EnviroCast

EnviroCast transforms live NASA satellite data into powerful climate forecasts—using AI and quantum computing to show how today’s actions can protect cities, ecosystems, and the future of our planet.

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Inspiration

EnviroCast was born out of a personal frustration with unreliable air quality data and the broader impact of air pollution on global health. Millions of people are exposed daily to PM2.5, ozone, and other pollutants, yet most forecasts lack precision or actionable health insights.

We were inspired by NASA's TEMPO satellite mission, which provides high-resolution atmospheric monitoring, and we saw an opportunity to combine this with quantum computing and AI to create a truly next-generation environmental intelligence platform. Our goal was not just to forecast air quality but to link environmental conditions with health outcomes, provide actionable guidance, and make this accessible through a developer-friendly API.

What it does

EnviroCast integrates quantum algorithms, agentic AI, and real-time data to provide a full-spectrum environmental intelligence solution:

  • Quantum Air Quality Forecasting

    • Uses Quantum Recurrent Neural Networks (QRNNs) with 16–32 qubits and 24-layer variational circuits.
    • Processes multi-pollutant interactions simultaneously with high predictive confidence.

  • Global Coverage & Real-Time Forecasts

    • Monitors 500+ cities worldwide with hourly 24-hour predictions.
    • Tracks PM2.5, PM10, O₃, NO₂, SO₂, and CO with 97%+ predictive accuracy.

  • Health Risk Analysis

    • Integrates health profiles including age, medical conditions, activity level, and exposure to provide personalized risk scores.
    • Offers recommendations for outdoor activity, medication adjustments, and exposure limits.

  • Interactive Dashboard & AI

    • Enviro AI Chatbot allows scenario exploration and future projections.
    • Real-time 3D globe visualization, predictive overlays, and disaster alerts.

  • API & Developer Integration

    • REST endpoints for /forecast, /health-risk, and /status.
    • JSON responses with confidence scores and quantum metrics.
    • Low latency (<50ms) and 99.9% uptime, supporting integration into health apps, smart cities, and research platforms.

How we built it

  1. Data Pipeline

    • Collected and normalized data from OpenMeteo, EPA AirNow, CPCB India, and EEA Europe.
    • Ingested real-time measurements from 12,000+ stations.

  2. Quantum Layer

    • Implemented QRNNs using IBM Quantum Runtime.
    • Encoded pollutant and health matrices into qubits for simultaneous processing.

  3. Dashboard & Visualization

    • Developed interactive 3D globe and AI assistant for scenario exploration.
    • Added real-time alerts, AQI tracking, and health recommendations.

  4. API & Backend

    • Designed REST endpoints for easy developer access.
    • Enabled real-time quantum predictions with detailed confidence metrics.

Challenges we ran into

  • Mapping multi-dimensional pollutant interactions into quantum circuits while maintaining coherence.
  • Handling massive real-time datasets from thousands of monitoring stations.
  • Entangling health profiles with environmental data to ensure accurate personalized risk scores.
  • Maintaining sub-second dashboard performance and reliable alerting systems.
  • Balancing quantum backend reliability with classical fallbacks.

Accomplishments that we're proud of

  • Quantum Air Quality Forecasting

    • Successfully implemented QRNNs with 16–32 qubits and 24-layer variational circuits, achieving 95.4%+ prediction accuracy for multi-pollutant AQI forecasts.

  • Global Coverage & Real-Time Data

    • Monitored 1K+ cities worldwide with hourly 24-hour forecasts.
    • Integrated data from 12,000+ active monitoring stations in real-time.

  • Personalized Health Risk Analysis

    • Developed AI-powered risk assessments linking pollutant exposure with health conditions, age, and lifestyle.
    • Provided actionable recommendations for sensitive groups (e.g., asthmatics, elderly).

  • Interactive Enviro AI Dashboard

    • Built a 3D globe visualization with quantum-enhanced data overlays.
    • Enabled scenario exploration, time travel projections (0–6 years), and disaster tracking.

  • Developer-Friendly API

    • Released REST API endpoints for forecasting, health-risk analysis, and system status.
    • Achieved <50ms response time and 99.9% uptime, allowing integration into health apps, smart cities, and research platforms.

  • Recognition & Validation

    • Built using NASA TEMPO data and IBM Quantum computing.
    • Demonstrated measurable quantum advantage (+23% vs classical) in environmental predictions.
    • Applied in competitions and hackathons, winning awards for environmental and AI innovation.

What we learned

  • Quantum computing improves multi-variable forecasting compared to classical methods.
  • Entangled environmental and health parameters allow instantaneous and accurate risk scoring.
  • Open APIs and intuitive dashboards are critical for public accessibility and adoption.
  • Cross-disciplinary collaboration between quantum engineers, AI developers, and environmental scientists is essential.

What's next for EnviroCast

  • Expand QRNNs to 64+ qubits for more detailed pollutant interactions.
  • Add population-level health risk mapping for urban planners and public health agencies.
  • Increase coverage beyond 500+ cities with additional real-time stations.
  • Enhance disaster prediction, early-warning systems, and scenario simulations.
  • Build SDKs and tutorials for developers.
  • Explore VR/immersive visualizations to engage communities in air quality awareness.

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