Ralph I. Larsen

An Air Quality Data Analysis System for Interrelating Effects, Standards and Needed Source Reductions.

Forced expiratory volume in 1 second (FEV1) was measured in 21 men exercising while exposed to four O3 concentrations (0.0, 0.08, 0.10, and 0.12 ppm). A lognormal multiple linear regression model was fitted to their mean FEV1 measurements to predict FEV1 percent decrease as a function of O3 concentration and exposure duration. The exercise level used was probably comparable to heavy manual labor. The longest O3 exposure studied was 6 h. Extrapolating cautiously to an 8-h workday of heavy manual labor, the model predicts that O3 concentrations of 0.08, 0.10, and 0.12 ppm would decrease FEV1 by 9, 15, and 20 percent, respectively.

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...

Calculating Air Quality and Its Control

Air quality is shown as a function of averaging times of five minutes to one year for carbon monoxide, hydrocarbons, nitric oxide, nitrogen dioxide, nitrogen oxides, oxidant, and sulfur dioxide in Chicago, Cincinnati, Los Angeles, New Orleans, Philadelphia, San Francisco, and Washington, D. C. Concentrations are approximately lognormally distributed for all pollutants in all cities for all averaging times. Maximum concentration is inversely proportional to averaging time to an exponent. The exponent is a function of the standard geometric deviation. General air quality and control parameters are derived and shown for one example, nitrogen oxides in Washington, D. C. These values are compared to one air quality standard.

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 ...

An Air Quality Data Analysis System For Interrelating Effects, Standards, And Needed Source Reductions—Part 2

The United States national ambient air quality standards specify, for averaging times of 24 hours or less, that the standard concentration is not to be exceeded more than once a year. If the expected annual maximum concentration (expected to occur an average of once a year) is used as the design value in determining source reductions, then these standards will probably be violated (exceeded more than once a year) in about 1 year out of 8. Similarly, annual standards will probably be violated in about 1 year out of 8 if the expected 5 year maximum concentration is selected as the design value in determining source reductions needed to achieve these annual standards. Use of such design values is recommended. The averaging-time mathematical model has generally been used with a 1 year base. From now on, it is recommended that it be used with a 5 year base. The tables needed for a 5 year base are given, and their use for calculating design values is shown.

Relating air pollutant effects to concentration and control.

Examples are cited relating air pollutant effects to concentration and control. The examples are organized under a dozen possible steps that could be used to proceed from air quality criteria to clean air. It is hoped that this paper will be of some assistance to State, regional, and local air managers and their air management boards in deriving the air quality standards and emission standards needed to prevent various unwanted pollutant effects.

Determining Reduced-Emission Goals Needed to Achieve Air Quality Goals— A Hypothetical Case

Air management steps involved in determining reduced-emission goals include determining the effects of various pollutant concentrations on man, animals, plants, and property; deciding which effects to prevent; selecting ambient air quality goals that will prevent these effects; measuring and evaluating pollutant concentrations from sources and in the ambient atmosphere; calculating over-all source reductions needed to achieve selected ambient air quality goals; and finally, determining reduced-emission goals for the various source types. Examples are cited of the various decisions and actions involved in determining a set of reduced-emission goals for stationary and mobile combustion sources.

Improving the dynamic response of continuous air pollutant measurements with a computer

A first-order differential equation describes the dynamic response of many continuous air sampling instruments: Ct = Ci + p(dCi/dt), where Ct is true concentration, Ci is indicated concentration, p is the time constant, and t is time. The time constant, lag time, delay time, and response time are all functions of the volume and flow through the sensor reservoir. All of them can be expressed by the same general equation: t = k3V/Q, where t is the selected time variable, k3 is a constant appropriate to the particular system and selected time variable, V is sensor reservoir volume, and Q is the flow rate through the reservoir. The time constant is the time a sampler takes to indicate 63.2 percent of its final response. Select time constants equal to about half of the shortest desired averaging time. Solve the second equation for the reagent flow to give the desired time constant. Selection of such a time constant eliminates spurious “noise” produced by a fast-responding system. It also provides values within...

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

Presented is a method that was devised to help meet new national ozone air quality standards. Mathematical models that can be used to calculate the reduction in emissions needed to fall within ozone standards are given. Seasonal variations that affect ozone levels are accounted for in the method. (9 graphs, 1 map, 16 references)

An Air Quality Data Analysis System for Interrelating Effects, Standards, and Needed Source Reductions: Part 5. N02 Mortality in Mice

Mice have been exposed for durations of 6 min to 1 yr to N02 concentrations of 0.5 to 28 ppm. Exposed mice and control mice have then inhaled an aerosol containing a lung pathogen and the excess mortality of exposed mice (compared with control mice) has been determined. A mathematical model (similar to a previously-developed model for vegetation injury) has been developed from an analysis of the resulting data to calculate expected excess mortality as a function of N02 concentration and exposure duration. Excess mortality was found to be proportional to N02 concentration multiplied by exposure duration raised to the 0.33 power. The concentration (c) expected to cause a certain mortality level (z), as a function of the hours of exposure (t), can be expressed as c = 9.55(2.42) zt0-33 The model has been used to calculate expected excess mortality (1.1 %) if mice had inhaled the ambient N02 concentrations measured in downtown Chicago for each hour of 1974 (1 yr arithmetic mean of 0.05 ppm, the same as the pre...