Full Terms & Conditions of access and use can be found at https://www.tandfonline.com/action/journalInformation?journalCode=taem20 Architectural Engineering and Design Management ISSN: 1745-2007 (Print) 1752-7589 (Online) Journal homepage: https://www.tandfonline.com/loi/taem20 Impact of ventilation method on residential indoor PM dispersion during dust storm events in Saudi Arabia Amos Kalua, Soo Jeong Jo, Seyedreza Fateminasab, Sana’a Al-Rqaibat & Christoph Opitz To cite this article: Amos Kalua, Soo Jeong Jo, Seyedreza Fateminasab, Sana’a Al-Rqaibat & Christoph Opitz (2019): Impact of ventilation method on residential indoor PM dispersion during dust storm events in Saudi Arabia, Architectural Engineering and Design Management To link to this article: https://doi.org/10.1080/17452007.2019.1652797 Published online: 16 Aug 2019. Submit your article to this journal View Crossmark data
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Full Terms & Conditions of access and use can be found athttps://www.tandfonline.com/action/journalInformation?journalCode=taem20
Impact of ventilation method on residential indoorPM dispersion during dust storm events in SaudiArabia
Amos Kalua, Soo Jeong Jo, Seyedreza Fateminasab, Sana’a Al-Rqaibat &Christoph Opitz
To cite this article: Amos Kalua, Soo Jeong Jo, Seyedreza Fateminasab, Sana’a Al-Rqaibat &Christoph Opitz (2019): Impact of ventilation method on residential indoor PM dispersion duringdust storm events in Saudi Arabia, Architectural Engineering and Design Management
To link to this article: https://doi.org/10.1080/17452007.2019.1652797
Impact of ventilation method on residential indoor PM dispersionduring dust storm events in Saudi ArabiaAmos Kaluaa,b, Soo Jeong Joa, Seyedreza Fateminasaba, Sana’a Al-Rqaibata andChristoph Opitza
aSchool of Architecture+Design, Virginia Polytechnic Institute and State University, Blacksburg, VA, USA;bDepartment of The Built Environment, Mzuzu University, Mzuzu, Malawi
ABSTRACTDust storms are a major environmental hazard in arid regions such as theMiddle East and other parts of Asia including China and Mongolia. Theprevalence of dust storms has been widely linked with high atmosphericParticulate Matter (PM) concentrations. The atmospheric PMconcentration is considered as being positively correlated with the PMconcentration of the indoor environment. High indoor concentrations ofPM compromise the Indoor Air Quality (IAQ) leading to adverse impactson building occupants’ health with increased occurrences of asthmaattacks, spread of disease-causing fungus, skin allergies and otherailments. There is, therefore, an increased risk of compromised IAQ inplaces that experience dust storms. This risk may be exacerbated by theincorporation of mechanical ventilation systems in attempts to conditionthe indoor ambient air. Instead of minimizing the problem of PMcontamination, these systems may potentially nurture it. UsingComputational Fluid Dynamics analyzes in ANSYS Fluent, this studyseeks to investigate the impact of ventilation method on outdoor-originating indoor PM dispersion within the occupied indoorenvironment during dust storm events in Saudi Arabia. Four ventilationmethods are studied and the one that yields the least amount ofparticle trajectories terminating within the breathing zone isrecommended for use.
ARTICLE HISTORYReceived 16 January 2019Accepted 23 May 2019
KEYWORDSDust storm; particulatematter; computational fluiddynamics; indoor air quality;Saudi Arabia
Background and introduction
Dust storm events are a common meteorological phenomenon in arid regions (Goudie, 2014; Indoitu,Orlovsky, & Orlovsky, 2012; Middleton, 2017; Shao & Dong, 2006). They are characterized by turbulentwind systems which carry substantial amounts of sand and dust particles.
The prevalence of dust storms has been widely linked with high atmospheric PM concentrations. Astudy by Leys, Heidenreich, Strong, McTainsh, and Quigley (2011) reported excessively high PM con-centrations at several locations in Australia during a dust storm event dubbed Red Dawn. In anotherstudy, Draxler, Gillette, Kirkpatrick, and Heller (2001) undertook periodic measurements of PM con-centration at several locations in the Middle East and they found that higher concentrations wereattributable to dust storm events in the region. Prasad and Singh (2007), investigating the seasonalvariability of Aerosol Parameters during dust storm events in India, found that the Aerosol OpticalDepth (AOD) increased with the prevalence of dust storms indicating an increase in the atmospheric
CONTACT Amos Kalua [email protected] School of Architecture+Design, Virginia Polytechnic Institute and StateUniversity, Blacksburg, VA 24061, USA; Department of The Built Environment, Mzuzu University, Private Bag 201, Mzuzu 2, Malawi
ARCHITECTURAL ENGINEERING AND DESIGN MANAGEMENThttps://doi.org/10.1080/17452007.2019.1652797
PM concentration. Another study by Alam, Trautmann, Blaschke, and Subhan (2014) in the MiddleEast and South West Asia showed a similar trend.
