Abstract: “Multiple linear regression models were constructed to characterize ground-level O3 metrics in Bangkok Metropolis Region where meteorological parameters are different from other studies in cold cities. SAS® 9.2 software analyzed 2.9-million hourly data during 1997 – 2011 including O3, NO2 and meteorological variables such as temperature (T), rainfall (RF), relative humidity (RH), pressure (P), solar radiation (SR), wind speed (WS) and wind direction (WD). The results showed O3 was highest in winter because of clearest sky and an atmospheric inversion. O3 had negatively correlated with RH and RF and positively correlated with SR and previous day O3 (O3(d-1)) Natural logarithm transformed O3 was used for 3 O3 metrics (daily average, daily maximum and daytime average) for 4 periods (annual, summer, winter and rainy season). Regression results showed that the lnO3(d-1) was a main positive predictor and RH is the strongest negative predictor following by a positive SR predictor. In winter, m”

Keywords: ozone, meteorology, multiple linear regression, seasonal effects