Meteorological pattern classification and application for forecasting air pollution episode potential in a mountain-valley area

In this study, we developed an automated scheme to classify the synoptic meteorological conditions governing over the northern Thailand during the winter period (November–January) into six-synoptic patterns representing different development stages of the regional pressure system. The meteorological classification scheme is hybrid in nature combining the air mass and the flow pattern based approaches. The classification is done though the principal component analysis of data from six regional surface meteorological stations and one upper-air station at 01:00 Thailand Local Standard Time daily for 5-year winter periods, which is followed by a two-stage clustering technique. Examination of the SO2 pollution levels in the complicated terrain of the Mae Moh valley in the northern Thailand, where a coal-fired power plant is located, shows that the scheme successfully identifies the patterns that are conducive to high pollutant and those that are conducive to low pollutant build up in the valley. Levels and spatial distributions of the daily highest 1-h SO2 concentrations in the valley exhibited similar characteristics within each pattern, and distinct differences between the six-synoptic patterns. The scheme is recommended for the early warning of the episode potentials in the valley. The stepwise regression was performed to develop prediction models for the two most polluted synoptic patterns, which have high frequencies of exceeding the National Ambient Air Quality Standards. The regression equations have R2 values above 60% and produce the daily highest 1-h SO2 concentrations with average relative errors of around 33%.