The quantification of NO<sub><i>x</i></sub> and SO<sub>2</sub> point source emission flux errors of mobile differential optical absorption spectroscopy on the basis of the Gaussian dispersion model: a simulation study

<p>Mobile differential optical absorption spectroscopy (mobile DOAS) has become an important tool for the quantification of emission sources, including point sources (e.g., individual power plants) and area emitters (e.g., entire cities). In this study, we focused on the error budget of mobile...

Full description

Bibliographic Details
Main Authors: Y. Huang, A. Li, T. Wagner, Y. Wang, Z. Hu, P. Xie, J. Xu, H. Ren, J. Remmers, X. Fang, B. Dang
Format: Article
Language:English
Published: Copernicus Publications 2020-11-01
Series:Atmospheric Measurement Techniques
Online Access:https://amt.copernicus.org/articles/13/6025/2020/amt-13-6025-2020.pdf
id doaj-a5040b1e01db44c39518e06ccd76ad94
record_format Article
collection DOAJ
language English
format Article
sources DOAJ
author Y. Huang
Y. Huang
A. Li
T. Wagner
Y. Wang
Z. Hu
P. Xie
P. Xie
P. Xie
J. Xu
H. Ren
H. Ren
J. Remmers
X. Fang
B. Dang
spellingShingle Y. Huang
Y. Huang
A. Li
T. Wagner
Y. Wang
Z. Hu
P. Xie
P. Xie
P. Xie
J. Xu
H. Ren
H. Ren
J. Remmers
X. Fang
B. Dang
The quantification of NO<sub><i>x</i></sub> and SO<sub>2</sub> point source emission flux errors of mobile differential optical absorption spectroscopy on the basis of the Gaussian dispersion model: a simulation study
Atmospheric Measurement Techniques
author_facet Y. Huang
Y. Huang
A. Li
T. Wagner
Y. Wang
Z. Hu
P. Xie
P. Xie
P. Xie
J. Xu
H. Ren
H. Ren
J. Remmers
X. Fang
B. Dang
author_sort Y. Huang
title The quantification of NO<sub><i>x</i></sub> and SO<sub>2</sub> point source emission flux errors of mobile differential optical absorption spectroscopy on the basis of the Gaussian dispersion model: a simulation study
title_short The quantification of NO<sub><i>x</i></sub> and SO<sub>2</sub> point source emission flux errors of mobile differential optical absorption spectroscopy on the basis of the Gaussian dispersion model: a simulation study
title_full The quantification of NO<sub><i>x</i></sub> and SO<sub>2</sub> point source emission flux errors of mobile differential optical absorption spectroscopy on the basis of the Gaussian dispersion model: a simulation study
title_fullStr The quantification of NO<sub><i>x</i></sub> and SO<sub>2</sub> point source emission flux errors of mobile differential optical absorption spectroscopy on the basis of the Gaussian dispersion model: a simulation study
title_full_unstemmed The quantification of NO<sub><i>x</i></sub> and SO<sub>2</sub> point source emission flux errors of mobile differential optical absorption spectroscopy on the basis of the Gaussian dispersion model: a simulation study
title_sort quantification of no<sub><i>x</i></sub> and so<sub>2</sub> point source emission flux errors of mobile differential optical absorption spectroscopy on the basis of the gaussian dispersion model: a simulation study
publisher Copernicus Publications
series Atmospheric Measurement Techniques
issn 1867-1381
1867-8548
publishDate 2020-11-01
description <p>Mobile differential optical absorption spectroscopy (mobile DOAS) has become an important tool for the quantification of emission sources, including point sources (e.g., individual power plants) and area emitters (e.g., entire cities). In this study, we focused on the error budget of mobile DOAS measurements from point sources, and we also offered recommendations for the optimum settings of such measurements via a simulation with a modified Gaussian plume model. Following the analysis, we conclude that (1) the proper sampling resolution should be between 5 and 50&thinsp;m. (2) When measuring far from the source, undetectable flux (measured slant column densities (SCDs) are under the detection limit) resulting from wind dispersion is the main error source. The threshold for the undetectable flux can be lowered by larger integration time. When measuring close to the source, low sampling frequency results in large errors, and wind field uncertainty becomes the main