The atmospheric PM concentration is considered as being positively correlated with the PM con-centration of the indoor environment. Several studies have shown that high outdoor PM concen-trations are generally responsible for high indoor PM concentrations (Braniš, Řezáčová, & Guignon,2002; Jamriska, Thomas, Morawska, & Clark, 1999; Jones, Thornton, Mark, & Harrison, 2000; Latifet al., 2014; Morawska, Jayaratne, Mengersen, Jamriska, & Thomas, 2002; Tippayawong, Khuntong,Nitatwichit, Khunatorn, & Tantakitti, 2009). The high indoor concentrations of PM compromise theIndoor Air Quality leading to adverse impact on occupants’ health (Gaffin et al., 2017).
There is an increased risk of compromised IAQ in places that experience dust storms. This risk maybe exacerbated through the incorporation of mechanical ventilation systems in attempts to conditionthe indoor ambient air. Instead of minimizing the problem of contamination, these systems maypotentially nurture it.
This study seeks to investigate the impact of ventilation method on outdoor-originating indoorPM dispersion within the occupied indoor environment during dust storm events in Saudi Arabia.Four ventilation methods are studied and a recommendation of the one that yields the leastamount of particle trajectories terminating within the breathing zone is recommended for use.
Literature review
Dust storms are a major environmental hazard in arid regions such as the Middle East and other partsof Asia including China and Mongolia. These events have been variably defined by different authors.Nonetheless, there seems to be a general consensus that they are severe weather events character-ized by substantial concentrations of dust and sand carried by strong winds (Bobrowsky, 2013; Wang,Wang, Zhou, & Shang, 2005). The atmospheric dust and sand can be transported over great distancesfrom their points of origin (Goudie & Middleton, 2006). During dust storm events, the atmosphericParticulate Matter concentration can rise sharply to hazardous levels. A study by Alghamdi et al.(2015) compared the atmospheric PM concentration during a dust storm and non-dust stormevents in western Saudi Arabia. Their findings showed that the PM concentration rises sharplyduring dust storms. In a similar study conducted in Iran, Shahsavani et al. (2012) reported a similarpositive correlation between dust storm occurrence and atmospheric Particulate Matter concen-tration. The same trend was also reported in several other studies (Alam et al., 2014; Draxler et al.,2001; Leys et al., 2011).
The rise in atmospheric PM concentration is a major health hazard to human beings. A study byJohnston, Hanigan, Henderson, Morgan, and Bowman (2011) reported a 15% increase in non-acci-dental mortality in the immediate aftermath of dust storm events in Australia. In a similar study,Lee et al. (2014) reported a higher increase of up to 18% for South Korea, Taiwan and Japan. Alower figure was reported for the USA at 7.4% (Crooks et al., 2016). The PM carried by the duststorms has been linked to increased occurrences of asthma attacks (Gyan et al., 2005; Kanataniet al., 2010; Wang, Chen, & Lin, 2014), the spread of disease causing fungus (Leathers, 1981) andsymptoms of dry eyes, dry itchy throat and skin allergies amongst others (Zhao & Wang, 2010).The increase in the mortality rates can be attributed to either direct exposure to the PM in theoutdoor environment or indirect exposure through the occupation of indoor environments whoseair quality has been compromised by way of infiltration of the outdoor originating PM. The latteris a major problem for Indoor Air Quality (IAQ) research. Several studies have been undertaken toinvestigate outdoor originating indoor particulate matter infiltration, dispersion, removal anddeposition.
In one study, Zhang and Chen (2006), used a CFD tool with a Lagrangian Particle Trackingmethod to study the dispersion and concentration of particles in ventilated rooms. Three venti-lation systems namely ceiling, side wall and underfloor air distribution were evaluated on thebasis of their particle removal performance. With the particle source at floor level for all the
2 A. KALUA ET AL.
three systems, it was found that the underfloor air distribution system yielded the bestperformance.
Similar studies were conducted by Gao and Niu (2007), Zhong, Yang, and Kang (2010), Jurelio-nis et al. (2015) and Ansaripour, Abdolzadeh, and Sargazizadeh (2016). Gao and Niu (2007) inves-tigated particle dispersion and deposition rates under 3 ventilation systems namely,displacement, mixing and Under Floor Air Distribution (UFAD) Systems. Zhong et al. (2010) inves-tigated the effect of pollutant source location and ventilation strategy on the dispersion of theparticles within a ventilated room. The ventilation systems studied were the mixing and underfl-oor air distribution. Using both experimental and numerical approaches, Jurelionis et al. (2015)studied the impact of displacement and mixing ventilation methods on the aerosol particle dis-persion and removal. The study showed that mixing ventilation method yielded better pollutantremoval efficiency. Ansaripour et al. (2016) sought to investigate the effects of displacement andmixing ventilation systems on the transportation and distribution pattern of particles emittedfrom a laser jet printer located within a ventilated room. A seated manikin, under heated andunheated conditions, was included in the study, and the particle concentration within its breath-ing zone investigated under the different ventilation configurations. It was found that underheating, the particle concentration in the manikin’s breathing zone was higher. It was alsofound that the mixing ventilation resulted into a lower particle concentration than the displace-ment ventilation systems.
