How could be the concentration of airborne pathogens in a specific indoor space be measured?

How could be the concentration of airborne pathogens in a specific indoor space be measured?

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I wonder if it is possible to measure the concentration of airborne pathogens in a specific indoor space in order to extract a percentage value. This value would be helpful to determine specific infection control strategies that would decrease this percentage and improve the indoor air quality.

I have no idea how practical it would be for your intended application, but people do estimate airborne concentrations of fungi and bacteria by sampling aerosols (i.e., filtering a volume of area to extract dust etc. that can carry the target organisms) and then using quantitative PCR with primers targeted at generic fungal or bacterial DNA sequences to estimate concentrations of particular classes of organisms.

Luhung, Irvan, Yan Wu, Chun Kiat Ng, Dana Miller, Bin Cao, and Victor Wei-Chung Chang. “Protocol Improvements for Low Concentration DNA-Based Bioaerosol Sampling and Analysis.” PLoS ONE 10, no. 11 (November 30, 2015).

Hospital infection control: reducing airborne pathogens

Much attention is focused today on pathogenic microorganisms that have developed resistance to antibiotic treatment, or entire types or classes of antibiotics. The loss of effective antibiotic treatment undermines the ability of healthcare professionals to fight infectious diseases and manage their complications among immunocompromised patients.

The Centers for Disease Control and Prevention (CDC) estimates that more than two million people in the United States are sickened every year with antibiotic-resistant infections, with at least 75,000 dying (2013) as a result. Healthcare associated infections (HAIs) kill more people in this country than AIDS, breast cancer and auto accidents combined.

Healthcare associated infections are also known as nosocomial infections or hospital-acquired infections. They are transmitted by a variety of vectors, including person-to-person, through injection/insertion of medical devices, airborne contact of open wounds, and by respiration of airborne particles. Some emerging diseases, such as Middle East Respiratory Syndrome (MERS) are not yet understood well enough to positively identify the transmission vector.

The most dangerous HAI pathogens are those that have the potential to spread by the airborne route (Kowalski 2006). Many of these pathogens, such as Methicillin-resistant Staphylococcus aureus (MRSA), are now called &ldquosuperbugs&rdquo because they are virtually invincible to standard drug treatments. Favorable indoor environments tend to self-perpetuate these agents, adding to the concern by infection control specialists everywhere.

According to the CDC and World Health Organization, antibiotic-resistant HAIs are on the rise. Support for airborne disease transmission is also on the rise (Fletcher et al. 2003). Evidence exists for airborne nosocomial transmissions of Acinetobacter, Pseudomonas, and MRSA (Allen and Green 1987), (Ryan et al. 2011) and (Farrington et al. 1990), and airborne transmission can spread rapidly and pervasively through a non-immune population (Weinstein 2004).

If mechanical and functional operations have remained unchanged, other sources of drug-resistant contamination must exist, presumably associated also with allusive paths of transmission. Therefore, source and pathway management should involve airborne transmission and especially enhanced methods of its control even though the primary route is considered to be direct contact. This article discusses several control methods.

Infection controls

Addressing infection control in hospitals requires integrating HVAC and air-pressure-control with dedicated infection-control systems, and minimizing unplanned airflows through building envelopes and interior spaces. It also benefits from the application of ultraviolet UV-C equipment from what is typically referred to in the healthcare industry (and the CDC) as ultraviolet germicidal irradiation (UVGI).

There are four methods used to reduce the concentrations of airborne infectious agents: dilution, filtration, pressurization, and disinfection. Following is a brief discussion of each method, with a focus on disinfection.

Dilution ventilation helps to control infectious particles by introducing outdoor air, usually 2 to 5 air changes/hour (ACH), to dilute space air and then exhausting that amount as contaminated air. If 100 percent of all supply air were outdoor air, nearly that amount of airborne infectious particles might be exhausted. However, conditioning that amount of outdoor air would be cost prohibitive and therefore considered out of the question.

The combination of filtration equipment and airflow rates are often misunderstood or underappreciated for the effect they have on the concentration of infectious agents in any conditioned space. Filtration should be considered healthcare&rsquos first line of defense against infectious agents as it removes a large percentage of them with every complete air change through an air handler. If the filter efficiency and/or air change rate is increased, a larger number of infectious agents would be removed per pass.

Therefore, it&rsquos important to view the return air and filtration system as a removal method of space generated contaminants - and not the air distribution side as a pathogen source, with the possible exception of some smaller viral particles. If there is concern here, the application of UV-C in the air handler equipment is warranted.

Current design guidelines suggest air change rates up to 25 (up to five of them as outdoor air) for new facilities, depending on the space served. Because most of the airborne pathogens originate from patients and room occupants, increasing the rates of supply air above design guidelines will bring diminishing returns. Thus, the incremental benefit in preventing cross-transmission is much more difficult to demonstrate beyond 25 ACH. This can be seen in this simple equation:

Concentration (particles/cu.ft)= generation rate (no. of people/activity)

cfm x filter eff. (removal rate)

If either the cfm or filter efficiency is increased, the particle concentrations will decrease mathematically. However, the algorithm favors reducing in-room source or generation rates of infectious agents, often referred to as source control or source reduction.


Pressurization protects against cross contamination from the infiltration of air from one space type to that of another. This is of great importance in healthcare settings, but it is very difficult to control. Frequently opened or propped open doors are all too common making corridors, etc., a conduit of contaminated air to other spaces. Although ORs and other areas are designed to be under positive pressure with respect to external spaces, this may not be the case when an air handler&rsquos airflow has been compromised!

Note that air handler design resistance is the sum of all pressure losses through the system, including elbows, dampers, filters and coils, etc. The shape of a resistance curve will change when pressure losses change (Greenheck 1999). For instance, as filter resistance increases, system air volume is reduced. But, like the filter, it&rsquos also common for the coil pressure drop to increase, even double, which will result in a higher system pressure drop as well. The curves show that as the system&rsquos total resistance increases, air volume and system pressure capability are reduced (at a constant fan RPM). This reduction in &lsquodesign-pressure&rsquo occurs often today from reduced coil cleaning procedures, but its effect is not at all obvious, and it&rsquos not just a reduction in air volume, although that&rsquos important, it&rsquos also a reduction in relative room pressure capability! When one space is said to be negative in relation to other spaces, it assumes that the adjoining spaces are all &lsquopositive&rsquo. A higher coil pressure drop will potentially negate that and permit the infiltration of contaminated air.

Measuring the pressure drop across a coil and comparing it to as-built design data, or better, the manufacturer&rsquos coil performance data, is one way to determine a potential loss in airflow. If the coil pressure drop is higher, the actual airflow should be measured to confirm that it matches as-built criteria. If the coil is fouled (common) and air pressure compromised, the 2011 ASHRAE Handbook &ndash HVAC Applications recommends the installation of UV-C lamps to clean the coil and keep it clean continuously. A clean coil will assure proper airflow and pressure relationships with the added benefit of restoring as-built cooling capacity (i.e., the heat-transfer characteristics). According to ASHRAE, UV-C will also eliminate the growth of coil plenum mold and bacteria removing the possibility of microbial products carryover and transfer to conditioned spaces.


In addition to opening and propping open of doors as a contaminant transfer mode, the entering and exiting of people also provides a contaminant source. It&rsquos known that the concentration of airborne bacteria is proportional to the number of personnel in the room (Mangram et al. 1999, Duvlis and Drescher 1980, Moggio et al. 1979, Kundsin 1976). The amount of surface contamination is also related to airborne contamination from occupation and activity since these microbes settle continuously. The World Health Organization (WHO 1998) recommends a limit of 100 cfu (colony forming units)/m3 for bacteria and 50 cfu/m3 for fungi for sensitive areas. There are no published cfu standards in the U.S.

While the use of UV-C equipment, or disinfection, to control infectious agents in healthcare settings is one of its oldest uses, today the technology is under-utilized. In the past seventy years it has used to disinfect upper air, ventilation air, and to sterilize medical equipment and water, with measured and successful results. However, its use waned as dependence on antibiotics began in the late 1950s and beyond. UV-C destroys all microorganisms&hellip and its use is extremely simple and inexpensive, but like all controls, it alone is not a complete answer.

UV-C for infection control

There are three primary means of applying UV-C systems against infectious agents: upper-air (upper-room), coil irradiation, and airstream disinfection. Upper-air systems are installed in room spaces, such as above patient beds and in waiting rooms, corridors and break areas, etc. Coil irradiation and airstream disinfection systems are installed within air handling units or duct runs. Upper room and HVAC applications are described below.

Upper Air/Room

The primary objective of upper-air UV-C placement is to interrupt the transmission of airborne infectious diseases in patient rooms, waiting rooms and other known microbial pathways such as lobbies, stairwells, laundry chutes, and emergency entrances and corridors, all of which can be effectively and affordably treated with UV-C (ASHRAE 2011). Airborne droplets containing infectious agents can remain in room air for 6 minutes and longer. Upper Air UV-C fixtures can destroy those microbes in a matter of seconds. Operating 24 hours a day, upper-air systems are also especially effective at notably reducing the potential viability of surface microbes that settle out of room air.

Humans are the source of airborne agents which infect people (Nardell and Macher: ACGIH 1999). Again, upper-air systems intercept microbes where they are generated, thereby controlling them at the source (First et al. 1999). They have been shown to be effective against viruses and bacteria, including chickenpox, measles, mumps, varicella, TB, and cold viruses. Studies of Mycobacterium tuberculosis have shown that they can be equivalent to 10&ndash25 air changes per hour (CDC 2005). In a study by Escombe et al. (2009), guinea pigs were exposed to exhaust air from a TB ward, of which 35 percent of the controls developed TB infections while only 9.5 percent developed infections where upper air UV-C was used, yielding a decrease of 74 percent in the infection rate.

Measles and influenza viruses and the tuberculosis bacteria are diseases known to be transmitted by means of shared air between infected and susceptible persons. Studies indicate that there are two transmission patterns: (I) within-room exposure such as in a congregate space (II) transmissions beyond a room through corridors, and through entrainment within ventilation ductwork where air is then recirculated throughout the building. Since the 1930s (Wells 1955 Riley and O&rsquoGrady 1961) and continuing to the present day (Miller et al. 2002 Xu et al. 2003 First et al. 2007), numerous experimental studies have demonstrated the efficacy of upper-air UV-C. In addition, effectiveness has been shown for reducing measles transmission in a school, and influenza transmission within a hospital (McLean 1961). What&rsquos more, newer fixtures available today provide more output and coverage at less cost and power. They also utilize inexpensive and commonly available lamps!

HVAC systems

HVAC systems provide an excellent growth area for mold and some bacteria in and around cooling coils drain pans (Levetin et al. 2001), plenum walls and filters. Growth of these microbial deposits also leads to coil fouling which will increase coil pressure drop and reduce airflow and heat exchange efficiency (Montgomery and Baker 2006). As performance degrades, so does the quality, amount and pressurization capability of air supplied to conditioned spaces (Kowalski 2006/2009).

Because hospital codes call for high-efficiency filters to be located downstream of the cooling coil, they can also become damp and often wet from saturated air in that location. As such, air filters are considered a growth medium for mold and bacteria and an infectious-disease agent reservoir. ASHRAE recommends UV-C lighting to be installed downstream of the cooling coil so if a 360 degree UV-C system is installed there, it will disinfect both the coil and the filter to destroy all microbes in and upon both devices. It should also be noted that when using a 360-degree lamp in a &ldquocommon&rdquo coil-irradiation system, it will also kill infectious diseases in the airstream. For example, up to a 35 percent kill ratio of many infectious agents is achieved, thus providing a measurable increase in the combined removal rate of the two devices (Kowalski 2009).

UV-C design guidance

Importantly, science has not found a microorganism that is resistant to the destructive effects of the 254-nm germicidal wavelength, including superbugs and all other microbes associated with HAIs. The question then &ndash how are UV-C systems sized and applied?

Historically, engineers and facility practitioners wanting to apply UV-C lacked specific guidance for systems design, sizing and specifications. ASHRAE undertook the process by forming a technical committee (TC 2.9 Ultraviolet Air and Surface Treatment) to author chapters in their 2008, 2011, and 2012 ASHRAE Handbooks, which have been referenced herein. HVAC trade publications have also published several technical articles to help provide additional design guidance these articles are cited in the sidebar, Technical Articles for Engineers. In total, these articles provide all practitioners with the guidance needed to successfully design, install, operate, and maintain successful UV-C applications in HVAC systems.

UV-C at large

UV-C's rising popularity beyond ASHRAE has also generated research by lesser-known organizations, such as the Air Purification Consortium (APC) the Air Cleaning Industry Expert Advisory Panel (ACIEAP) and The National Center for Energy Management and Building Technologies (NCEMBT). UV-C energy has been crucial to achieving each of their goals, whether to save energy, reduce biological contamination and maintenance or to reduce absenteeism. Their members are involved in high-stakes projects such as Homeland Security where application of UV-C is a crucial defense against bioterrorism.

