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cosuleabianca
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eea-pm25-forecasting

Tasks:
Time Series Forecasting
Tabular Regression
Languages:
English
Size:
1M<n<10M
Tags:
air-quality
pm25
forecasting
environment
europe
eea
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metadata 
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 

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 

from datasets import load_dataset

dataset = load_dataset("cosuleabianca/eea-pm25-dataset")

Load Raw Parquet Files 

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:

# 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:

@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

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.