Update README.md

README.md

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----
-license: cc-by-4.0
----
+---
+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.