Transparent & Verified Data

How we monitor
your city's air.

AirSense doesn't just guess. We synthesize ground-level measurements, satellite imagery, and meteorological models using advanced AI to deliver the most accurate hyper-local AQI in India.

Our Multi-Modal Approach

Triple-Verified Intelligence

By combining official reference data with satellite observation and low-cost sensor networks, we eliminate blind spots in urban monitoring.

Gold Standard
Reference Monitors
Direct integration with official regulatory-grade monitoring stations across the country.
  • CPCB (Central Pollution Control Board) nodes
  • Continuous Monitoring Stations (CAAQMS)
  • High-precision chemical analyzers
  • 15-minute refresh cycle
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Space-Grade
Sentinel-5P Satellite
ESA's Earth observation eyes providing vertical column density for gaseous pollutants.
  • TROPOMI high-resolution spectrometer
  • Detection of NO₂, SO₂, O₃ and Formaldehyde
  • Aerosol Optical Depth (AOD) mapping
  • Global coverage for gap-filling
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Critical-Alert
NASA FIRMS Satellite
Real-time thermal detection of active fires and agricultural burning via MODIS and VIIRS.
  • Active hotspot detection (375m/1km)
  • Fire Radiative Power (FRP) intensity
  • Thermal anomaly plume modeling
  • 24/7 global monitoring feed
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Predictive
Weather Models
Real-time atmospheric conditions to model how pollution disperses across wards.
  • Wind speed & direction mapping
  • Boundary layer height analysis
  • Humidity & temperature influence
  • 72-hour dispersion forecasting
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Next-Gen
IoT Sensor Mesh
Hyper-local, ward-level measurements from our dense network of low-cost sensors.
  • Hyper-local PM2.5 and PM10 data
  • Ward-by-ward density deployment
  • Continuous automated calibration
  • Real-time edge computing
Contextual
Land Use Logic
Geospatial data on traffic flow, construction zones, and industrial clusters.
  • Traffic congestion analytics
  • Construction permit monitoring
  • Industrial emission inventories
  • Biomass burning detection
Proprietary
AI Inference Layer
Our core engine that cross-references all streams to filter anomalies.
  • Removal of hardware outliers
  • Machine learning calibration curves
  • Source apportionment (Fingerprinting)
  • Confidence score for every reading
The Process

How we turn raw data into civic action.

Raw sensor data is often noisy. A passing truck can trigger a temporary PM spike, or high humidity can skew optical sensors. Our pipeline ensures that what you see on the dashboard is verified.

01

Ingestion & Sanitization

We collect millions of data points hourly. Our first layer removes spikes caused by sensor maintenance or transient local anomalies.

02

AI Cross-Calibration

We use high-precision government monitors to dynamically 'train' and calibrate low-cost sensors in their vicinity every few minutes.

03

Spatial Interpolation

Using the Inverse Distance Weighting (IDW) algorithm, we estimate AQI for the gaps between sensors, giving you a full city map.

04

Biomass Detection & Policy

Finally, we correlate PM spikes with NASA FIRMS hotspots to distinguish crop burning from traffic, enabling targeted bans and alerts.

Quality Score: 98.4%

Our internal validation scores show a 0.94 correlation between AirSense interpolated estimates and official reference-grade measurements.

Accuracy CorrelationHigh Confidence
Did you know?

Pollution levels can double from one city ward to another. That's why we prioritize hyper-local over city-wide averages.

Transparency First

Data Methodology FAQ

Have questions about our numbers? We've got answers.

Empower your city with data.

Are you a city official or an urban researcher? Get access to our high-resolution data streams and AI source apportionment models today.