README.md

metadata

license: mit
task_categories:
  - time-series-forecasting
language: []
tags:
  - air-quality
  - spatio-temporal
  - graph-neural-networks
  - missing-data
pretty_name: AirQualityBench
size_categories:
  - 1G-10G

AirQualityBench

A global-scale air quality forecasting benchmark featuring 3,720 monitoring stations across the world with authentic missing patterns and physical-scale evaluation.

Dataset Summary

AirQualityBench provides a realistic testbed for air quality prediction by preserving raw observational characteristics that prior benchmarks have removed through imputation. The dataset spans 5 years (2021–2025) of hourly measurements across 6 primary pollutants (PM2.5, PM10, NO₂, O₃, SO₂, CO).

Key Features

• Global scale: 3,720 active stations spanning 7 continents, sourced from OpenAQ
• Authentic missingness: No imputation — evaluation uses boolean masks to score only valid observations
• Physical-scale metrics: All statistics computed in original concentration units (μg/m³ for gases, mg/m³ for CO)
• Multi-pollutant: 6 pollutants evaluated jointly

Data Structure

Each yearly HDF5 file (aq_compact_{year}.h5) contains:

Dataset	Shape	Description
values	(T, 3720, 6)	Hourly concentrations (float32)
masks	(T, 3720, 6)	Valid observation flags (bool)
station_ids	(3720,)	Station identifiers
params	(6,)	Pollutant names

Additional files:

• adj_mx_10.pkl — KNN adjacency matrix (k=10, Haversine distance)
• scaler.csv — Per-pollutant mean/std for normalization
• global_quality_stats.csv — Per-station, per-pollutant coverage rates
• selected_nodes_metadata.csv — Station coordinates and metadata
• all_stations_coords.csv — Raw station coordinates from OpenAQ

Data Splits

Split	Years	Approx. Hours
Train	2021–2023	~26,280
Validation	2024	~8,784
Test	2025	~8,760

Usage

This dataset uses the HDF5 format with multiple internal arrays of different shapes, which is incompatible with the datasets library's HDF5 loader. Use h5py to read the files directly:

import h5py

with h5py.File("aq_compact_2021.h5", "r") as f:
    values = f["values"][:]       # (T, 3720, 6) float32
    masks = f["masks"][:]         # (T, 3720, 6) bool
    params = f["params"][:]       # (6,) pollutant names
    station_ids = f["station_ids"][:]  # (3720,) station IDs

Benchmark Code

Training and evaluation code: github.com/Star-Learning/AirQualityBench

Citation

@article{xu2026airqualitybench,
  title={AirQualityBench: A Realistic Evaluation Benchmark for Global Air Quality Forecasting},
  author={Xu, Xing and Wang, Xu and Zhang, Yudong and Zhao, Huilin and Zhou, Zhengyang and Wang, Yang},
  journal={arXiv preprint arXiv:2605.05854},
  year={2026}
}