An indoor air model

Aerobiologia 15: 115–120, 1999. © 1999 Kluwer Academic Publishers. Printed in the Netherlands. 115 An indoor air model Paul Comtois∗ and Nancy Marcoux Laboratoire d’aérobiologie, département de géographie, Université de Montréal, CP 6128, Montréal, H3C 3J7, Canada (∗ author for correspondence, e-mail: comtoisp@ere.umontreal.ca; fax: +1 514 3438008) Received 21 August 1998; accepted in final form 11 January 1999 Key words: aerobiology, building, contamination, dispersal, fungal spores, HVAC systems, indoor air quality, model Abstract Indoor aerobiological data sets are often difficult to handle because they are not easily related to climatic parameters similar to those found outdoors. However, in large buildings, where ventilation systems allow a systematic knowledge of air flows, the transfer of a power distribution model to airborne spore concentration can simplify the task of reaching a decision on contamination presence. The use of the absorption percentage of each component of the ventilation system (air intake, filters, cooling, humidification, air return) can identify abnormalities (i.e. concentrations not following the general expected model, which could mean contamination requiring action), permit direct cleaning, and allow the restoration of a normal state after a short period of time. . . . If you would not bring infection and diseases upon yourselves, change the air you breathe, change it many times in a day by opening the windows. Erasmus Darwin 1. Introduction Indoor air quality has recently been a very active field of research. Abundant information is now becoming available to the scientist and the general public on its main causes and consequences. It is not, however, a recent problem. From the neolithic period through the middle ages, men were confronted with indoor pollution, merely due to heating and cooking in enclosed areas. It is with the erection of chimneys in residential areas that this problem was transfered to the outdoors. For most people, and up to the end of the XIXth century, indoor air quality and diseases were often linked (Brimblecome, 1987). This was largely forgotten with the generalized use of electricity and natural gas, and pollution became an outdoor problem. In the 1970’s, however, the energy crisis and “tighter”, more efficient, buildings have shifted our concerns about pollutants back to indoors, where problems can surpass those caused by outdoor pollution. This phenomena is highly visible in indoor microbial contamination (Kay et al., 1991). But, it is much more difficult to set up exposure guidelines for airborne biological contamination – which are, and will always be, part of the human atmospheric environment – than for man-made chemical pollutants. The aerobiology sampling protocol was recently standardized with a view to adequately compare different sites (Macher et al., 1995). As a result, we have reached a stage where we can gain a certain control over these problems. But there are still difficulties in making accurate assessments in complex situations where gross contamination is not present. The question that remains to be answered is whether modelisation of indoor fungal spores and CFUs in building ventilation systems can help to identify “abnormalities” in biological concentrations in such a way that precise interventions could be planned. 2. Rationale Many models have been proposed for the dispersal of outdoor pathogens. However, there are important 116 differences between the outdoor and indoor situations. In the former, most parameters that have an effect on the aerobiological pathway are known (e.g. temperature, precipitation, wind speed, etc.). For we have a “moving box” of air that collects, then dilutes concentrations; and we reach very quickly a homogeneous situation. These concentrations will of course be site to site and season to season specific, but for a given place and period, they will be a recognizable pattern. Indoors, by contrast, we have an immobile “black box” where air travels, according to external influences and accumulates contamination of anthropic origin, thus creating heterogeneous situations. Indoors, it is almost like a Maxwellian model of aerobiology, where trapdoors are open or closed to contamination. Therefore, the aerobiological analysis of domestic interiors requires a lot of data