An Air Quality Data Analysis System for Interrelating Effects, Standards, and Needed Source Reductions: Part 9. Calculating Effective Ambient Air Quality Parameters

Abstract

The overall purpose of air pollution control is to reduce or eliminate adverse effects, such as plant injury or crop reduction. In order to study and to control such adverse effects efficiently, air quality parameters are needed that correlate closely with the effects, as does the effective mean concentration:

wherec _h, is the 1-h average ambient air pollutant concentration measured at a site for each daytime hour (defined here as 9:00 A.M.-4:00 P.M., always standard time) in a plant’s growing season, n is the number of hours of such available data, and vis a concentration-time parameter (−0.376 is always used here, based on previous studies). 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. This 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,m_gae , and an effective standard geometric deviation,s_gae , calculated from the effective and arithmetic means:

where exp indicates thate, 2.718, is raised to the power in brackets. 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.