Graduate Institute of Environmental Engineering, National Taiwan University, 71 Chou-Shan Rd, Taipei, 106, Taiwan,
Environ Monit Assess. 2014 Dec;186(12):8317-28. doi: 10.1007/s10661-014-4005-1. Epub 2014 Aug 22.
This study established a cause-effect relationship between ground-level ozone and latent variables employing partial least-squares analysis at an urban roadside site in four distinct seasons. Two multivariate analytic methods, factor analysis, and cluster analysis were adopted to cite and identify suitable latent variables from 14 observed variables (i.e., meteorological factors, wind and primary air pollutants) in 2008-2010. Analytical results showed that the first six components explained 80.3 % of the variance, and eigenvalues of the first four components were greater than 1. The effectiveness of this model was empirically confirmed with three indicators. Except for surface pressure, factor loadings of observed variables were 0.303-0.910 and reached statistical significance at the 5 % level. Composite reliabilities for latent variables were 0.672-0.812 and average variances were 0.404-0.547, except for latent variable "primary" in spring; thus, discriminant validity and convergent validity were marginally accepted. The developed model is suitable for the assessment of urban roadside surface ozone, considering interactions among meteorological factors, wind factors, and primary air pollutants in each season.
本研究采用偏最小二乘法，在四个不同季节的城市路边点建立了地面臭氧与潜在变量之间的因果关系。在 2008 年至 2010 年期间，采用了两种多元分析方法，即因子分析和聚类分析，从 14 个观测变量（即气象因素、风和主要空气污染物）中引用和识别合适的潜在变量。分析结果表明，前六个分量解释了 80.3%的方差，前四个分量的特征值大于 1。该模型的有效性通过三个指标得到了实证验证。除地面气压外，观测变量的因子负荷为 0.303-0.910，在 5%水平上达到统计学意义。潜在变量的综合可信度为 0.672-0.812，平均方差为 0.404-0.547，除春季的潜在变量“主要”外；因此，可接受判别有效性和收敛有效性。考虑到每个季节气象因素、风因素和主要空气污染物之间的相互作用，所开发的模型适用于城市路边地面臭氧的评估。