In other studies, using CFD simulations, Zhou, Deng, Wu, and Cao (2017) sought to investigate theeffect of ventilation and heating systems on indoor particulate matter concentration in buildingslocated in the northern part of China. It was found that higher ventilation velocity rates and temp-eratures yielded faster particle concentration decay rates. A very similar study was undertaken by Jur-elionis, Stasiuliene, Prasauskas, and Martuzevicius (2018) who sought to investigate the impact offloor heating systems on the dispersion of particulate matter from flooring materials such ascarpets. It was reported that floor heating minimized pollutant dispersion. Zhuang, Yang, Long,and Hu (2017) investigated two air conditioning systems namely central and split type, withregard to the efficiency with which they removed indoor air particulate matter and the resultingdeposition of the particles. It was found that the central air conditioning system removed particlesmore efficiently while depositing them on the floor surface. The split type system on the otherhand, was found to deposit the particles on the walls.
Hänninen et al. (2004) note that outdoor PM2.5 can have very high infiltration factors into theindoor environment, at times reaching as high as 1.0. In a study conducted to investigate the infiltra-tion of ambient PM2.5 in residences of four European cities, the infiltration factors were found torange from 0.59 in Helsinki to 0.70 in Athens, with Basle and Prague in between. Yang, Kang, Gao,and Zhong (2015) sought to investigate the effect of wall transparency ratio on the infiltration oftraffic generated PM into the indoor environment.
In another study, Kearney et al. (2014) investigated infiltration of PM into residential houses inCanada. They studied seasonal variations in infiltration factors in summer and winter and theirresults showed a range between 0.10 and 0.99. The summer registered higher infiltration factorsdue to the longer hours that windows remained opened as opposed to the winter season.
Wan et al. (2015) sought to investigate the effect of window airtightness on indoor concentrationof PM2.5 in two office buildings in Beijing, China. It was found that particle concentrations werehigher with lower window airtightness. This study also showed that however high the airtightness,atmospheric PM2.5 can still infiltrate into the indoor environment.
In spite of the considerable amount of research work that has been undertaken in this area, thereappears to have remained a lack of interest on the interaction between outdoor originating PM2.5and indoor ventilation systems, and particularly within the context of dust storm events in theMiddle East and similarly characterized geographical locations. The present study seeks to contributetowards efforts in filling this existing literature gap.
ARCHITECTURAL ENGINEERING AND DESIGN MANAGEMENT 3
Methodology
This study was primarily dependent on Computational Fluid Dynamics simulations in ANSYS Fluent. Acomputational model was developed to replicate a room in a Saudi Arabian residential housing unit.Simulations were then executed to study the impact of four different ventilation methods on the dis-persion trajectories of Particulate Matter infiltrating into the indoor environment from the outsideduring a dust storm event.
Simulation environment
This study is based on a housing project being undertaken by the King Abdulaziz City for Science andTechnology (KACST) in Saudi Arabia and the Virginia Tech Centre for High Performance Environments(CHPE) in the USA. This is a Saudi Arabian government funded project aimed at developing prefab-ricated housing units that combine automation, structural integrity and energy efficiency whilecutting on the construction time and site labor intensity (KACST). A floor plan of one of the prototy-pical housing units in this project is shown in Figure 1:
For the purposes of this study, the investigation zeroed in on the dining room that appearsencircled in red in Figure 1 above. The dining room was selected due to its large wall transparencyratio at 6.5 m2 and thus the significant potential for outdoor originating particulate matter infiltrationinto the indoor environment.
The study identified four possible methods that may be used in delivering ventilation air in theprototypical housing units. These included the ducted method, Under-Floor Air Distribution(UFAD) system, wall mounted mini split system and ceiling mounted mini split system. Accordingto Alrashed and Asif (2014) and Shash and Al-Mulla (2002) these methods constitute some of themost widely used and recently emerging in Saudi Arabia.
Basing on the aforementioned ventilation methods, four scenarios were defined for this study asdescribed in Table 1 and Figure 2:
Inlet boundary conditionsThe boundary conditions in ANSYS Fluent were set such that the ventilation methods achieved 14 AirChanges per Hour (ACH) as required by ASHRAE (ANSI/ASHRAE, 2007). The PM infiltration flow ratewas set at 0.001 ms−1 while the ventilation air supply rate was set at 1.5 ms−1. For ease of manage-ment of the simulation environment, all PM infiltration had been resolved to occur from the center ofthe window opening over a total surface area of 0.3 m2, being 5% of the total window opening area.This infiltration area was characterized as a surface injection point, injecting particles having an
Figure 1. Floor plan of prototypical housing unit and the dining room (CHPE, 2018; KACST).
4 A. KALUA ET AL.
average diameter of about 1e–06 m at a velocity of 1e–03 ms−1 and a total flow rate of 1e–20 Kgs−1.Dust particles range in size from about 1 μm to 100 μm (Mahowald et al., 2014). This study was par-ticularly interested in particles having a diameter of about 2.5 μm which constitute the majorproblem for indoor air quality (Hänninen et al., 2004; Kearney et al., 2014; Rivas et al., 2015; Wanet al., 2015).
Outlet boundary conditionsThere are two exhaust outlets of the outflow type, one on each of the side walls adjoining the windowopening, where the dining room connects with the negative pressure zones created by the exhaustfans in the kitchen and bathrooms. The dining room measures 4.5 × 3.9 m.
