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...     

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.     

Carbon dioxide separation and capture by adsorption: a review

Environmental Chemistry Letters - Rising adverse impact of climate change caused by anthropogenic activities is calling for advanced methods to reduce carbon dioxide emissions. Here, we...     

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...     

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.     

Simultaneous determination of hydroxylamine and phenol using a nanostructure-based electrochemical sensor

The electrochemical oxidation of hydroxylamine on the surface of a carbon paste electrode modified with carbon nanotubes and 2,7-bis(ferrocenyl ethyl)fluoren-9-one is studied. The electrochemical response characteristics of the modified electrode toward hydroxylamine and phenol were investigated. The results showed an efficient catalytic activity of the electrode for the electro-oxidation of hydroxylamine, which leads to lowering its overpotential. The modified electrode exhibits an efficient electron-mediating behavior together with well-separated oxidation peaks for hydroxylamine and phenol. Also, the modified electrode was used for determination of hydroxylamine and phenol in some real samples.     

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.     

Integrating Geographic Information System network analysis and nighttime light satellite imagery to optimize landfill regionalization on a regional level

More than half of financial resources allocated for municipal solid waste management are typically spent on waste collection and transportation. An optimized landfill siting and waste collection system can save fuel costs, reduce collection truck emissions, and provide higher accessibility with lower traffic impacts. In this study, a data-driven analytical framework is developed to optimize population coverage by landfills using network analysis and satellite imagery. Two scenarios, SC1 and SC2, with different truck travel times were used to simulate generation-site–disposal-site distances in three Canadian provinces. Under status quo conditions, Landfill Regionalization Index (LFRI) ranging from 0 to 2 population centers per landfill in all three jurisdictions. LFRI consistently improved after optimization, with average LFRI ranging from 1.3 to 2.0 population centers per landfill. Lower average truck travel times and better coverage of the population centers are generally observed in the optimized systems. The proposed analytical method is found effective in improving landfill regionalization. Under SC1 and SC2, LFRI percentages of improvement ranging from 58.3% to 64.5% and 22.7% to 59.4%, respectively. Separation distance between the generation and disposal sites and truck capacity appear not a decisive factor in the optimization process. The proposed optimization framework is generally applicable to regions with different geographical and demographical attributes, and is particularly applicable in rural regions with sparsely located population centers.