The Center has conducted various statistical analyses including compliance analysis of U.S. EPA criteria pollutants and air toxics based on National Ambient Air Quality Standard (NAAQS), significance tests (t-test, chi-square), regression analysis, time series analysis (ARIMA, long-term trends analysis), spatial analysis, ANOVA, factor analysis (PCA) and so on. Kolmogorv-Zurbenko filtering technique has been applied along with multi-linear regression to evaluate meteorologically adjusted long-term trends of pollutants.
The meteorological analyses for air quality researches evaluate influence of meteorological parameters on air quality, long-range transport, and regional and local pattern of air pollution distribution. These analyses include time series, regression, and spatial analysis. The meteorological analyses are coupled with enhanced statistical analyses such as multi-linear regression and artificial neural network(ANN) for air quality forecasting. Back-trajectory analysis is utilized to assess regional long-range transport and source-receptor relationship using NOAA HYSPLIT4 trajectory model. A cluster algorithm is implemented to analyze the backward trajectories and identify average regime patterns of air particles influencing on monitoring sites.
The Center has expertise in source apportionment and identification of source location with PM2.5 speciation, air toxics (Mercury, Arsenic), and volatile organic compounds (VOCs). Positive Matrix Factorization (PMF), UNMIX, Chemical Mass Balance (CMB8), and Principal Component Analysis (PCA) have been used for the source apportionment. The Center has also applied potential source contribution function (PSCF) and conditional probability function (CPF) to identify possible source locations.