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

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.

Determinant Powers of Socioeconomic Factors and Their Interactive Impacts on Particulate Matter Pollution in North China

“…5 and PM 10 at the same time. The results showed that the two pollutants have a strong relationship [17]. Due to the city being the spatial carrier of the above sources, one changes in response to the changes of others.…”

“…Puentes et al took Santiago, the capital of Chile, as an example, and used a bivariate regression prediction model to estimate the PM levels related to PM 2.5 and PM 10 at the same time. The results showed that the two pollutants have a strong relationship [ 17 ]. Due to the city being the spatial carrier of the above sources, one changes in response to the changes of others.…”

Application of Photo Texture Analysis and Weather Data in Assessment of Air Quality in Terms of Airborne PM10 and PM2.5 Particulate Matter

“…Models were created for daily averaged data from two stations using weather data and addictional seasonal informations. A similar approach was presented by the authors of [ 4 ]. The authors used a bivariate predictive model based on GBS distribution to predict next-day maximum PM 10 and PM 2.5 levels.…”

“…In summer, emissions from the municipal and household sectors decrease; therefore, emissions only come from households that use solid-fuel furnaces to heat water. As in other highly polluted cities, such as Santiago, Chile, it should be noted that topographic (poor ventilation), meteorological (low temperature at winter), and socio-economic (coal-fired home heating) conditions negatively affect air quality in the city, especially during the winter period [ 4 ]. Another source of emissions that is visible especially in large cities and agglomerations is transport, which accounts for around 5% of PM 10 emissions, 4% of PM 2.5 emissions, and 44% of NO x emissions for the whole voivodeship [ 30 ].…”

“…PM has a considerable negative effect on human health, including increased rates of cardiovascular, cerebrovascular, and respiratory diseases [ 2 , 3 ]. Puentes et al in [ 4 ] showed that every 50 µg/m 3 increase in PM 10 caused 4–12% growth of hospital visits for children with respiratory syndromes. Many techniques are available to measure the mass concentration of PM in air.…”