Combining homogeneous and heterogeneous chemistry to model inorganic compound concentrations in indoor environments: the H<sup>2</sup>I model (v1.0)

Abstract

Abstract. Homogeneous reactivity has been extensively studied in recent years through outdoor air-quality simulations. However, indoor atmospheres are known to be largely influenced by another type of chemistry, which is their reactivity with surfaces. Despite progress in the understanding of heterogeneous reactions, such reactions remain barely integrated into numerical models. In this paper, a room-scale, indoor air-quality (IAQ) model is developed to represent both heterogeneous and homogeneous chemistry. Thanks to the introduction of sorbed species, deposition and surface reactivity are treated as two separate processes, and desorption reactions are incorporated. The simulated concentrations of inorganic species are compared with experimental measurements acquired in a real room, thus allowing calibration of the model's undetermined parameters. For the duration of the experiments, the influence of the simulation's initial conditions is strong. The model succeeds in simulating the four inorganic species concentrations that were measured, namely NO, NO2, HONO and O3. Each parameter is then varied to estimate its sensitivity and to identify the most prevailing processes. The air-mixing velocity and the building filtration factor are uncertain parameters that appear to have a strong influence on deposition and on the control of transport from outdoors, respectively. As expected, NO2 surface hydrolysis plays a key role in the production of secondary species. The secondary production of NO by the reaction of sorbed HONO with sorbed HNO3 stands as an essential component to integrate into IAQ models.

Gas-particle partitioning of toluene oxidation products: an experimental and modeling study

“…The kinetics of this reaction takes into account the gas diffusion and the uptake. It is estimated according to the method proposed in Fiorentino et al (2021) considering an uptake coefficient of 10 -5 as recommended in Jacob (2000).…”

Improvement in Modeling of OH and HO2 Radical Concentrations during Toluene and Xylene Oxidation with RACM2 Using MCM/GECKO-A

“…An additional reaction representing the heterogeneous reaction of NO 2 on the walls of the AFT was also added (e.g., [23]; see Table 2). The kinetics of this heterogeneous reaction was calculated according to the method described by Fiorentino et al [24], which integrates impaction and reaction and takes into account the area and volume of the AFT. The uptake coefficient of NO 2 was fixed to 10 −5 (in the (10 −6 ;10 −3 ) range recommended by Jacob, 2000) to obtain a simulated distribution of the mass charge ratio (m/z) of the formed compound closest to the experimental data, especially for the concentration of hydroperoxides (formed by reaction of RO 2 with HO 2 ) and organic nitrates formed in the presence of NO X .…”

“…The modified RACM2 mechanism improved the simulated HO X concentrations compared with MCM/GECKO-A, especially under low NO X conditions. The use of the modified mechanism will therefore be particularly significant in environments with high concentrations of monoaromatic compounds and relatively low NO X concentrations, such as in indoor air [24] or in industrial regions with high VOC emissions.…”

Predicting Critical Properties and Acentric Factors of Fluids Using Multitask Machine Learning

“…Therefore, knowledge of critical temperature ( T c ), pressure ( P c ), density (ρ c ), and the acentric factor (ω) is required for many widely used EOSs, such as Peng-Robinson and Soave-Redlich-Kwong . Other than EOSs, various models use critical properties as inputs to predict physiochemical properties, such as diffusion coefficients, − surface tension, , and solubilities. − In addition, they are used to predict Lennard-Jones parameters, which are required to model transport and collisions in a reaction rate calculation. ,− It is clear that critical properties and acentric factors are widely used in a multitude of fields, and thus it is important to have accurate values for the same. However, obtaining these molecular properties through experiments is time-intensive and expensive and therefore, the development of computational prediction tools is necessary.…”