Antioxidant enzymes and compounds complement each other during arsenic detoxification in shoots of Isatis cappadocica Desv.

Isatis cappadocica has been reported to be an arsenic (As) hyperaccumulator. Antioxidant enzymes and compounds have been proposed to play an important role in the detoxification and tolerance of As. In the present study, As-induced oxidative stress and antioxidant responses were investigated on I. cappadocica grown hydroponically in response to application of arsenate (0–1200?μmol). As accumulation increased with an increase in arsenate concentration in the medium. Along with a significant increase in arsenate concentration, a build up in hydrogen peroxide, indicators of oxidative stress, was observed. The activity of superoxide dismutase and peroxidase was induced after arsenate treatment, reached a maximal value at 800?μmol arsenate and then declined at the highest arsenate treatment. Glutathione reductase activity and contents of non-enzymatic antioxidants (carotenoids, flavonoids and anthocyanins) increased significantly as arsenate concentration augmented. These results indicated that high efficient antioxidant system may play significant roles in As detoxification and improve I. cappadocica tolerance against As toxicity.     

Simultaneous Cr (VI) reduction and diazinon oxidation with organometallic sludge formation under photolysis: kinetics,degradation pathways,and mechanism

Environmental Science and Pollution Research - In this study, simultaneous removal of an organic matter (diazinon, DIZ) and an inorganic substance (chromium, Cr) was used. Breaking down of organic...     

Interaction effects of salinity,sewage sludge,and earthworms on the fractionations of Zn and Cu,and the metals uptake by the earthworms in a Zn- and Cu-contaminated calcareous soil

Environmental Science and Pollution Research - This study assessed the effects of salinity and sewage sludge on the fractionation of Zn and Cu in a soil around a lead-zinc mine as well as their...     

Understanding the effects of different social data on selecting priority conservation areas

Conservation success is contingent on assessing social and environmental factors so that cost‐effective implementation of strategies and actions can be placed in a broad social–ecological context. Until now, the focus has been on how to include spatially explicit social data in conservation planning, whereas the value of different kinds of social data has received limited attention. In a regional systematic conservation planning case study in Australia, we examined the spatial concurrence of a range of spatially explicit social values and land‐use preferences collected using a public participation geographic information system and biological data. We used Zonation to integrate the social data with the biological data in a series of spatial‐prioritization scenarios to determine the effect of the different types of social data on spatial prioritization compared with biological data alone. The type of social data (i.e., conservation opportunities or constraints) significantly affected spatial prioritization outcomes. The integration of social values and land‐use preferences under different scenarios was highly variable and generated spatial prioritizations 1.2–51% different from those based on biological data alone. The inclusion of conservation‐compatible values and preferences added relatively few new areas to conservation priorities, whereas including noncompatible economic values and development preferences as costs significantly changed conservation priority areas (48.2% and 47.4%, respectively). Based on our results, a multifaceted conservation prioritization approach that combines spatially explicit social data with biological data can help conservation planners identify the type of social data to collect for more effective and feasible conservation actions.     

A novel process for ethanol or biogas production from cellulose in blended-fibers waste textiles

A novel process has been developed for separation of the cellulose, i.e. cotton and viscose, from blended-fibers waste textiles. An environmentally friendly cellulose solvent, N-methylmorpholine-N-oxide (NMMO) was used in this process for separation and pretreatment of the cellulose. This solvent was mixed with blended-fibers textiles at 120 °C and atmospheric pressure to dissolve the cellulose and separate it from the undissolved non-cellulosic fibers. Water was then added to the solution in order to precipitate the cellulose, while both water and NMMO were reused after separation by evaporation. The cellulose was then either hydrolyzed by cellulase enzymes followed by fermentation to ethanol, or digested directly to produce biogas. The process was verified by testing 50/50 polyester/cotton and 40/60 polyester/viscose-blended textiles. The polyesters were purified as fibers after the NMMO treatments, and up to 95% of the cellulose fibers were regenerated and collected on a filter. A 2-day enzymatic hydrolysis and 1-day fermentation of the regenerated cotton and viscose resulted in 48 and 50 g ethanol/g regenerated cellulose, which were 85% and 89% of the theoretical yields, respectively. This process also resulted in a significant increase of the biogas production rate. While untreated cotton and viscose fibers were converted to methane by respectively, 0.02% and 1.91% of their theoretical yields in 3 days of digestion, the identical NMMO-treated fibers resulted into about 30% of yield at the same period of time.     

