A Pilot Indoor-Outdoor Study of Organic Particulate Matter and Particulate Mutagenicity

Abstract

Many large metropolitan areas experience elevated concentrations of ground-level ozone pollution during the summertime “smog season”. Local environmental or health agencies often need to make daily air pollution forecasts for public advisories and for input into decisions regarding abatement measures and air quality management. Such forecasts are usually based on statistical relationships between weather conditions and ambient air pollution concentrations. Multivariate linear regression models have been widely used for this purpose, and well-specified regressions can provide reasonable results. However, pollution-weather relationships are typically complex and nonlinear—especially for ozone—properties that might be better captured by neural networks. This study investigates the potential for using neural networks to forecast ozone pollution, as compared to traditional regression models. Multiple regression models and neural networks are examined for a range of cities under different climate and ozone regimes, enabling a comparative study of the two approaches. Model comparison statistics indicate that neural network techniques are somewhat (but not dramatically) better than regression models for daily ozone prediction, and that all types of models are sensitive to different weather-ozone regimes and the role of persistence in aiding predictions.     

Improved Predictions of Plume Perception with a Human Visual System Model

This paper describes incorporation of a human visual system model in the widely used plume visibility model PLUVUE. The results will be of interest to all involved with siting new sources for which visibility of the plume is a concern and to visibility researchers. The human visual system model allows inclusion of size and shape effects on the perceptibility of a plume. Example calculations are given for 2250- and 1600-MW power plants which show that size and shape effects can reduce the predicted perceptibility by up to a factor of three.     

A Review of Treatment Alternatives for Wastes Containing Treatment Technologies For Hazardous Wastes: Part V

The Hazardous and Solid Waste Amendments to the Resource Conservation and Recovery Act direct the Environmental Protection Agency to determine the available treatment technologies for a number of hazardous waste streams, including halogenated organics. If it is determined that existing technology and capacity is sufficient for the safe management of the designated halogenated organic wastes, these wastes will be prohibited from land disposal, effective July 8,1987. This article summarizes the general characteristics and treatment alternatives for this waste category.     

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.     

Receptor Modeling Applied to Patterns in Space (RMAPS)Part III–Apportionment of Airborne Particulate Sulfur in Western Washington State

Abstract

Data obtained from 24 of the 31 sites of the Pacific Northwest Regional Visibility Experiment Using Natural Tracers (PREVENT) study were analyzed by the Receptor Model Applied to Patterns in Space (RMAPS) multivariate receptor model. Four spatial patterns were found and interpreted as showing the effect of the coal-fired power plant in Centralia, WA; transport from the northwest; the Se-attle-Tacoma urban area; and transport from the southeast. In Mt. Rainier National Park, up to one-third of the sulfate can be attributed to the Centralia power plant. In the North Cascades National Park, 65-82% of the sulfur is accounted for by transport from Canada. The model was applied separately to sites in the northern and southern sections of the study area. The southern sites were affected only by the Centralia, urban, and southeast transport sources; the northern sites were affected only by the northwest transport, urban, and southeast transport sources. This gave two independent estimates of the normalized source contributions of the urban and southeast transport factors, which had a correlation coefficient of more than 0.90.     

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.     

Seasonal Variability in CH4 Emissions from a Landfill in a Cool,Semiarid Climate

ABSTRACT

Methane exchange with the atmosphere was measured during three seasons at the Rooney Road landfill in Jefferson County, CO. Substantial spatial and temporal variability in exchange rates were observed. Mean fluxes to the atmosphere were 534, 1290, and 538 mg CH₄/m²/day, respectively, in the fall of 1994, winter of 1994–1995, and summer of 1995. Median fluxes were 12.42, 8.62, and 5.65 mg CH₄/m²/day, respectively, during those seasons. Forty-three of 177 measurements had small negative fluxes, suggesting methanotrophic activity in the landfill cover soils. Despite probable methanotrophic activity in cover soils, landfills without gas collection systems may emit substantial CH₄ to the atmosphere, with large spatial and seasonal variability.     

The 1999 Fresno Particulate Matter Exposure Studies: Comparison of Community,Outdoor, and Residential PM Mass Measurements

ABSTRACT

Two collaborative studies have been conducted by the U.S. Environmental Protection Agency (EPA) National Exposure Research Laboratory (NERL) and National Health and Environmental Effects Research Laboratory to determine personal exposures and physiological responses to par-ticulate matter (PM) of elderly persons living in a retirement facility in Fresno, CA. Measurements of PM and other criteria air pollutants were made inside selected individual residences within the retirement facility and at a central outdoor site on the premises. In addition, personal PM exposure monitoring was conducted for a subset of the participants, and ambient PM monitoring data were available for comparison from the NERL PM research monitoring platform in central Fresno. Both a winter (February 1-28, 1999) and a spring (April 19-May 16, 1999) study were completed so that seasonal effects could be     

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.     

Source Apportionment of Phoenix PM2.5 Aerosol with the Unmix Receptor Model

Abstract

The multivariate receptor model Unmix has been used to analyze a 3-yr PM_2.5 ambient aerosol data set collected in Phoenix, AZ, beginning in 1995. The analysis generated source profiles and overall average percentage source contribution estimates (SCEs) for five source categories: gasoline engines (33 ± 4%), diesel engines (16 ± 2%), secondary SO₄ ^2? (19 ± 2%), crustal/soil (22 ± 2%), and vegetative burning (10 ± 2%). The Unmix analysis was supplemented with scanning electron microscopy (SEM) of a limited number of filter samples for information on possible additional low-strength sources. Except for the diesel engine source category, the Unmix SCEs were generally consistent with an earlier multivariate receptor analysis of essentially the same data using the Positive Matrix Factorization (PMF) model. This article provides the first demonstration for an urban area of the capability of the Unmix receptor model.