Turbulent air flow model
This study employed the Reynolds Averaged Navier Stokes (RANS) equations and the Standard k-[model to solve the incompressible turbulent air flow in a ventilated room.
The RANS equations are the conservation equations governing the continuity, momentum andenergy of the air flow in the room. They are given as below:
Continuity Equation:
∂�ui∂xi
= 0
Table 1. Scenario definition.
Scenario number Ventilation method
Scenario 1 Ducted SystemScenario 2 Under Floor Air DistributionScenario 3 Ceiling Mounted Mini SplitScenario 4 Wall Mounted Mini Split
Figure 2. Scenario geometrical configuration.
ARCHITECTURAL ENGINEERING AND DESIGN MANAGEMENT 5
Momentum Equation:
∂�ui∂t
+ ∂
∂xi(�ui�uj) = − 1
r
∂�p∂xi
+ ∂
∂xj(v + vt)
∂�ui∂xj
( )+ gib(�T − T0)
Energy Equation:
∂�T∂t
+ ∂
∂xj(�uj�T ) = ∂
xj(a+ at)
∂�T∂xj
( )
where r is the density of the fluid, �ui and �uj are the average fluid velocities in x, y and z directions, �pis the average pressure of the fluid, v and vt are kinematic viscosity and turbulent kinematicviscosity respectively, b is the coefficient of thermal expansion, �T is the temperature of the fluid,T0 is the reference temperature, a and at are the thermal diffusivity and turbulent thermal diffu-sivity (Zhou et al., 2017).
The turbulent kinematic viscosity is solved using the standard k-[ model which is described asfollows:
∂
∂t(rmk)+∇.(rm vm
��k) = ∇. ∂mt,m
sk∇k
( )+ Gk,m − rm [
and
∂
∂t(rm [)+∇.(rm vm
��[) = ∇. ∂mt,m
sk∇ [
( )+[
k(C1[Gk,m − C2[rm [ )
where the mixture density and velocity, rm and vm��, are computed from
rm =∑Ni=1
airi
and
vm�� =
∑Ni=1 airi vi
�∑Ni=1 airi
the turbulent viscosity, mt,m, is computed from
mt,m = rmCmk2
e
and the production of turbulence kinetic energy, Gk,m, is computed from
Gk,m = mt,m(∇ vm��+ (∇ vm
��)T:∇vm���
)
Spatial discretization
For the pressure interpolation scheme, this study used PRESTO!. This setting had been used withsuccess in previous studies (Zhou et al., 2017). The momentum, turbulent kinetic energy, turbulentdissipation rate and energy were modeled using the second order upwind. Blocken (2015) rec-ommends the use of higher order spatial discretization schemes. At the minimum, second-order dis-cretization is recommended for use (Roache, Ghia, & White, 1986). First-order discretization schemesare known to cause problems with numerical diffusion (Blocken, 2015).
6 A. KALUA ET AL.
Convergence criteria
Franke (2007) note that the convergence termination criterion of 1e–03 which is used in industrialapplications is too high for a converged solution. Instead, they recommend reducing the residualsby at least four orders of magnitude. In the present study, the convergence criterion for the residualswas set at 1e–04.
Discrete phase particle model
This study employed the Lagrangian Discrete Particle Model to study the dispersion trajectory of par-ticles within the ventilated room. The dispersion trajectory is predicted by integrating the force balanceacting on the particles in motion. The Lagrangian particle model treats PM as a discrete phase andtracks particle trajectories by solving the dynamic particle models (Chang & Hu, 2008; Chang, Kao, &Chang, 2012; Li & Ahmadi, 1993). This model can accurately provide detailed temporal and spatial infor-mation of PM trajectories and dispersion history (Zhang & Chen, 2007). It has been demonstrated ashaving many advantages over other Eulerian modeling (Lai & Chen, 2007; Zhang & Chen, 2007).
The particle force balance can be expressed as below:
dup�dt
= FD(ua�− up
�)+ �g(rp − ra)
rp+ Fa
�
where up� is the velocity of the particles in motion, ua
� is the air velocity, FD is the drag force per unitparticle mass, rp is the particle density ra is the air density, �g is the acceleration due to gravity and Fa
�represents additional forces on the particles.
The drag force can be expressed as below:
FD�= 18m
rpd2pCc(ua�− up
�)
where m is the fluid viscosity, dp is the particle diameter, Cc is the Cunningham correction factor
Cc = 1+ 2ldp
(1.257+ 0.4e−(1.1dp/(2l)))
where l is the mean free path of the air molecules.In the study, the incoming ventilation air supply was set at a temperature of 20°C. The thermo-
phoresis occasioned on the particles as a result of the thermal gradient so created was accountedfor by the inclusion of the thermophoretic force in the force balance equation. Further, seeing thatsome of the particles under study were likely to have sub-micronian aerodynamic diameters, itbecame important to account for the Saffman lift force and the Brownian force.
