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---
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]
<!-- Provide the basic links for the dataset. -->
- **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
<!-- This section describes suitable use cases for the dataset. -->
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
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
[More Information Needed]
#### Who are the source data producers?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
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**APA:**
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
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## More Information [optional]
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## Dataset Card Authors [optional]
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## Dataset Card Contact
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