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Experimental investigation of outdoor and indoor mean concentrations and concentration fluctuations of pollutants
Atmospheric Environment 45 (2011) 6534e6545
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Atmospheric Environment journal homepage: www.elsevier.com/locate/atmosenv
Experimental investigation of outdoor and indoor mean concentrations and concentration fluctuations of pollutants J.M. Santosa, *, I. Mavroidisb, N.C. Reis Jr. c, E.C. Pagelc a
Departamento de Engenharia Ambiental, UFES, Av. Fernando Ferrari 514, 29.060-910 Vitoria, ES, Brazil Hellenic Open University, Patras, Greece c Departamento de Engenharia Ambiental, UFES, Vitoria, ES, Brazil b
a r t i c l e i n f o
a b s t r a c t
Article history: Received 29 April 2011 Received in revised form 25 July 2011 Accepted 17 August 2011
Tracer gas was released upwind of a two-compartment complex shaped building under unstable atmospheric conditions. The mean wind direction was normal to or at 45 to the long face of the building. The general patterns of concentration distribution on the building external walls and inside the building were analysed and the influence of natural and mechanical ventilation on indoor concentration distributions was discussed. Mean concentration levels, as well as the concentration fluctuation intensity, were higher on the windward walls of the building, although concentration levels varied along each wall. Concentration fluctuations measured inside the building were lower than those measured outside. Inside the two compartments of the building, the time series of concentrations had a similar general behaviour; however, gas concentrations took approximately 1.5 times longer to reach the mean maximum concentration value at the downwind compartment 02 while they also decreased more rapidly in the upwind compartment 01 after the source was turned off. The highest indoor concentration and concentration fluctuation values were observed at the detectors located close to the windward walls, especially when the building windows were open. Experiments with and without natural ventilation suggested that infiltration and exfiltration of contaminants is much faster when the building windows are open, resulting to higher indoor concentration levels. Furthermore, mechanical ventilation tends to homogenize concentrations and suppress concentration fluctuations, leading to lower maximum concentration values. Ó 2011 Elsevier Ltd. All rights reserved.
Keywords: Atmospheric dispersion Building Indoor air quality Infiltration Outdoor source Ventilation Concentration fluctuations
1. Introduction Indoor air quality is a great matter of concern because people usually spend more than 90% of their time inside confined spaces either at work, home, transport or even leisure areas (Hoppe and Martinac, 1998). There can exist two sources of ambient air contamination affecting air quality at indoor environments: (i) the indoor sources (internal) represented by all sort of materials for construction, painting, furniture, cleaning products, combustion, etc that emit chemical substances such as CO2, HCHO (formaldehydes), volatile organic compounds (VOC) and smoke, for instance; and also (ii) the outdoor sources (external) represented by vehicles, industrial activities, wastewater treatment plants and other sources that can emit air pollutants which infiltrate indoor environments. The influence of outdoor sources on the indoor concentration levels and distributions depends, among other effects, on atmospheric
* Corresponding author. Fax: þ55 27 33352648. E-mail addresses: [email protected], [email protected] (J.M. Santos). 1352-2310/$ e see front matter Ó 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.atmosenv.2011.08.049
dispersion in the vicinity of buildings which will determine the concentration patterns near the buildings and on the building surfaces, and thus the infiltration of outdoor pollutants inside the buildings. The sustainable approach to improve indoor air quality would be to reduce or eliminate external and internal sources, while a more end-of-the-pipeline approach would be to efficiently manage ventilation systems. The ventilation systems should promote air exchange in order to exhaust the air contaminants emitted by internal sources and mix homogeneously the indoor air contaminants emitted by external sources, in order to avoid contaminants being accumulated at certain indoor locations where air would be stagnated. The indoor air quality depends on the ventilation rates promoted either by natural or mechanical (forced) ventilation systems and on the air patterns inside the building, which should drive building design and orientation (relatively to the prevailing local meteorological conditions). Some researchers have been concerned with internal sources affecting human health (for example, Moschandreas, 1985; Kotzias, 2005; Weschler, 2009), while several studies have examined the
J.M. Santos et al. / Atmospheric Environment 45 (2011) 6534e6545
links between indoor air quality and air exchange rates at indoor environments, through analysing natural and mechanical ventilation systems (for instance, Sekhar et al., 2002; Engvall et al., 2003; Liao et al., 2003; Wong and Huang, 2004; Dimitroulopoulou et al., 2006). Finally, a number of studies are examining the relation between indoor and outdoor air quality (for example, Kukadia and Palmer, 1998; Lee et al., 1999; Chaloulakou and Mavroidis, 2002; Chaloulakou et al., 2003; Sawant et al., 2004; Goyal and Khare, 2009; López-Aparicio et al., 2011). Most of the studies related to air quality in buildings evaluate mean concentrations, averaged over a long period of time (usually 1 h). Mean concentrations are an important measure to relate indoor air quality and health effects when long-term human exposure to atmospheric pollutants is of concern. On the other hand, information on concentration fluctuations (of a timescale of 1 s or so) due to atmospheric turbulence is very important in the case of odorant, flammable or highly toxic gases, since concentration fluctuations are related to odour perception, combustion or acute health effects, respectively. There are several experimental studies on turbulent concentration fluctuations around buildings (for example, Sada and Sato, 2002; Ohba et al., 2004; Santos et al., 2005; Mavroidis et al., 2007), but there is a lack of studies on turbulent concentration fluctuations of gases inside buildings. The aim of this work is first to examine mean concentrations as well as concentration fluctuations of a tracer gas, emitted by an external source, around and inside a building and then to investigate the effect of natural and mechanical ventilation on the concentration and concentration fluctuation patterns inside the building. 2. Materials and methods 2.1. Experimental details The experiments were conducted at flat terrain at Dugway Proving Ground, about 137 km southeast of Salt Lake City, USA. A detailed description of the field experiments can be found in Santos et al. (2005) and Santos (2000). The experiments involved placing a source of propylene gas at a fixed distance from a twocompartment building and using gas detectors (Photo Ionisation Detectors e PID’s) to measure concentration time-series close to the external walls and inside the building. Four experiments involving two different experimental configurations are examined in the present paper: (i) Two experiments (namely line01 and line02) involved an external continuous line source located 3.5 building heights (x ¼ 3.5 Hb ¼ 11.9 m) upwind from the northwest corner of the building, at a height of 0.5 Hb. The atmospheric conditions ranged from slightly to strongly unstable (Pasquill AeC) and the mean wind direction was northwest (at 45 to the building faces). The fans (mechanical ventilation) located inside the building to increase air mixing were switched on during both experiments; the building windows were open (i.e. pushed up 4 cm) during trial line01 and closed during trial line02. During experiment line01, the line source was turned on for 32 min and 10 s in order to fill the building with tracer gas, and then it was switched off. Data acquisition started when the source was switched on and the duration of data acquisition was 54 min and 30 s, in order to monitor the infiltration and exfiltration of the tracer gas from the two building compartments. During experiment line02, the line source was turned on for 53 min and 45 s in order to fill the building with tracer gas, and then it was switched off. Data acquisition started when the source was switched on and the duration of data acquisition was 87 min.
6535
(ii) Two experiments (namely pnt01 and pnt02) involved an external continuous point source located 3.5 building heights (x ¼ 3.5 Hb ¼ 11.9 m) upwind from the windward wall, at a height of 0.5 Hb. The atmospheric conditions were slightly unstable (Pasquill C) and the mean wind direction was west (normal to the long face of the building). The building windows were closed during both experiments; the fans located inside the building were turned on during trial pnt01 and off during trial pnt02. Results from experiment line01 are used to demonstrate the main characteristics of gas dispersion and infiltration examining outdoor and indoor concentration patterns. The other experiments are mainly used to demonstrate the effects of the different experimental conditions (i.e. natural and mechanical ventilation) on the behaviour of concentrations monitored inside the building. Fig. 1 presents a view of the building and a schematic representation of the tracer gas sources (point and line sources), while Table 1 summarizes the experimental conditions. The building involved in the trials has a complex shape and is separated into two compartments, connected by a door that was closed during the experiments. Fig. 2 presents both an outside and an inside view of the building used in the trials and shows a schematic representation of its exact dimensions. Furthermore, in the same figure the location of all the PID’s is indicated. Thirty-two detectors were deployed: 16 were located externally (denoted with the symbol e) and 16 were located inside the building (denoted with the symbol i). The detectors were located at half building height (0.5 Hb), except for detectors 15e and 16e (located on the roof of the building), detectors 8e, 4i and 15i (located at a height of 0.25 Hb) and detectors 7e, 5i and 16i (which were located at a height of 0.97 Hb). The digital, fast-response, PID’s used in the experiments were made by Aurora Scientific Inc. and have a response time corresponding to about 50 Hz. They sample the ambient air at a flow rate of 1 l min1 and this sample is exposed to a high energy ultra-violet light, which ionises the tracer gas. Molecules having ionisation energy levels below the lamp photon energy are ionised. At low concentrations (in the range up to approximately 800 ppm) the instrument response is linearly proportional to the concentration of the contaminant gas. The ions formed are collected on an electrode system providing an electrical field, producing an ion current that is proportional to tracer gas concentration. The ion current is amplified and converted to a voltage signal whose value is proportional to the gas concentration. The instruments were calibrated regularly during the field campaign by exposure to metered concentrations of tracer gas in the ambient air. A baseline correction was applied to remove the baseline signal from the data and then the voltage signals recorded by the acquisition system were converted to concentration values (ppm). Meteorological data were obtained by an ultrasonic anemometer located at a height of 0.88 Hb and at a distance of 30 m west from the west wall of the building. The ultrasonic anemometer measurements were complemented by measurements of ambient pressure, humidity and temperature provided by a meteorological station located within 500 m of the building. The meteorological conditions are reported in Table 2. 2.2. Methodology of data analysis The basic 50 Hz data were initially converted to 1 s averaged data, since these are more easy to handle while they contain sufficient information about fluctuations of concentration due to turbulent eddies (Santos et al., 2005). Thus, concentration time series presented in this study represent the time evolution of the
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J.M. Santos et al. / Atmospheric Environment 45 (2011) 6534e6545
Fig. 1. (a) Photograph of the building at Dugway Proving Ground (view looking SSW) and sketch of the experimental configuration for the (b) point source and (c) line source experiments.
