AQI (Air Quality Index) Calculator (US EPA)

Convert 24-hour PM2.5 and PM10 concentrations (µg/m³) to US EPA AQI and review truncation, breakpoints, and formula steps.

Use this calculator for learning and quick checks. For official reporting, rely on published authority values.

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Presets and sharing

Inputs

Advanced options

Calculation result

Concentration after truncation Cp
Use breakpoint
expression

Breakpoint (US EPA)

Category AQI range Concentration range (µg/m³)

Note: PM2.5 is rounded down to the nearest 0.1, and PM10 is rounded down to the nearest whole number.

Assumptions, source, and limits

FAQ

What is AQI?

AQI maps air pollution to a 0 to 500 scale and links each range to likely health impact.

Why round down and then calculate?

US EPA rules truncate PM2.5 to 0.1 units and PM10 to whole numbers before AQI conversion.

Why doesn't it match the officially announced value?

Official values may differ because of averaging windows, NowCast processing, station choice, or publisher rounding rules.

What should I do first on this page?

Start with the minimum required inputs or the first action shown near the primary button. Keep optional settings at defaults for a baseline run, then change one setting at a time so you can explain what caused each output change.

Why does this page differ from another tool?

Different pages often use different defaults, units, rounding rules, or assumptions. Align those settings before comparing outputs. If differences remain, compare each intermediate step rather than only the final number.

How to use AQI (Air Quality Index) Calculator (US EPA) effectively

What this calculator does

This page is for estimating outcomes by changing inputs in one controlled workflow. The model keeps your focus on variables, not output shape. Start with stable assumptions, then test sensitivity by changing one key input at a time to observe directional impact.

Input meaning and unit policy

Each input has an expected unit and a typical range. For reliable interpretation, check whether you are using the same unit system, period, and base assumptions across all runs. Unit mismatch is the most common source of unexpected drift in numeric results.

Use-case sequence

A practical sequence is: first run with defaults, then create a baseline log, then run one alternative scenario, and finally compare only the changed output metric. This sequence reduces cognitive load and prevents false pattern recognition in early experiments.

Common mistakes to avoid

Avoid changing too many variables at once, mixing incompatible data sources, and interpreting a one-time output without checking robustness. A single contradictory input can flip conclusions, so keep each experiment minimal and document assumptions as part of your note.

Interpretation guidance

Review both magnitude and direction. Direction tells you whether a strategy moves outcomes in the desired direction, while magnitude helps you judge practicality. If both agree, you can proceed; if not, rebuild the baseline and verify constraints before deciding.