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Atmospheric Chemistry and Physics An interactive open-access journal of the European Geosciences Union
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Volume 17, issue 21 | Copyright
Atmos. Chem. Phys., 17, 13297-13316, 2017
https://doi.org/10.5194/acp-17-13297-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 09 Nov 2017

Research article | 09 Nov 2017

A multi-model approach to monitor emissions of CO2 and CO from an urban–industrial complex

Ingrid Super1,2, Hugo A. C. Denier van der Gon2, Michiel K. van der Molen1, Hendrika A. M. Sterk3, Arjan Hensen4, and Wouter Peters1,5 Ingrid Super et al.
  • 1Meteorology and Air Quality Group, Wageningen University, P.O. Box 47, 6700 AA Wageningen, the Netherlands
  • 2Department of Climate, Air and Sustainability, TNO, P.O. Box 80015, 3508 TA Utrecht, the Netherlands
  • 3National Institute for Public Health and the Environment, P.O. Box 1, 3720 BA Bilthoven, the Netherlands
  • 4Energy research Centre of the Netherlands, P.O. Box 1, 1755 ZG Petten, the Netherlands
  • 5Centre for Isotope Research, Energy and Sustainability Research Institute Groningen, University of Groningen, Nijenborgh 4, 9747 AG Groningen, the Netherlands

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 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 11ppm CO2 and 40ppb 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 9.7ppm, observed, against 8.8ppm, 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 0.6ppb ppm−1, which agrees better with the observed standard deviation of 0.4ppb 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  ∼ 10km radius from the observation sites.

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In this research we examined the use of different models to simulate CO2 concentrations in and around urban areas. We find that in the presence of large stack emissions in a gridded model is insufficient to represent the small dimensions of the CO2 plumes. A plume model improves this representation up to 10–14 km from the stack. Better model results can improve the estimate of CO2 emissions from urban areas and assist in identifying efficient emission reduction policies.
In this research we examined the use of different models to simulate CO2 concentrations in and...
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