1 s averaged data. Similarly, the mean concentration, the deviation of the measured values in relation to the mean (variance e s2c ) and other statistical parameters were calculated using the 1 s averaged data. Concentrations are presented in a normalised form as C * ¼ CUb Hb2 =Q 106 where C is the concentration in ppm, Ub is the wind speed at the building height in m s1, Hb is the building height in m and Q is the volumetric flow rate emitted from the source in m3 s1. This facilitates comparison of concentration results from different experiments, while actual concentration levels would not provide additional, useful, information since these are tracer gas experiments. Time was normalised using the expression t* ¼ tUb/Hb as commonly presented in studies of building influenced dispersion. In order to analyse the mean concentration data obtained during the stationary regime, the selection of an adequate averaging time is required. As pointed out by Mavroidis et al. (2003), a minimum averaging time is required to obtain stable repeatable data, due to the randomness of the turbulent fluctuations. Beyond this, the degree to which the components that correspond to variations in wind speed and direction are included in experiments (by further increasing the averaging time) is to some extent subjective. The authors suggest that the use of averaging times of the order of hundreds of Hb/Ub, is adequate; furthermore at points close to the source, stable mean concentrations can be obtained for an averaging time of 2 or 3 min. In the present work, the data time Table 1 Summary of the experimental configurations. Trial
Pnt01 Pnt02 Line01 Line02
Source type
Flowrate (l min1)
Windows
Point Point Line Line
50 50 110 80
X X
Closed
Fans Open
Off
X X X
X
On
X X
Source location W W NW NW
series are averaged over 5 min periods to obtain mean concentration statistics (Santos et al., 2005). In order to obtain information about turbulent fluctuations, it is important to analyse concentration fluctuation values in relation to the levels of the mean concentration, since a certain absolute value of fluctuation can represent a relatively large fluctuation if the mean value is low or a relatively small fluctuation if the mean is high. Thus, concentration fluctuation intensity, i, is presented here as the ratio of the concentration standard deviation to the mean concentration. Furthermore, a detector will experience periods of zero concentration due to pockets of clean air brought into the plume by the small-scale atmospheric turbulence and due to the large-scales of turbulence that will cause the plume to meander. The term intermittency is used to describe this characteristic of the concentration time series. Intermittency is defined here as the proportion of time for which concentration is at or below a threshold value, close to zero concentration. Another parameter of interest is the cumulative frequency, as represented by the cumulative distribution function (CDF) of a concentration time series. The CDF gives the proportion of concentration readings which are below a given concentration (expressed as the ratio between the instantaneous and mean concentration values). The slope of the central portion of the CDF curve reflects the value of the concentration fluctuation intensity (the lower the intensity the steeper the gradient), while the value of intermittency is indicated by the point on the CDF at which the concentration is equal to the threshold value, which will be approximately equal to the point at which the CDF crosses the vertical axis (Higson et al., 1996). The CDF curve also provides information on the ratio between peak and mean concentrations, obtained from the value where the CDF reaches 1. 3. Results and discussion The results presented here are divided into three main sections. In the first two sections, results from trial line01 are presented
J.M. Santos et al. / Atmospheric Environment 45 (2011) 6534e6545
6537
Fig. 2. (a) Schematic representation of the building dimensions, including top, west and south views (clockwise) e dimensions are expressed in metres. (b) Schematic representation of the external detector locations and numbering on the building surfaces e perspective view looking from SE e and (c) perspective view looking from NW. (d) Top view of the interior of the building (showing the locations of internal detectors, windows, doors and fans) and internal photograph.
in order to investigate the distribution of concentrations and concentration fluctuations outdoors (on the building external walls, section 3.1) and inside the building (section 3.2). Section 3.3 examines the influence of mechanical and natural ventilation on indoor concentration and concentration fluctuation distributions, through the results from trials line02, pnt01 and pnt02.
the two upwind building faces (1e, 2e, 3e, 4e, 5e and 6e) the mean concentration levels are about 0.16e0.19, (ii) for detectors 13e, 14e, 9e and 10e located further downwind and at the boundaries of the wake region, mean concentration levels are about 0.12e0.14, and finally, (iii) for detectors 11e and 12e, located in the lee of the building and at the wake region, mean concentration levels take the lowest values observed on the building walls (about 0.07e0.08). These concentrations are less than half of those observed on the windward walls. Concentration fluctuation statistics are presented in Fig. 3b. The results show that concentration fluctuation intensity is higher on the windward walls (detectors 1e, 2e, 3e, 4e, 5e and 6e) and it takes much lower values (nearly half) on the leeward walls (detectors 9e, 10e, 11e and 12e), mainly due to the mixing occurring in the near-wake region, which is characterised by a boundary layer separation with high levels of turbulent kinetic energy generated by mechanical forces. Small-scale turbulence induced by the obstacle tends to increase the mixing within the plume, resulting in a quite well-mixed recirculation region behind the obstacle. Furthermore, concentrations in the recirculation region of an obstacle are much less intermittent. It is not possible to identify the same three levels of values for the concentration
3.1. Outdoor mean concentration and concentration fluctuation patterns During experiment line01 the windows were open and the three fans (mechanical ventilation) were on. Fig. 3a presents the concentration distribution on the building external walls averaged over a 5 min period. This 5 min period corresponds to the last 5 min before the source was switched off in order to ensure that concentrations have reached a stationary regime. The mean concentration levels are higher on the north and west walls (windward walls), which are located closer to the source, than on the other walls. Mean concentrations vary significantly, especially as measurement location moves further downwind and away from the source. There are three different levels of concentrations observed on the building walls: (i) for external detectors located on
Table 2 Meteorological parameters during each experiment. Trial
Pnt01 Pnt02 Line01 Line02
sq
s4
Wind speed
(degrees)
(degrees)
(m s1)
37.48 24.75 25.25 47.57
10.54 6.52 6.54 10.21
3.02 4.55 3.72 1.63
Wind dir.
W W NW NW
L
u*
T
Hf
(m)
(m s1)
( C)
(W m2)
37.74 36.19 23.36 3.1
0.46 0.33 0.36 0.15
42 42 36 37
263.03 130.53 143.34 105.92
Pasquill stability class e Stability condition C e slightly unstable C e slightly unstable C e slightly unstable A e extremely unstable
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J.M. Santos et al. / Atmospheric Environment 45 (2011) 6534e6545
a
0.2
0.2
1e
0.2
5e 9e
0.15
0.15 14e
C*
Northwest wind direction
6e
13e
0.15
0.2
4e
2e 3e
0.15 10e
0.1
0.1
0.1
0.1
12e
11e
0.05
0
0.05
0.05
0
0.5
1
0
0
0.5
d/Lwest
b
1
0
0.05
0
0.5
1
0
1
1
1
0.8
0.8
0.8
0.8
0.6
0.6
13e
i
2e 14e
0.4
0.6
3e
4e
5e
6e
0.4
0.2
0.4
0.2
0
1
Northwest wind direction
1e
0.4 9e
0
0.5
d/Lsouth
1
0.6
0
d/Least
d/Lnorth
0.5
d/Lwest
1
0
10e
0.2
0
0.5
d/Lnorth
1
0
12e
11e
0.2
0
0.5
d/Least
1
0
0
0.5
1
d/Lsouth
Fig. 3. (a) Complete normalised mean concentration distribution and (b) complete concentration fluctuation intensity, on external building walls averaged over the 5 min before the source was turned off (trial line01).
fluctuations as for the mean concentrations, since concentration fluctuation intensity takes similar values for all detectors located on the windward walls, although it takes lower values for all detectors located on the leeward walls and in the near-wake region. Due to the complex shape of the building and the wind direction (NW) the region where detectors 13e and 14e are installed can explain the somewhat low values of mean concentration, which are comparable to those for detectors 9e and 10e. On the other hand, detectors 9e and 10e are inside the near-wake region where increased mixing occurs and this can explain the lower values of concentration fluctuation intensity monitored by these detectors, as compared to detectors 13e and 14e. With regard to detectors 13e and 14e, the same pattern was observed by Santos et al. (2005) for a wind direction normal to the west building wall, under neutral and unstable conditions. Fig. 4 presents the concentration time series and the CDF curves of the concentration data for detectors 2e, 5e, 10e and 12e located at the centre of the west, north, east and south building walls, respectively. Detectors 2e and 5e on the windward walls monitor higher mean concentration values as well as concentration fluctuation intensities in comparison with detectors 10e and 12e, since the latter are located in the near-wake region. The CDF curves suggest that for detectors 2e and 5e the peak values of instantaneous concentration are about 2.4e2.9 times the mean concentration, while for the detectors located in the near-wake this ratio is about 1.5e1.7. The above results are characteristic of the mixing occurring in the near-wake region of a building. Finally, intermittency is very low for all detectors.
3.2. Effect of infiltration on indoor mean concentration and concentration fluctuation patterns Fig. 5a shows the concentration time series for the first 10 min of trial line01 (t* ¼ 657) averaged over all external and all internal detectors. Although averaging through all sensors tends to filter the spatial concentration distribution and to smooth the concentration fluctuations, it can be seen that the concentration time series based on the average of the external sensors exhibits a significantly higher level of fluctuation in relation to the concentration time series based on the average of the internal sensors. This behaviour is related to the increased mixing occurring inside the building, where turbulence scales are significantly smaller than turbulence scales outside the building, since the fluid flow is confined, and where mechanical ventilation produces a larger homogenization of concentrations. In addition, the concentration time series for the external sensors reaches a quasi-stationary mean well before the concentration time series for the internal sensors, which tends to increase gradually and continuously, due to the gradual infiltration of tracer gas, mainly through the open windows. Fig. 5b shows the concentration time series for internal detectors 1i and 7i in compartments 01 and 02 respectively and external detector 2e on the windward wall. These data illustrate that the mean concentrations averaged over all detectors are representative of the general behaviour of the time series of concentrations at individual detectors. Fig. 6a presents the concentration time series of the internal detectors averaged over compartments 01 and 02, for the whole
J.M. Santos et al. / Atmospheric Environment 45 (2011) 6534e6545
a
6539
1 0.5
0.8
CDF
C*
0.4 0.3
0.6 0.4
0.2 0.2
0.1
0
0 0
50
100
150
200
250
300
0
350
0.5
1
1.5
2
2.5
3
C\C mean
t*
b
1 0.5
0.8
CDF
C*
0.4 0.3
0.6 0.4
0.2 0.2
0.1
0
0 0
50
100
150
200
250
300
0
350
0.5
1
1.5
2
2.5
3
2
2.5
3
C\C mean
t* 1
c 0.5
0.8
CDF
C*
0.4 0.3
0.6 0.4
0.2 0.2
0.1
0
0 0
50
100
150
200
250
300
0
350
0.5
1
1.5 C\C mean
t*
d
1 0.5
0.8
CDF
C*
0.4 0.3
0.6 0.4
0.2 0.2
0.1
0
0 0
50
100
150
200
250
300
350
t*
0
0.5
1
1.5
2
2.5
3
C\C mean
Fig. 4. Time series of concentration and CDF curves for external detectors (a) 2e, (b) 5e, (c) 10e and (d) 12e, for the 5 min period before the source was turned off (trial line01).
