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.

Les titres et les descriptions proviennent des sources de données, en anglais.

Source
Lignes: 2 945
Colonnes: 29
Cadence de la source: Annuelle
Dernière actualisation: 30 sept. 2026
Thème: environment

Colonne	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.

10 premières lignes d’exemple — un aperçu, pas le jeu de données complet.

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

30 sept. 2026Actuelle
20260930T125429Z-42e631335af4 · sha256 42e631335af4…
2 945 lignes · premier instantané

Dirigez n’importe quel LLM vers le point d’accès des métadonnées — la documentation ci-dessus est aussi lisible par machine (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)

Point d’accès API : https://datazimuts.com/v1/datasets/us_county_aqi_intel/us_county_aqi_annual

Astuce : récupérez /llms.txt pour le catalogue complet lisible par machine.

D’où viennent ces données et ce qui en a été fait. Le travail des autres apparaît sous forme de décomptes ; seuls les projets partagés sont nommés.

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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/fr/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/fr/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=fr&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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