Correction methods for organic carbon artifacts when using quartz-fiber filters in large particulate matter monitoring networks: the regression method and other options

Sampling and handling artifacts can bias filter-based measurements of particulate organic carbon (OC). Several measurement-based methods for OC artifact reduction and/or estimation are currently used in research-grade field studies. OC frequently is not artifact-corrected in large routine sampling networks (e.g., U.S. Environmental Protection Agency (EPA)'s Chemical Speciation Network). In some cases, the OC artifact has been corrected using a regression method (RM) for artifact estimation. In this method, the gamma-intercept of the regression of the OC concentration on the fine particle (PM2.5) mass concentration is taken to be an estimate of the average OC sampling artifact (net of positive and negative artifacts). This paper discusses options for artifact correction in large routine sampling networks. Specifically, the goals are to (1) articulate the assumptions and limitations inherent to the RM, (2) describe other artifact correction approaches, and (3) suggest a cost-effective method for artifact correction in large monitoring networks. The RM assumes a linear relationship between measured OC and PM mass: a constant slope (OC mass fraction) and a constant intercept (RM artifact estimate). These assumptions are not always valid. Additionally, outliers and other individual data points can have a large influence on the RM artifact estimates. The RM yields results within the range of measurement-based methods for some datasets and not for others. Given that the adsorption of organic gases increases with atmospheric concentrations of organics, subtraction of an average artifact from all samples (e.g., across multiple sites) will underestimate OC for lower-concentration samples (e.g., clean sites) and overestimate OC for higher-concentration samples (e.g., polluted sites). For relatively accurate, simple, and cost-effective artifact OC estimation in large networks, the authors suggest backup filter sampling on at least 10% of sampling days at all sites with artifact correction on a sample-by-sample basis as described herein.     

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.     

Relationship of Destruction Parameters to the Destruction/Removal Efficiency of PCBs

The destruction/removal efficiency (DRE), and the ability to accurately measure it, is a function of the concentration of the chemical compound in the input waste, the incinerator design and operation, sampling methods, and the analytical procedures. All of these are interrelated. This paper discusses the basic DRE equation [DRE = W_In W_out)/W_in × 100] and how it relates to some of the other destruction parameters. Some example data from the literature are presented. While PCBs have been used as the example, the equations and graphs are equally valid for some other hazardous compounds (POHCs), with the substitution of the 99.99% DRE requirement in lieu of the 99.9999% DRE for PCBs. The use of the relationships discussed in this paper should allow incinerator operators to more efficiently plan demonstration test burns which will adequately demonstrate the DRE.     

Solar driven nitrous acid formation on building material surfaces containing titanium dioxide: A concern for air quality in urban areas?

The photoenhanced uptake of nitrogen dioxide (NO₂) to the surface of commercially available self-cleaning window glass has been studied under controlled laboratory conditions. This material is one of an array of modern building products which incorporate titanium dioxide (TiO₂) nanoparticles and are finding increasing use in populated urban areas. Amongst the principal drivers for the use of these materials is that they are thought to facilitate the irreversible removal of pollutants such as NO₂ and organic molecules from the atmosphere and thus act to remediate air quality. While it appears that TiO₂ materials do indeed remove organic molecules from built environments, in this study we show that the photoenhanced uptake of NO₂ to one example material, self-cleaning window glass, is in fact accompanied by the substantial formation (50–70%) of gaseous nitrous acid (HONO). This finding has direct and serious implications for the use of these materials in urban areas. Not only is HONO a harmful respiratory irritant, it is also readily photolysed by solar radiation leading to the formation of hydroxyl radicals (OH) together with the re-release of NO_x as NO. The net effect of subsequent OH initiated chemistry can then be the further degradation of air quality through the formation of secondary pollutants such as ozone and VOC oxidation products. In summary, we suggest that a scientifically conceived technical strategy for air quality remediation based on this technology, while widely perceived as universally beneficial, could in fact have effects precisely opposite to those intended.     

The oldest haplogyne spider (Araneae: Plectreuridae), from the Middle Jurassic of China

New fossil spiders (Arachnida: Araneae) from Middle Jurassic (ca. 165 Ma) strata of Daohugou, Inner Mongolia, China are described as Eoplectreurys gertschi gen. et sp. nov. and referred to the modern haplogyne family Plectreuridae. This small family is restricted to southwestern USA, Mexico, and the adjacent Caribbean area today and hitherto has only a sparse Cenozoic fossil record. The morphology of Eoplectreurys is remarkably similar to modern forms and thus demonstrates great evolutionary conservatism. This new discovery not only extends the fossil record of the family by at least 120 Ma to the Middle Jurassic but also supports the hypothesis of a different distribution of the family in the past than today and subsequent extinction over much of its former range.     

Principal response curves technique for the analysis of multivariate biomonitoring time series

Although chemical and biological monitoring is often used to evaluate the quality of surface waters for regulatory purposes and/or to evaluate environmental status and trends, the resulting biological and chemical data sets are large and difficult to evaluate. Multivariate techniques have long been used to analyse complex data sets. This paper discusses the methods currently in use and introduces the principal response curves method, which overcomes the problem of cluttered graphical results representation that is a great drawback of most conventional methods. To illustrate this, two example data sets are analysed using two ordination techniques, principal component analysis and principal response curves. Whereas PCA results in a difficult-to-interpret diagram, principal response curves related methods are able to show changes in community composition in a diagram that is easy to read. The principal response curves method is used to show trends over time with an internal reference (overall mean or reference year) or external reference (e.g. preferred water quality or reference site). Advantages and disadvantages of both methods are discussed and illustrated.     

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.