the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 15 May 2023
A simplified non-linear chemistry-transport model for analyzing NO2 column observations
Abstract. Satellites monitoring air pollutants (e.g., nitrogen oxides, NOx = NO + NO2) or greenhouse gases (GHGs) are widely utilized to understand the spatiotemporal variability and evolution of emission characteristics, chemical transformations, and atmospheric transport over anthropogenic "hotspots'". Recently, the joint use of space-based long-lived GHGs (e.g., carbon dioxide, CO2) and short-lived pollutants has made it possible to improve our understanding of emission characteristics. Some previous studies, however, lack consideration of the non-linear NOx chemistry or complex atmospheric transport. Considering the increase in satellite data volume and the demand for emission monitoring at higher spatiotemporal scales, it is crucial to construct a local-scale emission optimization system that can handle both long-lived GHGs and short-lived pollutants in a coupled and effective manner. This need motivates us to develop a Lagrangian chemical transport model that accounts for NOx chemistry and fine-scale atmospheric transport (STILT-NOx); and investigate how physical and chemical processes, anthropogenic emissions, and background may affect the interpretation of tropospheric NO2 columns (tNO2).
Interpreting emission signals from tNO2 commonly involves either an efficient statistical model or a sophisticated chemical transport model. To balance computational expenses and chemical complexity, we describe a simplified representation of the NOx chemistry that bypasses an explicit solution of individual chemical reactions while preserving the essential non-linearity that links NOx emissions to its concentrations. This NOx chemical parameterization is then incorporated into an existing Lagrangian modeling framework that is widely applied in the GHG community. We further quantify uncertainties associated with the wind field and chemical parameterization and evaluate modeled columns against retrieved columns from the TROPOspheric Monitoring Instrument (TROPOMI v2.1). Specifically, simulations with alternative model configurations of emissions, meteorology, chemistry, and inter-parcel mixing are carried out over three US power plants and two urban areas across seasons. Using EPA-reported emissions for power plants with non-linear NOx chemistry improves the model-data alignment in tNO2 (a high bias of ≤ 10 % on an annual basis), compared to simulations using either EDGAR or without chemistry (bias approaching 100 %). The largest model-data mismatches are associated with substantial biases in wind directions or conditions of slower atmospheric mixing and photochemistry. More importantly, our model development illustrates (1) how NOx chemistry affects the relationship between NOx and CO2 in terms of the spatial and seasonal variability and (2) how assimilating tNO2 can quantify systematic biases in modeled wind directions and emission distribution in prior inventories of NOx and CO2, which laid a foundation for a local-scale multi-tracer emission optimization system.
-
Notice on discussion status
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
-
Preprint
(13397 KB)
-
Supplement
(50749 KB)
-
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
(13397 KB) - Metadata XML
-
Supplement
(50749 KB) - BibTeX
- EndNote
- Final revised paper
Journal article(s) based on this preprint
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2023-876', Anonymous Referee #1, 13 Jun 2023
Satellite retrievals of NO2 columns are used to determine NOx emissions from power plants and cities. They are increasingly used alongside CO2 retrievals to calculate emissions from these sources. However, the effect of NOx chemistry and transport on NO2 columns is often overlooked. To address this issue, Wu et al. have developed a model that incorporates a simplified representation of NOx chemical loss within the STILT Lagrangian particle dispersion model. It includes additional features such as a column weighting module to account for retrieval averaging kernel profiles and an error analysis module. The model is evaluated against TROPOMI NO2 observations from three power plants and two cities. The manuscript covers the model's advantages, limitations, and applications such as using NO2-to-CO2 enhancement ratios to estimate CO2 emissions and identifying wind biases in meteorological data.
While this work is generally sound and well-presented, there are areas that I think need attention.
- NOx chemical tendency (Sect. 2.1):
- It appears that the model excludes heterogeneous NOx chemistry in aerosols. If this is indeed the case, it is important to discuss the resulting errors arising from this omission. Alternatively, if heterogeneous NOx chemistry is included, please clarify, and modify Fig. 1 accordingly to reflect this information.
