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

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.

A Comparison of Regression Techniques for Estimation of Above-Ground Winter Wheat Biomass Using Near-Surface Spectroscopy

“…This may be because the RF method randomly changes the input variable and validates the importance of the input data, thus generating a large number of decision trees and reducing the impact of noise; this result corresponds to the results of previous studies [25,89,90]. Our results (Figure 7) show that MLR is more sensitive to noise than PLSR, which is consistent with the findings of Zhao et al [91]. The results of Atzberger et al [7] indicate that the noise immunity of PCR, PLSR, and SMLR is ranked as PCR > PLSR > MLR, which is exactly the same ranking as obtained in the present work ( Figure 7).…”

Chemical composition of ambient aerosol, ice residues and cloud droplet residues in mixed-phase clouds: single particle analysis during the Cloud and Aerosol Characterization Experiment (CLACE 6)

“…Hinz et al, 1999) and ART2a neural networks (e.g. Zhao et al, 2005;Zhou et al, 2006) were used and discussed in the literature. A comparison of different clustering algorithm is presented by Hinz and Spengler (2007) and Rebotier and Prather (2007).…”

Quantitative results from single‐particle characterization data

“…Subsequently, Zhao et al [30] have used similar mass concentrations and size-segregated bulk sample composition data to permit the prediction of the bulk aerosol composition based on the single-particle ATOFMS data. Zhao et al used 50 samples to further test the prediction ability of the calibration model.…”