Characterization of Benthic Communities and Physical Habitat in the Stanislaus, Tuolumne, and Merced Rivers, California

The primary goal of this study was to characterize physical habitat and benthic communities (macroinvertebrates) in the Stanislaus, Tuolumne and Merced Rivers in California’s San Joaquin Valley in 2003. These rivers have been listed as impaired water bodies (303 (d) list) by the State of California due to the presence of organophosphate (OP) insecticides chlorpyrifos and diazinon, Group A pesticides (i.e., organochlorine pesticides), mercury, or unknown toxicity. Based on 10 instream and riparian physical habitat metrics, total physical habitat scores in the Stanislaus River ranged from 124 to 188 (maximum possible total score is 200). The highest total habitat score was reported at the upstream site. Tuolumne River physical habitat scores ranged from 86 to 167. Various Tuolumne River physical habitat metrics, including total habitat score, increased from downstream to upstream in this river. Merced River physical habitat scores ranged from 121 to 170 with a significant increase in various physical habitat metrics, including total habitat score, reported from downstream to upstream. Channel flow (an instream metric) and bank stability (a riparian metric) were the most important physical habitat metrics influencing the various benthic metrics for all three rivers. Abundance measures of benthic macroinvertebrates (5,100 to 5,400 individuals) were similar among the three rivers in the San Joaquin watershed. Benthic communities in all three rivers were generally dominated by: (1) Baetidae species (mayflies) which are a component of EPT taxa generally considered sensitive to environmental degradation; (2) Chironomidae (midges) which can be either tolerant or sensitive to environmental stressors depending on the species; (3) Ephemerellidae (mayflies) which are considered sensitive to pollution stress; and (4) Naididae (aquatic worms) which are generally considered tolerant to environmental stressors. The presence of 117 taxa in the Stanislaus River, 114 taxa in the Tuolumne River and 96 taxa in the Merced River implies that the benthic communities in these streams are fairly diverse but without a clear definition of benthic community expectations it is unknown if these water bodies are actually impaired.     

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.     

A multi-objective assessment of an air quality monitoring network using environmental,economic, and social indicators and GIS-based models

In the United States, air pollution is primarily measured by Air Quality Monitoring Networks (AQMN). These AQMNs have multiple objectives, including characterizing pollution patterns, protecting the public health, and determining compliance with air quality standards. In 2006, the U.S. Environmental Protection Agency issued a directive that air pollution agencies assess the performance of their AQMNs. Although various methods to design and assess AQMNs exist, here we demonstrate a geographic information system (GIS)-based approach that combines environmental, economic, and social indicators through the assessment of the ozone (O₃) and particulate matter (PM10) networks in Maricopa County, Arizona. The assessment was conducted in three phases: (1) to evaluate the performance of the existing networks, (2) to identify areas that would benefit from the addition of new monitoring stations, and (3) to recommend changes to the AQMN. A comprehensive set of indicators was created for evaluating differing aspects of the AQMNs’ objectives, and weights were applied to emphasize important indicators. Indicators were also classified according to their sustainable development goal. Our results showed that O₃ was well represented in the county with some redundancy in terms of the urban monitors. The addition of weights to the indicators only had a minimal effect on the results. For O₃, urban monitors had greater social scores, while rural monitors had greater environmental scores. The results did not suggest a need for adding more O₃ monitoring sites. For PM10, clustered urban monitors were redundant, and weights also had a minimal effect on the results. The clustered urban monitors had overall low scores; sites near point sources had high environmental scores. Several areas were identified as needing additional PM10 monitors. This study demonstrates the usefulness of a multi-indicator approach to assess AQMNs. Network managers and planners may use this method to assess the performance of air quality monitoring networks in urban regions.

Implications:The U.S. Environmental Protection Agency issued a directive in 2006 that air pollution agencies assess the performance of their AQMNs; as a result, we developed a GIS-based, multi-objective assessment approach that integrates environmental, economic, and social indicators, and demonstrates its use through assessing the O₃ and PM₁₀ monitoring networks in the Phoenix metropolitan area. We exhibit a method of assessing network performance and identifying areas that would benefit from new monitoring stations; also, we demonstrate the effect of adding weights to the indicators. Our study shows that using a multi-indicator approach gave detailed assessment results for the Phoenix AQMN.     

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.     

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.