International Conference on Future Trends In Bio-Informatics and Environmental Science - FTBES 2014
Author(s) : BUNDIT APISAMAJARAKUL , SITTHICHOK PUANGTHONGTHUB
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, major predictors are RH, NO2, WD and lnO3(d-1). In raining season, P and SR played significant positive predictors. In summer, RH is only a main predictor. For validation analysis, the lnO3 daily maximum and daytime average in summer show the highest R2 values at 0.573 and 0.568 respectively. This work investigated the effects of Bangkok tropical climate parameters influencing O3 metrics.