the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 20 Apr 2022
A model for simultaneous evaluation of NO2, O3 and PM10 pollution in urban and rural areas: handling incomplete data sets with multivariate curve resolution analysis
Abstract. A powerful methodology, based on multivariate curve resolution alternating least squares (MCR-ALS) with quadrilinearity constraints, is proposed to handle complex and incomplete four-way atmospheric data sets, providing concise and easy interpretable results. Changes in air quality by nitrogen dioxide (NO2), ozone (O3) and particular matter (PM10) in eight sampling stations located in Barcelona metropolitan area and other parts of Catalonia during the COVID-19 lockdown (2020) with respect to previous years (2018 and 2019) are investigated using such methodology. MCR-ALS simultaneous analysis of the 3 contaminants among the 8 stations and for the 3 years allows the evaluation of potential correlations among the pollutants even when having missing data blocks. NO2 and PM10 show correlated profiles due to similar pollution sources (traffic and industry), evidencing a decrease in 2019 and 2020 due to traffic restriction policies and COVID-19 lockdown, especially noticeable in the most transited urban areas (i.e., Vall d’Hebron, Granollers and Gràcia). Ozone evidences an opposed inter-annual trend, showing higher amounts in 2019 and 2020 respect to 2018 due to the decreased titration effect, more significant in rural areas (Begur) and in the control site (Obserbatori Fabra).
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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.
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Preprint
(1830 KB)
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Supplement
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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
(1830 KB) - Metadata XML
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Supplement
(938 KB) - BibTeX
- EndNote
- Final revised paper
Journal article(s) based on this preprint
Interactive discussion
Status: closed
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RC1: 'Comment on egusphere-2022-117', Anonymous Referee #1, 06 May 2022
This study attempts to apply multivariate curve resolution alternating least squares with quadrilinearity constraints to handle complex and incomplete four-way atmospheric data sets. Three air pollutants including nitrogen dioxide, ozone and particular matter in eight sampling stations located in Barcelona metropolitan area other parts of Catalonia during the COVID-19 lockdown (2020) with respect to previous years (2018 and 2019) are used for analysis. The new method indeed generates an interesting result, which has been well interpreted. Overall, the manuscript is well written and worthy of publication. However, this reviewer disagrees on one issue, i.e., estimation of missing data always suffered from uncertainties, whatever any approach to be used. The uncertainty should be included in data interpretation.
Citation: https://doi.org/10.5194/egusphere-2022-117-RC1 -
AC1: 'Reply on RC1', Eva Gorrochategui, 10 May 2022
We thank the reviewer for the comment. And yes, we agree that measurement uncertainties were not included in the bilinear model factor decomposition estimations. The environmental agency source data did not provide them. Otherwise, we could have applied the weighted version of ALS where data uncertainties are included as weights in the least squares estimations. On the other hand, missing data blocks were not included in the least squares estimations, this is the advantage of the proposed method, linear equations were only solved for the known data blocks. Therefore this should not be a limitation. What is true is that some parts of the factor solutions (those corresponding to the missing blocks) are not so overdetermined from a least squares point of view as the other data blocks without missing values, and this can be reflected in the reliability of the estimations of the later. This is an aspect that deserves a deeper study and needs further investigation.
Citation: https://doi.org/10.5194/egusphere-2022-117-AC1
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AC1: 'Reply on RC1', Eva Gorrochategui, 10 May 2022
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RC2: 'Comment on egusphere-2022-117', Vasil Simeonov, 12 Jun 2022
The present study proves convincingly the advatage of the chemometric technique multivariate curve resolution - alternating lesdt squares. The reasons for such an estimations could be summarized as follows:
1. Reliable options for apportionment of pollution sources
2. Realing with four-way constructed data sets
3. Elimination of missing data
The study is performed on a very high theoretical level and, additionally, delivers very useful practical information. The style is very sound, the conclusions - important.
I recommend acceptance in its present form.
Citation: https://doi.org/10.5194/egusphere-2022-117-RC2 -
AC2: 'Reply on RC2', Eva Gorrochategui, 13 Jun 2022
We kindly appreciate this reviewer's comment. We also agree that this study highlights the advantages of chemometrics in the analysis of complex four-way environmental data sets containing missing data blocks.
Citation: https://doi.org/10.5194/egusphere-2022-117-AC2
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AC2: 'Reply on RC2', Eva Gorrochategui, 13 Jun 2022
Interactive discussion
Status: closed
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RC1: 'Comment on egusphere-2022-117', Anonymous Referee #1, 06 May 2022
This study attempts to apply multivariate curve resolution alternating least squares with quadrilinearity constraints to handle complex and incomplete four-way atmospheric data sets. Three air pollutants including nitrogen dioxide, ozone and particular matter in eight sampling stations located in Barcelona metropolitan area other parts of Catalonia during the COVID-19 lockdown (2020) with respect to previous years (2018 and 2019) are used for analysis. The new method indeed generates an interesting result, which has been well interpreted. Overall, the manuscript is well written and worthy of publication. However, this reviewer disagrees on one issue, i.e., estimation of missing data always suffered from uncertainties, whatever any approach to be used. The uncertainty should be included in data interpretation.
Citation: https://doi.org/10.5194/egusphere-2022-117-RC1 -
AC1: 'Reply on RC1', Eva Gorrochategui, 10 May 2022
We thank the reviewer for the comment. And yes, we agree that measurement uncertainties were not included in the bilinear model factor decomposition estimations. The environmental agency source data did not provide them. Otherwise, we could have applied the weighted version of ALS where data uncertainties are included as weights in the least squares estimations. On the other hand, missing data blocks were not included in the least squares estimations, this is the advantage of the proposed method, linear equations were only solved for the known data blocks. Therefore this should not be a limitation. What is true is that some parts of the factor solutions (those corresponding to the missing blocks) are not so overdetermined from a least squares point of view as the other data blocks without missing values, and this can be reflected in the reliability of the estimations of the later. This is an aspect that deserves a deeper study and needs further investigation.
Citation: https://doi.org/10.5194/egusphere-2022-117-AC1
-
AC1: 'Reply on RC1', Eva Gorrochategui, 10 May 2022
-
RC2: 'Comment on egusphere-2022-117', Vasil Simeonov, 12 Jun 2022
The present study proves convincingly the advatage of the chemometric technique multivariate curve resolution - alternating lesdt squares. The reasons for such an estimations could be summarized as follows:
1. Reliable options for apportionment of pollution sources
2. Realing with four-way constructed data sets
3. Elimination of missing data
The study is performed on a very high theoretical level and, additionally, delivers very useful practical information. The style is very sound, the conclusions - important.
I recommend acceptance in its present form.
Citation: https://doi.org/10.5194/egusphere-2022-117-RC2 -
AC2: 'Reply on RC2', Eva Gorrochategui, 13 Jun 2022
We kindly appreciate this reviewer's comment. We also agree that this study highlights the advantages of chemometrics in the analysis of complex four-way environmental data sets containing missing data blocks.
Citation: https://doi.org/10.5194/egusphere-2022-117-AC2
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AC2: 'Reply on RC2', Eva Gorrochategui, 13 Jun 2022
Peer review completion
Journal article(s) based on this preprint
Data sets
Air quality data NO2, O3, PM10 Catalonia Department of Air Monitoring and Control Service of the Generalitat de Catalunya https://doi.org/10.1007/s11356-021-17137-7
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Eva Gorrochategui
Isabel Hernandez
Romà Tauler
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
(1830 KB) - Metadata XML
-
Supplement
(938 KB) - BibTeX
- EndNote
- Final revised paper