Concentration and Recovery of Viruses from Water: A Comprehensive Review

Enteric viruses are a cause of waterborne disease worldwide, and low numbers in drinking water can present a significant risk of infection. Because the numbers are often quite low, large volumes (100–1,000 L) of water are usually processed. The VIRADEL method using microporous filters is most commonly used today for this purpose. Negatively charged filters require the addition of multivalent salts and acidification of the water sample to effect virus adsorption, which can make large-volume sampling difficult. Positively charged filters require no preconditioning of samples, and are able to concentrate viruses from water over a greater pH range than electronegative filters. The most widely used electropositive filter is the Virosorb 1MDS; however, the Environmental Protection Agency has added the positively charged NanoCeram filters to their proposed Method 1615. Ultrafilters concentrate viruses based on size exclusion rather than electrokinetics, but are impractical for field sampling or processing of turbid water. Elution (recovery) of viruses from filters following concentration is performed with organic (e.g., beef extract) or inorganic solutions (e.g., sodium polyphosphates). Eluates are then reconcentrated to decrease the sample volume to enhance detection methods (e.g., cell culture infectivity assays and molecular detection techniques). While the majority of available filters have demonstrated high virus retention efficiencies, the methods to elute and reconcentrate viruses have met with varying degrees of success due to the biological variability of viruses present in water.     

Using decision science to evaluate global biodiversity indices

Global biodiversity indices are used to measure environmental change and progress toward conservation goals, yet few indices have been evaluated comprehensively for their capacity to detect trends of interest, such as declines in threatened species or ecosystem function. Using a structured approach based on decision science, we qualitatively evaluated 9 indices commonly used to track biodiversity at global and regional scales against 5 criteria relating to objectives, design, behavior, incorporation of uncertainty, and constraints (e.g., costs and data availability). Evaluation was based on reference literature for indices available at the time of assessment. We identified 4 key gaps in indices assessed: pathways to achieving goals (means objectives) were not always clear or relevant to desired outcomes (fundamental objectives); index testing and understanding of expected behavior was often lacking; uncertainty was seldom acknowledged or accounted for; and costs of implementation were seldom considered. These gaps may render indices inadequate in certain decision-making contexts and are problematic for indices linked with biodiversity targets and sustainability goals. Ensuring that index objectives are clear and their design is underpinned by a model of relevant processes are crucial in addressing the gaps identified by our assessment. Uptake and productive use of indices will be improved if index performance is tested rigorously and assumptions and uncertainties are clearly communicated to end users. This will increase index accuracy and value in tracking biodiversity change and supporting national and global policy decisions, such as the post-2020 global biodiversity framework of the Convention on Biological Diversity.     

Using Continuous PM2.5 Monitoring Data to Report an Air Quality Index

Abstract

As stated in 40 CFR 58, Appendix G (2000), statistical linear regression models can be applied to relate PM_2.5 continuous monitoring (CM) measurements with federal reference method (FRM) measurements, collocated or otherwise, for the purpose of reporting the air quality index (AQI). The CM measurements can then be transformed via the model to remove any bias relative to FRM measurements. The resulting FRM-like modeled measurements may be used to provide more timely reporting of a metropolitan statistical area’s (MSA’s) AQI.¹ Of considerable importance is the quality of the model used to relate the CM and FRM measurements. The use of a poor model could result in misleading AQI reporting in the form of incorrectly claiming either good or bad air quality.

This paper describes a measure of adequacy for deciding whether a statistical linear regression model that relates FRM and continuous PM_2.5 measurements is sufficient for use in AQI reporting. The approach is the U.S. Environmental Protection Agency’s (EPA’s) data quality objectives (DQO) process, a seven-step strategic planning approach to determine the most appropriate data type, quality, quantity, and synthesis for a given activity.² The chosen measure of model adequacy is r², the square of the correlation coefficient between FRM measurements and their modeled counterparts. The paper concludes by developing regression models that meet this desired level of adequacy for the MSAs of Greensboro/Winston-Salem/High Point, NC; and Davenport/Moline/Rock Island, IA/IL. In both cases, a log transformation of the data appeared most appropriate. For the data from the Greens-boro/Winston-Salem/High Point MSA, a simple linear regression model of the FRM and CM measurements had an r² of 0.96, based on 227 paired observations. For the data from the Davenport/Moline/Rock Island MSA, due to seasonal differences between CM and FRM measurements, the simple linear regression model had to be expanded to include a temperature dependency, resulting in an r² of 0.86, based on 214 paired observations.     

