Estimating the contribution of precursor compounds in consumer exposure to PFOS and PFOA

The exposure of humans to perfluorooctane sulfonate (PFOS) and perfluorooctanoic acid (PFOA) was quantified with emphasis on assessing the relative importance of metabolic transformation of precursor compounds. A Scenario-Based Risk Assessment (SceBRA) approach was used to model the exposure to these compounds from a variety of different pathways, the uptake into the human body and resulting daily doses. To capture the physiological and behavioral differences of age and gender, the exposure and resulting doses for seven consumer groups were calculated. The estimated chronic doses of a general population of an industrialized country range from 3.9 to 520 ng/(kg day) and 0.3 to 140 ng/(kg day) for PFOS and PFOA, respectively. The relative importance of precursor-based doses of PFOS and PFOA was estimated to be 2-5% and 2-8% in an intermediate scenario and 60-80% and 28-55% in a high-exposure scenario. This indicates that sub groups of the population may receive a substantial part of the PFOS and PFOA doses from precursor compounds, even though they are of low importance for the general population. Similar to a preceding study, uptake of perfluorinated acids from contaminated food and drinking water was identified as the most important pathway of exposure for the general population. The biotransformation yields of telomer-based precursors and to a lesser extent perfluorooctanesulfonylfluoride-based precursors were identified as influential parameters in the uncertainty analysis. Fast food consumption and fraction of food packaging paper treated with PFCs were influential parameters for determining the doses of PFOA.     

Cultural shift towards sustainability in the construction industry of Hong Kong

Sustainable development is forward-looking; it is a continuous mission for future developments of human society. A genuinely sustainable society is one that initiates developments in sustainable ways. The development of a genuinely sustainable society is supported by its citizens who think and act according to a recognized code of conduct - the sustainable culture. Similar to other forms of culture, sustainable culture of a society is not static, but changes over time. The changes found in a sustainable culture are reflections of the status of sustainability in a society and these changes should be measured from time to time. The resulting measurement gives very important information for decision-makers, in the government and in the private sector, to examine the magnitude of changes that have taken place in a given period of time. The results will also enable them to review and adjust policies in order to better accommodate changes according to the trends of society.This paper provides a method – the T-model, to investigate and measure the extent of change of sustainable culture through two extensive surveys among participants of the construction industry of Hong Kong. The change in sustainable culture is reflected by the change in attitude and practice among construction participants, this can be found in their performance in project development, design and construction operations. The data of these changes are collected and converted to numerical scores. The T-model synthesized these scores and revealed the change of sustainable culture within the specific study time frame.     

Carbon dissipation from surgical cotton production wastewater using macroalgae,microalgae, and activated sludge microbes

Surgical cotton production has drastically been increased in the past few years due to excessive use by medical health professionals especially in countries like India, which is among the top three exporters of cotton worldwide. The effluent generated from surgical cotton industries differ from textile effluents by the conspicuous absence of dyeing chemicals. This wastewater has a high concentration of suspended particles, COD, dissolved ions, organic carbon, and alkaline pH. Several studies have been published on the treatment of textile effluents and the degradation of dyeing chemicals, while the treatment studies on surgical cotton wastewater have been rarely reported in spite of their potential to cause pollution in receiving land/water bodies. Activated sludge microbes have been extensively studied and well documented in the treatment of several industrial effluent but does not match to the production of valuable biomass from algae. The global energy demand has prompted the scientific community to investigate and explore the possibility of using algae for energy production with simultaneous wastewater treatment. To the best of the authors’ knowledge, no research articles have been published which compare the effectiveness of activated sludge microorganisms, microalgae, and macroalgae in removing contaminants from real wastewater. To date, there is a knowledge gap in understanding and selecting the right choice of biological system for effective and economical effluent treatment. In an attempt to minimize this gap, carbon removal by microalgae, macroalgae, and activated sludge microbes were investigated on real effluent from surgical cotton industries. It was observed that the strain of Chlorella vulgaris could dissipate 83% of COD from real wastewater, while consortia of macroalgae (consisting predominantly of Ulvaceae and Chaetomorpha) and activated sludge microbes could remove 81% and 69% of the carbon, respectively. The microalgal growth (in terms of wet weight) increased from 0.15 to 0.3 g, whereas the macroalgal wet weight increased from 1.5 to 3 g in over 7 days of batch experiments conducted in triplicates. This indicated the superlative performance of microalgae over activated sludge microbes in carbon dissipation.

     

Selection of both habitat and genes in specialized and endangered caribou

Genetic mechanisms determining habitat selection and specialization of individuals within species have been hypothesized, but not tested at the appropriate individual level in nature. In this work, we analyzed habitat selection for 139 GPS-collared caribou belonging to 3 declining ecotypes sampled throughout Northwestern Canada. We used Resource Selection Functions comparing resources at used and available locations. We found that the 3 caribou ecotypes differed in their use of habitat suggesting specialization. On expected grounds, we also found differences in habitat selection between summer and winter, but also, originally, among the individuals within an ecotype. We next obtained Single Nucleotide Polymorphisms (SNPs) for the same caribou individuals, we detected those associated to habitat selection, and then identified genes linked to these SNPs. These genes had functions related in other organisms to habitat and dietary specializations, and climatic adaptations. We therefore suggest that individual variation in habitat selection was based on genotypic variation in the SNPs of individual caribou, indicating that genetic forces underlie habitat and diet selection in the species. We also suggest that the associations between habitat and genes that we detected may lead to lack of resilience in the species, thus contributing to caribou endangerment. Our work emphasizes that similar mechanisms may exist for other specialized, endangered species.     

Predicting landscape-scale biodiversity recovery by natural tropical forest regrowth

Natural forest regrowth is a cost-effective, nature-based solution for biodiversity recovery, yet different socioenvironmental factors can lead to variable outcomes. A critical knowledge gap in forest restoration planning is how to predict where natural forest regrowth is likely to lead to high levels of biodiversity recovery, which is an indicator of conservation value and the potential provisioning of diverse ecosystem services. We sought to predict and map landscape-scale recovery of species richness and total abundance of vertebrates, invertebrates, and plants in tropical and subtropical second-growth forests to inform spatial restoration planning. First, we conducted a global meta-analysis to quantify the extent to which recovery of species richness and total abundance in second-growth forests deviated from biodiversity values in reference old-growth forests in the same landscape. Second, we employed a machine-learning algorithm and a comprehensive set of socioenvironmental factors to spatially predict landscape-scale deviation and map it. Models explained on average 34% of observed variance in recovery (range 9–51%). Landscape-scale biodiversity recovery in second-growth forests was spatially predicted based on socioenvironmental landscape factors (human demography, land use and cover, anthropogenic and natural disturbance, ecosystem productivity, and topography and soil chemistry); was significantly higher for species richness than for total abundance for vertebrates (median range-adjusted predicted deviation 0.09 vs. 0.34) and invertebrates (0.2 vs. 0.35) but not for plants (which showed a similar recovery for both metrics [0.24 vs. 0.25]); and was positively correlated for total abundance of plant and vertebrate species (Pearson r = 0.45, p = 0.001). Our approach can help identify tropical and subtropical forest landscapes with high potential for biodiversity recovery through natural forest regrowth.