One-day-ahead prediction of maximum carbon monoxide concentration in urban environments

The adverse health effects associated with exposure to CO range from the more subtle cardiovascular effects at low concentrations to death after acute or chronic exposure to higher concentrations. The forecasting of the daily CO maximum levels is therefore essential in every attempt for protecting and improving public health in urban areas. The objective of this work is to create a suite of statistical models for predicting the one-day-ahead maximum CO levels based on both the meteorological and the pollutant data recorded in six monitoring sites in the greater area of Athens, Greece. The meteorological variables used as input consist of hourly values of the surface air Temperature, the Relative Humidity, the Wind Speed and the Wind Direction, while the pollutant parameters consist of hourly concentrations of nitrogen oxide, nitric dioxide, ozone and sulfur dioxide, all corresponding to the 7-year-period between 2001 and 2007. The models were developed on a seasonal (warm vs. cold period) and hebdomadal (workdays vs. weekends) basis and revealed that the influence of the air pollution levels recorded one day before (day m−1) on the maximum CO concentrations of day m is quite variable and depends on the site/type of the station, the local meteorology and the emission sources. Additionally, the analysis revealed that the CO concentrations are influenced by both local and/or wider area CO sources, suggesting a strong persistence of the CO levels, while only local meteorology (e.g. in the vicinity of the station and especially during working days) plays a role in the formation of present day’s CO levels. The derived models were validated against an independent yearlong data set (2008) through the use of a classical set of validation parameters known as the Model Validation Kit. Indices assessing the ability of the models to predict the CO exceedances of the EC limit value were also used. On the whole, it was found that the prognostic models introduced here manage to predict the CO maximum daily values in a satisfactory level, with Pearson’s correlation coefficients ranging between 0.62 and 0.76 during the warm period and between 0.51 and 0.80 during the cold period of the year. Similarly the index of agreement ranges between 0.50–0.95 during the warm period and 0.57–0.81 during the cold period of the year, revealing a rather adequate model performance.