Trusted Air Quality Intelligence

AirSense: Intelligent Air Quality Intelligence for India

AirSense is an open civic-tech platform that monitors air pollution in real time, identifies pollution sources using AI, and empowers city administrators with actionable policy recommendations — ward by ward.

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Cities Monitored
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Real-Time Updates
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AI-Powered Insights
The Context

India's Air Quality Crisis

Air pollution is one of India's most pressing public health challenges. While national monitoring networks exist, they often lack the density required for hyper-local awareness. Pollution levels can vary significantly from one street corner to the next, yet policies are often based on city-wide averages.

There is a critical gap between high-level reference monitors and actual neighborhood-level exposure. Without precise data on where pollution is coming from and how it moves across a city, both citizens and administrators are left in the dark.

Citizens unknowingly commute through toxic hotspots, while city officials struggle to justify localized industrial or traffic restrictions without clear evidence. This lack of actionable data prevents the implementation of effective, ward-level interventions.

AirSense was born to bridge this data gap. By combining multiple data streams—from the ground, the sky, and the weather—we create a comprehensive, real-time map of urban air quality that enables data-driven governance.

AQI Color Scale

Good0 - 50
Moderate51 - 100
Unhealthy for Sensitive Groups101 - 150
Unhealthy151 - 200
Very Unhealthy201 - 300
Hazardous301 - 500+

The US EPA AQI scale used by AirSense

How it Works

Our end-to-end intelligence pipeline transforms raw atmospheric data into actionable civic policy.

1

Data Collection

AirSense ingests real-time data from meteorological APIs and multiple satellite constellations — including Sentinel-5P for gaseous pollutants and NASA FIRMS for thermal fire detection.

2

AQI Computation

Raw pollutant concentrations are converted to AQI scores using the US EPA formula. A spatial interpolation algorithm (Inverse Distance Weighting) estimates AQI in areas between monitoring stations.

3

AI Source Detection

A machine learning classifier analyzes chemical fingerprints and temporal patterns to identify the likely source of pollution — traffic, construction dust, biomass burning, or industrial emissions.

4

Policy Recommendations

When anomalies are detected, a Retrieval-Augmented Generation (RAG) system powered by Google Gemini synthesizes forecasts and source data to generate structured, actionable recommendations for administrators.

Our Data Sources

We aggregate data from a diverse network of ground stations, satellites, and meteorological services.

OpenAQ Network

A global, open-source air quality data platform aggregating measurements from government monitoring stations. Used for ground-truth pollutant concentrations (PM2.5, PM10, NO₂, SO₂, O₃, CO).

openaq.org

OpenWeatherMap / Open-Meteo

Meteorological data including wind speed, wind direction, temperature, humidity, and boundary layer height. Critical for dispersion modeling and 72-hour AQI forecasting.

openweathermap.org

Copernicus Sentinel-5P

ESA's Earth observation satellite providing high-resolution NO₂ tropospheric column data and aerosol optical depth. Used to estimate AQI in areas without ground stations.

sentinel.esa.int
Critical

NASA FIRMS

NASA's Fire Information for Resource Management System. Provides near real-time thermal hotspots from MODIS and VIIRS satellites to track biomass burning.

firms.modaps.eosdis.nasa.gov
Roadmap

IoT Sensor Network

A planned dense grid of calibrated low-cost sensors across city wards for hyper-local, ground-level measurements. Integration in progress.

roadmap.airsense.in

Data Refresh Rate

SourceUpdate FrequencyCoverage
OpenAQEvery 15 minutesAvailable stations
MeteorologicalEvery 15 minutesAll locations
Satellite (Sentinel-5P)DailyIndia-wide
NASA FIRMS (Thermal)Every 3 hoursGlobal/National
Interpolated estimatesEvery 15 minutesGap-filled

Designed for City Administrators

AirSense provides a secure, role-based dashboard for municipal administrators and pollution control officials. City-level admins see ward-by-ward data for their jurisdiction. Central administrators can monitor all cities across India. No manual data collection required — the platform delivers automated anomaly detection and AI-generated policy briefs directly to your dashboard.

Request Admin Access

Built with Modern, Open Technologies

Next.jsTypeScriptSupabaseMapbox GL JSApache Kafka (roadmap)Google GeminiSentinel HubOpenAQRechartsTailwind CSSVercelPostgreSQL
BP

Bibhu Pradhan

Software Developer & GenAI Enthusiast

Passionate about building technology that creates meaningful impact in society. AirSense is a vision to bridge the gap between environmental data and actionable civic governance.

Frequently Asked Questions

Everything you need to know about our methodology and data.

Ready to check your local air?