and, as a result, need careful understanding of the spatial and temporal framing of samplings. However, the situation is totally different in mechanically ventilated rooms. Indeed, a ventilation system acts like a bottleneck where all available air to the rooms has to move in a relatively short period of time. This, however, generates another problem. Since there is always an outdoor component, and since local contamination is not always negligible, what type of methodology should be used to determine contamination presence? Do we apply a threshold rule (v.g. 1000 CFU/m3 ) (Morey et al., 1984)? Do we specify an indoor/outdoor ratio (v.g. 1/3) (Brooks and Davis, 1992)? Do we consider the correlation of species presence indoors and outdoors? (see Rao and Burge, 1996 for a review). This paper proposes a model that reduces large tables of airborne spore data to simple indices, thus allowing the objective discernment of contamination presence. mm Petri dishes, one containing 35 ml of a Sabouraud medium (peptone, glucose, agar), the other 35 ml of a malt 2% medium (malt extract, peptone, agar). Sample exposure times varied from 3 to 4 minutes and were taken while the ventilation systems were in operation. Flow rates (which are fixed on these instruments) were checked before and after each set of samples were taken (i.e. after 20 minutes of operation). In each system, samples were taken at five different sites (Figure 1): at the fresh air intake (1); after the filters (2); after the humidifier (of the water jet type) (3); after the coils (4); and in the air return ducts from the rooms (5). From a total of 110 sites, 440 samples were taken. All samples were taken during the same week (October 29th to November 2nd), between 10:00 AM and 15:00 PM. In this period, mean air temperature was 5.02 ◦ C and mean relative humidity level was of 80.8%. Therefore, humidification was not needed and 100% fresh air was admitted. Outdoor “control” air was sampled twice on each day, at ground level, near the entrance of the building, on the same side of the building as the fresh air intake. 4. The model From outdoors to the rooms, we hypothesize the following behavior of spores concentration: a sharp decline after the filters, a small increase either after humidification or after cooling, or both (due to microbial contamination associated with high moisture levels), and another decline in the rooms (because of mixing with “uncontaminated” air). In order to locate abnormalities from this simple model (in fact, an inverted exponential distribution, characteristic of a constant death rate), we can calculate the efficiency of any part of the system as: Efficiency (“E”): 3. Sampling All 22 ventilation systems of a 9 story building of an university campus in Montréal were sampled exhaustively and systematically in fall 1990. Samples of total spores were taken, in pair, with Burkard personal samplers (Burkard Manufacturing, Inc., Rickmansworth, U.K.) at a flow rate of 11 litres of air/minute on a gelatine (30%)/glycerine (70%) coated 75 × 25 mm microscopic slide. Samples of viable spores were taken, in pair, with Burkard portable air samplers for agar plates, drawing 15 litres of air/minute on 100 exhaust − supply return − supply v.g. for the filters, “EF”: (site 2 − site 1)/(site 5 − site 1). However, the application of this simple index to our data set has generated a lot of negative values. This error is the result of using a closed system model, while, in reality, the sampled sites mimic an open model, i.e. are influenced by the outdoor air. Indeed, the value of the numerator (site 2 − site 1) for filters was expected to be negative by our model (see also Figure 2 in the results). At this point, we decided to use only the output of each component (yield or “Y”) 117 Figure 1. Diagram of air flux in each ventilation system and localisation of sampling sites. and to integer all the elements for each system (for filters, “FY”: site 1/site 2,; for humidifier, “HY”: site 3/site 2; and for cooling, “CY”: site 4/site 2). Since our system is partially serial and partially parallel (graphically Y shaped, see Figure 1), we apply to the rooms the power distribution model of electrical circuits1 , so that: (CFUs) on Sabouraud and on Malt, and total spores respectively. Mean concentration outdoors was 140 CFU/m3 or 1120 spores/m3. Indoor, we found 100 CFU/m3 or 1830 spores/m3. There was no statistical difference between the use of the Sabouraud or the Malt medium, the mean difference was 11 CFU/m3 (Kendall Tau of 0.333; p of 0.034). Room yield or “RY” : (site 5/site 1)/(FY∗(HY + CY)). Yield (“Y”) can also be expressed as the capability of absorbing the external contamination, i.e. Absorption (“A”) : (1 − Y)∗100, which will be expressed as percentages. 