Tagging Diesel and Residential Oil Furnace Emissions in Roanoke,Virginia, with Enriched Isotopes of Samarium

Stable isotopic tracers were used in Roanoke, Virginia, to tag particulate emissions from diesel trucks and residential oil furnaces, two sources of soot and PAHs which cannot be differentiated on the basis of known constituents. Approximately 1.6 g of enriched ¹⁴⁹Sm were used to tag 264 m³ of diesel fuel burned by the city bus and truck fleets; 0.39 g of ¹⁵⁰Sm were used to tag 106 m³ of residential heating oil. Picogram amounts of the tracers were determined simultaneously by thermal-ionization mass spectrometry in fine particles collected within the city at signal-to-noise ratios as large as 6000. These results demonstrate the feasibility of tracing particles from multiple combustion sources with stable, separated isotopes.     

Comparing the effectiveness of education and technology in reducing wood smoke pollution: A field experiment

This study describes a field experiment assessing the effectiveness of education and technological innovation in reducing air pollution generated by domestic wood heaters. Two-hundred and twenty four households from a small regional center in Australia were randomly assigned to one of four experimental conditions: (1) Education only – households received a wood smoke reduction education pack containing information about the negative health impacts of wood smoke pollution, and advice about wood heater operation and firewood management; (2) SmartBurn only – households received a SmartBurn canister designed to improve combustion and help wood fires burn more efficiently, (3) Education and SmartBurn, and (4) neither Education nor SmartBurn (control). Analysis of covariance, controlling for pre-intervention household wood smoke emissions, wood moisture content, and wood heater age, revealed that education and SmartBurn were both associated with significant reduction in wood smoke emissions during the post-intervention period. Follow-up mediation analyses indicated that education reduced emissions by improving wood heater operation practices, but not by increasing health risk perceptions. As predicted, SmartBurn exerted a direct effect on emission levels, unmediated by wood heater operation practices or health risk perceptions.     

Narratives Can Motivate Environmental Action: The Whiskey Creek Ocean Acidification Story

Even when environmental data quantify the risks and benefits of delayed responses to rapid anthropogenic change, institutions rarely respond promptly. We propose that narratives complementing environmental datasets can motivate responsive environmental policy. To explore this idea, we relate a case study in which a narrative of economic loss due to regionally rapid ocean acidification—an anthropogenic change—helped connect knowledge with action. We pose three hypotheses to explain why narratives might be particularly effective in linking science to environmental policy, drawing from the literature of economics, environmental policy, and cognitive psychology. It seems that yet-untold narratives may hold similar potential for strengthening the feedback between environmental data and policy and motivating regional responses to other environmental problems.     

Integrated Measures of Anthropogenic Stress in the U.S. Great Lakes Basin

Integrated, quantitative expressions of anthropogenic stress over large geographic regions can be valuable tools in environmental research and management. Despite the fundamental appeal of a regional approach, development of regional stress measures remains one of the most important current challenges in environmental science. Using publicly available, pre-existing spatial datasets, we developed a geographic information system database of 86 variables related to five classes of anthropogenic stress in the U.S. Great Lakes basin: agriculture, atmospheric deposition, human population, land cover, and point source pollution. The original variables were quantified by a variety of data types over a broad range of spatial and classification resolutions. We summarized the original data for 762 watershed-based units that comprise the U.S. portion of the basin and then used principal components analysis to develop overall stress measures within each stress category. We developed a cumulative stress index by combining the first principal component from each of the five stress categories. Maps of the stress measures illustrate strong spatial patterns across the basin, with the greatest amount of stress occurring on the western shore of Lake Michigan, southwest Lake Erie, and southeastern Lake Ontario. We found strong relationships between the stress measures and characteristics of bird communities, fish communities, and water chemistry measurements from the coastal region. The stress measures are taken to represent the major threats to coastal ecosystems in the U.S. Great Lakes. Such regional-scale efforts are critical for understanding relationships between human disturbance and ecosystem response, and can be used to guide environmental decision-making at both regional and local scales.