Total population and Share of days with unhealthy air
Where Total population is higher, Share of days with unhealthy air tends to be higher. The link is moderate: +0.49 across 802 counties, on a scale from −1 (opposite) to +1 (in step). That figure compares places with the same population density, income and education, so those differences can't explain it.
Holds after adjusting
Every place, plotted
One dot for each place reporting both measures. Hover over or tap a dot to see its name and values. The thin line is the straight line that best fits the dots. The bold line, shown for counties, is the link after adjusting for density, income and education. A dot far from the lines is a place that does not follow the pattern.
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What each measure is
Total population
How many people live here, as counted by the American Community Survey. This comes from the survey, not from the Census Bureau's separate population estimates.
Census ACS 5-year · ACS table B01003_001
Share of days with unhealthy air
The percentage of days when the Air Quality Index was over 100, which is unhealthy for sensitive groups or worse. The index runs from 0 to 500 and is set by the worst pollutant that day. Smoke from wildfires and summer ozone cause most of these days. It counts only the days the monitors gave a reading, and a year counts only if there were at least 274 of them. Only about 800 counties have a monitor, mostly the larger ones.
EPA Air Quality System · EPA AQS annual_aqi_by_county, Unhealthy for Sensitive Groups + Unhealthy + Very Unhealthy + Hazardous Days, divided by Days with AQI
Highest and lowest
Where each measure is highest and lowest among the places reporting both (2023), with the other measure beside it. This is shown for every size of place where both are published. Metro-area values are added up from their counties. A measure that cannot be added up across counties, such as a middle value (median), has no metro figure. Counts are shown per 1,000 residents, since raw totals would only list the largest places. Cities and towns and ZIP codes are shown where the Census survey or the CDC publishes them; most other sources stop at the county. They are shown for places of 5,000 residents or more, and only where the survey’s margin of error is small enough for the figure to be usable. The CDC publishes its health estimates for cities in a version adjusted for age, so places with older or younger people can be compared fairly, but not for ZIP codes.
Counties
By total population
| Total population, per 1,000 residents | Share of days with unhealthy air | |
|---|---|---|
| Highest | ||
| Fresno County, CA | 1000.05 | 15.9 |
| Bay County, MI | 1000.05 | 1.65 |
| Maury County, TN | 1000.05 | 0 |
| Lowndes County, GA | 1000.04 | 1.11 |
| Cecil County, MD | 1000.04 | 1.67 |
| Lowest | ||
| Allen County, OH | 999.95 | 2.19 |
| Pima County, AZ | 999.95 | 1.92 |
| Flathead County, MT | 999.95 | 2.19 |
| Oswego County, NY | 999.96 | 0.281 |
| Berkeley County, WV | 999.96 | 1.81 |
By share of days with unhealthy air
| Total population, per 1,000 residents | Share of days with unhealthy air | |
|---|---|---|
| Highest | ||
| San Bernardino County, CA | 999.99 | 32.3 |
| Riverside County, CA | 1000.00 | 31.2 |
| Los Angeles County, CA | 1000.00 | 23.8 |
| Tulare County, CA | 999.99 | 22.5 |
| Kern County, CA | 1000.00 | 19.2 |
| Lowest | ||
| Routt County, CO | 1000.00 | 0 |
| Montezuma County, CO | 1000.00 | 0 |
| Mesa County, CO | 1000.03 | 0 |
| Gunnison County, CO | 1000.00 | 0 |
| Santa Barbara County, CA | 1000.01 | 0 |
States — Share of days with unhealthy air is not published for states
By total population
| Total population, per 1,000 residents | Share of days with unhealthy air | |
|---|---|---|
| Highest | ||
| Michigan | 1000.04 | — |
| Pennsylvania | 1000.04 | — |
| Georgia | 1000.04 | — |
| Delaware | 1000.03 | — |
| Rhode Island | 1000.03 | — |
| Lowest | ||
| North Carolina | 999.97 | — |
| Hawaii | 999.97 | — |
| New Hampshire | 999.98 | — |
| Nebraska | 999.98 | — |
| New York | 999.98 | — |
Metro areas — Share of days with unhealthy air is not published for metro areas
By total population
| Total population, per 1,000 residents | Share of days with unhealthy air | |
|---|---|---|
| Highest | ||
| Town of Pecos, TX | 1123.11 | — |
| Big Spring, TX | 1084.21 | — |
| Susanville, CA | 1080.25 | — |
| Cleveland, MS | 1041.25 | — |
| Greenville, MS | 1041.20 | — |
| Lowest | ||
| Wildwood-The Villages, FL | 907.47 | — |
| Jefferson, GA | 910.00 | — |
| Myrtle Beach-Conway-North Myrtle Beach, SC | 928.20 | — |
| Lakeland-Winter Haven, FL | 929.89 | — |
| Cedar City, UT | 937.55 | — |
Cities and towns — Share of days with unhealthy air is not published for cities and towns
By total population
| Total population, per 1,000 residents | Share of days with unhealthy air | |
|---|---|---|
| Highest | ||
| San Mateo city, CA | 1000.05 | — |
| Inglewood city, CA | 1000.05 | — |
| Sugar Land city, TX | 1000.05 | — |
| Manchester city, NH | 1000.04 | — |
| Clearwater city, FL | 1000.04 | — |
| Lowest | ||
| Lynn city, MA | 999.95 | — |
| Vacaville city, CA | 999.95 | — |
| Burbank city, CA | 999.95 | — |
| Green Bay city, WI | 999.95 | — |
| Ponce zona urbana, PR | 999.95 | — |
ZIP codes — Share of days with unhealthy air is not published for ZIP codes
By total population
| Total population, per 1,000 residents | Share of days with unhealthy air | |
|---|---|---|
| Highest | ||
| ZIP 37013 (Nashville-Davidson metropolitan government (balance), TN) | 1000.05 | — |
| ZIP 77084 (Houston city, TX) | 1000.05 | — |
| ZIP 79936 (El Paso city, TX) | 1000.03 | — |
| ZIP 78660 (Pflugerville city, TX) | 1000.03 | — |
| ZIP 90650 (Norwalk city, CA) | 1000.02 | — |
| Lowest | ||
| ZIP 11385 (New York city, NY) | 999.95 | — |
| ZIP 90011 (Los Angeles city, CA) | 999.96 | — |
| ZIP 08701 (Ocean County, NJ) | 999.97 | — |
| ZIP 92336 (Fontana city, CA) | 999.97 | — |
| ZIP 77494 (Fort Bend County, TX) | 999.98 | — |
Counties furthest from the pattern
Compared with the straight line that best fits all 803 counties: the five with far more share of days with unhealthy air than their total population would suggest, and the five with far less.
