The AeroSentinel platform

One atmosphere. Thousands of signals. One clear forecast.

AeroSentinel turns fragmented air-quality data into a local, one-hour-ahead PM10 estimate and a separate high-event risk signal.

01 / Input

See the full atmospheric context

The platform combines what monitors are measuring now with the conditions that move, trap, wash out or create particulate matter.

  • Continuous PM10 monitorsCurrent + history
  • Nearby and upwind stationsTransport-aware
  • Weather and boundary layerHourly
  • NOAA near-surface smoke+1 hour
  • EPA emissions inventoriesLocal context
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02 / Forecast

PM10, one hour ahead

A continuous concentration estimate in µg/m³, designed for short-term awareness at monitored locations.

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03 / Alert

High-event probability

A separate risk score flags when PM10 may cross elevated thresholds—without distorting the everyday forecast.

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04 / Confidence

Uncertainty, shown honestly

Forecast ranges and data-quality warnings help users understand when the system is confident and when conditions are changing fast.

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05 / Deploy

Built to transfer

The pipeline can be retrained for another county using local monitors, weather, smoke guidance, emissions and a strict held-out test.

Why hybrid intelligence

Physics for movement. Machine learning for local correction.

Smoke forecasts understand regional transport. Monitor histories understand what is actually happening near the ground. AeroSentinel combines both, then learns the local bias.

1
ObserveMonitor and weather signals arrive
Now
2
TransportUpwind and smoke guidance add context
Physics
3
CorrectLocal model learns what guidance misses
ML
4
CommunicateForecast, range and event risk
+1 hour

See the model in your region.

We’ll walk through data availability, evaluation and a pilot path.

Book a technical briefing ↗