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* Metric Name: Potential Total Smoke Production Index *  

* Tier: 1  

* Data Vintage: 2022. Includes disturbances through the end of 2022.  

* Unit Of Measure: 0 - 1 **,** a unitless number serving as an index; on a per 30-m pixel basis  

* Represent element and pillar: Yes  

* Type and distribution of data: Right-skewed distribution representing index values. Raw data values range from 0 to 1.  

* Translation: Where is potential total smoke production index the lowest?  

* Translation method and outcome: Raw data values translated to range +1 to -1 representing more favorable or less favorable conditions, respectively. The proposition of more favorable is based on the objective of lower potential total smoke production index (negative linear slope). More favorable (translated to +1) set at 0, less favorable (translated to -1) set at > 0.2.

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Figure . Histogram and scoring of potential total smoke production index in
Southern California.

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Figure . Histogram of translated potential total smoke production index in
Southern California.

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Figure . Maps displaying raw metric and translated metric potential total
smoke production index in Southern California.

  

* Metric Definition and Relevance: This metric is an index of the potential smoke production (represented by particulate matter that is 2.5 microns or less in diameter, or PM2.5) that could be emitted for a given 30-meter pixel under fire weather conditions that produce high severity fire effects. By showing spatial variation in potential smoke emissions under standardized fuel moisture conditions, this index is intended to help identify potential emissions hotpots within a region if a high severity wildfire occurs in the future. It may be useful for regional scale planning and/or prioritization.

However, the actual moistures and fire weather conditions under which these
fuels may convert to smoke will vary; therefore, the map does not represent
actual smoke production (PM2.5 emissions) during an actual fire event. For
data users interested in near-term smoke forecasts that reflect the
environmental drivers of emissions, project-specific modeling tools are
recommended. For example, the BlueSky Playground
(https://tools.airfire.org/playground) can tailor model inputs based on the
fuel and moisture conditions observed or planned for in the project area of
interest.

Potential smore emissions do not consider the probability of a fire or the
transport of smoke to more distant locations; they only reflect what would
happen locally if a pixel were to burn.

  

* Credits: LANDFIRE FCCS ([LANDFIRE Program: Data Products – Fuel – Fuel Characteristic Classification System Fuelbeds](https://www.landfire.gov/fccs.php)) 2022 Rocky Mountain Research Station https://www.firelab.org/project/fofem-fire- effects-model