India

Air Quality

Issue

  • India has 14 out of the 15 most polluted cities in the world in terms of PM (particulate matter) 2.5 concentrations (World Health Organization (WHO) Global Ambient Air Quality Database)
  • Enabling users to understand and track the level of air pollution, as well as to receive forecasts along with information about potential sources of pollution, could provide the basis for taking effective pollution control measures.

Approach

Objective: Provide data and engagement to help residents understand pollution levels and causes so that they can take appropriate action

Team 1

Designed a temporal forecasting solution to predict real-time and fine-grained air quality information based on historical data reported by Central Pollution Control board.

The solution predicts the air quality over the next 24 hours based on the level of different air pollutants including sulphur dioxide (SO2), nitrogen dioxide (NOx), PM2.5 and PM10 and tracks the seasonal variations of the major pollutants and the potential sources of pollution at different points in time.

The team developed an advanced machine learning model called CLair using LSTM techniques, deploying their solution approach on the Google Cloud Platform to automatically generate predictions every few hours on this website for five Delhi locations.

Team 2

Developed an Android smartphone application called Air Cognizer. This application allows users to upload an input image of the sky horizon taken from their smartphone camera.

Based on certain features of the sky, such as how blue it is, the app predicts air quality particulate matter indicator, PM2.5 concentration, with an error less than 5%. The application combines image processing with machine learning using Tensorflow Lite to generate estimates by combining a pre-training machine learning model with a model trained online for each location based on all the user-uploaded photos.

Two key challenges that students solve are preprocessing the data collected from different smartphone cameras so that the machine learning model works accurately and deploying this machine learning model on the smartphone with Tensorflow Lite to enable a low-latency real-time prediction experience

Results

  • Eight students scoped issues and built prototypes, using AI and machine learning, to educate people in Delhi about the levels and sources of pollution.
  • Learn more from the videos highlighting the student-developed solutions

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