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Mar 10, 2010 - a 5 mL aliquot of a 1.0 × 107 L. pneumophila (strain GS3.11) bacteria/mL aqueous suspension into the reaction chamber as droplets in a high ...
Environ. Sci. Technol. 2010, 44, 2605–2611

Photocatalytic Treatment of Bioaerosols: Impact of the Reactor Design ´ B A S T I E N J O S S E T , †,‡ SE ´ RO JE ˆ ME TARANTO,† NICOLAS KELLER,† ´ R I E K E L L E R , * ,† A N D VALE MARIE-CLAIRE LETT‡ Laboratoire des Mate´riaux, Surfaces et Proce´de´s pour la Catalyse (LMSPC), CNRS, Strasbourg University, 25 rue Becquerel, 67087 Strasbourg, France, and Laboratoire Ge´ne´tique Mole´culaire, Ge´nomique, Microbiologie (GMGM), CNRS, Strasbourg University, 28 rue Goethe, 67028 Strasbourg, France

Received October 9, 2009. Revised manuscript received February 19, 2010. Accepted February 19, 2010.

Comparing the UV-A photocatalytic treatment of bioaerosols contaminated with different airborne microorganisms such as L. pneumophila bacteria, T2 bacteriophage viruses and B. atrophaeus bacterial spores, pointed out a decontamination sensitivity following the bacteria > virus > bacterial spore ranking order, differing from that obtained for liquid-phase or surface UV-A photocatalytic disinfection. First-principles CFD investigation applied to a model annular photoreactor evidenced that larger the microorganism size, higher the hit probability with thephotocatalyticsurfaces.Appliedtoacommercialphotocatalytic purifier case-study, the CFD calculations showed that the performances of the studied purifier could strongly benefit from rational reactor design engineering. The results obtained highlighted the required necessity to specifically investigate the removal of airborne microorganisms in terms of reactor design, and not to simply transpose the results obtained from studies performed toward chemical pollutants, especially for a successful commercial implementation of air decontamination photoreactors. This illustrated the importance of the aerodynamics in air decontamination, directly resulting from the microorganism morphology.

Introduction The regulation of volatile organic compounds (VOC) has recently created a strong incentive for innovative sustainable environmental research. As a result, the indoor air quality control is receiving a growing interest due to the public concern over human health. Targets are not only VOC or more generally chemical pollutants, usually malodorous, toxic, or contributing to global warming, but include also airborne microorganisms such as bacteria, viruses, or spores. The U.S. Environmental Protection Agency considers the indoor air pollution as one of the top five environmental risks to public health, since we spend 70-90% of our time indoors, where pollutant contents are higher (1, 2). Biological pollutants are particularly threatening because of the con* Corresponding author phone: +33(0)368852736; fax: +33(0)368852761; e-mail: [email protected]. † Laboratoire des Mate´riaux, Surfaces et Procédés pour la Catalyse. ‡ Laboratoire Ge´ne´tique Mole´culaire, Génomique, Microbiologie. 10.1021/es902997v

