Monitoring and Predicting Air Quality using Machine Learning

Local Chapter Mumbai, India Chapter

Coordinated byIndia ,

Aishwarya NEVREKAR
, Avikumar Talaviya
, Manpreet Singh

Status: Completed

Project Duration: 01 Mar 2023 - 30 Apr 2023

Open Source resources available from this project

Github Repository

Project background.

Exploratory Data Analysis (EDA) is an approach to analyse the data using visual techniques. It is used to discover trends, and patterns, or to check assumptions with the help of statistical summaries and graphical representations. Machine learning is a growing technology that enables computers to learn automatically from past data. Deep learning is a subset of machine learning that can automatically learn and improve functions by examining algorithms.

The problem.

Air is what keeps humans alive. Since industrialization, there has been an increasing concern about environmental pollution. As mentioned in the WHO report 7 million premature deaths annually are linked to air pollution, air pollution is the world’s largest single environmental risk. Moreover, as reported in the NY Times article, India’s Air Pollution Rivals China’s as World’s Deadliest, it has been found that India’s air pollution is deadlier than even China’s.

Monitoring it and understanding its quality is of immense importance to our well-being. Using this dataset one can explore India’s air pollution levels at a more granular scale.

Project goals.

The goals of this project can be broken down into the following: Adopt or revise and implement **national air quality standards according to the latest WHO Air Quality Guidelines.**- **Monitor air quality** and** identify sources** of air pollution. - **Support the transition to exclusive use of clean household energy** for cooking, heating, and lighting. - **Build safe and affordable public transport systems** and pedestrian- and cycle-friendly networks - **Implement stricter vehicle emissions and efficiency standards**, and enforce mandatory inspection and maintenance for vehicles. - **Invest in energy-efficient housing** and power generation - **Improve industry and municipal waste** management - **Reduce agricultural waste incineration**, forest fires, and certain agro-forestry activities (e.g. charcoal production) - **Include air pollution in curricula for health professionals** and provide tools for the health sector to engage.

Project plan.

  • Week 1

    Researching, identifying and gathering potential datasets

  • Week 4

    Machine learning modelling to predict future AQI based on input features

  • Week 5

    Testing model and deploying on cloud

Learning outcomes.

Exploratory Data Analysis, Data Visualization, project management, communication, and Machine Learning, Deep Learning

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