error source of SO<span class="inline-formula"><sub>2</sub></span> flux (for NO<span class="inline-formula"><sub><i>x</i></sub></span> this error also increases, but other error sources dominate). More measurement times can lower the flux error that results from wind field uncertainty. The proper wind speed for mobile DOAS measurements is between 1 and 4&thinsp;m&thinsp;s<span class="inline-formula"><sup>−1</sup></span>. (3) The remaining errors by [NO<span class="inline-formula"><sub><i>x</i></sub></span>]&thinsp;<span class="inline-formula">∕</span>&thinsp;[NO<span class="inline-formula"><sub>2</sub></span>] ratio correction can be significant when measuring very close. To minimize the [NO<span class="inline-formula"><sub><i>x</i></sub></span>]&thinsp;<span class="inline-formula">∕</span>&thinsp;[NO<span class="inline-formula"><sub>2</sub></span>] ratio correction error, we recommend minimum distances from the source, at which 5&thinsp;% of the NO<span class="inline-formula"><sub>2</sub></span> maximum reaction rate is reached and thus NO<span class="inline-formula"><sub><i>x</i></sub></span> steady state can be assumed. (4) Our study suggests that emission rates <span class="inline-formula">&lt;</span>&thinsp;30&thinsp;g&thinsp;s<span class="inline-formula"><sup>−1</sup></span> for NO<span class="inline-formula"><sub><i>x</i></sub></span> and <span class="inline-formula">&lt;</span>&thinsp;50&thinsp;g&thinsp;s<span class="inline-formula"><sup>−1</sup></span> for SO<span class="inline-formula"><sub>2</sub></span> are not recommended for mobile DOAS measurements.</p> <p>Based on the model simulations, our study indicates that mobile DOAS measurements are a very well-suited tool to quantify point source emissions. The results of our sensitivity studies are important to make optimum use of such measurements.</p>
url https://amt.copernicus.org/articles/13/6025/2020/amt-13-6025-2020.pdf
work_keys_str_mv AT yhuang thequantificationofnosubixisubandsosub2subpointsourceemissionfluxerrorsofmobiledifferentialopticalabsorptionspectroscopyonthebasisofthegaussiandispersionmodelasimulationstudy
AT yhuang thequantificationofnosubixisubandsosub2subpointsourceemissionfluxerrorsofmobiledifferentialopticalabsorptionspectroscopyonthebasisofthegaussiandispersionmodelasimulationstudy
AT ali thequantificationofnosubixisubandsosub2subpointsourceemissionfluxerrorsofmobiledifferentialopticalabsorptionspectroscopyonthebasisofthegaussiandispersionmodelasimulationstudy
AT twagner thequantificationofnosubixisubandsosub2subpointsourceemissionfluxerrorsofmobiledifferentialopticalabsorptionspectroscopyonthebasisofthegaussiandispersionmodelasimulationstudy
AT ywang thequantificationofnosubixisubandsosub2subpointsourceemissionfluxerrorsofmobiledifferentialopticalabsorptionspectroscopyonthebasisofthegaussiandispersionmodelasimulationstudy
AT zhu thequantificationofnosubixisubandsosub2subpointsourceemissionfluxerrorsofmobiledifferentialopticalabsorptionspectroscopyonthebasisofthegaussiandispersionmodelasimulationstudy
AT pxie thequantificationofnosubixisubandsosub2subpointsourceemissionfluxerrorsofmobiledifferentialopticalabsorptionspectroscopyonthebasisofthegaussiandispersionmodelasimulationstudy
AT pxie thequantificationofnosubixisubandsosub2subpointsourceemissionfluxerrorsofmobiledifferentialopticalabsorptionspectroscopyonthebasisofthegaussiandispersionmodelasimulationstudy
AT pxie thequantificationofnosubixisubandsosub2subpointsourceemissionfluxerrorsofmobiledifferentialopticalabsorptionspectroscopyonthebasisofthegaussiandispersionmodelasimulationstudy
AT jxu thequantificationofnosubixisubandsosub2subpointsourceemissionfluxerrorsofmobiledifferentialopticalabsorptionspectroscopyonthebasisofthegaussiandispersionmodelasimulationstudy
AT hren thequantificationofnosubixisubandsosub2subpointsourceemissionfluxerrorsofmobiledifferentialopticalabsorptionspectroscopyonthebasisofthegaussiandispersionmodelasimulationstudy
AT hren thequantificationofnosubixisubandsosub2subpointsourceemissionfluxerrorsofmobiledifferentialopticalabsorptionspectroscopyonthebasisofthegaussiandispersionmodelasimulationstudy
