Solution to Pollution
THE DHARAVI CASE STUDY
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Dharavi Slum Case Study: AI Solutions for Pollution and Poverty, Study notes of Forestry
A case study of dharavi, one of the world's largest slums, focusing on the challenges of pollution and poverty. It highlights the area's high population density, health issues, and economic activities. The document proposes integrating artificial intelligence (ai) to address these problems by optimizing business sectors, distributing wealth evenly, and improving living conditions. It also outlines a technical solution involving iot devices for data collection, cloud services for processing, and ai models for analysis and hotspot identification. The document uses data visualization techniques to identify pollution hotspots and suggest appropriate interventions, aiming to improve the quality of life in dharavi through technological solutions.
Typology: Study notes
2025/2026
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Solution to Pollution
THE DHARAVI CASE STUDY
The other side of the financial capital
of India
Dharavi is a residential area in Mumbai, Maharashtra, India. It has often been considered to be one of the world's largest slums Dharavi has an area of just over 2.39 square kilometers (0.92 sq mi; 590 acres) and a population of about 1,000,000.With a population density of over 418,410/km 2 (1,083,677/sq mi), Dharavi is one of the most densely populated areas in the world.
Shocking statistics
Unlike any other slum, an astonishing share of 85-90% of total population of Dharavi are employed Over 5000 small scale businesses and cottage industries are run in the slums Over 500 million to 1 billion USD revenue is generated in Dharavi annually Dharavi hosts numerous small-scale industries, including waste recycling, pottery, leather product manufacturing, and textile production Over 80% of Mumbai’s plastic is recycled in Dharavi
So why does poverty still exist in
Dharavi?
Though corruption is one of the major reasons of poverty the other reason is lack of awareness The goods produced in these areas are sold at very low prices due to limited knowledge of prevailing market rates. The working population has limited access to the broader markets beyond their locality.
Technical solution
The idea is, we split the entire solution part into three sub solutions They being: Identifying Processing/ Analysing Addressing
Identifying
IoT devices help us pick up the data following the different forms of raw data from pinpointed locations to identify the hotspots The raw data is then sent to a data cloud, which will store the data The data processed to the cloud can then be retrieved and processed individually
The following devices are employed to collect raw data: DHT temperature, humidity, air quality index sensors Carbon, sulphur and other gas sensors Air thickness and density sensors Water purity sensors And other microcontrollers
Processing
The idea of processing here being sending all the data to a common cloud and then processing data. Usage of 3rd^ party cloud services like ThingSpeak to process the data properly without any hiccups or noise. Use of AI to identify the hotspots and regression plots to analyse the data and employ proper techniques to address the issues
The data here is visualised using Mathplotlib it can be later used in various regression models to identify the hotspots with more than usual levels of pollution.