https://github.com/armahdavi/data_pipeline_analytics_statistics_ml_pm_psd_residential_qff
Sharing all the data pipelines and processing codes, statistical modellings, descriptive statistics, plot visualizations, and machine learning from Mahdavi & Siegel (2021) (Indoor Air) Project Miestone: 2017 - 2020 Full-length article: https://onlinelibrary.wiley.com/doi/abs/10.1111/ina.12782
https://github.com/armahdavi/data_pipeline_analytics_statistics_ml_pm_psd_residential_qff
data-science data-visualization dust hvac indoor-air-quality jupyter-notebook machine-learning matplotlib-pyplot numpy pandas python scikit-learn scipy-stats spyder spyder-python-ide statistics
Last synced: about 2 months ago
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Sharing all the data pipelines and processing codes, statistical modellings, descriptive statistics, plot visualizations, and machine learning from Mahdavi & Siegel (2021) (Indoor Air) Project Miestone: 2017 - 2020 Full-length article: https://onlinelibrary.wiley.com/doi/abs/10.1111/ina.12782
- Host: GitHub
- URL: https://github.com/armahdavi/data_pipeline_analytics_statistics_ml_pm_psd_residential_qff
- Owner: armahdavi
- Created: 2024-07-06T15:41:25.000Z (10 months ago)
- Default Branch: main
- Last Pushed: 2025-02-22T19:04:11.000Z (2 months ago)
- Last Synced: 2025-02-22T20:18:45.483Z (2 months ago)
- Topics: data-science, data-visualization, dust, hvac, indoor-air-quality, jupyter-notebook, machine-learning, matplotlib-pyplot, numpy, pandas, python, scikit-learn, scipy-stats, spyder, spyder-python-ide, statistics
- Language: Jupyter Notebook
- Homepage: https://onlinelibrary.wiley.com/doi/abs/10.1111/ina.12782
- Size: 2.44 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
## Summary
This repository summarizes all the data pipeline design including the data pre-processing, statistical modeling, plot visualization, and ML modeling codes from Mahdavi & Siegel (2020) (Indoor Air)
Project Milestone: 2017 - 2021
## PSD and Concentration: Two Important Determinants of the Health Effects of Exposure to PM
Particle size distribution (PSD) and concentration are among the most important physical characteristics of particulate matter (PM), directly influencing the health effects of exposure to PM through inhalation, dermal contact, or ingestion. Samples of PM can be found and studied in the dust collected on HVAC filters installed in residential buildings. Therefore, the application of HVAC filters in collecting and analyzing PM indoors is a useful and cost-effective strategy.
## The Hidden Story in Our Air Filters
HVAC filters serve a dual purpose in homes, not only purifying the air by capturing harmful particles but also acting as passive samplers, preserving a record of airborne contaminants. This makes them a valuable tool for experts in Indoor Air Quality (IAQ), Indoor Environmental Engineering, and Health. By analyzing the dust accumulated on used filters, researchers can identify and assess the particles and contaminants present in indoor environments.
This analytical approach, 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 enables the accumulation of particles over the entire filter service life and across the entire conditioned indoor space, allowing for a temporally and spatially representative calculation of PM concentration and particle size distribution (PSD). These metrics are essential for exposure assessments in chronic health studies. This quantitative methodology is referred to as Quantitative Filter Forensics (QFF).