- The NOx chemical tendency is parameterized as a function of NOx concentration and the solar zenith angle. It is unclear why these were the only two variables chosen and whether they account for most of the variation in the NOx chemical tendency. Knowing the fraction of variation explained by these variables would be useful. I expected temperature (as a proxy for seasons) and the NO2/NO ratio to be potentially important variables.
- The calculation of the NOx chemical tendency was based on WRF-Chem simulations for three cities: Los Angeles, Shanghai, and Madrid. The rationale behind selecting these specific cities seems arbitrary. They are unrelated to the power plants and cities that were chosen for model evaluation. It would be helpful to have an explanation for this choice.
- The assumption of the NOx chemical tendency being independent of height (Eq. 2) is not accurate considering the vertical gradients of NOx near the surface during nighttime and early mornings. It seems important to discuss any limitations arising from this.
- It would be useful to assess the consistency of the NOx chemical lifetime from WRF-Chem to the available observations, although limited.
- NO2-to-NOx ratio (Sect. 2.2):
- The reactions of NO + HO2 and NO + RO2 to form NO2 are excluded, but they are important in the boundary layer.
- Line 242: Please clarify how the NOx chemical tendency in the model change when ozone is titrated near high emitters.
- Eq 1: The processes of NO2 dry deposition and mixing between the mixed layer and the free troposphere seem to be neglected.
- Eq. 5 assumes that the model transport and chemistry errors are independent, when in fact these errors are related.
- Fig. 5 and elsewhere: Please clarify how tNO2, the tropospheric NO2 column, is converted to a volume mixing ratio.
- Line 382: Considering the model’s sensitivity to errors in the wind direction and speed, it may be better to use winds from reanalyses datasets or to bias correct the model using nearby observations. This seems important for the inversion work planned in the future. Are there other ways to reduce this error?
- Lines 564-570: These lines are unclear.
Citation: https://doi.org/10.5194/egusphere-2023-876-RC1 - AC3: 'Reply on RC1', Dien Wu, 19 Aug 2023
- NOx chemical tendency (Sect. 2.1):
-
RC2: 'Comment on egusphere-2023-876', Anonymous Referee #2, 16 Jun 2023
This paper introduces STILT-NOx, a Lagrangian chemical transport model, evaluates it against satellite based column observations of NOx, and presents various sensitivity studies. The paper is well written, and I recommend publishing after the following minor comments are addressed.
General comments:
Interparcel-mixing: I was a bit surprized to see the 3-hour timescale for mixing within a volume with a horizontal of 1km x 1km and a vertical extend from surface to PBL top described as “relatively fast mixing”. Given the fact that the plume emitted from a power plant, when transported over 3 hours (or 30 km for typical winds speeds of 10 m/s within a well-developed PBL), and given the shape of typical plumes seen from satellite imagery, can the mixing time scale not be estimated from that? Based on that I would expect that within three hours mixing likely occurs over much larger “boxes”. This is along the thought that dispersion/mixing is similar when running LPDMs backward and forward as shown in the Lin et al. 2003 paper, so forward mixing as needed for chemistry should be similar to backward mixing (or spreading of particles emitted at the same time). Would a smaller mixing timescale increase the impact of mixing, as with a 3-hour time the impact was found to be less than 5% (line 356)? Was that assessed when comparing no-mixing (i.e. infinite timescale) with mixing turned on? I think this deserves a bit of attention, as it keeps puzzling me that given the quite nonlinear property of NOx chemistry mixing at those scales does not seem to matter in chemistry-transport simulations.
Rotation of wind: As the wind changes significantly within the atmospheric boundary layer with height (the Ekman spiral), differences between modelled wind direction and the direction apparent from the observed plume can also be related to inaccuracies in the plume release height distribution, potentially associated with plume rise of the buoyant exhaust. I would recommend this to be discussed.
Specific comments:
Fig. 3: which WRF-Chem runs were used? Before three different cities were mentioned, are all those simulations included in Fig. 3?
L200: Were the WRF-Chem simulations also selected for cloud-free conditions?
Fig. 5 b and d: the color code is missing, or am I overlooking something?
