Applying the zoo model to conservation of threatened exceptional plant species

Maintaining a living plant collection is the most common method of ex situ conservation for plant species that cannot be seed banked (i.e., exceptional species). Viability of living collections, and their value for future conservation efforts, can be limited without coordinated efforts to track and manage individuals across institutions. Using a pedigree-focused approach, the zoological community has established an inter-institutional infrastructure to support long-term viability of captive animal populations. We assessed the ability of this coordinated metacollection infrastructure to support the conservation of 4 plant species curated in living collections at multiple botanic gardens around the world. Limitations in current practices include the inability to compile, share, and analyze plant collections data at the individual level, as well as difficulty in tracking original provenance of ex situ material. The coordinated metacollection framework used by zoos can be adopted by the botanical community to improve conservation outcomes by minimizing the loss of genetic diversity in collections. We suggest actions to improve ex situ conservation of exceptional plant species, including developing a central database to aggregate data and track unique individuals of priority threatened species among institutions and adapting a pedigree-based population management tool that incorporates life-history aspects unique to plants. If approached collaboratively across regional, national, and global scales, these actions could transform ex situ conservation of threatened plant species.     

Estimating Potential Climate Change Effects on the Upper Neuse Watershed Water Balance Using the SWAT Model

Climate change poses water resource challenges for many already water stressed watersheds throughout the world. One such watershed is the Upper Neuse Watershed in North Carolina, which serves as a water source for the large and growing Research Triangle Park region. The aim of this study was to quantify possible changes in the watershed’s water balance due to climate change. To do this, we used the Soil and Water Assessment Tool (SWAT) model forced with different climate scenarios for baseline, mid‐century, and end‐century time periods using five different downscaled General Circulation Models. Before running these scenarios, the SWAT model was calibrated and validated using daily streamflow records within the watershed. The study results suggest that, even under a mitigation scenario, precipitation will increase by 7.7% from the baseline to mid‐century time period and by 9.8% between the baseline and end‐century time period. Over the same periods, evapotranspiration (ET) would decrease by 5.5 and 7.6%, water yield would increase by 25.1% and 33.2%, and soil water would increase by 1.4% and 1.9%. Perhaps most importantly, the model results show, under a high emission scenario, large seasonal differences with ET estimated to decrease by up to 42% and water yield to increase by up to 157% in late summer and fall. Planning for the wetter predicted future and corresponding seasonal changes will be critical for mitigating the impacts of climate change on water resources.     

From vineyards to feedlots: a fund-flow scanning of sociometabolic transition in the Vallès County (Catalonia) 1860–1956–1999

Regional Environmental Change - We analyse the changes to agricultural metabolism in four municipalities of Vallès County (Catalonia, Iberia) by accounting for their agroecosystem funds and...     

Source characterization and risk of exposure to atmospheric polychlorinated biphenyls (PCBs) in Ghana

With recent evidence that persistent organic pollutants (POPs) such as polychlorinated biphenyls (PCBs) are emerging in environmental media in some developing countries that otherwise have sparing production and usage history, it has become important to identify such contemporary source factors of PCBs and the risks this may pose, in line with the global consensus on POP management and elimination. The present study investigated contaminations from atmospheric PCBs in Ghana, deciphered source factors, and accessed risk of exposure to dioxin-like PCBs (DL-PCBs). Atmospheric PCBs were monitored by deployment of PUF-disk passive air samplers (PAS) at several sites across Ghana for 56 days. Atmospheric ∑₁₉₀PCB concentration in Ghana ranged from 0.28 ng/m³ in Kumasi to 4.64 ng/m³ at Agbogbloshie, a suburb in Accra noted for informal electronic waste (e-waste) recycling activities. As high as 11.10 ng/m³ of PCB concentration was measured in plumes from uncontrolled open burning of e-wastes at Agbogbloshie. Applying statistical source characterization tools, it emerged that e-wastes were a major contributor to the environmental burden of atmospheric PCBs in Ghana. The risk of DL-PCB toxicity via inhalation in the Agbogbloshie area was 4.2 pg TEQ/day, within similar order of magnitude of an estimated risk of 3.85 pg TEQ/day faced by e-waste workers working averagely for 8 h per day. It is suggested that elimination of e-waste sites would help to significantly reduce PCB-related toxicity issues in Ghana.

