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Deposition of aerosol particles on rough surfaces inside a test chamber
Building and Environment 44 (2009) 2056–2063
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Deposition of aerosol particles on rough surfaces inside a test chamber Tareq Hussein a, Lenka Kubincova´ b, Lucie D zumbova´ c, Alesˇ Hrusˇka d, Pavla Doha´nyosova´ c, d c, * Jirˇı´ Hemerka , Jirˇı´ Smolı´k a
University of Helsinki, Department of Physics, P.O. Box 64, FI–00014 University of Helsinki, Finland ´ 4, SK-04002 Kosˇice, Slovak Republic Technical University of Kosˇice, Civil Engineering Faculty, Institute of Building and Environmental Engineering, Vysokosˇkolska c ´ 135, CZ-16502 Praha 6, Prague, Czech Republic Institute of Chemical Process Fundamentals, Laboratory of Aerosol Chemistry and Physics, Rozvojova d ´ 4, CZ- 166 07 Praha 6, Prague, Czech Republic Czech Technical University of Prague, Department of Environmental Engineering, Technicka b
a r t i c l e i n f o
a b s t r a c t
Article history: Received 1 December 2008 Received in revised form 16 February 2009 Accepted 17 February 2009
We investigated the deposition rate of aerosol particles (diameter between 0.03 and 5 mm) on rough surfaces of wallpapers, wall-plasters, and two types of carpets inside a test chamber. Compared to a smooth aluminum surface, the deposition rate of aerosol particles on the tested surfaces was up to 20 times depending on the surface roughness, mixing intensity, and particle size. A rough surface with a dimensionless surface roughness height kþ < 0.06 can be treated as a hydraulically smooth. The estimated deposition rates in this study and those predicted by a deposition model, which incorporates surface roughness, were in good agreement for coarse mode particles (diameter > 1 mm) when kþ < 1.04 and for ultrafine particles (diameter < 0.1 mm) when kþ < 0.48. The agreement between the model prediction and our estimation was better for coarse mode particles than for ultrafine particles. Deposition of aerosol particles, especially fine particles, needs more empirical investigations aiming at improving the existing models. Ó 2009 Elsevier Ltd. All rights reserved.
Keywords: Deposition rate Hydraulically smooth Surface roughness Model evaluation Carpet Wall material
1. Introduction Deposition of aerosol particles in real-life indoor environments has been treated by assuming perfectly smooth indoor surfaces. Discrepancies between the empirical results and model were often explained by either not including some processes in the model or due to unknown artifacts in the experimental setup. Generally speaking, deposition of aerosol particles is a very complicated process that depends on the particle size, airflow characteristics, geometries of the indoor environment, and most importantly the surface roughness [1]. Deposition of aerosol particles indoors may involve several processes. Brownian and eddy diffusion are most important processes for fine particles (diameter < 1 mm) whereas gravitational settling and impaction are the significant processes for coarse particles. Other processes such as thermophoresis due to temperature gradients, electrostatic drifting due to charged particles or electric field nearby charged surfaces, or turbophoresis due to gradients in the turbulent fluctuating velocity components nearby the surface can be also important factors [2–7]. Even though deposition of aerosol particles on smooth surfaces has been well understood, there has been slow development on the
* Corresponding author. Tel.: þ420 220 390 247; fax: þ420 220 920 661. E-mail address: [email protected] (J. Smolı´k). 0360-1323/$ – see front matter Ó 2009 Elsevier Ltd. All rights reserved. doi:10.1016/j.buildenv.2009.02.009
theory of deposition on rough surfaces. The slow development has been mainly due to lack of experimental investigations. The effect of surface roughness has been quantified by using sandpapers, large-scale obstruction with regular geometry, or using textile materials commonly used indoors [8–14]. Some of these studies were applied for collection efficiency of aerosol particles inside duct lines or precipitators and the materials used in these tests are well described by producer but don’t exist in real buildings. In reallife conditions the surface roughness of indoor surfaces might vary from several microns (such as smooth wallpapers) up to several millimeters (such as carpets). Generally speaking and according to the empirical results reported in literature, deposition rate of aerosol particles on rough surfaces is higher than that on smooth surfaces [3,8–10,15–20]. Modeling efforts have been increased aiming to quantify deposition on rough surfaces by proposing different assumptions and parameterizations regarding both the structure of the boundary layer and the flow of the fluid within the boundary layer [16,21–23]. Previously, Lai et al. [10] explained the increased deposition rate on rough surfaces to be due to some modifications in the boundary layer over the rough surface. If the thin boundary layer is completely destroyed or at least made thinner due to the surface roughness, the transportation progress across this layer can be greatly enhanced. This explanation was in analogy with heat transfer experiments [24,25]. Guha [3] proposed a unified theory
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aiming to quantify the deposition of aerosol particles onto smooth and rough surfaces under different conditions. Following Wood [26], Guha [3] assumed in his unified theory that the origin of the velocity profile over a rough surface is shifted away from the wall. Similar assumptions were also proposed by Browne [27] following previous work by Perry et al. [28] and Grass [29]. Recently, Zhao and Wu [15] developed a new model where they used a modified assumption for the change in the boundary layer thickness over a rough surface. Their results were plausible when their model was evaluated against experimental observations. In this study we investigated the deposition rate of aerosol particles on rough surfaces commonly used in real-life situations: wallpapers, wall-plasters, and carpets. We performed our measurements inside a test chamber; we previously investigated the deposition rates of aerosol particles on smooth aluminum surfaces inside the same test chamber [30]. Our analysis included semi-empirical estimation for the deposition rates and comparison to existing deposition models and previous studies. Many of previous studies considered a limited size range or the measurement conditions were not well controlled. Here we present a widerange set of measurements covering the particle size range from 30 nm up to 5 mm under controlled conditions inside a test chamber. According to our knowledge, such data set has never been published and intensively analyzed as presented in this study.
2. Experimental setup and measurement We constructed a sealed chamber (1 1 1 m3) made of smooth aluminum surfaces (Fig. 1) according to the principles described by Hussein et al. [30], which reported deposition rates of aerosol particles on smooth aluminum surfaces inside this test chamber. The main findings by Hussein et al. [30] can be summarized as follows: (1) the deposition rate of aerosol particles on smooth aluminum surfaces was increased by increasing the fan speed (indoor mixing) and the ventilation rate. This is illustrated in Fig. 2.
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(2) the effect of particle coagulation was found negligible when particle number concentrations inside the chamber was below 104 cm3. (3) deposition of aerosol particles on aluminum surfaces via thermophoresis or electrostatic drifting can be neglected because the chamber was grounded and thermally insulated (with polystyrene foam sheets). (4) turbophoresis and impaction processes were seen significant for particles larger than 5 mm when the fan speed exceeded 548 rpm. (5) the influence of the ventilation rate on the deposition rate on aluminum surfaces was only observed during still-air (nomixing) conditions.
