---
license: cc0-1.0
task_categories:
- time-series-forecasting
- tabular-regression
language:
- en
tags:
- air-quality
- meteorology
- synthetic-data
- time-series
- kolkata
- spatiotemporal
- missing-data
- graph-learning
pretty_name: Synthetic Kolkata Air Quality & Meteorology 100M
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*.parquet
  - split: validation
    path: data/validation-*.parquet
  - split: test
    path: data/test-*.parquet
---

# Synthetic Kolkata Air Quality & Meteorology 100M

A reproducible **fully synthetic** spatiotemporal benchmark inspired by broad Kolkata, West Bengal climatological and air-quality behavior. The release contains exactly **100,000,000 station-hour rows** from **1,000 explicitly synthetic sensor sites**.

This is not an official CPCB/WBPCB/IMD monitoring archive and is not a reconstruction of historical measurements. Synthetic coordinates, event effects and station classes are benchmark constructs.

## Intended use

Suitable for:

- multivariate time-series forecasting
- graph/spatiotemporal forecasting
- missing-data imputation
- anomaly/event detection
- synthetic time-series benchmarking
- robustness experiments with variable-specific sensor outages

Not suitable for:

- epidemiological exposure estimates
- legal/regulatory compliance
- historical claims about actual Kolkata pollutant concentrations
- identifying real monitoring stations
- treating event multipliers as measured causal effects

## Dataset grain and coverage

| Property | Value |
|---|---|
| Grain | one synthetic site × one hourly timestamp |
| Sites | 1,000 |
| Hours/site | 100,000 |
| Total rows | **100,000,000** |
| Start | 2015-01-01 00:00 Asia/Kolkata |
| End | 2026-05-29 15:00 Asia/Kolkata |
| Train | first 80,000 hours/site = 80,000,000 rows |
| Validation | next 10,000 hours/site = 10,000,000 rows |
| Test | final 10,000 hours/site = 10,000,000 rows |
| Spatial graph | directed 10-nearest-neighbor Haversine graph |

The regular hourly grid is always preserved. Missing sensor values remain as nulls rather than deleting timestamps.

## Main variables

| Field | Unit / encoding | Generation logic |
|---|---|---|
| `timestamp` | timezone-aware | Asia/Kolkata in Parquet export |
| `station_id` | uint16 | synthetic site ID |
| `temperature_c` | °C | monthly climatology + diurnal cycle + persistent synoptic + microclimate |
| `relative_humidity_pct` | % | coupled to temperature and rainfall |
| `wind_speed_ms` | m/s | seasonal positive-skew process + cyclone gust regime |
| `rainfall_mm` | mm/hour | Markov wet/dry process + clustered storms + monthly interannual variability |
| `pm25_ugm3` | µg/m³ | season + diurnal + ventilation + wet scavenging + site + dynamic spatial field |
| `pm10_ugm3` | µg/m³ | PM2.5 + independently varying coarse component |
| `no2_ugm3` | µg/m³ | source class + rush-hour/weekend + ventilation + dynamic combustion field |
| `co_mgm3` | mg/m³ | combustion-linked but independently perturbed |
| `so2_ugm3` | µg/m³ | industrial/source effects + ventilation + independent perturbation |
| `o3_ugm3` | µg/m³ | solar/temperature photochemistry + NO2 titration + separate spatial field |
| `event_code` | uint8 | 0 normal, 1 Diwali stress, 2 lockdown, 3 cyclone, 4 heatwave |
| `obs_mask` | uint16 | variable-specific observation bit mask |
| `split_code` | uint8 | 0 train, 1 validation, 2 test |

See `DATA_DICTIONARY.md` for the exact bit layout of `obs_mask`.

## Spatial structure

The generator uses **24 time-varying spatial latent fields** so geographic proximity carries meaningful statistical structure rather than merely sharing one city-wide signal. Each station mixes its four nearest latent knots with Gaussian distance weights. Separate fields are used for fine particulate matter, coarse material, combustion-related pollutants and ozone.

