Predicting bulk ambient aerosol compositions from ATOFMS data with ART-2a and multivariate analysis

Author(s): Hopke, P. | Prather, K.A. | Zhao, W.X. | Qin, X.Y. |

Year: 2005

Citation: ANALYTICA CHIMICA ACTA Volume: 549 Issue: 1-2 Pages: 179-187

Abstract: The aerosol time-of-flight mass spectrometry (ATOFMS) has not generally been used to provide a quantitative estimation of chemical compositions of ambient aerosols. In an initial study, the possibility of developing a calibration model to predict chemical compositions from ATOFMS data was demonstrated, but because of the limited number of samples (only 12), the ability of the calibration model was not fully realized. In this study, 50 samples were created to further test the prediction ability of the calibration model. The conceptual framework is to relate the mass concentrations of the particles in the identified classes to the average aerosol compositions for each sampling time interval using a calibration model based on ART-2a and multivariate analysis. There may be some non-linearity between cluster mass concentrations and ambient species concentrations because of measurement errors, the scaling equations used to estimate particle mass and various assumptions required for building the model. Thus, in this study, PLS regression was integrated with radial basis functions (RBF-PLS) to obtain better prediction effects and compared to partial least square (PLS) regression alone. Compared with an earlier study, these results provide better and a more convincing demonstration of the ability of the calibration model to estimate the chemical compositions from ATOFMS data. The results also suggest that the model would be able to provide carbon data and thus substitute for thermal optical reflectance (TOR) measurements. Additionally, the calibration model based on RBF-PLS showed more accurate predictions in the cases with some non-linearity. Some of the key steps in the modeling effect are also discussed in detail.

Topics: Machine Learning, Applications: Chemical Analysis, Models: ART 2-A,

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