The final force balance equation that was used is expressed as below:
dup�dt
= FD(ua�− up
�)+ �g(rp − ra)
rp+ Fb
�+ Fth��+ Fs
�
where Fb�
, Fth��
, and Fs�
represent the Brownian, thermophoretic and Saffman forces respectively.The instantaneous turbulent velocity fluctuations on the trajectories resulting into the turbulent
dispersion of the particles were accounted for by using stochastic tracking capabilities of the DiscreteRandomWalk (DRW) model which assumes that the fluctuating velocities obey a Gaussian probabilitydistribution such that:
u′a�� = j
�����u′2a��√
= j������2k/3
√where k is the turbulent kinetic energy and j is the normally distributed random number.
ARCHITECTURAL ENGINEERING AND DESIGN MANAGEMENT 7
Validation
The turbulent air flow and discrete phase particle models that were used in the study have been vali-dated for use in indoor applications under non-isothermal conditions using data from Zhang andChen (2006) who experimentally studied the airflow field and particle concentration distribution inan UFAD ventilation system under non-isothermal conditions. The geometrical configuration andparametric description of the experimental environment are provided in Figure 3 and Table 2respectively.
A comparison between the experimental results from Zhang and Chen (2006) and the simulationresults provided by the turbulent air flow model used in the present study showed good agreementas shown in Figure 4:
For the discrete phase particle model, a point by point comparison between the measured andsimulated results was not possible as ANSYS Fluent does not directly calculate the particle concen-tration. Previous researchers have used the Particle Source In-Cell (PSI-C) scheme (Zhang & Chen,2006) and the Kernel method (Zhuang et al., 2017) to calculate the particle concentration separately.The latter is based on the number of particles in the computational domain, their mass, spatial pos-ition and a smoothing length which are weighted by a Kernel function. In the present study, the con-centration was evaluated as the particle mass per cell volume in kgm−3. The number of particles in acell was expected to be directly proportional to the particle mass in the cell. Naturally, it was antici-pated that the further away from the particle source, the lower the number of particles in cells andhence the lower the particle mass concentration in the cells. As anticipated, simulated points furtheraway from the particle source recorded exceedingly small particle mass concentrations such that afeasible comparison between the measured and simulated results was only possible at one locationnearest to the particle source, P4. The profiles of the measured and simulated results in Figure 5 showreasonable agreement.
Figure 3. Geometrical configuration of experimental environment; where V and P are velocity and particle concentration measure-ment points respectively in the XZ axis, adapted from Zhang and Chen (2006).
Table 2. Parametric description of experimental environment, adapted from Zhang and Chen (2006).
Surface Surface temperature (°C) Boundary Surface temperature (°C) Heat power (W)
Scenario 1 introduces air into the room through a duct in the ceiling as shown in Figure 6:
Figure 4. Comparison of measured and simulated velocity profiles (Triangular points –measured velocity, black line – simulated byZhang and Chen (2006), red line – simulated in the present study).
Figure 5. Comparison of measured and simulated particle concentration profile (Triangular points –measured concentration, blackline – simulated by Zhang and Chen (2006), red line – simulated in the present study).
ARCHITECTURAL ENGINEERING AND DESIGN MANAGEMENT 9
As this air enters at a velocity of 1.5 ms−1, it generates a downward jet which changes directionupon impact with the floor. The continuous stream of the supply air results into the formation of aquasi-circular flow pattern in the room as shown in Figure 7:
The air flowing along this pattern pushes the infiltrating particulate matter downwards, draggingit along the flow path and raising it upwards into the breathing zone before the air velocity drops tonear 0 ms−1 at about midway through the room height as shown in Figure 8:
The ASHRAE Standard (ANSI/ASHRAE, 2007), defines the breathing zone as ranging from about0.08 m to 1.8 m along the height of the room. For the purposes of this study, seeing that thespace under investigation was a dining room, the breathing zone was defined as ranging fromabout 0.8 m to 1.8 m along the height of the room. It can be seen that the steady flow of the air sig-nificantly minimizes the gravitational force on the particles such that once the near 0 ms−1 air velocityis reached midway through the room height, instead of dropping to the floor, the particles remainsuspended until parameters other than the supply air velocity, such as the room occupants’ move-ment amongst others, come into play.
Figure 6. Scenario 1 isometric projection.
Figure 7. Scenario 1 YZ axis projection – flow vectors.
10 A. KALUA ET AL.
Scenario 2
Scenario 2 employs an Under-Floor Air Distribution (UFAD) system as shown in Figure 9:Like in scenario 1, the air enters the room at a velocity of 1.5 ms−1, but this time, generating an
upward jet which only changes course upon impact with the ceiling as shown in Figure 10:The infiltrating particles are carried along this flow path until the air velocity begins to drop. The
particles then succumb to the gravitational force and begin to gradually drop into the breathing zoneas shown in Figure 11:
Scenario 3
Scenario 3 employs a centrally mounted ventilation unit supplying air in 4 directions at an angle of45° as shown in Figure 12:
The air enters the room at 1.5 ms−1. The quasi-circular pattern of flow that is evident in the pre-vious scenarios is prevented from developing due to the multi directional air supply as shown inFigure 13:
Figure 8. Scenario 1 YZ axis projection – particle dispersion.
Figure 9. Scenario 2 isometric projection.
ARCHITECTURAL ENGINEERING AND DESIGN MANAGEMENT 11
Figure 10. Scenario 2 YZ axis projection – flow vectors.
Figure 11. Scenario 1 YZ axis projection – particle dispersion.
