An Air Quality Data Analysis System for Interrelating Effects,Standards, and Needed Source Reductions: Part 3. Vegetation Injury

Acute leaf injury data are analyzed for 19 plant species exposed to ozone or sulfur dioxide. The data can be depicted by a new leaf injury mathematical model with two characteristics: (1) a constant percentage of leaf surface is injured by an air pollutant concentration that is inversely proportional to exposure duration raised to an exponent; (2) for a given exposure duration, the percent leaf injury as a function of pollutant concentration tends to fit a lognormal frequency distribution. Leaf injury as a function of laboratory exposure duration is modeled and compared with ambient air pollutant concentration measurements for various averaging times to determine which exposure durations are probably most important for setting ambient air quality standards to prevent or reduce visible leaf injury. The 8 hour average appears to be most important for most of the plants investigated for most sites, 1 hr concentrations are important for most plants at a few sites, and 3 hr S0₂ concentrations are important for some plants, especially those exposed to isolated point sources of the pollutant. The 1, 3, and 8 hr threshold injury concentrations are listed for each of the 19 plant species studied. To prevent or reduce acute leaf injury, fixed, nonoverlapping ambient air quality measurements and standards are recommended for averaging times of 1, 3, and 8hr.     

An Air Quality Data Analysis System for Interrelating Effects,Standards, and Needed Source Reductions: Part 8. An Effective Mean O3 Crop Reduction Mathematical Model

A plant injury mathematical model, applied previously to acute and chronic leaf injury data, is used here to model National Crop Loss Assessment Network (NCLAN) data for 15 cultivars and to calculate species parameters from the cultivar analyses. Percent crop yield reduction is estimated as a function of a new parameter, the effective mean O₃ concentration: m_e = [(Σ c_h ^?1/v)/n]^?v, where c_h is the hourly average ambient O₃ concentration for each daytime hour (defined here as 9:00 A.M.–4:00 P.M., always standard time) of data available at an air sampling site for summer (defined here as June 1–August 31), n is the total number of such available hours, and v is an exposure time-concentration parameter, calculated here to be approximately –0.376. Crop yield reduction for soybean is calculated here as z = 0.478 In (tm_e ^2-66) – 0.42, where z is the Gaussian transform of percent crop reduction, t is the hours of exposure (525 h is used here; 7 h/day for 75 days), and In indicates that the natural logarithm is taken of the quantity within parentheses. Crop yield reductions for seven plant species are estimated with similar equations for each of the 1824 site-years of 1981–1983 hourly O₃ concentration data available in the National Aerometric Data Bank (NADB). County-average effective mean O₃ concentrations are indicated by shading on a U.S. map. State-average O₃ parameters and estimated percent crop yield reductions are tabulated. The National Ambient Air Quality Standard (NAAQS) for O₃ specifies that, on the average, the second highest daily maximum 1-h average O₃ concentration in a year shall not exceed 0.12 ppm. For years 1981-1983,71% of the NADB sites recorded annual second highest daily maximum 1-h average O3 concentrations below 0.125 ppm (for summer daytime hours). Ambient O₃ concentrations reduced the total U.S. crop yield an estimated 5% for years 1981–1983. (Summer, daytime, and all acronyms are always used herein as defined above.)     

An Air Quality Data Analysis System for Interrelating Effects,Standards, and Needed Source Reductions: Part 4. A Three-Parameter Averaging-Time Model

Urban air pollutant concentration data often tend to fit a two-parameter averaging-time model having three characteristics: (1) pollutant concentrations are (two-parameter) lognormally distributed for all averaging times; (2) median concentrations are proportional to averaging time raised to an exponent; and (3) maximum concentrations are approximately inversely proportional to averaging time raised to an exponent. Concentration data obtained near many isolated point sources and in some urban areas often do not fit a two-parameter lognormal distribution. An increment (either positive or negative) can be added to each such concentration in order to fit the data instead to a three-parameter lognormal distribution. This increment has been incorporated as the third parameter in a new three-parameter averaging-time model that can be used in both point-source and urban settings. Examples show how this new model can be used to analyze SO₂ concentration data obtained near a point source to determine the degree of emission reduction needed to achieve the national ambient air quality standards.     

An Air Quality Data Analysis System for Interrelating Effects,Standards, and Needed Source Reductions: Part 10. Potential Ambient O3 Standards to Limit Soybean Crop Reduction

Soybean percent crop reduction was estimated as a function of ambient O₃ concentrations for each of 80 agricultural sites in the National Aerometric Data Bank (NADB) for each available year of data for years 1981-1985. Fourteen O₃ concentration statistics were calculated for each of the resulting 320 site-years of data. The two statistics that correlated best with estimated crop reduction were an effective mean O₃ concentration (1 percent of variance unexplained) and an arithmetic mean O₃ concentration (4 percent unexplained). The worst correlation of the 14 was for the statistic used in the present O₃ National Ambient Air Quality Standard (NAAQS), the second highest daily maximum 1-h O₃ concentration (42 percent unexplained). The number of site-years for estimated percent soybean yield reductions was plotted versus increasing O₃ concentrations for each of the 14 O₃ statistics. A maximum crop reduction line was drawn on each plot. These lines were used to estimate (and list) potential ambient O₃ standards for each of the 14 statistics that would limit soybean crop reduction at agricultural. NADB sites to 5, 10, 15, or 20 percent.     

An Air Quality Data Analysis System for Interrelating Effects,Standards, and Needed Source Reductions: Part 6. Calculating Concentration Reductions Needed to Achieve the New National Ozone Standard

The new ozone National Ambient Air Quality Standard specifies that the expected number of days per calendar year that the 1 hour average ozone concentration can exceed 0.12 ppm must be equal to or less than 1. This paper describes a method to calculate design frequency, design concentration, and the percentage concentration reduction necessary to achieve this standard. The design frequency is once per year (1/365) if daily maximum hour ozone concentrations are available at a particular site for an equal number of days in summer (April through September) and winter (October through March). An equation is used to adjust design frequency as a function of the number of summer and winter samples available. The design concentration (the ambient concentration measured at the design frequency) needs to be reduced to 0.12 ppm. Graphical and digital methods for determining the design concentration are presented. Percentage concentration reductions needed to achieve the standard are calculated for each site which has ozone concentration data available in the National Aerometric Data Bank for at least half of the days in one summer of years 1975 through 1977. The degree of reduction calculated for the site with the highest concentrations in each county is indicated by shading on a map of the United States.