 2010 American Chemical Society

Published on Web 03/10/2010

tinuously increasing resistance of microorganisms against medical treatments and their dissemination due to the intensification of human transports, as shown with worldwide damages (SARS, avian, or porcine flu). If many airborne microorganisms (AMO) show no or a low virulence, an impressive variety of AMO are a real hazard to safety, such as bacteria, viruses, fungi, with a huge societal impact in terms of mortality and cost (3). The removal of airborne chemical or biological pollutants is therefore a challenging task for which photocatalysis has attracted attention since decades for acting as an efficient air treatment technology because of the oxidizing power of UVA-irradiated semiconductors (4). The analogy between chemical and biological targets results from the organic nature of the microorganism constituents that photocatalysis can oxidize through oxidizing photoholes or •OH radicals, similarly to liquid and gas phase organics. The cell walls being a complex assembly of high molecular weight organic compounds (MW > 10 000), contact with TiO2 causes oxidative damage to cell membrane, considered as the first barrier maintaining the vital cell functions and the first target for photocatalysis, and leads to inactivate bacteria, viruses, spores, yeasts (5). This photocatalysis/biology interface has been pioneered by the photoelectrochemical sterilization of microbial cells by platinized semiconductors, which opened the door to the application of photocatalysis to the life science and enlarged its potential applications (6). In contrast to photocatalysis applied to chemicals, photocatalysis applied to biological targets remained mainly focused on the treatment of liquids and on self-decontaminating surfaces, mainly targeting bacteria (especially Escherichia coli bacteria), viruses, fungi, algae, and protozoa (7, 8). By contrast, despite the interest in terms of public health and a large spectrum of applications, the photocatalytic disinfection of contaminated air remained scarcely studied, due to the complexity of working with bioaerosols, which combines difficulties inherent to microbiology and to aerosol sciences. Works on bioaerosols concerned E. coli, Microbacterium sp., Bacillus subtilis, Bacillus cereus, Staphylococcus aureus, Aspergillus niger, a Candida famata yeast or the MS2 and λ phage viruses (9-13). Our previous works were devoted to the UV-A photocatalytic treatment of flowing air contaminated by E. coli and L. pneumophila (14, 15). This paper reports on the need of photoreactors specifically designed for biological applications, since up to now, the chemical approach was the main concern. When targeting biological agents, the main researchs concerned the increase in the biocidal properties mainly by metallic promotion of the photocatalyst (5). In contrary to works reporting on innovative designs proposed for removing chemical pollutants and on the corresponding tools developed for their scaling-up, works for reducing the biological contamination level by optimizing the reactor geometry remained scarce, with few articles focused on the photocatalytic treatment of bioaerosols with reactor design (9-11, 14, 15). Novel designs of photoreactors should be engineered for overcoming restrictive efficiency limitations and for meeting the requirements for achieving a commercial implementation (16). Paradoxically, many commercial photocatalytic devices claim their efficiency for inactivating AMOs at high flow rates, although a large part of them have only been designed and tested for chemical applications, only assuming the risky hypothesis that a similar efficiency toward pathogens was reached. Results usually obtained raise many questions about the efficiencies of photoreactors for removing AMOs at high VOL. 44, NO. 7, 2010 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

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FIGURE 1. Side-view scheme of the self-driven tangential fan decontamination photoreactor with 2D simulation of the velocity magnitude in the cross section of the reactor: (1) contaminated air inlet, (2) purified and decontaminated air outlet, (3) fan blades, (4) air duct housing. flow rates, since they were only assessed at low labscale rates. However, more than in the case of VOCs for which any concentration reduction is valuable, air treatment for reducing infections due to AMOs only makes sense if the removal rate is high (see the dose-response function to pathogenic exposure fitted using a beta-Poisson law in Supporting Information (SI) Figure S1). Commercial devices should thus absolutely incorporate only highly efficient reactors. The impact of AMOs on the photoactive surface appeared to be critical, and the development of efficient photocatalytic systems has to focus on that point. Hence, the design of photoreactors for decontaminating bioaerosols substantially differs from that targeting the treatment of chemical pollutants and it appeared necessary to specifically develop photoreactors devoted to the reduction of the biological pollution under realistic conditions, like was done in Grinshpun et al. (12). Recently, first-principles computational fluid dynamics (CFD) has become a promising tool for the design of photoreactors devoted to the removal of VOCs, since it easily takes into account the complex interactions between the flow field, the light activation, and the reactions kinetics (1, 17-21). CFD might be helpful for bioaerosol remediation too, because working with bioaerosols remains time- and material-consuming. Although concepts like the adsorption or the diffusion of chemicals cannot be easily transferred to the microbial inactivation, this tool could help in designing efficient biocidal reactors by enhancing the impact probability of the microorganisms on the surface.