2.1. Wall materials and surface roughness We placed different wall materials on the interior surfaces (walls, ceiling, and ground) of the chamber. We used smooth and rough surfaces of the commonly used wallpapers and wall-plasters in addition to two different types of carpets. Even though we used smooth wall materials, they were not as smooth as the aluminum surface used in our previous tests by Hussein et al. [30]. In this context, the term ‘‘smooth’’ for these wall materials is relative and only to distinguish those from the corresponding rough ones. Note that wall-plasters are usually used as a surface layer on walls and ceilings construction. Wallpapers are commonly used as surface finishing on vertical wall and they are alternatives to wall-plasters. Wood, strand boards, carpets, linoleum, ceramic tiles etc. are used as the wear layer on the floor. The mean surface roughness height (k) of the wallpapers and wall-plasters was measured with the surface contacting profilometer TALYSURF 6 (Taylor-Hobson). The mean surface roughness height refers to the absolute value of the surface roughness height averaged over the surface. One sample (about 10 10 cm2) from each surface type was used for surface roughness analysis. A total of three measurements with about 10 cm tracing length were performed over each sample.
Fig. 1. A schematic diagram illustrating the measurement chamber, aerosol particle generators, and instrumentation [30].
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Fig. 2. Deposition rates of aerosol particles on smooth aluminum surfaces inside the test chamber as reported by Hussein et al. [30]: (a) still-air (no-mixing), (b) mixing mode-15 (fan speed 375 rpm), (c) mixing mode-20 (fan speed 548 rpm), and (d) mixing mode-25 (fan speed 706 rpm). The predicted deposition rates (lines) are according to Lai and Nazaroff [38] and Zhao and Wu [15].
We could not obtain surface roughness measures for the carpets with the TALYSURF 6 because their surface material was too soft. The texture of the first carpet was loop-pile with 3 mm polyamide fibers whereas for the second carpet it was frieze with 4.5 mm polypropylene fibers. The surface roughness height of wall-plasters depends on granularity of its material. A wall-plaster material is a mixture of cement and filling aggregates such as sand. We made two types of wall-plasters: smooth and rough with aggregate granularity 0–0.4 mm and 0–4 mm, respectively. The mean surface roughness height was, respectively, 68–102 and 306–368 mm. We used two types of commercial smooth and rough wallpapers made of paper and expanded vinyl. The mean surface roughness height was 47–59 and 300–332 mm, respectively. 2.2. Aerosol particle generation and measurement Before each measurement we flushed the chamber with clean air at high ventilation rate. We injected the aerosol particle sample with a well-defined airflow rate. When the desired concentration of aerosol particles was achieved in the chamber we closed the tubing from the aerosol generator and at the same time we compensated for the outgoing air, which was the sampling flow rate required for the aerosol instruments, with a clean dry air. This acted as the ventilation process of the chamber to be either 0.018 h1 (SMPS sampling flow rate 0.3 l/min) or 0.3 h1 (APS sampling flow rate 5 l/ min), respectively, during the measurement scenarios for ultrafine or coarse particles; as will be described later in this section.
During the measurement sessions we maintained rather constant conditions (temperature, relative humidity, pressure, and indoor air mixing). The air mixing inside the chamber was performed with an air mixer (fan) mounted in the middle of the chamber. The fan was a simple four-blade propeller that induced an upward air stream. In this study we operated the fan at two mixing modes: 375 and 548 rpm (revolution-per-minute). Through out this article we will refer to these mixing schemes as mixing mode15 and mixing mode-20, respectively. When the fan was turned off we will recall the case as no-mixing. We generated two types of aerosol particles: ammonium sulphate (NH4)2SO4 and Di-2-Ethylhexyl-Sebacate (DEHS). Ammonium sulphate particles were generated with the AGK 2000 (Palas, Germany). The AGK generator is based on atomization of (NH4)2SO4 solution followed by drying to generate fine particles within the diameter size range 0.02–0.3 mm. The DEHS particles were generated with the MAG 3000 (Palas, Germany). The MAG generator is based on heterogeneous condensation of DEHS to produce nearly monodisperse liquid particles in the diameter range of 0.5–5 mm with particle density 0.914 g/cm3. Because the generated salt particles were not perfectly monodispersed, we used a Differential Mobility Analyzer (DMA, TSI model 3081) to select monodispersed samples with the desired diameter. In this study we assumed that the generated aerosol particles were spherical. We measured the particle number size distributions inside the chamber with a Scanning Mobility Particle Sizer (SMPS, TSI model 3934: sample flow rate 0.3 l/min, sample-to-sheath flow ratio 1:10)
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and/or an Aerodynamic Particle Sizer (APS, TSI model 3321: flow rate 5 l/min). The SMPS consisted of an Electrostatic Clasifier (EC, TSI model 3071) and Condensation Particle Counter (CPC, TSI model 3022). The sampling inlets were mounted through the floor of the chamber to measure the particle number size distributions in the middle at 15 cm above the floor of the chamber. The SMPS and APS measure aerosol particles based on different principles; and therefore, the aerodynamic diameter of DEHS particles as measured with the APS should be converted to geometric diameters [31]. However, the generated DEHS particles were monodispersed liquid droplets with density 0.914 g/cm3 and according to Hinds [32]
sffiffiffiffiffiffiffiffi
r0 c rp
dg ¼ da
(1)
where dg and da are, respectively, the geometric and aerodynamic diameters, r0 (1 g/cm3) and rp (0.914 g/cm3) are, respectively, the unit and particle densities, and c is the shape factor of DEHS particles (assumed to be 1). The difference between the aerodynamic and geometric diameter for DEHS particles is expected to be less than 5%.
2.3. Estimation and prediction of the deposition rate 2.3.1. Semi-empirical estimation Usually the deposition rate of aerosol particles is determined indirectly (semi-empirical) by monitoring the concentrations of airborne aerosol particles or by directly measuring the amount of deposited particles on indoor surfaces [33,30 and therein references]. Our analysis in this study included semi-empirical estimation for the deposition rate of aerosol particles: we can describe the dynamic behavior of aerosol particles inside the chamber with a simple indoor aerosol model by assuming the indoor air is well mixed and we further ignore re-suspension, condensation/evaporation, and coagulation [33]. The time change rate of the particle umber concentration of each particle size-class can be mathematically described as follows
d N ¼ l þ ld;i Ni dt i
(2)
where Ni [cm3] is the particle number concentration of a certain particle size fraction denoted by i, which has a well-defined and constant physical-chemical properties. l [s1] is the ventilation rate of the chamber and ld,i [s1] is the deposition rate of aerosol particles on the internal surfaces of the chamber. The analytical solution for this simple indoor aerosol model can be easily verified in the form
Ni ðtÞ ln N0;i
!
