Effectiveness of Smokeless Ashtrays

Abstract

Most environmental tobacco smoke (ETS) issues from the tips of smoldering cigarettes between puffs. Smokeless ashtrays are designed to reduce ETS exposure by removing particulate and/or gas-phase contaminants from this plume. This paper describes an experimental investigation of the effectiveness of four smokeless ashtrays: two commercial devices and two prototypes constructed by the authors. In the basic experimental protocol, one or more cigarettes was permitted to smolder in a room. Particulate or gas-phase pollutant concentrations were measured in the room air over time. Device effectiveness was determined by comparing pollutant concentrations with the device in use to those obtained with no control device. A lung deposition model was applied to further interpret device effectiveness for particle removal. The commercial ashtrays were found to be substantially ineffective in removing ETS particles because of the use of low-quality filter media and/or the failure to draw the smoke through the filter. A prototype ashtray using HEPA filter material achieved better than 90% particle removal efficiency. Gasphase pollutant removal was tested for only one prototype smokeless ashtray, which employed filters containing activated carbon and activated alumina. Removal efficiencies for the 18 gas-phase compounds measured (above the detection limit) were in the range of 70 to 95%.     

Air Pollution and Mortality: Issues and Uncertainties

Abstract

Results from 31 epidemiology studies linking air pollution with premature mortality are compared and synthesized. Consistent positive associations between mortality and various measures of air pollution have been shown within each of two fundamentally different types of regression studies and in many variations within these basic types; this is extremely unlikely to have occurred by chance. In this paper, the measure of risk used is the elasticity, which is a dimensionless regression coefficient defined as the percentage change in the dependent variable associated with a 1% change in an independent variable, evaluated at the means. This metric has the advantage of independence from measurement units and averaging times, and is thus suitable for comparisons within and between studies involving different pollutants. Two basic types of studies are considered: time-series studies involving daily perturbations, and cross-sectional studies involving longer-term spatial gradients. The latter include prospective studies of differences in individual survival rates in different locations and studies of the differences in annual mortality rates for various communities.

For a given data set, time-series regression results will vary according to the seasonal adjustment method used, the covariates included, and the lag structure assumed. The results from both types of cross-sectional regressions are highly dependent on the methods used to control for socioeconomic and personal lifestyle factors and on data quality. Amajor issue for all of these studies is that of partitioning the response among collinear pollution and weather variables. Previous studies showed that the variable with the least exposure measurement error may be favored in multiple regressions; assigning precise numerical results to a single pollutant is not possible under these circumstances. We found that the mean overall elasticity as obtained from timeseries studies for mortality with respect to various air pollutants entered jointly was about 0.048, with a range from 0.01 to 0.12. This implies that about 5% of daily mortality is associated with air pollution, on average. The corresponding values from population-based cross-sectional studies were similar in magnitude, but the results from the three recent prospective studies varied from zero to about five times as much. Long-term responses in excess of short-term responses might be interpreted as showing the existence of chronic effects, but the uncertainties inherent in both types of studies make such an interpretation problematic.     

Mapping Odor Sources from Complaint Statistics I: Identifying the Major Source

Odor is acknowledged as a major community air pollution nuisance, but the problems of analysis, particularly in Identifying the origin of odor, are also well recognised. One approach to the problem has been the development of computational schemes which use wind directions at the times of odor observations to locate the likely source of odor. An earlier technique, PONG, has now been superseded by a more sophisticated version, PONG2, which incorporates Gaussian plume modeling techniques. This paper describes the operation of PONG2 in the odor source mapping mode and provides an analysis of the Insensitivity of the technique to errors in input. Then, using specific Australian examples of community odor nuisance emanating from a large sewage treatment complex near Melbourne, and an irrigated golf course within metropolitan Darwin, the paper outlines the utility of PONG2 in resolving problem odor sources at a range of scales and levels of complexity.     

The Cost Estimation of Thermal and Catalytic Incinerators for the Control of VOCs

In an earlier paper, major commercially available incinerator technologies designed to treat dilute waste gas streams containing volatile organic compounds (VOCs) were described in a qualitative comparison. In addition, a simplified procedure was outlined through which important sizing parameters could be calculated given certain information about the waste gas. This paper describes the use of these parameters in developing a study (±30percent) cost estimate for the total capital investment and the total annual costs incurred in treating a waste stream of volatile organic compounds (VOCs). An illustrative problem used in the former paper is completed here in order to contrast some of the economic differences between thermal and catalytic incinerator systems.     

Maximum Ground-Level Concentrations with Downwash

Equations are derived from the Gaussian plume mode! and prescribe the critical downwind distance, wind speed, and plume rise values that result in maximum ground-level concentrations (MGLC) under downwash conditions. The derivations apply to bent-over plumes and encompass the Schulman-Scire and Huber-Snyder building downwash treatments.     

