Clean Air Score Formula: How We Rank U.S. City Air Quality

TL;DR: How Clean Air Scores Work

• Data Source: U.S. EPA Air Quality System (AQS) — official federal monitoring data covering 2021–2025.
• Scoring Formula: Clean Air Score = 0.40 × Annual PM2.5 + 0.25 × Unhealthy Days + 0.20 × 5-Year Trend + 0.10 × Seasonal Variability + 0.05 × Extreme Events.
• Update Frequency: Scores are refreshed annually after EPA finalizes the previous year’s dataset (usually late spring).
• Goal: One number, 0–100, designed for long-term decisions like homebuying — not daily weather-checking.

CleanAirData ranks hundreds of U.S. cities using a 0–100 Clean Air Score built entirely from EPA AQS monitoring data. The formula weights five components: annual PM2.5 average (40%), unhealthy AQI days (25%), 5-year trend direction (20%), seasonal variability (10%), and extreme pollution events (5%). We update scores once per year when EPA closes the books on the previous year’s data. Below you’ll find the exact formula, data sources, and scoring rationale for every component.

If you want to jump around:

• Home | Browse all cities
• See examples: Austin, TX | Honolulu, HI | Bakersfield, CA

Data Source & Method Summary

Item	Detail
Primary source	U.S. EPA Air Quality System (AQS) — https://aqs.epa.gov/
Pollutant focus	PM2.5 (fine particulate matter) + daily AQI values
Time window	2021–2025 (5 full years)
Cities covered	Hundreds of U.S. cities across the 2026 dataset
Update cycle	Annual, after EPA finalizes previous year’s data
Scoring range	0–100, mapped to letter grades A–F
Editorial policy	No paid placements, no city can buy a better score

Scoring formula at a glance:

Component	Weight	What it measures
Annual Air Quality	40%	Most recent 12-month PM2.5 average
Unhealthy Days	25%	% of days with AQI > 100
5-Year Trend	20%	Direction of PM2.5 change over 5 years
Seasonal Variability	10%	Month-to-month PM2.5 consistency
Extreme Events	5%	Days with AQI > 200

Full formulas and rationale for each component are detailed below.

Where the data comes from

Everything starts with the EPA’s Air Quality System (AQS)—the official federal database that state and local agencies feed their monitoring data into. It’s the same source regulators use for compliance tracking, so it’s as authoritative as you’ll get in the US.

What we pull:

• https://aqs.epa.gov/ — main EPA portal
• https://aqs.epa.gov/aqsweb/documents/data_api.html — technical docs if you want to dig into the API

Specifically:

• Daily AQI values to count unhealthy and extreme days
• PM2.5 readings (fine particulate matter) for annual averages, seasonal patterns, and multi-year trends
• Monitor locations so we know which stations belong to which city and how complete the coverage is

Time window: 2021 through 2025—five full years.

Update cycle: Once per year, after EPA closes the books on the previous year’s data (usually late spring).

Coverage: We currently publish Clean Air Scores for hundreds of U.S. cities in the 2026 dataset. Cities generally need enough monitor coverage and stable EPA records across the 2021-2025 window to make the cut. If a city’s monitoring data has gaps covering more than 20% of the days in our window, we skip it. Unreliable data makes for unreliable scores.

Quality controls we run:

• Multi-monitor averaging: Cities like LA have dozens of monitors; small cities might have two. We average across all stations so one outlier site doesn’t skew the whole city.
• Outlier flagging: Wildfire smoke is real and we count it, but we flag it separately under “Extreme Events” so you know what you’re dealing with.
• Gap-filling: If a monitor goes offline for a day or two, we can interpolate. Three days tops. Longer outages, we leave as gaps and track them in the completeness metric.

Data completeness examples

Here’s what we track for every city. Your build pipeline can auto-populate this table from the actual JSON output:

City	Monitors	Expected days (2021–2025)	Observed days	Completeness
Austin, TX	(auto)	(auto)	(auto)	(auto)
Honolulu, HI	(auto)	(auto)	(auto)	(auto)
Bakersfield, CA	(auto)	(auto)	(auto)	(auto)
Minneapolis, MN	(auto)	(auto)	(auto)	(auto)

Note: “Expected days” depends on monitor uptime schedules reported to EPA. We calculate completeness the same way for every city.

How to verify a city score yourself

If you open any city page, you should be able to audit the score without reading this whole methodology again.

• Raw input signals: annual PM2.5, unhealthy-day rate, 5-year trend direction, seasonal variability, and extreme-event risk
• Data quality context: monitor coverage note and completeness percentage
• Weighted math: each component score is shown alongside its weight so you can see what moved the final number

This is intentional. A score is only useful if a careful user can pressure-test it.

