https://github.com/armahdavi/data_analytics_statistics_plotting_pm_airborne_sampling

All codes for the data pipelines processing, statistical modellings, descriptive statistics and plot visualizations from airborne phase of Mahdavi et al. (2021) (Environmental Pollution) Project Miestone: 2018 - 2021
https://github.com/armahdavi/data_analytics_statistics_plotting_pm_airborne_sampling

data-science data-visualization machine-learning matplotlib-pyplot numpy pandas python scikit-learn scipy-stats statistics

Last synced: 9 months ago
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All codes for the data pipelines processing, statistical modellings, descriptive statistics and plot visualizations from airborne phase of Mahdavi et al. (2021) (Environmental Pollution) Project Miestone: 2018 - 2021

• Host: GitHub
• URL: https://github.com/armahdavi/data_analytics_statistics_plotting_pm_airborne_sampling
• Owner: armahdavi
• Created: 2024-07-07T00:16:44.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2025-02-22T19:18:38.000Z (about 1 year ago)
• Last Synced: 2025-02-22T20:19:15.790Z (about 1 year ago)
• Topics: data-science, data-visualization, machine-learning, matplotlib-pyplot, numpy, pandas, python, scikit-learn, scipy-stats, statistics
• Language: Jupyter Notebook
• Homepage: https://www.sciencedirect.com/science/article/abs/pii/S0269749120370779
• Size: 577 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
## Summary

This repository summarizes all the data pipeline design including data pre-processing, statistical modeling, plot visualizations, and ML modeling codes from the first phase of Mahdavi et al. (2021) (Environmental Pollution) (The QFF evaluation phase which is Phase 2 is in another repository).

Project Milestone: 2018 - 2021  

## The Hidden Story in Our Air Filters

HVAC filters play a dual role in our homes. Not only do they purify the air we breathe by trapping harmful particles, but they also act as silent samplers, collecting a record of these airborne particles. This makes them a valuable resource for experts in Indoor Air Quality (IAQ), Indoor Environmental Engineering, and Health to analyze indoor contaminants. By analyzing the dust accumulated on used filters, researchers gain insights into the particles and contaminants we may be inhaling.

The analysis of dust collected from HVAC filters, known as Filter Forensics, becomes even more powerful when combined with metadata from HVAC systems—such as airflow rate, runtime, and filter efficiency. This integration allows for the accumulation of particles over the entire filter service life and across the conditioned indoor space, enabling a temporally and spatially representative calculation of airborne particle concentration and composition. This representativeness is crucial for exposure assessments in chronic health studies, which rely on long-term and comprehensive approaches. This quantitative methodology is known as Quantitative Filter Forensics (QFF).

## Airborne Measurements

To ensure QFF is a valid approach, it is necessary to evaluate it. This evaluation can be performed by comparing QFF with an alternative airborne sampling technique. In this study, the alternative aiborne sampling technique consisted of small 37-mm filter and Sioutas Cascade Impactor (SCI) samplers, and this repository showcases all the codes developed for this "alternative" airborne sampling in the paper.