2002 Annual

Tuesday, 15 January 2002: 11:00 AM
A daily trend of pollutant concentrations referred to as the representative day
T. Tirabassi, ISAO-CNR, Bologna, Italy
The interpretation of phenomena governing air pollution diffusion presupposes a synthesis of information derived from temporal data series. The recording of air pollution concentration values involves the measurement of a large volume of data, giving rise to the need for a rapid method of summarising such information. Generally, automatic selectors and explicators are used, many of which are provided by statistics.

Currently, hourly concentration means are the most widespread method employed and represents the maximum desegregation with which pollution data are generally collected. It is often adopted in applications and allows the evaluation of average concentrations over long periods, such as a day, a season or a year. In addition hourly concentrations are used to define specific “typical” periods that may be of particular interest in the study of pollutant diffusion (for example, a typical working day, a typical holiday, a typical seasonal day etc.).

The work presents a daily trend of pollutant concentrations, referred to as the “representative day”, i.e. the day for which the overall sum of the mean-square differences between its concentration, averaged within each hour, and the concentrations for all other days at the same hour, is a minimum. The approach also allows the identification of the “least representative day” (the daily series that maximises the mean sum of squared residuals), which usually indicates an anomalous situation in pollutant dispersion, characterised by maximum concentration at the ground.

The purpose of such typifying is that of outlining characteristic scenarios. In fact the representative day is an actual day, so it allows the identification of the date on which the representative trend occurred and, thus, a knowledge of the meteorological and emission parameters that characterised it for a given period under investigation. Than mathematical models make it possible to attempt simulations of a typical period trend, without the need to simulate all the days of the time interval covered by the typical period.

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