Disinfection and removal of human pathogenic bacteria in arctic waste stabilization ponds

Wastewater stabilization ponds (WSPs) are commonly used to treat municipal wastewater in Arctic Canada. The biological treatment in the WSPs is strongly influenced by climatic conditions. Currently, there is limited information about the removal of fecal and pathogenic bacteria during the short cool summer treatment season. With relevance to public health, the objectives of this paper were to determine if treatment in arctic WSPs resulted in the disinfection (i.e., removal of fecal indicator bacteria, Escherichia coli) and removal of selected human bacterial pathogens from the treated effluent. The treatment performance, with focus on microbial removal, was assessed for the one-cell WSP in Pond Inlet (Nunavut [NU]) and two-cell WSP in Clyde River (NU) over three consecutive (2012–2014) summer treatment seasons (late June-early September). The WSPs provided a primary disinfection treatment of the wastewater with a 2–3 Log removal of generic indicator E. coli. The bacterial pathogens Salmonella spp., pathogenic E. coli, and Listeria monocytogenes, but not Campylobacter spp. and Helicobacter pylori, were detected in the untreated and treated wastewater, indicating that human pathogens were not reliably removed. Seasonal and annual variations in temperature significantly (p < 0.05) affected the disinfection efficiency. Improved disinfection and pathogen removal was observed for the two-cell system in Clyde River as compared to the one-cell system in Pond Inlet. A quantitative microbial risk assessment should be performed to determine if the release of low levels of human pathogens into the arctic environment poses a human health risk.     

Circumpolar status of Arctic ptarmigan: Population dynamics and trends

Rock ptarmigan (Lagopus muta) and willow ptarmigan (L. lagopus) are Arctic birds with a circumpolar distribution but there is limited knowledge about their status and trends across their circumpolar distribution. Here, we compiled information from 90 ptarmigan study sites from 7 Arctic countries, where almost half of the sites are still monitored. Rock ptarmigan showed an overall negative trend on Iceland and Greenland, while Svalbard and Newfoundland had positive trends, and no significant trends in Alaska. For willow ptarmigan, there was a negative trend in mid-Sweden and eastern Russia, while northern Fennoscandia, North America and Newfoundland had no significant trends. Both species displayed some periods with population cycles (short 3–6 years and long 9–12 years), but cyclicity changed through time for both species. We propose that simple, cost-efficient systematic surveys that capture the main feature of ptarmigan population dynamics can form the basis for citizen science efforts in order to fill knowledge gaps for the many regions that lack systematic ptarmigan monitoring programs.

     

Characterizing spatial structure of sediment E. coli populations to inform sampling design

Escherichia coli can persist in streambed sediments and influence water quality monitoring programs through their resuspension into overlying waters. This study examined the spatial patterns in E. coli concentration and population structure within streambed morphological features during baseflow and following stormflow to inform sampling strategies for representative characterization of E. coli populations within a stream reach. E. coli concentrations in bed sediments were significantly different (p?=?0.002) among monitoring sites during baseflow, and significant interactive effects (p?=?0.002) occurred among monitoring sites and morphological features following stormflow. Least absolute shrinkage and selection operator (LASSO) regression revealed that water velocity and effective particle size (D ₁₀) explained E. coli concentration during baseflow, whereas sediment organic carbon, water velocity and median particle diameter (D ₅₀) were important explanatory variables following stormflow. Principle Coordinate Analysis illustrated the site-scale differences in sediment E. coli populations between disconnected stream segments. Also, E. coli populations were similar among depositional features within a reach, but differed in relation to high velocity features (e.g., riffles). Canonical correspondence analysis resolved that E. coli population structure was primarily explained by spatial (26.9–31.7 %) over environmental variables (9.2–13.1 %). Spatial autocorrelation existed among monitoring sites and morphological features for both sampling events, and gradients in mean particle diameter and water velocity influenced E. coli population structure for the baseflow and stormflow sampling events, respectively. Representative characterization of streambed E. coli requires sampling of depositional and high velocity environments to accommodate strain selectivity among these features owing to sediment and water velocity heterogeneity.     

Ten ways remote sensing can contribute to conservation

In an effort to increase conservation effectiveness through the use of Earth observation technologies, a group of remote sensing scientists affiliated with government and academic institutions and conservation organizations identified 10 questions in conservation for which the potential to be answered would be greatly increased by use of remotely sensed data and analyses of those data. Our goals were to increase conservation practitioners’ use of remote sensing to support their work, increase collaboration between the conservation science and remote sensing communities, identify and develop new and innovative uses of remote sensing for advancing conservation science, provide guidance to space agencies on how future satellite missions can support conservation science, and generate support from the public and private sector in the use of remote sensing data to address the 10 conservation questions. We identified a broad initial list of questions on the basis of an email chain‐referral survey. We then used a workshop‐based iterative and collaborative approach to whittle the list down to these final questions (which represent 10 major themes in conservation): How can global Earth observation data be used to model species distributions and abundances? How can remote sensing improve the understanding of animal movements? How can remotely sensed ecosystem variables be used to understand, monitor, and predict ecosystem response and resilience to multiple stressors? How can remote sensing be used to monitor the effects of climate on ecosystems? How can near real‐time ecosystem monitoring catalyze threat reduction, governance and regulation compliance, and resource management decisions? How can remote sensing inform configuration of protected area networks at spatial extents relevant to populations of target species and ecosystem services? How can remote sensing‐derived products be used to value and monitor changes in ecosystem services? How can remote sensing be used to monitor and evaluate the effectiveness of conservation efforts? How does the expansion and intensification of agriculture and aquaculture alter ecosystems and the services they provide? How can remote sensing be used to determine the degree to which ecosystems are being disturbed or degraded and the effects of these changes on species and ecosystem functions?     

