M‐quantile regression shrinkage and selection via the Lasso and Elastic Net to assess the effect of meteorology and traffic on air quality
M‐quantile regression shrinkage and selection via the Lasso and Elastic Net to assess the effect of meteorology and traffic on air quality
In this work, we intersect data on size-selected particulate matter (PM) with vehicular traffic counts and a comprehensive set of meteorological covariates to study the effect of traffic on air quality. To this end, we develop an M-quantile regression model with Lasso and Elastic Net penalizations. This allows (i) to identify the best proxy for vehicular traffic via model selection, (ii) to investigate the relationship between fine PM concentration and the covariates at different M-quantiles of the conditional response distribution, and (iii) to be robust to the presence of outliers. Heterogeneity in the data is accounted by fitting a B-spline on the effect of the day of the year. Analytic and bootstrap-based variance estimates of the regression coefficients are provided, together with a numerical evaluation of the proposed estimation procedure. Empirical results show that atmospheric stability is responsible for the most significant effect on fine PM concentration: this effect changes at different levels of the conditional response distribution and is relatively weaker on the tails. On the other hand, model selection allows to identify the best proxy for vehicular traffic whose effect remains essentially the same at different levels of the conditional response distribution.
B-splines, additive models, cross-validation, influence function, robust regression
Ranalli, M. Giovanna
aec65b36-08c7-467c-ae6e-77e8308cffd3
Salvati, Nicola
9be298e5-de55-4a24-9361-054a2ec09726
Petrella, Lea
bf351458-2a5a-452e-be73-496a19c4060a
Pantalone, Francesco
c1b85bef-a71c-4851-9807-7776bc0b5ded
December 2023
Ranalli, M. Giovanna
aec65b36-08c7-467c-ae6e-77e8308cffd3
Salvati, Nicola
9be298e5-de55-4a24-9361-054a2ec09726
Petrella, Lea
bf351458-2a5a-452e-be73-496a19c4060a
Pantalone, Francesco
c1b85bef-a71c-4851-9807-7776bc0b5ded
Ranalli, M. Giovanna, Salvati, Nicola, Petrella, Lea and Pantalone, Francesco
(2023)
M‐quantile regression shrinkage and selection via the Lasso and Elastic Net to assess the effect of meteorology and traffic on air quality.
Biometrical Journal, 65 (8), [2100355].
(doi:10.1002/bimj.202100355).
Abstract
In this work, we intersect data on size-selected particulate matter (PM) with vehicular traffic counts and a comprehensive set of meteorological covariates to study the effect of traffic on air quality. To this end, we develop an M-quantile regression model with Lasso and Elastic Net penalizations. This allows (i) to identify the best proxy for vehicular traffic via model selection, (ii) to investigate the relationship between fine PM concentration and the covariates at different M-quantiles of the conditional response distribution, and (iii) to be robust to the presence of outliers. Heterogeneity in the data is accounted by fitting a B-spline on the effect of the day of the year. Analytic and bootstrap-based variance estimates of the regression coefficients are provided, together with a numerical evaluation of the proposed estimation procedure. Empirical results show that atmospheric stability is responsible for the most significant effect on fine PM concentration: this effect changes at different levels of the conditional response distribution and is relatively weaker on the tails. On the other hand, model selection allows to identify the best proxy for vehicular traffic whose effect remains essentially the same at different levels of the conditional response distribution.
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Biometrical J - 2023 - Ranalli - M‐quantile regression shrinkage and selection via the Lasso and Elastic Net to assess the
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Accepted/In Press date: 11 April 2023
e-pub ahead of print date: 24 September 2023
Published date: December 2023
Additional Information:
Funding Information:
The work of Ranalli has been carried out with the support of the project AIDMIX, Fondo di ricerca di Ateneo, edizione 2021, Universita degli Studi di Perugia. The work of Salvati has been carried out with the support of the project InGRID 2 (Grant Agreement N. 730998) and of the project LOCOMOTION (Grant Agreement N. 821105). The authors are grateful to the PMetro project for providing the data and to David Cappelletti for useful discussions.
Keywords:
B-splines, additive models, cross-validation, influence function, robust regression
Identifiers
Local EPrints ID: 483029
URI: http://eprints.soton.ac.uk/id/eprint/483029
ISSN: 0323-3847
PURE UUID: ecfc5ef2-cc97-48af-8874-f0c8b09cccc6
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Date deposited: 19 Oct 2023 17:04
Last modified: 18 Mar 2024 04:04
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Contributors
Author:
M. Giovanna Ranalli
Author:
Nicola Salvati
Author:
Lea Petrella
Author:
Francesco Pantalone
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