duration of the data acquisition (54 min and 30 s, i.e. t* ¼ 3578). The time series of concentration have similar behaviour inside the two compartments of the building. Furthermore, in both compartments measured concentrations start to have values above zero almost at the same time, as the windows are open. However, the gas infiltrates more slowly the downwind compartment 02, as it takes about 1.5 times longer to reach the maximum concentration value in this compartment. Concentration averaged over all the detectors decreases more rapidly in compartment 01 after the source is turned off. This behaviour can be explained by the fact that clean air also infiltrates more rapidly in compartment 01 when the source is turned off. In the more confined compartment 02 clean air infiltrates more slowly, while contaminated air also escapes the room at a slower rate. It is also possible: (a) that the contaminated air which
has infiltrated in compartment 01 also affects compartment 02 since it can infiltrate compartment 02 through small gaps, even though the connecting door is closed, and (b) that contaminated air which is recirculated in the near-wake of the building affects compartment 02, infiltrating from the south and east windows. However, these effects are expected to be very small. Fig. 6b presents the time series for internal detectors 1i, 2i and 3i in compartment 02 for the whole duration of the data acquisition (54 min and 30 s, i.e. t* ¼ 3578). It can be seen that for all detectors the concentration increases up to t* y 1500 (t ¼ 22.9 min). After t* y 1500, concentration levels fluctuate around a stationary mean value and start to decrease at approximately t* ¼ 2220, some time after the source is turned off (t* ¼ 2111.7 or t ¼ 32 min and 10 s), since it takes a certain time for the tracer gas to stop filling the
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a
J.M. Santos et al. / Atmospheric Environment 45 (2011) 6534e6545
a
0.3
Internal Average
0.18 0.16
External Average 0.25
0.14 0.12
0.2 C*
0.1
C*
0.08
0.15 0.06 0.04
0.1
Comp. 01 Average 0.02
Comp. 02 Average
0
0.05
0
500
1000
1500
2000
2500
3000
3500
t*
0 0
100
200
300
400
500
600
t*
b
0.18 0.16
b
1i
0.45
2i
0.14
2e
0.4
3i
0.12
1i 0.35
7i C*
0.1 0.08
0.25
0.06
C*
0.3
0.04
0.2
0.02
0.15
0
0.1
0
500
1000
1500
2000
2500
3000
3500
t*
0.05
c
0 0
100
200
300
400
500
0.18 0.16
600
t*
7i
0.14
Fig. 5. Time series of mean concentrations for trial line01: (a) time series averaged over all external and internal detectors (in both compartments), (b) time series for external detector 2e and internal detectors 1i and 7i.
11i 14i
0.12
C*
0.1 0.08
compartments. The highest maximum instantaneous concentration value is observed at detector 1i (C* ¼ 0.1642), which is located close to the windward walls of compartment 02 and the respective windows, where the influence of external concentration fluctuations is more pronounced. The fluctuating behaviour of concentrations detected by sensor 1i can be attributed to the vicinity of this detector to the open windows, where there is a more direct effect of outdoor concentrations and also to instrument noise. The stationary regime (mean) of concentration values within the compartment presents differences between the sensors, but much smaller than those of the fluctuating values, possibly due to the overall mixing effect of the turbulence within the room. Fig. 6c presents the time series for internal detectors 7i, 11i and 14i for the whole duration of the data acquisition. Detector 7i presents the higher concentration values since it is located near the west windward wall. There is a similar general behaviour between the two compartments, however differences between the concentrations observed by the individual detectors in this compartment are somewhat more pronounced than in compartment 02, since this upwind compartment is much larger and the internal mixing cannot be as adequate. Furthermore, concentration levels approach a maximum value at about t* ¼ 1400, then they are almost stationary (decrease slowly) until approximately t* ¼ 2140e2160 (depending on the detector) when they start decaying rapidly. This behaviour, with compartment 01 responding before compartment
0.06 0.04 0.02 0 0
500
1000
1500
2000
2500
3000
3500
t*
Fig. 6. Time series of concentrations for trial line01: (a) averaged over all internal detectors for compartments 01 and 02; (b) for internal detectors 1i, 2i, 3i (compartment 02); (b) for internal detectors 7i, 11i, 14i (compartment 01).
02, is due to the fact that detectors in compartment 01 “see” the concentration built-up, as well as the concentration decay due to the closure of the source, somewhat earlier than those in compartment 02. Table 3 Indoor and outdoor concentrations and i/o ratios at the detectors located near the centres of the side walls of the two compartments. Internal detector No. C*
1i 0.11
3i 0.11
7i 0.14
11i 0.12
9i 0.11
2i 0.11
External detector No. C*
14e 0.12
11e 0.06
2e 0.17
10e 0.12
5e 0.17
12e 0.08
i/o ratio
0.92
1.83
0.82
1.00
0.65
1.38
J.M. Santos et al. / Atmospheric Environment 45 (2011) 6534e6545
Table 3 presents the maximum steady-state indoor concentrations together with the 5-min average outdoor concentrations and the resulting indoor/outdoor (i/o) ratio for the pairs of indoor and outdoor detectors located near each centre of the side walls of the two compartments (Fig. 2bed show the exact detector locations).
a
The values presented at Table 3 suggest that although the variability of average outdoor concentrations is large, depending on the location of the detectors, indoor concentrations present a relatively small variability, due to the mixing occurring within the building compartments. Therefore, the i/o ratio presented in Table 3 takes
0.2
1 0.8
CDF
C*
0.175
0.15
0.125
0.1
0.6 0.4 0.2 0
0
50
100
150
200
250
300
350
0
0.5
t*
b
0.2
C*
CDF 50
100
150
200
250
300
0
350
0.5
t*
1
1.5
2
1.5
2
1.5
2
C\Cmean
0.2
1 0.8
CDF
0.175
C*
2
0.4
0 0
0.15
0.125
0.6 0.4 0.2 0
0.1 0
50
100
150
200
250
300
0
350
0.5
t*
1
C\Cmean
0.2
1 0.8
CDF
0.175
C*
1.5
0.6
0.2
0.1
0.15
0.125
0.6 0.4 0.2 0
0.1 0
50
100
150
200
250
300
0
350
0.5
t*
1
C\Cmean
0.2
1 0.8
CDF
0.175
C*
2
0.8
0.125
e
1.5
1
0.15
d
1
C\Cmean
0.175
c
6541
0.15
0.125
0.6 0.4 0.2 0
0.1 0
50
100
150
200
t*
250
300
350
0
0.5
1
C\Cmean
Fig. 7. Time series of concentration and CDF curves for detectors (a) 1i, (b) 2i, (c), 3i, (d), 4i and (e) 5i, for the 5 min before the source is turned off (compartment 02, trial line01).
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smaller values for the pairs of detectors located near the upwind faces of the building, were outdoor concentrations are high, and much larger values at the lee of the building, and especially at the remotest pair of detectors (3i/11e), where outdoor concentrations are lower. Fig. 7 presents the time series of concentration and CDF curves for the detectors in compartment 02, for the 5 min before the source is turned off (in order to have a stationary time series of concentration for the analysis). It can be noted that detector 1i (located upwind, closer to the window and behind the fan, as show in Fig. 2) monitors the more fluctuating signal and therefore shows the higher concentration fluctuation intensity and larger higher ratio between peak and mean concentrations (about 1.3) when compared to the other detectors. Detector 1 shows a maximum concentration fluctuation intensity I ¼ 0.0812, which is approximately nine times higher than the mean concentration fluctuation intensity obtained by the other detectors (i ¼ 0.0093). Data from detectors 2i, 3i, 4i and 5i show a very steep slope for the CDF curve, which is indicative of a ratio between peak and mean concentrations very close to one, due to the increased mixing and the mechanical ventilation.
a
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3.3.1. Effect of natural ventilation As shown in Table 1 and discussed in section 2 of the paper, the difference between trials line01 and line02 is that in trial line01 the windows were open, while in trial line02 they were closed. Therefore these two experiments allow an examination of the influence of natural ventilation on the concentrations and concentration fluctuations observed inside the building. The fans were on for both trials. Fig. 8 presents time series of concentrations for trial line02 averaged over all internal detectors for compartments 01 and 02, as well as for individual detectors from the two compartments of the building, in order to allow comparison with case line01 (Fig. 6). For the same reason in Figs. 6a and 8a the same scaling was used for the y-axis, while for Fig. 8b and c, a different axis scaling was used to facilitate distinguishing data from different detectors. A comparison of Figs. 6 and 8 shows that infiltration is clearly slower in trial line02, when the windows are closed. Initially, it takes approximately t* ¼ 220 for the gas to reach compartment 01 and approximately t* ¼ 300 to infiltrate in compartment 02, so that concentrations will start increasing. Furthermore, the rate of concentration increase is much slower in case line02 for both compartments. The maximum, stationary, concentration is in general reached before t* ¼ 1500 for case line01 and after t* ¼ 1700 for case line02. Exfiltration of gas from the two compartments is also much slower in case line02. It is important to state that, although the duration of data acquisition for case line02 lasted for almost 34 min after the gas source was switched off, it was not possible to capture the whole profile of concentration decay from the two compartments. Finally, for trial line01 the concentration levels inside the two compartments remained much higher than the concentration levels for trial line02 during the whole duration of the trials. For the quasi-stationary period when the maximum concentration was reached, concentrations for case line01 were approximately 3 times higher than those observed for case line02. Ultimately, an interesting feature is that in case line02, when the windows are closed, detector 1i presents a much less fluctuating signal and the concentrations detected by this detector are lower as compared to detector 2i, since detector 1i is not in this case affected by the upwind windows nearby. This confirms the suggestion that in experiment line01 (Fig. 6) the fluctuating behaviour of
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3.3. Influence of mechanical and natural ventilation on indoor concentrations
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Fig. 8. Time series of concentrations for trial line02: (a) averaged over all internal detectors for compartments 01 and 02; (b) for internal detectors 1i, 2i, 3i (compartment 02); (b) for internal detectors 7i, 11i, 14i (compartment 01).
concentrations detected by sensor 1i is mainly due to the vicinity of this detector to the open windows at the upwind face of the building, close to the line source. 3.3.2. Effect of mechanical ventilation Trials pnt01 and pnt02 were selected to allow the examination of the influence of mechanical ventilation on concentration patterns inside the building. The experimental features and meteorological parameters of both trials are described in Tables 1 and 2 respectively. The building windows were closed during both trials. The difference between these two trials is that during trial pnt02 the fans were turned off, while during trial pnt01 the fans were on.
J.M. Santos et al. / Atmospheric Environment 45 (2011) 6534e6545
a 0.18 Comp. 01 Average
0.16
Comp. 02 Average 0.14 0.12 0.1 C*
Figs. 9 and 10 present time series of concentrations for trials pnt01 and pnt02 respectively, averaged over all internal detectors for compartments 01 and 02, as well as for individual detectors from the two compartments of the building (as presented in Figs. 6 and 8 for cases line01 and line02 respectively, in order to facilitate comparison). Several features are clear from Figs. 9 and 10. First of all, as Figs. 9a and 10a show, the fans tend to mix the air and homogenize concentration distribution inside the building as the concentration for all detectors increases more steadily and rapidly when the fans are turned on (case pnt01) and fluctuations of concentration are suppressed. A more pronounced fluctuating behaviour when the fans are switched off (case pnt02) is evident
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Fig. 10. Time series of concentrations for trial pnt02: (a) averaged over all internal detectors for compartments 01 and 02; (b) for internal detectors 1i, 2i, 3i (compartment 02); (b) for internal detectors 7i, 11i, 14i (compartment 01).
