||Air Quality Data Analysis System for Interrelating Effects, Standards, and Needed Source Reductions. Part 9. Calculating Effective Ambient Air Quality Parameters.
Larsen, R. I. ;
Heck, W. W. ;
||Environmental Protection Agency, Research Triangle Park, NC. Atmospheric Sciences Research Lab. ;North Carolina State Univ. at Raleigh.
Air pollution ;
Data processing ;
Geometric mean ;
Standard deviation ;
Mathematical models ;
Air quality data ;
Air pollution standards ;
Air pollution effects(Animals) ;
Air pollution effects(Humans) ;
Air pollution effects(Materials) ;
Air pollution effects(Plants)
||Some EPA libraries have a fiche copy filed under the call number shown.
||Ambient air quality data can often be characterized by the two characteristic parameters of the lognormal distribution, the geometric mean, and the standard geometric deviation, but some ambient data are far from lognormal. The paper suggests that even though an air quality data set is not lognormal, the effects of the concentrations can be characterized with an effective geometric mean and an effective standard geometric deviation calculated from the effective and arithmetic means. These two effective parameters can be used to characterize air quality at a site, in terms of its expected effects on plants, and to compare these parameters and the expected plant effects from site to site. (Copyright (c) 1985--Air Pollution Control Association.)
||Pub. in APCA Jnl., v35 n12 p1274-1279 Dec 85. Prepared in cooperation with North Carolina State Univ. at Raleigh.
|NTIS Title Notes
||Reprint: Air Quality Data Analysis System for Interrelating Effects, Standards, and Needed Source Reductions. Part 9. Calculating Effective Ambient Air Quality Parameters.
|PUB Date Free Form
||PC A02/MF A01