Opening air handler doors to fan sections must be minimized because it allows unfiltered air to enter and be dispersed to potentially sensitive areas, and/or it will disrupt pressure relationships in the spaces served by them. Shutting these systems down can also disrupt pressure relationships beyond the spaces served. Both of these functions, when necessary, should be coordinated with floor nurses so that all room doors may be closed before hand. Exposing filter surfaces to UV-C is an effective way to destroy microbes on media surfaces. However, synthetic media filters are not compatible with UV-C while filters with glass media are. Caution should also be exercised when using unsupported &ldquobag&rdquo style filters as they inherently collapse when being replaced to expel potentially microbe- laden contamination. The CDC also recommends that all used filters be bagged upon removal to prevent dispersion of microbial contamination during transport.

Where installed, facility staff should be trained how to inspect UV-C systems to ensure they are working properly. Controls should be installed to turn UV-C systems off when air-handler doors are opened. Eye and skin protection are needed to prevent exposure to UV-C light when working in any area where the lamps are on.

UV-C lamps are very similar in construction to fluorescent lamps, and therefore contain trace amounts of mercury. The use of encapsulated lamps is recommended to prevent air-handler contamination should lamp breakage occur. Like fluorescent lamps, UV-C lamps should be replaced and recycled annually in a scheduled fashion.

UV-C installations are a simple, effective, and relatively inexpensive means of reducing concentrations of airborne and surface pathogens that cause healthcare associated infections. Within patient rooms, waiting rooms, and other congregational areas, upper-air UV-C units will kill airborne microorganisms that inherently circulate into the path of the UV-C light. UV-C lamps can be installed within HVAC systems downstream of cooling coils to keep coils clean and to provide supplemental kill ratios in airstreams and on filter surfaces. Recent guidance from ASHRAE and published technical articles in HVAC trades provide healthcare engineers and facility staff with the resources needed to size, select, install, operate, and maintain UV-C systems.

Design considerations

&bull Concentration of airborne infectious agents is directly related to people activity

&bull Humans are the source of drug resistant microorganisms that effect humans

&bull Airborne transmission of infectious agents may be more prevalent than proven

&bull UV-C inactivates and destroys microorganisms rendering them harmless

&bull 70+ year old Upper Air UV-C technology is heavily researched and proven effective

&bull Newer Upper Air UV-C units are more affordable and much more effective

&bull At 6 ACH an aerosol of infectious agents can stay airborne for 10 minutes

&bull Upper Air UV can inactivate airborne infectious agents in a matter of seconds

&bull Source management will always prove to be the most effective means of control

&bull Increased coil pressure drop will lower system airflow and space pressurization

&bull Bathing coils with UV-C cleans them, improves airflow and heat transfer efficiency

Additional Tips

&bull Install UV-C on cooling coils and drain pans to kill mold and restore airflow

&bull Manage room pressure relationships, especially during visiting hours

&bull Review and manage all air handler service, especially filters and change-outs

&bull Manage air handler shutdowns, access door openings and coil pressure drop

&bull Install newer style upper air UV-C fixtures in all spaces known for HAI&rsquos

&bull Also install them in corridors and waiting rooms connected to these areas

&bull Once airflow is restored, upgrade air filters and efficiencies where possible

Concentrations and size distributions of airborne influenza A viruses measured indoors at a health centre, a day-care centre and on aeroplanes

The relative importance of the aerosol transmission route for influenza remains contentious. To determine the potential for influenza to spread via the aerosol route, we measured the size distribution of airborne influenza A viruses. We collected size-segregated aerosol samples during the 2009–2010 flu season in a health centre, a day-care facility and onboard aeroplanes. Filter extracts were analysed using quantitative reverse transcriptase polymerase chain reaction. Half of the 16 samples were positive, and their total virus concentrations ranged from 5800 to 37 000 genome copies m −3 . On average, 64 per cent of the viral genome copies were associated with fine particles smaller than 2.5 µm, which can remain suspended for hours. Modelling of virus concentrations indoors suggested a source strength of 1.6 ± 1.2 × 10 5 genome copies m −3 air h −1 and a deposition flux onto surfaces of 13 ± 7 genome copies m −2 h −1 by Brownian motion. Over 1 hour, the inhalation dose was estimated to be 30 ± 18 median tissue culture infectious dose (TCID50), adequate to induce infection. These results provide quantitative support for the idea that the aerosol route could be an important mode of influenza transmission.

1. Introduction

Influenza A viruses (IAVs) are transmitted through direct contact, indirect contact, large respiratory droplets and droplet nuclei (aerosols) that are left behind by the evaporation of larger droplets. The relative importance of each of these routes remains contentious. The aerosol transmission route has been particularly controversial since there is scant direct proof of infection mediated by virus-laden aerosols, partly owing to the difficulties in studies involving human subjects and partly owing to the challenges in detecting IAVs in ambient air [1–3].

Virus-laden aerosols may be released into air when infected people cough, sneeze, talk or breathe however, the aerosols are quickly diluted by ambient air to extremely low concentrations [4]. In addition, the relatively insensitive culture methods to detect viruses, potential inactivation during aerosol sampling and inhibition of detection methods by airborne contaminants present challenges to the measurement of airborne IAVs [1,5]. Consequently, despite the rapid development of detection methods for IAVs in clinical and laboratory settings, there are still very few measurements of them in the airborne environment. Even fewer studies have determined the size of influenza virus-laden particles, which is important because it determines how long particles will remain suspended in air before being removed by gravitational settling or Brownian diffusion, and where they will deposit in the respiratory system.

Quantitative reverse transcriptase–polymerase chain reaction (qRT–PCR), based on the detection of viral RNA, affords a sensitive and rapid approach for quantifying low levels of viruses. Chen et al. [6] applied this method to detect IAVs in a live poultry market, but their sampling method did not discriminate by particle size. Using qRT–PCR, Blachere et al. [7] measured aerosolized influenza viruses in a hospital emergency department for six days. Eighty-one air samples were collected with a modified National Institute for Occupational Safety and Health two-stage cyclone sampler that separated the aerosols into greater than 4, 1–4 and less than 1 µm fractions, and IAV RNA was detected in 11 of the samples. They found that 46, 49 and 4 per cent of the IAVs were collected in each of the size ranges, respectively. A more extensive follow-up study by Lindsley et al. [8] reported that IAVs were detected on 10 out of 11 days, with 17 per cent out of 385 samples confirmed to contain IAV RNA. Of the detected IAV RNA, 42 per cent was associated with particles 4.1 µm or less.

Public places with a susceptible population and/or a high population density, such as hospitals, day-care centres and aeroplanes, may harbour high concentrations of pathogens. Of 218 surfaces (toys, nappy-changing areas, toilet seat tops, etc.) tested in 14 different day-care centres, Boone & Gerba [9] detected influenza viruses on 23 and 53 per cent of the samples during autumn and spring, respectively. Infected individuals on an aeroplane may spread the influenza virus to other passengers [10]. The Alaska Airlines outbreak [11] has been presented as proof of airborne influenza transmission: a jet with 54 persons aboard was delayed on the ground for 3 h (during which the aeroplane ventilation system was inoperative), and 72 per cent of the passengers who stayed on the aeroplane were infected by an influenza-contracted passenger within 72 h.

To evaluate the prevalence of airborne IAVs in high-risk, public spaces, we collected aerosol samples from a health centre, a day-care facility and onboard three commercial passenger aeroplane flights during the 2009–2010 flu season. Particles were divided into five size fractions, and IAVs in each were analysed using qRT–PCR. The indoor influenza virus emission strength, the deposition flux onto the wall surfaces and risk for airborne infection were then estimated using our experimental data.

2. Material and methods

2.1. Reference viruses

Reference strains of influenza A were from our collection at the Department of Biomedical Sciences and Pathology, Center for Molecular Medicine and Infectious Disease at Virginia Tech. Prototype strains used to develop the qPCR method were A/PR/8/34 (H1N1) and A/swine/Minnesota/1145/2007 (H3N2). These two strains were used to construct and test the qRT–PCR concentration standards.

2.2. Detection of viral genome

2.2.1. Viral genomic RNA extraction

Influenza virus RNA collected on the filters was extracted using a Trizol–chloroform-based method modified from a protocol reported elsewhere [12,13]. Briefly, the filter was rolled and put into a 2 ml microcentrifuge tube containing 250 µl of phosphate-buffered saline (PBS) supplemented with 20 µg of glycogen (Ambion, TX, USA), 15 µg of glycoblue (Ambion) and 50 ng of human genomic DNA (Cat. no. 636401, Clontech Laboratories, Inc., CA, USA). A volume of 750 µl of Trizol LS (Invitrogen, CA, USA) was added, and the sample was vortexed thoroughly and incubated at room temperature for 10 min. The sample was then briefly centrifuged, and the supernatant was transferred to a 1.5 ml microcentrifuge tube, to which 230 µl of chloroform was added (Sigma-Aldrich, MO, USA). The sample was briefly vortexed, incubated at room temperature for 5 min and then centrifuged at 2100g for 5 min. The colourless upper aqueous phase was carefully transferred to a new 1.5 ml tube containing 600 µl of isopropanol (Sigma-Aldrich) for RNA precipitation for 1 h. Then, the RNA was pelleted by centrifuging for 12 min at 20 000g and was washed with 600 µl of 75 per cent ethanol. The RNA was finally dissolved in 20 µl of diethylpyrocarbonate-treated water (Sigma-Aldrich) and immediately converted to complementary DNA (cDNA) or stored at −80°C until use.

2.2.2. Reverse transcription

cDNA was generated with a TaqMan Reverse Transcription Reagents Kit (N8080234, Applied Biosystems, CA, USA) according to the manufacturer's instructions. A 20 µl reaction mixture was made with a final concentration of 1× TaqMan RT buffer, 5.5 mM of Mg 2+ , 500 µM of each dNTP, 2.5 µM of RT random hexamer primers, 0.4 U µl −1 of RNase inhibitor and 1.25 U µl −1 of MultiScribe Reverse Transcriptase, plus 7.7 µl of RNA. cDNA synthesis was carried out on a thermal cycler (1000-Series Thermal Cycling Platform, Bio-Rad, USA) at 25°C for 10 min, 48°C for 30 min and 95°C for 5 min.

2.2.3. Quantifying standard and standard curve preparation

The cDNA standard solution was constructed by ligation of the targeted gene fragment in a pCR2.1-TOPO vector according to the instructions of the TOPO TA Cloning Kit (Invitrogen). Two sets of IAV primers, one reported by Ward et al. [14] and the other by van Elden et al. [15], are widely used to detect the M1 protein gene of IAVs [6,14,16]. The primers by Ward et al. [14] have proved to be applicable for the currently circulating A (H3N2), seasonal A (H1N1) and pandemic A (H1N1) strains [17,18]. The genomic regions amplified by these two sets of primers are partially overlapping (table 1). We used the forward primer reported by Ward et al. [14] and the reverse primer reported by van Elden et al. [15] to amplify a segment that spans both genomic regions. The amplicon obtained for cloning was a 262 bp segment by RT–PCR from stocks of A/swine/Minnesota/1145/2007 (H3N2). The ligation plasmids were transformed into competent E. coli cells, and recombinant bacteria were selected on kanamycin-containing LB agar. Positive inserts were amplified by M13 primers embedded within the pCR2.1-TOPO vector according to the manufacturer's protocol. The resulting PCR products were sequenced and confirmed to be the target IAV gene fragment. The PCR products were quantified by a Molecular Imager Gel Doc XR system (Bio-Rad) and were used as the cDNA standard for qPCR. A standard stock solution was prepared at a concentration of 10 10 genome copies µl −1 . It was tested and confirmed to quantify successfully both the H3N2 and the H1N1 influenza virus strains.

Table 1. Primers and probes of influenza A virus.

a Aligned with A/PR/8/34 (H1N1) segment 7 (M gene).

The calibration curve was generated using serial 10-fold dilutions of the standard solution from 10 7 to 10 genome copies µl −1 in triplicate. A standard curve was generated each time that field samples were quantified, and the amount of IAV genome in field samples was determined according to the linear regression of cycle threshold (Ct) values against the known log concentrations (C0). Autoclaved ultrapure water (NANOpure Ultrapure Water System, Barnstead/Thermolyne, IA, USA) was used as a qPCR negative control during each run.

2.2.4. Quantitative polymerase chain reaction

The qPCR assay was performed in 96-well reaction plates (MicroAmp Optical, Applied Biosystems) on a 7300 Real Time PCR System (Applied Biosystems). Two sets of primers [14,15] were tested using an SYBR Green PCR Master Mix Kit (Applied Biosystems). The qPCR mixture consisted of a final concentration of 1× SYBR Green Master Mix, 200 nM of each primer, 5 µl of cDNA and autoclaved ultrapure water to bring the qPCR reaction volume to 25 µl. Cycling conditions were one cycle of AmpliTaq Gold enzyme activation at 95°C for 10 min, 40 cycles of denaturation of DNA at 95°C for 15 s and annealing and extension at 60°C for 1 min. The amplification was followed by a melting curve analysis with a dissociation stage from 60°C to 95°C.

