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oai:uvadoc.uva.es:10324/674282024-05-10T10:44:23Zcom_10324_36297com_10324_954com_10324_894col_10324_36298

Ranalli, M. Giovanna Salvati, Nicola Petrella, Lea Pantalone, Francesco 2024-05-08T08:50:41Z 2024-05-08T08:50:41Z 2024 Biometrical Journal, Septiembre, 2023 0323-3847 https://uvadoc.uva.es/handle/10324/67428 10.1002/bimj.202100355 Producción Científica 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 Bspline 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 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). application/pdf spa Wiley info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/4.0/ © The Authors Attribution 4.0 Internacional Meteorología Contaminación Additive models B-splines Cross-validation Influence function Robust regression 2509 Meteorología 2509.02 Contaminación Atmosférica M-quantile regression shrinkage and selection via the Lasso and Elastic Net to assess the effect of meteorology and traffic on air quality info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion https://onlinelibrary.wiley.com/doi/10.1002/bimj.202100355 SI