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Group Contribution Approach To Predict the Refractive Index of Pure Organic Components in Ambient Organic Aerosol
Author(s) -
Chen Cai,
Aleksandra Marsh,
Yunhong Zhang,
Jonathan P. Reid
Publication year - 2017
Publication title -
environmental science and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.851
H-Index - 397
eISSN - 1520-5851
pISSN - 0013-936X
DOI - 10.1021/acs.est.7b01756
Subject(s) - aerosol , polarizability , chemistry , halogen , refractive index , molecular descriptor , group (periodic table) , organic compound , molar refractivity , training set , particle (ecology) , group contribution method , organic component , thermodynamics , quantitative structure–activity relationship , environmental chemistry , organic chemistry , molecule , materials science , stereochemistry , phase equilibrium , alkyl , oceanography , optoelectronics , physics , phase (matter) , artificial intelligence , computer science , geology
We introduce and assess a group contribution scheme by which the refractive index (RI) (λ = 589 nm) of nonabsorbing components common to secondary organic aerosols can be predicted from the molecular formula and chemical functionality. The group contribution method is based on representative values of ratios of the molecular polarizability and molar volume of different functional groups derived from data for a training set of 234 compounds. The training set consists of 106 nonaromatic compounds common to atmospheric aerosols, 64 aromatic compounds, and 64 compounds containing halogens; a separate group contribution model is provided for each of these three classes of compound. The resulting predictive model reproduces the RIs of compounds in the training set with mean errors of ±0.58, ±0.36, and ±0.30% for the nonaromatic, aromatic, and halogen-containing compounds, respectively. We then evaluate predictions from the group contribution model for compounds with no previously reported RI, comparing values with predictions from previous treatments and with measurements from single aerosol particle experiments. We illustrate how such comparisons can be used to further refine the predictive model. We suggest that the accuracy of this model is already sufficient to better constrain the optical properties of organic aerosol of known composition.

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