The standard and Ward's primers were further tested with a TaqMan One-Step RT–PCR Master Mix Reagents Kit (Applied Biosystems), and the influenza A probe (6-FAM-5′ TTT GTG TTC ACG CTC ACC GT 3′- Black Hole Quencher 1) [14] was used. One-step RT–PCR was performed in 25 µl consisting of a final concentration of 1× Master Mix without UNG, 1× MultiScribe and RNase Inhibitor Mix (0.25 and 0.4 U µl −1 , respectively), 900 nM of each primer and 225 nM of the influenza A probe, plus 3 µl of viral RNA. The reaction mixture was held at 48°C for 30 min for cDNA synthesis, 95°C for 10 min for AmpliTaq Gold enzyme activation and 40 two-step cycles followed (95°C for 15 s for denaturation and 60°C for 1 min for primer annealing and extension). All qPCR assays were run in triplicate.

2.3. Virus spike recovery experiments

Virus spike recovery experiments were conducted to test the recovery efficiencies of the viral genome from the filters used to collect ambient particle samples and the PBS buffer used for RNA extraction. Two polytetrafluoroethylene (PTFE) filters, 25 and 37 mm in diameter (Cat. no. 225-1708 and 225-1709, SKC Inc., PA, USA), were used for sample collection. The H1N1 virus stock was diluted 2 × 10 −2 with autoclaved ultrapure water and used as a spiking solution. Filters were spiked with 50 µl of virus solution (2.5 µl per droplet, 20 droplets total for the 25 mm filter, and 5 µl per droplet, 10 droplets total for the 37 mm filter). Because the 37 mm filter was especially hydrophobic, the droplet volume had to be increased to 5 µl for it to be taken up from a pipette. For the PBS buffer, 50 µl of the virus solution was spiked into a 2 ml microcentrifuge tube containing 200 µl of PBS buffer (referred to as ‘PBS control’ hereafter). To test for possible decay of the virus with time, 50 µl of the virus solution was added into a 2 ml microcentrifuge tube without PBS (referred to as ‘decay control’ hereafter). All samples were placed in a biological safety cabinet for 2 h, allowing the virus solution droplets on the filters to dry out. The temperature in the cabinet was maintained at approximately 20°C. Aliquots of 50 µl of the same virus solution used for spiking were stored at 4°C for quantification of the spiked amount. All samples were supplemented with PBS to a final volume of 250 µl and subjected to viral RNA extraction as described above. All tests were conducted in duplicate.

2.4. Field sample collection

2.4.1. Sampling locations

Samples were collected from a health centre at Virginia Tech, a day-care centre in Blacksburg, Virginia, and aeroplanes corresponding to three cross-country flights between Roanoke and San Francisco. The health centre samples were collected from a waiting room, which is a semi-open space about 8.5 × 5 m. The mean indoor temperature (±s.d.) was 22.0 ± 1.0°C, with a mean relative humidity of 34.5 ± 11.4%. Design room air exchange rates (AERs) were 8–12 air changes h −1 (ACH). The day-care centre samples were collected in two toddlers' rooms and a babies' room. Each of the toddlers' rooms is about 8 × 4 m and holds 16 children plus four adults, and the babies' room is about 8 × 3.5 m and holds 12 children and four adults. The mean indoor temperature in the toddlers' rooms was 22.8 ± 1.7°C, with a mean relative humidity of 40.6 ± 5.1%. The mean indoor temperature in the babies' room was 25.1 ± 1.1°C, with a mean relative humidity of 32.9 ± 2.0%. The mean temperature in the aeroplanes (between Roanoke and San Francisco with a stopover) was 23.6 ± 3.1°C, with a mean relative humidity of 27.1 ± 11.9%. The ventilation systems were operating properly during all sampling periods.

2.4.2. Sample collection

A total of 16 samples were collected between 10 December 2009 and 22 April 2010, of which nine were collected from the health centre, four from the day-care centre and three from aeroplanes. A cascade impactor (Sioutas Cascade Impactor, SKC Inc.) and a pump running at 9 l min −1 (Leland Legacy, SKC Inc.) were used to collect the samples over 6–8 h. The impactor consists of four stages that allow the separation and collection of airborne particles in five size ranges: greater than 2.5, 1.0–2.5, 0.5–1.0, 0.25–0.5 and less than 0.25 µm. Particles larger than each cut-point were collected on 25 mm PTFE filters (Cat. no. 225-1708, SKC Inc.) those smaller than the 0.25 µm cut-point of the last stage were collected on a 37 mm PTFE after-filter (Cat. no. 225-1709, SKC Inc.).

In the health centre, the sampler was placed on a desk (approx. 0.5 m high) around which patients sit while waiting in the day-care centre, the sampler was placed on a shelf (approx. 1.0 m high) and on aeroplanes, it was placed near the seat pocket (less than 0.5 m high). Temperature and relative humidity were recorded every 2 min during sampling (OM-73, Omega Engineering, Inc., USA). After each sampling period, the impactor was washed with 10 per cent bleach, cleaned with ultrapure water and autoclaved (121°C, 30 min). New filters were loaded into the impactor, left overnight before sampling and used as device blank controls. Only those with results confirmed to have no detectable influenza virus RNA in the device blank controls were adopted.

3. Results

3.1. Quantitative reverse transcriptase–polymerase chain reaction

We tested two sets of influenza virus primers that have been widely cited in the literature [4,6,14–16]. Both sets of primers proved to be adequate they were able to specifically amplify the target gene segments from the H1N1 and H3N2 strains, as indicated by the dissociation curve (data not shown). We tested the efficiency of primers to detect a field isolate of the H3N2 and pandemic H1N1 strains. Ward's primers achieved better qPCR efficiency (91 versus 61%) and a lower detection limit (100 genome copies per reaction versus 1000 genome copies per reaction) than did van Elden's. Hence, we used Ward's primers in subsequent experiments. TaqMan qRT–PCR showed that with Ward's primers, the detection limit was 10 genome copies per reaction, with an efficiency around 100 per cent and R 2 > 0.99 for our samples.

3.2. Virus spike recovery experiments

According to qRT–PCR results, each spiked sample contained approximately 2.4 × 10 7 genome copies of the H1N1 virus. The recovery efficiencies were calculated by dividing the amount of virus detected by the number spiked into each sample (filter, PBS control or decay control). Results are reported in table 2. The viral genome recovery efficiencies were 40.5 per cent from the 25 mm filter (p = 0.00011), 62.0 per cent from the 37 mm filter (p = 0.0058), 86.6 per cent from the PBS-control samples (p = 0.077) and 91.2 per cent from the decay-control samples (p = 0.23). The control experiments showed that PBS had no significant adverse effect on the viral genome, and the natural decay of virus genome was insignificant within a 2 h period. By contrast, the recovery efficiencies from the two filters were significantly less than 100 per cent. During the RNA extraction step, only 800–900 µl of Trizol lysate was retrieved for phase separation by chloroform, with 100–200 µl of lysate retained by the filter. This loss accounts for a portion of the incomplete recovery from the filter.

Table 2. Virus recovery efficiency from PTFE filters and control solutions (virus solution in PBS and virus solution only) spiked with 2.4 ± 0.1 × 10 7 genome copies. Samples were incubated for 2 h and then analysed by qRT–PCR. Recovery efficiencies were significantly less than 100% with both filters.

a Recovery efficiency significantly less than 100%.

3.3. Concentrations of airborne influenza A viruses

Between 10 December 2009 and 22 April 2010, we collected 16 samples, listed in table 3. Half of the samples were confirmed to contain aerosolized IAVs: 33 per cent of the health centre samples (three of nine), 75 per cent of the day-care centre samples (three of four) and 67 per cent of the aeroplane samples (two of three). Concentrations in all of the field and laboratory blanks were below the detection limit. In the samples containing detectable amounts of IAVs, the average concentration was 1.6 ± 0.9 × 10 4 genome copies m −3 .

Table 3. Average ambient relative humidity and temperature and total airborne influenza A virus concentration in each of 16 samples. Humidity and temperature were recorded every 2 min, and each sample was collected using a cascade impactor for 6–8 h. IAV RNA was extracted from filters and quantified by qRT–PCR. It was not detected in half of the samples. In the samples containing detectable amounts of IAV, the average concentration was 1.6 ± 0.9 × 10 4 genome copies m −3 .

a Not available owing to a logging error.

b No detectable influenza A virus genome.

3.4. Virus-laden particle size distribution

The cascade impactor separated particles into five size fractions: greater than 2.5, 1.0–2.5, 0.5–1.0, 0.25–0.5 and less than 0.25 µm. The amounts of virus found in each fraction, summed over all samples, were 36, 28, 11, 10 and 15 per cent, respectively. As shown in figure 1, the virus-laden particle size distributions of the eight positive samples were diverse, and no obvious trend was observed. In some cases, the virus was relatively evenly distributed across the different particle sizes, while in others, it was found predominantly in the smallest and largest, or just the largest, size fractions.

Figure 1. Airborne IAV particle size distributions in each positive sample (date and location shown at top). Aerosol samples were collected over 6–8 h in each location using a cascade impactor with cut-point diameters of 0.25, 0.5, 1.0 and 2.5 µm. The y-axis indicates the percentage of total virus genome copies found in each size range. In seven of the eight cases, the majority of viruses were associated with fine particles smaller than 2.5 µm, which can remain suspended for hours, but there were no obvious trends in size distributions across different samples.

3.5. Indoor influenza virus emission and deposition flux by modelling

3.5.1. Indoor influenza A virus emission rate

To estimate the emission source strength of IAVs in airborne particles, we developed a mass-balance model. The model assumes that well-mixed, steady-state conditions apply at each sampling site: in a room of volume V (m 3 ), air flows in and out through the heating, ventilating and air-conditioning system with a flow rate of Q (m 3 h −1 ). Aerosolized viruses are generated by the occupants with an emission rate of E (genome copies h −1 ), disperse into ambient air and become well mixed immediately upon release. Assuming that the virus concentration in the air entering the room is zero, the indoor virus concentration is maintained at C (genome copies m −3 ) and the outlet virus concentration is also C, we establish the mass balance for the modelled room:

Typical AERs are 13 ACH in hospitals, 9 ACH in schools and 4 ACH in commercial offices [19]. The AER in commercial aircraft is usually higher with a typical value of 15 ACH [20]. Therefore, we adopt an AER of 10 ± 5 ACH to estimate the IAV emission rate in these public places, and for a measured indoor virus concentration of 1.6 ± 0.9×10 4 genome copies m −3 , the emission rate EV is 1.6 ± 1.2 × 10 5 genome copies m −3 h −1 .

3.5.2. Influenza A virus deposition on surfaces by Brownian motion

Applying the well-mixed model, we assume the virus-laden particles are evenly distributed throughout the room, except in a thin boundary layer alongside each wall surface, across which the virus-laden particles diffuse by Brownian motion and finally deposit onto the surface. The deposition flux can be calculated according to Fick's law:

We sum over all particle sizes and use the midpoint diameter of each range, assuming a minimum of 0.1 µm for the smallest one and a maximum of 10 µm for the largest one, to calculate the diffusion coefficients. Based on our measurements, the total diffusive flux of viruses to indoor surfaces is 13 ± 7 genome copies m −2 h −1 . This flux is sufficiently small that it can be neglected in the previous mass–balance model used to estimate the virus emission rate.

4. Discussion

4.1. Influenza A virus concentrations and size distributions in indoor facilities

To our knowledge, there have been only a few studies on the presence of airborne IAVs in a healthcare environment, and no airborne IAV detection has been reported in day-care centres or onboard passenger aeroplanes. Blachere et al. [7] reported airborne IAV concentrations in a health centre ranging from 460 to 16 278 median tissue culture infectious dose (TCID50)-equivalent RNA particles for an entire sample. The sampling time and flow rate were 3–5 h and 3.5 l min −1 , respectively, and if we assume a total sample volume of 840 l of air (4 h at 3.5 l min −1 ), then the corresponding concentrations were 5.5 × 10 2 to 1.9 × 10 4 TCID50-equivalent RNA particles m −3 . Their PCR was calibrated in TCID50 by using serial dilutions of a live-attenuated influenza virus quantified in TCID50 ml −1 . Lindsley et al. [8], in a more detailed study in the same clinic, detected 1.2 ± 4.4 pg RNA m −3 in examination rooms, 1.1 ± 3.0 pg RNA m −3 in procedure rooms and 0.3 ± 4.3 pg RNA m −3 in a waiting room. These values can be converted to 5.0 ± 18.5 TCID50 m −3 , 4.6 ± 12.6 TCID50 m −3 and 1.3 ± 18.1 TCID50 m −3 , respectively, using the ratio of approximately 4.2 TCID50 FluMist vaccine pg −1 RNA reported in the study.

