气候变化领域集成服务门户(CCPortal): Relative-humidity-dependent organic aerosol thermodynamics via an efficient reduced-complexity model

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DOI	10.5194/acp-19-13383-2019
	Relative-humidity-dependent organic aerosol thermodynamics via an efficient reduced-complexity model
	Gorkowski K.; Preston T.C.; Zuend A.
发表日期	2019
ISSN	16807316
起始页码	13383
结束页码	13407
卷号	19 期号:21
英文摘要	Water plays an essential role in aerosol chemistry, gas-particle partitioning, and particle viscosity, but it is typically omitted in thermodynamic models describing the mixing within organic aerosol phases and the partitioning of semivolatile organics. In this study, we introduce the Binary Activity Thermodynamics (BAT) model, a water-sensitive reduced-complexity model treating the nonideal mixing of water and organics. The BAT model can process different levels of physicochemical mixture information enabling its application in the thermodynamic aerosol treatment within chemical transport models, the evaluation of humidity effects in environmental chamber studies, and the analysis of field observations. It is capable of using organic structure information including OH:C, molar mass, and vapor pressure, which can be derived from identified compounds or estimated from bulk aerosol properties. A key feature of the BAT model is predicting the extent of liquid-liquid phase separation occurring within aqueous mixtures containing hydrophobic organics. This is crucial to simulating the abrupt change in water uptake behavior of moderately hygroscopic organics at high relative humidity, which is essential for capturing the correct behavior of organic aerosols serving as cloud condensation nuclei. For gas-particle partitioning predictions, we complement a volatility basis set (VBS) approach with the BAT model to account for nonideality and liquid-liquid equilibrium effects. To improve the computational efficiency of this approach, we trained two neural networks; the first for the prediction of aerosol water content at given relative humidity, and the second for the partitioning of semivolatile components. The integrated VBS + BAT model is benchmarked against high-fidelity molecular-level gas-particle equilibrium calculations based on the AIOMFAC (Aerosol Inorganic-Organic Mixtures Functional groups Activity Coefficient) model. Organic aerosol systems derived from α-pinene or isoprene oxidation are used for comparison. Predicted organic mass concentrations agree within less than a 5% error in the isoprene case, which is a significant improvement over a traditional VBS implementation. In the case of the -pinene system, the error is less than 2% up to a relative humidity of 94%, with larger errors past that point. The goal of the BAT model is to represent the bulk O:C and molar mass dependencies of a wide range of water-organic mixtures to a reasonable degree of accuracy. In this context, we discuss that the reduced-complexity effort may be poor at representing a specific binary water-organic mixture perfectly. However, the averaging effects of our reduced-complexity model become more representative when the mixture diversity increases in terms of organic functionality and number of components. © 2019 Author(s).
语种	英语
scopus关键词	aerosol; atmospheric modeling; complexity; mixing; relative humidity; thermodynamics; volatile organic compound; water
来源期刊	Atmospheric Chemistry and Physics
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/144064
作者单位	Department of Atmospheric and Oceanic Sciences, McGill University, Montreal, QC, Canada; Department of Chemistry, McGill University, Montreal, QC, Canada
推荐引用方式 GB/T 7714	Gorkowski K.,Preston T.C.,Zuend A.. Relative-humidity-dependent organic aerosol thermodynamics via an efficient reduced-complexity model[J],2019,19(21).
APA	Gorkowski K.,Preston T.C.,&Zuend A..(2019).Relative-humidity-dependent organic aerosol thermodynamics via an efficient reduced-complexity model.Atmospheric Chemistry and Physics,19(21).
MLA	Gorkowski K.,et al."Relative-humidity-dependent organic aerosol thermodynamics via an efficient reduced-complexity model".Atmospheric Chemistry and Physics 19.21(2019).