Consumer products, such as the biocide spray, have a variety of chemical substances which damage human health. Humans are exposed to the threatening substance released in daily life. The potential risks of consumer products should be assessed to ensure human safety. Modeling methods [1
] and experimental methods [7
] have been used to estimate the inhalation exposure concentration by the consumer products.
The modeling tools are the mathematical model of ordinary differential equations that can quickly estimate the inhalation exposure from the use of consumer products, such as biocide spray, cosmetics and cleaning products. The modeling tools assume that the aerosol particles spread rapidly throughout the space, so the air concentration results calculated by the tools are uniform throughout the space [1
]. The modeling tools cannot account for the inhalation exposure by considering spatial property and hence do not obtain the exposure information on the various spots in the room. So, the model was called the zero-dimensional spray model.
Unlike the zero-dimensional model’s assumption, [9
] showed that the concentration of pollutants is not uniform throughout the indoor space because the mean age of air varies depending on the measurement locations of the indoor space. Additionally, the exposure experimental results according to the measurement locations were up to 10 times higher than the results of the zero-dimensional spray model [4
]. In addition, a spatially divided multi-box zero-dimensional model has been developed, but the dynamic behavior of aerosols is still unpredictable by the instantaneous diffusion assumption [10
]. The zero-dimensional spray models are likely to underestimate the inhalation exposure. The model should consider the spatial property to predict the correct inhalation exposure under the inhomogeneous concentration in indoor spaces.
For solving the spatial problem of the exposure modeling tool, the exposure assessment has to be followed by an experimental method, which can consider the exposure results by the various measurement positions and the local ventilation rate by the difference in room structure and size. However, experimenting in various measurement locations generates a lot of equipment costs. Additionally, the execution time about the experimental analysis can be in the order of weeks and months for the exposure assessment according to the various indoor space specifications. That is, an appropriate methodology is needed to solve the problem of cost and time due to the experimental approach and the problem of non-spatial property due to the modeling method.
With the development of computational science, computational fluid dynamics (CFD) has been applied to many industrial fields to solve experimental limitations and cost problems [11
]. CFD can be used to predict the flow behavior in ventilated indoor spaces and calculate the concentration of pollutant by solving the partial differential equations with 3D tensor based on the physics law [14
]. In general, the dynamic behavior of particle/aerosol has been predicted with the discrete particle method (DPM) and volume-tracking represented by the volume of fluid (VOF) method in CFD [15
]. By adopting a numerical method based on the VOF method, various phase flow behaviors and gas–liquid and gas–gas interactions can be accurately described. The DPM has been used to track the dynamic behavior of solid particles in air. The micro-sized liquid aerosols from consumer product sprays are volatized into the solid phase rapidly. So, the DPM is suitable for application in the simulations of spray dynamic behavior. In addition, it is possible to analyze various information such as the dynamic behavior of aerosols that cannot be obtained in the experiment and the ventilation rate of each measurement location [24
However, despite the various applications of CFD that can solve the spatial problem of zero-dimensional spray models and time/cost problems of experimental methods, there are no examples of CFD used in the field of exposure science for estimating exposure by biocide spray. Therefore, this study proposes the feasibility of applying CFD for the inhalation exposure evaluation by comparing the experimental results and the zero-dimensional simulation results with CFD results. This study has two purposes, (1) to investigate the advantages and disadvantages of the zero-dimensional model based on the CFD results and experimental results of the biocide aerosol concentration, (2) to evaluate the feasibility of a new approach for the inhalation exposure assessment to solve the spatial problem of the zero-dimensional spray model and the cost/time problem of experiments by comparing the CFD results of the aerosol concentration at the various measurement positions with the experimental results of the biocide aerosol concentration at the same measurement positions. As far as we can tell, this paper is the first report to propose the new approach of exposure assessment by using CFD.
The research flow chart of this work is summarized in Figure 1
. First, the experiments, such as the particle size distribution (PSD) and mass generation rate measurement, are conducted to simulate the zero-dimensional model and CFD. The experiments of the model parameters are performed in a large well-controlled chamber. Then, zero-dimensional spray simulation, CFD spray simulation and the aerosol concentration measurement by spray are performed. The three results were performed based on the same chamber volume and particle information (PSD, mass generation rate and injection time). Finally, the feasibility of the zero-dimensional spray model result is evaluated by the CFD results and the measurement results. Additionally, the CFD results were compared with the experimental results of the biocide aerosol concentration at the same measurement positions in order to verify the feasible use of CFD for exposure estimation.
In this paper, we investigated the applicability of CFD for inhalation exposure assessment from biocide spray in order to solve the spatial problem of the zero-dimensional space model and the time/cost-consuming problem of measurement methods. The CFD results of the sprayed aerosol concentration in various positions were compared with measurement results and the zero-dimensional spray model results. The results of the three methods were compared for 3600 s of physics/simulation. The conclusions can be drawn as follows:
(1) The zero-dimensional spray model results, the experimental results in the far field and CFD in the far field show relatively good results when compared to each other. However, the spray model shows an underestimation of approximately five times when compared with the CFD results in the near field and experimental results in the near field. In this study, it was shown that exposure evaluation should be performed using CFD or experimental methods rather than the conventional zero-dimensional model.
(2) When the concentration measurement is performed at various locations using experimental methodology, the cost generated and time required are enormous. The results of measuring the concentration of aerosols at five locations and the CFD results at the same location were compared to show the possibility of evaluating inhalation exposure at various locations using CFD. The experimental result includes an error bar representing the minimum, average and maximum. It can be seen that the CFD value at each measurement location is within the experimental error range. In this study, it was shown that the experimental cost and time problem for inhalation exposure can be reasonably solved using CFD.
In other words, in the field of exposure science, a guideline for exposure evaluation using CFD was found that complements the shortcomings of the conventional methodology, the zero-dimensional spray model and measurement method.
However, CFD generates a relatively high computational cost for the simulation of exposure estimation. It is difficult to estimate the exposure according to the user’s needs. To address these computational cost problems, machine learning modeling methods based on simulation data are used in various industries [29
]. Therefore, in future research, we will develop a machine learning model with spatial information as an input variable and spatial concentration as an output variable to predict concentration according to various spatial structures and characteristics.