L394: in table S1 the RMSE values range from 0.11 to 0.25 ppb
L 412: “fast-growing” is relative, Baotou certainly has a faster growth in population than Phoenix
Fig. 8 a: What does RMSP stand for?
Table 1 in the supplement should be named “Table S1”
Fig. S6: please use axis titles that clearly indicate v1 and v2 in all figures
Fig. S7: the symbols in the legend don’t quite fit with those in the figure. Triangles should be should only for EDGAR estimates, not for EPA.
Fig. S10 a and b: please use fewer x-axis labels
Citation: https://doi.org/10.5194/egusphere-2023-876-RC2 - AC4: 'Reply on RC2', Dien Wu, 19 Aug 2023
-
CEC1: 'Comment on egusphere-2023-876', Juan Antonio Añel, 19 Jun 2023
Dear authors,
Unfortunately, after checking your manuscript, it has come to our attention that it does not comply with our "Code and Data Policy".
https://www.geoscientific-model-development.net/policies/code_and_data_policy.htmlYou state in your manuscript that you have not published the code used in your work and that you will do it later. This is highly irregular, and I must make clear that your manuscript should have never been accepted for Discussions given this issue. In this way, you must publish in a prompt manner the code of the model (and any other code that you use to produce your work) in one of the repositories listed in our policy. I should note that you mention GitHub in your submission; however, GitHub is not a suitable repository for scientific publication, which is clearly mentioned in our policy too. GitHub itself instructs authors to use other alternatives for long-term archival and publishing.
Moreover, in the title of your manuscript, you do not include the name and version of the model used. This is something required by our policy and author guidelines too.
Therefore, please, publish your code in one of the appropriate repositories, and reply to this comment with the relevant information (link and DOI), and a new title for your manuscript, as soon as possible, as it must be available for the Discussions stage. Also, please, include all the relevant primary input and output data.
Please, also remember that you must include in a potentially reviewed version of your manuscript the modified title and the "Code and Data Availability" section.
Note that if you do not fix all these issues, we will have to reject your manuscript for publication in our journal.
Juan A. Añel
Geosci. Model Dev. Exec. EditorCitation: https://doi.org/10.5194/egusphere-2023-876-CEC1 -
AC1: 'Reply on CEC1', Dien Wu, 20 Jun 2023
Dear Juan A. Añel, GMD Executive Editor,
We apologize for not providing the model script timely and have now released a fixed version of the STILT-NOx model code on Zenodo ("STILT-NOx v1 for GMD submission") with a doi of https://zenodo.org/record/8057850. The key input/output data including the preprocessed EDGARv6 emissions and the essential NOx curves are included in the /data subdirectory. The NOx chemistry module contains functions under r/src/chem_lifetime.
The revised title would be "A simplified non-linear chemistry-transport model for analyzing NO2 column observations: STILT-NOx v1".
The revised Code and Data Availability section is "The STILT-NOx v1 model is built on previous efforts of the X-STILT model in modeling NO2. The exact version used in the discussion paper is archived on Zenodo with a doi of https://zenodo.org/record/8057850. TROPOMI Level 2 NO2 data and OCO-3 Level 2 B10p4r XCO2 data were accessed from 10.5270/S5P-9bnp8q8 and 10.5067/970BCC4DHH24, respectively. EDGARv6.1 emissions are accessed from https://data.jrc.ec.europa.eu/dataset/df521e05-6a3b-461c-965a-b703fb62313e and have been preprocessed."
We will revise all relevant content in a potentially reviewed version of the manuscript. Thank you for the constructive comments.
Dien Wu
Citation: https://doi.org/10.5194/egusphere-2023-876-AC1 -
CEC2: 'Reply on AC1', Juan Antonio Añel, 20 Jun 2023
Dear authors,
Many thanks for your quick reply. The information that you provide is now sufficient. However, there are a few issues that you should address:
- First, I recommend that you clarify in the reviewed version of your Code and Data Availability section that the numbers that you provide for TROPOMI and OCO data are DOIs, something that, with the current wording, is unclear.
- In the Zenodo repository for the code, in the Readme file, it states "in this GitHub repository". I understand that this is inherited from moving the code from GitHub to Zenodo; however, it would be good if you could fix it and, in the information in the Zenodo repository, refer to it.
