| --- |
| 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} |
| } |
| ``` |
|
|