The ratio of viral particles to TCID50 can vary greatly depending on types of viruses (even for strains of the same type), culture methods and conditions (e.g. culturing cells, media and harvest time). For influenza viruses, this ratio has been reported to be in the range of hundreds to thousands: Fabian et al. [12] established a ratio of 300 copies Ward et al. [14] determined that 1000 genome copies ml −1 corresponded to 1 TCID50 ml −1 and Poon et al. [22] estimated that 1 TCID50 of A/California/04/2009 (H1N1) contained approximately 5000 copies of the M gene. In our experiment, 1 PFU of A/PR/8/34 (H1N1) stock was equivalent to 3 × 10 3 genome copies, or approximately 2.1 × 10 3 genome copies per TCID50 according to the relationship between TCID50 and PFU [23], and the ratio for the pandemic A/California/04/2009 (H1N1) strain was determined to be 452 ± 84 copies/TCID −1 50. Based on this ratio (i.e. 452), our results from the health centre correspond to airborne IAV concentrations of 12.8–81.9 TCID50 m −3 , one to two orders of magnitude lower than those observed by Blachere et al. [7] but slightly higher than those reported by Lindsley et al. [8]. With respect to size distributions, we found a larger fraction of total genome copies to be associated with fine particles: 80 per cent with particles smaller than 2.5 µm versus 53 per cent [7] and 42 per cent [8] with particles smaller than 4.1 µm.

For the three positive samples from the day-care centre, the total concentrations ranged from 1.6 × 10 4 to 3.7 × 10 4 genome copies m −3 , half of which were associated with particles greater than 2.5 µm and the other half with smaller particles. The average concentration in the day-care centre was nearly two times higher than that in the health centre. Considering that children are the primary susceptible population of influenza, the difference is not surprising. In addition, the IAV size distributions in the day-care centre differed from those in the health centre: a larger portion of genome copies was found in particles greater than 2.5 µm (50 versus 20%). This discrepancy could originate from the ways that viruses were released (from coughing, sneezing, talking or breathing) and/or differences in the droplet size distribution of different age groups [24]. Viruses were probably released from latent subjects in the day-care centre (children are sent home as soon as symptoms are apparent), whereas those released in the health centre are assumed to come from symptomatic patients. Whether there are any differences between virus-laden particles released at different stages of infection and between hosts of different age warrants further investigation.

Virus concentrations of the two positive aeroplane samples were very similar (1.4 × 10 4 and 1.1 × 10 4 genome copies m −3 ), and virus-laden particles were relatively evenly distributed across each size fraction (figure 1e,f). It is possible that the diverse ages of aeroplane passengers evened out the difference observed in particular groups such as college students in the university health centre and children in the day-care centre.

Although this discussion has focused on the positive results, half of the samples were negative for IAVs. These negative results could be attributable to inhibitors to qRT–PCR, as observed by Chen et al. [6]. However, since the RNA extraction method adopted in this study has the potential to eliminate such inhibitions [4], it is more likely that in these instances, there were no infected individuals in the sampling locations or that concentrations were below the detection limit. The fact that the total virus concentration in the measurable samples ranged over a factor of only six, rather than orders of magnitude, seems surprising but could possibly be explained by the presence of only one or two infected individuals and similar AERs in each setting.

While the qPCR method is a powerful tool for determining the presence of viral genomic material, it does not indicate whether the virus is viable or not. Therefore, the results presented here are an upper limit on the concentration of viable viruses. On the other hand, the recovery efficiency of viruses spiked onto filters was roughly 50 per cent across the two types of filters used in this study, so the reported concentration of genome copies may be underrepresented by a factor of approximately 2. However, the true recovery efficiency is unknown because sample collection by impacting particles onto filters is not equivalent to spiking them onto filters from solution. Additionally, viral RNA may be subject to decay during extended sampling times. One important question to address in the future that could not be answered by qPCR is, ‘Are the viruses found across different sizes of particles equally viable, or are those in one size fraction more so?’.

4.2. Risk of airborne influenza A virus infection

Assuming a uniform airborne IAV concentration of 1.6 ± 0.9 × 10 4 copies m −3 air (corresponding to 35.4 ± 21.0 TCID50 m −3 air) and a adult breathing rate of 20 m 3 d −1 [25], we estimate the inhalation doses during exposures of 1 h (for example, the duration of a clinical visit), 8 h (a workday) and 24 h to be 30 ± 18, 236 ± 140 and 708 ± 419 TCID50, respectively. Compared with the human infectious dose 50 per cent (ID50) by aerosols of 0.6–3 TCID50 [26], these doses are adequate to induce infection. In most instances, the measured concentration of airborne IAVs could be either over- or underestimated based on the sensitivity of the qRT–PCR assay. However, it is not our intent to imply that all the estimated amounts of airborne viral particles are infectious. Our results allow an accurate estimate of exposure to viral particles in air.

While illustrative, this calculation is subject to several limitations. First, the conversion from genome copies to TCID50 is based on the ratio determined with the laboratory strain A/California/04/2009 (H1N1) rather than with samples from the field, where infectivity may decay owing to environmental factors such as temperature, humidity and UV radiation. Therefore, a more sensitive method that can determine the infectivity of IAVs is needed to assess the exposure risk more accurately.

Second, the exposure doses calculated above are cumulative over 1–24 h, while the ID50 measured by Alford et al. [26] is based on inoculations completed within 1 min. In reality, viruses depositing within the respiratory tract will be cleared by mucociliary action instead of simply accumulating at the deposition sites [27]. Therefore, the exposure doses calculated in this study provide only the simplest estimation of the amount inhaled. Models taking into account particle deposition efficiency within the respiratory tract as well as host defence mechanisms are needed to estimate the infection risk more accurately. The deposition efficiency of particles can range from less than 1 per cent to nearly 100 per cent, depending on particle size, density, airway geometry and the individual's breathing pattern [21].

Third, the assumption of uniform concentrations throughout a room is complicated when accounting for true dispersion patterns from a point source and the existence of non-homogeneous ventilation. Finally, only IAVs were measured in this study, so it did not account for infection risk owing to the influenza B virus (IBV). However, according to the nationwide Weekly Influenza Surveillance by the US World Health Organization and National Respiratory and Enteric Virus Surveillance System [28], the IBV accounted for only 0–3.3% of samples that tested positive for influenza viruses during the period 4 October 2009 to 10 April 2010. Therefore, underestimation owing to exclusion of IBVs may be negligible.

On the other hand, the virus deposition flux to surfaces was estimated to be only 13 ± 7 genome copies m −2 h −1 . Over an 8 h workday, 106 ± 60 genome copies m −2 could accumulate on surfaces. This analysis suggests that all surfaces, not just those that come into direct contact with an infected host, can harbour influenza viruses, although they are not expected to survive beyond 2–3 days [29]. However, the amount deposited on surfaces via Brownian motion seems unlikely to produce infectious doses, as the surface–hand–nasal mucosa route requires transferring at least 10 4 TCID50 from the surface [29]. This estimation does not account for deposition owing to gravitational settling, which is important for larger particles and can result in higher deposition fluxes in the vicinity of emission.

5. Conclusions

The concentrations and size distributions of airborne influenza viruses were measured in a health centre, a day-care facility and aeroplanes by qRT–PCR. During the 2009–2010 flu season, 50 per cent of the samples collected (8/16) contained IAVs with concentrations ranging from 5800 to 37 000 genome copies m −3 . On average, 64 per cent of virus-laden particles were found to be associated with particles smaller than 2.5 µm, which can remain airborne for hours. Modelling of virus concentrations indoors suggests a source strength of 1.6 ± 1.2 × 10 5 genome copies m 3 h −1 and a deposition flux onto surfaces of 13 ± 7 genome copies m −2 h −1 . Doses of 30 ± 18, 236 ± 140 and 708 ± 419 TCID50 were estimated for 1, 8 and 24 h exposures, respectively. As a whole, these results provide quantitative support for the possibility of airborne transmission of influenza.


The authors gratefully acknowledge the Dutch COVID-19 response team consisting of colleagues from RIVM-LCI, RIVM-Centre for Infectious Diseases, Epidemiology and Surveillance (EPI), and Erasmus Medical Centre. Furthermore, the authors express gratitude to the RIVM COVID-19 molecular diagnostic team of Centre for Infectious Diseases Research, Diagnostics and Laboratory Surveillance (IDS) for use of diagnostic data on Dutch COVID-19 patients. The authors thank Municipal health services and hospital labs for sending in specimens of suspected COVID-19 patients and providing additional clinical data. Special thanks are extended to H. Vennema for fruitful discussions. The authors thank anonymous reviewers for valuable comments on this work. This work was funded by the Dutch Ministry of Health, Welfare, and Sports.


In the case of airborne diseases, pathogen copies are transmitted by droplets of respiratory tract fluid that are exhaled by the infectious that stay suspended in the air for some time and, after partial or full drying, inhaled as aerosols by the susceptible. The risk of infection in indoor environments is typically modelled using the Wells-Riley model or a Wells-Riley-like formulation, usually assuming the pathogen dose follows a Poisson distribution (mono-pathogen assumption). Aerosols that hold more than one pathogen copy, i.e. poly-pathogen aerosols, break this assumption even if the aerosol dose itself follows a Poisson distribution. For the largest aerosols where the number of pathogen in each aerosol can sometimes be several hundred or several thousand, the effect is non-negligible, especially in diseases where the risk of infection per pathogen is high. Here we report on a generalization of the Wells-Riley model and dose-response models for poly-pathogen aerosols by separately modeling each number of pathogen copies per aerosol, while the aerosol dose itself follows a Poisson distribution. This results in a model for computational risk assessment suitable for mono-/poly-pathogen aerosols. We show that the mono-pathogen assumption significantly overestimates the risk of infection for high pathogen concentrations in the respiratory tract fluid. The model also includes the aerosol removal due to filtering by the individuals which becomes significant for poorly ventilated environments with a high density of individuals, and systematically includes the effects of facemasks in the infectious aerosol source and sink terms and dose calculations.

Citation: Nordsiek F, Bodenschatz E, Bagheri G (2021) Risk assessment for airborne disease transmission by poly-pathogen aerosols. PLoS ONE 16(4): e0248004.

Editor: Ivan Kryven, Utrecht University, NETHERLANDS

Received: November 27, 2020 Accepted: February 17, 2021 Published: April 8, 2021

Copyright: © 2021 Nordsiek et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the manuscript and its Supporting information files.

Funding: This work has been funded by the BMBF as part of the B-FAST (Bundesweites Netzwerk Ange- wandte Surveillance und Teststrategie) project (01KX2021) within the NUM (Netzwerk Universitätsmedizin) and the Max Planck Society. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Health Effects of Inhalable Particles

Exposure to inhalable particles can affect both your lungs and your heart. Many studies directly link the size of particles to their potential for causing health problems. Small particles (less than 10 micrometers in diameter) can get deep into your lungs, and some may even get into your bloodstream. People with heart or lung diseases such as coronary artery disease, congestive heart failure, and asthma or chronic obstructive pulmonary disease (COPD), children and older adults may be at greater risk from PM exposure. Scientific studies have linked PM exposure to a variety of health impacts, including:

  • Eye, nose and throat irritation
  • Aggravation of coronary and respiratory disease symptoms and
  • Premature death in people with heart or lung disease.


Recent advances in high-throughput sequencing have generated a rush to characterize the microbiome of various environments, including indoor and outdoor air [1–4]. The built environment is of particular interest because humans spend over 90 % of their time indoors [5]. Researchers have observed that microbial communities are vastly different between different types of indoor environments such as schools, houses, and hospitals [6–8]. In fact, even different rooms within the same building (e.g., bedroom vs. bathroom) exhibit distinct microbiomes [9, 10].

Despite rapid advances in our ability to characterize airborne microbial communities through rRNA surveys, metagenomics, proteomics, and metabolomics, limited information is available about actual concentrations of airborne microorganisms in built environments. In one of the few studies of concentrations of total bacteria and viruses in indoor air, Prussin et al. [11] found virus-like and bacteria-like particle concentrations of

10 6 particles m −3 in various indoor environments and outdoor air, respectively. Shelton et al. [12] measured an average viable airborne fungi concentration of 80 colony-forming units (CFU) m -3 in samples collected from schools, hospitals, residences, and industrial buildings however, in some instances concentrations were as high as 10 4 CFU m −3 . These values are for kingdoms, or viruses, and not certain species. Concentrations at more detailed taxonomic ranks will enable much more powerful applications and analyses of the data. Such information should be forthcoming as methods for quantitative metagenomics analyses become more powerful [13–15].

The next chapter in understanding the airborne microbiome of the built environment is characterizing the various sources of microorganisms and the relative contribution of each. Ideally, source apportionment, as it is known in the air quality research community, would allow one to characterize the microorganism content in a sample, consult a database of sources, and then determine the relative contribution of each source. This approach is known as source tracking in the microbiome research community, although source tracking also appears to include identification of sources without quantification. Source identification could be based on operational taxonomic units (OTUs), mRNA, proteins, or any other quantifiable marker. For example, source apportionment of airborne microorganisms collected in a pet-friendly office could show that 40 % of them originate from humans, 30 % from outdoors, and 30 % from dogs. This information combined with estimations of actual emission rates could then be used to probe fundamental relationships between specific sources and human health, to design interventions to improve building health and human health, or even to provide evidence for forensic investigations. For example, a recent study showed that indoor bacterial phylotypes are able to predict whether a dog or cat lives in a home with 92 and 83 % accuracy, respectively [16].

