Particulate matter (PM) pollution is a serious environmental problem. Santiago of Chile is one of the most polluted cities in the world in terms of PM2.5 and PM10. Monitoring of environmental risk is useful for detecting and preventing adverse effects on human health in highly polluted cities. In this paper, we propose a methodology for monitoring PM pollution in Santiago based on bivariate quality control charts and an asymmetric distribution. A simulation study is carried out to evaluate performance of the proposed methodology. A case study with PM pollution real-world data from Santiago is provided, which shows that the methodology is suitable to alert early episodes of extreme air pollution. The results are in agreement with the critical episodes reported with the current model used by the Chilean health authority.
- air pollution
- bivariate nonnormal distributions
- Hotelling statistic
- maximum likelihood and Monte Carlo methods
- process control
- R software