¼ l þ ld;i t
(3)
where N0,i is the initial concentration (at time t ¼ 0) of the aerosol particles in the chamber. This initial concentration can be chosen in the beginning of the linear trend of the decaying particle number concentration according to Eq. (3). Following our estimation procedure as described by Hussein et al. [30], we used the least square method to best-fit the temporal variation of aerosol particle number concentrations according to Eq. (3). The deposition rate is then estimated from the slope of the best-fit line after taking into account the ventilation rate (l ¼ Q/V, where Q [m3/s] is the air exchange rate and V [m3] is the chamber volume). According to our measurement setup, the decaying trend of the particle number concentrations followed a perfect line
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according to Eq. (3), and the error of the linear fitting process was less than 5%. 2.3.2. Prediction with a deposition model Theoretically, deposition models have been developed in order to predict the deposition velocity of aerosol particles onto indoor surfaces. Deposition models assume a well-mixed profile of the indoor air and incorporate Brownian and eddy diffusion as well as gravitational settling as the most important processes. Some of the models additionally incorporate other processes such as thermophoresis, electrostatic drifting, and/or turbophoresis when their effects become significant [3,5,7,15,]. A deposition model predicts the deposition velocity on ground (horizontal facing up), ceiling (horizontal facing down), and walls (vertical) separately. The overall deposition rate ld,i on indoor surfaces can be estimated according to
ld;i ¼
1X Aj vd;i;j V
(4)
j
where V [m3] is again the volume of the indoor domain, Aj [m2] is the total surface area of a deposition surface, and vd,i,j [m/s] is the predicted deposition velocity on that surface. j is an identifier for the orientation of the deposition surface as horizontal facing up, horizontal facing down, or vertical. i is again an identifier for a certain particle size. Traditional deposition models required the particle size as well as a characteristic parameter (ke) that describes the turbulent intensity [3,5,35,36]. The friction velocity has been introduced in modern deposition models because the interpretation of ke is typically difficult to understand [2,16,34,37,38]. While many previous models assumed smooth surfaces, only very few considered the changes in the boundary layer near by rough surfaces [3,15] where the mean surface roughness height (k) is additionally needed as an input. 3. Results and discussion 3.1. Deposition on wallpapers The deposition rate of aerosol particles on smooth wallpaper was quite similar to that on smooth aluminum surface (Figs. 2 and 3a). The predicted values of the friction velocity (u*) according to the deposition model by Lai and Nazaroff [38] that describes these deposition rates on smooth wallpaper were 1.05, 8, and 10 cm/s, respectively, for the no-mixing, mixing mode-15, and mixing mode-20. Even though the smooth wallpaper used in this study can be assumed smooth to a certain extent, its surface roughness measurement indicated that the mean surface roughness height (k) ranged between 47 and 59 mm. We, therefore, compared our deposition rates by assuming k ¼ 50 mm according to the deposition model by Zhao and Wu [15]. In that case the predicted values of the friction velocity were 1.05, 5.5, and 6.5 cm/s, respectively, for nomixing, mixing mode-15, and mixing mode-20 (Fig. 3b). In practice, the friction velocity over a rough surface is not expected to have a smaller value in comparison with a smooth surface under the same indoor air mixing conditions. Therefore, our empirical results clearly suggest that this smooth wallpaper (k ¼ 47–59 mm) behaved as a smooth surface for the tested mixing modes and the deposition model by Lai and Nazaroff [38] is sufficiently capable of describing the deposition rate as a function of the particle diameter. As expected, the deposition rate of aerosol particles, especially ultrafine particles (diameter < 100 nm), on rough wallpaper (k ¼ 300–332 mm) was higher in comparison to that on the smooth wallpaper (Fig. 3c). Compared to a smooth aluminum surface for
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Fig. 3. Deposition rates of aerosol particles on wallpapers: (a, b) smooth and (c, d) rough.
the same tested condition of indoor mixing the deposition rate was up to 2.5 times higher on the rough wallpaper depending on the particle size and mixing intensity. Obviously, the deposition model by Lai and Nazaroff [38] is not capable of predicting the deposition rates for this case. According to the deposition model by Zhao and Wu [15] and by assuming k ¼ 315 mm the predicted values of the friction velocity were 3.5 and 4 cm/s, respectively, for mixing mode-15 and mixing mode-20 (Fig. 3d). Again, the friction velocity over a rough surface is not expected to have a smaller value in comparison with a smooth surface under the same indoor air mixing conditions. Therefore, we used the friction velocities 8 and 10 cm/s, which were previously predicted for a smooth surface under the same indoor air mixing conditions, and this case k was w65 mm in order to describe the deposition rates on the rough wall-paper (Fig. 3c). However, this value of the mean surface roughness height is very small compared to the actual one k ¼ 300–332 mm. 3.2. Deposition on wall-plasters The deposition rate of aerosol particles on wall-plasters was higher when compared to the deposition rate on wallpapers with only one exception for the smooth wall-plaster (k ¼ 68–100 mm) which can be treated as a smooth surface during the no-mixing case (Fig. 4a and c). This finding is, in general, expected because the tested wall-plasters in this study were rougher than the corresponding wallpapers, and therefore, the deposition rate of aerosol particles on wall-plasters was higher than that on wallpapers
especially during the air mixing conditions. Compared to deposition on smooth aluminum surface the deposition rate was up to 3.5 times higher on the smooth wall-plaster and up to 10 times higher on rough wall-plaster depending on the particle size and mixing intensity. As mentioned before, the smooth wall-plaster can be treated as a hydraulically smooth surface during the no-mixing conditions. And for this case we could explain the deposition pattern of aerosol particles as a function of their size by using the deposition model By Lai and Nazaroff [38] with a friction velocity 1.05 cm/s (Fig. 4a). This friction velocity is exactly the same as the predicted one for the smooth aluminum surface [30]. The other mixing modes (15 and 20) were not possible to be explained by this deposition model, and therefore we used the deposition model by Zhao and Wu [15] with k ¼ 80 mm. The predicted values of the friction velocity were 7 and 8.5 cm/s, respectively, for the mixing mode-15 and mixing mode20, respectively. Again, these values are lower than the predicted values (8 and 10 cm/s) for a smooth surface. We then used these friction velocities (8 and 10 cm/s) in the Zhao and Wu [15] and found out that the predicted deposition rates by this model exceeded our estimations unless we use k ¼ 58 mm, which is slightly lower than the lower limit of the measured value of k (Fig. 4b). The rough wall-plaster had a mean surface roughness height k ¼ 306–368 mm. Again, we compared the deposition rates predicted according to the deposition model by Zhao and Wu [15] to our estimated values, and we found out that we could explain the deposition rate of aerosol particles as a function of their size when we used friction velocities 1.05, 8, and 10 cm/s (values predicted for
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Fig. 4. Deposition rates of aerosol particles on wall-plasters (a, b) smooth and (c, d) rough.
a smooth surface) and k ¼ 100 mm (Fig. 4c), which is very small compared to the lower limit of the measured value of the mean surface roughness height. If we use more reasonable value for k (say 330 mm), the predicted friction velocities should be 1.05, 3.7, and 4.5 cm/s, respectively, for no-mixing, mixing mode-15, and mixing mode-20 (Fig. 4d). These friction velocities are again not realistic because they are lower than those predicted over a smooth surface under the same indoor mixing conditions. The reader might wonder why an increment in the mean surface roughness (k) required a reduction in the friction velocity (u*) in order to explain the estimated deposition rates in this study for the tested surfaces with the model prediction by Zhao and Wu [15]! The answer is quite complicated and needs a draw back to the model development itself. According to Zhao and Wu [15], the friction velocity (u*) and the mean surface roughness height (k) are combined together in another parameters as the dimensionless surface roughness height (kþ) according to
kþ ¼
ku*
n
(5)
where k [m] is again the mean roughness height of the surface, u* [m/s] is the friction velocity, and v [m2/s] is the kinematic viscosity of air. In fact, this is the parameter that describes the modification in the boundary layer over a rough surface and, in fact, for each rough surface and flow condition there is only one value for kþ that can be used to explain the deposition rate of aerosol particles as a function of their size over that rough surface. Therefore, and according to Eq. (5), the inverse relationship between k and u* assuming invariant kþ is easily explained here. In other words, kþ is
the direct parameter along with the particle diameter that is used in such a model in order to predict the deposition velocity. 3.3. Deposition on carpets The deposition rate of aerosol particles on the tested carpets was higher than that on wallpapers and wall-plasters (Fig. 5). The deposition rate on the pile-loop carpet and the frieze carpet was, respectively, up to 8 times and 20 times higher than that on a smooth aluminum surface. From the physical point of view, the surface roughness of the loop-pile carpet can be considered similar to a rough wall-plasters and this is why the deposition rate on the tested loop-pile carpet was of the same order of magnitude as that on the rough wall-plaster. The situations were more complicated over a frieze carpet. The surface structure was very complicated because long fibers might extend beyond the boundary layer resulting in complex behavior of aerosol particles’ movement within and above the boundary layer. The deposition of aerosol particles via electrostatic drifting on the tested carpets was also very probable because carpet materials (polymer material) easily build up surface charges [7]. Surprisingly, the estimated deposition rates did not change when we sprayed anti-static material on the frieze carpet (Fig. 5b). This, in fact, does not indicate that the frieze carpet did not accumulate surface charges; instead, it suggests that the used anti-static spray might not be efficient to remove the accumulated charges on the surface. Quantification of electrostatic drifting on the tested carpets is not possible here because of the lack of information about the surface charge distribution.