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Development of visibility forecasting modeling framework for the Lower Fraser Valley of British Columbia using Canada’s Regional Air Quality Deterministic Prediction System

Visibility degradation, one of the most noticeable indicators of poor air quality, can occur despite relatively low levels of particulate matter when the risk to human health is low. The availability of timely and reliable visibility forecasts can provide a more comprehensive understanding of the anticipated air quality conditions to better inform local jurisdictions and the public. This paper describes the development of a visibility forecasting modeling framework, which leverages the existing air quality and meteorological forecasts from Canada’s operational Regional Air Quality Deterministic Prediction System (RAQDPS) for the Lower Fraser Valley of British Columbia. A baseline model (GM-IMPROVE) was constructed using the revised IMPROVE algorithm based on unprocessed forecasts from the RAQDPS. Three additional prototypes (UMOS-HYB, GM-MLR, GM-RF) were also developed and assessed for forecast performance of up to 48 hr lead time during various air quality and meteorological conditions. Forecast performance was assessed by examining their ability to provide both numerical and categorical forecasts in the form of 1-hr total extinction and Visual Air Quality Ratings (VAQR), respectively. While GM-IMPROVE generally overestimated extinction more than twofold, it had skill in forecasting the relative species contribution to visibility impairment, including ammonium sulfate and ammonium nitrate. Both statistical prototypes, GM-MLR and GM-RF, performed well in forecasting 1-hr extinction during daylight hours, with correlation coefficients (R) ranging from 0.59 to 0.77. UMOS-HYB, a prototype based on postprocessed air quality forecasts without additional statistical modeling, provided reasonable forecasts during most daylight hours. In terms of categorical forecasts, the best prototype was approximately 75 to 87% correct, when forecasting for a condensed three-category VAQR. A case study, focusing on a poor visual air quality yet low Air Quality Health Index episode, illustrated that the statistical prototypes were able to provide timely and skillful visibility forecasts with lead time up to 48 hr.

Implications: This study describes the development of a visibility forecasting modeling framework, which leverages the existing air quality and meteorological forecasts from Canada’s operational Regional Air Quality Deterministic Prediction System. The main applications include tourism and recreation planning, input into air quality management programs, and educational outreach. Visibility forecasts, when supplemented with the existing air quality and health based forecasts, can assist jurisdictions to anticipate the visual air quality impacts as perceived by the public, which can potentially assist in formulating the appropriate air quality bulletins and recommendations.     

Silver near municipal wastewater discharges into western Lake Ontario,Canada

Because of the widespread use of silver nanoparticles in commercial products, discharges of municipal wastewater may be a point source of silver in the aquatic environment. We monitored two sites in western Lake Ontario impacted by discharges from wastewater treatment plants serving the City of Toronto. Concentrations of silver were elevated in bottom sediments and suspended sediments collected at the two sites. We also deployed two types of passive samplers in the water column at the two sites, the newly developed Carbon Nanotube Integrative Samplers for monitoring “CNIS-labile” silver and Diffusive Gradient in Thin Film samplers for monitoring “DGT-labile” silver. Results from these passive samplers indicated that the concentrations of silver at the two sites were either below detection limits or were in the ng/L range. In laboratory experiments where the sediments were re-suspended in Milli-Q water, a small proportion of the silver (i.e., <?25%) was labile and partitioned as colloidal or dissolved silver into the liquid phase after agitation. Nanoparticles tentatively identified as silver nanoparticles were detected by single-particle ICP-MS in suspension after agitation of both suspended and bottom sediments. Therefore, there is a need to assess whether silver species, including silver nanoparticles are transported from wastewater treatment plants into sediments in the aquatic environment. This study is unique in focusing on the in situ distribution of silver in natural waters and in sediments that are potentially impacted by urban sources of nanoparticles.     

