Current Conditions
Delhi, Delhi, India · 28.614, 77.209
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Source: Sample dataset (demo)Resolution: Station-level, hourly
Weather Summary
Current surface observations
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Source: Sample dataset (demo)Condition:
Pollutant Levels
Hover a card for units and health relevance
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Source: Sample dataset (demo)
Sources & Methodology
Data provenance, limitations, and how to wire in live sources
Datasets used in this dashboard
- City / country conditions & time seriesLive: current AQI and pollutant concentrations from WAQI (World Air Quality Index, nearest reporting station); current weather, forecasts, and pollutant history/forecast from OpenWeather. Composite AQI for history/forecast is derived from measured PM2.5 via the standard EPA breakpoint formula.
- AQI health categories & breakpointsU.S. EPA Air Quality Index technical guidance (2024) — Good through Hazardous, PM2.5 breakpoint table.
- Adult obesity prevalence (choropleth / cartogram)Illustrative values modeled on CDC Adult Obesity Prevalence Maps (BRFSS, self-reported BMI ≥ 30) reporting patterns.
- Smoking prevalence & cigarettes/dayIllustrative values modeled on CDC smoking surveillance reporting patterns (BRFSS / NHIS-style indicators).
- State PM2.5 / AQI annual layersIllustrative values modeled on EPA / AirNow annual summary reporting patterns.
- U.S. state boundariesUS Census TIGER/Line boundaries via the us-atlas TopoJSON distribution (Natural Earth / Census-derived, 1:10m), served from /api/geography/states.
Methodology notes
- Observed vs. predicted values are always labeled separately; forecast points carry a widening uncertainty band with lead time (currently modeled for AQI).
- The bivariate choropleth classifies each variable into terciles independently — a 3×3 grid, not a joint quantile.
- Environmental and public-health layers shown together are exploratory correlations, never causal claims.
- City/country conditions, forecasts, and the bivariate choropleth are live provider data (WAQI, OpenWeather), served through a normalized internal REST API (see Network tab, /api/**). U.S. state-level public-health layers (obesity, smoking, correlation research) remain illustrative sample data modeled on CDC/EPA reporting patterns, not live feeds.
- Missing data is never silently interpolated across incompatible geographies or time windows; gaps are left visible in the underlying series.