Establishing multiple regression models for ozone sensitivity analysis to temperature variation in Taiwan

Sensitivity of meteorological variation to air quality has attracted people's attention since climate change became a world issue. The goal of this study is to investigate the sensitivity of ground-level ozone concentrations to temperature variation in Taiwan. Several multivariate regression models were built based on historical data of ozone and meteorological variables at three cities located in northern, mid-western, and southern Taiwan. Results of descriptive statistics indicate that the severe pollution from the highest to the minor conditions following by the order of the southern (Pingtung), mid-western (Fengyuan), and the northern sites (Hsichih). Multiple regression models containing a principal component trigger variable effectively simulated the historical ozone exceedance during 2004-2009. Inclusion of the PC trigger were improved R2 from the lowest 0.38 to the highest 0.58. High probability of detection and critical success index (mostly between 85% and 90%) and low false alarm rates (0-2.6%) were achieved for predicting the high ozone days (≧100 ppb). The results of sensitivity analysis indicated that (1) the ozone sensitivity was positively correlated with the temperature variation, (2) the sensitivity levels were opposite to that of the ozone problem severity, (3) the sensitivity was mostly apparent in ozone seasons, and (4) the sensitivity strongly depended on the seasonality in the urban cities Hischih and Fengyuan, but weakly depended on seasonality in the rural city Pingtung.