A multi-model approach to monitor emissions of CO<sub>2</sub> and CO from an urban–industrial complex
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 (Wea...
Main Authors: | , , , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
Copernicus Publications
2017-11-01
|
Series: | Atmospheric Chemistry and Physics |
Online Access: | https://www.atmos-chem-phys.net/17/13297/2017/acp-17-13297-2017.pdf |
id |
doaj-59079b4e686c4ab0a5f8cb587c2f1f61 |
---|---|
record_format |
Article |
spelling |
doaj-59079b4e686c4ab0a5f8cb587c2f1f612020-11-24T22:55:12ZengCopernicus PublicationsAtmospheric Chemistry and Physics1680-73161680-73242017-11-0117132971331610.5194/acp-17-13297-2017A multi-model approach to monitor emissions of CO<sub>2</sub> and CO from an urban–industrial complexI. Super0I. Super1H. A. C. Denier van der Gon2M. K. van der Molen3H. A. M. Sterk4A. Hensen5W. Peters6W. Peters7Meteorology and Air Quality Group, Wageningen University, P.O. Box 47, 6700 AA Wageningen, the NetherlandsDepartment of Climate, Air and Sustainability, TNO, P.O. Box 80015, 3508 TA Utrecht, the NetherlandsDepartment of Climate, Air and Sustainability, TNO, P.O. Box 80015, 3508 TA Utrecht, the NetherlandsMeteorology and Air Quality Group, Wageningen University, P.O. Box 47, 6700 AA Wageningen, the NetherlandsNational Institute for Public Health and the Environment, P.O. Box 1, 3720 BA Bilthoven, the NetherlandsEnergy research Centre of the Netherlands, P.O. Box 1, 1755 ZG Petten, the NetherlandsMeteorology and Air Quality Group, Wageningen University, P.O. Box 47, 6700 AA Wageningen, the NetherlandsCentre for Isotope Research, Energy and Sustainability Research Institute Groningen, University of Groningen, Nijenborgh 4, 9747 AG Groningen, the NetherlandsMonitoring 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<sub>2</sub> 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<sub>2</sub> 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 : ΔCO<sub>2</sub> ratios, which can be used to identify dominant source types. We find that WRF-Chem is able to represent synoptic variability in CO<sub>2</sub> and CO (e.g. the median CO<sub>2</sub> 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 (<i>R</i><sup>2</sup> 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<sub>2</sub> 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 :  ΔCO<sub>2</sub> ratios decreases drastically from 1.5 to 0.6 ppb ppm<sup>−1</sup>, which agrees better with the observed standard deviation of 0.4 ppb ppm<sup>−1</sup>. This is partly due to improved wind fields (increase in <i>R</i><sup>2</sup> of 0.10) but also due to improved point source representation (increase in <i>R</i><sup>2</sup> of 0.05) and dilution (increase in <i>R</i><sup>2</sup> 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.https://www.atmos-chem-phys.net/17/13297/2017/acp-17-13297-2017.pdf |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
I. Super I. Super H. A. C. Denier van der Gon M. K. van der Molen H. A. M. Sterk A. Hensen W. Peters W. Peters |
spellingShingle |
I. Super I. Super H. A. C. Denier van der Gon M. K. van der Molen H. A. M. Sterk A. Hensen W. Peters W. Peters A multi-model approach to monitor emissions of CO<sub>2</sub> and CO from an urban–industrial complex Atmospheric Chemistry and Physics |
author_facet |
I. Super I. Super H. A. C. Denier van der Gon M. K. van der Molen H. A. M. Sterk A. Hensen W. Peters W. Peters |
author_sort |
I. Super |
title |
A multi-model approach to monitor emissions of CO<sub>2</sub> and CO from an urban–industrial complex |
title_short |
A multi-model approach to monitor emissions of CO<sub>2</sub> and CO from an urban–industrial complex |
title_full |
A multi-model approach to monitor emissions of CO<sub>2</sub> and CO from an urban–industrial complex |
title_fullStr |
A multi-model approach to monitor emissions of CO<sub>2</sub> and CO from an urban–industrial complex |
title_full_unstemmed |
A multi-model approach to monitor emissions of CO<sub>2</sub> and CO from an urban–industrial complex |
title_sort |
multi-model approach to monitor emissions of co<sub>2</sub> and co from an urban–industrial complex |
publisher |
Copernicus Publications |
series |
Atmospheric Chemistry and Physics |
issn |
1680-7316 1680-7324 |
publishDate |
2017-11-01 |
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 CO<sub>2</sub> 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<sub>2</sub>
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 : ΔCO<sub>2</sub> ratios, which can be used to identify
dominant source types. We find that WRF-Chem is able to represent synoptic
variability in CO<sub>2</sub> and CO (e.g. the median CO<sub>2</sub> 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 (<i>R</i><sup>2</sup> 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<sub>2</sub> 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 :  ΔCO<sub>2</sub> ratios decreases drastically from 1.5 to
0.6 ppb ppm<sup>−1</sup>, which agrees better with the observed standard
deviation of 0.4 ppb ppm<sup>−1</sup>. This is partly due to improved wind
fields (increase in <i>R</i><sup>2</sup> of 0.10) but also due to improved point source
representation (increase in <i>R</i><sup>2</sup> of 0.05) and dilution (increase in <i>R</i><sup>2</sup> 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. |
url |
https://www.atmos-chem-phys.net/17/13297/2017/acp-17-13297-2017.pdf |
work_keys_str_mv |
AT isuper amultimodelapproachtomonitoremissionsofcosub2subandcofromanurbanindustrialcomplex AT isuper amultimodelapproachtomonitoremissionsofcosub2subandcofromanurbanindustrialcomplex AT hacdeniervandergon amultimodelapproachtomonitoremissionsofcosub2subandcofromanurbanindustrialcomplex AT mkvandermolen amultimodelapproachtomonitoremissionsofcosub2subandcofromanurbanindustrialcomplex AT hamsterk amultimodelapproachtomonitoremissionsofcosub2subandcofromanurbanindustrialcomplex AT ahensen amultimodelapproachtomonitoremissionsofcosub2subandcofromanurbanindustrialcomplex AT wpeters amultimodelapproachtomonitoremissionsofcosub2subandcofromanurbanindustrialcomplex AT wpeters amultimodelapproachtomonitoremissionsofcosub2subandcofromanurbanindustrialcomplex AT isuper multimodelapproachtomonitoremissionsofcosub2subandcofromanurbanindustrialcomplex AT isuper multimodelapproachtomonitoremissionsofcosub2subandcofromanurbanindustrialcomplex AT hacdeniervandergon multimodelapproachtomonitoremissionsofcosub2subandcofromanurbanindustrialcomplex AT mkvandermolen multimodelapproachtomonitoremissionsofcosub2subandcofromanurbanindustrialcomplex AT hamsterk multimodelapproachtomonitoremissionsofcosub2subandcofromanurbanindustrialcomplex AT ahensen multimodelapproachtomonitoremissionsofcosub2subandcofromanurbanindustrialcomplex AT wpeters multimodelapproachtomonitoremissionsofcosub2subandcofromanurbanindustrialcomplex AT wpeters multimodelapproachtomonitoremissionsofcosub2subandcofromanurbanindustrialcomplex |
_version_ |
1725657481722462208 |