Air Quality Prediction Flow: Methodology and Significance

The ClearSkyIQ air quality forecasting system integrates multimodal environmental data from space-based, ground-based, and meteorological sources to generate accurate, real-time predictions of air quality and pollutant behavior. The overall methodology can be broken down into five major phases, representing the end-to-end data and model workflow.

Hybrid Deep Learning Model Architecture

The core innovation of ClearSkyIQ lies in its hybrid model, which integrates multiple deep learning paradigms to capture the spatial, temporal, and dynamic relationships governing air pollution.

1. Data Ingestion Layer

Purpose: Collect multimodal data (satellite, meteorological, and ground-based) from multiple sources.

AWS Lambda:
Scheduled functions (triggered via Amazon EventBridge) fetch data from NASA APIs (MODIS, CALIPSO), NOAA weather feeds, and OpenAQ ground sensors.
Amazon S3 (Raw Data Bucket):
Stores incoming raw data files (CSV, NetCDF, JSON, satellite raster images) for preprocessing.
AWS Glue:
Cleanses, normalizes, and joins datasets — preparing them for machine learning ingestion.Recommended air quality guidelines (AQG) levels and interim targets.

2. Machine Learning & Analytics Layer

Purpose: Train and deploy the hybrid AI model for air quality prediction and anomaly detection.

Amazon SageMaker:
Trains the hybrid deep learning model using CNNs, BiLSTM, GNNs, Attention, and Neural ODE architectures.
- Input: Processed data from S3.
- Output: Predicted AQI and pollutant concentrations (NO₂, O₃, PM2.5).
Amazon SageMaker Endpoint:
Hosts the trained model for real-time and batch inference.
- Lambda functions can invoke this endpoint hourly to refresh predictions.

3. Storage and Query Layer

Purpose: Store predicted results and make them queryable and visualizable.

Amazon OpenSearch Service:
Stores model outputs and time-series data for AQI, pollutants, and detected anomalies.
Enables advanced filtering, aggregation, and spatial queries for dashboard use.

4. API and Delivery Layer

Purpose: Expose processed and forecast data to the web frontend securely.

Amazon API Gateway:
Provides REST or GraphQL APIs for the frontend to access prediction data, heatmaps, and alerts.
AWS Lambda (API backend):
Acts as a serverless API handler fetching data from OpenSearch and DynamoDB.

5. Visualization and Hosting Layer

Purpose: Present forecast and visual data interactively to users.

Frontend (Next.js hosted on Vercel):
- Fetches data from AWS API Gateway endpoints.
- Displays features such as:
  - Dynamic Air Quality Timeline
  - 3D Atmospheric Layer View
  - Comparative City Dashboard
  - Correlation Heatmap
  - Historical Trend Explorer
- Uses Mapbox, D3.js, and Plotly.js for real-time visual rendering.

Full Flow

ClearSkyIQ introduces a hybrid deep learning approach that fuses spatial, temporal, and dynamic data from multiple sources. By combining AWS-managed AI/ML services with a lightweight, globally hosted Vercel frontend, it creates a powerful yet scalable system that not only visualizes current air quality but predicts future risks — empowering smarter environmental management and public health responses.

Benefits

ClearSkyIQ enables proactive decision-making by providing accurate, real-time pollution forecasts and early warnings for high-risk events. Governments can issue timely advisories, researchers can study pollution dynamics, and citizens can plan activities around safer air quality levels. It bridges the gap between complex satellite data and actionable public information.

Impact

The project aims to minimize human exposure to hazardous air pollution through predictive intelligence. By forecasting pollution hotspots before they occur, ClearSkyIQ helps protect public health, supports data-driven environmental policy, and promotes transparency in air quality management across urban and rural regions.

Tools, coding languages, or software used

Languages: Python (backend, ML), JavaScript/TypeScript (frontend)
Frameworks & Libraries: TensorFlow, Scikit-learn, Next.js, D3.js, Mapbox
AWS Cloud Services: Amazon SageMaker, Lambda, S3, Glue, OpenSearch, API Gateway, EventBridge, SNS, CloudWatch
Data Sources: NASA TEMPO, CALIPSO, MODIS, MERRA-2, NOAA weather feeds, OpenAQ ground sensors
Frontend Hosting: Vercel (Next.js deployment)

How is your project creative?

ClearSkyIQ innovates by combining diverse environmental datasets into a single predictive ecosystem powered by hybrid deep learning. Unlike traditional AQI dashboards, it visualizes spatial-temporal changes dynamically through 3D maps, pollutant composition wheels, and historical trend explorers—turning scientific forecasts into intuitive visual experiences accessible to the public.

What factors did your team consider?

The team focused on data reliability, scalability, and accessibility. We prioritized open data sources (NASA, NOAA, OpenAQ) and cloud-native services to ensure sustainability and real-time scalability. We also emphasized user-centered design—making complex environmental forecasts visually interactive and easy to interpret for policymakers, researchers, and everyday users.

2025 NASA Space Apps Challenge

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