C*
0.1 0.08 0.06 0.04 0.02 0 0
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t*
Fig. 9. Time series of concentrations for trial pnt01: (a) averaged over all internal detectors for compartments 01 and 02; (b) for internal detectors 1i, 2i, 3i (compartment 02); (b) for internal detectors 7i, 11i, 14i (compartment 01).
for concentrations detected in the larger, upwind, compartment 01 (Fig. 10a and c). Furthermore, the rate of concentration increase is much slower in case pnt02 for both compartments, but especially for the downwind compartment 02. The maximum, almost stationary, concentration is in general reached before t* ¼ 2200 for case pnt01 while for case pnt02 after t* ¼ 5200 for compartment 01 and after t* ¼ 6000 for compartment 02. It should be noted that all these values are much higher than those observed for trial line01, when the windows were open. Finally, for trial pnt02 the concentration levels inside the two compartments are higher than those observed for trial pnt01,
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possibly due to the reduced mixing in trial pnt02. The maximum concentration levels observed in case pnt02 are approximately 35% higher in compartment 01 and approximately 25% higher in compartment 02, as compared to case pnt01. The maximum concentration levels observed during case pnt01 are comparable to those observed in case line01, while those of case pnt02 are the highest observed during the experiments presented here. A comparison can also be made between experimental cases line02 and pnt01, since both include the same configuration in relation to the building: windows are closed and the mechanical ventilation is on. The results presented in Figs. 8 and 9 have certain similarities: in both cases, concentrations in compartment 01 increase somewhat more rapidly than in compartment 02, but also drop more rapidly after reaching a maximum. The maximum indoor concentration is reached at t* between 2000 and 2200 for case pnt01 and at t* between 1700 and 2000 for case line02. However, there are also some differences, apart from the somewhat faster increase of indoor concentrations in case line02: maximum non-dimensional concentrations are approximately 3 times higher (C*max z 0.12) in case pnt01 as compared to case line02 (C*max z 0.04), for both compartments of the building. The main reason for the observed differences is that the experimental setting of the source was quite different in these two experiments, since one involved a line source located NW of the building (case line02) and the other involved a point source located W of the building (case pnt01). 4. Conclusions This paper presents a field experimental investigation of concentrations and concentration fluctuations around and inside a complex shaped building. There is a lack of work in the literature related to turbulent concentration fluctuations for infiltrated or emitted gases inside buildings; such work is of importance when “instantaneous” peak values (due to atmospheric turbulence at a time scale of approximately 1 s) can affect comfort, health or safety. Thus, mean concentration as well as concentration fluctuation intensity and cumulative frequency of concentration time series of pollutants, emitted by external sources into buildings with natural and forced ventilation systems, are presented and analysed. The experiments were conducted in flat terrain under unstable atmospheric conditions. The general patterns of concentration distribution on the building external walls and inside the building were analysed and the influence of ventilation on indoor concentration distributions was discussed. Mean concentration levels were higher on the windward walls, closer to the source, although mean concentrations varied considerably along each wall of the complex shaped building. The concentration fluctuation intensity was also higher on the windward walls due to the direct effect of the source and large scale turbulent eddies (comparable to the building dimensions) while it had lower e nearly half e values on the leeward walls, due to the increased mixing and the high levels of turbulent kinetic energy generated by mechanical forces in the near-wake region. When the source was switched on, the external detectors measured concentration values above zero before the internal detectors. When the windows of the building were closed there was a significant delay in the infiltration of gas inside the building. The mean concentration for all internal detectors increased very slowly and steadily. Concentration fluctuations were lower inside the building in comparison with concentration fluctuations measured by the external detectors. Inside the two compartments of the building, the time series of mean concentrations for the whole experiment presented a similar general behaviour when the windows were open; however, the gas took 1.5 times longer to
reach the mean maximum concentration value at the downwind compartment 02 and decreased more rapidly in the upwind compartment 01 after the source was turned off. This can be explained by the fact that clean air also infiltrated more rapidly in compartment 01 after the source was turned off. The highest maximum mean concentration and concentration fluctuation values were observed at the detectors located close to the windward walls, where the influence of external fluctuating concentrations is more pronounced. For the other detectors, located further away from the windward walls, the peak-to-mean ratio was very close to one, due also to mechanical ventilation. Natural ventilation (open windows), resulted to higher rates of infiltration and exfiltration of contaminant from the building; concentration values increased more rapidly when the tracer gas source was switched on but also decayed more rapidly when it was switched off. In general, indoor concentration levels reached much higher values when the windows were open. The mechanical ventilation inside the building tended to homogenize concentration distributions and suppress concentration fluctuations; thus, mean concentrations for all detectors increased more steadily and rapidly when the mechanical ventilation was turned on. This pattern was more evident in compartment 01. Furthermore, when the mechanical ventilation was off, higher maximum concentration values were observed inside the building. Finally, indoor concentrations for two experimental cases that included the same configuration in relation to the building (closed windows and open fans) but different configurations in relation to the source (a line source located NW of the building and a point source located W of the building) showed the same qualitative behaviour with time in general; however, maximum nondimensional concentrations were approximately 3 times higher for the experiment involving a point source. Acknowledgements The authors would like to acknowledge the sponsorship of CNPq (The Brazilian Government Agency for Technological Development and Scientific Research). The authors would also like to acknowledge the Meteorological Division, Dugway Proving Ground, Utah, USA and staff of DSTL, Porton UK. Special thanks are due to Prof. Richard F. Griffiths of the University of Manchester and to Dr. Ian Roberts of the Defence Science & Technology Laboratories (Porton, UK) for leading the trials. References Chaloulakou, A., Mavroidis, I., 2002. Comparison of indoor and outdoor concentrations of CO at a public school. Evaluation of an Indoor Air Quality Model. Atmospheric Environment 36 (11), 1769e1781. Chaloulakou, A., Mavroidis, I., Douci, A., 2003. Indoor and outdoor carbon monoxide measurements at different microenvironments in the Athens area. Chemosphere 52 (6), 1007e1019. Dimitroulopoulou, C., Ashmore, M.R., Hill, M.T.R., Byrne, M., Kinnersley, R., 2006. INDAIR: a probabilistic model of indoor air pollution in the UK. Atmospheric Environment 40, 6362e6379. Engvall, K., Norby, C., Norback, D., 2003. Ocular, nasal, dermal and respiratory symptoms in relation to heating, ventilation, energy conservation, and reconstruction of older multi-family houses. Indoor Air: International Journal of Indoor Environment and Health 13 (3), 206e211. Goyal, R., Khare, M., 2009. Indooreoutdoor concentrations of RSPM in classroom of a naturally ventilated school building near an urban traffic roadway. Atmospheric Environment 43 (38), 6026e6038. Higson, H.L., Griffiths, R.F., Jones, C.D., Hall, D.J., 1996. Flow and dispersion around an isolated building. Atmospheric Environment 30 (16), 2859e2870. Hoppe, P., Martinac, I., 1998. Indoor climate and air quality. International Journal of Biometeorology 42 (1), 1e7. Kotzias, D., 2005. Indoor air and human exposure assessment e needs and approaches. Experimental and Toxicologic Pathology 57 (1), 5e7. Kukadia, V., Palmer, J., 1998. The effect of external atmospheric pollution on indoor air quality: a pilot study. Energy and Buildings 27 (3), 223e230.
J.M. Santos et al. / Atmospheric Environment 45 (2011) 6534e6545 Lee, S.C., Chang, M., Chan, K.Y.,1999. Indoor and outdoor air quality investigation at six residential buildings in Hong Kong. Environment International 25 (4), 489e496. Liao, C.M., Chen, J.W., Huang, S.J., 2003. Size-dependent PM10 indoor/outdoor/ personal relationships for a wind-induced naturally ventilated airspace. Atmospheric Environment 37 (22), 3065e3075. López-Aparicio, S., Smolík, J., Masková, L., Sou cková, M., Grøntoft, T., Ondrá cková, L., Stankiewicz, J., 2011. Relationship of indoor and outdoor air pollutants in a naturally ventilated historical building envelope. Building and Environment 46 (7), 1460e1468. Mavroidis, I., Griffiths, R.F., Hall, D.J., 2003. Field and wind tunnel investigations of plume dispersion around single surface obstacles. Atmospheric Environment 37 (21), 2903e2918. Mavroidis, I., Andronopoulos, S., Bartzis, J.G., Griffiths, R.F., 2007. Atmospheric dispersion in the presence of a three-dimensional cubical obstacle: modelling of mean concentration and concentration fluctuations. Atmospheric Environment 41, 2740e2756. Moschandreas, D.J., 1985. Characterization of indoor air pollution. Journal of Wind Engineering and Industrial Aerodynamics 21 (1), 39e49. Ohba, R., Kouchi, A., Hara, T., Vieillard, V., Nedelka, D., 2004. Validation of heavy and light gas dispersion models for the safety analysis of LNG tank. Journal of Loss Prevention in the Process Industries 17 (5), 325e337.
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Sada, K., Sato, A., 2002. Numerical calculation of flow and stack-gas concentration fluctuation around a cubical building. Atmospheric Environment 36 (35), 5527e5534. Santos J. M. 2000. Flow and Dispersion around Isolated Buildings, Ph.D. thesis. University of Manchester Institute of Science and Technology, England. Santos, J.M., Griffiths, R.F., Roberts, I.D., Reis Jr., N.C., 2005. A field experiment on turbulent concentration fluctuations of an atmospheric tracer gas in the vicinity of a complex shaped building. Atmospheric Environment 39 (28), 4999e5012. Sawant, A.A., Na, K., Zhu, X., Cocker, K., Butt, S., Song, C., Cocker III, D.R., 2004. Characterization of PM2.5 and selected gas-phase compounds at multiple indoor and outdoor sites in Mira Loma, California. Atmospheric Environment 38 (37), 6269e6278. Sekhar, S.C., Tham, K.W., Cheong, D., 2002. Ventilation characteristics of an airconditioned office building in Singapore. Building Environment 37 (3), 241e255. Weschler, C.J., 2009. Changes in indoor pollutants since the 1950s. Atmospheric Environment 43 (1), 153e169. Wong, N.H., Huang, B., 2004. Comparative study of the indoor air quality of naturally ventilated and air-conditioned bedrooms of residential building in Singapore. Building and Environment 39 (9), 1115e1123.