Figure 12. Scenario 3 isometric projection.
12 A. KALUA ET AL.
For this reason, the infiltrating particulate matter remains trapped within a localized zone close tothe point of origin as shown in Figure 14:
Scenario 4
Scenario 4 introduces air into the room by way of a ventilation unit mounted on a wall facing thewindow opening through which the infiltration of the particulate matter occurs as shown inFigure 15:
The air is supplied at an angle of 45° creating an angular but generally downward jet flow. The jetof air generates a flow pattern that is similar to that of scenario 2. Upon impact with the floor, the airflow path changes, rising upwards towards the ceiling as shown in Figure 16:
The particulate matter is carried upwards and across the room along the ceiling plane. Nonethe-less, seeing that the jet of air does not originate in a perpendicular orientation relative to the ceilingplane, the air does not have sufficient energy to travel far enough along the flow path before the
Figure 13. Scenario 3 YZ axis projection – flow vectors.
Figure 14. Scenario 3 YZ axis projection – particle dispersion.
ARCHITECTURAL ENGINEERING AND DESIGN MANAGEMENT 13
velocity begins to approach 0 ms−1. As the velocity drops, the particulate matter begins to yield in tothe gravitational force and gradually drop into the breathing zone as shown in Figure 17:
Summary of results
A summary of the results is presented in Figures 18 and 19. The figures provide a graphical compari-son of the dispersion trajectories that were obtained in each of the study scenarios.
Conclusions
The present study sought to investigate the impact of four ventilation methods on the dispersion ofactively infiltrating particulate matter within the indoor environment of residential buildings in SaudiArabia during dust storm events. The findings suggest that the different ventilation methods thatwere investigated impact on the particles’ dispersion trajectories differently with regard to thebreathing zone. It was shown that the centrally located ceiling mounted ventilation unit in Scenario
Figure 15. Scenario 4 YZ isometric projection.
Figure 16. Scenario 4 YZ axis projection – flow vectors.
14 A. KALUA ET AL.
3 demonstrated a capability of significantly minimizing the risk of contamination of the breathingzone. This unit ensured that the infiltrating particulate matter remains contained within a localizedzone close to the point of origin. The other ventilation methods were seen to facilitate particle
Figure 17. Scenario 4 YZ axis projection particle dispersion.
Figure 18. Comparison of the YZ axis projections.
ARCHITECTURAL ENGINEERING AND DESIGN MANAGEMENT 15
dispersion trajectories that terminated in zones whereby the breathing zone became vulnerable tocontamination. The study’s findings are notwithstanding previous research findings that havelauded the UFAD system’s performance over other ventilation methods reporting improvedenergy efficiency, thermal comfort, adaptability and cost efficiency (Bauman & Dally, 2003; Giles,2008). With regard to Indoor Air Quality, however, this study has shown that the UFAD systemmay not perform equally well.
Disclosure statement
No potential conflict of interest was reported by the authors.
References
Alam, K., Trautmann, T., Blaschke, T., & Subhan, F. (2014). Changes in aerosol optical properties due to dust storms in theMiddle East and Southwest Asia. Remote Sensing of Environment, 143, 216–227.
Alghamdi, M. A., Almazroui, M., Shamy, M., Redal, M. A., Alkhalaf, A. K., Hussein, M. A., & Khoder, M. I. (2015).Characterization and elemental composition of atmospheric aerosol loads during springtime dust storm in westernSaudi Arabia. Aerosol and Air Quality Research, 15(2), 440–453.
Alrashed, F., & Asif, M. (2014). Trends in residential energy consumption in Saudi Arabia with particular reference to theEastern Province. Journal of Sustainable Development of Energy, Water and Environment Systems, 2(4), 376–387.
Figure 19. Comparison of the XY axis projections.
16 A. KALUA ET AL.
Ansaripour, M., Abdolzadeh, M., & Sargazizadeh, S. (2016). Computational modeling of particle transport and distributionemitted from a Laserjet printer in a ventilated room with different ventilation configurations. Applied ThermalEngineering, 103, 920–933.
ANSI/ASHRAE. (2007). Standard 62.1–2007: Ventilation for acceptable indoor air quality. Atlanta: American NationalStandards Institute & American Society of Heating, Refrigerating and Air-Conditioning Engineers.
Bauman, F. S., & Dally, A. (2003). Underfloor air distribution (UFAD) design guide (Vol. 1064). Amer Soc of ElectroplatedPlastics.
Blocken, B. (2015). Computational fluid dynamics for urban physics: Importance, scales, possibilities, limitations and tentips and tricks towards accurate and reliable simulations. Building and Environment, 91, 219–245.
Bobrowsky, P. T. (2013). Encyclopedia of natural hazards. Dordrecht: Springer .Braniš, M., Řezáčová, P., & Guignon, N. (2002). Fine particles (PM1) in four different indoor environments. Indoor and Built
Environment, 11(4), 184–190.Chang, T.-J., & Hu, T.-S. (2008). Transport mechanisms of airborne particulate matters in partitioned indoor environment.
