Predicting PM2.5 and PM10 levels during critical episodes management in Santiago, Chile, with a bivariate Birnbaum-Saunders log-linear model

Puentes, Rodrigo; Marchant-Fuentes, Carolina; Leiva, Víctor; Figueroa-Zúñiga, Jorge I.; Ruggeri, Fabrizio

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dc.contributor.author	Puentes, Rodrigo
dc.contributor.author	Marchant-Fuentes, Carolina
dc.contributor.author	Leiva, Víctor
dc.contributor.author	Figueroa-Zúñiga, Jorge I.
dc.contributor.author	Ruggeri, Fabrizio
dc.date.accessioned	2021-12-14T12:28:12Z
dc.date.available	2021-12-14T12:28:12Z
dc.date.issued	2021
dc.identifier.uri	http://repositorio.ucm.cl/handle/ucm/3589
dc.description.abstract	Improving air quality is an important environmental challenge of our time. Chile currently has one of the most stable and emerging economies in Latin America, where human impact on natural resources and air quality does not go unperceived. Santiago, the capital of Chile, is one of the cities in which particulate matter (PM) levels exceed national and international limits. Its location and climate cause critical conditions for human health when interaction with anthropogenic emissions is present. In this paper, we propose a predictive model based on bivariate regression to estimate PM levels, related to PM2.5 and PM10, simultaneously. Birnbaum-Saunders distributions are used in the joint modeling of real-world PM2.5 and PM10 data by considering as covariates some relevant meteorological variables employed in similar studies. The Mahalanobis distance is utilized to assess bivariate outliers and to detect suitability of the distributional assumption. In addition, we use the local influence technique for analyzing the impact of a perturbation on the overall estimation of model parameters. In the predictions, we check the categorization for the observed and predicted cases of the model according to the primary air quality regulations for PM.	es_CL
dc.language.iso	en	es_CL
dc.rights	Atribución-NoComercial-SinDerivadas 3.0 Chile	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/3.0/cl/	*
dc.source	Mathematics, 9(6), 645	es_CL
dc.subject	Air pollution	es_CL
dc.subject	Birnbaum-Saunders distributions	es_CL
dc.subject	Bivariate regression models	es_CL
dc.subject	Data science	es_CL
dc.subject	Diagnostics techniques	es_CL
dc.subject	R software	es_CL
dc.title	Predicting PM2.5 and PM10 levels during critical episodes management in Santiago, Chile, with a bivariate Birnbaum-Saunders log-linear model	es_CL
dc.type	Article	es_CL
dc.ucm.facultad	Facultad de Ciencias Básicas	es_CL
dc.ucm.indexacion	Scopus	es_CL
dc.ucm.indexacion	Isi	es_CL
dc.ucm.uri	www.mdpi.com/2227-7390/9/6/645	es_CL
dc.ucm.doi	doi.org/10.3390/math9060645	es_CL