PREDICTION OF ULTRAFINE PARTICLE CONCENTRATIONS IN VARIOUS INDOOR ENVIRONMENTS

Transcription

1 PREDCTON OF ULTRAFNE PARTCLE CONCENTRATONS N VAROUS NDOOR ENVRONMENTS U Matson 1 and LE Ekberg 2,* 1 Building Services Engineering, Chalmers University of Technology, Sweden. 2 CT Energy Management AB, Chalmers ndustriteknik, Sweden ABSTRACT The present paper describes a method for estimation of indoor concentrations of ultrafine particles based on a particle number balance model. Previously reported data from measurements of particle sink rate, particle generation rate, air filter efficiency and outdoor concentrations were used as input data. The model was used to predict indoor concentrations in various simulated residential buildings, office buildings and classrooms. Calculations were made for cases with air change rates ranging from 0.2h -1 up to 5h -1, and under different assumptions about the efficiency of the supply air filtration. The simulated indoor-outdoor concentration ratios deviate less than 10% from measured values. The simulations indicate that ventilation systems can be designed to reduce the indoor concentration of ultrafine particles substantially, and consequently protect the population from a potential health hazard. The model can be used to distinguish the fraction of outdoor ultrafine particles in indoor air from the fraction originating from indoor sources. NDEX TERMS ndoor air quality, number concentration, measurements, air filtration, modelling NTRODUCTON Environmental medicine investigations have suggested a possible association between ultrafine particles (UFPs) and human health (Pekkanen et al. 2002; Wichmann and Peters 2000; Oberdörster 2001). Most studies of health effects from UFPs (particles smaller than 0.1µm) are based on outdoor measurements only. However, since people spend the majority of the time indoors, it is obvious that the indoor exposure should also be considered. Recent work by Matson (2004) indicates that indoor-outdoor (O) concentration ratios between 0.5 and 0.8 are common in Swedish office buildings, while O ratios substantially above unity can be expected temporarily in residential buildings. The dynamical behaviour of indoor particle concentrations is influenced by many factors. The indoor concentration depends on the outdoor concentration and its changes, outdoor to indoor transport, and sources and sinks indoors. Different mechanisms are involved in the removal of UFPs, for example by particle growth and agglomeration, surface removal, deposition etc (Sippola and Nazaroff 2003; Weschler 2003). The particle generation rate is known from laboratory experiments for various indoor sources (Afshari et al. 2005). Sink rate constants can also be found in the literature (Mosley et al. 2001). Data on the efficiency of various air filter classes are also available for particles in the ultrafine size range (Fisk et al. 2002). Previously, indoor contaminant concentrations have often been modelled based on a mass balance equation (Jamriska et al. 2000). However, UFPs dominate the number concentration of particles but their mass is very small compared to larger particles (Keywood et al. 1999; Morawska et al. 2004). Thus, when modelling UFP concentrations it should be appropriate to instead use a particle number-balance approach. The aim of this work is to simulate particle concentrations in various types of indoor environments with a particle number-balance model. MATERALS AND METHODS The input data to the model described below were mainly obtained by measurement of the total number concentration of particles in the size range 0.02µm to about 1µm. Thus, this measure may overestimate the total number of ultrafine particles larger than 0.02µm. However, the fraction of particles larger than the upper limit of the ultrafine size range, 0.1µm, can be expected to be rather small. * Corresponding author lars.ekberg@cit.chalmers.se 1581

