---
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.