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     

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.     

SITE Demonstration of Slurry-Phase Biodegradation of PAH Contaminated Soil

This paper summarizes a joint Superfund Innovative Technology Evaluation (SITE) project on slurry-phase biodegradation and a project to collect information for the data base on Best Demonstrated Available Technologies (BOAT). In this 12-week study, a creosote contaminated soil from the Burlington Northern Superfund site in Brainerd, Minnesota was used to evaluate the effectiveness of the bioslurry reactors. During the demonstration, five 64-liter stainless steel bioreactors, equipped with agitation, aeration and temperature controls were used. The pilot scale study employed a 30 percent slurry, an inoculum of indigenous polynuclear aromatic hydrocarbon (PAH) degraders, and inorganic nutrients.

Total PAH degradation averaged 93.4 ± 3.2 percent over all five operating reactors in the 12 weeks with 97.4 percent degradation of the 2- and 3-ring PAHs and 90 percent degradation of the 4- to 6-ring PAHs. A study of the air emissions, both semivolatile compounds such as naphthalene, anthracene, and phenanthrene and volatile compounds such as toluene, xylene, and benzene, from the units showed that the greatest amount of emissions occurred during the loading of the reactors and during the first few days of operation. Therefore, it may not be cost-effective to require elaborate emissions controls unless there are significant quantities of volatile compounds present in the soil or water to be treated in a bioslurry reactor system.     

Making neighborhoods safer: Examining predictors of residents’ concerns about neighborhood safety

This study examined what factors best predict residents’ concerns about neighborhood safety. One-hundred and twenty-two participants were selected from a large, Midwestern metropolitan area. All participants lived in high crime areas. Participants completed a 22-item questionnaire that assessed their perceptions of neighborhood safety and vigilance. These items were clustered as: 1) Community care and vigilance, 2) neighborhood safety concerns, 3) physical incivilities, and 4) social incivilities. Police crime data were also used in the analyses. Our findings suggest that aspects of the broken window theory, collective efficacy, and place attachments/territoriality play a role in affecting residents’ concerns about neighborhood safety.     

Volatile organic compounds in the unsaturated zone from radioactive wastes

Volatile organic compounds (VOCs) are often comingled with low-level radioactive wastes (LLRW), but little is known about subsurface VOC emanations from LLRW landfills. The current study systematically quantified VOCs associated with LLRW over an 11-yr period at the USGS Amargosa Desert Research Site (ADRS) in southwestern Nevada. Unsaturated-zone gas samples of VOCs were collected by adsorption on resin cartridges and analyzed by thermal desorption and GC/MS. Sixty of 87 VOC method analytes were detected in the 110-m-thick unsaturated zone surrounding a LLRW disposal facility. Chlorofluorocarbons (CFCs) were detected in 100% of samples collected. Chlorofluorocarbons are powerful greenhouse gases, deplete stratospheric ozone, and are likely released from LLRW facilities worldwide. Soil-gas samples collected from a depth of 24 m and a horizontal distance 100 m south of the nearest waste-disposal trench contained >60,000 ppbv total VOCs, including >37,000 ppbv CFCs. Extensive sampling in the shallow unsaturated zone (0-2 m deep) identified areas where total VOC concentrations exceeded 5000 ppbv at the 1.5-m depth. Volatile organic compound concentrations exceeded background levels up to 300 m from the facility. Maximum vertical diffusive fluxes of total VOCs were estimated to be 1 g m yr. Volatile organic compound distributions were similar but not identical to those previously determined for tritium and elemental mercury. To our knowledge, this study is the first to characterize the unsaturated zone distribution of VOCs emanating from a LLRW landfill. Our results may help explain anomalous transport of radionuclides at the ADRS and elsewhere.     

Cross-reactivity of anti-Salmonella egg-yolk antibodies to Salmonella serovars

The cross-reactivity of egg yolk antibodies specific to antigens of Salmonella Enteritidis and Salmonella Typhimurium to killed bacterial cells of common Salmonella serovars were tested using an indirect Enzyme Linked Immunosorbent Assay (ELISA). Egg yolk antibodies were produced against purified fimbriae, flagella and lipopolysaccharide (LPS) of S. Enteritidis strain ATCC13076 and flagella, LPS and outer membrane proteins (OMP) of S. Typhimurium strain ATCC13311. For immunological specificity of egg yolk antibodies against killed bacterial cells, we found that the titers of the anti-S. Enteritidis egg yolk antibodies were higher than those of the anti-S. Typhimurium antibodies. In the evaluation of cross-reactivity of these egg yolk antibodies to various Salmonella serovars, we observed that the anti-S. Enteritidis antibodies exhibited more specific affinity than those of the anti-S. Typhimurium antibodies. All S. Enteritidis strains reacted specifically with the anti-S. Enteritidis fimbrial and flagellar egg yolk antibody whereas anti-S. Enteritidis LPS and anti-S. Typhimurium LPS, OMP and flagellar antibodies displayed non-specific reactivity to all Salmonella serovars used in this study. This finding suggests that it may be possible to design a anti-fimbrial egg yolk antibody of S. Enteritidis as a diagnostic tool and a cocktail of OMP and LPS antigens of S. Enteritidis and S. Typhimurium could be used for administering broad spectrum passive immunity to protect against the colonization of pathogenic Salmonella strains in food animals.     

Modeling of Carbon Monoxide Hot Spots

This paper describes the results of a measurement and modeling study of carbon monoxide (CO) concentrations In the proximity of intersections. Analysis for model performance of paired observed and predicted CO concentrations are presented. Two methodologies of pollutant prediction were used: the Intersection Midblock Model (IMM) and a statistical multiple linear regression. The results showed that both methods underpredicted frequently and dispensed results that were site specific. In addition, correlations of IMM predicted concentrations to observed concentrations were poor (typically r² values <0.25). Various explanations for this observation are proposed. The statistical approach exhibited an improved accuracy over that of IMM. However, some of the independent variables used might be difficult to obtain as a routine measurement, and use of a one or two independent parameter model yielded adjusted R² values comparable to the r² values observed with IMM. Based on these results, an Intersection model applicable under a wide range of conditions of traffic, meteorology, and geometry is not available. Research Is needed to develop one, since its use would often be called on in the development of air quality sections of Environmental Assessments or Environmental Impact Statements.     

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 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.