Statistical analysis of aerosol species, trace gasses, and meteorology in Chicago
Statistical analysis of aerosol species, trace gasses, and meteorology in Chicago
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DOI：
10.1007/s10661-013-3101-y
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发表时间：
2013-09-01
影响因子：
3
通讯作者：
Fosco, Tinamarie
中科院分区：
文献类型：
--
作者：
Binaku, Katrina;O'Brien, Timothy;Fosco, Tinamarie
典型相关分析（CCA）和主成分分析（PCA）应用于大气气溶胶和痕量气体浓度和气象数据收集在芝加哥在2002年，2003年和2004年夏季。铵，钙，硝酸盐，硫酸盐和草酸盐颗粒物的浓度，以及气象参数温度，风速，风向和湿度进行CCA和PCA。臭氧和氮氧化物的混合比率也包括在数据集中。统计分析的目的是确定气象参数与污染物浓度之间存在的线性关系的程度，或缺乏这种关系，此外还减少原始数据的维数，以确定污染物的来源。在CCA中，得出的前三个典型变量对在0.05水平上具有统计学显著性。第一个典型变量对之间的典型相关为0.821，而第二个和第三个典型变量对之间的相关分别为0.562和0.461。第一个典型变量对表明，温度升高导致高的臭氧混合比，而第二个典型变量对风速和湿度的影响，当地铵浓度。在第三个变量对中没有发现新的信息。典型的负载也解释了数据集之间的关系的信息。主成分分析得到4个主成分，其方差占原始数据方差的77.0%.对PCs的解释表明，该区域有大量的二次气溶胶产生和/或输送（PC1）。此外，还沿着了局地气象综合指标（PC3），表达了臭氧光化学生成和风速对污染物的影响（PC2）。总之，CCA和PCA结果相结合，成功地揭示了芝加哥气象和空气污染物之间的线性关系，并有助于确定可能的污染源。
Both canonical correlation analysis (CCA) and principal component analysis (PCA) were applied to atmospheric aerosol and trace gas concentrations and meteorological data collected in Chicago during the summer months of 2002, 2003, and 2004. Concentrations of ammonium, calcium, nitrate, sulfate, and oxalate particulate matter, as well as, meteorological parameters temperature, wind speed, wind direction, and humidity were subjected to CCA and PCA. Ozone and nitrogen oxide mixing ratios were also included in the data set. The purpose of statistical analysis was to determine the extent of existing linear relationship(s), or lack thereof, between meteorological parameters and pollutant concentrations in addition to reducing dimensionality of the original data to determine sources of pollutants. In CCA, the first three canonical variate pairs derived were statistically significant at the 0.05 level. Canonical correlation between the first canonical variate pair was 0.821, while correlations of the second and third canonical variate pairs were 0.562 and 0.461, respectively. The first canonical variate pair indicated that increasing temperatures resulted in high ozone mixing ratios, while the second canonical variate pair showed wind speed and humidity's influence on local ammonium concentrations. No new information was uncovered in the third variate pair. Canonical loadings were also interpreted for information regarding relationships between data sets. Four principal components (PCs), expressing 77.0 % of original data variance, were derived in PCA. Interpretation of PCs suggested significant production and/or transport of secondary aerosols in the region (PC1). Furthermore, photochemical production of ozone and wind speed's influence on pollutants were expressed (PC2) along with overall measure of local meteorology (PC3). In summary, CCA and PCA results combined were successful in uncovering linear relationships between meteorology and air pollutants in Chicago and aided in determining possible pollutant sources.