Contents lists available at SciVerse ScienceDirect
Atmospheric Environment journal homepage: www.elsevier.com/locate/atmosenv
Experimental investigation of outdoor and indoor mean concentrations and concentration fluctuations of pollutants J.M. Santosa, *, I. Mavroidisb, N.C. Reis Jr. c, E.C. Pagelc a
Departamento de Engenharia Ambiental, UFES, Av. Fernando Ferrari 514, 29.060-910 Vitoria, ES, Brazil Hellenic Open University, Patras, Greece c Departamento de Engenharia Ambiental, UFES, Vitoria, ES, Brazil b
a r t i c l e i n f o
a b s t r a c t
Article history: Received 29 April 2011 Received in revised form 25 July 2011 Accepted 17 August 2011
Tracer gas was released upwind of a two-compartment complex shaped building under unstable atmospheric conditions. The mean wind direction was normal to or at 45 to the long face of the building. The general patterns of concentration distribution on the building external walls and inside the building were analysed and the influence of natural and mechanical ventilation on indoor concentration distributions was discussed. Mean concentration levels, as well as the concentration fluctuation intensity, were higher on the windward walls of the building, although concentration levels varied along each wall. Concentration fluctuations measured inside the building were lower than those measured outside. Inside the two compartments of the building, the time series of concentrations had a similar general behaviour; however, gas concentrations took approximately 1.5 times longer to reach the mean maximum concentration value at the downwind compartment 02 while they also decreased more rapidly in the upwind compartment 01 after the source was turned off. The highest indoor concentration and concentration fluctuation values were observed at the detectors located close to the windward walls, especially when the building windows were open. Experiments with and without natural ventilation suggested that infiltration and exfiltration of contaminants is much faster when the building windows are open, resulting to higher indoor concentration levels. Furthermore, mechanical ventilation tends to homogenize concentrations and suppress concentration fluctuations, leading to lower maximum concentration values. Ó 2011 Elsevier Ltd. All rights reserved.
Keywords: Atmospheric dispersion Building Indoor air quality Infiltration Outdoor source Ventilation Concentration fluctuations
1. Introduction Indoor air quality is a great matter of concern because people usually spend more than 90% of their time inside confined spaces either at work, home, transport or even leisure areas (Hoppe and Martinac, 1998). There can exist two sources of ambient air contamination affecting air quality at indoor environments: (i) the indoor sources (internal) represented by all sort of materials for construction, painting, furniture, cleaning products, combustion, etc that emit chemical substances such as CO2, HCHO (formaldehydes), volatile organic compounds (VOC) and smoke, for instance; and also (ii) the outdoor sources (external) represented by vehicles, industrial activities, wastewater treatment plants and other sources that can emit air pollutants which infiltrate indoor environments. The influence of outdoor sources on the indoor concentration levels and distributions depends, among other effects, on atmospheric
* Corresponding author. Fax: þ55 27 33352648. E-mail addresses: [email protected], [email protected] (J.M. Santos). 1352-2310/$ e see front matter Ó 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.atmosenv.2011.08.049
dispersion in the vicinity of buildings which will determine the concentration patterns near the buildings and on the building surfaces, and thus the infiltration of outdoor pollutants inside the buildings. The sustainable approach to improve indoor air quality would be to reduce or eliminate external and internal sources, while a more end-of-the-pipeline approach would be to efficiently manage ventilation systems. The ventilation systems should promote air exchange in order to exhaust the air contaminants emitted by internal sources and mix homogeneously the indoor air contaminants emitted by external sources, in order to avoid contaminants being accumulated at certain indoor locations where air would be stagnated. The indoor air quality depends on the ventilation rates promoted either by natural or mechanical (forced) ventilation systems and on the air patterns inside the building, which should drive building design and orientation (relatively to the prevailing local meteorological conditions). Some researchers have been concerned with internal sources affecting human health (for example, Moschandreas, 1985; Kotzias, 2005; Weschler, 2009), while several studies have examined the
J.M. Santos et al. / Atmospheric Environment 45 (2011) 6534e6545
links between indoor air quality and air exchange rates at indoor environments, through analysing natural and mechanical ventilation systems (for instance, Sekhar et al., 2002; Engvall et al., 2003; Liao et al., 2003; Wong and Huang, 2004; Dimitroulopoulou et al., 2006). Finally, a number of studies are examining the relation between indoor and outdoor air quality (for example, Kukadia and Palmer, 1998; Lee et al., 1999; Chaloulakou and Mavroidis, 2002; Chaloulakou et al., 2003; Sawant et al., 2004; Goyal and Khare, 2009; López-Aparicio et al., 2011). Most of the studies related to air quality in buildings evaluate mean concentrations, averaged over a long period of time (usually 1 h). Mean concentrations are an important measure to relate indoor air quality and health effects when long-term human exposure to atmospheric pollutants is of concern. On the other hand, information on concentration fluctuations (of a timescale of 1 s or so) due to atmospheric turbulence is very important in the case of odorant, flammable or highly toxic gases, since concentration fluctuations are related to odour perception, combustion or acute health effects, respectively. There are several experimental studies on turbulent concentration fluctuations around buildings (for example, Sada and Sato, 2002; Ohba et al., 2004; Santos et al., 2005; Mavroidis et al., 2007), but there is a lack of studies on turbulent concentration fluctuations of gases inside buildings. The aim of this work is first to examine mean concentrations as well as concentration fluctuations of a tracer gas, emitted by an external source, around and inside a building and then to investigate the effect of natural and mechanical ventilation on the concentration and concentration fluctuation patterns inside the building. 2. Materials and methods 2.1. Experimental details The experiments were conducted at flat terrain at Dugway Proving Ground, about 137 km southeast of Salt Lake City, USA. A detailed description of the field experiments can be found in Santos et al. (2005) and Santos (2000). The experiments involved placing a source of propylene gas at a fixed distance from a twocompartment building and using gas detectors (Photo Ionisation Detectors e PID’s) to measure concentration time-series close to the external walls and inside the building. Four experiments involving two different experimental configurations are examined in the present paper: (i) Two experiments (namely line01 and line02) involved an external continuous line source located 3.5 building heights (x ¼ 3.5 Hb ¼ 11.9 m) upwind from the northwest corner of the building, at a height of 0.5 Hb. The atmospheric conditions ranged from slightly to strongly unstable (Pasquill AeC) and the mean wind direction was northwest (at 45 to the building faces). The fans (mechanical ventilation) located inside the building to increase air mixing were switched on during both experiments; the building windows were open (i.e. pushed up 4 cm) during trial line01 and closed during trial line02. During experiment line01, the line source was turned on for 32 min and 10 s in order to fill the building with tracer gas, and then it was switched off. Data acquisition started when the source was switched on and the duration of data acquisition was 54 min and 30 s, in order to monitor the infiltration and exfiltration of the tracer gas from the two building compartments. During experiment line02, the line source was turned on for 53 min and 45 s in order to fill the building with tracer gas, and then it was switched off. Data acquisition started when the source was switched on and the duration of data acquisition was 87 min.
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(ii) Two experiments (namely pnt01 and pnt02) involved an external continuous point source located 3.5 building heights (x ¼ 3.5 Hb ¼ 11.9 m) upwind from the windward wall, at a height of 0.5 Hb. The atmospheric conditions were slightly unstable (Pasquill C) and the mean wind direction was west (normal to the long face of the building). The building windows were closed during both experiments; the fans located inside the building were turned on during trial pnt01 and off during trial pnt02. Results from experiment line01 are used to demonstrate the main characteristics of gas dispersion and infiltration examining outdoor and indoor concentration patterns. The other experiments are mainly used to demonstrate the effects of the different experimental conditions (i.e. natural and mechanical ventilation) on the behaviour of concentrations monitored inside the building. Fig. 1 presents a view of the building and a schematic representation of the tracer gas sources (point and line sources), while Table 1 summarizes the experimental conditions. The building involved in the trials has a complex shape and is separated into two compartments, connected by a door that was closed during the experiments. Fig. 2 presents both an outside and an inside view of the building used in the trials and shows a schematic representation of its exact dimensions. Furthermore, in the same figure the location of all the PID’s is indicated. Thirty-two detectors were deployed: 16 were located externally (denoted with the symbol e) and 16 were located inside the building (denoted with the symbol i). The detectors were located at half building height (0.5 Hb), except for detectors 15e and 16e (located on the roof of the building), detectors 8e, 4i and 15i (located at a height of 0.25 Hb) and detectors 7e, 5i and 16i (which were located at a height of 0.97 Hb). The digital, fast-response, PID’s used in the experiments were made by Aurora Scientific Inc. and have a response time corresponding to about 50 Hz. They sample the ambient air at a flow rate of 1 l min1 and this sample is exposed to a high energy ultra-violet light, which ionises the tracer gas. Molecules having ionisation energy levels below the lamp photon energy are ionised. At low concentrations (in the range up to approximately 800 ppm) the instrument response is linearly proportional to the concentration of the contaminant gas. The ions formed are collected on an electrode system providing an electrical field, producing an ion current that is proportional to tracer gas concentration. The ion current is amplified and converted to a voltage signal whose value is proportional to the gas concentration. The instruments were calibrated regularly during the field campaign by exposure to metered concentrations of tracer gas in the ambient air. A baseline correction was applied to remove the baseline signal from the data and then the voltage signals recorded by the acquisition system were converted to concentration values (ppm). Meteorological data were obtained by an ultrasonic anemometer located at a height of 0.88 Hb and at a distance of 30 m west from the west wall of the building. The ultrasonic anemometer measurements were complemented by measurements of ambient pressure, humidity and temperature provided by a meteorological station located within 500 m of the building. The meteorological conditions are reported in Table 2. 2.2. Methodology of data analysis The basic 50 Hz data were initially converted to 1 s averaged data, since these are more easy to handle while they contain sufficient information about fluctuations of concentration due to turbulent eddies (Santos et al., 2005). Thus, concentration time series presented in this study represent the time evolution of the
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Fig. 1. (a) Photograph of the building at Dugway Proving Ground (view looking SSW) and sketch of the experimental configuration for the (b) point source and (c) line source experiments.