A final 120-site × 6,000-hour diagnostic on the materialized dataset gives:

| PM2.5 spatial diagnostic | Result |
|---|---:|
| distance vs raw pairwise correlation | **-0.7125** |
| mean raw correlation <5 km | **0.9546** |
| mean raw correlation ≥30 km | **0.9133** |
| mean residualized correlation <5 km | **0.2610** |
| mean residualized correlation ≥30 km | **-0.0570** |

The exact coefficients are properties of this synthetic design, not empirical claims about a universal Kolkata covariance law. Their purpose is to ensure that geographic proximity carries meaningful statistical information.

## Structured, variable-specific missingness

Missingness combines whole-site telemetry outages with channel-specific clustered failures. Full-table rates are:

| Variable | Missing rate |
|---|---:|
| temperature | 1.860% |
| relative humidity | 2.051% |
| wind speed | 2.427% |
| rainfall | 2.524% |
| PM2.5 | 3.215% |
| PM10 | 3.772% |
| NO2 | 5.503% |
| CO | 6.452% |
| SO2 | 7.459% |
| O3 | 6.000% |

Missing periods occur in contiguous runs. For example, in the validator's 20-site sample, median missing-run lengths range from about 5 to 10 hours depending on channel, with longer high-percentile outages for gaseous-pollutant channels.

## Physical and logical validation

The materialized 100M-row table passes all hard validation rules in `validation_report.json`:

- exact 100,000,000 rows
- 1,000 sites × 100,000 timestamps
- timestamp order consistency = 1.0
- timestamp uniqueness = 1.0
- hourly regularity = 1.0
- grid completeness = 1.0
- zero station/timestamp key errors
- zero train/validation/test structure errors
- zero invalid observation-mask bits
- zero mask/null mismatches
- zero negative pollutant values
- zero invalid temperature/RH/wind/rain range violations
- zero observed rows with `PM10 < PM2.5`
- released 10-nearest-neighbor graph has exactly 10 outgoing neighbors/site and no self edges

Across the full table:

| Variable | Minimum | Mean | Maximum |
|---|---:|---:|---:|
| temperature | 7.743 °C | 27.089 °C | 42.330 °C |
| RH | 30.986% | 75.181% | 100% |
| wind | 0.030 m/s | 1.805 m/s | 21.910 m/s |
| rain | 0 | 0.201 mm/h | 69.722 mm/h |
| PM2.5 | 1.911 | 96.075 | 950 µg/m³ |
| PM10 | 4.590 | 140.716 | 1,200 µg/m³ |
| NO2 | 3.617 | 42.810 | 250 µg/m³ |
| CO | 0.170 | 1.396 | 6.321 mg/m³ |
| SO2 | 1.046 | 9.292 | 50.318 µg/m³ |
| O3 | 2.387 | 26.365 | 199.632 µg/m³ |

Safety caps are numerical backstops rather than major distributional drivers: only 15 observed PM2.5 values hit 950, 25 PM10 values hit 1,200, and one NO2 value hit 250 across 100M rows; CO, SO2 and O3 do not hit their upper caps.

The January temperature audit across 20 geographically spread synthetic sites is **8.36–31.35 °C**, correcting the physically implausible >40 °C winter behavior found in the older dataset.

## Relationship diagnostics

Station-0 full-history pairwise correlations include:

- PM2.5 vs PM10: 0.988
- PM2.5 vs wind: -0.381
- PM2.5 vs rain: -0.141
- NO2 vs O3: -0.212
- NO2 vs CO: 0.818

These are generator diagnostics, not target scientific constants. The validator checks signs and logical relationships rather than claiming exact empirical equivalence.

## Temporal fidelity

The generator is not independent row sampling. Persistent city and local AR processes, calendar effects, rainfall episodes, events and spatial latent fields create long-range structure.