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Fig. 5. Deposition rates of aerosol particles on (a) loop-pile carpet and (b) Frieze carpet with and without anti-static spray.
Our results also illustrated that a rough surface can be treated as hydraulically smooth surface for kþ < 0.06 (Figs. 3b and 4b). This contradicts with Wan [39] who showed that the velocity boundary layer over a rough surface is not altered (and the surface is considered hydraulically smooth) when kþ < 3. Applying this to our setup and measurement conditions (maximum value for kþ ¼ 2.28 for Frieze carpet and mixing mode-20), which is not proven to behave as a hydraulically smooth surface. The same argument holds for the rough wallpaper and rough wall-plaster. After all, the model developed by Zhao and Wu [15] is in good agreement with our measurements for coarse mode particles (diameter > 1 mm) when kþ < 1.04. On the other hand, the model was in good agreement with our measurements for ultrafine particles (diameter < 0.1 mm) when kþ < 0.48. Even though this agreement is based on the dimensionless surface roughness height (kþ), the model is not yet capable of predicting the deposition rates by using the actual surface roughness height (k) and there is a need for further development to improve the model performance for the fine particle size range. Additionally, the deposition of aerosol particles, especially fine particles, needs more empirical investigations aiming at improving the existing model. 4. Summary and conclusions In this study we investigated the deposition rate of aerosol particles (diameter between 0.03 and 5 mm) on rough surfaces of wallpapers, wall-plasters, and two types of carpets inside a test chamber. Our analysis included semi-empirical estimation for the deposition rates and validation for deposition models according to Zhao and Wu [15] and Lai and Nazaroff [38]. According to our setup and methods [30], the effects of coagulation, thermophoresis, and turbophoresis can be neglected. Our results emphasized that deposition of aerosol particles on rough surface is enhanced by increasing the surface roughness height (k) as well as the indoor air mixing intensity. Compared to a smooth aluminum surface, the deposition rate of aerosol particles on the tested wallpapers and wall-plasters was up to 10 times higher depending on the surface roughness, mixing intensity, and particle size. The deposition rate on the tested loop-pile carpet was similar to that on the rough wall-plaster because the used loop-pile carpet had a mean surface roughness height within the same order of magnitude of the rough wall-plaster. The deposition rate on the tested frieze carpet was more challenging because of the complex surface structure which consists of fibers that might extend over the velocity boundary layer. The enhancement in the deposition
rate on the frieze carpet was up to 20 times when compared to the deposition on smooth aluminum surface. Using anti-static sprays on the frieze carpet did not show any differences in the deposition patterns of aerosol particles. It was, therefore, not possible by this study to determine whether the enhanced deposition on the frieze carpet was also due to accumulated electrostatic charges on the surface material. As suggested by previous theories [3,10,34], the surface roughness of the tested carpets significantly modified the boundary layer where the transport of aerosol particles was enhanced. On the other hand, deposition by impaction of coarse particles is also probable here, especially on the frieze carpet, because the fibers might extend over the velocity boundary layer where they act as active intercepting objects for aerosol particles nearby the surface. This study showed that a rough surface can be treated as hydraulically smooth surface for kþ < 0.06, which contradicts with Wan [39] who showed that a rough surface is considered hydraulically smooth when kþ < 3. The outcome of our analysis showed that the deposition model developed by Zhao and Wu [15] is valid to predict the deposition velocity of coarse mode particles (diameter > 1 mm) when kþ < 1.04 and of ultrafine particles (diameter < 0.1 mm) when kþ < 0.48; though, the model is not yet capable of predicting the deposition velocities by using the actual mean surface roughness height (k). Therefore, further development and empirical investigations are needed to improve the model performance, especially for fie particles. Acknowledgements This work was supported by the GACR grants No. 101/04/1190 (Indoor aerosol deposition: An experimental study) and 101/07/ 1361 (Evaluation of dynamics of aerosol particles in indoor environment). Dr. Hussein acknowledges the financial support by the Va¨isa¨la¨ Foundation/Finnish Academy of Science and Letters (Indoor aerosol behavior). References [1] Lai ACK. Particle deposition indoors: a review. Indoor Air 2002;12:211–4. [2] Lai ACK. Investigation of electrostatic forces on particle deposition in a test chamber. Indoor and Built Environment 2006;15:179–86. [3] Guha A. A unified Eulerian theory of turbulent deposition to smooth and rough surfaces. Journal of Aerosol Science 1997;28:1517–37. [4] Chen BT, Yeh HC, Cheng YS. Evaluation of an environmental reaction chamber. Aerosol Science and Technology 1992;17:9–24.
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[21] Fan FG, Ahmadi G. A sublayer model for turbulent deposition of particles in vertical ducts with smooth and rough surfaces. Journal of Aerosol Science 1993;24:45–64. [22] Shimada M, Okuyama K, Kousaka Y, Ohshima K. Turbulent and Brownian diffusive deposition of aerosol particles onto a rough wall. Journal of Chemical Engineering of Japan 1987;20:57–64. [23] Oron A, Gutfinger C. On turbulent deposition of particles to rough surfaces. Journal of Aerosol Science 1986;17:903–20. [24] Kozlu H, Mikic BB, Patera AT. Turbulent heat transfer augmentation using microscale disturbances inside the viscous sublayer. Journal of Heat Transfer 1992;114:348–53. [25] Dawson DA, Trass O. Mass transfer at rough surfaces. International Journal of Heat and Mass Transfer 1972;15:1317–36. [26] Wood NB. A simple method for the calculation of turbulent deposition to smooth and rough surfaces. Journal of Aerosol Science 1981;12:275–90. [27] Browne LWB. Deposition of particles on rough surfaces during turbulent gasflow in a pipe. Atmospheric Environment 1974;8:801–16. [28] Perry AE, Schofield WH, Joubert PN. Rough wall turbulent boundary layers. Journal of Fluid Mechanics 1969;37:386–7. [29] Grass AJ. Structural features of turbulent flow over smooth and rough boundaries. Journal of Fluid Mechanics 1971;50:233–50. [30] Hussein T, Hrusˇka A, Doha´nyosova´ P, D zumbova´ L, Hemerka J, Kulmala M, et al. Evaluation of deposition rates of aerosol particles on smooth surfaces inside a test chamber. Atmospheric Environment 2009;43:905–14. [31] Khlystov A, Stanier C, Pandis SN. An algorithm for combining electrical mobility and aerodynamic size distributions data when measuring ambient aerosol. Aerosol Science and Technology 2004;38(S1):229–38. [32] Hinds WC. Aerosol technology. New York: John Wiley & Sons; 1999. [33] Hussein T, Kulmala M. Indoor aerosol modeling: basic principles and practical applications. Water, Air, and Soil Pollution: Focus 2008;8:23–34. [34] Zhao B, Wu J. Modeling particle deposition from fully developed turbulent flow in ventilation duct. Atmospheric Environment 2006;40:457–66. [35] Crump JG, Flagan RC, Seinfeld JH. Particle wall loss rates in vessels. Aerosol Science and Technology 1983;2:303–9. [36] Corner J, Pendlebury ED. The coagulation and deposition of a stirred aerosol. Proceedings of the Physical Society 1951;B64:645–54. [37] Sippola MR, Nazaroff WW. Modeling particle loss in ventilation ducts. Atmospheric Environment 2003;37:5597–609. [38] Lai ACK, Nazaroff WW. Modeling indoor particle deposition from turbulent flow onto smooth surfaces. Journal of Aerosol Science 2000;31:463–76. [39] Wan SG. Experimental study on turbulence near walls. Chinese Science Bulletin 1981;26:1145–8.