2 The model: Eq. (1) is valid under the assumption that the room air is completely mixed, which is an appropriate assumption in many offices and residential buildings. Another simplification, appropriate for Scandinavian buildings, is that the ventilation system is without air re-circulation. Furthermore, air infiltration through the building envelope is neglected, which is justified when simulating rather airtight Swedish buildings. The required input parameters are the outdoor concentration, filter efficiency, airflow rate, room volume, source strength and particle removal rate (sink effect). dc V& ( 1 E) C S V C R V O + = & + + (1) dt where C O = pollutant concentration outdoors (particles per m 3 ), C = pollutant concentration indoors (particles per m 3 ), V & = airflow rate (m 3 per hour), V = room volume (m 3 ), t = time (hour), S = strength of indoor sources (particles per hour), R = pollutant removal rate (particles per hour), E = supply air filter efficiency. The pollutant removal rate R in Eq. (1) is assumed to be proportional to the total number of particles present in the room air, according to Eq. (2). R = r V C (2) where r = particle removal (sink) rate constant (h -1 ). From Eqs. (1) and (2) a numerical solution is derived: C n+ V& t + r V S C O E V& CO E V& 1 (1 ) (1 ) n S = + C e (3) V& rv V& rv V& + rv V& + rv where n+1 C = indoor concentration at the end of the current time step (particles per m 3 ), t = time step (hours). Under the assumption of steady-state conditions the equation can be written as Eq (4): S + CO (1 E) V& C t = (4) ( ) V& + rv Outdoor concentrations: The outdoor concentration values have been published previously (Matson 2004). The measurements were made using a condensation particle counter, TS, model 8525 (P-Trak ), which measures the number concentration of particles larger than 0.02µm. Table 1 shows a summary of the measurements, which were made in the large city of Copenhagen, Denmark, the medium size city of Gothenburg, Sweden, and at a rural location, 60 km outside of Gothenburg (Borås). The data comprise a total of 29 diurnal measurement series, and were collected by continuous measurements for several days at each location using a 1-minute sampling interval. The background concentration presented for the large city corresponds well to the reported urban background value of 7500 particles per cm 3 (Jamriska et al. 2003). Table 1. Summary of measured outdoor UFP concentrations in three Scandinavian locations (Matson 2005). Site type Mean daily average Maximum daily Minimum daily average [p/cm 3 ] average [p/cm 3 ] (background) [p/cm 3 ] Large urban Medium urban Rural Filtration efficiency: Five levels of air filtration were simulated: no supply air filtration, filtration using new filters of class F5, F6, F7 and F8, according to the European filter standard EN 779. The filtration efficiencies were obtained from a previous publication (Fisk et al. 2002), as shown by the solid curves in Figure 1. For each curve the ASHRAE dust spot efficiency is indicated together with the approximately corresponding filter class. The dashed curve in Figure 1 shows the outdoor particle size distribution measured by an electrical low pressure impactor (ELP) at a suburban location (Matson 2004). 1582

3 dn/dlog(dp) (Number of particles/cm 3 ) ,01 0, Particle diameter (µm) dn/dlog(dp) 40-45% ~ F % ~ F % ~ F % ~ F7 Figure 1. Filtration efficiency for various filter classes (Fisk et al. 2002). The dashed curve shows an example of a suburban outdoor particle size distribution (Matson 2004). Filtration efficiency (%) By combining the efficiency curves with the size distribution shown in Figure 1 the total filtration efficiency, weighted with respect to the outdoor particle size distribution, can be estimated. For particles larger than 0.02µm this method gives efficiency values of 12%, 43%, 69% and 75% for the filter classes F5, F6, F7 and F8, respectively. These values were used as input data in the particle concentration simulations. t is acknowledged that this estimation may be considered as rough, since other size distributions would lead to somewhat different filtration efficiency values. Ventilation rate: Calculations were made using ventilation rate values between 0.2h -1 and 5h -1 (air changes per hour), simulating premises ranging from poorly ventilated residential buildings to well-ventilated offices and classrooms. ndoor particle generation and sink rate: Data of the internal particle generation rate was obtained from a previous study (Afshari et al. 2005). The data were determined by laboratory measurements, studying one source at a time, in a full-scale chamber (34m 3 ). Some examples of obtained source strengths are: candle burning (pure wax) particles per minute; cigarette burning particles per minute; meat frying particles per minute. Matson (2004) and Mosley et al. (2001) reported sink rate constant values between 0.2 h -1 and 1.0 h -1, which depend on the particle size. The lower value may be valid