Aerosol, trace gas, and meteorological data were collected in Chicago, Illinois during 2010-2012 summer air studies. Ozone, nitrogen oxides, acetate, formate, chloride, nitrate, sulfate, and oxalate concentrations as well as temperature, wind speed, wind direction, and humidity data were explored by both principal component analysis (PCA) and canonical correlation analysis (CCA). Multivariate statistical techniques were applied to uncover existing relationships between meteorology and air pollutant concentrations and also reduce data dimensions. In PCA, principal components (PC) revealed a relationship of ozone and nitrate concentrations with respect to temperature and humidity, coupled with transport of species from the south in relation to the sampling site (PC1). PC2 was a measure of secondary aerosols but also suggested acetate and formate presence was a result of primary emissions or transport. Both PC3 and PC4 contained residual information with the former representing days of lower pollution and the latter representing northerly wind transport of chloride, nitrate, and ozone to the sampling site. In CCA, three canonical functions were statistically significant. The first indicated high temperature and low wind speed had a strong linear relationship ozone, oxalate, and nitrogen oxides concentrations whereas the second function showed a strong influence of wind direction on acetate, formate, and chloride concentrations. Residuals of temperature, wind speed, trace gases, and oxalate also were in the second function. The only new information in the third function was humidity. Overall, PCA and CCA bring forth multi-variable relationships, not represented in descriptive statistics, useful in understanding pollution variability.
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.
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