Indoor secondary organic aerosols: Towards an improved representation of their formation and composition in models

Abstract

The formation of secondary organic aerosol (SOA) indoors is one of the many consequences of the rich and complex chemistry that occurs therein. Given particulate matter has well documented health effects, we need to understand the mechanism for SOA formation indoors and its resulting composition. This study evaluates some uncertainties that exist in quantifying gas-to-particle partitioning of SOA-forming compounds using an indoor detailed chemical model. In particular, we investigate the impacts of using different methods to estimate compound vapour pressures as well as simulating the formation of highly oxygenated organic molecules (HOM) via auto-oxidation on SOA formation indoors. Estimation of vapour pressures for 136 α-pinene oxidation species by six investigated methods led to standard deviations of 28-216%. Inclusion of HOM formation improved model performance across three of the six assessed vapour pressure estimation methods when comparing against experimental data, particularly when the NO₂ concentration was relatively high. We also explored the predicted SOA composition using two product classification methods, the first assuming the molecule is dominated by one functionality according to its name, and the second accounting for the fractional weighting of each functional group within a molecule. The SOA composition was dominated by the HOM species when the NO₂-to-α-terpineol ratio was high for both product classification methods, as these conditions promoted formation of the nitrate radical and hence formation of HOM monomers. As the NO₂-to-α-terpineol ratio decreased, peroxides and acids dominated the simple classification, whereas for the fractional classification, carbonyl and alcohol groups became more important.

Keywords: Highly oxygenated organic molecules; Indoor air chemistry; Secondary organic aerosol; Vapour pressure; Volatile organic compound.

Fig. 1.

The oxidation of VOCs and subsequent processes to form SOA.

Fig. 2.

(a) Range of predicted average Log₁₀ Vp (atmospheres) of each class of compounds for each of the 6 methods across the 136 species at 295K. (b) Log₁₀ Vp (atmospheres) for 7 selected species including their chemical structures (reading from the left to the right: dimethylcyclobutanecarboxylic acid, pinonaldehyde, pinonic acid, C₁₀H₁₈O₃, C₁₀H₁₆O₄, C₁₀H₁₇NO₄, C₁₀H₁₅NO₇), against molecular weight.

Fig. 3.

Comparison of measured and modelled SOA concentrations (μg m⁻³), following the addition of 100 ppb of O₃ to 100 ppb of α-pinene in the chamber. The measurements are shown with a shaded grey area that defines the standard deviation for the three experiments. The model runs are shown for the three different vapour pressure methods and with (EVAPHOM, MYHOM, NVPHOM) and without (EVAP, MY, NVP) HOM formation.

Fig. 4.

Comparison of SOA (μg m⁻³) modelled and measured SOA concentrations for 21 experiments for a range of initial α-terpineol and NO₂ concentrations. The model results show the EVAP, NVP and MY method with and without the inclusion of HOMs. The line function equations and the coefficient of determination for each method are presented in the Supplementary Information (see Table S4).

Fig. 5a.

Modelled/measured SOA concentrations at 30 min output for the 21 NO₂ and α-terpineol (AT) experiments for the EVAP, NVP and MY method with HOMs included against log₁₀ [NO₂]/[α-terpineol]. Note that the x-axis is shown using a log₁₀ scale and that the points furthest to the left are those for which NO₂ is zero.

Fig. 5b.

Modelled/measured SOA concentrations at 30 min output for the 21 NO₂ and α-terpineol experiments for the EVAP, NVP and MY method with HOMs included against measured SOA.

Fig. 6.

The ‘simple’ (upper) and ‘complex’ (lower) classification of the SOA composition for the EVAP method for the 21 sets of experimental conditions.