Yu Tai-Yi, Chang I-Cheng
Department of Risk Management and Insurance, Ming Chuan University, Taipei, Taiwan.
Environ Sci Pollut Res Int. 2006 Jul;13(4):268-75. doi: 10.1065/espr2005.12.288.
BACKGROUND, AIMS AND SCOPE: 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.
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.
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.
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.
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.
背景、目的与范围:本研究试图通过主成分分析确定同时影响五种空气污染物大气浓度的主导因素,并确定导致严重空气污染事件的气象相关参数。根据美国环境保护局(U.S. EPA)对次标准污染物指数(subPSI)和污染物标准指数(PSI)值的定义,计算了五种标准空气污染物(二氧化硫、一氧化碳、臭氧、可吸入颗粒物(PM10)和二氧化氮)的历史原始数据作为每日次标准污染物指数值。除了大气浓度外,本研究还同步收集了由中央气象局设立的台北气象站的地面气象参数。
进行主成分分析以筛选五种空气污染物(二氧化硫、一氧化碳、臭氧、可吸入颗粒物(PM10)和二氧化氮)的严重空气污染情景。将1995年至2001年在台湾北部17个空气质量监测站测量的各种空气污染物浓度转换为每日次标准污染物指数值。本研究纳入了五种空气污染物与四个气象参数(风速、温度、混合层高度和通风率)的相关性分析。在筛选出严重空气污染情景后,本研究识别出容易引发严重空气污染事件的天气形势。
分析结果表明,二氧化硫、一氧化碳、臭氧、可吸入颗粒物(PM10)和二氧化氮前两个主成分的特征值均大于1。五种空气污染物的第一主成分分别解释了二氧化硫、一氧化碳、臭氧、可吸入颗粒物(PM10)和二氧化氮次标准污染物指数方差的64%、64%、67%、76%和63%。只有二氧化氮和一氧化碳的相关系数具有统计学显著的正值(0.82);其他污染物对呈现中等(0.4至0.7)或低(0至0.4)的正值。空气污染物与三个气象参数(风速、混合层高度和通风指数)的相关系数为中等或低的负值。在台湾北部,春季最易引发高浓度污染,二氧化硫、一氧化碳、可吸入颗粒物(PM10)和二氧化氮第一主成分的成分得分较高;夏季是导致高臭氧事件的最糟糕季节。因此,第一主成分的因子载荷分析结果和各种来源的排放清单表明,移动源是影响台湾北部环境空气质量的主导因素。
根据对五种空气污染物的主成分分析结果,17个成分中的前两个被列为主要因素,解释了次标准污染物指数方差的71%。根据氮氧化物排放清单和第一主成分的因子载荷等值线图,台北市西南部的移动源占因子载荷值和排放清单值的最高比例。天气形势分析和主成分分析表明,三种天气形势(高压回流、锋前暖区和西南风系统)容易引发严重空气污染情景。总之,如果发生严重空气污染日,平均气象参数处于不利于空气污染物扩散的条件;即风速、混合层高度和通风指数的平均值分别低于2.1米/秒、360米和800平方米/秒。如果三种天气形势中的一种与不利气象条件同时出现,将引发严重空气污染事件。
通过利用天气形势,本研究发现三种天气系统容易在台湾北部引发严重空气污染事件。分析结果表明,在严重空气污染事件期间,风速和混合层高度分别小于2.1米/秒和360米。