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Building and Environment journal homepage: www.elsevier.com/locate/buildenv
Deposition of aerosol particles on rough surfaces inside a test chamber Tareq Hussein a, Lenka Kubincova´ b, Lucie D zumbova´ c, Alesˇ Hrusˇka d, Pavla Doha´nyosova´ c, d c, * Jirˇı´ Hemerka , Jirˇı´ Smolı´k a
University of Helsinki, Department of Physics, P.O. Box 64, FI–00014 University of Helsinki, Finland ´ 4, SK-04002 Kosˇice, Slovak Republic Technical University of Kosˇice, Civil Engineering Faculty, Institute of Building and Environmental Engineering, Vysokosˇkolska c ´ 135, CZ-16502 Praha 6, Prague, Czech Republic Institute of Chemical Process Fundamentals, Laboratory of Aerosol Chemistry and Physics, Rozvojova d ´ 4, CZ- 166 07 Praha 6, Prague, Czech Republic Czech Technical University of Prague, Department of Environmental Engineering, Technicka b
a r t i c l e i n f o
a b s t r a c t
Article history: Received 1 December 2008 Received in revised form 16 February 2009 Accepted 17 February 2009
We investigated the deposition rate of aerosol particles (diameter between 0.03 and 5 mm) on rough surfaces of wallpapers, wall-plasters, and two types of carpets inside a test chamber. Compared to a smooth aluminum surface, the deposition rate of aerosol particles on the tested surfaces was up to 20 times depending on the surface roughness, mixing intensity, and particle size. A rough surface with a dimensionless surface roughness height kþ < 0.06 can be treated as a hydraulically smooth. The estimated deposition rates in this study and those predicted by a deposition model, which incorporates surface roughness, were in good agreement for coarse mode particles (diameter > 1 mm) when kþ < 1.04 and for ultrafine particles (diameter < 0.1 mm) when kþ < 0.48. The agreement between the model prediction and our estimation was better for coarse mode particles than for ultrafine particles. Deposition of aerosol particles, especially fine particles, needs more empirical investigations aiming at improving the existing models. Ó 2009 Elsevier Ltd. All rights reserved.
Keywords: Deposition rate Hydraulically smooth Surface roughness Model evaluation Carpet Wall material
1. Introduction Deposition of aerosol particles in real-life indoor environments has been treated by assuming perfectly smooth indoor surfaces. Discrepancies between the empirical results and model were often explained by either not including some processes in the model or due to unknown artifacts in the experimental setup. Generally speaking, deposition of aerosol particles is a very complicated process that depends on the particle size, airflow characteristics, geometries of the indoor environment, and most importantly the surface roughness [1]. Deposition of aerosol particles indoors may involve several processes. Brownian and eddy diffusion are most important processes for fine particles (diameter < 1 mm) whereas gravitational settling and impaction are the significant processes for coarse particles. Other processes such as thermophoresis due to temperature gradients, electrostatic drifting due to charged particles or electric field nearby charged surfaces, or turbophoresis due to gradients in the turbulent fluctuating velocity components nearby the surface can be also important factors [2–7]. Even though deposition of aerosol particles on smooth surfaces has been well understood, there has been slow development on the
* Corresponding author. Tel.: þ420 220 390 247; fax: þ420 220 920 661. E-mail address: [email protected] (J. Smolı´k). 0360-1323/$ – see front matter Ó 2009 Elsevier Ltd. All rights reserved. doi:10.1016/j.buildenv.2009.02.009
theory of deposition on rough surfaces. The slow development has been mainly due to lack of experimental investigations. The effect of surface roughness has been quantified by using sandpapers, large-scale obstruction with regular geometry, or using textile materials commonly used indoors [8–14]. Some of these studies were applied for collection efficiency of aerosol particles inside duct lines or precipitators and the materials used in these tests are well described by producer but don’t exist in real buildings. In reallife conditions the surface roughness of indoor surfaces might vary from several microns (such as smooth wallpapers) up to several millimeters (such as carpets). Generally speaking and according to the empirical results reported in literature, deposition rate of aerosol particles on rough surfaces is higher than that on smooth surfaces [3,8–10,15–20]. Modeling efforts have been increased aiming to quantify deposition on rough surfaces by proposing different assumptions and parameterizations regarding both the structure of the boundary layer and the flow of the fluid within the boundary layer [16,21–23]. Previously, Lai et al. [10] explained the increased deposition rate on rough surfaces to be due to some modifications in the boundary layer over the rough surface. If the thin boundary layer is completely destroyed or at least made thinner due to the surface roughness, the transportation progress across this layer can be greatly enhanced. This explanation was in analogy with heat transfer experiments [24,25]. Guha [3] proposed a unified theory
T. Hussein et al. / Building and Environment 44 (2009) 2056–2063
aiming to quantify the deposition of aerosol particles onto smooth and rough surfaces under different conditions. Following Wood [26], Guha [3] assumed in his unified theory that the origin of the velocity profile over a rough surface is shifted away from the wall. Similar assumptions were also proposed by Browne [27] following previous work by Perry et al. [28] and Grass [29]. Recently, Zhao and Wu [15] developed a new model where they used a modified assumption for the change in the boundary layer thickness over a rough surface. Their results were plausible when their model was evaluated against experimental observations. In this study we investigated the deposition rate of aerosol particles on rough surfaces commonly used in real-life situations: wallpapers, wall-plasters, and carpets. We performed our measurements inside a test chamber; we previously investigated the deposition rates of aerosol particles on smooth aluminum surfaces inside the same test chamber [30]. Our analysis included semi-empirical estimation for the deposition rates and comparison to existing deposition models and previous studies. Many of previous studies considered a limited size range or the measurement conditions were not well controlled. Here we present a widerange set of measurements covering the particle size range from 30 nm up to 5 mm under controlled conditions inside a test chamber. According to our knowledge, such data set has never been published and intensively analyzed as presented in this study.