Experimental Section Photocatalytic Pilots for AMO-Contaminated Air Treatment. The micropilot used for performing the single-pass UV-A photocatalytic treatment of contaminated air has been previously detailed (scheme as SI S2), as well as the procedure for preparing and aerosolizing L. pneumophila aqueous suspension for generating a reproducible contaminated air flow, and that for recovering and numerating bacteria from the outlet stream (14). The single-pass tests were performed at a 5 m3/h air flow in an 70 mm wide and 300 mm long annular photoreactor with a central 19 mm diameter 8W UV-A actinic lamp (Philips, actinic BL, TL8). In this configuration, the reactor worked with an annular space of 25.5 mm. The TiO2 coating was performed by evaporating to 2606

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dryness aqueous slurry of the TiO2 and the coated reactor was dried at 110 °C for 1 h in air. Details on the reactor and on the coating method could be found in ref 22. Recirculation tests were performed with a tangential flow reactor at a 140 m3/h air flow in a 0.8 m3 glovebox adapted for AMOs and used as reaction chamber, targeting L. pneumophila, T2 naked virus, and B. atrophaeus spore. This reactor has been commercialized by the Biowind company (France) under the DPA label, schematized in Figure 1 with the lighting coming from the top of the fan (14). TiO2 was coated by dipping using an aqueous suspension of TiO2 and further drying at ambient temperature. This photoreactor has an inner volume of 1.2 L with a photoactive surface of 1040 cm2 and a TiO2 coating density of 1 mg/cm2. Details on the reactor and on the coating method could be found in ref 14. In both cases, TiO2 P25 (Degussa - Evonik) was used. Targeted AMOs. Extensive details on the preparation of the bacteria, virus and spore suspensions used for aerosolization are reported as SI S3. L. pneumophila of 1-2 µm size is a well-known AMO, well adapted for bioaerosol tests. The contaminated bioaerosol was obtained by aerosolizing a 5 mL aliquot of a 1.0 × 107 L. pneumophila (strain GS3.11) bacteria/mL aqueous suspension into the reaction chamber as droplets in a high flow rate air stream using a peristaltic pump (6.3 × 107 microorganisms per m3 of air). 50 nm mean size T2 naked bacteriophage viruses are used as models for airborne viruses. The contaminated bioaerosol was obtained by aerosolizing a 5 mL aliquot of a 1.51 × 108 T2 bacteriophages/mL suspension into the reaction chamber as droplets in a high flow rate air stream using a peristaltic pump (9.4 × 108 microorganisms per m3 of air). Bacterial endospores (≈1 µm) are considered to be the most resistant living form and are produced by Gram-positive bacteria to survive extreme situations. Bacillus atrophaeus spores, former labeled as B. subtilis, were used, since they are used in dry-heat disinfection norms (EN866-2), and were already positively used as a surrogate of environmentally resistant pathogenic microorganisms like anthrax (B. anthracis spores). The contaminated bioaerosol was obtained by aerosolizing a 5 mL aliquot (1.0 × 107 spores/mL) of a commercial suspension (CIP77.18, Pasteur Institute, Paris) into the reaction chamber as droplets in a high flow rate air stream using a peristaltic pump (6.4 × 107 microorganisms per m3 of air).

FIGURE 2. Probability for spherical particles to hit the photocatalytic surface of an annular photoreactor, as a function of the particle diameter on a normal scale (A), and on a log-scale (B), and of the annular space, at a constant input of 5 m3/h. Microorganism Numeration. Direct quantitative analysis of bacteria was achieved by epifluorescence microscopy using the “LIVE/DEAD Baclight Bacterial Viability kit” (Invitrogen) as appropriate viability indicator and staining method based on a membrane integrity test for easily distinguishing between live (with integrate membranes) and dead cells (with damaged ones), whatever their cultivability (5). Thus, the treatment efficiency can be directly derived from the viabilities (expressed in percents) at the inlet and the outlet of a photoreactor. This method is not suitable to viability of bacterial spores and the infectivity of viruses. However, since such microorganisms do not enter a “viable but noncultivable state” (VBNC), their counts can be performed using classical heterotrophic plate count. Details can be found in a review dealing with numeration methods (5) and as SI S4. Efficiency Indicator. The efficiency of a biocidal treatment for disinfecting liquids or surfaces was usually described with the “logarithmic reduction” (LR). This indicator compared the concentrations before and after the treatment, so that the treatment efficiency was defined by this ratio, and expressed on a log10 scale for overcoming the low precision of biological numerations. However, this calculation could not be used here, since the numeration should be performed over the totality of the bacteria. Thus, the viability of the bacteria bioaerosol could be defined as the ratio between live bacteria and both live and dead bacteria in an air flow sampling. The bacteria viability in the inlet air corresponded to the viability of the starting suspension (µin), whereas the bacteria viability in the outlet air corresponded to the viability of the collected suspension (µout). The absence of any influence of both aerosolization and collection processes on the bacteria viability has been checked. The LR could be easily expressed using this percentage viability, and thus be replaced by the logarithmic reduction in viability (LRV) which considers that a virtual quantity of microorganisms N0 entered the photoreactor with a µin viability and left it with a µout one (eq 1). LRV ) log