1 s averaged data. Similarly, the mean concentration, the deviation of the measured values in relation to the mean (variance e s2c ) and other statistical parameters were calculated using the 1 s averaged data. Concentrations are presented in a normalised form as C * ¼ CUb Hb2 =Q 106 where C is the concentration in ppm, Ub is the wind speed at the building height in m s1, Hb is the building height in m and Q is the volumetric flow rate emitted from the source in m3 s1. This facilitates comparison of concentration results from different experiments, while actual concentration levels would not provide additional, useful, information since these are tracer gas experiments. Time was normalised using the expression t* ¼ tUb/Hb as commonly presented in studies of building influenced dispersion. In order to analyse the mean concentration data obtained during the stationary regime, the selection of an adequate averaging time is required. As pointed out by Mavroidis et al. (2003), a minimum averaging time is required to obtain stable repeatable data, due to the randomness of the turbulent fluctuations. Beyond this, the degree to which the components that correspond to variations in wind speed and direction are included in experiments (by further increasing the averaging time) is to some extent subjective. The authors suggest that the use of averaging times of the order of hundreds of Hb/Ub, is adequate; furthermore at points close to the source, stable mean concentrations can be obtained for an averaging time of 2 or 3 min. In the present work, the data time Table 1 Summary of the experimental configurations. Trial
Pnt01 Pnt02 Line01 Line02
Source type
Flowrate (l min1)
Windows
Point Point Line Line
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X X
Closed
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Off
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X
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series are averaged over 5 min periods to obtain mean concentration statistics (Santos et al., 2005). In order to obtain information about turbulent fluctuations, it is important to analyse concentration fluctuation values in relation to the levels of the mean concentration, since a certain absolute value of fluctuation can represent a relatively large fluctuation if the mean value is low or a relatively small fluctuation if the mean is high. Thus, concentration fluctuation intensity, i, is presented here as the ratio of the concentration standard deviation to the mean concentration. Furthermore, a detector will experience periods of zero concentration due to pockets of clean air brought into the plume by the small-scale atmospheric turbulence and due to the large-scales of turbulence that will cause the plume to meander. The term intermittency is used to describe this characteristic of the concentration time series. Intermittency is defined here as the proportion of time for which concentration is at or below a threshold value, close to zero concentration. Another parameter of interest is the cumulative frequency, as represented by the cumulative distribution function (CDF) of a concentration time series. The CDF gives the proportion of concentration readings which are below a given concentration (expressed as the ratio between the instantaneous and mean concentration values). The slope of the central portion of the CDF curve reflects the value of the concentration fluctuation intensity (the lower the intensity the steeper the gradient), while the value of intermittency is indicated by the point on the CDF at which the concentration is equal to the threshold value, which will be approximately equal to the point at which the CDF crosses the vertical axis (Higson et al., 1996). The CDF curve also provides information on the ratio between peak and mean concentrations, obtained from the value where the CDF reaches 1. 3. Results and discussion The results presented here are divided into three main sections. In the first two sections, results from trial line01 are presented
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Fig. 2. (a) Schematic representation of the building dimensions, including top, west and south views (clockwise) e dimensions are expressed in metres. (b) Schematic representation of the external detector locations and numbering on the building surfaces e perspective view looking from SE e and (c) perspective view looking from NW. (d) Top view of the interior of the building (showing the locations of internal detectors, windows, doors and fans) and internal photograph.
in order to investigate the distribution of concentrations and concentration fluctuations outdoors (on the building external walls, section 3.1) and inside the building (section 3.2). Section 3.3 examines the influence of mechanical and natural ventilation on indoor concentration and concentration fluctuation distributions, through the results from trials line02, pnt01 and pnt02.
the two upwind building faces (1e, 2e, 3e, 4e, 5e and 6e) the mean concentration levels are about 0.16e0.19, (ii) for detectors 13e, 14e, 9e and 10e located further downwind and at the boundaries of the wake region, mean concentration levels are about 0.12e0.14, and finally, (iii) for detectors 11e and 12e, located in the lee of the building and at the wake region, mean concentration levels take the lowest values observed on the building walls (about 0.07e0.08). These concentrations are less than half of those observed on the windward walls. Concentration fluctuation statistics are presented in Fig. 3b. The results show that concentration fluctuation intensity is higher on the windward walls (detectors 1e, 2e, 3e, 4e, 5e and 6e) and it takes much lower values (nearly half) on the leeward walls (detectors 9e, 10e, 11e and 12e), mainly due to the mixing occurring in the near-wake region, which is characterised by a boundary layer separation with high levels of turbulent kinetic energy generated by mechanical forces. Small-scale turbulence induced by the obstacle tends to increase the mixing within the plume, resulting in a quite well-mixed recirculation region behind the obstacle. Furthermore, concentrations in the recirculation region of an obstacle are much less intermittent. It is not possible to identify the same three levels of values for the concentration
3.1. Outdoor mean concentration and concentration fluctuation patterns During experiment line01 the windows were open and the three fans (mechanical ventilation) were on. Fig. 3a presents the concentration distribution on the building external walls averaged over a 5 min period. This 5 min period corresponds to the last 5 min before the source was switched off in order to ensure that concentrations have reached a stationary regime. The mean concentration levels are higher on the north and west walls (windward walls), which are located closer to the source, than on the other walls. Mean concentrations vary significantly, especially as measurement location moves further downwind and away from the source. There are three different levels of concentrations observed on the building walls: (i) for external detectors located on
Table 2 Meteorological parameters during each experiment. Trial
Pnt01 Pnt02 Line01 Line02
sq
s4
Wind speed
(degrees)
(degrees)
(m s1)
37.48 24.75 25.25 47.57
10.54 6.52 6.54 10.21
3.02 4.55 3.72 1.63
Wind dir.
W W NW NW
L
u*
T
Hf
(m)
(m s1)
( C)
(W m2)
37.74 36.19 23.36 3.1
0.46 0.33 0.36 0.15
42 42 36 37
263.03 130.53 143.34 105.92
Pasquill stability class e Stability condition C e slightly unstable C e slightly unstable C e slightly unstable A e extremely unstable
6538
J.M. Santos et al. / Atmospheric Environment 45 (2011) 6534e6545
a
0.2
0.2
1e
0.2
5e 9e
0.15
0.15 14e
C*
Northwest wind direction
6e
13e
0.15
0.2
4e
2e 3e
0.15 10e
0.1
0.1
0.1
0.1
12e
11e
0.05
0
0.05
0.05
0
0.5
1
0
0
0.5
d/Lwest
b
1
0
0.05
0
0.5
1
0
1
1
1
0.8
0.8
0.8
0.8
0.6
0.6
13e
i
2e 14e
0.4
0.6
3e
4e
5e
6e
0.4
0.2
0.4
0.2
0
1
Northwest wind direction
1e
0.4 9e
0
0.5
d/Lsouth
1
0.6
0
d/Least
d/Lnorth
0.5
d/Lwest
1
0
10e
0.2
0
0.5
d/Lnorth
1
0
12e
11e
0.2
0
0.5
d/Least
1
0
0
0.5
1
d/Lsouth
Fig. 3. (a) Complete normalised mean concentration distribution and (b) complete concentration fluctuation intensity, on external building walls averaged over the 5 min before the source was turned off (trial line01).
fluctuations as for the mean concentrations, since concentration fluctuation intensity takes similar values for all detectors located on the windward walls, although it takes lower values for all detectors located on the leeward walls and in the near-wake region. Due to the complex shape of the building and the wind direction (NW) the region where detectors 13e and 14e are installed can explain the somewhat low values of mean concentration, which are comparable to those for detectors 9e and 10e. On the other hand, detectors 9e and 10e are inside the near-wake region where increased mixing occurs and this can explain the lower values of concentration fluctuation intensity monitored by these detectors, as compared to detectors 13e and 14e. With regard to detectors 13e and 14e, the same pattern was observed by Santos et al. (2005) for a wind direction normal to the west building wall, under neutral and unstable conditions. Fig. 4 presents the concentration time series and the CDF curves of the concentration data for detectors 2e, 5e, 10e and 12e located at the centre of the west, north, east and south building walls, respectively. Detectors 2e and 5e on the windward walls monitor higher mean concentration values as well as concentration fluctuation intensities in comparison with detectors 10e and 12e, since the latter are located in the near-wake region. The CDF curves suggest that for detectors 2e and 5e the peak values of instantaneous concentration are about 2.4e2.9 times the mean concentration, while for the detectors located in the near-wake this ratio is about 1.5e1.7. The above results are characteristic of the mixing occurring in the near-wake region of a building. Finally, intermittency is very low for all detectors.
3.2. Effect of infiltration on indoor mean concentration and concentration fluctuation patterns Fig. 5a shows the concentration time series for the first 10 min of trial line01 (t* ¼ 657) averaged over all external and all internal detectors. Although averaging through all sensors tends to filter the spatial concentration distribution and to smooth the concentration fluctuations, it can be seen that the concentration time series based on the average of the external sensors exhibits a significantly higher level of fluctuation in relation to the concentration time series based on the average of the internal sensors. This behaviour is related to the increased mixing occurring inside the building, where turbulence scales are significantly smaller than turbulence scales outside the building, since the fluid flow is confined, and where mechanical ventilation produces a larger homogenization of concentrations. In addition, the concentration time series for the external sensors reaches a quasi-stationary mean well before the concentration time series for the internal sensors, which tends to increase gradually and continuously, due to the gradual infiltration of tracer gas, mainly through the open windows. Fig. 5b shows the concentration time series for internal detectors 1i and 7i in compartments 01 and 02 respectively and external detector 2e on the windward wall. These data illustrate that the mean concentrations averaged over all detectors are representative of the general behaviour of the time series of concentrations at individual detectors. Fig. 6a presents the concentration time series of the internal detectors averaged over compartments 01 and 02, for the whole
J.M. Santos et al. / Atmospheric Environment 45 (2011) 6534e6545
a
6539
1 0.5
0.8
CDF
C*
0.4 0.3
0.6 0.4
0.2 0.2
0.1
0
0 0
50
100
150
200
250
300
0
350
0.5
1
1.5
2
2.5
3
C\C mean
t*
b
1 0.5
0.8
CDF
C*
0.4 0.3
0.6 0.4
0.2 0.2
0.1
0
0 0
50
100
150
200
250
300
0
350
0.5
1
1.5
2
2.5
3
2
2.5
3
C\C mean
t* 1
c 0.5
0.8
CDF
C*
0.4 0.3
0.6 0.4
0.2 0.2
0.1
0
0 0
50
100
150
200
250
300
0
350
0.5
1
1.5 C\C mean
t*
d
1 0.5
0.8
CDF
C*
0.4 0.3
0.6 0.4
0.2 0.2
0.1
0
0 0
50
100
150
200
250
300
350
t*
0
0.5
1
1.5
2
2.5
3
C\C mean
Fig. 4. Time series of concentration and CDF curves for external detectors (a) 2e, (b) 5e, (c) 10e and (d) 12e, for the 5 min period before the source was turned off (trial line01).
duration of the data acquisition (54 min and 30 s, i.e. t* ¼ 3578). The time series of concentration have similar behaviour inside the two compartments of the building. Furthermore, in both compartments measured concentrations start to have values above zero almost at the same time, as the windows are open. However, the gas infiltrates more slowly the downwind compartment 02, as it takes about 1.5 times longer to reach the maximum concentration value in this compartment. Concentration averaged over all the detectors decreases more rapidly in compartment 01 after the source is turned off. This behaviour can be explained by the fact that clean air also infiltrates more rapidly in compartment 01 when the source is turned off. In the more confined compartment 02 clean air infiltrates more slowly, while contaminated air also escapes the room at a slower rate. It is also possible: (a) that the contaminated air which
has infiltrated in compartment 01 also affects compartment 02 since it can infiltrate compartment 02 through small gaps, even though the connecting door is closed, and (b) that contaminated air which is recirculated in the near-wake of the building affects compartment 02, infiltrating from the south and east windows. However, these effects are expected to be very small. Fig. 6b presents the time series for internal detectors 1i, 2i and 3i in compartment 02 for the whole duration of the data acquisition (54 min and 30 s, i.e. t* ¼ 3578). It can be seen that for all detectors the concentration increases up to t* y 1500 (t ¼ 22.9 min). After t* y 1500, concentration levels fluctuate around a stationary mean value and start to decrease at approximately t* ¼ 2220, some time after the source is turned off (t* ¼ 2111.7 or t ¼ 32 min and 10 s), since it takes a certain time for the tracer gas to stop filling the
6540
a
J.M. Santos et al. / Atmospheric Environment 45 (2011) 6534e6545
a
0.3
Internal Average
0.18 0.16
External Average 0.25
0.14 0.12
0.2 C*
0.1
C*
0.08
0.15 0.06 0.04
0.1
Comp. 01 Average 0.02
Comp. 02 Average
0
0.05
0
500
1000
1500
2000
2500
3000
3500
t*
0 0
100
200
300
400
500
600
t*
b
0.18 0.16
b
1i
0.45
2i
0.14
2e
0.4
3i
0.12
1i 0.35
7i C*
0.1 0.08
0.25
0.06
C*
0.3
0.04
0.2
0.02
0.15
0
0.1
0
500
1000
1500
2000
2500
3000
3500
t*
0.05
c
0 0
100
200
300
400
500
0.18 0.16
600
t*
7i
0.14
Fig. 5. Time series of mean concentrations for trial line01: (a) time series averaged over all external and internal detectors (in both compartments), (b) time series for external detector 2e and internal detectors 1i and 7i.
