Lisbon air quality forecast using statistical methods
Ozone and particulate matter levels in Southern European countries are particularly high, exceeding the established limit values, and the information and alert thresholds (in the case of ozone). Therefore, it is relevant to develop a good prediction methodology for the concentrations of these pollutants. Statistical models based on Multiple Regression (MR) analysis and classification and regression trees analysis were developed successfully. The models were applied in forecasting the average daily concentrations for particulate matter and average maximum hourly ozone levels, for next day, for the group of existing air quality monitoring stations in the Metropolitan Area of North Lisbon in Portugal.
Keywords: statistical forecasting, particles, ozone levels, air pollution, air quality, environmental pollution, Portugal, particulate matter, PM10, Lisbon