The formula

We score five dimensions separately (each 0–100), then weight them according to what matters most for long-term living. Here’s the top-line formula:

$$\text{Clean Air Score} = 0.40 \times \text{Annual Air Quality} + 0.25 \times \text{Unhealthy Days} + 0.20 \times \text{5-Year Trend} + 0.10 \times \text{Seasonal Variability} + 0.05 \times \text{Extreme Events}$$

Weights in config:

# config/weights.yaml
annual_air_quality: 0.40
unhealthy_days: 0.25
five_year_trend: 0.20
seasonal_variability: 0.10
extreme_events: 0.05

How it flows

flowchart LR
  A[EPA AQS data<br/>PM2.5, AQI, monitors] --> B[Quality checks<br/>averaging, gaps, flags]
  B --> C1[Annual Air Quality]
  B --> C2[Unhealthy Days]
  B --> C3[5-Year Trend]
  B --> C4[Seasonal Variability]
  B --> C5[Extreme Events]
  C1 --> D[Weighted sum]
  C2 --> D
  C3 --> D
  C4 --> D
  C5 --> D
  D --> E[Clean Air Score<br/>0–100]

Annual Air Quality (40%)

What it is: Average PM2.5 concentration over the most recent 12 months, measured in $\mu g/m^3$. PM2.5 is fine particulate matter—small enough to get deep into your lungs and linked to cardiovascular disease, respiratory problems, and premature death in long-term studies.

Scoring:

• $\text{PM2.5} \le 8$ → score 100 (EPA’s “good” threshold)
• $\text{PM2.5} \ge 25$ → score 0 (approaching “unhealthy” territory)
• Everything in between scales linearly

$$\text{Annual Score} = \max\left(0, \min\left(100, 100 \times \frac{25 - \text{PM2.5}}{17}\right)\right)$$

Why 40%? This is your baseline. It’s what you’re breathing most days. If you spend ten years somewhere, this number matters more than anything else.

Unhealthy Days (25%)

What it is: Percentage of days last year where AQI exceeded 100. Above 100, the EPA says sensitive groups—kids, elderly, people with asthma—should start limiting outdoor activity.

$$r = \frac{\text{days with AQI} > 100}{\text{total valid days}}$$ $$\text{Unhealthy Days Score} = 100 \times (1 - r)$$

Why 25%? Because if your kid has asthma, even 15 bad days a year changes your life. You’re canceling soccer practice, keeping windows closed, paying attention. Annual averages don’t capture that disruption—day counts do.

5-Year Trend (20%)

What it is: Is the air getting cleaner or dirtier? We fit a simple trend line through five years of annual PM2.5 averages.

• Negative slope → air improving → score boost
• Positive slope → air worsening → score penalty

We cap extreme slopes so one freak year doesn’t dominate, and we keep the penalty/reward symmetrical.

Why 20%? If you’re buying a house, you’re thinking 5–10 years out. A city improving from “fair” to “good” might be a better bet than one that’s “good” today but sliding backward.

Seasonal Variability (10%)

What it is: How much does air quality swing month-to-month? We calculate the standard deviation of monthly PM2.5 averages. Low variability = predictable air. High variability = maybe three great months and two terrible ones.

Stable cities score higher. Cities with wild seasonal swings (often fire-prone areas) score lower.

Why 10%? It matters if you’re planning a kid’s outdoor birthday party in August or training for a marathon in June, but it’s less critical than the overall average or the number of truly bad days. So it gets a smaller weight.

Extreme Events (5%)

What it is: Days in the past year where AQI exceeded 200 (“very unhealthy”). These are rare in most places, but when they hit, schools close, events get canceled, and health departments issue warnings.

The penalty curve is steep at first (0 → 2 days hurts) then flattens (20 → 22 days barely moves the needle), so one disaster season doesn’t make the whole score useless.

Why 5%? Important, but rare. We don’t want this to become a wildfire-only index, so we acknowledge it without letting it dominate.

Pollen Outlook (Current Seasonal Forecast)

While the Clean Air Score focuses on long-term air pollution, we provide a Pollen Outlook module to help users understand their immediate environment.

What it measures: The Pollen Outlook tracks current levels for three major categories: Tree, Grass, and Weed pollen. It identifies which types are currently “In Season” and provides a risk level from “Very Low” to “Very High.”

Data Source: Data is sourced from the Google Pollen API, which uses sophisticated climate models and land-cover data to estimate pollen concentrations. Unlike our air quality data, this is not derived from federal monitoring stations.