Air pollution below WHO levels decreases by 40 % the links of terrestrial microbial networks

Air pollution has a deleterious impact on public health and the environment. There is few knowledge on the effect of air pollution on terrestrial microbial communities, despite the major role of microbes in ecosystems. Here, we designed an in situ trial ecosystem to assess the impact of moderate atmospheric pollution, below World Health Organization (WHO) thresholds, on an indigenous microbial communities, including bacteria, fungi, ciliates, algae, cyanobacteria, testate amoebae, rotifers and nematodes, extracted from terrestrial bryophytes. These micro-ecosystems were placed at a rural, an urban and an industrial site in France and were thus exposed to various levels of nitrogen dioxide (NO₂), from 6.6–67.9 μg·m^?3, and particulate matter, from 0.7–7.9 μg·m^?3. Microbial analysis was performed by microscopy. We determined atmospheric temperature, relative humidity and particulate matter with diameter lower than 10 µm (PM₁₀), Cu, Cr, Fe, Ni, Pb, Zn in PM₁₀, and (NO₂). Results show a significant impact of chronic moderate exposure to NO₂ and copper Cu-associated particulate matter on the global microbial network complexity. This is evidenced by a loss of about 40 % of microbial co-occurrence links during incubation. Most lost microbial links are ecologically positive links. Moreover, most changes in community co-occurrence networks are related to testate amoebae, a major top predator of microbes. Overall, our findings demonstrate that air pollution can have strong deleterious effects on microbial interactions, even at levels below WHO thresholds.     

Improved Spatial Allocation Methodology for On-Road Mobile Emissions

ABSTRACT

Emissions from automobiles and trucks operating on public roads represent a major portion of the air pollutants included in emission inventories. When emission data are prepared for air quality modeling studies, such as those supporting development of a State Implementation Plan, an emission processor matches the spatial and temporal resolution of the emissions to the requirements of the modeling study. However, the spatial location of vehicular emissions is not known and must be estimated. This paper presents a methodology for determining the spatial distribution of the roads belonging to a road class using geospatial data functions, such as those commonly provided by a geographic information system. Vehicle-miles traveled (VMT) are then allocated to medium-resolution (12 x 12-km) and fine-resolution (4 x 4-km) modeling grids using both this methodology and the existing top-down methodology, which uses population density. The results show a significant difference in the spatial distribution of VMT between these two methodologies. Based upon these results, we recommend using the road class-specific methodology in lieu of the population methodology for spatially allocating vehicular emissions for medium- and finer-resolution modeling grids.     

Improved spatial allocation methodology for on-road mobile emissions

Emissions from automobiles and trucks operating on public roads represent a major portion of the air pollutants included in emission inventories. When emission data are prepared for air quality modeling studies, such as those supporting development of a State Implementation Plan, an emission processor matches the spatial and temporal resolution of the emissions to the requirements of the modeling study. However, the spatial location of vehicular emissions is not known and must be estimated. This paper presents a methodology for determining the spatial distribution of the roads belonging to a road class using geospatial data functions, such as those commonly provided by a geographic information system. Vehicle-miles traveled (VMT) are then allocated to medium-resolution (12 x 12-km) and fine-resolution (4 x 4-km) modeling grids using both this methodology and the existing top-down methodology, which uses population density. The results show a significant difference in the spatial distribution of VMT between these two methodologies. Based upon these results, we recommend using the road class-specific methodology in lieu of the population methodology for spatially allocating vehicular emissions for medium- and finer-resolution modeling grids.     

Analyzing the factors affecting environmental risks of projects using a hybrid approach of DEMATEL‐ANP,artificial neural network: A case study

The increasing growth of the economy in each country necessitates a great amount of investment in infrastructure. The belief that projects involve various uncertainties, such as technical skills, management quality, and the like, indicates that most projects fail to achieve their aims, interests, costs, as well as their timeframes and space requirements. As the environment can pose significant uncertainty to any project, environmental risks should be deeply studied by project management departments. This study intends to analyze as a case the environmental risk management system within a consulting firm. From this analysis, each aspect of a project's environmental risk management is ranked using a fuzzy analytical network process (ANP), a neural network algorithm, and a decision‐making trial and evaluation laboratory (DEMATEL) methodology. From the organizational aspect, budget risk is the most significant. From the technical aspect, the risk of regulations is the most important one. Finally, the risk of project failure from poor communication is another identified main risk in this research. By studying high‐ranking items in this hierarchy, it can be understood that these criteria exist in different aspects; therefore, all aspects of the risk should be taken into account to cover and assess risk. A neural network algorithm for validating and reassessment of ranking is employed. Results of this application showed that, based on Spearman's rank correlation method, two different approaches resulted in similar rankings. Finally, some practical implications for responding to the most highly ranked risks are proposed.