Critical Episodes of PM10 Particulate Matter Pollution in Santiago of Chile, an Approximation Using Two Prediction Methods: MARS Models and Gamma Models

Alvarado, A O Sergio; Silva, Claudio Z.; Cáceres, Dante L.

doi:10.5772/32164

Cited by 1 publication

(1 citation statement)

References 19 publications

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“…In this study, data collected from the Pudahuel monitoring station from MACAM network were used during the year 2015 in the CEM period. The main reasons to work with 2015 data and the Pudahuel station are: (i) 2015 is the last year with the most validated measurements for each station; (ii) the Pudahuel station registered the highest levels of PM2.5 during 2015; (iii) the Pudahuel station is the most influential monitoring station in Santiago, informing administrative decisions based on predicted critical episodes [53]; and (iv) according to air quality regulation, if at least one monitoring station in Santiago reports situations defined as pre-emergency or emergency for PM10 and/or PM2.5, the authority will declare the condition of a critical episode in the city [54]. Hence, data from the Pudahuel station are considered relevant for pollutant investigation in the Santiago region.…”

Section: Data Variables and Modelmentioning

confidence: 99%

Predicting PM2.5 and PM10 Levels during Critical Episodes Management in Santiago, Chile, with a Bivariate Birnbaum-Saunders Log-Linear Model

et al. 2021

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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.

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Section: Data Variables and Modelmentioning

confidence: 99%