Building and Environment, 43(5), 886–895.Chang, K.-H., Kao, H.-M., & Chang, T.-J. (2012). Lagrangian modeling of particle concentration distribution in indoor
environment with different kernel functions and particle search algorithms. Building and Environment, 57, 81–87.CHPE. (2018). Virginia Tech Center for High Performance Environments.Crooks, J. L., Cascio, W. E., Percy, M. S., Reyes, J., Neas, L. M., & Hilborn, E. D. (2016). The association between dust storms
and daily non-accidental mortality in the United States, 1993–2005. Environmental Health Perspectives, 124(11), 1735.Draxler, R. R., Gillette, D. A., Kirkpatrick, J. S., & Heller, J. (2001). Estimating PM10 air concentrations from dust storms in
Iraq, Kuwait and Saudi Arabia. Atmospheric Environment, 35(25), 4315–4330.Franke, J. (2007). Best practice guideline for the CFD simulation of flows in the urban environment. Hamburg: Meteorological
Inst.Gaffin, J. M., Petty, C. R., Hauptman, M., Kang, C.-M., Wolfson, J. M., Awad, Y. A.,… Baxi, S. (2017). Modeling indoor par-
ticulate exposures in inner-city school classrooms. Journal of Exposure Science and Environmental Epidemiology, 27(5), 451.
Gao, N., & Niu, J. (2007). Modeling particle dispersion and deposition in indoor environments. Atmospheric Environment,41(18), 3862–3876.
Giles, J. R. (2008). Energy comparison of under floor air distribution heating ventilation and air conditioning systems in officebuildings. Ames: Iowa State University.
Goudie, A. S. (2014). Desert dust and human health disorders. Environment International, 63, 101–113.Goudie, A. S., & Middleton, N. J. (2006). Desert dust in the global system. Berlin: Springer Science & Business Media.Gyan, K., Henry, W., Lacaille, S., Laloo, A., Lamsee-Ebanks, C., McKay, S.,…Monteil, M. A. (2005). African dust clouds are
associated with increased paediatric asthma accident and emergency admissions on the Caribbean island ofTrinidad. International Journal of Biometeorology, 49(6), 371–376.
Hänninen, O., Lebret, E., Ilacqua, V., Katsouyanni, K., Künzli, N., Srám, R. J., & Jantunen, M. (2004). Infiltration of ambientPM2. 5 and levels of indoor generated non-ETS PM2. 5 in residences of four European cities. Atmospheric Environment,38(37), 6411–6423.
Indoitu, R., Orlovsky, L., & Orlovsky, N. (2012). Dust storms in Central Asia: Spatial and temporal variations. Journal of AridEnvironments, 85, 62–70.
Jamriska, M., Thomas, S., Morawska, L., & Clark, B. (1999). Relation between indoor and outdoor exposure to fine particlesnear a busy arterial road. Indoor Air, 9(2), 75–84.
Johnston, F., Hanigan, I., Henderson, S., Morgan, G., & Bowman, D. (2011). Extreme air pollution events from bushfiresand dust storms and their association with mortality in Sydney, Australia 1994–2007. Environmental Research,111(6), 811–816.
Jones, N., Thornton, C., Mark, D., & Harrison, R. (2000). Indoor/outdoor relationships of particulate matter in domestichomes with roadside, urban and rural locations. Atmospheric Environment, 34(16), 2603–2612.
Jurelionis, A., Gagytė, L., Prasauskas, T., Čiužas, D., Krugly, E., Šeduikytė, L., & Martuzevičius, D. (2015). The impact of the airdistribution method in ventilated rooms on the aerosol particle dispersion and removal: The experimental approach.Energy and Buildings, 86, 305–313.
Jurelionis, A., Stasiuliene, L., Prasauskas, T., & Martuzevicius, D. (2018). Dispersion of indoor air pollutants emitted at near-floor levels in rooms with floor heating and mixing ventilation. Indoor and Built Environment, 27(2), 205–218.
KACST. King Abdulaziz City for Science and Technology. Retrieved from https://www.kacst.edu.sa/eng/rd/pages/content.aspx?dID=94#Project_447
Kanatani, K. T., Ito, I., Al-Delaimy, W. K., Adachi, Y., Mathews, W. C., & Ramsdell, J. W. (2010). Desert dust exposure is associ-ated with increased risk of asthma hospitalization in children. American Journal of Respiratory and Critical CareMedicine, 182(12), 1475–1481.
Kearney, J., Wallace, L., MacNeill, M., Héroux, M.-E., Kindzierski, W., & Wheeler, A. (2014). Residential infiltration of fine andultrafine particles in Edmonton. Atmospheric Environment, 94, 793–805.
Lai, A. C., & Chen, F. (2007). Comparison of a new Eulerian model with a modified Lagrangian approach for particle dis-tribution and deposition indoors. Atmospheric Environment, 41(25), 5249–5256.
ARCHITECTURAL ENGINEERING AND DESIGN MANAGEMENT 17
Latif, M. T., Yong, S. M., Saad, A., Mohamad, N., Baharudin, N. H., Mokhtar, M. B., & Tahir, N. M. (2014). Composition of heavymetals in indoor dust and their possible exposure: A case study of preschool children in Malaysia. Air Quality,Atmosphere & Health, 7(2), 181–193.
Leathers, C. R. (1981). Plant components of desert dust in Arizona and their significance for man Desert Dust: Origin,Characteristics, and Effect on Man (Vol. 186, pp. 191–206): Geological Society of America Boulder.
