||In addition to air pollutants index (i.e. PSI), ambient air quality can be described by atmospheric visibility since it can be observed directly by general publics. In this study, atmospheric visibility observation, meteorological parameter monitoring, and aerosol particle sampling were conducted to investigate the influences of physicochemical properties of suspended particles and meteorological parameters on atmospheric visibility. This study further applied receptor model and multiple regression linear analysis to forecast atmospheric visibility and develop strategies for improving urban visual air quality at Taipei region.|
Results from regular visibility observation indicated that the average visibilities were 10.30, 8.05 and 6.00 km in the directions of Tamsui, Sonshan, and Shindian, respectively. Similar trend of visibility variation was also observed for intensive observation. Further analysis of synoptic chart and regular observation data during the period of January 2007–March 2008 showed that the lowest atmospheric visibility commonly occurred whenas the weather patterns were in sequence of eastward movement of rainy areas in southern China, southerly airstream, strong northeast monsoon, circus-sluice of high pressure outflow, and weak northeast monsoon.
Results from chemical analysis of suspended particles at Taipei region indicated that major water-soluble ionic species were SO42-, NO3-, and NH4+ and followed by Cl-, while major metallic content were Ca and K. Carbonaceous analysis showed that the mass ratio of OC/EC ranged from 1.65 to 1.91 for PM2.5 and from 1.37 to 1.88 for PM2.5-10. Ammonium nitrate, organic carbon, and ammonium sulfate were the major chemical species that influenced atmospheric visibility at Taipei region.
In this study, we choose the averaged atmospheric visibility in Sonshan as a dependent variable and PM10, NO2, SO2, O3, relative humidity (RH), wind direction (WD), and wind speed (WS) as independent variables to establish multiple linear regression models for forecasting the atmospheric visibility. Results of statistical analysis indicated that high correlation between forecasted and observed atmospheric visibilities was observed (R=0.7167). Furthermore, atmospheric visibility forecasting models were established for various weather patterns. The accuracies of atmospheric visibility verification (September~December, 2007) and forecasting (January~March, 2008) were 91.80% and 87.97%, respectively.
This study further applied SPSS stastistic software to conduct factor analysis for atmospheric visibility. Results from factor analysis of visibility indicated that the top three factors (PM10, NO2, and SO2) accounted for 71.13% of variance. Furthermore, variable correlation analysis showed that atmospheric visibility had positive correlation with wind speed and negative correlation with other variables (PM10, NO2, SO2, O3, RH, and WD). Besides, for the significant levels of α=0.01 or α=0.05, all variables were proven to be significantly correlated with atmospheric visibility except O3.
At Taipei region, the automobile tail emission was the major emission source causing low visibility, thus the most effective strategy for improving atmospheric visibility was to reduce the mission of automobiles and the formation of secondary aerosols containing ammonium nitrate and ammonium sulfate, which could effectively increase the atmospheric visibility at Taipei region.