(

) ( )

N0 µin µin ) log N0 µout µout

(1)

The survival probability P was the probability for a bacteria to come out of the reactor alive, given by P ) 10-LRV.

Results and Discussion Computational Fluid Dynamics (CFD) Study Applied to Annular Photoreactors. If annular photocatalytic reactors are irreplaceable for theoretical studies in the case of chemical applications because they enable determining the kinetic parameters and validating the mechanisms which can further be incorporated in CFD models, their efficiency for the removal of AMOs at high flow rates remains questionable. In order to illustrate that, the motion of spherical particles inside an annular photoreactor has been simulated using the “Fluent” CFD software, as a function of the annular space and of the particle size ranging from 10 nm to 10 µm, at a constant flow rate of 5 m3/h (Figure 2). In such a configuration, the photocatalytically active contact surface corresponded to the internal side of the external tube, with the flow passing through the inner space between both coaxial tubes, the inner tube being in our model the external surface of the UV-A actinic lamp tube. For this computation, no reactions have been incorporated in the model, the particles hitting the internal surface of the external wall being simply “trapped” on the photocatalytic surface. This hypothesis corresponded to a 100% inactivation efficiency of the biocidal surface, so that the computation described the motion of particles, but not the photocatalytic aspects of the reactors (i.e., 100% inactivation efficiency toward a microorganism if it impacts on the illuminated surface, at a given and constant UV-A irradiation rate). A Lagrangian approach was used, it focuses on particle tracks, rather than on a control volume like the Eulerian method. The system was considered as isothermal, the flow governed by the ideal gas law and the AMOs were assumed not to significantly influence the flow characteristics due to very low AMO concentrations. The velocity of the flow at the photoreactor inlet and the initial velocity of the particles were normal to the inlet boundary, and the outlet boundary was set at atmospheric pressure. Hence, this simplified model did not incorporate the effects of parameters related to the motion of the air flow, such as the rotation or the shape of fan blades when they are present. However, Sahle-Demessie et al. (23) detailed that these parameters have a significant influence on the process efficiency and should be included in the investigation of more realistic reactors. Moreover, thanks to the axisymetry of this geometry, this 3D problem VOL. 44, NO. 7, 2010 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

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TABLE 1. Computational Details of the Axisymetric Meshes Used to Simulate Annular Photoreactorsa Rint ) Rlamp ) 0.009 m Rext-Rint (m) 0.006 0.016 0.026 0.036 0.046

annular section (m2) -4

4.52 × 10 1.71 × 10-3 3.59 × 10-3 6.11 × 10-3 9.25 × 10-3

scaled residuals velocity (m/s) 0

3.07× 10 8.13 × 10-1 3.86 × 10-1 2.27 × 10-1 1.50 × 10-1

Re

number of particles

2420 1708 1320 1075 907

5.50 × 10 4.65 × 102 5.08 × 102 5.31 × 102 5.46 × 102 2

maximal cell sizes (m) -4

continuity -10

1.00 × 10 /9,10 × 10 1.80 × 10-4/4.40 × 10-9 2.00 × 10-4/8,68 × 10-9 2.00 × 10-4/1,12 × 10-8 4.07 × 10-4/2,14 × 10-8