2. Experimental setup and measurement We constructed a sealed chamber (1 1 1 m3) made of smooth aluminum surfaces (Fig. 1) according to the principles described by Hussein et al. [30], which reported deposition rates of aerosol particles on smooth aluminum surfaces inside this test chamber. The main findings by Hussein et al. [30] can be summarized as follows: (1) the deposition rate of aerosol particles on smooth aluminum surfaces was increased by increasing the fan speed (indoor mixing) and the ventilation rate. This is illustrated in Fig. 2.
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(2) the effect of particle coagulation was found negligible when particle number concentrations inside the chamber was below 104 cm3. (3) deposition of aerosol particles on aluminum surfaces via thermophoresis or electrostatic drifting can be neglected because the chamber was grounded and thermally insulated (with polystyrene foam sheets). (4) turbophoresis and impaction processes were seen significant for particles larger than 5 mm when the fan speed exceeded 548 rpm. (5) the influence of the ventilation rate on the deposition rate on aluminum surfaces was only observed during still-air (nomixing) conditions.
2.1. Wall materials and surface roughness We placed different wall materials on the interior surfaces (walls, ceiling, and ground) of the chamber. We used smooth and rough surfaces of the commonly used wallpapers and wall-plasters in addition to two different types of carpets. Even though we used smooth wall materials, they were not as smooth as the aluminum surface used in our previous tests by Hussein et al. [30]. In this context, the term ‘‘smooth’’ for these wall materials is relative and only to distinguish those from the corresponding rough ones. Note that wall-plasters are usually used as a surface layer on walls and ceilings construction. Wallpapers are commonly used as surface finishing on vertical wall and they are alternatives to wall-plasters. Wood, strand boards, carpets, linoleum, ceramic tiles etc. are used as the wear layer on the floor. The mean surface roughness height (k) of the wallpapers and wall-plasters was measured with the surface contacting profilometer TALYSURF 6 (Taylor-Hobson). The mean surface roughness height refers to the absolute value of the surface roughness height averaged over the surface. One sample (about 10 10 cm2) from each surface type was used for surface roughness analysis. A total of three measurements with about 10 cm tracing length were performed over each sample.
Fig. 1. A schematic diagram illustrating the measurement chamber, aerosol particle generators, and instrumentation [30].
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Fig. 2. Deposition rates of aerosol particles on smooth aluminum surfaces inside the test chamber as reported by Hussein et al. [30]: (a) still-air (no-mixing), (b) mixing mode-15 (fan speed 375 rpm), (c) mixing mode-20 (fan speed 548 rpm), and (d) mixing mode-25 (fan speed 706 rpm). The predicted deposition rates (lines) are according to Lai and Nazaroff [38] and Zhao and Wu [15].
We could not obtain surface roughness measures for the carpets with the TALYSURF 6 because their surface material was too soft. The texture of the first carpet was loop-pile with 3 mm polyamide fibers whereas for the second carpet it was frieze with 4.5 mm polypropylene fibers. The surface roughness height of wall-plasters depends on granularity of its material. A wall-plaster material is a mixture of cement and filling aggregates such as sand. We made two types of wall-plasters: smooth and rough with aggregate granularity 0–0.4 mm and 0–4 mm, respectively. The mean surface roughness height was, respectively, 68–102 and 306–368 mm. We used two types of commercial smooth and rough wallpapers made of paper and expanded vinyl. The mean surface roughness height was 47–59 and 300–332 mm, respectively. 2.2. Aerosol particle generation and measurement Before each measurement we flushed the chamber with clean air at high ventilation rate. We injected the aerosol particle sample with a well-defined airflow rate. When the desired concentration of aerosol particles was achieved in the chamber we closed the tubing from the aerosol generator and at the same time we compensated for the outgoing air, which was the sampling flow rate required for the aerosol instruments, with a clean dry air. This acted as the ventilation process of the chamber to be either 0.018 h1 (SMPS sampling flow rate 0.3 l/min) or 0.3 h1 (APS sampling flow rate 5 l/ min), respectively, during the measurement scenarios for ultrafine or coarse particles; as will be described later in this section.
During the measurement sessions we maintained rather constant conditions (temperature, relative humidity, pressure, and indoor air mixing). The air mixing inside the chamber was performed with an air mixer (fan) mounted in the middle of the chamber. The fan was a simple four-blade propeller that induced an upward air stream. In this study we operated the fan at two mixing modes: 375 and 548 rpm (revolution-per-minute). Through out this article we will refer to these mixing schemes as mixing mode15 and mixing mode-20, respectively. When the fan was turned off we will recall the case as no-mixing. We generated two types of aerosol particles: ammonium sulphate (NH4)2SO4 and Di-2-Ethylhexyl-Sebacate (DEHS). Ammonium sulphate particles were generated with the AGK 2000 (Palas, Germany). The AGK generator is based on atomization of (NH4)2SO4 solution followed by drying to generate fine particles within the diameter size range 0.02–0.3 mm. The DEHS particles were generated with the MAG 3000 (Palas, Germany). The MAG generator is based on heterogeneous condensation of DEHS to produce nearly monodisperse liquid particles in the diameter range of 0.5–5 mm with particle density 0.914 g/cm3. Because the generated salt particles were not perfectly monodispersed, we used a Differential Mobility Analyzer (DMA, TSI model 3081) to select monodispersed samples with the desired diameter. In this study we assumed that the generated aerosol particles were spherical. We measured the particle number size distributions inside the chamber with a Scanning Mobility Particle Sizer (SMPS, TSI model 3934: sample flow rate 0.3 l/min, sample-to-sheath flow ratio 1:10)
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and/or an Aerodynamic Particle Sizer (APS, TSI model 3321: flow rate 5 l/min). The SMPS consisted of an Electrostatic Clasifier (EC, TSI model 3071) and Condensation Particle Counter (CPC, TSI model 3022). The sampling inlets were mounted through the floor of the chamber to measure the particle number size distributions in the middle at 15 cm above the floor of the chamber. The SMPS and APS measure aerosol particles based on different principles; and therefore, the aerodynamic diameter of DEHS particles as measured with the APS should be converted to geometric diameters [31]. However, the generated DEHS particles were monodispersed liquid droplets with density 0.914 g/cm3 and according to Hinds [32]
sffiffiffiffiffiffiffiffi
r0 c rp
dg ¼ da
(1)
where dg and da are, respectively, the geometric and aerodynamic diameters, r0 (1 g/cm3) and rp (0.914 g/cm3) are, respectively, the unit and particle densities, and c is the shape factor of DEHS particles (assumed to be 1). The difference between the aerodynamic and geometric diameter for DEHS particles is expected to be less than 5%.
2.3. Estimation and prediction of the deposition rate 2.3.1. Semi-empirical estimation Usually the deposition rate of aerosol particles is determined indirectly (semi-empirical) by monitoring the concentrations of airborne aerosol particles or by directly measuring the amount of deposited particles on indoor surfaces [33,30 and therein references]. Our analysis in this study included semi-empirical estimation for the deposition rate of aerosol particles: we can describe the dynamic behavior of aerosol particles inside the chamber with a simple indoor aerosol model by assuming the indoor air is well mixed and we further ignore re-suspension, condensation/evaporation, and coagulation [33]. The time change rate of the particle umber concentration of each particle size-class can be mathematically described as follows
d N ¼ l þ ld;i Ni dt i
(2)
where Ni [cm3] is the particle number concentration of a certain particle size fraction denoted by i, which has a well-defined and constant physical-chemical properties. l [s1] is the ventilation rate of the chamber and ld,i [s1] is the deposition rate of aerosol particles on the internal surfaces of the chamber. The analytical solution for this simple indoor aerosol model can be easily verified in the form
Ni ðtÞ ln N0;i
!