Future ocean acidification will be amplified by hypoxia in coastal habitats

Ocean acidification is elicited by anthropogenic carbon dioxide emissions and resulting oceanic uptake of excess CO₂ and might constitute an abiotic stressor powerful enough to alter marine ecosystem structures. For surface waters in gas-exchange equilibrium with the atmosphere, models suggest increases in CO₂ partial pressure (pCO₂) from current values of ca. 390 μatm to ca. 700–1,000 μatm by the end of the century. However, in typically unequilibrated coastal hypoxic regions, much higher pCO₂ values can be expected, as heterotrophic degradation of organic material is necessarily related to the production of CO₂ (i.e., dissolved inorganic carbon). Here, we provide data and estimates that, even under current conditions, maximum pCO₂ values of 1,700–3,200 μatm can easily be reached when all oxygen is consumed at salinities between 35 and 20, respectively. Due to the nonlinear nature of the carbonate system, the approximate doubling of seawater pCO₂ in surface waters due to ocean acidification will most strongly affect coastal hypoxic zones as pCO₂ during hypoxia will increase proportionally: we calculate maximum pCO₂ values of ca. 4,500 μatm at a salinity of 20 (T = 10 °C) and ca. 3,400 μatm at a salinity of 35 (T = 10 °C) when all oxygen is consumed. Upwelling processes can bring these CO₂-enriched waters in contact with shallow water ecosystems and may then affect species performance there as well. We conclude that (1) combined stressor experiments (pCO₂ and pO₂) are largely missing at the moment and that (2) coastal ocean acidification experimental designs need to be closely adjusted to carbonate system variability within the specific habitat. In general, the worldwide spread of coastal hypoxic zones also simultaneously is a spread of CO₂-enriched zones. The magnitude of expected changes in pCO₂ in these regions indicates that coastal systems may be more endangered by future global climate change than previously thought.     

New investigation findings on the 2006 Danvers,MA explosion

On November 22, 2006 the largest explosion in the history of Massachusetts occurred in Danvers, MA at approximately 2:46 am. This paper presents a detailed analysis into the potential causes and lessons learned from the Danvers explosion. Other investigative groups concluded that the cause of the explosion was an overheated production tank. However, the analyses presented here demonstrate that their proposed scenario could not have occurred and that other potential causes are more likely.Using the computational fluid dynamics tool FLACS, it was possible to investigate the chain of events leading to the explosion, including: (1) evaluating various leak scenarios by modeling the dispersion and mixing of gases and vapors within the facility, (2) evaluating potential ignition sources within the facility of the flammable fuel–air mixture, and (3) evaluating the explosion itself by comparing the resulting overpressures of the exploding fuel–air cloud with the structural response of the facility and the observed near-field and far-field blast damage. These results, along with key witness statements and other analyses, provide valuable insight into the likely cause of this incident. Based on the results of our detailed analysis, lessons learned regarding the investigative procedure and methods for mitigating this and future explosions are discussed.     

Using computational fluid dynamics (CFD) for blast wave predictions

Explosions will, in most cases, generate blast waves. While simple models (e.g., Multi Energy Method) are useful for simple explosion geometries, most practical explosions are far from trivial and require detailed analyses. For a reliable estimate of the blast from a gas explosion it is necessary to know the explosion strength. The source explosion may not be symmetric; the pressure waves will be reflected or deflected when hitting objects, or even worse, the blast waves may propagate inside buildings or tunnels with a very low rate of decay. The use of computational fluid dynamics (CFD) explosion models for near and far field blast wave predictions has many advantages. These include more precise estimates of the energy and resulting pressure of the blast wave, as well as the ability to evaluate non-symmetrical effects caused by realistic geometries, gas cloud variations and ignition locations. This is essential when evaluating the likelihood of a given leak source as cause of an explosion or equally when evaluating the potential risk associated with a given leak source for a consequence analysis.In addition, unlike simple methods, CFD explosion models can also evaluate detailed dynamic effects in the near and far field, which include time dependent pressure loads as well as reflection and focusing of the blast waves. This is particularly valuable when assessing actual near-field blast damage during an explosion investigation or potential near-field damage during a risk analysis for a facility. One main challenge in applying CFD, however, is that these models require more information about the actual facility, including geometry details and process information. Collecting the necessary geometry and process data may be quite time consuming. This paper will show some blast prediction validation examples for the CFD model FLACS. It will also provide examples of how directional effects or interaction with objects can significantly influence the dynamics of the blast wave. Finally, the challenge of obtaining useful predictions with insufficient details regarding the geometry will also be addressed.