-7

9.90 × 10 8.40 × 10-4 5.60 × 10-4 3.00 × 10-4 4.50 × 10-4

x velocity -1

2.50 × 10 0 2.20 × 10-7 8.00 × 10-8 3.40 × 10-8 4.00 × 10-8

y velocity -12

3.70 × 10 5.70 × 10-9 3.60 × 10-9 2.00 × 10-9 1.90 × 10-9

k

ω -8

4.60 × 10 8.40 × 10-5 6.20 × 10-6 6.80 × 10-6 1.40 × 10-6

1.90 × 10-8 2.50 × 10-4 3.60 × 10-4 1.40 × 10-4 2.90 × 10-4

a The scaled residuals for the calculation of the flow characteristics indicate a very good convergence at the end of the iterations. (Constant input: 5 m3.h-1). A fine mesh was used for discretizing the boundary layers and the flow, without too high computational costs. For each annular space, a first mesh was produced with trigonal cells in the size range of 1-4 µm. The mesh, viz. the maximal cell size, was refined in the high velocity gradient zones after that a first solution leading to a convergence limit was first reached, so that the value of the maximal velocity gradient was finally divided by a factor 10. Both initial and final maximal cell sizes (initial/final) are shown here.

in the Cartesian frame could be replaced by a 2D one using a cylindrical frame. Details were reported in Table 1. The mass and the momentum conservation equations were approximated using the Reynolds Averaged NavierStokes equations, the most widely used model since it can model all turbulence scales (24). The Reynolds numbers reported in Table 1 were calculated by considering the hydraulic diameter for an annular duct as being the difference between both inner and outer diameters of the annular reactor. Used for the simulations, they indicated a flow globally ranged in a laminar to transitional regime. However, in order to accurately model the photocatalytic treatment of a stream, it is necessary to emphasize on the phenomena occurring in the boundary layer, near the photocatalytic surface. This near-wall zone is particularly complex because of the no-slip condition and the high shear stress at the wall which result in large gradients. Thus, the shear stress transport k-ω turbulence model (SSTKW) was used to model the continuous phase, since it is an empirical model well adapted for wall-bounded flows. The solution control was based on the coupled pressure velocity mode, and used explicit relaxation factors set at 0.75 for both momentum and pressure, second order for the pressure, and also third order muscle for the momentum, the turbulent kinetic energy and the specific dissipation rate. The motion of the particles was solved by integrating the force balance equation (written in the x direction), which equates the particle inertia with the forces acting on the particle, taking into account the drag and the Archimedes forces as well as an additional acceleration (e.g., Brownian forces). By contrast to particles having an aerodynamic diameter larger than 1 µm, it should be noted that working with particles smaller than 1 µm required to incorporate the Cunningham’s correction factor to the drag forces per unit particle mass expression derived from the Stockes law. The Cunningham correction factor rapidly and asymptotically decreases down to 1 with increasing the particle diameter under normal pressure. Details relative to that section are reported in SI S5 and in ref (24). Microorganisms have various morphologies and sizes, ranging from some 10s of nanometers for small viruses, to a few micrometers for bacteria or even larger for droplets containing a high number of germs, so that, both laws have to be employed depending on the studied microorganisms. Hence, Table 1 reported the computation results for the flow field characteristics, whereas Figure 2 evidenced the probability pattern of spherical particles with diameters ranging from 10 nm to 10 µm to hit the photocatalytic surface, in photoreactors with annular spaces ranging from 6 to 46 mm, at a constant flow input (5 m3/h). Thus, this describes the behavior of most of the microorganisms, classified in different aerodynamic groups, from viruses usually with sizes in the 10-100 nm range to bacteria and bacterial spores in the 0.5-3 µm range. Dust or droplets composed of more 2608