Lee, H., Honda, Y., Lim, Y.-H., Guo, Y. L., Hashizume, M., & Kim, H. (2014). Effect of Asian dust storms on mortality in threeAsian cities. Atmospheric Environment, 89, 309–317.
Leys, J. F., Heidenreich, S. K., Strong, C. L., McTainsh, G. H., & Quigley, S. (2011). PM10 concentrations and mass transportduring “Red Dawn”–Sydney 23 September 2009. Aeolian Research, 3(3), 327–342.
Li, A., & Ahmadi, G. (1993). Deposition of aerosols on surfaces in a turbulent channel flow. International Journal ofEngineering Science, 31(3), 435–451.
Mahowald, N., Albani, S., Kok, J. F., Engelstaeder, S., Scanza, R., Ward, D. S., & Flanner, M. G. (2014). The size distribution ofdesert dust aerosols and its impact on the Earth system. Aeolian Research, 15, 53–71.
Middleton, N. (2017). Desert dust hazards: A global review. Aeolian Research, 24, 53–63.Morawska, L., Jayaratne, E., Mengersen, K., Jamriska, M., & Thomas, S. (2002). Differences in airborne particle and gaseous
concentrations in urban air between weekdays and weekends. Atmospheric Environment, 36(27), 4375–4383.Prasad, A. K., & Singh, R. P. (2007). Changes in aerosol parameters during major dust storm events (2001–2005) over the
Indo-Gangetic Plains using AERONET and MODIS data. Journal of Geophysical Research: Atmospheres, 112, D9.Rivas, I., Viana, M., Moreno, T., Bouso, L., Pandolfi, M., Alvarez-Pedrerol, M.,…Querol, X. (2015). Outdoor infiltration and
indoor contribution of UFP and BC, OC, secondary inorganic ions and metals in PM2. 5 in schools. AtmosphericEnvironment, 106, 129–138.
Roache, P. J., Ghia, K. N., & White, F. M. (1986). Editorial policy statement on the control of numerical accuracy. Journal ofFluids Engineering, 108(1), 2–2.
Shahsavani, A., Naddafi, K., Haghighifard, N. J., Mesdaghinia, A., Yunesian, M., Nabizadeh, R.,… Saki, H. (2012). The evalu-ation of PM10, PM2. 5, and PM1 concentrations during the Middle Eastern dust (MED) events in Ahvaz, Iran, from Aprilthrough September 2010. Journal of Arid Environments, 77, 72–83.
Shao, Y., & Dong, C. (2006). A review on East Asian dust storm climate, modelling and monitoring. Global and PlanetaryChange, 52(1–4), 1–22.
Shash, A., & Al-Mulla, E. (2002, December). Major components of “Typical Villa” in Saudi Arabia for price/cost index devel-opment. Paper presented at the The 6th Saudi Engineering Conference, KFUPM, Dhahran.
Tippayawong, N., Khuntong, P., Nitatwichit, C., Khunatorn, Y., & Tantakitti, C. (2009). Indoor/outdoor relationships ofsize-resolved particle concentrations in naturally ventilated school environments. Building and Environment,44(1), 188–197.
Wan, Y., Chen, C., Wang, P., Wang, Y., Chen, Z., & Zhao, L. (2015). Infiltration characteristic of outdoor fine particulatematter (PM2. 5) for the window gaps. Procedia Engineering, 121, 191–198.
Wang, C.-H., Chen, C.-S., & Lin, C.-L. (2014). The threat of Asian dust storms on asthma patients: A population-based studyin Taiwan. Global Public Health, 9(9), 1040–1052.
Wang, S., Wang, J., Zhou, Z., & Shang, K. (2005). Regional characteristics of three kinds of dust storm events in China.Atmospheric Environment, 39(3), 509–520.
Yang, F., Kang, Y., Gao, Y., & Zhong, K. (2015). Numerical simulations of the effect of outdoor pollutants on indoor airquality of buildings next to a street canyon. Building and Environment, 87, 10–22.
Zhang, Z., & Chen, Q. (2006). Experimental measurements and numerical simulations of particle transport and distributionin ventilated rooms. Atmospheric Environment, 40(18), 3396–3408.
Zhang, Z., & Chen, Q. (2007). Comparison of the Eulerian and Lagrangian methods for predicting particle transport inenclosed spaces. Atmospheric Environment, 41(25), 5236–5248.
Zhao, C., & Wang, Z. (2010). Effects of sandstorm on health of adults. Journal of Environment and Health, 27(9), 776–779.Zhong, K., Yang, X., & Kang, Y. (2010). Effects of ventilation strategies and source locations on indoor particle deposition.
Building and Environment, 45(3), 655–662.Zhou, Y., Deng, Y., Wu, P., & Cao, S.-J. (2017). The effects of ventilation and floor heating systems on the dispersion and
deposition of fine particles in an enclosed environment. Building and Environment, 125, 192–205.Zhuang, C., Yang, G., Long, T., & Hu, D. (2017). Numerical comparison of removal and deposition for fully-distributed par-
ticles in central-and split-type air-conditioning rooms. Building and Environment, 112, 17–28.
