The field-study question
Schools are not controlled test chambers. They are active buildings with shifting occupancy, classroom traffic, outdoor air effects, varied HVAC conditions, and constant movement of students and staff. That is why real-world air purifier testing matters when the question is whether an air-cleaning intervention can support healthier occupied spaces.1,4,5
The seven-week study evaluated plasma-based room air purifiers in four parallel-controlled elementary schools: two treated schools and two untreated control schools. The goal was practical rather than theoretical: measure whether the intervention was associated with lower airborne bacterial concentrations, lower modeled infection probability, and lower illness-related absenteeism during the same study period.1
Parallel-controlled design
The study focused on two parallel-controlled comparisons. In each comparison, one elementary school served as the treated site and one served as an untreated control. Treated classrooms and common areas used plasma-based room air purifiers, while control schools operated without the added air purification intervention.1
The school pairs were selected for comparability across practical building and population factors, including building age, existing air systems, socioeconomic demographics, geography, and morning or afternoon sampling time. Matching cannot remove every real-world difference, but it helps make field comparisons more meaningful than a one-building demonstration.1
Two aerosol scientists sampled each treated/control pair simultaneously over seven weeks. At each school, samples were collected from five classrooms, one common area, and one outdoor location. Each comparison generated 490 bacterial air samples, giving the study repeated field data rather than a single point-in-time measurement.1
What ARE Labs measured
The study used three layers of evidence. First, ARE Labs measured airborne bacterial concentration in classrooms, reported as colony-forming units per cubic meter of air. Second, measured bacterial aerosol concentrations were used to estimate infection probability using Wells-Riley and exponential Quantitative Microbial Risk Assessment approaches. Third, illness-related absenteeism data were analyzed for the same study period.1
The modeled infection values were not clinical infection rates, and absenteeism was not treated as proof that every absence difference was caused by the air purifiers. The value of the design came from convergence: measured microbial air quality, modeled exposure interpretation, and attendance-related observations all moved in the same direction in the treated schools.1
| Study pair | School role | Average classroom bacterial concentration | Bacterial concentration comparison | Modeled infection probability | Illness-related absenteeism |
|---|---|---|---|---|---|
| Pair 1 | Treated | 30.2 cfu/m3 | 41.1% lower than control | 4.0% | 5.12% |
| Pair 1 | Control | 51.3 cfu/m3 | Reference | 6.4% | 10.99% |
| Pair 2 | Treated | 56.1 cfu/m3 | 21.9% lower than control | 7.0% | 4.75% |
| Pair 2 | Control | 71.8 cfu/m3 | Reference | 8.6% | 7.28% |
Modeled infection probability values come from report chart labels; absenteeism is an observed study-period outcome.
Lower classroom bacterial concentrations
The treated school in Pair 1 averaged 30.2 cfu/m3 across sampled classrooms, compared with 51.3 cfu/m3 in its untreated control school. That equates to a 41.1% lower average classroom bacterial concentration in the treated school. In Pair 2, the treated school averaged 56.1 cfu/m3, compared with 71.8 cfu/m3 in its untreated control school, a 21.9% lower concentration.1
Source: ARE Labs 2025 school study summary report.
- Values are average classroom bacterial concentrations in cfu per cubic meter.
- The chart does not represent clinical infection incidence.
The statistical analysis supported the observed differences. Pair 1 reported p = 0.00002 and Cohen's d = 1.874. Pair 2 reported p = 0.011 and Cohen's d = 1.844. For a school IAQ field study, these measured bacterial concentration differences were the technical anchor of the evidence package.1
| Statistical endpoint | Pair 1 | Pair 2 |
|---|---|---|
| t-statistic | -4.996 | -2.716 |
| t-critical value | 2.042 | 2.042 |
| p-value | 0.00002 | 0.011 |
| Cohen's d | 1.874 | 1.844 |
Modeled risk and absenteeism
The study then translated measured bacterial concentrations into risk-oriented interpretation. In Pair 1, the control school had an average modeled infection probability of 6.4%, compared with 4.0% in the treated school. In Pair 2, the control school had an average modeled infection probability of 8.6%, compared with 7.0% in the treated school.1
During the same seven-week period, Pair 1 showed illness-related absenteeism of 5.12% in the treated school versus 10.99% in the control school. Pair 2 showed 4.75% in the treated school versus 7.28% in the control school. That means absenteeism was 53.4% lower in treated Pair 1 and 34.8% lower in treated Pair 2 during the study window.1
Where ASHRAE 241 and CADR fit
ASHRAE Standard 241 is useful context because it puts equivalent clean airflow at the center of infectious aerosol risk reduction. That does not mean the school study measured building-level CADR directly. The better framing is that CADR-style testing, controlled bioaerosol efficacy testing, and field bioaerosol studies answer different but complementary questions.1,2,3
CADR testing can measure how quickly a room air cleaner removes defined particle challenges under controlled conditions. Room bioaerosol efficacy testing can evaluate airborne microbial reduction under controlled chamber or room-scale conditions. A field study can then evaluate how a deployed intervention behaves in occupied spaces with real building variability.1,2
What the study shows
For school districts, a field study can help translate air-cleaning investments into measurable outcomes: microbial air quality, modeled exposure risk, and attendance-related indicators. For manufacturers, it can strengthen the evidence package behind product claims without overstating what a device can prove.1,4,5
The defensibility comes from the field design. Matched comparison spaces, simultaneous or time-balanced sampling, outdoor/background measurements, repeated sampling across occupied periods, clear endpoint definitions, and a data analysis plan all helped separate measured results from modeled interpretation. That structure made the study useful for decision-making without reducing school IAQ to one device number.1
Summary
In summary, the seven-week school study showed lower average classroom bacterial concentrations, lower modeled infection probability, and lower illness-related absenteeism in treated schools during the same study window. The work helped the client and school IAQ decision-makers connect air-cleaning performance with occupied-building evidence. ARE Labs supported the work by keeping the protocol, field sampling, modeling, and interpretation tied to what the data could and could not show.1