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than one AMO should be considered as larger particles. The decreases of the residuals for the continuity and the velocity field components to values lower than 10-3 are usually considered as a good convergence indicator for the simulations (25). One should add that the reactor could be considered in a first approximation as being close to a plug flow reactor, due to residence times of microorganisms with a narrow distribution. Figure 2A shows that the probability for microorganisms to hit the photoactive surface of an annular photoreactor remains very low, mostly under 10%, whatever the annular space. This was especially true for submicronic particles, for example, viruses or small bacteria, which actually follow the main stream. This hit probability increased when increasing the passage time in the photoreactor, which is proportional to the open section, and thus to the annular space (Table 1). This observation was in agreement with the fact that larger the reactor, slower the particles, so that the probability they hit the photocatalytic boundary increased. One should also note that larger the microorganism, higher the hit probability because of the higher inertia which allows more important direction changes. This behavior resulted from the fact that small size particles (e.g., nanosize viruses compared to bacteria or bacterial spores) will follow the mean air flow and thus will be less impacted on the photocatalytic surface. For a given annular space, the hit probability increased when increasing the particle diameter, so that a hit probability of about 35% could be obtained for 10 µm diameter large-size microorganisms at low passage times. However, the illumination of the photoactive surface quickly decreases with the increase in the external radius of the annulus, so that, for an equal hit probability, the shortest annular space should be preferred, since the illuminated surface remains more active. Another possibility for enhancing the hit probability through the use of a large photoreactor with very low internal speed would consist in the use of an external illumination. Unfortunately, even if the efficiency of this configuration was already reported, it would require the use of many light sources for surrounding the reactor, leading to very restrictive extra costs. Bactericidal Efficiency of a Annular Photoreactor. The simulations showed that the hit probability of microorganisms increased with the size of the annular space, and reached a plateau for annular spaces larger than 25-30 mm (mainly for particles with diameter lower than 5 µm, that is, usual viruses, bacteria and bacterial spores). Indeed, large annular spaces lowered the flow velocity inside the reactor and increased the residence time; on the other hand, too large annular spaces decreased the hit probability on the biocidal surface since the surface-to-volume ratio decreased. Since too large annular spaces would lead to a decrease in the illumination of the photocatalytic surface and thus would negatively influence the decontamination efficiency, we

carried out the photocatalytic treatment of air contaminated by L. pneumophila inside the photoreactor previously described, with an annular space of 25.5 mm. The decontamination efficiency toward L. pneumophila obtained for two experiments series, with and without UVA, was expressed in terms of LRV and single pass survival probability, obtained through the TiO2-coated annular photoreactor working in the seep-flow mode (inlet air flow of 5 m3/h). Two grams of TiO2 was evenly coated onto the inner surface of the tube, corresponding to a TiO2 surface density of 3.5 mg/cm2). L. pneumophila was not sensitive to the aerosolization process, and therefore could be considered as a valuable microorganism for investigating the decontamination efficiency of the photocatalytic treatment. The TiO2-coated reactor did not show any bactericidal properties in the dark, with LRVs of -0.2 and 0.0, corresponding to single-pass survival probabilities of 118% and 100%, respectively (988 and 1584 bacteria observed, respectively). Obviously, obtaining a slightly negative LRV value and a survival probability slightly greater that 100% was not surprisingly in the microbiology field and has obviously no real meaning. This resulted from the use of biological agents (and AMO especially) and from the accuracy of the epifluorescence numeration method. Such levels corresponded to the absence of any decontamination activity. Thus the decontamination efficiency obtained under UV-A, if any, could totally be attributed to the UV-A photocatalysis on TiO2. Actually, even under UV-A, there was no statistically significant abatement, with a LRV of 0.1 corresponding to a single-pass survival probability of 79% (1931 bacteria observed). This LRV and this survival probability remained within the measurement error, greatly higher when working with AMOs than with VOCs, and therefore this reactor configuration could be considered as exhibiting no air decontamination, or air decontamination efficiency close to zero. This was in agreement with the simulation results and confirmed that the major part of the AMOs passes through the reactor without impacting on the biocidal surface. A Case-Study: A Commercial Tangential Flow Photoreactor. Hence, when targeting commercial implementation, the mass transfer of the AMO and the contact probability of the AMO with the photoactive surfaces have to be optimized, without generating too high pressure drops, even at very high flow rates. To afford this challenge, a so-called “tangential fan photocatalytic reactor” (DPA) has been engineered (Figure 1) and this configuration previously led to a LRV of 1.2 and to a 6% survival probability for the L. pneumophila bioaerosol decontamination in a “single-pass” mode at 5 m3/h (14). However, in order to simulate the decontamination of small rooms at high flow rates and to maintain the AMOs in aerosol, the recirculation rate has to be drastically increased (up to 140 m3/h in the present study), leading of course to a strong decrease in the single-pass efficiency. Therefore, the decontamination tests have to be performed in a recirculation mode inside a test chamber. At high air flow rates, this photoreactor uses the direction change and the vortex produced between the blades for generating strong shear forces and turbulences that enhance the separation of the AMO from the air flow (Figure 1) (14). The decontamination efficiency toward bioaerosols as a function of time for the DPA device was evidenced in a recirculation configuration at a 140 m3/h flow rate for L. pneumophila, T2 virus, and B. atrophaeus endospores (Figure 3). In such a configuration, the decontamination tests were probably also limited by mass transfer phenomena inside the reactor. Three photocatalytic tests and three blank tests were performed for the T2 virus aerosols, whereas four photocatalytic tests and four blank tests, and three photocatalytic tests and three

