https://github.com/armahdavi/unsupervised-clustering-ml---pm_source_detection---how-extensive-do-you-cook-or-smoke-

Indoor PM2.5 source detection algorithm using unsupervised clustering ML method (k-means clustering)
https://github.com/armahdavi/unsupervised-clustering-ml---pm_source_detection---how-extensive-do-you-cook-or-smoke-

climate-data jupyter-notebook kmeans kmeans-clustering matplotlib-pyplot numpy pandas python spyder-python-ide

Last synced: 4 days ago
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Indoor PM2.5 source detection algorithm using unsupervised clustering ML method (k-means clustering)

• Host: GitHub
• URL: https://github.com/armahdavi/unsupervised-clustering-ml---pm_source_detection---how-extensive-do-you-cook-or-smoke-
• Owner: armahdavi
• Created: 2024-07-07T14:49:21.000Z (4 months ago)
• Default Branch: main
• Last Pushed: 2024-11-10T17:57:23.000Z (6 days ago)
• Last Synced: 2024-11-10T18:29:42.875Z (6 days ago)
• Topics: climate-data, jupyter-notebook, kmeans, kmeans-clustering, matplotlib-pyplot, numpy, pandas, python, spyder-python-ide
• Language: Python
• Homepage: https://www.sciencedirect.com/science/article/abs/pii/S0269749120370779
• Size: 15.6 KB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Unsupervised Clustering Machine Learning (ML) to Estimate Indoor PM Sources 

## How much do you cook or smoke indoors?

Combustion activities such as cooking or smoking are significant contributors to indoor particulate matter (PM) emissions. Given we spend a substantial portion of our lives indoors (> 85%), primarily at home, understanding and identifying combustion-driven PM sources is crucial. Determining the duration and intensity of these activities enables us to assess the urgency of developing effective exposure mitigation strategies—such as reducing smoke or cook time and using a range hood—to address airborne particulate matter (PM) exposure. PM poses significant long-term health risks, including lung cancer, heart disease, asthma exacerbation, and premature death.

## This Repository

In this repository, I developed an unsupervised Machine Learning (ML) algorithm using k-means clustering to detect and characterize combustion-driven source regimes for PM2.5. The algorithm calculates the duration and emission rate of PM2.5 within these regimes. This approach is feasible due to the unique temporal patterns exhibited by PM2.5 concentrations during source emission regimes compared to other courses. The absence of labeled data on PM2.5 sources necessitated an unsupervised learning framework. This research is a side part of the Mahdavi et al. (2021) paper published in "Environmental Pollution" where PM concentrations were measured using optical sensors in a single-family home for a duration of close to 6 weeks in Summer 2018. 

The full-length article can be found in the "About" section.