ISC3 (Industrial Source Complex) model is a popular steady-state Gaussian plume model which can be used to assess pollutant concentrations from a wide variety of sources associated with an industrial complex.

This model can account for the following:

• Point, area, line, and volume sources
• Settling and dry deposition of particles
• Downwash
• Separation of point sources
• Limited terrain adjustment

ISC3 operates in both long-term and short-term modes. The screening version of ISC3 is SCREEN3.

Very recently, the status of ISC3 as a Preferred/Recommended Model of the US Environmental Protection Agency has been withdrawn, but it can still be used as an alternative to the Preferred/Recommended models in regulatory applications with case-by-case justification to the reviewing authority.

Input data [link]

ISC short term version required two sets of data: source data and hourly averaged meteorological data:

Source data [link]

• Dimensions of the source
• Emission discharge rate
• Release height of the emission source

Meteorological data [link]

• Ambient temperature, K
• Wind direction
• Wind speed, m/s
• Atmospheric stability classes (A through F, entered as 1 through 6)
• Urban and rural mixing height, m

See also [link]

• Bibliography of atmospheric dispersion modeling
• Atmospheric dispersion modeling
• List of atmospheric dispersion models

Further reading [link]

For those who are unfamiliar with air pollution dispersion modelling and would like to learn more about the subject, it is suggested that either one of the following books be read:

• Turner, D.B. (1994). Workbook of atmospheric dispersion estimates: an introduction to dispersion modeling (2nd Edition ed.). CRC Press. ISBN 1-56670-023-X.  www.crcpress.com

• Beychok, M.R. (2005). Fundamentals Of Stack Gas Dispersion (4th Edition ed.). self-published. ISBN 0-9644588-0-2.  www.air-dispersion.com

External links [link]

• ISC3 User's Guide, Volume I
• ISC3 User's Guide, Volume II
• Meteorological Monitoring Guidance for Regulatory Modeling Applications