---
license: mit
---

# Dataset Card for MIL-QUALAIR

<!-- Provide a quick summary of the dataset. -->

The dataset has been constructed for urban air pollution forecasting in task the Milan metropolitan area and includes Sentinel-5P satellite observations, meteorological conditions, topographical features, and ground monitoring station measurements.
## Dataset Details

### Dataset Description

<!-- Provide a longer summary of what this dataset is. -->
The dataset encompasses a compilation of various data sources, including Sentinel-5 satellite observations, Digital Elevation Model (DEM) data, land cover information, meteorological records, and ground-level measurements, spanning the period from 2018 to 2023 within the metropolitan area of Milan. It is curated to support the task of forecasting the concentrations of five major pollutants namely PM10, PM25, NO2, O3, SO2. This dataset has been utilized and introduced in the study *Urban Air Pollution Forecasting: A Machine Learning Approach Leveraging Satellite Observations and Meteorological Forecasts*.


- **Curated by:** LINKS Foundation
- **Funded by:** [UP2030](https://up2030-he.eu/) project
- **License:** MIT License

### Dataset Sources [optional]

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- **Paper:**
```latex
@INPROCEEDINGS{10424071,
  author={Blanco, Giacomo and Barco, Luca and Innocento, Lorenzo and Rossi, Claudio},
  booktitle={2024 IEEE International Workshop on Metrology for Living Environment (MetroLivEnv)}, 
  title={Urban Air Pollution Forecasting: A Machine Learning Approach Leveraging Satellite Observations and Meteorological Forecasts}, 
  year={2024}
}

```

## Uses

<!-- Address questions around how the dataset is intended to be used. -->
The dataset is intended to serve as a comprehensive resource for researchers and practitioners interested in studying urban air quality dynamics and developing pollution forecasting models. With its diverse array of environmental data sources, including Sentinel-5 satellite observations, Digital Elevation Model (DEM) data, land cover information, meteorological records, and ground-level measurements, the dataset offers rich insights into the complex interplay of factors influencing air pollution levels in the Milan metropolitan area. Researchers can utilize this dataset to investigate correlations between different environmental variables and pollutant concentrations, identify patterns and trends over time, and develop and validate predictive models for air quality forecasting.

### Direct Use

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Major dataset use case is for the development of air pollution forecasting models. By combining various data sources within the dataset, users can create a comprehensive feature set for each day. This aggregated feature set provides a robust foundation for predicting the levels of the five supported pollutants with greater accuracy. The repository presents each data source separately, allowing users to follow the aggregation process outlined in the associated paper or develop their own methodology tailored to specific research objectives.


## Dataset Structure

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## Dataset Creation

### Curation Rationale

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### Source Data

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#### Data Collection and Processing

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#### Who are the source data producers?

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### Recommendations

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Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.

## Citation [optional]

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**BibTeX:**

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**APA:**

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## Glossary [optional]

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## Dataset Card Authors [optional]

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## Dataset Card Contact

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