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Biology

Log reduction (microbial kill) calculator

How many orders of magnitude a treatment reduces a microbial count.

Published 9 August 2026 · Updated 24 September 2026

What this calculator does

Log reduction expresses microbial kill on a base-ten logarithmic scale. A 1-log reduction removes 90% of the organisms, 2-log removes 99%, 3-log 99.9%, and so on. Each additional log is another factor of ten.

The log scale is used because percentages become useless at the top end. The difference between 99.99% and 99.9999% looks trivial written out and is in fact a hundredfold difference in survivors. Expressed as 4-log against 6-log, the gap is obvious, which is why disinfection standards are written this way.

The formula

FormulaLog reduction = log₁₀(N₀/N); % reduction = (1 − N/N₀) × 100

Log reduction is the base-ten logarithm of the starting count divided by the surviving count. Percent reduction is one minus the surviving fraction, expressed as a percentage. The times-reduced figure is simply the ratio of the two counts, which is ten raised to the log reduction.

TermMeaning
Log reductionlog₁₀ of the ratio of starting count to surviving count.
1-logA tenfold reduction, removing 90% of organisms.
6-logA millionfold reduction, 99.9999%, a common sterilisation benchmark.
D-valueThe time required for a 1-log reduction under set conditions. Not calculated here.

The inputs explained

FieldWhat to enter
Initial count (CFU)Starting microbial count, typically in colony-forming units.
Final count (CFU)Surviving count after treatment, in the same units.

When to use it

Validating a disinfection process

Standards specify performance in log reductions, so test results as raw counts have to be converted before they can be compared against a requirement.

Comparing two treatments

Two processes at 99.9% and 99.99% sound almost identical and differ tenfold in survivors. Converting to logs makes that immediately visible.

Understanding a water treatment specification

Water treatment requirements are written as log removal values for specific organism classes, and converting to percentages helps with interpreting what they mean.

Worked examples

Every figure in the tables below is produced by this page’s own calculator at build time, so the numbers and the tool always agree. Select any row to load that scenario.

What does each log reduction mean as a percentage?

A range of surviving counts from a starting population of one million.

Starting from 1,000,000 organisms
Surviving countLog reductionPercent reductionTimes reduced
1,000,0000.000.000%1×
100,0001.0090.0%10×
10,0002.0099.0%100×
1004.00100.0%10,000×
105.00100.0%100,000×
16.00100.0%1,000,000×
The percentage column stops being informative after the third row, where it reads 100.0% for everything from a 4-log reduction upward despite the survivors falling from 100 to 1. The log column keeps distinguishing them, which is exactly why the scale exists.

Questions

What does 6-log reduction mean?

A reduction by a factor of one million, or 99.9999%. Starting from a million organisms it leaves one. It is a common benchmark for sterilisation, and in some contexts the requirement is stated as a probability of a single surviving organism rather than as a log value.

How does log reduction relate to percentage?

Each log is a factor of ten in survivors. 1-log is 90%, 2-log 99%, 3-log 99.9%, and every further log adds another nine. The percentage form becomes unusable past about 4-log, since the numbers round to 100% while the actual survivor counts still differ enormously.

What is a D-value?

The time needed to achieve one log reduction under specified conditions of temperature and agent concentration. It lets a required log reduction be converted into a required treatment time, which is how sterilisation cycles are designed. This calculator gives the reduction achieved rather than the time to achieve it.

Can log reduction be a fraction?

Yes, and usually is in real data. A reduction from 1,000,000 to 5,000 is about 2.3 logs. Whole numbers appear in standards and specifications because they are targets, not because measured results land on them.

For bacterial growth in the other direction, see the bacterial growth calculator. For mutation rates in a large population, see the mutation frequency calculator.