FIGURE 3. Results of the decontamination tests with (A) T2 bacteriophage viruses (three tests), (B) B. atrophaeus spores (four tests) and (C) L. pneumophila bacteria (three tests) with UV-A (empty squares) and without UV-A (filled squares) photocatalytic activation, at 140 m3 · h-1 (spores: 37-51% RH at 22-24 °C; bacteriophages: 31-39% RH at 21-25 °C; bacteria: 34-55% RH at 20-25 °C). blank tests were performed over B. atrophaeus and L. pneumophila, respectively. Nonlinear fits of classical apparent first order decontamination kinetics were obtained for the AMOs, so that apparent time constants were used for characterizing the decontamination efficiencies. A natural decay was observed during blank tests whatever the microorganisms, resulting (i) from the high flow rate used during the experiments at VOL. 44, NO. 7, 2010 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

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TABLE 2. Decontamination Time Constants for the T2 Bacteriophage Viruses, Bacillus atrophaeus Spores and Legionella pneumophila Bacteria, with (On), Without (Off) and after Correction (corr.) time constant

T2 bacteriophage viruses

B. atrophaeus spores

L. pneumophila bacteria

τon τoff τcorr

22 min (95%CI: 21-23 min)a 295 min (95%CI: 241-374 min) 24 min (95%CI: 22-25 min)

48 min (95%CI: 44-52 min) 302 min (95%CI: 270-337 min) 57 min (95%CI: 51-64 min)

13 min (95%CI: 9-17 min) 59 min (95%CI: 50-73 min) 17 min (95%CI: 10-26 min)

a

CI: Confidence Interval.

140 m3/h and leading to impact some microorganisms onto the chamber walls, and (ii) from the possible instability of the bioaerosols during the test. Therefore, the natural decay observed during blank tests was considered by defining the τoff apparent time constant, whereas the τon apparent time constant was derived from the photocatalytic tests, also including the natural decay of the bioaerosols. The results of the photocatalytic tests (%Con) have thus to be corrected with those of the blank tests (%Coff) for isolating the photocatalytic abatement from the abatement due to the experimental settings. Thus, assuming for all cases first order kinetics, and starting with a 100% initial value, the %Ccorr photocatalytic abatement and the corresponding corrected time constant, that is, τcorr, were derived from eqs 8-9. on off %C on(t) 100%.e-t/τ ) 100%.(e-t/τ +t/τ ) ) off -t/τoff %C (t) 100%.e (8) on

%C corr(t) )

τ corr. )

τ onτ off τ - τ on off

(9)