Why it is separate from the Clean Air Score:

• Short-term vs. Long-term: The Clean Air Score is based on 5 years of historical EPA data for relocation decisions. The Pollen Outlook is a 1-5 day forecast for daily activity planning.
• Model vs. Monitor: Clean Air Scores use physical sensors; Pollen Outlook uses predictive models.
• Health Impact: Pollen is a seasonal allergen, whereas PM2.5 is a regulated pollutant with different long-term health implications.

Limitations: Pollen forecasting is an estimate based on local vegetation and weather patterns. It does not provide annual “grades” (A-F) or long-term rankings because pollen seasons vary significantly by year and local flora. It should not be used for long-term health or relocation conclusions.

Letter grades

Some people think in grades. Here’s how we map scores to A–F:

Score	Grade	What it means
85–100	A	Excellent air—safe for nearly everyone year-round
70–84	B	Good overall—occasional bad days, but not many
55–69	C	Moderate—sensitive groups should pay attention
40–54	D	Fair—bad days are common, plan accordingly
0–39	F	Poor—not recommended for sensitive populations

Why not just use EPA’s AQI colors? Because AQI is designed for today’s air—it tells you whether to go for a run this afternoon. Our score is designed for choosing where to live—it synthesizes years of data, adds trend and variability, and focuses on long-term patterns. Different tool, different job.

What this score can’t do

No scoring system is perfect. Here’s where ours has limits:

Monitor density varies. Big cities have tons of monitors; smaller cities might have just one or two. That means the “city average” is more reliable in some places than others. We publish monitor counts and completeness percentages for every city so you can see how confident to be.

We focus on PM2.5 and AQI. We don’t break out ozone, $NO_2$, or other pollutants separately in the score. Why? PM2.5 has the strongest evidence base for long-term health effects, and it’s measured consistently enough to compare hundreds of cities. If ozone specifically matters to you (summer smog, for instance), check EPA’s pollutant-specific tools in addition to this score.

Weights are judgment calls. We based ours on public health research and what we think matters for home-buying decisions, but there’s no universally “correct” weighting. We publish the weights openly and version them. If we change them based on user feedback or new research, you’ll see exactly what changed and when.

There’s a time lag. Right now we’re scoring through 2025. We update yearly. That keeps scores stable and comparable, but it means the score won’t catch a brand-new highway, a factory closure, or a sudden wildfire until the next annual refresh.

Personal sensitivity varies wildly. What’s livable for one person might be rough for another. Someone with severe asthma will care more about unhealthy days; someone planning to stay 20 years might weight trend more heavily. This score is a starting point, not a prescription. Talk to your doctor if you have health concerns. Check local air quality advisories. Don’t make major decisions based on a single number.

We maintain complete editorial independence. We maintain complete editorial independence and do not accept payment for rankings. No paid placements. No city can buy a better ranking. If we ever run ads or take on partners, we’ll disclose it clearly and keep the scoring completely separate.

Research Citation

Why this matters (research context)

Air quality isn’t just a health issue—it’s an economic one. Multiple studies have found that PM2.5 pollution depresses home values. An NBER working paper, for example, estimates that each additional $1\ \mu g/m^3$ of PM2.5 is associated with roughly 0.5–1% lower home prices in the areas they studied (NBER working paper 25489). Translation: clean air is worth real money when you’re buying or selling.

Public health agencies agree PM2.5 is a big deal. The World Health Organization’s air quality guidelines flag long-term PM2.5 exposure as a major risk factor for respiratory and cardiovascular disease (WHO fact sheet). The EPA’s own guidance explains why fine particles matter and how AQI categories map to risk (EPA AQS and AQI basics).

Trends also carry signal. Environmental policy research shows that improving or worsening air quality often reflects changes in regulation, industry mix, transportation patterns, and regional climate. That’s why we include a 5-year trend—it’s not just “where you are,” it’s “where you’re headed.”

Common questions

My city’s score seems off. Why? We use five years of data, not last month’s weather or a news story you read. A city can feel cleaner recently but still have a track record of frequent bad days baked into the score—or vice versa. Check the trend chart on the city page to see what’s been happening over time.

Do you update the scores? Yes, once a year after EPA finalizes the latest annual data. We also republish this methodology page if we change any part of the calculation.

Can I use the score to predict future air quality? The 5-year trend gives you a sense of direction, but it can’t predict wildfires, new construction, sudden policy changes, or weird weather. Treat it as one input, not a crystal ball.

Why isn’t [famously polluted city] an F? Some cities have gotten a lot better in the past few years. Our trend and unhealthy-day components pick that up. Reputations lag reality.

Can I adjust the weights to fit my priorities? Not yet. We built one standard score so city-to-city comparisons stay consistent. Custom weights are on the roadmap—if we add them, the default score will stay as-is and fully documented.