Environmental Insights

Environmental Insights#

Repo README.md Contents#

Environmental Insights

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A Python package for democratizing access to ambient air pollution data and predictive analytics.

📖 Description

Environmental Insights provides easy-to-use functions to download, process, and analyze ambient air pollution and meteorological data over England.

  • Implements supervised machine-learning pipelines to predict hourly pollutant concentrations on a 1 km² grid.

  • Supplies both “typical day” aggregates (percentiles) and full hourly model outputs.

  • Includes geospatial utilities for mapping, interpolation, and uncertainty analysis.

⚙️ Installation

Install from PyPI:

pip install environmental-insights

Or from source:

git clone https://github.com/liamjberrisford/Environmental-Insights.git
cd Environmental-Insights
python -m build
pip install dist/environmental_insights-0.2.1b0-py3-none-any.whl

📂 Data Sources

This package downloads and processes two primary CEDA datasets:

  1. Synthetic Hourly Air Pollution Prediction Averages for England (SynthHAPPE)
    Berrisford, L. (2025). Synthetic Hourly Air Pollution Prediction Averages for England (SynthHAPPE). NERC EDS Centre for Environmental Data Analysis.
    DOI: 10.5285/4cbd9c53ab07497ba42de5043d1f414b

    Representative “typical day” profiles of NO₂, NO, NOₓ, O₃, PM₁₀, PM₂.₅ and SO₂ on a 1 km² grid, with 5th, 50th & 95th percentiles.

  2. Machine Learning for Hourly Air Pollution Prediction in England (ML-HAPPE)
    Berrisford, L. (2025). Machine Learning for Hourly Air Pollution Prediction in England (ML-HAPPE). NERC EDS Centre for Environmental Data Analysis.
    DOI: 10.5285/fc735f9878ed43e293b85f85e40df24d

    Full-year (2018) hourly modelled concentrations of NO₂, NO, NOₓ, O₃, PM₁₀, PM₂.₅ and SO₂ on a 1 km² grid, including 5th, 50th & 95th percentiles and underlying training data.

For full examples, see the Jupyter-Book tutorial in book/tutorial_environmental_insights.ipynb.

📚 Documentation

Build and view locally:

jupyter-book build book/

Then open book/_build/html/index.html in your browser.
Highlights:

  • API Reference: book/docs/api/environmental_insights/

  • Tutorial Notebook: book/tutorial_environmental_insights.ipynb

The documentation is also avaiable via the GitHub Pages Site

✅ Testing

Run the full test suite:

pytest

Integration and unit tests are under tests/.

🤝 Contributing

Contributions and bug-reports are very welcome! Please see CONTRIBUTING.md for details on:

  • Code style

  • Pull request process

  • Issue reporting

📑 Citation

If you use Environmental Insights in your work, please cite:

Berrisford, L. J. (2025). Environmental Insights: Democratizing access to ambient air pollution data and predictive analytics (Version 0.2.1b0) [Software]. GitHub. https://github.com/liamjberrisford/Environmental-Insights

Also cite the underlying datasets:

  • Berrisford, L. (2025). SynthHAPPE: Synthetic Hourly Air Pollution Prediction Averages for England. NERC EDS CEDA. DOI: 10.5285/4cbd9c53ab07497ba42de5043d1f414b

  • Berrisford, L. (2025). ML-HAPPE: Machine Learning for Hourly Air Pollution Prediction in England. NERC EDS CEDA. DOI: 10.5285/fc735f9878ed43e293b85f85e40df24d

📜 License

This project is released under the GPL-3.0-or-later.

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