11i 14i
0.12
C*
0.1 0.08
compartments. The highest maximum instantaneous concentration value is observed at detector 1i (C* ¼ 0.1642), which is located close to the windward walls of compartment 02 and the respective windows, where the influence of external concentration fluctuations is more pronounced. The fluctuating behaviour of concentrations detected by sensor 1i can be attributed to the vicinity of this detector to the open windows, where there is a more direct effect of outdoor concentrations and also to instrument noise. The stationary regime (mean) of concentration values within the compartment presents differences between the sensors, but much smaller than those of the fluctuating values, possibly due to the overall mixing effect of the turbulence within the room. Fig. 6c presents the time series for internal detectors 7i, 11i and 14i for the whole duration of the data acquisition. Detector 7i presents the higher concentration values since it is located near the west windward wall. There is a similar general behaviour between the two compartments, however differences between the concentrations observed by the individual detectors in this compartment are somewhat more pronounced than in compartment 02, since this upwind compartment is much larger and the internal mixing cannot be as adequate. Furthermore, concentration levels approach a maximum value at about t* ¼ 1400, then they are almost stationary (decrease slowly) until approximately t* ¼ 2140e2160 (depending on the detector) when they start decaying rapidly. This behaviour, with compartment 01 responding before compartment
0.06 0.04 0.02 0 0
500
1000
1500
2000
2500
3000
3500
t*
Fig. 6. Time series of concentrations for trial line01: (a) averaged over all internal detectors for compartments 01 and 02; (b) for internal detectors 1i, 2i, 3i (compartment 02); (b) for internal detectors 7i, 11i, 14i (compartment 01).
02, is due to the fact that detectors in compartment 01 “see” the concentration built-up, as well as the concentration decay due to the closure of the source, somewhat earlier than those in compartment 02. Table 3 Indoor and outdoor concentrations and i/o ratios at the detectors located near the centres of the side walls of the two compartments. Internal detector No. C*
1i 0.11
3i 0.11
7i 0.14
11i 0.12
9i 0.11
2i 0.11
External detector No. C*
14e 0.12
11e 0.06
2e 0.17
10e 0.12
5e 0.17
12e 0.08
i/o ratio
0.92
1.83
0.82
1.00
0.65
1.38
J.M. Santos et al. / Atmospheric Environment 45 (2011) 6534e6545
Table 3 presents the maximum steady-state indoor concentrations together with the 5-min average outdoor concentrations and the resulting indoor/outdoor (i/o) ratio for the pairs of indoor and outdoor detectors located near each centre of the side walls of the two compartments (Fig. 2bed show the exact detector locations).
a
The values presented at Table 3 suggest that although the variability of average outdoor concentrations is large, depending on the location of the detectors, indoor concentrations present a relatively small variability, due to the mixing occurring within the building compartments. Therefore, the i/o ratio presented in Table 3 takes
0.2
1 0.8
CDF
C*
0.175
0.15
0.125
0.1
0.6 0.4 0.2 0
0
50
100
150
200
250
300
350
0
0.5
t*
b
0.2
C*
CDF 50
100
150
200
250
300
0
350
0.5
t*
1
1.5
2
1.5
2
1.5
2
C\Cmean
0.2
1 0.8
CDF
0.175
C*
2
0.4
0 0
0.15
0.125
0.6 0.4 0.2 0
0.1 0
50
100
150
200
250
300
0
350
0.5
t*
1
C\Cmean
0.2
1 0.8
CDF
0.175
C*
1.5
0.6
0.2
0.1
0.15
0.125
0.6 0.4 0.2 0
0.1 0
50
100
150
200
250
300
0
350
0.5
t*
1
C\Cmean
0.2
1 0.8
CDF
0.175
C*
2
0.8
0.125
e
1.5
1
0.15
d
1
C\Cmean
0.175
c
6541
0.15
0.125
0.6 0.4 0.2 0
0.1 0
50
100
150
200
t*
250
300
350
0
0.5
1
C\Cmean
Fig. 7. Time series of concentration and CDF curves for detectors (a) 1i, (b) 2i, (c), 3i, (d), 4i and (e) 5i, for the 5 min before the source is turned off (compartment 02, trial line01).
J.M. Santos et al. / Atmospheric Environment 45 (2011) 6534e6545
smaller values for the pairs of detectors located near the upwind faces of the building, were outdoor concentrations are high, and much larger values at the lee of the building, and especially at the remotest pair of detectors (3i/11e), where outdoor concentrations are lower. Fig. 7 presents the time series of concentration and CDF curves for the detectors in compartment 02, for the 5 min before the source is turned off (in order to have a stationary time series of concentration for the analysis). It can be noted that detector 1i (located upwind, closer to the window and behind the fan, as show in Fig. 2) monitors the more fluctuating signal and therefore shows the higher concentration fluctuation intensity and larger higher ratio between peak and mean concentrations (about 1.3) when compared to the other detectors. Detector 1 shows a maximum concentration fluctuation intensity I ¼ 0.0812, which is approximately nine times higher than the mean concentration fluctuation intensity obtained by the other detectors (i ¼ 0.0093). Data from detectors 2i, 3i, 4i and 5i show a very steep slope for the CDF curve, which is indicative of a ratio between peak and mean concentrations very close to one, due to the increased mixing and the mechanical ventilation.
a
0.18 0.16
Comp. 02 Average Comp. 01 Average
0.14 0.12 0.1 C*
6542
0.08 0.06 0.04 0.02 0 0
500
1000
1500
2000
2500
3000
t*
b
0.06 1i 0.05
2i 3i
3.3.1. Effect of natural ventilation As shown in Table 1 and discussed in section 2 of the paper, the difference between trials line01 and line02 is that in trial line01 the windows were open, while in trial line02 they were closed. Therefore these two experiments allow an examination of the influence of natural ventilation on the concentrations and concentration fluctuations observed inside the building. The fans were on for both trials. Fig. 8 presents time series of concentrations for trial line02 averaged over all internal detectors for compartments 01 and 02, as well as for individual detectors from the two compartments of the building, in order to allow comparison with case line01 (Fig. 6). For the same reason in Figs. 6a and 8a the same scaling was used for the y-axis, while for Fig. 8b and c, a different axis scaling was used to facilitate distinguishing data from different detectors. A comparison of Figs. 6 and 8 shows that infiltration is clearly slower in trial line02, when the windows are closed. Initially, it takes approximately t* ¼ 220 for the gas to reach compartment 01 and approximately t* ¼ 300 to infiltrate in compartment 02, so that concentrations will start increasing. Furthermore, the rate of concentration increase is much slower in case line02 for both compartments. The maximum, stationary, concentration is in general reached before t* ¼ 1500 for case line01 and after t* ¼ 1700 for case line02. Exfiltration of gas from the two compartments is also much slower in case line02. It is important to state that, although the duration of data acquisition for case line02 lasted for almost 34 min after the gas source was switched off, it was not possible to capture the whole profile of concentration decay from the two compartments. Finally, for trial line01 the concentration levels inside the two compartments remained much higher than the concentration levels for trial line02 during the whole duration of the trials. For the quasi-stationary period when the maximum concentration was reached, concentrations for case line01 were approximately 3 times higher than those observed for case line02. Ultimately, an interesting feature is that in case line02, when the windows are closed, detector 1i presents a much less fluctuating signal and the concentrations detected by this detector are lower as compared to detector 2i, since detector 1i is not in this case affected by the upwind windows nearby. This confirms the suggestion that in experiment line01 (Fig. 6) the fluctuating behaviour of
0.03
0.02
0.01
0 0
c
500
1000
1500 t*
2000
2500
3000
0.06
7i
0.05
11i 14i
0.04
C*
3.3. Influence of mechanical and natural ventilation on indoor concentrations
C*
0.04
0.03
0.02
0.01
0 0
500
1000
1500
2000
2500
3000
3500
t*
Fig. 8. Time series of concentrations for trial line02: (a) averaged over all internal detectors for compartments 01 and 02; (b) for internal detectors 1i, 2i, 3i (compartment 02); (b) for internal detectors 7i, 11i, 14i (compartment 01).
concentrations detected by sensor 1i is mainly due to the vicinity of this detector to the open windows at the upwind face of the building, close to the line source. 3.3.2. Effect of mechanical ventilation Trials pnt01 and pnt02 were selected to allow the examination of the influence of mechanical ventilation on concentration patterns inside the building. The experimental features and meteorological parameters of both trials are described in Tables 1 and 2 respectively. The building windows were closed during both trials. The difference between these two trials is that during trial pnt02 the fans were turned off, while during trial pnt01 the fans were on.
J.M. Santos et al. / Atmospheric Environment 45 (2011) 6534e6545
a 0.18 Comp. 01 Average
0.16
Comp. 02 Average 0.14 0.12 0.1 C*
Figs. 9 and 10 present time series of concentrations for trials pnt01 and pnt02 respectively, averaged over all internal detectors for compartments 01 and 02, as well as for individual detectors from the two compartments of the building (as presented in Figs. 6 and 8 for cases line01 and line02 respectively, in order to facilitate comparison). Several features are clear from Figs. 9 and 10. First of all, as Figs. 9a and 10a show, the fans tend to mix the air and homogenize concentration distribution inside the building as the concentration for all detectors increases more steadily and rapidly when the fans are turned on (case pnt01) and fluctuations of concentration are suppressed. A more pronounced fluctuating behaviour when the fans are switched off (case pnt02) is evident
6543
0.08 0.06 0.04 0.02 0
a
0
0.18 0.16
Comp. 01 Average
2000
3000
b 0.18
Comp. 02 Average
0.14
1000
6000
7000
4000
5000
6000
7000
2i
0.12
3i
0.14
C*
0.12
0.08 C*
0.1
0.06
0.08
0.04
0.06
0.02
0.04
0 0
1000
2000
3000
4000
0.02
5000
t*
b
5000
1i
0.16
0.1
4000 t*
0 0
1000
2000
3000
0.18
t* 1i
0.16
2i
c
0.3
3i
0.14
7i 11i
0.25
0.12
14i 0.1 C*
0.2
C*
0.08 0.06 0.04
0.15
0.1
0.02
0.05
0 0
1000
2000
3000
4000
5000
t*
c
0 0
0.18 7i
0.16
11i 0.14
14i
0.12
1000
2000
3000
4000
5000
6000
7000
t*
Fig. 10. Time series of concentrations for trial pnt02: (a) averaged over all internal detectors for compartments 01 and 02; (b) for internal detectors 1i, 2i, 3i (compartment 02); (b) for internal detectors 7i, 11i, 14i (compartment 01).