- Also, in the Zenodo repository, there is no license listed for the code. If you do not include a license, despite what you state saying that the license is "Other", the code continues to be your property, and nobody can use it, precluding the possibility of reproducing your work. Therefore, you should include a license for your code. You could want to choose a free software/open-source (FLOSS) license. We recommend the GPLv3. You only need to include the file 'https://www.gnu.org/licenses/gpl-3.0.txt' as LICENSE.txt with your code. Also, you can choose other options that Zenodo provides: GPLv2, Apache License, MIT License, etc.
Regards,
Juan A. Añel
Geosci. Model Dev. Executive Editor
Citation: https://doi.org/10.5194/egusphere-2023-876-CEC2
-
CEC2: 'Reply on AC1', Juan Antonio Añel, 20 Jun 2023
-
AC1: 'Reply on CEC1', Dien Wu, 20 Jun 2023
- AC2: 'Comment on egusphere-2023-876', Dien Wu, 19 Aug 2023
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2023-876', Anonymous Referee #1, 13 Jun 2023
Satellite retrievals of NO2 columns are used to determine NOx emissions from power plants and cities. They are increasingly used alongside CO2 retrievals to calculate emissions from these sources. However, the effect of NOx chemistry and transport on NO2 columns is often overlooked. To address this issue, Wu et al. have developed a model that incorporates a simplified representation of NOx chemical loss within the STILT Lagrangian particle dispersion model. It includes additional features such as a column weighting module to account for retrieval averaging kernel profiles and an error analysis module. The model is evaluated against TROPOMI NO2 observations from three power plants and two cities. The manuscript covers the model's advantages, limitations, and applications such as using NO2-to-CO2 enhancement ratios to estimate CO2 emissions and identifying wind biases in meteorological data.
While this work is generally sound and well-presented, there are areas that I think need attention.
- NOx chemical tendency (Sect. 2.1):
- It appears that the model excludes heterogeneous NOx chemistry in aerosols. If this is indeed the case, it is important to discuss the resulting errors arising from this omission. Alternatively, if heterogeneous NOx chemistry is included, please clarify, and modify Fig. 1 accordingly to reflect this information.
- The NOx chemical tendency is parameterized as a function of NOx concentration and the solar zenith angle. It is unclear why these were the only two variables chosen and whether they account for most of the variation in the NOx chemical tendency. Knowing the fraction of variation explained by these variables would be useful. I expected temperature (as a proxy for seasons) and the NO2/NO ratio to be potentially important variables.
- The calculation of the NOx chemical tendency was based on WRF-Chem simulations for three cities: Los Angeles, Shanghai, and Madrid. The rationale behind selecting these specific cities seems arbitrary. They are unrelated to the power plants and cities that were chosen for model evaluation. It would be helpful to have an explanation for this choice.
- The assumption of the NOx chemical tendency being independent of height (Eq. 2) is not accurate considering the vertical gradients of NOx near the surface during nighttime and early mornings. It seems important to discuss any limitations arising from this.
- It would be useful to assess the consistency of the NOx chemical lifetime from WRF-Chem to the available observations, although limited.
- NO2-to-NOx ratio (Sect. 2.2):
- The reactions of NO + HO2 and NO + RO2 to form NO2 are excluded, but they are important in the boundary layer.
- Line 242: Please clarify how the NOx chemical tendency in the model change when ozone is titrated near high emitters.
- Eq 1: The processes of NO2 dry deposition and mixing between the mixed layer and the free troposphere seem to be neglected.
- Eq. 5 assumes that the model transport and chemistry errors are independent, when in fact these errors are related.
- Fig. 5 and elsewhere: Please clarify how tNO2, the tropospheric NO2 column, is converted to a volume mixing ratio.
- Line 382: Considering the model’s sensitivity to errors in the wind direction and speed, it may be better to use winds from reanalyses datasets or to bias correct the model using nearby observations. This seems important for the inversion work planned in the future. Are there other ways to reduce this error?
- Lines 564-570: These lines are unclear.
