Room model

Room context only matters if it changes what happens next.

IAQng organizes room health risk through four operating lenses: air, people, service, and context. The model is not a diagnosis. It is a way to explain what changed in a room and help a facility team decide what to do next.

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The IAQng model

Traditional IAQ measures conditions. IAQng explains room risk.

Indoor air quality gives facilities a necessary vocabulary: particulate matter, VOCs, carbon dioxide, humidity, temperature, ventilation, filtration, and source control. But the same reading can mean different things depending on the room, the people in it, what happened there, and what is happening around the building. 1

Room health risk What does this room need right now?

The model keeps indoor air signals in view, then adds use, service history, and external context so the next action is explainable.

01 / Air

Indoor air conditions

Particles, dust, VOCs, carbon dioxide, temperature, humidity, ventilation, and filtration proxies.

02 / People

How people use it

People count, density, dwell time, activity, talking, respiratory activity, and utilization.

03 / Service

What happened here

Utilization since last cleaned, verification checks, service records, occupant reports, and recurring issues.

04 / Context

The room and what surrounds it

Square footage, room type, capacity, layout, outdoor AQI, weather, wastewater trends, and local signals.

Output

Prioritize cleaning, inspection, filtration, ventilation, targeted service, or continued monitoring.

From context to action

The same four lenses should carry through the operating loop.

IAQng should not stop at a score. The model is useful only when it helps teams detect what changed, decide what matters, act, verify the result, and learn.

Room context

What is happening in the space?

People release particles when they breathe, talk, cough, and sneeze. Smaller aerosols can stay suspended, build up, and travel with the room's airflow. 7 8 9

Outdoor AQI, wildfire smoke, heat, humidity, wastewater trends, and local respiratory activity can change the posture of the room without being controlled by the room itself. 5 6

Operating loop

What should happen next?

Detect

Watch room conditions, usage, behavior, service history, and drift.

Contextualize

Normalize the reading to the room, its purpose, and outside signals.

Prioritize

Find where intervention has the highest practical value.

Dispatch

Route cleaning, inspection, filtration, ventilation, or reset work.

Verify

Document what changed and whether the driver improved.

Learn

Turn every event into better thresholds and better operations.

Applied example

What changes in a waiting room?

A waiting room is not just square footage. It is people arriving in waves, sitting close together, talking, coughing, waiting, leaving residue, and changing the room's air and service posture over time.

Try the room risk model

People

Load changes quickly.

Density and dwell time make the same air reading mean more during peak use.

Air

Signals drift.

CO2, particles, VOCs, humidity, and ventilation proxies show whether the room is returning to baseline.

Service

Readiness is time-bound.

Service records, inspections, occupant reports, and issue persistence change the next best action.

Context

Outside conditions matter.

Smoke, heat, outdoor air quality, wastewater trends, and local illness signals can change room strategy.

Model assumptions

Research-informed, not research-complete.

IAQng should be honest about what the model knows and what it is still asking the field to improve. The current model borrows its shape from established airborne-risk, ventilation, and filtration frameworks, but the coefficients are still visible assumptions that should improve with evidence.

Airborne dose logic

Risk is not a flat weighted average.

Wells-Riley style models treat airborne infection risk as a dose problem shaped by source strength, exposure time, breathing, room mixing, and removal. That is why the model accelerates when people, activity, viral context, and weak clean air combine.

Presence and rebreathed air

Community context changes the baseline.

Rudnick and Milton's CO2/rebreathed-air work points toward the next version: replacing rough prevalence and occupancy proxies with room-level evidence about shared air.

Clean-air removal

Ventilation and filtration change the dose.

ASHRAE 241, CDC/NIOSH ventilation guidance, EPA IAQ guidance, and CADR-style thinking all point in the same direction: equivalent clean air, source control, filtration, and outdoor-air constraints have to be operational inputs.

Documented assumptions

The numbers should be argued with.

The current slider-to-risk conversions are transparent starting assumptions, not validated coefficients. The point is to make them visible enough for researchers, engineers, operators, and field data to improve them.

Model boundaries

What this model should not claim.

Not diagnosis

It does not identify who is sick.

The model describes room conditions and operating context, not individual health status.

Not replacement

Service does not replace air control.

Cleaning, inspection, and verification matter, but they do not replace ventilation, filtration, and exposure management.

Not certainty

External signals are context, not proof.

Wastewater, outdoor air, heat, smoke, and local illness trends should change posture without pretending to predict individual risk.

Next step

Use the tools to test the model against a real room.

The room model explains the four lenses. The risk model and intervention planner make the assumptions visible enough to compare drivers, constraints, and practical next moves.

Try the risk model Plan interventions

Sources