Researchers are beginning to apply source apportionment to the airborne microbiome using approaches that are based on the concept of mass balance. That is, the mixture of microorganisms in a sample is assumed to be a linear combination of those released by specific sources whose emissions have fixed proportions of various species. By comparing dissimilarity between pairs of samples, Bowers et al. [17] assigned relative contributions of three sources—soil, leaf surfaces, and animal feces—to samples of bacteria collected in outdoor air of cities in the Midwestern US. A recent study of airborne allergenic fungal particles in a classroom used a mass balance approach to apportion them between indoor and outdoor sources [18].

Originally developed to detect sample contamination, a Bayesian approach dubbed SourceTracker can identify sources and their relative contributions in marker gene and functional metagenomics studies [19]. We are aware of three studies that have applied SourceTracker to airborne microorganisms. Leung et al. [20] estimated the contribution of various outdoor locations in Hong Kong (i.e., the sources) to the bacterial community found in different subway lines (i.e., the receptors or “sinks” in SourceTracker’s terminology). In a meta-analysis of 23 studies, Adams et al. [21] assessed the contribution of outdoor air, soil, and human-associated sources to indoor air and other samples. Hoisington et al. [22] found that 17 % of sequences on filters from the heating, ventilation, and air-conditioning (HVAC) systems of retail stores originated from humans.

While numerous studies have characterized the community composition of airborne microorganisms in various settings in the built environment, less is known about specific sources and even less about their emission rates. A recent meta-analysis concluded that “outdoor air and unidentified sources dominated the sources for indoor air environments,” accounting for an average of 52 and 43 %, respectively, of observed bacteria [21]. The goal of this work is to identify major categories of sources of airborne microorganisms in the built environment, illustrated in Fig. 1. The targets are whole microorganisms and not the broader category of bioaerosols, which also encompass pollen, tiny invertebrates, skin flakes, and other biological parts that may be airborne. Based on knowledge about sources of particles in indoor air [23–26] and studies of microbial community structures indoors [7, 27–29], we generated an initial list of source categories and refined it further through literature found in a search on Google Scholar of each source combined with the following terms: bioaerosols, concentrations, emitted, bacteria, virus, fungi, or indoor air. We followed up with forward and reserve citation searches of pertinent papers. The final list contained eight major source categories: humans, pets, plants, plumbing systems, HVAC systems, mold, dust resuspension, and the outdoor environment.

Sources of microbial bioaerosols in the built environment may include humans pets plants plumbing systems heating, ventilation, and air-conditioning systems mold resuspension of settled dust and outdoor air. The green and red dots represent microorganisms that may be beneficial or detrimental to human health, respectively. Artwork by Tim Skiles

Specifically, we examine the role of humans as sources of airborne microorganisms, including those released from the respiratory system and the skin. Likewise, pets and plants are also a source. Building infrastructure, such as plumbing (showers, sinks, and toilets) and HVAC systems, can generate airborne microorganisms, as can mold growing on building materials. Resuspension of microorganisms from the floor, clothing, and furniture acts as a secondary source. Finally, recent studies have shown that outdoor air might shape the indoor air microbiome [30, 31]. Through improved knowledge about the various sources of airborne microorganisms, we will gain deeper insight into the factors that influence the microbiome of indoor air and how we might be able to optimize it for human health and well-being.

Humans as sources of airborne microorganisms

As humans carry 10 12 microorganisms on their epidermis and 10 14 microorganisms in their alimentary tract, we might be one of the greatest sources of bioaerosols in the built environment [32]. Respiration and the shedding of millions of skin cells daily contribute to bioaerosols in the built environment. In fact, human occupancy might be the most important factor affecting the total number and community structure of bioaerosols present in the built environment, especially in poorly ventilated or heavily occupied environments [30]. Qian et al. [33] quantified microorganism emission rates and found that 3.7 × 10 7 and 7.3 × 10 6 bacterial and fungal genome copies, respectively, were emitted per person-hour. The corresponding mass emission rate was

30 mg per person-hour. Table 1 summarizes source strengths of microorganisms from this and other studies.

Not only does human occupancy affect the total airborne microbial load but it also affects the community structure [27, 29]. Meadow et al. [29] found that microbial communities in indoor air were significantly influenced by ventilation and occupancy. Although community structure in indoor air was closely associated with that of outdoor air, human-associated bacteria were over two times more abundant in an occupied indoor environment. Bouillard et al. [34] found that Micrococcus spp., Staphylococcus spp., and Streptococcaceae spp. were the most common species found in the air of a healthy office building. These bacteria are representative of the normal human flora, providing further evidence that human occupancy shapes the bacterial communities in indoor air to some degree. Kloos and Musselwhite [35] showed that Staphylococcus spp., Micrococcus spp., Acinetobacter spp., Bacillus spp., and Streptomyces spp. are part of the normal human skin flora. Charlson et al. [36] found high relative abundances of Staphylococcaceae spp., Propionibacteriaceae spp., Corynebacteriaceae spp., Streptococcaceae spp., Veillonellaceae spp., Prevotellaceae spp., Fusobacteriaceae spp., and Neisseriaceae spp. in healthy human nasopharynx and oropharynx tracts, and many of these have been identified in indoor air. Kembel et al. [7] reported that airborne bacteria indoors contain many taxa that are absent in outdoor air, including taxa related to human pathogens, indicating the importance from a health-based perspective of human occupancy on microbial communities in the built environment. Barberan et al. [16] even suggested there might be differences in the microbiomes created by male vs. female occupancy. The researchers showed that homes with a higher fraction of male occupants had greater relative abundances of Corynebacterium spp., Dermabacter spp., and Roseburia spp., while homes occupied predominantly by females had greater relative abundance of Lactobacillus spp.

Certain species of fungi are associated with human skin [37] and may be released as bioaerosols upon shedding. Yamomoto et al. [18] found that floor dust in classrooms was enriched in skin-associated yeasts, such as the genera Rhodotorula, Candida, Cryptococcus, Malassezia, and Trichosporon [18]. However, studies have shown that fungi in indoor air are dominated by those from outdoor air [16, 31]. Samples collected in a library building in Singapore by Goh et al. [38] revealed that fungal levels in indoor air were approximately 50 times lower than in outdoor air contrastingly, bacterial levels were approximately 10 times higher indoors than outdoors. Furthermore, the researchers found that fungal levels in indoor air were unaffected by the number of occupants, while human occupancy did affect bacterial loads. Adams et al. [31] asserted that none of the fungal taxa found in a university housing facility were suggestive of indoor air, and room and occupant behavior did not significantly affect the airborne fungal community.

Although humans are a primary source for many pathogenic viruses, there remains a knowledge gap regarding airborne viral communities and how human occupancy affects the community structure and total microbial load [39]. With the development of quantitative polymerase chain reaction (qPCR), researchers have been able to target and study specific viruses in air however, the majority of the literature has focused solely on influenza virus. Yang et al. [40] collected aerosol samples in a health center, daycare facility, and airplane cabins during the 2009–2010 flu season and found influenza A virus concentrations as high as 3.7 × 10 5 genome copies m −3 . Milton et al. [41] found that patients who have tested positive for influenza exhale as many as 2.6 × 10 5 genome copies of influenza virus per hour. More concerning, the researchers found that fine particles contained almost nine-fold more influenza genome copies than did coarse particles, meaning that large numbers of the virus may remain airborne for hours. Lindsley et al. [42] sought to quantify aerosol particles generated during a cough when a person is infected with influenza. The researchers found that patients produced on average 75,400 particles cough −1 (38.3 pl aerosol volume) while infected compared to 52,200 particles cough −1 (26.4 pl aerosol volume) after recovering. Presumably, the particles emitted by infected patients contain virus, and thus, people with the flu are probably a greater source of airborne virus than are healthy people. The same may be true for other respiratory infections.

Mycobacterium tuberculosis, the bacterium responsible for tuberculosis, has also been shown to be aerosolized and remain viable when a patient coughs [43]. Humans carry many other types of bacteria and viruses in the respiratory tract and saliva and discharge the microorganisms into the built environment in aerosols during coughing, sneezing, talking, and even just breathing [44–47] this topic provides excellent avenues for future research.

Recent studies have shown that dust and bioaerosols generated by dogs are beneficial to infant and child health [48–52]. Barberan et al. [16] examined the role of pets, specifically dogs and cats, in shaping the indoor microbiome. The researchers found that 56 and 24 bacterial genera were significantly more abundant in homes with dogs and cats, respectively. Dogs were associated with higher abundances of Porphyromonas spp., Moraxella spp., Bacteroides spp., Arthrobacter spp., Blautia spp., and Neisseria spp., while cats were associated with higher abundances of Prevotella spp., Porphyromonas spp., Jeotgalicoccus spp., Sporosarcina spp., Moraxella spp., and Bifidobacterium spp. It remains to be seen whether microorganisms that are specific to pets are responsible for improvements in certain measures of health or whether the pets simply increase exposure to resuspended dust by their movement and perhaps to outdoor microorganisms if they venture outside.


Microorganisms are present on the surfaces of plants and in the soil. Furthermore, certain fungi may release spores into the air as part of their life cycle. While one study found that house plants contribute minimally to certain airborne fungi, agitation such as from watering or strong air currents produced elevated levels of airborne Cladosporium, Penicillium, Alternaria, Epicoccum, and Pithomyces genera of fungi [53]. The same could also be true for microorganisms present in fruits and vegetables brought indoors. Based on this study and others, the authors of an opinion article contend that plants are a source of airborne microorganisms in the built environment [54], although we are not aware of any other studies on this topic.

Plumbing systems

The United States Environmental Protection Agency estimates that the average American family of four uses 1500 L of water daily, with 60 % of that water being used in toilets, showers, and faucets in the built environment [55]. When these fixtures are used, they generate millions of aerosols, some of which contain microorganisms. Thus, plumbing systems may be a major contributor to bioaerosols in the built environment.

Over half of the total solids in feces are bacteria, and these may be aerosolized upon flushing the toilet [56]. Each toilet flush produces up to 145,000 aerosol particles, >99 % of which are less than 5 μm [57]. Particles of this size can remain suspended for minutes to hours. In patients with intestinal diseases, concentrations of 10 5 –10 9 Shigella spp., 10 4 –10 8 Salmonella spp., and 10 8 –10 9 norovirus particles per gram of stool have been reported [58, 59].

Some of the initial work showing that plumbing systems are a source of bioaerosols was completed in the 1970s when Gerba et al. [60] seeded household toilets with virus (MS-2 bacteriophage) and bacteria (Escherichia coli) prior to flushing. The major finding from this study was that after flushing, both the virus and bacteria were found on all bathroom surfaces sampled (wall, floor, toilet seat, toilet rim, flush handle, bathtub, sink, and cabinet), indicating that the microorganisms aerosolized by a toilet flush remained viable and airborne long enough to travel throughout the bathroom and settle on surfaces. Another finding from this study was that even after seven toilet flushes in a row, a measurable fraction of virus and bacteria remained in the toilet, suggesting that they had the potential to be aerosolized long after their initial introduction into a toilet. This hypothesis was confirmed by Barker and Jones [61], who showed that toilets seeded with Serratia spp. continued to produce aerosolized bacteria even after three flushes. Additionally, the researchers showed that, 60 min after flushing, viable Serratia spp. were still detected in the air.

Other studies focusing on toilets in regular use have also confirmed that they are a source of bioaerosols. Verani et al. [62] sampled aerosols near unseeded toilets being used regularly in office buildings and hospitals. The researchers found that 62 and 77 % of air samples were positive for human adenovirus in offices and hospitals, respectively. Additionally, Torque teno virus appeared in 18 and 15 % of air samples collected above toilets in offices and hospitals, respectively, confirming that toilets are an important source of viral bioaerosols. Additional information about the microbial community associated with toilets would be beneficial, as it could be valuable for improved disease prevention and control.

Each person in the USA uses approximately 95 L of water when showering and using sink faucets. Their use can produce millions of bacterial and fungal bioaerosols. There has been an abundance of literature showing that Legionella bacteria can be aerosolized when showering and using hot water faucets [63–66]. Legionella can cause Legionnaires disease and Pontiac fever, which are respiratory diseases that exhibit symptoms similar to pneumonia and may be deadly in elderly people. Bollin et al. [66] reported that 90 % of aerosol particles produced by showers were between 1 and 5 μm, and 50 % of aerosol particles produced by sink faucets were between 1 and 8 μm, small enough to penetrate into the lower human respiratory system and cause disease. Multiple studies found extremely high levels, between 10 5 and 10 6 cells m −3 air, of Legionella in nursing homes and health care facilities [63–65]. In exploring the airborne microbial communities produced by showers in a hospital, Perkins et al. [67] found concerning levels of Mycobacterium mucogenicum and Pseudomonas aeruginosa.

