Generalized linear modelling based monitoring methods for air quality surveillance

Abstract

Rising industrial pollution, exacerbated by climate change, underscores the need for effective environmental monitoring. Leveraging sensor advancements and Birnbaum-Saunders distribution, this study introduces a novel surveillance method for environmental data, crucial for shaping impactful industrial policies. Simulation studies demonstrate the method's performance, and a case study on nitrogen oxide levels in Italy validates its efficacy in the early detection of severe air pollution events.

Keywords

• Birnbaum-Saunders Regression model
• Deviance residuals
• Environmental Pollution
• Standardized residuals
• Statistical process monitoring