AT jremmers thequantificationofnosubixisubandsosub2subpointsourceemissionfluxerrorsofmobiledifferentialopticalabsorptionspectroscopyonthebasisofthegaussiandispersionmodelasimulationstudy
AT xfang thequantificationofnosubixisubandsosub2subpointsourceemissionfluxerrorsofmobiledifferentialopticalabsorptionspectroscopyonthebasisofthegaussiandispersionmodelasimulationstudy
AT bdang thequantificationofnosubixisubandsosub2subpointsourceemissionfluxerrorsofmobiledifferentialopticalabsorptionspectroscopyonthebasisofthegaussiandispersionmodelasimulationstudy
AT yhuang quantificationofnosubixisubandsosub2subpointsourceemissionfluxerrorsofmobiledifferentialopticalabsorptionspectroscopyonthebasisofthegaussiandispersionmodelasimulationstudy
AT yhuang quantificationofnosubixisubandsosub2subpointsourceemissionfluxerrorsofmobiledifferentialopticalabsorptionspectroscopyonthebasisofthegaussiandispersionmodelasimulationstudy
AT ali quantificationofnosubixisubandsosub2subpointsourceemissionfluxerrorsofmobiledifferentialopticalabsorptionspectroscopyonthebasisofthegaussiandispersionmodelasimulationstudy
AT twagner quantificationofnosubixisubandsosub2subpointsourceemissionfluxerrorsofmobiledifferentialopticalabsorptionspectroscopyonthebasisofthegaussiandispersionmodelasimulationstudy
AT ywang quantificationofnosubixisubandsosub2subpointsourceemissionfluxerrorsofmobiledifferentialopticalabsorptionspectroscopyonthebasisofthegaussiandispersionmodelasimulationstudy
AT zhu quantificationofnosubixisubandsosub2subpointsourceemissionfluxerrorsofmobiledifferentialopticalabsorptionspectroscopyonthebasisofthegaussiandispersionmodelasimulationstudy
AT pxie quantificationofnosubixisubandsosub2subpointsourceemissionfluxerrorsofmobiledifferentialopticalabsorptionspectroscopyonthebasisofthegaussiandispersionmodelasimulationstudy
AT pxie quantificationofnosubixisubandsosub2subpointsourceemissionfluxerrorsofmobiledifferentialopticalabsorptionspectroscopyonthebasisofthegaussiandispersionmodelasimulationstudy
AT pxie quantificationofnosubixisubandsosub2subpointsourceemissionfluxerrorsofmobiledifferentialopticalabsorptionspectroscopyonthebasisofthegaussiandispersionmodelasimulationstudy
AT jxu quantificationofnosubixisubandsosub2subpointsourceemissionfluxerrorsofmobiledifferentialopticalabsorptionspectroscopyonthebasisofthegaussiandispersionmodelasimulationstudy
AT hren quantificationofnosubixisubandsosub2subpointsourceemissionfluxerrorsofmobiledifferentialopticalabsorptionspectroscopyonthebasisofthegaussiandispersionmodelasimulationstudy
AT hren quantificationofnosubixisubandsosub2subpointsourceemissionfluxerrorsofmobiledifferentialopticalabsorptionspectroscopyonthebasisofthegaussiandispersionmodelasimulationstudy
AT jremmers quantificationofnosubixisubandsosub2subpointsourceemissionfluxerrorsofmobiledifferentialopticalabsorptionspectroscopyonthebasisofthegaussiandispersionmodelasimulationstudy
AT xfang quantificationofnosubixisubandsosub2subpointsourceemissionfluxerrorsofmobiledifferentialopticalabsorptionspectroscopyonthebasisofthegaussiandispersionmodelasimulationstudy
AT bdang quantificationofnosubixisubandsosub2subpointsourceemissionfluxerrorsofmobiledifferentialopticalabsorptionspectroscopyonthebasisofthegaussiandispersionmodelasimulationstudy
_version_ 1724427755822514176
spelling doaj-a5040b1e01db44c39518e06ccd76ad942020-11-25T04:07:50ZengCopernicus PublicationsAtmospheric Measurement Techniques1867-13811867-85482020-11-01136025605110.5194/amt-13-6025-2020The quantification of NO<sub><i>x</i></sub> and SO<sub>2</sub> point source emission flux errors of mobile differential optical absorption spectroscopy on the basis of the Gaussian dispersion model: a simulation studyY. Huang0Y. Huang1A. Li2T. Wagner3Y. Wang4Z. Hu5P. Xie6P. Xie7P. Xie8J. Xu9H. Ren10H. Ren11J. Remmers12X. Fang13B. Dang14Key Laboratory of Environmental Optics and Technology, Anhui Institute of Optics and Fine Mechanics, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, 230031, ChinaUniversity of Science and Technology of China, Hefei, 230026, ChinaKey Laboratory of Environmental Optics and Technology, Anhui Institute of Optics and Fine