Simulation of the daily average PM10 concentrations at Ta-Liao with Box–Jenkins time series models and multivariate analysis

Southern Taiwan has experienced severe PM10 problems for over a decade. The present paper describes the establishment of a simulation model for the daily average PM10 concentrations at Ta-Liao, southern Taiwan. The study used a regression with time series error models (RTSE models) (multivariate ARIMA time series model), including an explanatory variable resulting from principal component analyses to complete the PM10 simulation. Factor 1 estimated from the factor analyses explained the variance of 44–49%, which indicated the important contribution from the neighbor-city PM10 at Mei-Nung, Lin-Yuang, Zuoying, Chao-Chou, local ozone and NOx. Factor 1 can be interpreted with regional PM10 plus photochemical reactions. To improve the predictability of extremely high PM10, different results from the principal component analysis were introduced to the RTSE models. We constructed four kinds of RTSE models: RTSE model without PC, with PC4S (PM10 at Mei-Nung, Lin-Yuang, Zuoying, and Chao-Chou), with PCTL (meteorological variables and co-pollutants at Ta-Liao), and with PCTL4S (the combination of the above two) and evaluated the statistics model performance. Ozone, dew point temperature, NOx, wind speed, wind directions, and the PC trigger were the significant variables in the RTSE models most of time. When the neighbor-city PM10 was included in the PC trigger, the predictability was apparently improved. The closeness of fit with the inclusion of PC4S and PCTL4S was improved by reducing SEE from 0.117 to 0.092. Using the RTSE models with PC4S or PCTL4S, POD was improved by an increase of 33%, FAR was reduced 30%, and CSI was increased 39%, when simulating the daily average PM10 > 150 μg m−3. Evidently we need to survey source impacts prior to establishing a simulation model. Factor analysis is a useful method to investigate sources that contributed PM10 to a target site prior to establishing a simulation model.