| | --- |
| | license: cc-by-4.0 |
| | task_categories: |
| | - time-series-forecasting |
| | - tabular-regression |
| | tags: |
| | - air-quality |
| | - pm25 |
| | - forecasting |
| | - environment |
| | - europe |
| | - eea |
| | language: |
| | - en |
| | pretty_name: EEA PM2.5 Air Quality Dataset (Europe) |
| | size_categories: |
| | - 1M<n<10M |
| | --- |
| | |
| | # EEA PM2.5 Air Quality Dataset |
| |
|
| | Hourly air quality measurements from the European Environment Agency (EEA) for PM2.5 forecasting research. |
| |
|
| | ## Dataset Description |
| |
|
| | This dataset contains hourly air pollutant concentrations and meteorological data from monitoring stations across 5 European cities, prepared for machine learning forecasting tasks. |
| |
|
| | ### Data Sources |
| | - **Air Quality**: European Environment Agency (EEA) Air Quality Portal |
| | - **Weather**: Open-Meteo Archive API |
| |
|
| | ### Coverage |
| | - **Time Period**: 2018-01-08 to 2024-12-31 |
| | - **Countries**: 5 (AT, BE, ES, FI, FR) |
| | - **Cities**: Wien, Paris, Madrid, Antwerpen, Helsinki |
| | - **Monitoring Stations**: 38 |
| | - **Total Records**: 1,945,153 hourly observations |
| | - **Total Features**: 81 columns |
| |
|
| | ## Dataset Files |
| |
|
| | ### Raw Data (Parquet) |
| | | File | Description | |
| | |------|-------------| |
| | | `PM2.5.parquet` | PM2.5 concentrations (all sites) | |
| | | `PM2.5_filtered.parquet` | PM2.5 (filtered to quality sites) | |
| | | `NO2.parquet` | NO2 concentrations (all sites) | |
| | | `NO2_filtered.parquet` | NO2 (filtered sites) | |
| | | `PM10.parquet` | PM10 concentrations (all sites) | |
| | | `PM10_filtered.parquet` | PM10 (filtered sites) | |
| |
|
| | ### ML-Ready Dataset |
| | | File | Description | Size | |
| | |------|-------------|------| |
| | | `ml_ready_dataset_full_realistic.csv` | Feature-engineered dataset | ~1.6 GB | |
| |
|
| | ## Features (81 columns) |
| |
|
| | ### Metadata |
| | - `Start`: Original timestamp |
| | - `Country`: Country code (AT, BE, ES, FI, FR) |
| | - `SiteNumber`: Station identifier |
| | - `dt_utc`: Timestamp in UTC |
| | - `dt_local`: Timestamp in local timezone |
| |
|
| | ### Site Metadata (Geographic) |
| | - `Latitude`, `Longitude`, `Altitude` |
| |
|
| | ### Station Type (One-Hot Encoded) |
| | - `StationType_background` |
| | - `StationType_industrial` |
| | - `StationType_traffic` |
| |
|
| | ### Station Area (One-Hot Encoded) |
| | - `StationArea_rural` |
| | - `StationArea_rural-nearcity` |
| | - `StationArea_suburban` |
| | - `StationArea_urban` |
| |
|
| | ### Weather Features (Open-Meteo API) |
| | - `temperature_2m`: Air temperature at 2m (°C) |
| | - `relative_humidity_2m`: Relative humidity (%) |
| | - `dew_point_2m`: Dew point temperature (°C) |
| | - `wind_u`: East-west wind component (m/s) |
| | - `wind_v`: North-south wind component (m/s) |
| | - `precipitation`: Hourly precipitation (mm) |
| | - `surface_pressure`: Surface pressure (hPa) |
| |
|
| | ### Target Variable |
| | - `PM2.5`: Current PM2.5 concentration (µg/m³) |
| |
|
| | ### Pollutant Features |
| | - `NO2`: Current NO2 concentration |
| | - `PM10`: Current PM10 concentration |
| |
|
| | ### Temporal Features |
| | - `hour`, `day_of_week`, `day_of_month`, `month`, `year` |
| | - `is_weekend`: Weekend indicator (0/1) |
| | - `season`: Season indicator |
| | - `hour_sin`, `hour_cos`: Cyclical hour encoding |
| | - `month_sin`, `month_cos`: Cyclical month encoding |
| |
|
| | ### Lag Features (1h, 2h, 3h, 6h, 12h, 24h, 168h) |
| | - `PM2.5_lag_1h`, `PM2.5_lag_2h`, `PM2.5_lag_3h`, `PM2.5_lag_6h`, `PM2.5_lag_12h`, `PM2.5_lag_24h`, `PM2.5_lag_168h` |
| | - `NO2_lag_1h`, `NO2_lag_2h`, `NO2_lag_3h`, `NO2_lag_6h`, `NO2_lag_12h`, `NO2_lag_24h`, `NO2_lag_168h` |
| | - `PM10_lag_1h`, `PM10_lag_2h`, `PM10_lag_3h`, `PM10_lag_6h`, `PM10_lag_12h`, `PM10_lag_24h`, `PM10_lag_168h` |
| |
|
| | ### Rolling Mean Features (3h, 6h, 12h, 24h windows) |