NOx Removal with Combined Selective Catalytic Reduction and Selective Noncatalytic Reduction: Pilot-Scale Test Results

Pilot-scale tests were conducted to develop a combined nitrogen oxide (NO_x) reduction technology using both selective catalytic reduction (SCR) and selective noncatalytic reduction (SNCR). A commercially available vanadium- and titanium-based composite honeycomb catalyst and enhanced urea (NH₂CONH₂) were used with a natural-gas-fired furnace at a NO_x concentration of 110 ppm. Changes in SNCR chemical injection temperature and stoichiometry led to varying levels of post-furnace ammonia (NH₃), which acts as the reductant feed to the downstream SCR catalyst. The urea-based chemical could routinely achieve SNCR plus SCR total NO_x reductions of 85 percent with less than 3 ppm NH₃ slip at reductant/NO_x stoichiometries ranging from about 1.5 to 2.5 and SCR space velocities of 18,000 to 32,000 h^?1. This pilot-scale research has shown that SNCR and SCR can be integrated to achieve high NO_x removal. SNCR provides high temperature reduction of NO_x followed by further removal of NO_x and minimization of NH₃ slip by a significantly downsized (high-space velocity) SCR.     

A Simple Neural Network for Estimating Emission Rates of Hydrogen Sulfide and Ammonia from Single Point Sources

Abstract

Neural networks have shown tremendous promise in modeling complex problems. This work describes the development and validation of a neural network for the purpose of estimating point source emission rates of hazardous gases. This neural network approach has been developed and tested using experimental data obtained for two specific air pollutants of concern in West Texas, hydrogen sulfide and ammonia. The prediction of the network is within 20% of the measured emission rates for these two gases at distances of less than 50 m. The emission rate estimations for ground level releases were derived as a function of seven variables: downwind distance, crosswind distance, wind speed, downwind concentration, atmospheric stability, ambient temperature, and relative humidity. A backpropagation algorithm was used to develop the neural network and is also discussed here. The experimental data were collected at the Wind Engineering Research Field Site located at Texas Tech University in Lubbock, Texas. Based on the results of this study, the use of neural networks provides an attractive and highly effective tool to model atmospheric dispersion, in which a large number of variables interact in a nonlinear manner.     

The Application of the Karl Fischer Oven for the Determination of Water in Consumer Products

Abstract

The direct Karl Fischer (KF) titration method has known interferences for measuring water content. In addition, in analyzing some paints, KF can fail to produce an accurate analysis. The California Air Resources Board (GARB) staff has developed a KF procedure that can be used to determine the water content of consumer products. The procedure uses an oven accessory to the titration system, and is based on a distillation method developed by the California Polytechnical University at San Luis Obispo (Cal Poly). Samples are diluted in l-methoxy-2-propanol (MPA), and an aliquot is injected into an enclosed oven system, where the MPA/water azeotrope is swept directly into the KF titration vessel. The technique is accurate and precise and, thus far, proves to be a fast and reliable method for analysis.     

Statistical Considerations in Determining the Health Significance of Constituents of Airborne Particulate Matter

ABSTRACT

Because of the U.S. Environmental Protection Agency’s (EPA) new ambient air quality standard for fine particles, the need is likely to continue for more detailed scientific investigation of various types of particles and their effects on human health. Epidemiology studies have become the method of choice for investigating health responses to such particles and to other air pollutants in community settings. Health effects have been associated with virtually all of the gaseous criteria pollutants and with the major constituents of airborne particulate matter (PM), including all size fractions less than about 20 gm, inorganic ions, carbonaceous particles, metals, crustal material, and biological aerosols. In many of the more recent studies, multiple pollutants or agents (including weather variables) have been significantly associated with health responses, and various methods have been used to suggest which ones might be the most important. In an ideal situation, classical least-squares regression methods are capable of performing this task. However, in the real world, where most of the pollutants are correlated with one another and have varying degrees of measurement precision and accuracy, such regression results can be misleading. This paper presents some guidelines for dealing with such collinearity and model comparison problems in both single- and multiple-pollutant regressions. These techniques rely on mean effect (attributable risk) rather than statistical significance per se as the preferred indicator of importance for the pollution variables.     

Daily Mortality in the Philadelphia Metropolitan Area and Size-Classified Particulate Matter

ABSTRACT

Time-series of daily mortality data from May 1992 to September 1995 for various portions of the seven-county Philadelphia, PA, metropolitan area were analyzed in relation to weather and a variety of ambient air quality parameters. The air quality data included measurements of size-classified PM, SO₄ ^2-, and H+ that had been collected by the Harvard School of Public Health, as well as routine air pollution monitoring data. Because the various pollutants of interest were measured at different locations within the metropolitan area, it was necessary to test for spatial sensitivity by comparing results for different combinations of locations. Estimates are presented for single pollutants and for multiple-pollutant models, including gaseous pollutants and mutually exclusive components of PM (PM_2.5 and coarse particles, SO₄ ^2- and non-SO₄ ^2- portions of total suspended particulate [TSP] and PM₁₀), measured on the day of death and the previous day.

We concluded that associations between air quality and mortality were not limited to data collected in the same part of the metropolitan area; that is, mortality for one part may be associated with air quality data from another, not necessarily neighboring, part. Significant associations were found for a wide variety of gaseous and particulate pollutants, especially for peak O₃. Using joint regressions on peak O₃ with various other pollutants, we found that the combined responses were insensitive to the specific other pollutant selected. We saw no systematic differences according to particle size or chemistry. In general, the associations between daily mortality and air pollution depended on the pollutant or the PM metric, the type of collection filter used, and the location of sampling. Although peak O₃ seemed to exhibit the most consistent mortality responses, this finding should be confirmed by analyzing separate seasons and other time periods.