Mechanics, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, 230031, ChinaMax Planck Institute for Chemistry, Mainz, GermanyMax Planck Institute for Chemistry, Mainz, GermanyKey Laboratory of Environmental Optics and Technology, Anhui Institute of Optics and Fine Mechanics, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, 230031, ChinaKey Laboratory of Environmental Optics and Technology, Anhui Institute of Optics and Fine Mechanics, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, 230031, ChinaUniversity of Science and Technology of China, Hefei, 230026, ChinaCAS Center for Excellence in Regional Atmospheric Environment, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen, 361000, ChinaKey Laboratory of Environmental Optics and Technology, Anhui Institute of Optics and Fine Mechanics, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, 230031, ChinaKey Laboratory of Environmental Optics and Technology, Anhui Institute of Optics and Fine Mechanics, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, 230031, ChinaUniversity of Science and Technology of China, Hefei, 230026, ChinaMax Planck Institute for Chemistry, Mainz, GermanyChinese Academy of Meteorological Science, Beijing, 100081, ChinaBeijing Municipal Climate Center, Beijing, 100089, China<p>Mobile differential optical absorption spectroscopy (mobile DOAS) has become an important tool for the quantification of emission sources, including point sources (e.g., individual power plants) and area emitters (e.g., entire cities). In this study, we focused on the error budget of mobile DOAS measurements from point sources, and we also offered recommendations for the optimum settings of such measurements via a simulation with a modified Gaussian plume model. Following the analysis, we conclude that (1) the proper sampling resolution should be between 5 and 50&thinsp;m. (2) When measuring far from the source, undetectable flux (measured slant column densities (SCDs) are under the detection limit) resulting from wind dispersion is the main error source. The threshold for the undetectable flux can be lowered by larger integration time. When measuring close to the source, low sampling frequency results in large errors, and wind field uncertainty becomes the main error source of SO<span class="inline-formula"><sub>2</sub></span> flux (for NO<span class="inline-formula"><sub><i>x</i></sub></span> this error also increases, but other error sources dominate). More measurement times can lower the flux error that results from wind field uncertainty. The proper wind speed for mobile DOAS measurements is between 1 and 4&thinsp;m&thinsp;s<span class="inline-formula"><sup>−1</sup></span>. (3) The remaining errors by [NO<span class="inline-formula"><sub><i>x</i></sub></span>]&thinsp;<span class="inline-formula">∕</span>&thinsp;[NO<span class="inline-formula"><sub>2</sub></span>] ratio correction can be significant when measuring very close. To minimize the [NO<span class="inline-formula"><sub><i>x</i></sub></span>]&thinsp;<span class="inline-formula">∕</span>&thinsp;[NO<span class="inline-formula"><sub>2</sub></span>] ratio correction error, we recommend minimum distances from the source, at which 5&thinsp;% of the NO<span class="inline-formula"><sub>2</sub></span> maximum reaction rate is reached and thus NO<span class="inline-formula"><sub><i>x</i></sub></span> steady state can be assumed. (4) Our study suggests that emission rates <span class="inline-formula">&lt;</span>&thinsp;30&thinsp;g&thinsp;s<span class="inline-formula"><sup>−1</sup></span> for NO<span class="inline-formula"><sub><i>x</i></sub></span> and <span class="inline-formula">&lt;</span>&thinsp;50&thinsp;g&thinsp;s<span class="inline-formula"><sup>−1</sup></span> for SO<span class="inline-formula"><sub>2</sub></span> are not recommended for mobile DOAS measurements.</p> <p>Based on the model simulations, our study indicates that mobile DOAS measurements are a very well-suited tool to quantify point source emissions. The results of our sensitivity studies are important to make optimum use of such measurements.</p>https://amt.copernicus.org/articles/13/6025/2020/amt-13-6025-2020.pdf