environment_air_quality_county_year
County x year x metric grain. CDC/EPA Downscaler air quality estimates — PM2.5 annual mean (ug/m3) and ozone annual mean (ppb) at county level, 2001-2020. Long format: one row per (county_id, year, metric). Tier B modeled estimates — Bayesian spatial fusion of EPA AQS monitor data with CMAQ model output. Contiguous US only; AK/HI/territories absent. Do not use for personal exposure or precise rural values (model-dependent). Opens the us_environment domain.
overview
~62,000 county-year rows in wide format. PM2.5 annual mean and ozone annual mean for all US counties.
current vintage — 2020
history — Yes — 2001–2020
source & licensing
fields
| name | type | definition |
|---|---|---|
| country_id | string | ISO alpha-2 country code (always 'US' for domestic tables). |
| county_fips | string | Five-character FIPS code identifying the county. Matches the contract column county_id; retained as a source-native identifier. |
| county_idkey | string | 5-character FIPS code identifying the county.Part of primary key. Joins dim.counties on county_id. |
| data_basis | string | Label describing the methodological basis of the modeled estimate for this county-year (e.g., monitor-informed vs. model-only). Values reflect how much EPA AQS monitor coverage contributed relative to CMAQ output. |
| ozone_annual_mean_ppb | float64 · parts per billion (ppb) | Bayesian spatial-fusion estimate of annual mean ozone (O₃) concentration for the county-year, derived from EPA AQS monitor data fused with CMAQ model output via the CDC/EPA Downscaler. Higher values indicate worse air quality. |
| ozone_stdd | float64 · parts per billion (ppb) | Posterior standard deviation of the ozone annual mean estimate from the Downscaler Bayesian model. Higher values indicate greater uncertainty in the modeled estimate for that county-year. |
| ozone_tract_day_count | int64 · count | Number of census tract-day observations that were aggregated to produce the county-year ozone estimate. Lower counts suggest sparser underlying data and potentially less reliable estimates. |
| pm25_annual_mean_ug_m3 | float64 · micrograms per cubic meter (µg/m³) | Bayesian spatial-fusion estimate of annual mean fine particulate matter (PM2.5) concentration for the county-year, derived from EPA AQS monitor data fused with CMAQ model output via the CDC/EPA Downscaler. Higher values indicate worse air quality. |
| pm25_stdd | float64 · micrograms per cubic meter (µg/m³) | Posterior standard deviation of the PM2.5 annual mean estimate from the Downscaler Bayesian model. Higher values indicate greater uncertainty in the modeled estimate for that county-year. |
| pm25_tract_day_count | int64 · count | Number of census tract-day observations that were aggregated to produce the county-year PM2.5 estimate. Lower counts suggest sparser underlying data and potentially less reliable estimates. |
| state_id | string | 2-character FIPS code identifying the state.Joins dim.states on state_id. |
| yearkey | int64 | Reference year of the observation.Part of primary key. |
joins
how to use this table
Bayesian spatial downscaling model fusing EPA AQS monitoring data with CMAQ deterministic air quality model output.
Regulatory compliance or permitting — use EPA AQS monitor data directly.