| Total population, per 1,000 residents | Share of days with unhealthy air | |
|---|---|---|
| Above the line | ||
| San Bernardino County, CA | 999.99 | 32.3 |
| Riverside County, CA | 1000.00 | 31.2 |
| Los Angeles County, CA | 1000.00 | 23.8 |
| Tulare County, CA | 999.99 | 22.5 |
| Kern County, CA | 1000.00 | 19.2 |
| Below the line | ||
| Maury County, TN | 1000.05 | 0 |
| Santa Fe County, NM | 1000.03 | 0 |
| Bonneville County, ID | 1000.03 | 0 |
| Ponce Municipio, PR | 1000.03 | 0 |
| Mesa County, CO | 1000.03 | 0 |
Two series, one county
Both measures over time for a county you pick. The two charts are stacked, so they share only the years along the bottom. You can pick from the thirty largest counties reporting both. This panel covers 2010–2024, and a measure published only once shows as a single dot.
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Every version of the link
Each row measures the same link, from −1 to +1, in a different way. If the rows mostly agree, the link does not depend on one choice of method.
| Before adjusting for anything | +0.494 | 802 counties reporting both |
| After adjusting for density, income and education | +0.488 | the part of the link that is not just how urban, rich and educated a place is |
| Also adjusting for population size | +0.401 | size taken out as well as density |
| Also comparing only within each state | +0.482 | each county is compared only with others in its own state, so this is no longer a comparison between states |
| Giving bigger counties more weight | +0.759 | the adjusted link for the average resident rather than the average county. With this weighting, the data counts for about 165 equally weighted counties |
| Middle value inside single states | +0.582 | the link worked out inside each of 14 states with at least 20 counties reporting |
| Using ranks, after adjusting | +0.059 | uses each county’s rank, not its value, so a few extreme counties cannot create the link |
How much to believe it
Could it be chance?
The p-value is the chance of seeing a link this strong if there were really none. A value of 0.05 or less means the link is unlikely to be chance. Very small values are written in short form: 3.1e-16 means 0.00000000000000031, and the bigger the number after “e-”, the smaller the value. Treating every county as a separate case gives p = 4.2e-49. But counties in the same state tend to resemble each other, so the better figure allows for that: p = 1.7e-6. The site tested 24,090 pairs, and testing so many turns up some flukes. The q-value allows for that too. For this pair it is 2.4e-48.
Do nearby places look alike?
Moran’s I measures how much nearby places look alike: 0 means no map pattern, and higher means neighbours are more similar. For the part of each county that the adjustment leaves unexplained, it is +0.33 (p = 0.001). That unexplained part still clusters on the map. So some regional factor shared by both measures is not accounted for.
Did they change together?
The figures above compare counties at one moment. This asks a different question: where total population rose most, did share of days with unhealthy airmove too? Each county’s change is compared with the average change in its own state. That takes out ups and downs that hit a whole region at once. Each change is the gap between two estimates that both contain some error. That error pulls these links towards zero, so a weak one is weak partly for that reason. The q column shows whether a link could be a fluke, allowing for the many pairs tested: 0.05 or less means it is unlikely to be chance.
| Period | Counties | Change together | Within states | q |
|---|---|---|---|---|
| 2014–2019 the main test: each Census survey figure averages five years, and these two do not overlap; before the pandemic | 700 | -0.03 | -0.05 | 0.441 |
| 2019–2023 a repeat check: its two five-year survey figures both include 2019, and it covers the COVID years | 755 | -0.03 | +0.07 | 0.120 |
Over the main period, any link between the changes could easily be chance. How change is measured.
Related pairs
- Share of days with unhealthy air and Population (WONDER) — +0.56
- Share of days with unhealthy air and Tests taken — +0.54
- Women who gave birth in the past year and Share of days with unhealthy air — +0.52
- Share of days with unhealthy air and Unemployed — +0.52
- Share of days with unhealthy air and People who did not move — +0.49
- Share of days with unhealthy air and Population — +0.49
- Share of days with unhealthy air and Population for whom poverty status is determined — +0.49
- Population in the same house one year ago and Share of days with unhealthy air — +0.49
Association only. Nothing here shows that either measure changes the other. This describes counties, not the people in them. Two measures going together across places does not mean the same people have both. How the US county data works · Explore Total population and Share of days with unhealthy air