Predicting PM2.5 and PM10 Levels during Critical Episodes Management in Santiago, Chile, with a Bivariate Birnbaum-Saunders Log-Linear Model
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 whic...
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MDPI AG
2021-03-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/9/6/645 |
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| author | Rodrigo Puentes Carolina Marchant Víctor Leiva Jorge I. Figueroa-Zúñiga Fabrizio Ruggeri |
| author_facet | Rodrigo Puentes Carolina Marchant Víctor Leiva Jorge I. Figueroa-Zúñiga Fabrizio Ruggeri |
| author_sort | Rodrigo Puentes |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T13:08:32Z |
| format | Article |
| id | doaj.art-3ae9627d2bdc497a8dd05e0ee890d0af |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-10T13:08:32Z |
| publishDate | 2021-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-3ae9627d2bdc497a8dd05e0ee890d0af2023-11-21T10:57:11ZengMDPI AGMathematics2227-73902021-03-019664510.3390/math9060645Predicting PM2.5 and PM10 Levels during Critical Episodes Management in Santiago, Chile, with a Bivariate Birnbaum-Saunders Log-Linear ModelRodrigo Puentes0Carolina Marchant1Víctor Leiva2Jorge I. Figueroa-Zúñiga3Fabrizio Ruggeri4National Medical Devices, Innovation and Development Agency, Instituto de Salud Pública de Chile, Santiago 7780050, ChileFaculty of Basic Sciences, Universidad Católica del Maule, Talca 3480112, ChileSchool of Industrial Engineering, Pontificia Universidad Católica de Valparaíso, Valparaíso 2362807, ChileDepartment of Statistics, Universidad de Concepción, Concepción 4070386, ChileConsiglio Nazionale delle Ricerche, Istituto di Matematica Applicata e Tecnologie Informatiche, 20133 Milano, ItalyImproving 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.https://www.mdpi.com/2227-7390/9/6/645air pollutionBirnbaum-Saunders distributionsbivariate regression modelsdata sciencediagnostics techniquesR software |
| spellingShingle | Rodrigo Puentes Carolina Marchant Víctor Leiva Jorge I. Figueroa-Zúñiga Fabrizio Ruggeri Predicting PM2.5 and PM10 Levels during Critical Episodes Management in Santiago, Chile, with a Bivariate Birnbaum-Saunders Log-Linear Model Mathematics air pollution Birnbaum-Saunders distributions bivariate regression models data science diagnostics techniques R software |
| title | Predicting PM2.5 and PM10 Levels during Critical Episodes Management in Santiago, Chile, with a Bivariate Birnbaum-Saunders Log-Linear Model |
| title_full | Predicting PM2.5 and PM10 Levels during Critical Episodes Management in Santiago, Chile, with a Bivariate Birnbaum-Saunders Log-Linear Model |
| title_fullStr | Predicting PM2.5 and PM10 Levels during Critical Episodes Management in Santiago, Chile, with a Bivariate Birnbaum-Saunders Log-Linear Model |
| title_full_unstemmed | Predicting PM2.5 and PM10 Levels during Critical Episodes Management in Santiago, Chile, with a Bivariate Birnbaum-Saunders Log-Linear Model |
| title_short | Predicting PM2.5 and PM10 Levels during Critical Episodes Management in Santiago, Chile, with a Bivariate Birnbaum-Saunders Log-Linear Model |
| title_sort | predicting pm2 5 and pm10 levels during critical episodes management in santiago chile with a bivariate birnbaum saunders log linear model |
| topic | air pollution Birnbaum-Saunders distributions bivariate regression models data science diagnostics techniques R software |
| url | https://www.mdpi.com/2227-7390/9/6/645 |
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