The apparent time constants obtained for the AMOs and characterizing the decontamination efficiency toward the AMO bioaerosols are reported in Table 2 with the 95% confidence intervals of the parameters. We showed that working with different kinds of AMOs such as viruses, bacteria and spores is of high interest when studying air decontamination. The nature of the microorganisms is very important for assessing their sensitivities toward UV-A photocatalysis, and Huang et al. reviewed that the sensitivity of microorganisms to TiO2 photocatalysis for water treatment was likely in the viruses > bacterial cell > bacterial spores order (25). They suggested that the different microorganisms respond differently to photocatalysis due to structural differences, particularly in terms of complexity and of thickness of the cell envelop. For instance, the Gram negative bacteria are more affected than Gram positive one, as a result of structural differences in the outer membrane (5). In a first approximation, the sensitivities of microorganisms toward TiO2 photocatalysis were usually in agreement with those obtained for classical disinfection chemicals, viruses being the most sensitive microorganisms, followed by Gram (-) bacteria, then by Gram (+) bacteria, and by bacterial endospores, which are known to be one of the most resistant living forms (5). However, the observed sensitivity order was almost only related and assigned to differences in terms of chemical compositions of the microorganisms, but neither the influence of the microorganism morphology nor that of their aerodynamic characteristics were pointed out, because the disinfection results were usually obtained in liquid phase using suspensions of photocatalysts mixed with microorganisms. In the present work, decontamination apparent time constants of 24 min, 17 min and 57 min were obtained for T2 viruses, L. pneumophila and B. atrophaeus respectively, evidencing that the sensitivity would here differ from the expected order, and thus would follow a bacteria > viruses 2610

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> bacterial spores order, significantly differing from that obtained for liquid-phase or surface UV-A photocatalytic disinfection. Hence, the results obtained with AMOs illustrated the importance of the aerodynamics in air decontamination at the biological level, especially at high flow rates, when compared to liquid-phase decontamination or selfdecontaminating surface applications, for which the aerodynamic aspects resulting from the microorganism morphology have logically not been taken into account. As a result, the efficiency shown by photocatalytic reactors toward gaseous and liquid pollutants cannot be directly transposed to the photocatalytic removal or airborne microbiological targets. CFD Investigation Applied to the DPA Photocatalytic Purifier Case-Study. In order to understand the flow field inside the tangential reactor, a 2D CFD model has been developed starting with a trigonal mesh with cells in the millimeter range. Figure 1 shows the velocity magnitude computed inside the 2D cross section of the tangential fan reactor working at a flow rate of 140 m3/h, corresponding to an input velocity of 5 m/s and to a rotor radial velocity of 3800 rpm (398 rad/s). Again, the k-ω model was used for the viscosity, and the mesh was refined in the high velocity gradient zones after that a first solution was first reached, so that the value of the maximal velocity gradient was finally divided by a factor 10. The absolute convergence criterions on the residuals for the continuity and for the components of the velocity field, reached values lower than 10-8, indicating that a stable solution was reached. The CFD calculation showed that a nonnegligible part of the air flow was directly sucked to the reactor output (zone indicated by a red circle), so that the flowsand the AMO targets especiallyscould not impact on the photocatalyst, which was coated on the rotating blades of the fan and on the internal surface of the duct housing (dashed line). Tiny AMOs such as viruses are more likely to follow the air pathways than larger particles such as vegetative bacteria or bacterial spores, which have more stochastic moves due to their inertia, leading to a higher probability to hit one of the biocidal surfaces. This could explain the unexpected resistance of the 40 nm diameter T2 bacteriophage virus to the photocatalytic treatment when compared to the micrometer-sized L. pneumophila. This pointed out the promising and beneficial role that CFD computation could play for improving the efficiency of photoreactors, which strongly depends on the kind of targets, especially when materialand time-consuming AMO inactivation experiments should be necessarily conducted. So, decontamination tests performed in recirculation showed that the DPA photocatalytic device was well active for removing AMOs from air, but CFD calculations evidenced that further improvements could be obtained by reducing the contact-free bypass inside the reactor, especially when small size AMOs like naked viruses should be inactivated. Specific designs have thus to be rationally engineered for taking into account the particularity of AMOs compared to chemical targets, for looking for a commercial implementation of photoreactors with high air decontamination efficiency.

Acknowledgments The Alsace Regional Council, France, is deeply thanked for financial support through the projects N° 653/04 and 1096/ 07 and the Ph.D. Grant of Dr. Sébastien Josset. The Biowind Company, France, is acknowledged for supplying the DPA photocatalytic air purifier.

Supporting Information Available S1, Dose-response function to pathogenic exposure. S2, Single-pass experimental setup scheme. S3, Preparation of the microorganism suspensions. S4, Microorganism numeration. S5, Reynolds numbers, force balance equation, Stockes law, and Cunningham’s correction factor. This material is available free of charge via the Internet at http:// pubs.acs.org.

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