Air Pollution–Weather Coupled Forecasting System (Delhi NCR Focus)
“Current air quality forecasts in Delhi fail because they treat weather and pollution as separate things. This project asks you to build a smart system that understands how pollution changes the weather and how weather traps pollution, providing a 72-hour accurate prediction for the city.”
Deconstructed Problem Blueprint
WHAT TO BUILD?
A high-resolution, coupled forecasting software platform that integrates atmospheric physics with chemical dispersion modeling.
WHO IS IT FOR?
Primary: Ministry of Earth Sciences (MoES) and NCMRWF. Secondary: Delhi NCR disaster management authorities, urban planners, and the general public.
WHY DOES IT EXIST?
Existing models ignore the 'feedback loop'—where smog blocks sunlight, changing local temperatures and wind, which in turn traps more smog. This leads to massive errors in current AQI predictions during winter months.
INPUT → PROCESS → OUTPUT ARCHITECTURE PIPELINE
Satellite aerosol optical depth (AOD) data, ground-based sensor networks (PM2.5, PM10, O3, NOx), meteorological data (temp, wind, humidity, PBL height), and stubble-burning fire count data (MODIS/VIIRS).
Numerical Weather Prediction (NWP) integration, WRF-Chem model execution, atmospheric inversion detection algorithms, and time-series forecasting using LSTM or Transformer-based architectures.
A 72-hour interactive dashboard with heatmaps, inversion strength indicators, plume dispersion animations, and automated early-warning alerts for pollution spikes.
CONSTRAINTS
- Must handle high-resolution spatial data for the Delhi NCR region.
- Must account for the non-linear feedback between aerosols and solar radiation.
- 72-hour forecasting window requirement.
- Must be computationally efficient enough to run on standard cloud infrastructure.
JURY METRICS
- Mean Absolute Error (MAE) of PM2.5 predictions compared to ground truth.
- Inversion detection accuracy.
- Dashboard latency and UI/UX responsiveness.
- Model convergence time for the 72-hour forecast.
CORE BOTTLENECK
The 'Two-way Feedback' implementation: accurately calculating how aerosol concentrations modify the Planetary Boundary Layer (PBL) height and local radiation balance in real-time.
Why You Should Choose This PS (Pros)
- High national priority and social impact.
- Clear, scientific, and research-oriented problem statement.
- Strong potential for government adoption if the model shows high accuracy.
Challenges & Risks to Watch (Cons)
- High mathematical and domain-specific complexity.
- Requires deep understanding of atmospheric science, not just coding.
- Data preprocessing for satellite and sensor fusion is time-consuming.
Recommended Tech Stack & Grand Finale Features
Killer Features to Impress the SIH Jury
View Official SIH 2026 Portal Raw Text▼
Traditional Air Quality Index (AQI) forecasting models typically treat meteorology and pollution dispersion as separate entities. However, in highly polluted urban landscapes like Delhi NCR, there is a critical, dynamic feedback loop between the weather and pollutants. During peak pollution seasons (such as the winter stubble-burning period), atmospheric inversion layers trap particulate matter close to the ground. Conversely, dense concentrations of aerosols (PM2.5) block sunlight,altering local temperatures, wind patterns, and planetary boundary layer (PBL) heights. Ignoring these coupled meteorological-chemical feedback loops leads to significant inaccuracies in standard AQI predictions. To achieve high-accuracy, actionable insights, there is an urgent need for an integrated system that simulates real-time interactions between atmospheric physics and chemical transport.
The challenge is to build a high-resolution, coupled forecasting system specifically tailored for Delhi NCR that predicts AQI for the next 72 hours.
The solution must leverage advanced weather-chemistry models (such as WRF-Chem or similar open-source coupled frameworks) to dynamically interlink meteorology with pollution dispersion(specifically PM2.5 and Ground-level Ozone). A core focus should be accurately modeling the impact of atmospheric inversion on external pollution spikes, such as regional stubble burning,and how those trapped pollutants subsequently alter local weather conditions.Implement a workflow that handles two-way feedback between meteorology (temperature, wind,PBL height) and chemistry (PM2.5, PM10, O3,NOx). A user-friendly, real-time dashboard displaying high-resolution AQI forecasts for Delhi NCR with a 72-hour outlook. Features that explicitly track atmospheric inversion strength and predict how stubble-burning plumes will disperse under prevailing weather conditions.