Citation: https://doi.org/10.5194/egusphere-2023-876-RC1 - AC3: 'Reply on RC1', Dien Wu, 19 Aug 2023
- NOx chemical tendency (Sect. 2.1):
-
RC2: 'Comment on egusphere-2023-876', Anonymous Referee #2, 16 Jun 2023
This paper introduces STILT-NOx, a Lagrangian chemical transport model, evaluates it against satellite based column observations of NOx, and presents various sensitivity studies. The paper is well written, and I recommend publishing after the following minor comments are addressed.
General comments:
Interparcel-mixing: I was a bit surprized to see the 3-hour timescale for mixing within a volume with a horizontal of 1km x 1km and a vertical extend from surface to PBL top described as “relatively fast mixing”. Given the fact that the plume emitted from a power plant, when transported over 3 hours (or 30 km for typical winds speeds of 10 m/s within a well-developed PBL), and given the shape of typical plumes seen from satellite imagery, can the mixing time scale not be estimated from that? Based on that I would expect that within three hours mixing likely occurs over much larger “boxes”. This is along the thought that dispersion/mixing is similar when running LPDMs backward and forward as shown in the Lin et al. 2003 paper, so forward mixing as needed for chemistry should be similar to backward mixing (or spreading of particles emitted at the same time). Would a smaller mixing timescale increase the impact of mixing, as with a 3-hour time the impact was found to be less than 5% (line 356)? Was that assessed when comparing no-mixing (i.e. infinite timescale) with mixing turned on? I think this deserves a bit of attention, as it keeps puzzling me that given the quite nonlinear property of NOx chemistry mixing at those scales does not seem to matter in chemistry-transport simulations.
Rotation of wind: As the wind changes significantly within the atmospheric boundary layer with height (the Ekman spiral), differences between modelled wind direction and the direction apparent from the observed plume can also be related to inaccuracies in the plume release height distribution, potentially associated with plume rise of the buoyant exhaust. I would recommend this to be discussed.
Specific comments:
Fig. 3: which WRF-Chem runs were used? Before three different cities were mentioned, are all those simulations included in Fig. 3?
L200: Were the WRF-Chem simulations also selected for cloud-free conditions?
Fig. 5 b and d: the color code is missing, or am I overlooking something?
L394: in table S1 the RMSE values range from 0.11 to 0.25 ppb
L 412: “fast-growing” is relative, Baotou certainly has a faster growth in population than Phoenix
Fig. 8 a: What does RMSP stand for?
Table 1 in the supplement should be named “Table S1”
Fig. S6: please use axis titles that clearly indicate v1 and v2 in all figures
Fig. S7: the symbols in the legend don’t quite fit with those in the figure. Triangles should be should only for EDGAR estimates, not for EPA.
Fig. S10 a and b: please use fewer x-axis labels
Citation: https://doi.org/10.5194/egusphere-2023-876-RC2 - AC4: 'Reply on RC2', Dien Wu, 19 Aug 2023
-
CEC1: 'Comment on egusphere-2023-876', Juan Antonio Añel, 19 Jun 2023
Dear authors,
Unfortunately, after checking your manuscript, it has come to our attention that it does not comply with our "Code and Data Policy".
https://www.geoscientific-model-development.net/policies/code_and_data_policy.htmlYou state in your manuscript that you have not published the code used in your work and that you will do it later. This is highly irregular, and I must make clear that your manuscript should have never been accepted for Discussions given this issue. In this way, you must publish in a prompt manner the code of the model (and any other code that you use to produce your work) in one of the repositories listed in our policy. I should note that you mention GitHub in your submission; however, GitHub is not a suitable repository for scientific publication, which is clearly mentioned in our policy too. GitHub itself instructs authors to use other alternatives for long-term archival and publishing.
Moreover, in the title of your manuscript, you do not include the name and version of the model used. This is something required by our policy and author guidelines too.
Therefore, please, publish your code in one of the appropriate repositories, and reply to this comment with the relevant information (link and DOI), and a new title for your manuscript, as soon as possible, as it must be available for the Discussions stage. Also, please, include all the relevant primary input and output data.
Please, also remember that you must include in a potentially reviewed version of your manuscript the modified title and the "Code and Data Availability" section.