for particles of the size 0.1µm and the higher value for 0.02µm particles. For particles in the size-interval µm the sink rate constant value can be estimated to be about 0.4h -1, which was selected as the base case in the calculations presented below. RESULTS AND DSCUSSON As an initial verification of the validity of the model and the selected input data, the results of the calculations were compared to measurement results from four different office buildings. The ventilation rates were determined by a tracer gas technique and the O concentration ratios obtained by measurements are average values for 3-4 days of continuous measurements in each building. Although the concentrations were fluctuating during the measurements, the O values can be expected to reflect the ratios that would prevail under steady-state conditions. No obvious UFP sources were active indoors during the measurements. The calculated results were obtained by Eq. (4) assuming that the internal particle generation is zero (S=0). As shown in Table 2, the calculated values deviate less than 10% from the measured values, except for building SS. Table 2. Comparison of calculated and measured O concentration ratios. The calculations are based on Eq (4) with the parameters S=0 and r=0.4h -1. UFP Deviation Type of Ventilation Measured Calculated building rate (h -1 Filter class filtration (calculated- ) O ratio O ratio efficiency measured) Office T 1.6 F6 43% % Office EM 2.6 F5 12% % Office SS 1.1 F5 12% % Office SS 1.1 F5 25% % Office PO 2.0 No filters 0% % The reason for the larger deviation in building SS may in this case be due to the air filters actually having a higher 1583

4 efficiency than originally estimated by the procedure given above. This could be the case if the outdoor particle size distribution is different from the one used when estimating the overall filtration efficiency. Alternatively, if the filter has accumulated dust for a long time, the efficiency can be expected to be higher than the originally assumed value for new filters. The result of a second calculation, using a higher filtration efficiency value, is added to the table. Figure 2 summarizes the steady-state O concentration ratio calculated for various air change rates and various supply air filter classes assuming no internal particle generation. The sink rate constant is given the value 0.4h -1. Air change rates between 0.2 and 0.5 h -1 represent residential buildings while values between 1.0 and 3.0 h -1 are common in office buildings. The high end of the scale, air change rates up to 5.0 h -1, may represent well-ventilated school premises with high occupancy density. Thus, the figure shows that the indoor UFP concentration in residential buildings may be between 10% and 60% of the outdoor concentration, depending on the quality of air filtration and the air change rate. The corresponding range is roughly 20% - 90% in both office buildings and classrooms. The low values correspond to cases with a high class of air filtration (F7-F8), and the high values to the case without air filtration. As pointed out, these ranges are valid only if the internal generation of ultrafine particles is negligible. The O concentration ratio given by Figure 2 can be used together with the outdoor concentrations shown in Table 1 for prediction of the indoor concentration of UFPs originating outdoors. O concentration ratio 1,0 0,9 0,8 0,7 0,6 0,5 0,4 0,3 0,2 0,1 0, No filter F5 (12%) F6 (43%) F7 (69%) F8 (75%) Air change rate (h -1 ) Figure 2. O concentration ratio calculated under assumption of no internal particle generation. The sink rate constant is given the value 0.4h -1. Matson (2004) showed by field measurements that the internal UFP generation in Scandinavian office buildings may be close to zero, unless tobacco smoking occurs indoors. t appears reasonable to assume that this is the case also in classrooms. On the other hand, the same study showed that there are major UFP sources likely to be active in residential buildings. Cooking and candle burning are examples of strong indoor UFP souces. Since these sources typically are active for rather short periods, steady-state concentrations may never be reached. Thus, simulations considering such sources should be made by Eq (3) instead of Eq (4). Calculations representing a single family house showed that burning one candle for 30 minutes caused the indoor concentration to increase from particles per cm 3 to about particles