Fungal bioaerosols are also produced by showers and sink faucets. Aerosolization of Fusarium spp. and Aspergillus spp. has been documented in hospitals after running showers or sink faucets [68, 69]. Fungi can be re-aerosolized from surfaces, such as shower floors or sink basins, when water splashes them. Lee et al. [70] isolated Aspergillus spp. from air samples and surface samples collected in a hospital however, no fungal spores were isolated from the water supply. The researchers concluded that spores must be aerosolized from surfaces when impacted by water droplets. Anaissie et al. [68] reported that simply cleaning the floors of shower facilities in hospitals reduced the mean airborne concentrations of Aspergillus spp. from 12 to 4 CFU m −3 . In addition to Fusarium spp. and Aspergillus spp., other fungi including Penicillium spp., Paecilomyces variotii, Alternaria alternata, Cladosporium spp., and Acremonium spp. have been identified in bioaerosols generated by residential showers [71]. Future work should address how best to control and prevent bioaerosols from being created when people use showers and sink faucets. Building upon results for fungi, researchers may wish to examine the re-aerosolization of bacteria and viruses from showers, sinks, and surfaces during use.

HVAC systems

HVAC systems typically provide a mixture of outdoor air and recirculated indoor air at supply vents, but the systems themselves can be a source of airborne microorganisms due to contamination [72–75]. Bernstein et al. [74] showed that improperly maintained HVAC systems supported abundant growth of Penicillium spp. and resulted in 50 to 80 times higher concentrations of airborne fungi in an affected office compared to an unaffected one. Dondero et al. [73] identified the cause of an outbreak of Legionnaires’ disease as an air-conditioning cooling tower contaminated with Legionella pneumophila. Ager and Tickner [72] demonstrated that HVAC systems provide favorable conditions for the growth of Legionella spp. However, the researchers also noted that through regular maintenance and cleaning, the risk of microbial exposure was greatly reduced. Therefore, building users have some degree of control over HVAC systems as a source of airborne microorganisms.

Water-damaged materials

It is well established that water-damaged homes are associated with adverse respiratory effects [76–79]. Dales et al. [80] examined bioaerosol samples in over 400 homes and found that water damage was associated with a 50 % increase in total viable fungi in dust samples. Additionally, when moldy odors were reported, total viable fungi concentrations were 2.55 × 10 5 CFU g −1 of dust. When mold and water damage was reported, Aspergillus and Penicillium levels were twice as high compared to when these conditions were absent. Flappan et al. [81] examined airborne levels of Stachybotrys atra, a particular species of mold that is known to be very toxigenic, in water-damaged homes and found levels as high as 420 spores m −3 air. These levels were particularly alarming as Etzel et al. [82] concluded that infants experiencing pulmonary hemorrhage and hemosiderosis were 16 times more likely to live in water-damaged homes and be exposed to S. atra than were infants living in a healthy built environment. Although fungal spores appear to be the dominant type of microorganisms found at elevated levels in water-damaged homes, some bacterial spores may be associated with such environments. Andersson et al. [83] found high levels of Gram-negative bacteria and mycobacteria at water-damaged sites however, the researchers did not examine whether the bacteria became airborne.

In water-damaged homes, bioaerosol production can be controlled and oftentimes completely eliminated. In order to grow, fungi need moisture, so simply controlling moisture levels (e.g., using a dehumidifier in basements) will in most cases limit fungal spore production [84, 85]. Additionally, there are many indicators of a fungal spore problem in a water-damaged home, such as moldy odors and the visual presence of mold that gives homeowners an indication that intervention is needed. Unfortunately, many homeowners do not remediate moldy and damp environments until it is too late, at which point it becomes costly to fully remove all the fungi.

Dust resuspension

It has been estimated that the average home collects as much as 18 kg of dust each year, and exposure to dust mediates health and homeostasis, including allergies and the gut microbiome [48, 86, 87]. In fact, resuspended dust is estimated to constitute up to 60 % of the total particulate matter in indoor air [88, 89]. Dust is found almost everywhere in the home, including floors, clothing, mattresses, and furniture, among other surfaces. Concentrations of microorganisms in household dust are highly variable, ranging from undetectable to 10 9 cells g −1 [90]. Studies have shown that bacterial microbial communities in house dust are diverse. They may contain up to 112,000 phylotypes (across samples from

1200 households) and are dominated by skin-associated and Gram-positive bacteria [16, 90–93]. The most abundant bacterial genera found in household dust are Staphylococcus, Corynebacterium, Lactococcus, Firmicutes, and Actinobacteria. The fungal flora of household dust is also diverse, containing up to 57,000 phylotypes, and tends to include fungal species that are found outdoors: household molds such as Cladosporium spp., Penicillium spp., and Aspergillus spp. wood-degrading fungi and those associated with humans such as Candida spp. and Saccharomyces spp. [16, 93, 94]. Occupancy, air-conditioning, ventilation, moisture, and pets can affect the types of fungi found indoors [16, 93, 94].

The microbial community of household dust is probably correlated with that in air, so as a first approximation, its source profile could be approximated by that of air. However, certain microorganisms may be enhanced or diminished in dust while it resides on a surface. Growth and decay rates in dust are likely to vary by species. If certain microorganisms tend to be associated with larger carrier particles, then they may be enriched in dust due to their higher settling velocities. On the other hand, microorganisms associated with smaller carrier particles may be less likely to be resuspended if surface forces between the floor and particle are high compared to its weight.

Resuspension of settled dust, as by walking [95], can be considered a secondary source of microorganisms that were previously airborne, settled on a surface, and then reentered the air. Ferro et al. [96] reported resuspension emission rates of particulate matter 2.5 μm and less (PM2.5) and PM5 as high as 0.5 and 1.4 mg min −1 , respectively, when two people were walking in a room. Resuspension rates are highly dependent on flooring type a carpet has been shown to have significantly higher particle resuspension rates than a hard floor, such as vinyl tile [97]. Khare and Marr [98] simulated the vertical concentration gradient of influenza virus in dust resuspended from the floor by walking. They suggested that the concentration of resuspended influenza virus at 1 m above the floor would be up to 40 % higher than at 2 m. One implication of this research is that sampling height may influence the population of microorganisms that is collected.

While walking produces the highest resuspension emission rates, other activities such as vacuuming, making the bed, and folding clothes also produce resuspended particles, including microorganisms potentially. Knibbs et al. [99] reported a median emission rate of 4 × 10 4 bacterial genome copies min −1 from measurements of 21 vacuum cleaners (Table 1). Even sleeping can generate resuspended microorganisms. Adults spend approximately 34 % of their time sleeping on a mattress, which is known to contain abundant allergens, fungal spores, and bacteria [5]. Boor et al. [100] found dust resuspension rates to be 10 −3 to 10 1 particles h −1 from mattresses and bedding. The intake fraction during sleeping was 10 2 –10 4 particles inhaled per million resuspended, so inhalation exposure to microorganisms resuspended during sleeping can be substantial. Dirty clothing has shown to have a significantly higher dust resuspension rate compared to clean clothing [101]. In summary, once microorganisms deposit on a surface, we cannot assume they have been permanently removed from the air, as there are many opportunities for resuspension. Future studies are needed to verify the relationship between exposure to microorganisms in resuspended dust and health outcomes.

Outdoor air: a major driver of the indoor air microbiome

It is well known that PM is able to penetrate effectively from outdoor air into the built environment [102, 103]. In fact, in some cases variation in outdoor PM explains the majority of variation in PM in the built environment [103–106]. In a review of indoor bioaerosols, Nazaroff [107] suggested that the penetration efficiency of bioaerosols is close to 100 % in a naturally ventilated building, meaning that all bioaerosols flowing through leaks and openings in the building environment arrive indoors. In fact, Prussin et al. [11] showed that concentrations of bacteria-like and virus-like particles were approximately two times higher in outdoor air than in indoor air, suggesting that human occupancy might not be the only component in shaping the microbial structure of air in the built environment. The microbial community structure of outdoor air varies geographically [10, 93, 108], so a single community profile cannot be applied to all indoor settings to account for the influence of outdoor air.

Adams et al. [30] sought to determine how outdoor air and human occupancy affected bacterial microbial communities in a mechanically ventilated, office-like building. Although the authors found that human occupancy was associated with increased levels of bioaerosols associated with the human body, occupancy did not have the most profound effect on the microbiome. Rather, microbial communities observed in indoor air were closely related with those in outdoor air, and changes in microbial communities in outdoor air were mirrored by changes in indoor air. The authors found an overlap in the microbial taxa in aerosol samples collected in indoor and outdoor air. The authors found high abundances indoors of Burkholderiales spp., Pseudomonadales spp., Flavobacteriales spp., and Streptophyta spp., which are typically classified as outdoor-associated taxa. The study led to the conclusion that outdoor air might exert a stronger influence on microbial communities than does human occupancy in the built environment that is well ventilated and has moderate occupancy.

Compared to airborne bacteria, fungi are even more strongly correlated between indoor and outdoor air [31, 109]. Typically most airborne fungi found indoors are presumed to originate from outdoors, except in water-damaged buildings. In residential homes, Adams et al. [31] showed that indoor and outdoor air were dominated by Cryptococcus victoriae, Cladosporium spp., Epicoccum spp., and Penicillium spp. and that the fungal community structure varied seasonally. Lee et al. [109] found an indoor/outdoor (I/O) ratio of 0.345 for total fungal spores and 0.025 for pollen grains. Additionally, indoor fungal and pollen concentrations followed trends in outdoor air concentrations. The low I/O ratio for pollen grains reflected the low penetration efficiency of large particles into the built environment compared to smaller spores.

Although the relationship between airborne viruses in the built environment and those outdoors has not been explicitly studied, it is fair to assume that viruses from outdoor air influence the viral bioaerosol community in the built environment, as seen for bacteria and fungi. Viruses are smaller than bacteria and fungi and thus may be able to penetrate indoors more efficiently. Nevertheless, future research should address how outdoor air affects viral bioaerosol communities in the built environment.

Theory and formulation

Infection risk assessment models should be based on theories and mathematical equations that are biologically plausible or conformable to clinical or laboratorial evidences. Airborne respiratory pathogens can be generated from expiration actions and other activities that introduce pathogen-laden aerosols into the air. Pathogens released from the infectious source must reach the target infection site of the receptor to commence the infection. Even after the pathogen has successfully reached the target infection site, it must survive the immune defenses of the receptor organism to induce infection. A number of influencing factors affect this process and the outcome. They are listed in Table 1. These factors add complexities to the exposure and risk assessment of pathogenic microorganisms. Many of them are not well-understood, especially the pathogen–host interactions. As a result, statistics and probabilities are often employed to formulate quantitative infection risk assessment models.

Factor Description
Dispersion and distribution of airborne pathogens How airborne pathogens disperse and distribute in the room air governs the exposure levels of the susceptible persons. The spatial distribution of airborne pathogens depends on the proximity to the infectious source, ventilation, and the geometry of the premises. The susceptible people would generally have different exposure levels and hence different degrees of infection risk. Assuming a uniform airborne pathogen distribution may cause significant error in the assessment ( Noakes and Sleigh, 2008 ).
Ventilation strategy Airborne pathogens can be dispersed to different locations by airflow. The ventilated airflow pattern has strong correlation to the spreading of airborne transmissible diseases ( Li et al., 2007 ). The spatial distribution of infectious particles is very dependent on the airflow pattern. Infectious particles can be removed from the air by ventilation dilution, which depends on the ventilation rate.
Survival of pathogen Pathogens may lose viability to cause infection by biological decay during the airborne stage, which is a sinking mechanism for respiratory pathogens. Airborne survival of pathogens often depends on the temperature and humidity (e.g., Schaffer et al., 1976 ).
Aerosol size Expiratory aerosols and many other bioaerosols are polydispersed. The transport of aerosols depends on their aerodynamic size. Therefore, the dispersion of pathogen-laden aerosols is dependent on aerodynamic size and the exposure levels to these aerosols usually have spatial variations. The deposition loss of infectious particles also depends on their aerosol size ( Chao et al., 2008 ).
Respiratory deposition When airborne pathogens are inhaled by the receptor organism, not all but a fraction of the inhaled pathogen-laden aerosols may deposit on the target infection site in the respiratory tract. In addition, because of aerosol dynamics, the respiratory deposition of these aerosols is dependent on aerodynamic size. Because of the difference in respiratory deposition of aerosols with different sizes, the aerosols have different deposition fractions on different regions of the respiratory tract. For example, aerosols with sizes >6 μm are trapped increasingly on the upper respiratory tract, aerosols with sizes >20 μm generally do not deposited on the lower respiratory tract and those with sizes >10 μm generally do not reach the alveolar region ( Hinds, 1999 Tellier, 2006 ).
Heterogeneous infectivity Different regions of the respiratory tract may have different immune mechanisms. In other words, pathogens generally have different infectivity in different regions of the respiratory tract. For example, the ID50 of influenza virus is about two orders higher when the virus was introduced to the nasal cavity by intranasal drop than introduced to lower respiratory tract via aerosol inhalation ( Alford et al., 1966 Douglas, 1975 ). As the respiratory deposition of aerosols depends on their sizes, the variation of pathogen infectivity when carried by infectious particles of different sizes was also observed, as shown by many experimental infection studies (e.g., Day and Berendt, 1972 Wells, 1955 ).
Air turbulence As induced by air turbulence, airborne pathogens trend to be randomly distributed in air. Any estimated exposure level or intake dose would be an expected value rather than an exact value. Air turbulence also exists in respiratory tracts. Respiratory deposition fraction of aerosols is also an expected value rather than an exact value ( Hinds, 1999 ). In other words, when the respiratory deposition fraction of aerosols with a particular size is β, each aerosol with this size would have a probability of successful deposition equal to β.
Pathogen–host interaction When a host organism is exposed to the pathogen, whether the organism will be infected or not depending on the infectivity of the pathogen and the immune status of the host organism ( Haas et al., 1999 ).
Control measures Control measures such as respiratory protection, ultraviolet irradiation and particle filtration can reduce the exposure level of the susceptibles to airborne pathogens ( Nazaroff et al., 1998 ).

