## Unmix

The EPA Unmix model “unmixes” the concentrations of chemical species measured in the ambient air to identify the contributing sources. Chemical profiles of the sources are not required, but instead are generated internally from the ambient data by Unmix, using a mathematical formulation based on a form of factor analysis.

concentrationschemical species
322

#### Authorship

Is authorship not correct? Feed back

#### Classification(s)

Application-focused categoriesNatural-perspectiveAtmospheric regions

#### Model Description

English {{currentDetailLanguage}} English

EPA’s Unmix Model is a mathematical receptor model developed by EPA scientists that provides scientific support for the development and review of the air and water quality standards, exposure research, and environmental forensics. Unmix can analyze a wide range of environmental sample data: sediments, wet deposition, surface water, ambient air, and indoor air. EPA’s Unmix model reduces the large number of variables in complex analytical data sets to combinations of species called source types and source contributions. The source types are identified by comparing them to measured profiles. Source contributions are used to determine how much each source contributed to a sample.

## What are the benefits of using Unmix 6.0 Model?

Unmix quantifies the sources of contaminants contributing to sediment, water and air samples based on chemically speciated data. Unmix determines the number of sources impacting the samples based on user provided data. The model is free of charge, and does not require a license or other software to use.

## Who should use the Unmix 6.0 Model?

Scientists and engineers who generate or analyze speciated sample data and want to quantify the sources contributing to environmental samples can use Unmix 6.0. In addition, chemists can use Unmix 6.0 to evaluate instrument data.

## How does the Unmix 6.0 Model work?

Users of EPA’s Unmix model provide files of sample species concentrations, which the model uses to calculate the number of source types, profiles, relative contributions, and a time-series of contributions. Unmix does not make any assumptions as to the number and composition of the sources, relying instead on the correlations of the observed species. The species concentrations are apportioned by a principal components analysis using constraints to assure non-negative and realistic sources compositions and contributions. The Unmix model software uses graphical user interfaces that ease data input, generation, evaluation and exporting of results. Algorithms used in the Unmix model have been peer reviewed by leading air quality management scientists.

## System Requirements:

Unmix 6.0 works on Windows 2000, Windows XP, and Windows Vista. The computer should have at least a 2.0 GHz processor and 512 MB of memory. Microsoft Excel is required for saving and exporting output. Unmix may not be entirely compatible with newer versions of Windows (e.g. Windows 7, Windows 8).

## How do I get help?

The EPA Unmix 6.0 Fundamentals & User Guide provides references and details on how to use Unmix.
Unmix 6.0 Fundamentals & User Guide

## Technical Contact:

Model Domain
{{domain}}

Principles
{{principle}}
Incorporated Models
{{incorporatedModel}}
Model part of larger framework: {{metadata.design.framework}}
Incorporated Models
{{process}}

Inputs
{{input}}
Outputs
{{output}}

#### How to Cite

US Environmental Protection Agency (2019). Unmix, Model Item, OpenGMS, https://geomodeling.njnu.edu.cn/modelItem/0615d8f8-c71e-4e6d-a08d-70eb64549bb2

#### Comment(s)

{{comment.date}}
{{comment.content}}
{{subComm.date}}
{{subComm.content}}

#### Authorship

Is authorship not correct? Feedback

#### QR Code

• {{curRelation.name}}
{{curRelation.name}}

{{curRelation.overview}}
{{curRelation.author.join('; ')}}
{{curRelation.journal}}

You can link related {{typeName}} from repository to this model item, or you can create a new {{typeName.toLowerCase()}}.

Related Items
{{ props.row.description }}
{{ props.row.description }}
Related Items
{{props.row.name}}

You can link resource from repository to this model item, or you can create a new {{typeName.toLowerCase()}}.

{{ props.row.description }}
{{ props.row.description }}
Drop the file here, orclick to upload.
File size should not exceed 10m.
Select From My Space

These authorship information will be submitted to the contributor to review.

Cancel Submit
Model Classifications
Cancel Submit
{{ item.label }} {{ item.value }}
{{props.row.localName}}
Model Name :
Cancel Submit
Name:
Version:
Model Type:
Model Domain:
Scale:
Purpose:
Principles:
Incorporated models:

Model part of

larger framework

Process:
Information:
Initialization:
Hardware Requirements:
Software Requirements:
Inputs:
Outputs:
Cancel Submit
Title Author Date Journal Volume(Issue) Pages Links Doi Operation
Cancel Submit

Yes, this is it Cancel

OK
Cancel Confirm
Model Classifications 1
Model Classifications 2
Title Author Date Journal Volume(Issue) Pages Links Doi Operation

#### NEW

Name:
Affiliation:
Email:
Homepage:

Yes, this is it Cancel

Confirm
path
:
/{{path.label}}
search results of '{{searchContentShown}}'

#### No content to show

{{item.label}}

.

{{item.suffix}}

.{{item.suffix}}

{{item.fileName}}.{{item.suffix}}

Copy
Delete
Rename
/{{path.label}}
Change
/{{path.label}}
Select File
Cancel Confirm
path
:
/{{path.label}}
/..
Cancel Confirm
{{ node.label }}
##### You have select  {{multipleSelection.length+multipleSelectionMyData.length}} data .
• Output Data
• {{item.computableName}}@{{formatDate(item.runTime)}}
{{scope.row.type}}
{{ scope.row.tag }}