How do I simulate a carbon cycle?

Open this page and use the live Air Quality Model tool below — set your inputs and the simulation runs instantly in your browser using real numerics. No install, no account needed.

Yes. The simulation runs free in your browser. A one-time unlock or a Pro plan adds advanced parameters, saved presets, data import, and clean exports.

Can I use my own numbers?

Absolutely — every input is adjustable, and with data import you can drive a carbon cycle from your own measurements.

Use case · powered by Air Quality Model

Air Quality Model for a carbon cycle

Simulate a carbon cycle live in your browser. This runs the real Air Quality Model solver — adjust the inputs, watch it respond instantly, and export the result. No install, no account.

For:Students Researchers Educators Engineers Hobbyists & Makers K-12 Students First Responders

Hazmat Plume

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Hazmat Plume DispersionLive

Gaussian plume · protective action

Controls

Presets

Release rate (g/s)

Wind speed (m/s)

Action level (mg/m³)

A Gaussian plume estimates how a gas or vapor release disperses downwind. Stability class (A unstable to F stable) sets how fast the plume spreads. The dashed line is the protective action distance where concentration falls below your action level. Screening estimate only — use ERG and monitors on scene.

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Data Inspector

Protective distance>20 km

StabilityD neutral

Wind4 m/s

Governing equation

C (x, y) = \frac{50}{2 π 4 σ _{y} σ _{z}} exp (- \frac{y ^{2}}{2 σ _{y}^{2}})

Reading this result: Concentration falls off as the wind stretches and vertical mixing thins the plume — stronger wind and less stable air pull the protective distance in toward the source.

Runs locally in your browser — free forever. Scale to the cloud when reality gets heavy.

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Spatial Interpolation

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Spatial Interpolation (IDW)Live

filling the gaps between measurements

Controls

Presets

Power parameter

Given scattered measurements — rainfall gauges, soil samples, air-quality sensors — inverse-distance weighting estimates values everywhere in between. Each sample's influence falls off with distance raised to a power: a low power gives smooth, blended surfaces; a high power makes each sample dominate its neighborhood in sharp bull's-eyes. The workhorse of GIS mapping.

▶ Run in Python

Data Inspector

Samples8

Power p2.0

Characterbalanced

Governing equation

\overset{z}{^} (x) = \frac{\sum _{i} w _{i} z _{i}}{\sum _{i} w _{i}}, w_{i} = \frac{1}{d _{i}^{2.0}}

Reading this result: At power 2.0 nearby samples win but distant ones still contribute — a balanced surface between smooth and spiky.

Runs locally in your browser — free forever. Scale to the cloud when reality gets heavy.

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About this simulation

The full Air Quality Model tool models a carbon cycle with the same numerics engineers and scientists use — running entirely client-side. Change any parameter and the result updates in real time, so you can build intuition, check a design, or teach the concept without spreadsheets or installs.

More you can do with Air Quality Model

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Other ways to simulate a carbon cycle

Energy Balance for a carbon cycle Daisyworld for a carbon cycle Milankovitch Cycles for a carbon cycle Tsunami Propagation for a carbon cycle Carbon Cycle for a carbon cycle Earthquake Shaking for a carbon cycle

Frequently asked questions

How do I simulate a carbon cycle?: Open this page and use the live Air Quality Model tool below — set your inputs and the simulation runs instantly in your browser using real numerics. No install, no account needed.
Is it free?: Yes. The simulation runs free in your browser. A one-time unlock or a Pro plan adds advanced parameters, saved presets, data import, and clean exports.
Can I use my own numbers?: Absolutely — every input is adjustable, and with data import you can drive a carbon cycle from your own measurements.