¼ l þ ld;i t
(3)
where N0,i is the initial concentration (at time t ¼ 0) of the aerosol particles in the chamber. This initial concentration can be chosen in the beginning of the linear trend of the decaying particle number concentration according to Eq. (3). Following our estimation procedure as described by Hussein et al. [30], we used the least square method to best-fit the temporal variation of aerosol particle number concentrations according to Eq. (3). The deposition rate is then estimated from the slope of the best-fit line after taking into account the ventilation rate (l ¼ Q/V, where Q [m3/s] is the air exchange rate and V [m3] is the chamber volume). According to our measurement setup, the decaying trend of the particle number concentrations followed a perfect line
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according to Eq. (3), and the error of the linear fitting process was less than 5%. 2.3.2. Prediction with a deposition model Theoretically, deposition models have been developed in order to predict the deposition velocity of aerosol particles onto indoor surfaces. Deposition models assume a well-mixed profile of the indoor air and incorporate Brownian and eddy diffusion as well as gravitational settling as the most important processes. Some of the models additionally incorporate other processes such as thermophoresis, electrostatic drifting, and/or turbophoresis when their effects become significant [3,5,7,15,]. A deposition model predicts the deposition velocity on ground (horizontal facing up), ceiling (horizontal facing down), and walls (vertical) separately. The overall deposition rate ld,i on indoor surfaces can be estimated according to
ld;i ¼
1X Aj vd;i;j V
(4)
j
where V [m3] is again the volume of the indoor domain, Aj [m2] is the total surface area of a deposition surface, and vd,i,j [m/s] is the predicted deposition velocity on that surface. j is an identifier for the orientation of the deposition surface as horizontal facing up, horizontal facing down, or vertical. i is again an identifier for a certain particle size. Traditional deposition models required the particle size as well as a characteristic parameter (ke) that describes the turbulent intensity [3,5,35,36]. The friction velocity has been introduced in modern deposition models because the interpretation of ke is typically difficult to understand [2,16,34,37,38]. While many previous models assumed smooth surfaces, only very few considered the changes in the boundary layer near by rough surfaces [3,15] where the mean surface roughness height (k) is additionally needed as an input. 3. Results and discussion 3.1. Deposition on wallpapers The deposition rate of aerosol particles on smooth wallpaper was quite similar to that on smooth aluminum surface (Figs. 2 and 3a). The predicted values of the friction velocity (u*) according to the deposition model by Lai and Nazaroff [38] that describes these deposition rates on smooth wallpaper were 1.05, 8, and 10 cm/s, respectively, for the no-mixing, mixing mode-15, and mixing mode-20. Even though the smooth wallpaper used in this study can be assumed smooth to a certain extent, its surface roughness measurement indicated that the mean surface roughness height (k) ranged between 47 and 59 mm. We, therefore, compared our deposition rates by assuming k ¼ 50 mm according to the deposition model by Zhao and Wu [15]. In that case the predicted values of the friction velocity were 1.05, 5.5, and 6.5 cm/s, respectively, for nomixing, mixing mode-15, and mixing mode-20 (Fig. 3b). In practice, the friction velocity over a rough surface is not expected to have a smaller value in comparison with a smooth surface under the same indoor air mixing conditions. Therefore, our empirical results clearly suggest that this smooth wallpaper (k ¼ 47–59 mm) behaved as a smooth surface for the tested mixing modes and the deposition model by Lai and Nazaroff [38] is sufficiently capable of describing the deposition rate as a function of the particle diameter. As expected, the deposition rate of aerosol particles, especially ultrafine particles (diameter < 100 nm), on rough wallpaper (k ¼ 300–332 mm) was higher in comparison to that on the smooth wallpaper (Fig. 3c). Compared to a smooth aluminum surface for
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Fig. 3. Deposition rates of aerosol particles on wallpapers: (a, b) smooth and (c, d) rough.
the same tested condition of indoor mixing the deposition rate was up to 2.5 times higher on the rough wallpaper depending on the particle size and mixing intensity. Obviously, the deposition model by Lai and Nazaroff [38] is not capable of predicting the deposition rates for this case. According to the deposition model by Zhao and Wu [15] and by assuming k ¼ 315 mm the predicted values of the friction velocity were 3.5 and 4 cm/s, respectively, for mixing mode-15 and mixing mode-20 (Fig. 3d). Again, the friction velocity over a rough surface is not expected to have a smaller value in comparison with a smooth surface under the same indoor air mixing conditions. Therefore, we used the friction velocities 8 and 10 cm/s, which were previously predicted for a smooth surface under the same indoor air mixing conditions, and this case k was w65 mm in order to describe the deposition rates on the rough wall-paper (Fig. 3c). However, this value of the mean surface roughness height is very small compared to the actual one k ¼ 300–332 mm. 3.2. Deposition on wall-plasters The deposition rate of aerosol particles on wall-plasters was higher when compared to the deposition rate on wallpapers with only one exception for the smooth wall-plaster (k ¼ 68–100 mm) which can be treated as a smooth surface during the no-mixing case (Fig. 4a and c). This finding is, in general, expected because the tested wall-plasters in this study were rougher than the corresponding wallpapers, and therefore, the deposition rate of aerosol particles on wall-plasters was higher than that on wallpapers
especially during the air mixing conditions. Compared to deposition on smooth aluminum surface the deposition rate was up to 3.5 times higher on the smooth wall-plaster and up to 10 times higher on rough wall-plaster depending on the particle size and mixing intensity. As mentioned before, the smooth wall-plaster can be treated as a hydraulically smooth surface during the no-mixing conditions. And for this case we could explain the deposition pattern of aerosol particles as a function of their size by using the deposition model By Lai and Nazaroff [38] with a friction velocity 1.05 cm/s (Fig. 4a). This friction velocity is exactly the same as the predicted one for the smooth aluminum surface [30]. The other mixing modes (15 and 20) were not possible to be explained by this deposition model, and therefore we used the deposition model by Zhao and Wu [15] with k ¼ 80 mm. The predicted values of the friction velocity were 7 and 8.5 cm/s, respectively, for the mixing mode-15 and mixing mode20, respectively. Again, these values are lower than the predicted values (8 and 10 cm/s) for a smooth surface. We then used these friction velocities (8 and 10 cm/s) in the Zhao and Wu [15] and found out that the predicted deposition rates by this model exceeded our estimations unless we use k ¼ 58 mm, which is slightly lower than the lower limit of the measured value of k (Fig. 4b). The rough wall-plaster had a mean surface roughness height k ¼ 306–368 mm. Again, we compared the deposition rates predicted according to the deposition model by Zhao and Wu [15] to our estimated values, and we found out that we could explain the deposition rate of aerosol particles as a function of their size when we used friction velocities 1.05, 8, and 10 cm/s (values predicted for
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Fig. 4. Deposition rates of aerosol particles on wall-plasters (a, b) smooth and (c, d) rough.
