An Approach to Air Quality Management Standards

The Clean Air Amendments of 1970 impose a stringent set of uniform air quality standards and a rigid timetable for achieving them. Such an approach is open to serious question, since it fails to take account of variations in relevant local conditions. This paper sketches a proposal for a more realistic approach to air quality. It suggests a program of management standards that would reflect the needs and circumstances of particular regions. An institutional framework is outlined that would consider the technical, economic, and social constraints that determine how quickly and how much any area can improve its air quality. The framework would provide the needed flexibility, and would employ a feedback approach that avoids the need to resolve all uncertainties before any progress can be made. At the same time, it would impose a timetable and planning process to assure that all feasible progress in air quality improvement is realized nationwide.     

A Quantitative Approach to the Traffic Air Quality Problem: The Traffic Air Quality Index

Abstract

The Traffic Air Quality (TAQ) model is a simple tool to estimate traffic fine particulate emissions on roadways (g/km) and can be used for both real-time analysis and for localized conformity analysis (“hot-spot” analysis for nonattainment areas) as defined by 40 CFR 93.123. This paper is a follow-up to a study published earlier regarding the development of the TAQ model. This paper shows how local air quality levels can be a factor in traffic management in nonattainment areas. Similar to the industrial source quotas measured in tons per year, it is proposed that road segments are to be assigned emission quotas (or TAQ indices) measured in pollutant mass emitted per road length (g/km) above which traffic-measures have to be taken to reduce the fine-particulates emissions on such road links. The TAQ model as well as traffic-rerouting measures along with the Intelligent Transportation System (ITS) protocols can be used to have a real-time control of the traffic conditions along expressways to maintain the fine-particulates emissions below the quota assigned per road link and consequently improving the over all local air quality in nonattainment areas.     

An Index and Model of Human Response to Air Quality

This study undertook an empirical Investigation of human response to air quality. Home interviews of 475 respondents living in 22 neighborhoods of Los Angeles County had elicited information on respondent socioeconomic characteristics, behavioral patterns, and measures of human response to air quality. This data base was then augmented with nine measures of actual air quality for six time frames for each neighborhood.

An observer-based air quality index (OBAQI) was constructed based upon which combination of air quality variables correlated best with the percentage of neighborhood respondents who perceived “smoggy air.” The best combination (OBAQI 3) consisted of prevailing visibility, O₃, and SO₂, each measured as the annual number of days that a selected standard had been equalled or exceeded. Subsequently, multiple regression models were constructed using this index as a predictor of aggregated perception of air quality. In addition to a general model for all neighborhoods, separate models were constructed for clusters of neighborhoods of similar micrometeorology as approximated by uniform elevations and/or coastal distances. Zones of homogeneous air quality and micrometeorology were then defined. Within these zones variation in four measures of human response to air quality was associated with respondent socioeconomic characteristics and behavioral patterns. Both the index and model could prove useful in gauging public response to proposed actions of air quality management districts.     

An Efficient Gaussian-Plume Multiple-Source Air Quality Algorithm

The information presented in this paper is directed to air pollution scientists with an interest in applying air quality simulation models. RAM is the three letter designation for this efficient Gaussian-plume multiple-source air quality algorithm. RAM is a method of estimating short-term dispersion using the Gaussian steady-state model. This algorithm can be used for estimating air quality concentrations of relatively stable pollutants for averaging times from an hour to a day in urban areas from point and area sources. The algorithm is applicable for locations with level or gently rolling terrain where a single wind vector for each hour is a good approximation to the flow over the source area considered. Calculations are performed for each hour. Hourly meteorological data required are wind direction, wind speed, stability class, and mixing height. Emission information required of point sources consists of source coordinates, emission rate, physical height, stack gas volume flow and stack gas temperature. Emission information required of area sources consists of south-west corner coordinates, source area, total area emission rate and effective area source height. Computation time is kept to a minimum by the manner in which concentrations from area sources are estimated using a narrow plume hypothesis and using the area source squares as given rather than breaking down all sources to an area of uniform elements. Options are available to the user to allow use of three different types of receptor locations: 1 ) those whose coordinates are input by the user, 2) those whose coordinates are determined by thé model and are downwind óf significant point and area sources where maxima are likely to occur, and 3) those whose coordinates are determined by the model to give good area coverage of a specific portion of the region. Computation time is also decreased by keeping the number of receptors to a minimum.     

