Long-term variation in surface ozone and its precursors in Athens, Greece: a forecasting tool

Photochemical pollution is a very complex process involving meteorological, topographic, emission and chemical parameters. The most important chemical mechanisms involved in the atmospheric process have already been identified and studied. However, many unknown parameters still exist because of the large number of participating chemical reactions.

The present study investigates the processes involved in the photochemical pollution effect of an urban station located in the greater area of the Athens basin and gives a plausible explanation for the different seasonal ozone development between that station and another rural one. Furthermore, the distribution of the mean monthly surface ozone observed at the urban station during 1987-2001 is examined in order to create a relevant forecasting tool.

Averaged hourly data of O3 and NOx observations monitored at the above mentioned stations, during 1987-2001, have been used in order to derive the daytime (7:00-15:00) values. Trajectories calculated by using a 2D-trajectory code and meteorological data, during the period 1988-1996, have also been used.

At the urban station, the percentage negative trend of NO and NOx data in winter and summer is higher than that in spring and autumn, while the percentage ozone trend is maximum in the summer. On the contrary, the negative surface ozone trend at the rural station exhibits a minimum in summer and a maximum in autumn and winter. The mean seasonal wind-rose for the selected months shows that the northward wind flow dominates during June, the month of the lowest negative ozone trend in the rural station. Finally, the development of the forecasting tool shows that the mean monthly surface ozone data during the period (1987-2001) demonstrates a semi-log distribution.

Air transport effect on the air pollution of the rural station (not blocked by mountains) is deduced as a possible reason for the different seasonal ozone development observed between the rural and the urban station. Finally, the discrepancies between the theoretical probabilities deduced by the model and the empirical ones appear to be very small, and the corresponding correlation coefficient is 0.99.

However, to interpret the aforementioned statistical results about the negative trends in ozone and its precursors, additional parameters can be taken into account. Changes in NOx concentrations, for instance, can result not only from changes in emissions or meteorological conditions. There might also be a contribution through changes in the atmospheric composition. A study of the contribution of changes in atmospheric composition to trends of observed NOx concentrations requires that a series of steps be taken (removal of meteorological influence in the time series, calculation of trends in OH concentrations, etc.).