Infection risk assessment consists of two components in general: the estimation of the intake dose of the infectious agent and the estimation of probability of infection under a given intake dose. The intake dose is the amount of the infectious agent reaching the target infection site. For airborne pathogens, estimation of intake dose requires knowledge of the exposure level to the infectious agent, pulmonary ventilation rate, exposure time interval, and the respiratory deposition of the infectious particles. Knowing the intake dose, the probability of infection can then be modeled by a mathematical function.

Infection risk assessment models can be divided into two categories: deterministic models and stochastic models. In deterministic models, each individual is hypothesized to have an inherent tolerance dose toward the infectious agent ( Haas et al., 1999 ). When a receptor organism intakes a dose of pathogens equivalent to or exceeding his/her tolerance dose, infection will occur. Lower than this tolerance dose, the receptor organism will remain uninfected. Following this hypothesis, the model can determine whether an individual will be infected or not under a certain intake dose. On the contrary, stochastic models do not determine whether an individual will acquire infection or not under a certain intake dose. Instead, the models estimate the probability of acquiring the infection under the intake dose. More details on these two concepts will be discussed in further sections.

Some infection risk assessment models are classified as threshold models. When a population intakes a dose lower than the threshold dose, none of the individuals would acquire the infection, i.e., the infection risk would be zero. It should be noticed that the threshold dose is different from the tolerance dose in the deterministic models ( Haas et al., 1999 ). Threshold dose is the minimum amount of pathogens required to initiate infection. When the intake dose exceeds the threshold dose, there will be a non-zero probability of infection. Tolerance dose is a deterministic indicator. When an individual receives an intake dose exceeding his/her tolerance dose, that individual will be infected. Examples and the assumptions of threshold models will be discussed in further sections.

Wells–Riley model: the quantum of infection and the Poisson probability distribution

Wells (1955) proposed a hypothetical infectious dose unit: the quantum of infection. A quantum is defined as the number of infectious airborne particles required to infect the person and may consist of one or more airborne particles. These particles are assumed to be randomly distributed throughout the air of confined spaces. Riley et al. (1978) considered the intake dose of airborne pathogens in terms of the number of quanta to evaluate the probability of escaping the infection as a modification of the Reed-Frost equation ( Abbey, 1952 ). Together with the Poisson probability distribution describing the randomly distributed discrete infectious particles in the air, the Wells–Riley equation was derived as follows:

where PI is the probability of infection, C is the number of infection cases, S is the number of susceptibles, I is the number of infectors, p is the pulmonary ventilation rate of a person, q is the quanta generation rate, t is the exposure time interval, and Q is the room ventilation rate with clean air. The quanta generation rate, q, cannot be directly obtained, but estimated epidemiologically from an outbreak case where the attack rate of the disease during the outbreak is substituted into PI. If the exposure time and ventilation rate are known, the quanta generation rate of the disease can be calculated from Equation 1.

The exponential term of any exponential equation should always be dimensionless. Following the definition by Wells (1955) , a ‘quantum’ has a unit describing the number of infectious particles (or the number of airborne pathogens). Hence, the exponential term in Equation 1 is not dimensionless but has the unit of the number of infectious particles. Two different interpretations can be made on Equation 1:

There is a unity infectivity term, with the unit of per infectious particle, in the exponential term.

The infectivity term is implicitly included in the backward calculated quanta generation rate in the equation, i.e., q = infectivity term × number of quanta/unit time. The infectivity term may not be one.

Adding an infectivity term to the exponential term would make it dimensionless. The infectivity term describes the probability of each infectious particle to initiate the infection. It should be noticed that in the first interpretation, a unity infectivity term implies that the host is completely vulnerable to the pathogen. This will make the Wells–Riley equation only suitable for diseases such as tuberculosis, in which the definition of tuberculosis infection fulfills this condition ( Huebner et al., 1993 ). A unity infectivity term also indicates that one quantum is equal to one infectious particle/pathogen and makes the model deterministic, because the individual is determined to be infected if his/her intake dose is equal to or greater than one pathogen. The first interpretation has also assumed that all inhaled infectious particles will successfully deposit on the target infection site in the respiratory tracts, which is not correct in general. Adopting the second interpretation, the equation is applicable to many diseases and it is a stochastic model. Respiratory deposition of infectious particles is implicitly considered in the calculated quanta generation rate. The calculated quanta generation rate will be a combination of infectivity of the pathogen and the infectious source strength in the outbreak. When the Wells–Riley equation is used in risk assessment of pathogens with a threshold dose greater than one pathogen, it will provide more conservative assessment results at low intake doses. A more accurate approach is to use a multiple-hit exponential form ( Haas, 1983 Nicas et al., 2005 ).

The Wells–Riley equation assumes well-mixed room air and a steady-state infectious particle concentration which varies with the ventilation rate. Although Riley et al. (1978) assumed that the biological decay of the airborne pathogen could be neglected, the biological decay of the pathogen during aerosolization and in the airborne state is implicitly considered in the calculated quanta generation rate. Many complexities in airborne disease transmission are also implicitly considered in the quanta generation rate.

The Wells–Riley equation provides a simple and quick assessment of the infection risk of airborne transmissible diseases. The basic reproduction number of the infection is calculated as C/I, which can be used to estimate the disease spreading risk in a large community. Many epidemic modeling studies have used the Wells–Riley equation as part of their mathematical models (e.g., Liao et al., 2005, 2008 Noakes et al., 2006 ).

Dose-response model: deterministic and stochastic models

Dose-response type infection risk assessment models require infectious dose data to construct the dose-response relationship. The term ‘dose’ refers to the quantity of the pathogen (WHO, 2003). Infectious dose data are obtained from experimental infections of test animals (or human subjects) by the pathogen. For example, when a group of test animals is exposed to a certain dose of pathogens and half of the test animals acquire the infection, this particular dose of pathogen is the 50% infectious dose. Interspecies extrapolation may be required when human infectious dose data are unavailable. There are both deterministic and stochastic types of dose-response models, which interpret the dose-response relationships in different ways.

Deterministic models are empirical models. Following the tolerance dose concept, infectious dose data are interpreted as the dose of pathogens that exceeds the tolerance dose of a portion of the population. For example, 50% infectious dose (ID50) exceeds the tolerance dose of half of the susceptible population. When each person in a susceptible population intakes a dose of the pathogen equal to ID50, half of the people will be infected. When the frequency distribution of this tolerance dose is known, the infection risk of a certain intake dose can be assessed. Figure 1 illustrates this idea. The cumulative curve describes the dose-response relationship. When each member of a susceptible population receives the same dose of pathogens, the infection risk is equal to the (cumulative) relative frequency of infection at this dose. The tolerance dose concept is biologically plausible in the sense that the immune status and the host’s sensitivity to the pathogen vary between individuals as do their tolerance doses. In addition, some infection symptoms may only be observed after the host acquires a certain amount of pathogen in the body. However, it is not biologically plausible in the sense that the pathogens would inherently be assumed to be acting cooperatively, by which infection is the consequence of their joint action ( Armitage et al., 1965 Haas, 1983 ). Some examples of deterministic dose-response models are shown in Table 2.

An illustration of the frequency distribution of the tolerance dose

Model name Description
Deterministic model
Lognormal Some experimental infection results suggested that the distribution of tolerance doses can be described lognormally (e.g., Nicas and Hubbard, 2002 ). Therefore, the lognormal model is one of the deterministic dose-response models: where N is the intake dose, μ and σ are the mean and SD of natural logarithm of the tolerance dose, respectively. Equation 2 can be rewritten as the cumulative distribution function: where erf is the error function. μ and σ are determined by fitting the infectious dose data of the disease. The infectivity of the pathogen and the pathogen–host interactions are implicitly considered by the probability distribution of the tolerance doses, hence μ and σ.
Log-logistic, Weibull These two deterministic models use different probability distributions in describing the distribution of the tolerance dose ( Haas et al., 1999 ).
Stochastic model
Exponential The host organism must intake a dose containing at least one pathogen. At least one of the pathogens has to reach the infection site and survive until symptoms are provoked on the host. These conditions can be expressed by the following equation: where P1(j) is the probability of inhaling a number of j pathogens, P2(k | j) is the probability of a number of k pathogens from those j inhaled pathogens surviving inside the host to initiate the infection. The pathogens, as discrete matters, are distributed in a medium in a random manner described by the Poisson probability distribution. When the medium is aerosolized, the pathogen distribution in the aerosols and hence their distribution in the air also follows the Poisson probability distribution. Substituting the Poisson probability function into P1(j) in Equation 4 and using a constant, r, to express the probability of a pathogen surviving inside the host to initiate the infection, the probability of infection with an intake dose, N, is derived ( Haas, 1983 ): Simplifying the summation series, it becomes the exponential dose-response model: The infectivity of the pathogen and the pathogen–host interactions are implicitly considered by r.
Beta-Poisson The variation of host sensitivity is not considered in the exponential dose-response model. To complement that, a distribution of the value of r rather than a fixed value can be considered. It is believed that the beta-distribution is the most plausible description for the r values ( Moran, 1954 ). This results in the beta-Poisson model: where Γ is the Gamma function. The equation can be approximated as follows ( Furumoto and Mickey, 1967 ): The infectivity of the pathogen and the pathogen–host interactions are implicitly considered by r, α, and β in the equations. Similar to μ and σ in Equations 2 and 3, r in Equation 6 as well as α and β in Equations 7 and 8 are determined by fitting the infectious dose data of the disease a,b . The approximate form does not work well when β is small and/or N is large. In the example of Norwalk virus, the estimates are α = 0.040 and β = 0.055 ( Teunis et al., 2008 ). If N = 25 virus, the exact equation predicts a 50% chance of infection, whereas the approximation only predicts a 22% chance of infection.
  • a When calculating the fitting parameters, whether or not the respiratory deposition of pathogen-laden aerosols should be considered is dependent on the infectious dose data. If the infectious dose data refer to the inhaled dose, respiratory deposition of pathogen-laden aerosols can be implicitly considered by the fitting parameters. The intake dose would be: N = pCt, where Ct is the total exposure concentration to viable pathogens. If the infectious dose data refer to the deposited dose of pathogen-laden aerosols on to the respiratory tract, the deposition fraction of pathogen-laden aerosols should be considered explicitly. The intake dose would be: N = βpCt, where β is the deposition fraction of pathogen-laden aerosols onto the respiratory tract.
  • b Taking r in the exponential form as an example, with an ID50 data, r can be calculated by substituting 0.5 to PI and the ID50 value into N in Equation 6, which equals to −ln 0.5/ID50.

In contrast to the deterministic models, stochastic models are semi-empirical models. They assume that at any intake dose, the host will have a probability of getting infected. Generally, the greater the intake dose, the greater the probability of infection will be. In the stochastic single-hit models, the host must intake a dose containing at least one pathogen. At least one of the pathogens has to reach the infection site and survive until symptoms are provoked on the host. The models are formulated by solving these conditional probabilities. Some examples are shown in Table 2.

Stochastic dose-response models are more biologically plausible than the deterministic ones, as they are not based on the tolerance dose concept. In addition, some stochastic properties regarding the exposure and intake of the pathogens cannot be considered by the deterministic models. For example, the pathogens, as discrete matters, are randomly distributed in the suspension medium. The distribution of these pathogens in the air is also in a random manner as induced by air turbulence. Therefore, the estimated exposure level and intake dose of airborne pathogens are always expected values rather than exact values. Deterministic models often regard the intake dose as an exact value and ignore this randomness, which may cause error in the assessment.

In practice, the model providing the best fit to the infectious dose data on the disease should be selected for infection risk assessment. The selection of the model will also depend on the availability of the infectious dose data. If there is only one available infectious dose value, only the exponential model can be used as the other models require at least two infectious dose values to calculate the fitting parameters.

