Air Pollution Prediction System | Multi-Source AI for Environmental Forecasting - 01

Speakers

Air Pollution Prediction System | Multi-Source AI for Environmental Forecasting

Team Kridha — a collective of environmental technologists and data scientists from IIT Patna — is leveraging artificial intelligence to tackle one of India's most pressing public health crises: air pollution. With a commitment to bridging the gap between environmental science and technology, Team Kridha is building predictive systems that empower citizens, public health officials, and policymakers to act before pollution becomes a crisis.

What are they building

An AI-powered Air Quality Prediction System that harnesses the power of multi-source data integration to forecast air pollution levels with unprecedented accuracy and spatial coverage. Unlike traditional air quality apps that rely solely on scattered sensor networks, this system fuses historical pollution data, meteorological reanalysis datasets, and satellite imagery to generate location-specific forecasts that work even in regions lacking ground monitoring stations.

The platform automatically preprocesses and merges diverse data sources, trains advanced machine learning and deep learning models to extract spatial-temporal patterns, and delivers predictive insights through an interactive, geospatial dashboard. Citizens, health authorities, and environmental policymakers gain the foresight needed to take preventive action — from issuing health advisories to implementing pollution control measures.

Key Features Include:

Multi-Source Data Integration — Combines ground monitoring data (CPCB / OpenAQ), meteorological reanalysis (ERA5 from ECMWF), and satellite imagery (NASA MODIS / Sentinel-5P TROPOMI) for robust, comprehensive insights
Predictive Modeling — Provides both short-term and long-term Air Quality Index (AQI) forecasts using LSTM, Random Forest, CNN-LSTM hybrids, and XGBoost models
Dynamic Visualization Dashboard — Displays real-time and forecasted pollution maps with interactive trend charts, spatial heat maps, and temporal patterns
Intelligent Alert System — Notifies citizens, health agencies, and authorities about predicted high pollution events in advance
Explainable AI Insights — Highlights which meteorological and spatial features most influence air quality, providing actionable understanding rather than black-box predictions
High Spatial Resolution — Generates granular, location-specific predictions across regions, even where traditional monitoring stations don't exist

Why are they building or to solve what?

Air pollution is a silent killer affecting millions across India and globally. Major cities experience unpredictable spikes in hazardous pollutants like PM2.5, PM10, NO₂, and CO, triggering respiratory diseases, cardiovascular complications, and reduced life expectancy. Yet current air quality monitoring remains fundamentally reactive and reactive — sensors report what has already happened, leaving citizens and authorities with little time to respond.

Traditional monitoring networks are sparse, covering only select urban centers while leaving vast regions blind to pollution risks. When data does exist, it's fragmented across platforms, disconnected from meteorological and satellite context, and rarely translated into actionable foresight.

This system changes that paradigm. By combining historical patterns, real-time meteorological data, and satellite observations, Team Kridha's platform provides proactive, predictive intelligence — enabling health advisories before pollution peaks, allowing schools to plan indoor activities, and empowering policymakers to deploy interventions strategically. The inclusion of explainable AI ensures that predictions aren't just numbers; they're insights citizens and officials can understand and act upon with confidence.

Team Kridha's vision is rooted in environmental justice: everyone deserves clean air, and that starts with knowing what's coming.

Scope

Air Pollution Prediction System: From Data to Defense

Team Kridha's Air Pollution Prediction System represents a paradigm shift in environmental intelligence. While traditional air quality apps passively display current conditions from scattered sensors, this system is proactive, predictive, and intelligent — integrating machine learning with multi-source environmental data to forecast pollution before it happens. This fundamental shift from reaction to prevention is what enables real public health impact.

What makes this system truly transformational is its data fusion architecture. Historical pollution patterns reveal seasonal trends and long-term shifts. Meteorological reanalysis (ERA5) provides high-resolution atmospheric context — wind patterns, temperature inversions, humidity — that directly influence pollutant dispersion. Satellite data (MODIS and TROPOMI) captures emissions from industrial sources, wildfires, and vehicular traffic at scale, offering spatial coverage no ground network alone can achieve. By weaving these three data streams together, Team Kridha's models achieve prediction accuracy and geographic reach that existing systems simply cannot match — especially in undermonitored regions where air quality data is most scarce but health impacts are most acute.

The platform is built with explainability at its core, recognizing that prediction accuracy means nothing if users don't understand why pollution is forecast to spike. Advanced visualization techniques highlight the meteorological and spatial drivers of poor air quality, transforming raw forecasts into actionable intelligence. Citizens learn not just that pollution will be high, but why — enabling informed decisions about exercise, outdoor activities, and medication needs. Policymakers gain evidence-based insights to deploy resources effectively and measure the impact of interventions.

Team Kridha's roadmap is ambitious and impact-focused. The team is working on hyperlocal forecasting that can predict air quality at neighborhood scales, not just city-wide averages, enabling targeted public health responses. Integration with IoT sensor networks is underway, creating hybrid systems that combine official data with community-contributed observations. Advanced features like health impact modeling — forecasting hospital admissions based on predicted pollution levels — and source apportionment analysis — identifying whether pollution originates from local traffic, industries, or long-range transport — are in active development. Partnerships with municipal corporations, environmental agencies, and public health institutions are being pursued to deploy the system across India's most polluted cities. The team is also exploring mobile-first interfaces to ensure accessibility for citizens in resource-constrained areas, and multi-language support to serve India's diverse population.

A Breath of Fresh Data

At its core, Team Kridha's vision is simple yet profound: data should protect lives, not just record them. Air pollution doesn't announce itself — it builds gradually, influenced by weather, geography, human activity, and countless unseen factors. By harnessing AI to synthesize diverse data sources and forecast these invisible threats, the Air Pollution Prediction System gives society the foresight needed to adapt, protect, and act. In a world where air quality directly impacts health, productivity, and economic outcomes, this system transforms environmental data into the ultimate public good: advance warning that saves lives and shapes healthier, cleaner cities.

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