Datasets:
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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](https://openaq.org/)
- **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:
```python
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](https://github.com/Star-Learning/AirQualityBench)
## Citation
```bibtex
@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}
}
```
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