Annual county-level air-quality exposure panel built from the EPA Air Quality System (AQS) daily AQI-by-county bulk files. Per county and year: reported-day coverage, AQI max/p90, day counts by EPA category, unhealthy-plus (AQI>100) day counts, longest unhealthy streak, Jun-Aug wildfire-season smoke days, dominant pollutant, PM2.5/ozone shares, year-over-year change, a fixed-cut burden tier, and a 0-100 composite burden score. Covers ~990 counties with EPA monitors (50 states + DC). Joins on ISO date + county FIPS for demand-disruption modeling, seasonal staffing, and territory planning.

Source
Rows: 2,945
Columns: 29
Source cadence: Yearly
Last refreshed: Sep 30, 2026
Theme: environment

Column	Type	Description
snapshot_date	string	Date this snapshot was built (ISO).
vintage_year	integer	Calendar year of the EPA annual file. (unit: year)
county_fips	string	5-digit county FIPS (state 2-digit + county 3-digit).
county_name	string	County name as published by EPA AQS.
state_code	string	USPS postal abbreviation.
state_name	string	State name as published by EPA AQS.
country_code	string	ISO alpha-3 country code (always USA).
n_days_reported	integer	Days with at least one EPA monitor reporting AQI. (unit: days)
coverage_pct	float	n_days_reported / days in year * 100. (unit: percent)
aqi_max	integer	Highest daily AQI recorded in the county that year. (unit: AQI)
aqi_max_date	string	Date of the highest daily AQI (first on ties).
aqi_p90	float	90th percentile of daily AQI values. (unit: AQI)
n_good	integer	Days with AQI 0-50 (EPA 'Good'). (unit: days)
n_moderate	integer	Days with AQI 51-100 (EPA 'Moderate'). (unit: days)
n_unhealthy_sensitive	integer	Days with AQI 101-150. (unit: days)
n_unhealthy	integer	Days with AQI 151-200. (unit: days)
n_very_unhealthy	integer	Days with AQI 201-300. (unit: days)
n_hazardous	integer	Days with AQI 301+. (unit: days)
n_unhealthy_plus	integer	Days with AQI > 100 (all unhealthy bands). (unit: days)
unhealthy_plus_pct	float	n_unhealthy_plus / n_days_reported * 100. (unit: percent)
longest_unhealthy_streak	integer	Longest run of consecutive calendar days with AQI > 100. (unit: days)
summer_smoke_days	integer	Jun-Aug days with AQI > 100 (wildfire-season exposure window). (unit: days)
dominant_parameter	string	Most frequent EPA defining parameter (usually PM2.5 or Ozone).
pm25_share_pct	float	Share of reported days driven by PM2.5. (unit: percent)
ozone_share_pct	float	Share of reported days driven by Ozone. (unit: percent)
yoy_change_unhealthy_plus	float	Year-over-year change in unhealthy-plus days vs prior vintage (null for 2023). (unit: days)
burden_tier	string	Fixed-cut tier on n_unhealthy_plus: minimal (0), low (1-3), moderate (4-8), high (9-20), very_high (>20).
aqi_burden_score	float	0-100 composite: 100(0.5minmax(n_unhealthy_plus) + 0.3minmax(aqi_p90) + 0.2minmax(longest_unhealthy_streak)); min-max across counties within the vintage year. (unit: score 0-100)
row_hash	string	sha256('AQI\|<county_fips>\|<year>')[:16], unique row id.

First 10 sample rows — a preview, not the complete dataset.

