INTRODUCTION: The effect of diurnal changes in strengths of volatile organic compound (VOC) sources on the performances of positive matrix factorization (PMF) and principal component analysis (PCA) was investigated using ambient measurement results that were taken during daytime and nighttime hours between March 24 and May 14, 2011, within Davutpasa Campus of Yildiz Technical University (Istanbul, Turkey). METHODS: Forty-five VOC species, ranging from C(5) to C(11) in volatility, were measured in the samples, 40 of which are included in the analyses. Ambient samples were grouped as daytime, nighttime, and all day datasets, and both PMF and PCA were applied to each dataset. A total of six source groups were extracted from each dataset: solvent use, general industrial paint use, gasoline and diesel vehicle exhausts, and biogenic as well as evaporative emissions. Estimated source contributions showed great diurnal variations. RESULTS: The results suggested that extraction of possible sources by PCA depends greatly on the number of samples and the strength of the sources, while PMF produced stable results regardless of number of samples and source strengths. CONCLUSION: Although PMF was unable to resolve gasoline vehicle and evaporative emissions, it was found to be successful in explaining diurnal fluctuations in source strengths, while the performance of PCA depends on the strength of emission source.
INTRODUCTION: The effect of diurnal changes in strengths of volatile organic compound (VOC) sources on the performances of positive matrix factorization (PMF) and principal component analysis (PCA) was investigated using ambient measurement results that were taken during daytime and nighttime hours between March 24 and May 14, 2011, within Davutpasa Campus of Yildiz Technical University (Istanbul, Turkey). METHODS: Forty-five VOC species, ranging from C(5) to C(11) in volatility, were measured in the samples, 40 of which are included in the analyses. Ambient samples were grouped as daytime, nighttime, and all day datasets, and both PMF and PCA were applied to each dataset. A total of six source groups were extracted from each dataset: solvent use, general industrial paint use, gasoline and diesel vehicle exhausts, and biogenic as well as evaporative emissions. Estimated source contributions showed great diurnal variations. RESULTS: The results suggested that extraction of possible sources by PCA depends greatly on the number of samples and the strength of the sources, while PMF produced stable results regardless of number of samples and source strengths. CONCLUSION: Although PMF was unable to resolve gasoline vehicle and evaporative emissions, it was found to be successful in explaining diurnal fluctuations in source strengths, while the performance of PCA depends on the strength of emission source.