5. Results Overall, 67 different taxa were identified indoors and 23 outdoors. This difference can be related to the number of sites sampled: the ratio species/sites being respectively of 3.04 and 3.83. Species composition will not be discussed here, but it can be said that, as expected for fall outdoors samples, Cladosporium dominates total spores (60%) and CFUs (71%). The other genera, in rank order, include: Penicillium spp., Aspergillus spp., and Alternaria spp. Indoors, Cladosporium spp. dominate the viable recoveries, but Cladosporium spore counts were similar to those of the Aspergillus/Penicillium type. Results from each of the ventilation systems are presented in Tables 1, 2 and 3, showing viable units 6. Discussion The average situation found in the systems corresponds exactly to the general behavior hypothesized in our model: an inverted exponential distribution (Figure 2). From a statistical point of view, only the presence of filters (between sites 1 and 2) is significant (t: 2.56; p: 0.007). While the net contribution of the humidifier site (the 2/3 ratio) or cooling site (the 3/4 ratio) were generally positive, they were not significant (−0.26 > t > −0.81; 0.60 < p < 0.79). Of course, operating humidifier or cooling systems could totally change this result. Mean absorption for the studied systems was 67%, which is exactly what the theory was predicting for a “normal” behavior (i.e. 66%, or an indoor/outdoor ratio of 1/3). Our model seems, therefore, to adequately represent the airborne situation of fungal spores, even if sampling was limited to specific (but systematic) sites. The use of our absorbtion coefficient (“A”) (Table 4) allows the visualisation of the situation at each system. It can also be seen from this table that room absorption was well correlated with the average 118 Figure 2. Mean frequencies distribution of airborne colonies forming units (CFU) in the studied ventilation system components (see Figure 1). absorption level of each system (Figure 3) (Pearson’s r of 0.987, p of 0.0001). Moreover, there was a large agreement between a low indoor/outdoor ratio and a high absorption percentage (Pearson’s r of 0.91). All negative “A” correspond to an indoor/outdoor ratio <1. The net advantage of our model upon a simple indoor/outdoor ratio is that it allows the precise localisation of the contamination. It is, therefore, a significant addition to the indoor/outdoor comparison (which is still necessary). Possible individual absorption percentage range from 17% (#C4) to 99% (#C3). However, two systems (B2 and C5) present negative absorption. This suggests that these systems disperse more fungal particles than they receive from the outside. We therefore recommended that these two systems be cleaned. After cleaning, the 2 systems presenting an abnormal behavior were re-sampled (after 3 months, i.e. February 1991) with the same methodology. This second sampling showed that the situation has again become normal, with a mean indoor/outdoor ratio of 37% (instead of −3034%), or a mean decrease of concentrations of 96%. 7. Conclusion The use of a simple index of absorption has allowed us to handle a large data set of airborne spores and 119 Figure 3. Simple Linear Regression model between room and ventilation system absorption levels. Table 1. Fungal colonies forming units (CFU) concentrations per cubic meter of air on Sabouraud media, at each site (see Figure 1) of each 22 ventilation systems. Table 2. Fungal colonies forming units (CFU) concentrations per cubic meter of air on Malt media, at each site (see Figure 1) of each 22 ventilation systems. Systems CFU/m3 Site 1 Site 2 Site 3 Site 4 Site 5 Mean[ ] St. dev. Systems CFU/m3 Site 1 Site 2 Site 3 Site 4 Site 5 Mean[ ] St. dev. A1 A2 A3 A4 A5 A9 B1 B2 B3 B4 B7 B8 B9 B10 C1 C2 C3 C4 C5 C6 C7 C8 97 97 93 93 21 64 89 87 62 44 96 108 58 – 152 353 639 727 44 99 420 20 20 82 71 10 40 112 136 118 156 95 88 171 78 