Threshold models versus non-threshold models

Exponential dose-response model and the beta-Poisson model belong to the category of non-threshold models, as they assume that an infection could be initiated by a single pathogen reaching the infection site and surviving in the host. In threshold models, infection risk is generally zero if the intake dose is lower than the threshold dose. Figure 2 illustrates the difference between threshold and non-threshold models. The threshold dose will be reflected in the distribution of the tolerance dose when deterministic model is used. For stochastic model, to incorporate the effect of the threshold dose, a multiple-hit model needs to be used ( Nicas et al., 2005 ). A simple multiple-hit model can be obtained by modifying Equation 5 ( Haas et al., 1999 ):

An illustration of the difference between a non-threshold model and a threshold model

where kmin is the threshold dose. More complicated threshold models can be found in Haas et al. (1999) .

Although the threshold dose concept is not the same as the tolerance dose concept, threshold models also inherently assume that the pathogens act cooperatively ( Rubin, 1987 ). This assumption is not biologically plausible, as pathogen attacks on an organ or cell are spontaneous and independent actions and they do not have ‘joint actions’ or ‘cooperative attacks’. In addition, after pathogens successfully attack the organ or cell, they may quickly replicate inside the host body and eventually reach a critical amount sufficient to provoke infection symptoms in the host. There is sufficient evidence to support the argument that only a single pathogen is required to commence infection of some diseases, including tuberculosis and smallpox ( Nicas et al., 2004 Wells, 1955 ). However, some arguments suggest that threshold models do provide more accurate assessment results for some diseases, especially under low intake doses. Some experimental infection studies have observed threshold dose among the test animals (e.g., Cafruny and Hovinen, 1988 Dean et al., 2005 ). In such cases, the threshold models would provide a better fit to these infectious dose data.

The observation of a threshold dose may involve some complex biology. It could also be attributed to the limited number of the test animals when conducting experimental infection study. To obtain the dose-response relationship of a pathogen, different doses of the pathogen are given to different groups of test animals in experimental infection study. For instance, if each group consists of 10 test animals and the given dose has its true probability of infection to the test animals less than 0.05, it is most likely that no test animal in that group would be infected. If no test animal is infected under this given dose or other doses lower than this given dose, this given dose will be an observed threshold dose of the pathogen. With this limitation, a threshold dose may be observed even if the pathogen does not have such a threshold.

After all, the model providing the best fit to the infectious dose data of the pathogen should be used in the infection risk assessment.

Poor air quality in classrooms detrimental to wellbeing and learning

UNSW researchers found concentrations of carbon dioxide in classrooms were significantly higher than limits prescribed by the National Construction Code. Credit: Shutterstock

Many Australian school kids could be learning in classrooms with poor indoor air quality that exceeds safety guidelines.

A team from the UNSW Sydney's School of Built Environment found concentrations of carbon dioxide (CO2) in classrooms peaked significantly higher than the 850-ppm threshold prescribed by the National Construction Code due to a lack of proper ventilation.

The study also showed that low ventilation rates raise the concentration of other contaminants in a classroom environment, such as emissions from the building materials and furniture and particulate matter from indoor/outdoor sources.

Poor indoor air quality and high indoor air temperatures in classrooms are critical problems worldwide. This is only worsened when ventilation rates are insufficient to remove excessive heat or pollutants.

The lead author of the study, Associate Lecturer Dr. Shamila Haddad, said children are particularly vulnerable to the impacts of poor air quality.

"Poor indoor air quality in classrooms is a critical problem given that students spend a substantial amount of their daytime in the classroom," Dr. Haddad said. "Pollutant exposure during developmental stages may produce lifelong issues such as respiratory infections and upper and lower airways disorders."

Ventilation, not just air-conditioning

Poor air quality in the classroom doesn't just affect student health and wellbeing but also learning capacity through concentration loss, tiredness and fatigue.

"High concentration of CO2 released by the occupants of the classroom can lead to fatigue, concentration loss, and poor learning performance. Elevated CO2 concentrations can cause headache, sleepiness, and tiredness." Dr. Haddad said.

"If we want to improve productivity in the classroom, we need to revise the shortcomings of the building itself to enhance health, wellbeing and comfort.

"Improving indoor thermal and environmental quality is as important as improving the teaching material in the classroom."

Previous research conducted by UNSW Professor Mat Santamouris found CO2 levels of up to 4000ppm in classrooms, more than four times the recommended threshold.

"Under these conditions, both the teacher and the students are sleepy and tired, and their learning capacity is reduced tremendously," Professor Santamouris said.

While each state in Australia has its guidelines for indoor air quality in schools, school classroom ventilation typically relies upon natural and manual airing, which is not always possible. Often, windows are closed to avoid discomfort caused by external noise from people, traffic and construction and extreme weather to prevent drafts.

However, without adequate ventilation, high concentrations of pollutants build up inside the school, and microbes are likely to circulate the environment.

"Adequate ventilation and indoor air quality in classrooms cannot be achieved by split-type air-conditioners without the supply of fresh air leading to an accumulation of contaminants," Dr. Haddad said.

"A good ventilation system inside classrooms, on the other hand, can ensure good air quality and thermal comfort, which can enhance learning capacity and also protect students against the transmission of airborne diseases, like COVID-19."

During the study, the research team installed a demand-controlled ventilation system inside a classroom to monitor air quality and pollutants. The system uses real-time feedback to regulate indoor air quality in line with safe recommended levels by adjusting the ventilation rate in response to occupancy numbers and pollutant concentration.

Once the air quality exceeds the school classrooms' recommended thresholds, the ventilation system supplies fresh air and flushes out polluted air based on measured air quality in the classroom.

"Demand controlled ventilation with air extraction removes excessive heat and stale air and allows fresh cool air to enter the classroom and maintain indoor air quality and thermal comfort," Dr. Haddad said.

"It utilizes both natural and mechanical ventilation systems and provides an effective opportunity for controlling indoor air quality in school buildings by adjusting airflow rates based on indoor air quality measures such as CO2, Total Volatile Organic Compound (TVOC) and thermal comfort parameters."

The demand control ventilation system is more reliable than natural ventilation and is more efficient and cheaper to run than other conventional ventilation systems that use open-loop controls with constant ventilation rates, Dr. Haddad said.

Child-based design guidelines for schools

Dr. Haddad said the study supports a growing case for developing specific health guidelines that consider the combination of indoor- based ventilation and thermal comfort needed specifically for schools.

"School kids aren't just little adults, due to several reasons, namely age-dependent morphological, physiological, and psychological factors," Dr. Haddad said. "They need specific environmental conditions to be comfortable."

"This study provides evidence-based findings, which can be taken forward to develop a new set of child-based design guidelines for future school buildings. where students' thermal comfort and satisfaction, health and energy consumption measures are undertaken."

Fact Sheet: Microbial Ecology in the Built Environment

Microorganisms (living things too small to be seen unaided) are found in every corner of the earth, from miles below the surface to boiling hot springs to Antarctic ice. Also referred to as microbes, microorganisms include the familiar bacteria, fungi, and viruses, as well as several less-familiar types. Microbiologists estimate that hundreds of millions of species of microbes inhabit our world, most of which are, at best, poorly understood. Though microbes are best known as the causes of disease, they also play critical roles in almost all physical systems, changing atmospheric chemistry, enabling digestion in larger organisms, and breaking down organic matter. Microbes affect global geochemical cycles and of course, help us with the creation of beer and wine.

In the lab, we often study a single microbial type, “culturing” it by growing it in isolation in a dish or flask. Culturing is a powerful method for understanding the biology of a single type of microbe, allowing for many controlled experiments. Culturing also has its limitations. In nature, microbes do not live in isolation, but are components of complex dynamic communities, frequently including “macroorganisms” like plants and animals, as well. These microbial communities interact with their environment in a myriad of ways, influencing, and being influenced by, their surroundings. For example, lichen (actually a community of algae and fungi) slowly degrade the rocks they inhabit through the release of carbonic acid.

What is Microbial Ecology?

The field of “microbial ecology” seeks to understand how microbes interact with other organisms (including both macroorganisms and other microbes) and with the environment. As with any other environment, buildings and other man-made objects provide rich habitats for microbes. Buildings provide space and nutrients for microbes, and the people (and various animals and plants) passing through continually bring new species to the community.

Traditionally, the study of microbes in buildings has focused on known pathogens or on microbes that can damage the structure – things like black mold on the walls, Legionella bacteria (named for the deadly outbreak of respiratory infections it caused at an American Legion conference) in the water, and airborne pathogens. Researchers have emphasized the most visible or virulent agents associated with widespread outbreaks or exposure to allergens. In particular they have looked for associations between the presence of a particular microbe and some measured health effect on humans. These microbes have often been studied in labs, out of the context in which they cause problems, and without regard to the communities from which they came. These organisms are isolated in culture and assessed only for their risk to human health, or building integrity. This can be problematic because the behavior of many microbes depends strongly on the other organisms present in a community.

Studying natural communities of microbes

However, studies of microbes in their native habitats have been challenging for many reasons. Microbes are, by definition, hard to observe directly, and direct observation can be of limited value. The physical appearance of a microbe is generally not a useful indicator of what the microbe is (very different microbes can look very, very similar) or of its potential to cause trouble (e.g., pathogenic forms of bacteria can look identical to non pathogenic forms).

Thus, many researchers use indirect means to characterize microbes in their natural habitats, for example, by studying the byproducts of microbial activity. In the late 1980s, Norm Pace and colleagues pioneered a new approach to microbial ecology. Researchers began to examine microbes in their natural habitats by isolating DNA or RNA from environmental samples, and studying it in the laboratory. The analysis of the DNA/RNA allows researchers to examine the inner workings of microbes, and make more accurate inferences about both the kind of microbes that are present in a location as well as the biological potential (what they can do) of those microbes.

History of DNA/RNA based studies

In the 1980s and 1990s, DNA-based studies of microbes took off, and revealed stunning details about microbial communities. For example, DNA-based studies discovered that there were many, many more kinds of microbes present in most samples than anyone had been able to grow in the lab. In other words, most of the kinds of microbes present in samples were “uncultured” and we basically knew nothing about them. DNA-based studies also highlighted the immense scale of microbial diversity – some samples (e.g., a few grams of soil) contained thousands and thousands of species, most of which had never been grown in the lab, or studied in any way.

For many years, the focus of DNA-based studies of microbial ecology was on small-scale surveys of the kinds of microbes present in specific samples, due to limitations in DNA characterization methods. Large-scale surveys (either examining many samples or deeply characterizing individual samples) were unheard of. This changed with advances in DNA sequencing technology, driven in large part by “genome” projects such as the Human Genome Project, and related activities. DNA sequencing involves reading the order of the nucleotide bases found in specific pieces of DNA. The genome projects aimed to read the entire “sequence” of nucleotide bases for specific organisms. Technological innovation driven by genome programs led to exponential decreases in the cost, and massive increases in the speed, of DNA sequencing. Though the general focus of the people developing these technologies was on humans, the technology could be used for any DNA samples, and the aid to other fields was a great side benefit.

A revolution in microbial ecology

Thus, in the early years of the 21st century, a new revolution began in microbial ecology with the application of high throughput, low cost DNA sequencing to the study of microbes in the environment. This has even led to a new general approach called “metagenomics,” wherein one takes samples from an environment, collects all of the DNA present, and then sequences portions of that DNA. With cheap DNA sequencing, we can sequence more and more environmental DNA, and approach a representative sampling of the microbes present in the original sample.

In the last few years, metagenomic, and related studies of microbial communities have been conducted in a diversity of ecosystems, from the oceans, to boiling hot springs, to the “planet” of the human body. These studies are transforming our understanding of these ecosystems and revealing not just “who is there” in these systems but helping determine “what they are doing” in their communities. We have discovered new enzymes and biological pathways, quantified the relative natural abundance of numerous species and for the first time, have been able to compare different microbial communities on a large scale.

Though most of the early DNA-based or metagenomic-based studies of microbial communities focused on so-called “natural” ecosystems, recently, some researchers have turned the focus to the built environment. Results from these early studies are exciting, suggesting, for example, that organisms present in showerheads are a very distinct, and potentially worrisome, subset of those present in the municipal water system.

Sloan Foundation and microBEnet

A number of research labs are engaged in work on the “microbiology of the built environment”. The Alfred P. Sloan Foundation Indoor Environment Program funds some of this work and is supporting the development of tools that can be applied more broadly in indoor environments.

As part of its efforts to facilitate the connection between microbial ecologists and indoor air scientists, the Sloan Foundation is funding the microBEnet project. Led by Jonathan Eisen of UC Davis and Hal Levin of the Building Ecology Research Group, microBEnet is assembling information about the relevant work of Sloan grantees, and other resources, to encourage the exchange of information between and among microbial ecologists and indoor air scientists.

This document was produced by microBEnet. It was written by David Coil and Jonathan Eisen, and edited by Hal Levin and Elizabeth Lester.

Bacteria image from Talking Glossary of Genetic Terms, Bacteria in culture photo by David Coil (no copyright restrictions), Mold photo from Wikipedia Commons, Norm Pace photo from Pace lab website, DNA sequencing photo from Wikipedia Commons, Showerhead photo from Wikipedia Commons.