| --- |
| license: cc-by-4.0 |
| task_categories: |
| - time-series-forecasting |
| - tabular-regression |
| tags: |
| - air-quality |
| - pm25 |
| - forecasting |
| - environment |
| - europe |
| language: |
| - en |
| pretty_name: PM2.5 Air Quality Forecasting Models (Europe) |
| --- |
| |
| # PM2.5 Air Quality Forecasting Models |
|
|
| Pre-trained models for predicting PM2.5 concentrations 1-24 hours ahead across European cities. |
|
|
| ## Model Overview |
|
|
| These models were trained on European Environment Agency (EEA) air quality data from 2018-2022 and evaluated on 2023-2024 data. They predict PM2.5 at multiple forecast horizons: **1h, 3h, 6h, 12h, and 24h**. |
|
|
| ### Training Data |
| - **Countries**: 5 (AT, BE, ES, FI, FR) |
| - **Cities**: Wien, Paris, Madrid, Antwerpen, Helsinki |
| - **Stations**: 38 monitoring stations |
| - **Records**: 1.9M+ hourly observations |
|
|
| ## Available Models |
|
|
| | Model | Type | File Pattern | Description | |
| |-------|------|--------------|-------------| |
| | **Linear Regression** | Statistical | `lr_h{horizon}.pkl` | Baseline linear model | |
| | **GAM** | Statistical | `gam_h{horizon}.pkl` | Generalized Additive Model | |
| | **Random Forest** | ML | `rf_h{horizon}.pkl` | Tuned Random Forest | |
| | **XGBoost** | ML | `xgb_h{horizon}.pkl` | Tuned XGBoost | |
| | **LightGBM** | ML | `lgb_h{horizon}.pkl` | Tuned LightGBM | |
| | **LSTM** | Deep Learning | `lstm_global_h{horizon}.keras` | Basic LSTM (168h lookback) | |
| | **LSTM-Residual** | Deep Learning | `lstm_residual_h{horizon}.keras` | Residual connections | |
| | **LSTM-Attention** | Deep Learning | `lstm_attention_h{horizon}.keras` | Global attention mechanism | |
| | **LSTM-CNN** | Deep Learning | `lstm_cnn_h{horizon}.keras` | Hybrid CNN-LSTM | |
|
|
| ## Performance (1-hour horizon) |
|
|
| ### Protocol A: Full Dataset (606,635 test samples) |
|
|
| | Model | MAE (µg/m³) | RMSE (µg/m³) | R² | |
| |-------|-------------|--------------|-----| |
| | Persistence | 1.50 | 2.64 | 0.872 | |
| | Linear Regression | 1.49 | 2.51 | 0.885 | |
| | LightGBM | 1.44 | 2.45 | 0.890 | |
|
|
| ### Protocol B: Sequence-Eligible Subset (375,906 test samples) |
|
|
| | Model | MAE (µg/m³) | RMSE (µg/m³) | R² | |
| |-------|-------------|--------------|-----| |
| | LSTM-Attention | 1.19 | 2.18 | 0.916 | |
|
|
| *Protocol B uses stations with sufficient sequential data for LSTM (168h+ continuous sequences). See full results in the [GitHub repository](https://github.com/CosuleaBianca/eea-pm25).* |
|
|
| ## Usage |
|
|
| ### Download Models |
|
|
| ```python |
| from huggingface_hub import hf_hub_download |
| |
| # Download a specific model |
| model_path = hf_hub_download( |
| repo_id="cosuleabianca/eea-pm25-models", |
| filename="models_lgb/lgb_h1.pkl" |
| ) |
| |
| # Load with joblib (for sklearn/xgboost/lightgbm models) |
| import joblib |
| model = joblib.load(model_path) |
| ``` |
|
|
| ### Load Keras Models |
|
|
| ```python |
| from huggingface_hub import hf_hub_download |
| from tensorflow import keras |
| |
| model_path = hf_hub_download( |
| repo_id="cosuleabianca/eea-pm25-models", |
| filename="lstm_attention_models/lstm_attention_h1.keras" |
| ) |
| model = keras.models.load_model(model_path) |
| ``` |
|
|
| ## Input Features |
|
|
| All models expect the same feature set (81 features total): |
|
|
| ### Pollutant Features |
| - **PM2.5**: lag_1h, lag_2h, lag_3h, lag_6h, lag_12h, lag_24h, lag_168h, rolling_mean_3h/6h/12h/24h, rolling_std_3h/6h/12h/24h |
| - **NO2**: current, lags (1h-168h), rolling_mean_3h/6h/12h/24h, rolling_std_3h/6h/12h/24h |
| - **PM10**: current, lags (1h-168h), rolling_mean_3h/6h/12h/24h, rolling_std_3h/6h/12h/24h |
| |
| ### Weather Features (Open-Meteo) |
| - temperature_2m, relative_humidity_2m, dew_point_2m |
| - wind_u, wind_v (east-west and north-south components) |
| - precipitation, surface_pressure |
| |
| ### Temporal Features |
| - hour_sin, hour_cos, month_sin, month_cos |
| - day_of_week, is_weekend, season |
|
|
| ### Station Metadata |
| - Latitude, Longitude, Altitude |
| - StationType (background, industrial, traffic) |
| - StationArea (rural, suburban, urban) |
|
|
| ## Repository Structure |
|
|
| ``` |
| ├── models_rf/ # Random Forest models |
| ├── models_lgb/ # LightGBM models |
| ├── models_gam/ # GAM models |
| ├── lstm_global_models/ # Basic LSTM |
| ├── lstm_residual_models/# Residual LSTM |
| ├── lstm_attention_models/# Attention LSTM |
| ├── lstm_cnn_models/ # CNN-LSTM hybrid |
| └── scalers/ # Per-station scalers (for LSTM) |
| ``` |
|
|
| ## Citation |
|
|
| If you use these models, please cite: |
|
|
| ```bibtex |
| @misc{eea-pm25-forecasting, |
| author = {Chisilev Bianca-Iuliana}, |
| title = {PM2.5 Air Quality Forecasting Models for Europe}, |
| year = {2025}, |
| publisher = {Hugging Face}, |
| url = {https://huggingface.co/cosuleabianca/eea-pm25-models} |
| } |
| ``` |
|
|
| ## Links |
|
|
| - **GitHub Repository**: [Github repository](https://github.com/CosuleaBianca/eea-pm25) |
| - **Dataset**: [Dataset](https://huggingface.co/datasets/cosuleabianca/eea-pm25-forecasting) |
|
|
| ## License |
|
|
| CC BY 4.0 - You are free to share and adapt, with attribution. |
|
|
