SIH26082/Clean & Green Technology

Delhi air pollution forecasting dashboard

A web dashboard that uses a coupled weather-chemistry model to predict 72-hour AQI and atmospheric inversion for Delhi NCR.

Ministry of Earth Sciences (MoES) · National Centre for Medium Range Weather Forecasting (NCMRWF) · Software

Teams already in: Not read yet
Buildable in 36 hours: LowOur estimate, from their wording
Hard blockers: NoneNothing you must source first
Submissions close: 15 Sept9 days left

Make this one of our two Compare with others

Signing in saves it for your whole team — everyone on your invite link sees the same two entries. Anything you shortlisted while signed out comes with you.

Submit on sih.gov.in

Whether this suits your team

Why this fits you

This organisation declared winners on 9 problem statement(s) in SIH 2025 and on 6 in SIH 2024. It has awarded 2 joint winner(s), so it sometimes splits a prize between two teams.

What you would have to build

Decomposed from what the description asks for. Nothing added.

01
Weather Chemistry Engine
Integrates atmospheric physics and chemical transport to simulate two-way feedback between meteorology and pollutants.
02
Inversion and Plume Tracker
Tracks atmospheric inversion strength and predicts how regional stubble-burning plumes will disperse.
03
72-Hour Forecasting Module
Generates high-resolution pollution and weather predictions for the next 72 hours in Delhi NCR.
04
Real-Time Dashboard
Displays the forecasted AQI, temperature, wind, and boundary layer heights in a user-friendly interface.

What they ask for, and what they leave open

Both columns are read off the brief's own wording. Nothing here is inferred from the ministry's name.

They state

The jury will check if your system implements a two-way feedback loop between meteorology and chemistry, accurately models atmospheric inversion, and provides a 72-hour AQI forecast dashboard for Delhi NCR.

They never say

Exact data sources and APIs to be used for initial meteorological and pollution inputs
Specific deployment infrastructure or server requirements

A jury can still ask about these. Decide them deliberately rather than by accident.

What they'll ask you

Generated from the brief's own wording and the competition's published rules — never from a guess about what this ministry prefers.

Exact data sources and APIs to be used for initial meteorological and pollution inputs?
The brief never answers this, so a panel will. Whatever you decide, say it the same way twice.
Specific deployment infrastructure or server requirements?
The brief never answers this, so a panel will. Whatever you decide, say it the same way twice.
Has any part of this been shown at a previous event, hackathon or college project?
The guidelines are explicit: your solution must not have appeared in any previous event or programme, of any sort. A recycled project is what a team under time pressure reaches for.

8 more— the ones a panel gets to if there is time

The brief asks for weather chemistry engine. How would you build that?
Decoded from SIH26082 itself — Integrates atmospheric physics and chemical transport to simulate two-way feedback between meteorology and pollutants. The brief asks for it by name.

Before you commit

Each one is quoted from a gap in the brief, not a guess about your team.

No specific user named
The description asks for a user-friendly dashboard but never names who the specific end user or operator of the dashboard is.

The data

What the organisers attached, and what the brief assumes you can get.

Dataset from the organisers: None attached
Does the brief need data?: Nothing in the text says it depends on a dataset we would have to find.

Also from Ministry of Earth Sciences (MoES)

Same organisation, same year. Reading two of theirs tells you more about what they care about than reading one.

Take this to an AI

Pick what you are about to do and copy the prompt. It carries the organisers' own wording, the constraints they never spell out, and an instruction not to invent requirements they never set.

Who has this problem, what already exists, and what you would have to find out.

# Context: India's Smart India Hackathon 2026

I am on a student team entering SIH 2026. Problem statement SIH26082, set by Ministry of Earth Sciences (MoES).
Its official title, word for word: "Air Pollution–Weather Coupled Forecasting System (Delhi NCR Focus)"
Theme: Clean & Green Technology. Category: software.

In plain terms: A web dashboard that uses a coupled weather-chemistry model to predict 72-hour AQI and atmospheric inversion for Delhi NCR.

## The organisers' full description, verbatim

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.

## What the brief never says

- Exact data sources and APIs to be used for initial meteorological and pollution inputs
- Specific deployment infrastructure or server requirements
## Constraints that are not negotiable

- The judged build is a prototype demonstrated in roughly 36 hours by students, not a product.
- A jury evaluates it against this problem statement's own wording, not against general merit.
- We submit on sih.gov.in; the problem statement number and title above must match exactly.
- Your solution must not have appeared in any previous event or programme, of any sort.
- Known obstacles in the brief: No named user.

## What I want from you

1. Who actually has this problem in India, and what they do about it today.
2. What already exists — government systems, open-source projects, published research — that we should not rebuild.
3. For each item under 'what the brief never says', what the realistic answer is likely to be and how I could verify it.
4. The two or three things about this domain that a student team would get wrong without local knowledge.
## How to answer

- Everything above between quotation marks is the organisers' own wording. Treat it as the requirement.
- Do NOT invent requirements the brief does not state. If something matters and is not stated, say so and ask me, rather than assuming.
- Where you are inferring rather than reading, mark it as your inference.
- Do not guess what this ministry wants from its name. Its wording above is the only evidence.

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Problem statement facts assembled by Wynit from Ministry of Earth Sciences (MoES)'s published brief for SIH 2026.
The plain-English summary, the list of parts and the evaluator read are our interpretation of that text; the quoted title and the full description are theirs.

The government's original description (241 words)

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.