a smooth surface) and k ¼ 100 mm (Fig. 4c), which is very small compared to the lower limit of the measured value of the mean surface roughness height. If we use more reasonable value for k (say 330 mm), the predicted friction velocities should be 1.05, 3.7, and 4.5 cm/s, respectively, for no-mixing, mixing mode-15, and mixing mode-20 (Fig. 4d). These friction velocities are again not realistic because they are lower than those predicted over a smooth surface under the same indoor mixing conditions. The reader might wonder why an increment in the mean surface roughness (k) required a reduction in the friction velocity (u*) in order to explain the estimated deposition rates in this study for the tested surfaces with the model prediction by Zhao and Wu [15]! The answer is quite complicated and needs a draw back to the model development itself. According to Zhao and Wu [15], the friction velocity (u*) and the mean surface roughness height (k) are combined together in another parameters as the dimensionless surface roughness height (kþ) according to
kþ ¼
ku*
n
(5)
where k [m] is again the mean roughness height of the surface, u* [m/s] is the friction velocity, and v [m2/s] is the kinematic viscosity of air. In fact, this is the parameter that describes the modification in the boundary layer over a rough surface and, in fact, for each rough surface and flow condition there is only one value for kþ that can be used to explain the deposition rate of aerosol particles as a function of their size over that rough surface. Therefore, and according to Eq. (5), the inverse relationship between k and u* assuming invariant kþ is easily explained here. In other words, kþ is
the direct parameter along with the particle diameter that is used in such a model in order to predict the deposition velocity. 3.3. Deposition on carpets The deposition rate of aerosol particles on the tested carpets was higher than that on wallpapers and wall-plasters (Fig. 5). The deposition rate on the pile-loop carpet and the frieze carpet was, respectively, up to 8 times and 20 times higher than that on a smooth aluminum surface. From the physical point of view, the surface roughness of the loop-pile carpet can be considered similar to a rough wall-plasters and this is why the deposition rate on the tested loop-pile carpet was of the same order of magnitude as that on the rough wall-plaster. The situations were more complicated over a frieze carpet. The surface structure was very complicated because long fibers might extend beyond the boundary layer resulting in complex behavior of aerosol particles’ movement within and above the boundary layer. The deposition of aerosol particles via electrostatic drifting on the tested carpets was also very probable because carpet materials (polymer material) easily build up surface charges [7]. Surprisingly, the estimated deposition rates did not change when we sprayed anti-static material on the frieze carpet (Fig. 5b). This, in fact, does not indicate that the frieze carpet did not accumulate surface charges; instead, it suggests that the used anti-static spray might not be efficient to remove the accumulated charges on the surface. Quantification of electrostatic drifting on the tested carpets is not possible here because of the lack of information about the surface charge distribution.
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Fig. 5. Deposition rates of aerosol particles on (a) loop-pile carpet and (b) Frieze carpet with and without anti-static spray.
Our results also illustrated that a rough surface can be treated as hydraulically smooth surface for kþ < 0.06 (Figs. 3b and 4b). This contradicts with Wan [39] who showed that the velocity boundary layer over a rough surface is not altered (and the surface is considered hydraulically smooth) when kþ < 3. Applying this to our setup and measurement conditions (maximum value for kþ ¼ 2.28 for Frieze carpet and mixing mode-20), which is not proven to behave as a hydraulically smooth surface. The same argument holds for the rough wallpaper and rough wall-plaster. After all, the model developed by Zhao and Wu [15] is in good agreement with our measurements for coarse mode particles (diameter > 1 mm) when kþ < 1.04. On the other hand, the model was in good agreement with our measurements for ultrafine particles (diameter < 0.1 mm) when kþ < 0.48. Even though this agreement is based on the dimensionless surface roughness height (kþ), the model is not yet capable of predicting the deposition rates by using the actual surface roughness height (k) and there is a need for further development to improve the model performance for the fine particle size range. Additionally, the deposition of aerosol particles, especially fine particles, needs more empirical investigations aiming at improving the existing model. 4. Summary and conclusions In this study we investigated the deposition rate of aerosol particles (diameter between 0.03 and 5 mm) on rough surfaces of wallpapers, wall-plasters, and two types of carpets inside a test chamber. Our analysis included semi-empirical estimation for the deposition rates and validation for deposition models according to Zhao and Wu [15] and Lai and Nazaroff [38]. According to our setup and methods [30], the effects of coagulation, thermophoresis, and turbophoresis can be neglected. Our results emphasized that deposition of aerosol particles on rough surface is enhanced by increasing the surface roughness height (k) as well as the indoor air mixing intensity. Compared to a smooth aluminum surface, the deposition rate of aerosol particles on the tested wallpapers and wall-plasters was up to 10 times higher depending on the surface roughness, mixing intensity, and particle size. The deposition rate on the tested loop-pile carpet was similar to that on the rough wall-plaster because the used loop-pile carpet had a mean surface roughness height within the same order of magnitude of the rough wall-plaster. The deposition rate on the tested frieze carpet was more challenging because of the complex surface structure which consists of fibers that might extend over the velocity boundary layer. The enhancement in the deposition
rate on the frieze carpet was up to 20 times when compared to the deposition on smooth aluminum surface. Using anti-static sprays on the frieze carpet did not show any differences in the deposition patterns of aerosol particles. It was, therefore, not possible by this study to determine whether the enhanced deposition on the frieze carpet was also due to accumulated electrostatic charges on the surface material. As suggested by previous theories [3,10,34], the surface roughness of the tested carpets significantly modified the boundary layer where the transport of aerosol particles was enhanced. On the other hand, deposition by impaction of coarse particles is also probable here, especially on the frieze carpet, because the fibers might extend over the velocity boundary layer where they act as active intercepting objects for aerosol particles nearby the surface. This study showed that a rough surface can be treated as hydraulically smooth surface for kþ < 0.06, which contradicts with Wan [39] who showed that a rough surface is considered hydraulically smooth when kþ < 3. The outcome of our analysis showed that the deposition model developed by Zhao and Wu [15] is valid to predict the deposition velocity of coarse mode particles (diameter > 1 mm) when kþ < 1.04 and of ultrafine particles (diameter < 0.1 mm) when kþ < 0.48; though, the model is not yet capable of predicting the deposition velocities by using the actual mean surface roughness height (k). Therefore, further development and empirical investigations are needed to improve the model performance, especially for fie particles. Acknowledgements This work was supported by the GACR grants No. 101/04/1190 (Indoor aerosol deposition: An experimental study) and 101/07/ 1361 (Evaluation of dynamics of aerosol particles in indoor environment). Dr. Hussein acknowledges the financial support by the Va¨isa¨la¨ Foundation/Finnish Academy of Science and Letters (Indoor aerosol behavior). References [1] Lai ACK. Particle deposition indoors: a review. Indoor Air 2002;12:211–4. [2] Lai ACK. Investigation of electrostatic forces on particle deposition in a test chamber. Indoor and Built Environment 2006;15:179–86. [3] Guha A. A unified Eulerian theory of turbulent deposition to smooth and rough surfaces. Journal of Aerosol Science 1997;28:1517–37. [4] Chen BT, Yeh HC, Cheng YS. Evaluation of an environmental reaction chamber. Aerosol Science and Technology 1992;17:9–24.
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