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AQI AI: High-Resolution Surface Air Quality & HCHO Hotspot Mapping Across India Using Satellite Data

Leveraging satellite remote sensing, meteorology, and machine learning to estimate surface AQI and detect formaldehyde hotspots for public health and policy in India.

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AQI AI — Surface AQI Mapping & HCHO Hotspot Detection across India Team Name: AQI AI | Team Leader: Vineet Dilip Dagade Problem Statement: Development of surface AQI & identification of formaldehyde (HCHO) hotspots over India using satellite data. This presentation summarises our approach: combining satellite remote sensing, meteorology, and machine learning to estimate high-resolution surface AQI and to detect/monitor HCHO emission hotspots for public health and policy action.
Team Members & Roles Vineet Dilip Dagade — Team Lead Ajeenkya DY Patil School of Engineering (ADYPSOE), Pune. Responsible for overall project management, model development and satellite data processing pipelines. Shavi Gongale — Remote Sensing Specialist Vishwakarma Institute of Technology, Pune. Focus: preprocessing of Sentinel/TROPOMI/VIIRS, atmospheric correction, colocation with ground stations. Team Member 2 — Machine Learning Engineer Focus: surface AQI retrieval models, HCHO hotspot detection models, uncertainty quantification. Team Member 3 — Domain Analyst & Visualisation Focus: epidemiological context, map visualisation, stakeholder reporting and web dashboards. We propose a cross-functional team bridging remote sensing, ML, atmospheric science and public health for operational deliverables.
Opportunity & Differentiation • How different: Converts satellite column data (TROPOMI, Sentinel-5P) to surface-level AQI using physics-informed ML and dispersion correction — not just raw columns. • How it solves the problem: Integrates satellites, meteorology (ERA5), land use, and limited ground monitors to infer surface pollutant levels and identify statistically significant HCHO hotspots with temporal persistence metrics. • USP: Rapid, scalable hotspot alerts + spatially continuous AQI maps for cities, industrial corridors and rural regions lacking monitors; uncertainty estimates for every pixel to support decision making.
Key Features of the Proposed Solution 1 Multi-Source Ingestion Automatedretrieval& harmonisationof TROPOMI,Sentinel-2, VIIRS, MODIS, ERA5 and governmental groundAQ sensors. 2 Column-to-Surface Inference Physics-awareML modelcombining radiative transfer corrections,boundary layer height and meteorologyto estimate surface NO2, HCHO, PM2.5 proxies and convertto AQI. 3 HCHO Hotspot Detection Spatial statistical detection (e.g., Getis-Ord Gi*, temporalpersistence scoring)to flag industrial, biomass burning or transport-relatedHCHO hotspots. 4 Uncertainty & Explainability Per-pixel uncertainty estimates, feature-importanceexplanations to aid regulatoryinterpretation. 5 Operational Dashboards & APIs Interactive web maps, time series, alerting APIs, and exportablereports tailored for stakeholders (municipalities, health agencies, researchers). Visual aids: We recommendincluding samplemaps and hotspot overlays whenpresenting to stakeholdersto make impacts tangible.
Process Flow — From Satellite to Surface AQI & Hotspots Deliver Detect HCHO Hotspots Infer Surface AQI Preprocess Ingest Caption: The pipeline ingests daily satellite swaths, applies cloud & quality filters, collocates meteorological and land-use layers, runs the inference model to produce gridded surface AQI and HCHO anomaly layers, then runs hotspot detection and serves results via dashboard and APIs.
Wireframes & Mockups (Preview) Map View High-resolution AQI tiles, HCHO hotspot markers, on/off layers (ground stations, land use). Click a pixel for time series and uncertainty band. Time Series & Diagnostics Temporal plots (daily/weekly), model residuals vs ground monitors, anomaly detection controls to tune hotspot sensitivity. Optional features: downloadable CSV/KML, automated stakeholder email alerts for persistent hotspots, mobile-friendly simplified views.
Architecture — Scalable & Reproducible Data Ingestion (Cloud) Automated fetch from ESA/Google Earth Engine/TROPOMI, VIIRS, and meteorological APIs into a data lake (object storage). Preprocessing & Feature Store QA/QC, regridding, collocation, derived features (BLH, AOD proxies) stored for model consumption. Model Training & Serving Physics-informed ML (hybrid neural network + physical correction). Batch retraining and real-time inference endpoints. Analytics & Hotspot Engine Statistical hotspot detection, persistence scoring, and prioritisation queue for human review. Delivery Web dashboard, APIs, periodic reports and alerting channels (email/SMS/webhooks). The architecture emphasises modularity, reproducibility (containerised workflows) and cost-effective cloud compute for batch model runs.
Technologies & Tools Python Ecosystem Data processing(xarray,rasterio), ML (TensorFlow/ PyTorch),geospatial libs (rasterio, geopandas). Satellite & Geospatial GoogleEarthEngine for rapid prototyping,ESA/TROPOMIfor HCHO/NO2columns,Sentinel-2for landcovercontext. Infrastructure Containerisedpipelines(Docker),orchestration(Kubernetes), cloudobject storageand serverlessendpointsfor APIs. Storage & Serving PostGIS/TimescaleDBfor ground observations and time series; tile server (vector/raster)for fast map delivery. Visualization Web map frameworks (Leaflet/Mapbox),Plotly/D3for charts, RESTful APIs for integrations. Emphasis on open standards and reproducibility so the platform can be adopted by government and research institutions. Estimated Implementation Effort & Timeline Proposed phased delivery (4–6 weeks total): 1. Week 0–1: Data pipeline & basic ingestion; gather ground monitor datasets and baseline satellite preprocessing. 2. Week 1–3: Development of column-to-surface model, initial validation against monitors; prototype maps. 3. Week 3–4: Hotspot detection module, dashboard prototype and stakeholder review. 4. Week 4–6: Scaling, uncertainty quantification, final validation, API & alerting, deployment. Cost considerations (high-level): cloud compute for ingest/model training, storage, engineering hours, dashboard hosting. We recommend a pilot budget allocation for the first 6 months to cover development and a limited production deployment.
AQI AI — Development of surface AQI & HCHO Hotspot Identification over India using Satellite Data Contact:Vineet Dilip Dagade (7264802008) College: Ajeenkya DY Patil School of Engineering (ADYPSOE), Pune