Statistical classification of synoptic weather patterns associated with Tehran air pollution

Abstract

Introduction: Poor air quality in the heavily polluted cities like Tehran is often the main city problem that influences people health and comfort. The main goals of this study are summarized as: 1) Seasonal pollutants mean variations during 2005, meteorological conditions effects on pollutant concentration; 2) Meteorological conditions case study and pollution spatial distribution for three determining synoptic patterns (MET1, MET4, MET5);

3) Further analysis of the episode from 30^th November to 13^th December 2005 (MET4); 4) Episode analysis from 30^th November to 13^th December 2005 (MET4) and 5) Episode analysis from 12^th-22^th of September 2005 (MET5). These are systematic weather patterns that usually affect the air pollution levels in Tehran.

Materials and methods: Concentration changes of CO, PM10, SO2 and O3, as the relationship between the air pollution extreme events and atmospheric

conditions in Tehran have been investigated. The hourly air pollution data from 11 representative monitoring sites were used. To understand the relationship between local meteorological synoptic patterns and air pollution, the principal component analysis (PCA) method has been applied to meteorological data. Then for minimizing the data complication the varimax rotations (VR) was used and five synoptic perspectives weather patterns have resulted for highly polluted periods.

Results: Pollutants correlation investigation of the five patterns showed that air quality was highly dependent on middle tropospheric high geopotential ridge development, local southerly wind with strong static stability.

Conclusion: The most polluted periods were associated with a weak pressure gradient, a weak wind, severe air descent, and radiation inversion.