snapshot_date	vintage_year	county_fips	county_name	state_code	state_name	country_code	n_days_reported	coverage_pct	aqi_max	aqi_max_date	aqi_p90	n_good	n_moderate	n_unhealthy_sensitive	n_unhealthy_plus	unhealthy_plus_pct	longest_unhealthy_streak	summer_smoke_days	dominant_parameter	pm25_share_pct	ozone_share_pct	yoy_change_unhealthy_plus	burden_tier	aqi_burden_score	row_hash
2026-09-30	2,023	01003	Baldwin	AL	Alabama	USA	347	95.07	90	2023-05-03	59.4	236	111	0	0	0	0	0	PM2.5	68.9	31.1	—	minimal	10	f07b5a4d98c6ba9f
2026-09-30	2,024	01003	Baldwin	AL	Alabama	USA	355	96.99	90	2024-10-13	57.6	278	77	0	0	0	0	0	PM2.5	64.2	35.8	0	minimal	10	361610dae23f62ee
2026-09-30	2,025	01003	Baldwin	AL	Alabama	USA	355	97.26	87	2025-04-10	55	268	87	0	0	0	0	0	PM2.5	61.7	38.3	0	minimal	10	b4ef618e9ec88e98
2026-09-30	2,023	01027	Clay	AL	Alabama	USA	327	89.59	85	2023-03-22	60.4	221	106	0	0	0	0	0	PM2.5	100	0	—	minimal	10	bb8edca031b712b9
2026-09-30	2,024	01027	Clay	AL	Alabama	USA	354	96.72	75	2024-03-21	52	307	47	0	0	0	0	0	PM2.5	100	0	0	minimal	10	a236d12061bdec10
2026-09-30	2,025	01027	Clay	AL	Alabama	USA	354	96.99	133	2025-01-26	54.7	287	66	1	1	0.28	1	0	PM2.5	100	0	1	low	10	590023896dfc5aa1
2026-09-30	2,023	01049	DeKalb	AL	Alabama	USA	365	100	133	2023-06-07	65.6	212	151	2	2	0.55	1	2	PM2.5	55.6	44.4	—	low	10	4164b62a00e1ebc3
2026-09-30	2,024	01049	DeKalb	AL	Alabama	USA	366	100	87	2024-08-27	57	277	89	0	0	0	0	0	Ozone	33.9	66.1	-2	minimal	10	dc99fd6b7e216fe4
2026-09-30	2,025	01049	DeKalb	AL	Alabama	USA	365	100	93	2025-03-12	56	282	83	0	0	0	0	0	Ozone	36.2	63.8	0	minimal	10	b4695b7cdf561127
2026-09-30	2,023	01051	Elmore	AL	Alabama	USA	238	65.21	90	2023-06-08	49	221	17	0	0	0	0	0	Ozone	0	100	—	minimal	10	51656003e197fcba

Sep 30, 2026Current
20260930T125429Z-42e631335af4 · sha256 42e631335af4…
2,945 rows · first snapshot

Point any LLM at the metadata endpoint — the documentation above is machine-readable too (JSON-LD + Croissant).

cURL

curl "https://datazimuts.com/v1/datasets/us_county_aqi_intel/us_county_aqi_annual" | jq '{title, rows, columns_count, license}'

Python

import requests

ds = requests.get("https://datazimuts.com/v1/datasets/us_county_aqi_intel/us_county_aqi_annual").json()
print(ds["title"], ds["rows"], "rows")

# Sample rows for an LLM context window
for row in ds.get("sample_rows", [])[:5]:
    print(row)

API endpoint: https://datazimuts.com/v1/datasets/us_county_aqi_intel/us_county_aqi_annual

Tip: fetch /llms.txt for the full machine-readable catalog.

Where this data comes from and what was made from it. Other people's work shows as counts; only shared projects are named.

Cite this snapshot

Pinned to snapshot 20260930T125429Z-42e631335af4 and its content hash, so readers get exactly the data you used.

APA

. (2026). US county air-quality exposure intelligence (annual, 2023-2025) [Data set, snapshot 20260930T125429Z-42e631335af4, sha256 42e631335af4]. Datazimuts. Retrieved 2026-09-30, from https://datazimuts.com/en/datasets/us_county_aqi_intel/us_county_aqi_annual?snapshot=20260930T125429Z-42e631335af4

BibTeX

@misc{dz_us_county_aqi_intel_us_county_aqi_annual_42e63133,
  title        = {{US county air-quality exposure intelligence (annual, 2023-2025)}},
  author       = {{}},
  year         = {2026},
  publisher    = {Datazimuts},
  howpublished = {\url{https://datazimuts.com/en/datasets/us_county_aqi_intel/us_county_aqi_annual?snapshot=20260930T125429Z-42e631335af4}},
  note         = {Snapshot 20260930T125429Z-42e631335af4, sha256 42e631335af4f5a99106a76618154fe3bd7000af7fa2cdaef6cc9d892f3ea3a4; accessed 2026-09-30}
}

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<iframe src="https://datazimuts.com/embed/chart?dataset=us_county_aqi_intel%2Fus_county_aqi_annual&lang=en&theme=auto&snapshot=20260930T125429Z-42e631335af4&x=snapshot_date&y=vintage_year&agg=avg" title="US county air-quality exposure intelligence (annual, 2023-2025)" width="100%" height="380" style="border:0" loading="lazy"></iframe>

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US county air-quality exposure intelligence (annual, 2023-2025)

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