Spatiotemporal features of severe air pollution in northern Taiwan.
OBJECTIVE
This research attempted to identify the dominant factors simultaneously affecting the airborne concentrations of five air pollutants with principal component analysis and to determine the meteorologically related parameters that cause severe air-pollution events. According to the definition of subPSI and PSI values through the U.S. EPA, the historical raw data of five criteria air pollutants, SO2, CO, O3, PM10 and NO2, were calculated as daily subPSI values. In addition to the airborne concentrations, this study simultaneous collected the surface meteorological parameters of the Taipei meteorological station, established by the Central Weather Bureau.


METHODS
Principal component analysis was conducted to screen severe air pollution scenarios for five air pollutants: SO2, CO, O3, PM10 and NO2. The concentrations of various air pollutants measured at 17 air-quality stations in northern Taiwan from 1995 to 2001 were transformed into daily subPSI values. The correlation analysis of the five air pollutants and four meteorological parameters (wind speed, temperature, mixing height and ventilation rate) were included in this research. After screening severe air pollution scenarios, this study recognized the synoptic patterns easily causing the severe air-pollution events.


CONCLUSIONS
Analytical results showed that the eigenvalues of the first two principal components for SO2, CO, O3, PM10 and NO2 were greater than 1. The first component of five air pollutants explained 64, 64, 67, 76 and 63% of subPSI variance for SO2, CO, O3, PM10 and NO2, respectively. Only the correlation coefficient of NO2 and CO had statistically significant positive values (0.82); other pollutant pairs presented medium (0.4 to 0.7) or low (0 to 0.4) positive values. The correlation coefficients for air pollutants and three meteorological parameters (wind speed, mixing height and ventilation index) were medium or low negative values. In northern Taiwan, spring was most likely induced high concentrations and the component scores of the first component for SO2, CO, PM10 and NO2; summer was the worst season that caused high O3 episodes. Consequently, the analytical results of factor loadings for the first principal component and emission inventory of various sources revealed that mobile sources were dominant factors affecting ambient air quality in northern Taiwan.


CONCLUSIONS
According to the results of principal component analysis for the five air pollutants, the first two of 17 components were cited as major factors and explained 71% of subPSI variance. Based on the inventory of NOx emissions and the isopleth diagram of factor loading for the first component, mobile sources in the southwest Taipei City accounted for the highest factor loading values and emission inventory values. Synoptic analysis and principal component analysis demonstrated that three types of weather patterns (high-pressure recirculation, prefrontal warm sector and the southwesterly wind system) easily caused the severe air-pollution scenarios. In summary, if severe air-pollution days occurred, the average meteorological parameters experienced adverse conditions for diffusing air pollutants; that is, the average values of wind speed, mixing height and ventilation index were lower than 2.1 ms(-1), 360 m and 800 m2s(-1), respectively. If one of the three synoptic patterns were to occur in combination with adverse meteorological conditions, severe air-pollution events would be developed.


CONCLUSIONS
By utilizing synoptic patterns, this work found three weather systems easily caused severe air-pollution events over northern Taiwan. Analytical results showed, respectively, the wind speed and mixing height were less than 2.1 m/s and 360 m during severe air-pollution events.
Yu
Tai-Yi
TY
Department of Risk Management and Insurance, Ming Chuan University, Taipei, Taiwan. yutaiyi@gmail.com
Chang
I-Cheng
IC
D000392:Air Movements; D000393:Air Pollutants / Q000032:analysis*; D002248:Carbon Monoxide / Q000032:analysis; D004784:Environmental Monitoring / Q000379:methods*; D006801:Humans; D009589:Nitrogen Oxides / Q000032:analysis; D010126:Ozone / Q000032:analysis; D010316:Particle Size; D025341:Principal Component Analysis / Q000379:methods*; D011634:Public Health; D012621:Seasons; D013458:Sulfur Dioxide / Q000032:analysis; D013624:Taiwan; D013696:Temperature; D001335:Vehicle Emissions / Q000009:adverse effects / Q000032:analysis*; D014835:Volatilization
D000393:Air Pollutants; D009589:Nitrogen Oxides; D001335:Vehicle Emissions; D013458:Sulfur Dioxide; D010126:Ozone; D002248:Carbon Monoxide