per cm 3. f burnt for 2-3 hours the calculated steady state concentration of particles per cm 3 was approached. These figures are in approximate accordance with the measured results presented by Matson (2004). n the same model house the UFP concentration increase caused by the side stream smoke from a cigarette was calculated to about p/cm 3 after 5 minutes. CONCLUSONS AND MPLCATONS The deviation between the simulated O ratios and the values obtained by measurements is less than 10%. The simulations indicate that ventilation systems can be designed to reduce the indoor concentration of UFPs substantially, and consequently protect the population from a potential health hazard. n offices and classrooms the indoor concentration can be expected to be about 90% of the outdoor concentration if the supply air is not filtered. The results indicate that the indoor concentration can be reduced by 70-80% if supply air filters of class F7 or F8 are used. n residential buildings without indoor UFP generation, the indoor concentration may be about 60% of the outdoor concentration without supply air filtration. This value may be reduced to 10%-20% by the use of filters of class F7 or F8. f strong indoor UFP sources are active, which can be expected especially in residential buildings, the indoor concentration will temporarily reach values far above the outdoor concentration. The results also showed that the model can be used as a tool to distinguish the fraction of outdoor UFPs in indoor air from the 1584

5 fraction originating from indoor sources. This may be important since the health effects possibly are different for these groups of UFPs. ACKNOWLEDGEMENTS This research project was supported by The Swedish Research Council for Environment, Agricultural Sciences and Spatial Planning. The authors gratefully acknowledge the contribution from Adjunct Prof. Ove Strindehag, Chalmers University of Technology. REFERENCES Afshari A., Matson U., and Ekberg LE Characterisation of indoor sources of fine and ultrafine particles: A study conducted in a full-scale chamber. ndoor Air, 15, pp Fisk WJ., Faulkner D., Palonen J., and Seppanen O Performance and costs of particle air filtration technologies. ndoor Air, 12, pp Jamriska M., Morawska L. and Clark BA Effect of ventilation and filtration on submicrometer particles in an indoor environment. ndoor Air, 10, pp Jamriska M., Morawska L. and Ensor DS Control strategies for sub-micrometer particles indoors: model study of air filtration and ventilation. ndoor Air, 13, pp Keywood MD., Ayers GP., Gras JL., Gillett RW. and Cohen DD Relationships between size segregated mass concentration data and ultrafine particle number concentrations in urban areas. Atmospheric Environment, 33, pp Matson U Ultrafine Particles in ndoor Air: Measurements and Modelling, Dissertation, Document D2004:06, Chalmers University of Technology, Gothenburg, Sweden. Matson U ndoor and outdoor concentrations of ultrafine particles in some Scandinavian rural and urban areas. Science of The Total Environment, 343, pp Morawska L., Thomas S., Hofmann W., Ristovski Z., Jamriska M., Rettenmoser T. and Kagerer S Exploratory cross-sectional investigations on ambient submicrometer particles in Salzburg, Austria. Atmospheric Environment, 38, pp Mosley RB., Greenwell DJ., Sparks LE., Guo Z., Tucker WG., Fortmann R. and Whitfield C Penetration of ambient fine particles into the indoor environment. Aerosol Science and Technology, 34, pp Oberdörster, G Pulmonary effects of inhaled ultrafine particles. nternational Archives of Occupational and Environmental Health, 74, pp.1-8. Pekkanen J., Peters A., Hoek G., Tiittanen P., Brunekreef B., de Hartog J., Heinrich J., bald-mulli A., Kreyling W G., Lanki T., Timonen KL. and Vanninen E Particulate air pollution and risk of ST-segment depression during repeated submaximal exercise tests among subjects with coronary heart disease: The exposure and risk assessment for fine and ultrafine particles in ambient air (ULTRA) study. Circulation, 106, pp Sippola MR. and Nazaroff WW Modeling particle loss in ventilation ducts. Atmospheric Environment, 37, pp Weschler CJ ndoor/outdoor connections exemplified by processes that depend on an organic compound's saturation vapor pressure. Atmospheric Environment, 37, pp Wichmann HE. and Peters A Epidemiological evidence of the effects of ultrafine particle exposure. Philosophical Transactions: Mathematical, Physical and Engineering Sciences, 358, pp