A Cost-Benefit Approach to Air Pollution Control

Air pollution has plagued the urban areas of our Nation for many years, both as a serious health hazard and as a costly economic burden. Users of the air as a waste disposal medium have treated it as a free resource with no regard for the damages incurred by receptors of the dirty air. Unfortunately, no self-regulating market forces exist that will bring this diseconomy into an acceptable equilibrium. An outside regulating force, which can affect abatement of air pollution, is needed to achieve control of air pollution to a level acceptable to a community. A prerequisite of any proposed regulatory action to be applied by an outside force, however, is the need to define an acceptable level of pollution. One means of accomplishing the task is to apply an economic evaluation, in the form of a cost-benefit analysis, to the problem. Previous analyses of the economic aspects of air pollution have stressed only one side of the problem. That is, they have dealt only with the cost of control or the damages caused by air pollution. No analysis has combined both aspects into a meaningful analysis of the overall economic impact. The objective of this paper is to present a technique that can be used to find the level of pollution abatement in an area that balances the cost of controlling pollution with the benefits received from its control. To present a clearer picture of this technique and its results, it has been applied to the Washington, D. C. area to find the optimum level of particulate abatement. This method is generally applicable to any area, in which an air pollution problem exists.     

Analysis of Air Quality Observations with the Aid of the Source-Receptor Relationship Approach

Abstract

In this study, an attempt was made to analyze time series of air quality measurements (O₃, SO₂, SO₄ ^2?NO_x) conducted at a remote place in the eastern Mediterranean (Finokalia at Crete Island in 1999) to obtain concrete information on potential contributions from emission sources. For the definition of a source-receptor relationship, advanced meteorological and dispersion models appropriate to identify “areas of influence” have been used. The model tools used are the Regional Atmospheric Modeling System and the Lagrangian-type particle dispersion model (forward and backward in time), with capabilities to derive influence functions and definition of “areas of influence.” When high levels of pollutants have been measured at the remote location of Finokalia, particles are released from this location (receptor) and traced backward in time. The influence function derived from particle distributions characterizes dispersion conditions in the atmosphere and also provides information on potential contributions from emission sources within the modeling domain to this high concentration. As was shown in the simulation results, the experimental site of Finokalia in Crete is influenced during the selected case studies, primarily by pollutants emitted from the urban conglomerate of Athens. Secondarily, it is influenced by polluted air masses arriving from Italy and/or the Black Sea Region. For some specific cases, air pollutants monitored at Finokalia were possibly related to war activities in the West Balkan Region (Kosovo).     

Ozone Air Quality Models

Abstract

The field of ozone air quality modeling, or as it is commonly referred to, photochemical air quality modeling, has undergone rapid change in recent years. Improvements in model components, as well as in methods of interpreting model performance, have contributed to this change. Attendant with this rapid change has been a growing need for those developing and using air quality models and policy makers to have a common understanding of the use and role of models in the decision making process. This Critical Review highlights recent advances and continuing problem areas in photochemical air quality modeling. Emphasis is placed on the components and input data for such models, model performance evaluation, and the implications for their use in regulatory decisions.     

“分析家”被动采样器在区域空气质量评估中的应用

简要介绍“分析家”（Analyst）被动采样器的结构、原理及在中意项目：“分析家”被动采样器在苏州空气监测系统预评估阶段的应用。     

The Annual Average: An Alternative to the Second Highest Value as a Measure of Air Quality

It is common practice to use the second highest value both in determining compliance with the once-per-year air quality standards (AQS) and as a measure of air quality in pollutant trend studies and rollback calculations. A study of the variation in the second highest 8 hr carbon monoxide concentration observed at the CAMP stations 1962-72 is presented. It is shown that, for a given annual average, the second highest value can differ by a factor of 2 due to random variation. The annual average is linearly related to the observed average of the second highest value and is shown to be a good predictor of the percent of time the carbon monoxide AQS is exceeded. It is concluded that the annual average, which is less variable and not as greatly influenced by erroneous measurements, is the preferred measure of air quality for trend studies and air quality projections.     

Monte Carlo Simulation Techniques Applied to Air Quality Impact Assessment

Hammersly and Handscomb¹ describe Monte Carlo Methods as a branch of experimental mathematics concerned with experiments on random numbers. The purpose of this paper is to suggest methods through which this powerful technique can be applied to the problem of evaluating the return period of a concentration estimate.     

Air Quality Control Equipment Guarantees

The purpose of this paper is to discuss considerations regarding guarantees of air quality control equipment. Topics include specification design parameters to cover ranges of expected operating conditions, negotiation of performance guarantees, design changes during construction, reliability, performance testing and comparison of test conditions to the worst expected conditions for which the guarantees apply. Comments are also offered regarding corrective actions in event the guarantees are not complied with.