Note that if you do not fix all these issues, we will have to reject your manuscript for publication in our journal.
Juan A. Añel
Geosci. Model Dev. Exec. EditorCitation: https://doi.org/10.5194/egusphere-2023-876-CEC1 -
AC1: 'Reply on CEC1', Dien Wu, 20 Jun 2023
Dear Juan A. Añel, GMD Executive Editor,
We apologize for not providing the model script timely and have now released a fixed version of the STILT-NOx model code on Zenodo ("STILT-NOx v1 for GMD submission") with a doi of https://zenodo.org/record/8057850. The key input/output data including the preprocessed EDGARv6 emissions and the essential NOx curves are included in the /data subdirectory. The NOx chemistry module contains functions under r/src/chem_lifetime.
The revised title would be "A simplified non-linear chemistry-transport model for analyzing NO2 column observations: STILT-NOx v1".
The revised Code and Data Availability section is "The STILT-NOx v1 model is built on previous efforts of the X-STILT model in modeling NO2. The exact version used in the discussion paper is archived on Zenodo with a doi of https://zenodo.org/record/8057850. TROPOMI Level 2 NO2 data and OCO-3 Level 2 B10p4r XCO2 data were accessed from 10.5270/S5P-9bnp8q8 and 10.5067/970BCC4DHH24, respectively. EDGARv6.1 emissions are accessed from https://data.jrc.ec.europa.eu/dataset/df521e05-6a3b-461c-965a-b703fb62313e and have been preprocessed."
We will revise all relevant content in a potentially reviewed version of the manuscript. Thank you for the constructive comments.
Dien Wu
Citation: https://doi.org/10.5194/egusphere-2023-876-AC1 -
CEC2: 'Reply on AC1', Juan Antonio Añel, 20 Jun 2023
Dear authors,
Many thanks for your quick reply. The information that you provide is now sufficient. However, there are a few issues that you should address:
- First, I recommend that you clarify in the reviewed version of your Code and Data Availability section that the numbers that you provide for TROPOMI and OCO data are DOIs, something that, with the current wording, is unclear.
- In the Zenodo repository for the code, in the Readme file, it states "in this GitHub repository". I understand that this is inherited from moving the code from GitHub to Zenodo; however, it would be good if you could fix it and, in the information in the Zenodo repository, refer to it.
- Also, in the Zenodo repository, there is no license listed for the code. If you do not include a license, despite what you state saying that the license is "Other", the code continues to be your property, and nobody can use it, precluding the possibility of reproducing your work. Therefore, you should include a license for your code. You could want to choose a free software/open-source (FLOSS) license. We recommend the GPLv3. You only need to include the file 'https://www.gnu.org/licenses/gpl-3.0.txt' as LICENSE.txt with your code. Also, you can choose other options that Zenodo provides: GPLv2, Apache License, MIT License, etc.
Regards,
Juan A. Añel
Geosci. Model Dev. Executive Editor
Citation: https://doi.org/10.5194/egusphere-2023-876-CEC2
-
CEC2: 'Reply on AC1', Juan Antonio Añel, 20 Jun 2023
-
AC1: 'Reply on CEC1', Dien Wu, 20 Jun 2023
- AC2: 'Comment on egusphere-2023-876', Dien Wu, 19 Aug 2023
Peer review completion
Journal article(s) based on this preprint
Model code and software
STILT for simulating NO2 columns (STILT-NOx) Dien Wu https://github.com/uataq/X-STILT
Viewed
| HTML | XML | Total | Supplement | BibTeX | EndNote | |
|---|---|---|---|---|---|---|
| 437 | 211 | 24 | 672 | 57 | 11 | 6 |
- HTML: 437
- PDF: 211
- XML: 24
- Total: 672
- Supplement: 57
- BibTeX: 11
- EndNote: 6
Viewed (geographical distribution)
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
Joshua L. Laughner
Junjie Liu
Paul I. Palmer
John C. Lin
Paul O. Wennberg
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
(13397 KB) - Metadata XML
-
Supplement
(50749 KB) - BibTeX
- EndNote
- Final revised paper