C*
0.1 0.08 0.06 0.04 0.02 0 0
1000
2000
3000
4000
5000
t*
Fig. 9. Time series of concentrations for trial pnt01: (a) averaged over all internal detectors for compartments 01 and 02; (b) for internal detectors 1i, 2i, 3i (compartment 02); (b) for internal detectors 7i, 11i, 14i (compartment 01).
for concentrations detected in the larger, upwind, compartment 01 (Fig. 10a and c). Furthermore, the rate of concentration increase is much slower in case pnt02 for both compartments, but especially for the downwind compartment 02. The maximum, almost stationary, concentration is in general reached before t* ¼ 2200 for case pnt01 while for case pnt02 after t* ¼ 5200 for compartment 01 and after t* ¼ 6000 for compartment 02. It should be noted that all these values are much higher than those observed for trial line01, when the windows were open. Finally, for trial pnt02 the concentration levels inside the two compartments are higher than those observed for trial pnt01,
6544
J.M. Santos et al. / Atmospheric Environment 45 (2011) 6534e6545
possibly due to the reduced mixing in trial pnt02. The maximum concentration levels observed in case pnt02 are approximately 35% higher in compartment 01 and approximately 25% higher in compartment 02, as compared to case pnt01. The maximum concentration levels observed during case pnt01 are comparable to those observed in case line01, while those of case pnt02 are the highest observed during the experiments presented here. A comparison can also be made between experimental cases line02 and pnt01, since both include the same configuration in relation to the building: windows are closed and the mechanical ventilation is on. The results presented in Figs. 8 and 9 have certain similarities: in both cases, concentrations in compartment 01 increase somewhat more rapidly than in compartment 02, but also drop more rapidly after reaching a maximum. The maximum indoor concentration is reached at t* between 2000 and 2200 for case pnt01 and at t* between 1700 and 2000 for case line02. However, there are also some differences, apart from the somewhat faster increase of indoor concentrations in case line02: maximum non-dimensional concentrations are approximately 3 times higher (C*max z 0.12) in case pnt01 as compared to case line02 (C*max z 0.04), for both compartments of the building. The main reason for the observed differences is that the experimental setting of the source was quite different in these two experiments, since one involved a line source located NW of the building (case line02) and the other involved a point source located W of the building (case pnt01). 4. Conclusions This paper presents a field experimental investigation of concentrations and concentration fluctuations around and inside a complex shaped building. There is a lack of work in the literature related to turbulent concentration fluctuations for infiltrated or emitted gases inside buildings; such work is of importance when “instantaneous” peak values (due to atmospheric turbulence at a time scale of approximately 1 s) can affect comfort, health or safety. Thus, mean concentration as well as concentration fluctuation intensity and cumulative frequency of concentration time series of pollutants, emitted by external sources into buildings with natural and forced ventilation systems, are presented and analysed. The experiments were conducted in flat terrain under unstable atmospheric conditions. The general patterns of concentration distribution on the building external walls and inside the building were analysed and the influence of ventilation on indoor concentration distributions was discussed. Mean concentration levels were higher on the windward walls, closer to the source, although mean concentrations varied considerably along each wall of the complex shaped building. The concentration fluctuation intensity was also higher on the windward walls due to the direct effect of the source and large scale turbulent eddies (comparable to the building dimensions) while it had lower e nearly half e values on the leeward walls, due to the increased mixing and the high levels of turbulent kinetic energy generated by mechanical forces in the near-wake region. When the source was switched on, the external detectors measured concentration values above zero before the internal detectors. When the windows of the building were closed there was a significant delay in the infiltration of gas inside the building. The mean concentration for all internal detectors increased very slowly and steadily. Concentration fluctuations were lower inside the building in comparison with concentration fluctuations measured by the external detectors. Inside the two compartments of the building, the time series of mean concentrations for the whole experiment presented a similar general behaviour when the windows were open; however, the gas took 1.5 times longer to
reach the mean maximum concentration value at the downwind compartment 02 and decreased more rapidly in the upwind compartment 01 after the source was turned off. This can be explained by the fact that clean air also infiltrated more rapidly in compartment 01 after the source was turned off. The highest maximum mean concentration and concentration fluctuation values were observed at the detectors located close to the windward walls, where the influence of external fluctuating concentrations is more pronounced. For the other detectors, located further away from the windward walls, the peak-to-mean ratio was very close to one, due also to mechanical ventilation. Natural ventilation (open windows), resulted to higher rates of infiltration and exfiltration of contaminant from the building; concentration values increased more rapidly when the tracer gas source was switched on but also decayed more rapidly when it was switched off. In general, indoor concentration levels reached much higher values when the windows were open. The mechanical ventilation inside the building tended to homogenize concentration distributions and suppress concentration fluctuations; thus, mean concentrations for all detectors increased more steadily and rapidly when the mechanical ventilation was turned on. This pattern was more evident in compartment 01. Furthermore, when the mechanical ventilation was off, higher maximum concentration values were observed inside the building. Finally, indoor concentrations for two experimental cases that included the same configuration in relation to the building (closed windows and open fans) but different configurations in relation to the source (a line source located NW of the building and a point source located W of the building) showed the same qualitative behaviour with time in general; however, maximum nondimensional concentrations were approximately 3 times higher for the experiment involving a point source. Acknowledgements The authors would like to acknowledge the sponsorship of CNPq (The Brazilian Government Agency for Technological Development and Scientific Research). The authors would also like to acknowledge the Meteorological Division, Dugway Proving Ground, Utah, USA and staff of DSTL, Porton UK. Special thanks are due to Prof. Richard F. Griffiths of the University of Manchester and to Dr. Ian Roberts of the Defence Science & Technology Laboratories (Porton, UK) for leading the trials. References Chaloulakou, A., Mavroidis, I., 2002. Comparison of indoor and outdoor concentrations of CO at a public school. Evaluation of an Indoor Air Quality Model. Atmospheric Environment 36 (11), 1769e1781. Chaloulakou, A., Mavroidis, I., Douci, A., 2003. Indoor and outdoor carbon monoxide measurements at different microenvironments in the Athens area. Chemosphere 52 (6), 1007e1019. Dimitroulopoulou, C., Ashmore, M.R., Hill, M.T.R., Byrne, M., Kinnersley, R., 2006. INDAIR: a probabilistic model of indoor air pollution in the UK. Atmospheric Environment 40, 6362e6379. Engvall, K., Norby, C., Norback, D., 2003. Ocular, nasal, dermal and respiratory symptoms in relation to heating, ventilation, energy conservation, and reconstruction of older multi-family houses. Indoor Air: International Journal of Indoor Environment and Health 13 (3), 206e211. Goyal, R., Khare, M., 2009. Indooreoutdoor concentrations of RSPM in classroom of a naturally ventilated school building near an urban traffic roadway. Atmospheric Environment 43 (38), 6026e6038. Higson, H.L., Griffiths, R.F., Jones, C.D., Hall, D.J., 1996. Flow and dispersion around an isolated building. Atmospheric Environment 30 (16), 2859e2870. Hoppe, P., Martinac, I., 1998. Indoor climate and air quality. International Journal of Biometeorology 42 (1), 1e7. Kotzias, D., 2005. Indoor air and human exposure assessment e needs and approaches. Experimental and Toxicologic Pathology 57 (1), 5e7. Kukadia, V., Palmer, J., 1998. The effect of external atmospheric pollution on indoor air quality: a pilot study. Energy and Buildings 27 (3), 223e230.
J.M. Santos et al. / Atmospheric Environment 45 (2011) 6534e6545 Lee, S.C., Chang, M., Chan, K.Y.,1999. Indoor and outdoor air quality investigation at six residential buildings in Hong Kong. Environment International 25 (4), 489e496. Liao, C.M., Chen, J.W., Huang, S.J., 2003. Size-dependent PM10 indoor/outdoor/ personal relationships for a wind-induced naturally ventilated airspace. Atmospheric Environment 37 (22), 3065e3075. López-Aparicio, S., Smolík, J., Masková, L., Sou cková, M., Grøntoft, T., Ondrá cková, L., Stankiewicz, J., 2011. Relationship of indoor and outdoor air pollutants in a naturally ventilated historical building envelope. Building and Environment 46 (7), 1460e1468. Mavroidis, I., Griffiths, R.F., Hall, D.J., 2003. Field and wind tunnel investigations of plume dispersion around single surface obstacles. Atmospheric Environment 37 (21), 2903e2918. Mavroidis, I., Andronopoulos, S., Bartzis, J.G., Griffiths, R.F., 2007. Atmospheric dispersion in the presence of a three-dimensional cubical obstacle: modelling of mean concentration and concentration fluctuations. Atmospheric Environment 41, 2740e2756. Moschandreas, D.J., 1985. Characterization of indoor air pollution. Journal of Wind Engineering and Industrial Aerodynamics 21 (1), 39e49. Ohba, R., Kouchi, A., Hara, T., Vieillard, V., Nedelka, D., 2004. Validation of heavy and light gas dispersion models for the safety analysis of LNG tank. Journal of Loss Prevention in the Process Industries 17 (5), 325e337.
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Sada, K., Sato, A., 2002. Numerical calculation of flow and stack-gas concentration fluctuation around a cubical building. Atmospheric Environment 36 (35), 5527e5534. Santos J. M. 2000. Flow and Dispersion around Isolated Buildings, Ph.D. thesis. University of Manchester Institute of Science and Technology, England. Santos, J.M., Griffiths, R.F., Roberts, I.D., Reis Jr., N.C., 2005. A field experiment on turbulent concentration fluctuations of an atmospheric tracer gas in the vicinity of a complex shaped building. Atmospheric Environment 39 (28), 4999e5012. Sawant, A.A., Na, K., Zhu, X., Cocker, K., Butt, S., Song, C., Cocker III, D.R., 2004. Characterization of PM2.5 and selected gas-phase compounds at multiple indoor and outdoor sites in Mira Loma, California. Atmospheric Environment 38 (37), 6269e6278. Sekhar, S.C., Tham, K.W., Cheong, D., 2002. Ventilation characteristics of an airconditioned office building in Singapore. Building Environment 37 (3), 241e255. Weschler, C.J., 2009. Changes in indoor pollutants since the 1950s. Atmospheric Environment 43 (1), 153e169. Wong, N.H., Huang, B., 2004. Comparative study of the indoor air quality of naturally ventilated and air-conditioned bedrooms of residential building in Singapore. Building and Environment 39 (9), 1115e1123.