The macroeconomic determinants of volatility in precious metals markets

This paper models the monthly price volatilities of four precious metals (gold, silver, platinum and palladium prices) and investigates the macroeconomic determinants (business cycle, monetary environment and financial market sentiment) of these volatilities. Gold volatility is shown to be explained by monetary variables, but this is not true for silver. Overall, there is limited evidence that the same macroeconomic factors jointly influence the volatility processes of the four precious metal price series, although there is evidence of volatility feedback between the precious metals. These results are consistent with the view that precious metals are too distinct to be considered a single asset class, or represented by a single index. This finding is of importance for portfolio managers and investors.     

Effects of particle size on chemical speciation and bioavailability of copper to earthworms (Eisenia fetida) exposed to copper nanoparticles

To investigate the role of particle size on the oxidation, bioavailability, and adverse effects of manufactured Cu nanoparticles (NPs) in soils, we exposed the earthworm Eisenia ferida to a series of concentrations of commercially produced NPs labeled as 20- to 40-nm or < 100-nm Cu in artificial soil media. Effects on growth, mortality, reproduction, and expression of a variety of genes associated with metal homeostasis, general stress, and oxidative stress were measured. We also used X-ray absorption spectroscopy and scanning X-ray fluorescence microscopy to characterize changes in chemical speciation and spatial distribution of the NPs in soil media and earthworm tissues. Exposure concentrations of Cu NPs up to 65 mg kg(-1) caused no adverse effects on ecologically relevant endpoints. Increases in metallothionein expression occurred at concentrations exceeding 20 mg kg(-1) of Cu NPs and concentrations exceeding 10 mg kg(-1) of CuSO4. Based on the relationship of Cu tissue concentration to metallothionein expression level and the spatial distribution and chemical speciation of Cu in the tissues, we conclude that Cu ions and oxidized Cu NPs were taken up by the earthworms. This study suggests that oxidized Cu NPs may enter food chains from soil but that adverse effects in earthworms are likely to occur only at relatively high concentrations (> 65 mg Cu kg(-1) soil).     

Preventing corruption in humanitarian assistance: perceptions, gaps and challenges

Corruption is a threat to the purpose of humanitarian assistance. Until fairly recently, humanitarian assistance has not been considered an important arena in broader efforts aimed at curbing corruption, and corruption has not always been considered a particularly important concern for humanitarian assistance despite the obviously challenging nature of the context of humanitarian emergencies. Corruption, though, is a threat to humanitarian action because it can prevent assistance from getting to the people who most need it, and because it can potentially undermine public support for such assistance. This paper examines perceptions of corruption and its affects, documents best practices, and outlines gaps in understanding. It suggests recommendations for improving the capacity of humanitarian agencies to prevent and manage the risk of corruption. Agencies have taken steps to combat corruption and improve accountability--downwards and upwards--but scope remains for improvement and for greater sharing of learning and good practice.     

Phospholipid fatty acid biomarkers in a freshwater periphyton community exposed to uranium: discovery by non-linear statistical learning

Phospholipid fatty acids (PLFA) have been widely used to characterize environmental microbial communities, generating community profiles that can distinguish phylogenetic or functional groups within the community. The poor specificity of organism groups with fatty acid biomarkers in the classic PLFA-microorganism associations is a confounding factor in many of the statistical classification/clustering approaches traditionally used to interpret PLFA profiles. In this paper we demonstrate that non-linear statistical learning methods, such as a support vector machine (SVM), can more accurately find patterns related to uranyl nitrate exposure in a freshwater periphyton community than linear methods, such as partial least squares discriminant analysis. In addition, probabilistic models of exposure can be derived from the identified lipid biomarkers to demonstrate the potential model-based approach that could be used in remediation. The SVM probability model separates dose groups at accuracies of ∼87.0%, ∼71.4%, ∼87.5%, and 100% for the four groups; Control (non-amended system), low dose (amended at 10 μg U L⁻¹), medium dose (amended at 100 μg U L⁻¹), and high dose (500 μg U L⁻¹). The SVM model achieved an overall cross-validated classification accuracy of ∼87% in contrast to ∼59% for the best linear classifier.