# Environmental Insights
[](https://pypi.org/project/environmental-insights)
[](https://github.com/liamjberrisford/Environmental-Insights/releases)
[](https://github.com/liamjberrisford/Environmental-Insights/actions)
[](https://github.com/liamjberrisford/Environmental-Insights/actions/workflows/testing.yml)
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:
```bash
pip install environmental-insights
```
Or from source:
```bash
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](https://dx.doi.org/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](https://dx.doi.org/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:
```bash
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](https://liamjberrisford.github.io/Environmental-Insights/home_page.html)
---
## ✅ Testing
Run the full test suite:
```bash
pytest
```
Integration and unit tests are under `tests/`.
---
## 🤝 Contributing
Contributions and bug-reports are very welcome! Please see [CONTRIBUTING.md](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](LICENSE).
Raw data
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"description": "# Environmental Insights\n\n[](https://pypi.org/project/environmental-insights)\n[](https://github.com/liamjberrisford/Environmental-Insights/releases)\n[](https://github.com/liamjberrisford/Environmental-Insights/actions)\n[](https://github.com/liamjberrisford/Environmental-Insights/actions/workflows/testing.yml)\n\n\n\nA Python package for democratizing access to ambient air pollution data and predictive analytics.\n\n---\n\n## \ud83d\udcd6 Description\n\n**Environmental Insights** provides easy-to-use functions to download, process, and analyze ambient air pollution and meteorological data over England. \n- Implements supervised machine-learning pipelines to predict hourly pollutant concentrations on a 1 km\u00b2 grid. \n- Supplies both \u201ctypical day\u201d aggregates (percentiles) and full hourly model outputs. \n- Includes geospatial utilities for mapping, interpolation, and uncertainty analysis.\n\n---\n\n## \u2699\ufe0f Installation\n\nInstall from PyPI:\n\n```bash\npip install environmental-insights\n```\n\nOr from source:\n\n```bash\ngit clone https://github.com/liamjberrisford/Environmental-Insights.git\ncd Environmental-Insights\npython -m build\npip install dist/environmental_insights-0.2.1b0-py3-none-any.whl\n```\n\n---\n\n## \ud83d\udcc2 Data Sources\n\nThis package downloads and processes two primary CEDA datasets:\n\n1. **Synthetic Hourly Air Pollution Prediction Averages for England (SynthHAPPE)** \n Berrisford, L. (2025). *Synthetic Hourly Air Pollution Prediction Averages for England (SynthHAPPE).* NERC EDS Centre for Environmental Data Analysis. \n DOI: [10.5285/4cbd9c53ab07497ba42de5043d1f414b](https://dx.doi.org/10.5285/4cbd9c53ab07497ba42de5043d1f414b) \n > Representative \u201ctypical day\u201d profiles of NO\u2082, NO, NO\u2093, O\u2083, PM\u2081\u2080, PM\u2082.\u2085 and SO\u2082 on a 1 km\u00b2 grid, with 5th, 50th & 95th percentiles.\n\n2. **Machine Learning for Hourly Air Pollution Prediction in England (ML-HAPPE)** \n Berrisford, L. (2025). *Machine Learning for Hourly Air Pollution Prediction in England (ML-HAPPE).* NERC EDS Centre for Environmental Data Analysis. \n DOI: [10.5285/fc735f9878ed43e293b85f85e40df24d](https://dx.doi.org/10.5285/fc735f9878ed43e293b85f85e40df24d) \n > Full-year (2018) hourly modelled concentrations of NO\u2082, NO, NO\u2093, O\u2083, PM\u2081\u2080, PM\u2082.\u2085 and SO\u2082 on a 1 km\u00b2 grid, including 5th, 50th & 95th percentiles and underlying training data.\n\n---\n\nFor full examples, see the Jupyter-Book tutorial in `book/tutorial_environmental_insights.ipynb`.\n\n## \ud83d\udcda Documentation\n\nBuild and view locally:\n\n```bash\njupyter-book build book/\n```\n\nThen open `book/_build/html/index.html` in your browser. \nHighlights:\n\n- **API Reference**: `book/docs/api/environmental_insights/` \n- **Tutorial Notebook**: `book/tutorial_environmental_insights.ipynb`\n\nThe documentation is also avaiable via the [GitHub Pages Site](https://liamjberrisford.github.io/Environmental-Insights/home_page.html)\n\n---\n\n## \u2705 Testing\n\nRun the full test suite:\n\n```bash\npytest\n```\n\nIntegration and unit tests are under `tests/`.\n\n---\n\n## \ud83e\udd1d Contributing\n\nContributions and bug-reports are very welcome! Please see [CONTRIBUTING.md](CONTRIBUTING.md) for details on:\n\n- Code style \n- Pull request process \n- Issue reporting \n\n---\n\n## \ud83d\udcd1 Citation\n\nIf you use *Environmental Insights* in your work, please cite:\n\n> 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 \n\nAlso cite the underlying datasets:\n\n- Berrisford, L. (2025). *SynthHAPPE*: Synthetic Hourly Air Pollution Prediction Averages for England. NERC EDS CEDA. DOI: 10.5285/4cbd9c53ab07497ba42de5043d1f414b \n- Berrisford, L. (2025). *ML-HAPPE*: Machine Learning for Hourly Air Pollution Prediction in England. NERC EDS CEDA. DOI: 10.5285/fc735f9878ed43e293b85f85e40df24d \n\n---\n\n## \ud83d\udcdc License\n\nThis project is released under the [GPL-3.0-or-later](LICENSE). \n\n",
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