2 signals integrated over the time a plant has grown. Here, we present the 14C analysis results for 89 individual maize (Zea mays) plant samples from 51 different locations that were gathered in the Netherlands in the years 2010 to 2012, and from western Germany and France in 2012. We describe our sampling strategy and results, and include a comparison to a model simulation of the Δ14CO2 that would be accumulated in each plant over a growing season. Our model simulates the Δ14CO2 signatures in good agreement with observed plant samples, resulting in a root-mean-square deviation (RMSD) of 3.30‰. This value is comparable to the measurement uncertainty, but still relatively large (20–50%) compared to the total signal. It is also comparable to the spread in Δ14CO2 values found across multiple plants from a single site, and to the spread found when averaging across larger regions. We nevertheless find that both measurements and model capture the large-scale (>100 km) regional Δ14CO2 gradients, with significant observation-model correlations in all three countries in which we collected samples. The modeled plant results suggest that the largest gradients found in the Netherlands and Germany are associated with emissions from energy production and road traffic, while in France, the 14CO2 enrichment from nuclear sources dominates in many samples. Overall, the required model-based interpretation of plant samples adds additional uncertainty to the already relatively large measurement uncertainty in Δ14CO2, and we suggest that future fossil fuel monitoring efforts should prioritize other strategies such as direct atmospheric sampling of CO2 and Δ14CO2.

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Simulating the integrated summertime Δ<sup>14</sup>CO<sub>2</sub> signature from anthropogenic emissions over Western Europe

Cited by 21 publications

References 86 publications

A multi-model approach to monitor emissions of CO<sub>2</sub> and CO from an urban–industrial complex

A multi-model approach to monitor emissions of CO<sub>2</sub> and CO from an urban–industrial complex

Interpreting continuous in-situ observations of carbon dioxide and carbon monoxide in the urban port area of Rotterdam

Three Years of Δ¹⁴CO₂Observations from Maize Leaves in the Netherlands and Western Europe

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Simulating the integrated summertime Δ&lt;sup&gt;14&lt;/sup&gt;CO&lt;sub&gt;2&lt;/sub&gt; signature from anthropogenic emissions over Western Europe

Cited by 21 publications

References 86 publications

A multi-model approach to monitor emissions of CO&lt;sub&gt;2&lt;/sub&gt; and CO from an urban–industrial complex

A multi-model approach to monitor emissions of CO&lt;sub&gt;2&lt;/sub&gt; and CO from an urban–industrial complex

Interpreting continuous in-situ observations of carbon dioxide and carbon monoxide in the urban port area of Rotterdam

Three Years of Δ14CO2Observations from Maize Leaves in the Netherlands and Western Europe

Contact Info

Product

Resources

About

Simulating the integrated summertime Δ<sup>14</sup>CO<sub>2</sub> signature from anthropogenic emissions over Western Europe

A multi-model approach to monitor emissions of CO<sub>2</sub> and CO from an urban–industrial complex

A multi-model approach to monitor emissions of CO<sub>2</sub> and CO from an urban–industrial complex

Three Years of Δ¹⁴CO₂Observations from Maize Leaves in the Netherlands and Western Europe