Predicting PM2.5 and PM10 levels during critical episodes management in Santiago, Chile, with a bivariate Birnbaum-Saunders log-linear model
Autor
Puentes, Rodrigo
Marchant-Fuentes, Carolina
Leiva, Víctor
Figueroa-Zúñiga, Jorge I.
Ruggeri, Fabrizio
Fecha
2021Resumen
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.
Fuente
Mathematics, 9(6), 645Link de Acceso
Click aquí para ver el documentoIdentificador DOI
doi.org/10.3390/math9060645Colecciones
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