Example station-0 autocorrelation:

| Variable | lag 1 h | lag 24 h | lag 168 h |
|---|---:|---:|---:|
| temperature | 0.983 | 0.976 | 0.903 |
| PM2.5 | 0.967 | 0.888 | 0.835 |
| NO2 | 0.901 | 0.728 | 0.681 |
| O3 | 0.945 | 0.969 | 0.948 |

`validation_report.json` also stores first-difference summaries, monthly profiles, event-window ratios and spatial-correlation bins.

## Synthetic event regimes

`event_code` supplies controlled stress regimes. Multipliers are deliberately documented as **synthetic scenario parameters**, not measured causal effects. The benchmark is therefore suitable for algorithmic stress tests but not for estimating historical policy or event effects.

## Reproducibility

Generation seed: `20260917`.

```bash
pip install -r requirements.txt
python generate.py --init --start-station 0 --end-station 1000
python validate.py
python export_parquet.py \
  --input air_quality_100m.npy \
  --stations stations.csv \
  --graph spatial_graph_knn10.csv \
  --out data
```

Generation is resumable by station range. The canonical NumPy materialization is deterministic for the same code, seed and dependency behavior.

## Research rationale

Recent work supports evaluating synthetic sequential data beyond static marginals:

1. **Kwon et al. (2026), Seq2Synth, arXiv:2607.15606** shows that static distribution fidelity can hide timestamp and trajectory failures and motivates direct checks of regularity, first differences, autocorrelation and cross-sectional dynamics.
2. **Vapsi et al. (2026), Dynamic Linear Coregionalization, arXiv:2604.05064** motivates time-varying and lag-aware multivariate dependence rather than fixed cross-channel correlations.
3. **Xu et al. (2026), AirQualityBench, arXiv:2605.05854** emphasizes explicit observation masks, physical-scale metrics, spatial graph structure and distance-aware correlation analysis for realistic air-quality forecasting benchmarks.

Those papers motivate the **validation principles**, not a claim that this synthetic generator reproduces their datasets.

See `RESEARCH_VALIDATION.md` for more detail.

## Selected domain references

- India Meteorological Department, Kolkata/Alipore climatological and extreme-weather summaries.
- Central Pollution Control Board, National Ambient Air Quality Standards, India.
- Singh, V., Singh, S., & Biswal, A. (2021). *Exceedances and trends of particulate matter (PM2.5) in five Indian megacities*. Science of the Total Environment 750, 141461. DOI: 10.1016/j.scitotenv.2020.141461.
- Chatterjee, A. et al. (2013). *Air Quality during Diwali Festival over Kolkata – A Mega-City in India*. Aerosol and Air Quality Research 13, 1133–1144. DOI: 10.4209/aaqr.2012.03.0062.

## Important limitation

This release passes **internal synthetic-design validation**, but that is different from proving empirical equivalence to a real Kolkata monitoring archive. A future external-fidelity study should compare against an authoritative, redistributable real reference set using matched seasonal/diurnal distributions, spectra, autocorrelation, cross-correlation, extreme-event statistics, spatial covariance and train-synthetic/test-real forecasting utility.

Accordingly, the correct description is **literature-informed synthetic benchmark**, not historical reconstruction or digital twin.

## External calibration snapshot

A separate `EXTERNAL_CALIBRATION.md` compares selected synthetic summaries with published Kolkata evidence. In particular, a 100-site sample has a Jan–Feb PM2.5 mean of about **176.5 µg/m³**, while a CPCB report gives **175 µg/m³** for Kolkata winter; the synthetic highest/lowest monthly mean ratio is **5.90**, versus **6.98** in a six-year Kolkata study. These are broad plausibility checks only because the periods, stations and aggregation rules differ.

## License

CC0-1.0 for the synthetic dataset and release metadata. Third-party references remain under their original terms.