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.
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:
- 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.
- Vapsi et al. (2026), Dynamic Linear Coregionalization, arXiv:2604.05064 motivates time-varying and lag-aware multivariate dependence rather than fixed cross-channel correlations.
- 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.