| | - `PM2.5_rolling_mean_3h`, `PM2.5_rolling_mean_6h`, `PM2.5_rolling_mean_12h`, `PM2.5_rolling_mean_24h` |
| | - `NO2_rolling_mean_3h`, `NO2_rolling_mean_6h`, `NO2_rolling_mean_12h`, `NO2_rolling_mean_24h` |
| | - `PM10_rolling_mean_3h`, `PM10_rolling_mean_6h`, `PM10_rolling_mean_12h`, `PM10_rolling_mean_24h` |
| |
|
| | ### Rolling Std Features (3h, 6h, 12h, 24h windows) |
| | - `PM2.5_rolling_std_3h`, `PM2.5_rolling_std_6h`, `PM2.5_rolling_std_12h`, `PM2.5_rolling_std_24h` |
| | - `NO2_rolling_std_3h`, `NO2_rolling_std_6h`, `NO2_rolling_std_12h`, `NO2_rolling_std_24h` |
| | - `PM10_rolling_std_3h`, `PM10_rolling_std_6h`, `PM10_rolling_std_12h`, `PM10_rolling_std_24h` |
| |
|
| | ## Data Quality |
| |
|
| | ### Filtering Criteria |
| | Stations included meet these quality thresholds: |
| | - **Train completeness**: ≥50% (2018-2022) |
| | - **Test completeness**: ≥50% (2023-2024) |
| | - **Maximum gap**: ≤168 hours |
| |
|
| | ### Preprocessing |
| | - Sentinel values (<0) replaced with NaN |
| | - Time-based lag/rolling features (handles data gaps correctly) |
| | - Weather data merged by nearest hour |
| | - Local timezone conversion for temporal features |
| | - **No missing values** in final dataset |
| |
|
| | ## Stations by Country |
| |
|
| | | Country | City | Stations | |
| | |---------|------|----------| |
| | | AT | Wien | 10 | |
| | | BE | Antwerpen | 8 | |
| | | ES | Madrid | 9 | |
| | | FI | Helsinki | 5 | |
| | | FR | Paris | 6 | |
| |
|
| | ## Usage |
| |
|
| | ### Load with Pandas |
| |
|
| | ```python |
| | import pandas as pd |
| | |
| | # Load ML-ready dataset |
| | df = pd.read_csv("ml_ready_dataset_full_realistic.csv") |
| | |
| | # Train/test split (temporal) |
| | train = df[df['dt_utc'] < '2023-01-01'] |
| | test = df[df['dt_utc'] >= '2023-01-01'] |
| | ``` |
| |
|
| | ### Load with Hugging Face Datasets |
| |
|
| | ```python |
| | from datasets import load_dataset |
| | |
| | dataset = load_dataset("cosuleabianca/eea-pm25-dataset") |
| | ``` |
| |
|
| | ### Load Raw Parquet Files |
| |
|
| | ```python |
| | import pandas as pd |
| | |
| | pm25 = pd.read_parquet("PM2.5_filtered.parquet") |
| | no2 = pd.read_parquet("NO2_filtered.parquet") |
| | ``` |
| |
|
| | ## Train/Test Split |
| |
|
| | | Split | Period | Purpose | |
| | |-------|--------|---------| |
| | | Train | 2018-01-08 to 2022-12-31 | Model training | |
| | | Test | 2023-01-01 to 2024-12-31 | Evaluation | |
| |
|
| | This temporal split simulates real-world forecasting scenarios. |
| |
|
| | ## Regenerating the Dataset |
| |
|
| | If you prefer to regenerate from raw EEA data: |
| |
|
| | ```bash |
| | # Clone the repository |
| | git clone https://github.com/CosuleaBianca/eea-pm25 |
| | cd eea-pm25 |
| | |
| | # Install dependencies |
| | pip install -r requirements.txt |
| | |
| | # Run data pipeline |
| | python dataset_build/src/download_pollutants.py |
| | python dataset_build/src/filter_pm25_sites.py |
| | python dataset_build/src/process_data.py |
| | python dataset_build/src/prepare_ml_dataset.py |
| | python dataset_build/src/coverage_only_v6.py |
| | python dataset_build/src/dataset_full_realistic_v6.py |
| | ``` |
| |
|
| | ## Citation |
| |
|
| | If you use this dataset, please cite: |
| |
|
| | ```bibtex |
| | @misc{eea-pm25-dataset, |
| | author = {Chisilev Bianca-Iuliana}, |
| | title = {EEA PM2.5 Air Quality Dataset for Europe}, |
| | year = {2025}, |
| | publisher = {Hugging Face}, |
| | url = {https://huggingface.co/datasets/cosuleabianca/eea-pm25-dataset} |
| | } |
| | ``` |
| |
|
| | ## Links |
| |
|
| | - **GitHub Repository**: [Github repository](https://github.com/CosuleaBianca/eea-pm25) |
| | - **Pre-trained Models**: [Models](https://huggingface.co/cosuleabianca/eea-pm25) |
| |
|
| | ## License |
| |
|
| | CC BY 4.0 - You are free to share and adapt, with attribution. |
| |
|
| | Original data from the European Environment Agency is provided under the EEA standard reuse policy. |
| |
|