57 56 1 63 8 54 81 29 30 24 91 130 66 53 56 151 92 80 103 268 155 430 140 21 51 7 21 32 12 0 18 250 45 49 44 72 63 117 207 171 132 250 81 120 106 33 9 656 61 39 7 59 13 22 20 40 11 20 24 67 79 103 23 48 58 40 – 48 63 0 14 45 9 38 21 83 67 77 45 41 64 112 117 114 79 150 115 145 101 62 95 273 166 43 42 109 20 99 33 36 36 22 31 34 53 47 45 101 48 162 42 52 146 343 314 8 45 175 6 A1 A2 A3 A4 A5 A9 B1 B2 B3 B4 B7 B8 B9 B10 C1 C2 C3 C4 C5 C6 C7 C8 76 40 134 134 141 80 114 54 104 51 80 162 156 – 318 524 624 410 43 90 475 40 71 40 93 10 70 152 240 139 228 101 176 135 201 50 24 10 50 32 36 36 48 20 80 108 110 102 63 90 138 77 96 112 188 232 534 84 7 37 20 21 8 32 10 37 326 115 10 52 99 75 249 287 234 141 235 81 335 92 87 0 350 23 16 62 29 25 11 50 55 44 0 48 66 70 103 63 16 67 50 – 56 18 0 7 63 18 39 21 113 71 80 68 75 89 161 125 153 94 139 135 255 75 98 118 209 99 33 48 120 29 122 38 49 49 52 38 80 96 71 37 89 66 186 22 127 227 272 174 22 29 199 3 Mean[ ] 165 71 91 117 38 96 48 Mean[ ] 183 89 99 133 41 109 53 120 Table 3. Fungal spores concentrations per cubic meter of air at each site (see Figure 1) of each 22 ventilation systems. Systems Spores/m3 Site 1 Site 2 Site 3 Site 4 A1 A2 A3 A4 A5 A9 B1 B2 B3 B4 B7 B8 B9 B10 C1 C2 C3 C4 C5 C6 C7 C8 1037 2991 2799 1352 2234 1227 879 157 912 345 1604 2360 1132 – 1417 1171 25638 23244 219 2297 8439 722 471 1038 1038 659 188 2201 1383 407 1384 1384 1824 533 1195 1354 407 723 282 283 313 439 755 219 565 – 1037 1036 1635 1098 1100 596 471 1131 1887 849 1574 2266 535 1572 723 503 345 439 – 281 3913 840 982 Mean[ ] Site 5 Mean[ ] St. dev. 767 1338 1503 816 1088 1181 1854 4175 616 861 1598 943 1372 1658 912 938 5631 5102 433 855 2503 364 447 1209 771 460 846 625 469 8166 530 601 726 813 955 527 435 454 11193 10145 213 809 3962 217 1305 1614 1296 1415 346 1227 94 1006 1635 910 125 943 440 816 565 785 124 943 18774 188 126 1352 93 2295 378 628 343 28092 157 1353 – 1228 975 849 377 1321 189 535 943 754 534 597 502 314 503 408 189 1030 Table 4. Mean absorption percentage of outdoor concentrations in each 22 ventilation systems and for the ventilated rooms, for colonies on Sabouraud media, colonies on Malt media, and spores. Mean absortion % Systems Sabouraud Malt A1 A2 A3 A4 A5 A9 B1 B2 B3 B4 B7 B9 B10 C1 C2 C3 C4 C5 C6 C7 C8 77.32 79.38 56.99 88.17 4.79 62.50 24.72 9.20 −66.13 47.73 50.00 46.30 # Value! 68.42 82.15 100.00 98.07 −2.27 90.91 90.95 −5.00 colonies concentrations (more than 15,000 identifications were done) from a single building. Contamination, defined as “abnormalities” in concentrations and not necessarily as health hazards, were easily detected. Cleaning of only 4% of the ventilation systems components has allowed the restoration of a normal state in a very short period of time. Aerobiological models are, therefore, a very useful tool in locating contamination and in testing the efficiency of cleaning, but it does requires thoroughly planned samplings. However, it must be stated clearly that application of this or similar models will not replace the aerobiological expertise of the investigator. It is a tool, not a panacea. Acknowledgements The authors would like to recognize the helpful work of Stéphane Boucher, Lize Fafard, Chantal Paulin and Claire Vachier. Note 1. This model was applied, even if it refers to equal resistance on each side, which is not necessary true in our case. References Spores System mean Room 85.53 66.63 76.49 90.60 −25.00 96.86 50.41 # Value! 58.96 41.59 52.51 50.60 67.16 90.75 82.03 85.00 100.00 80.30 61.69 88.98 40.00 53.95 52.15 73.74 42.11 85.89 50.91 83.79 −29.63 −11859.96 −3959.47 −321.85 0.96 86.18 7.01 69.54 −23.53 73.04 32.41 87.19 80.00 76.43 68.81 92.64 58.64 85.47 63.47 76.93 # Value! # Value! # Value! # Value! 82.39 93.12 81.31 32.08 96.56 96.78 91.83 43.59 100.00 99.26 99.75 96.92 98.29 95.94 97.44 58.72 −46.51 −143.84 −64.21 −24.82 80.00 78.15 83.02 61.68 80.00 78.15 83.02 61.68 47.50 73.82 38.77 56.99 Brimblecome P.: 1987, The Big Smoke. Methuen, London, 185 pp. Brooks B.O. and Davis W.F.: 1992, Understanding Indoor Air Quality. CRC press, Boca Raton, 189 pp. Kay J.G., Keller G.E. and Miller J.F.: 1991, Indoor Air Pollution. Lewis publishers, Chelsea, 259 pp. Macher J.M., Chatigny M.A. and Burge H.: 1995, Sampling airborne micro-organisms and aeroallergens. 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