Phytotoxicity of short-term exposure to excess zinc or copper in Scots pine seedlings in relation to growth,water status,nutrient balance,and antioxidative activity

The toxic effects of heavy metals pose a significant threat to the productivity and stability of forest ecosystems. Changes in the agrochemical properties of polluted forest soils due to global climate changes can increase the bioavailability of previously immobilized heavy metals. To test this hypothesis, we studied the effects of short-term shock exposure to ZnSO₄ (50, 150, 300 μM) or CuSO₄ (2.5, 5, 10 μM) in hydroculture on 4- to 6-week-old seedlings of Scots pine (Pinus sylvestris L.) with well-developed root systems. The effects of the excess heavy metals on mineral nutrients and the functioning of low-molecular-weight antioxidants and glutathione in protecting plants from oxidative damage were studied. Even short-term exposure to exogenous metals led to their rapid accumulation in the root system and their subsequent transport to aboveground organs. An increase in the 4-hydroxyalkenals content in seedling needles exposed to excess Cu led to an increase in the content of proanthocyanidins and catechins, which act as scavengers of reactive oxygen species. The impact of both metals led to the rapid development of mineral nutrient imbalances in the seedlings, which were most pronounced in the presence of excess Zn. Exposure to excess Zn led to a disruption in the translocation of Fe and a decrease in the Fe content in the needles. The most dramatic consequence of Zn exposure was the development of Mn deficiency in the roots, which was the likely cause of the inhibition of phenolic compound synthesis. A deficiency in phenolic compounds can have serious environmental consequences for pine populations that are at risk of contamination by Zn and Cu salts.

     

Influence of pyrolysis temperature on production and nutrient properties of wastewater sludge biochar

The important challenge for effective management of wastewater sludge materials in an environmentally and economically acceptable way can be addressed through pyrolytic conversion of the sludge to biochar and agricultural applications of the biochar. The aim of this work is to investigate the influence of pyrolysis temperature on production of wastewater sludge biochar and evaluate the properties required for agronomic applications. Wastewater sludge collected from an urban wastewater treatment plant was pyrolysed in a laboratory scale reactor. It was found that by increasing the pyrolysis temperature (over the range from 300 °C to 700 °C) the yield of biochar decreased. Biochar produced at low temperature was acidic whereas at high temperature it was alkaline in nature. The concentration of nitrogen was found to decrease while micronutrients increased with increasing temperature. Concentrations of trace metals present in wastewater sludge varied with temperature and were found to primarily enriched in the biochar.     

Scenario simulations of CO2 injection feasibility, plume migration and storage in a saline aquifer, Scania, Sweden

Deep saline aquifers have large capacity for geological CO₂ storage, but are generally not as well characterized as petroleum reservoirs. We here aim at quantifying effects of uncertain hydraulic parameters and uncertain stratigraphy on CO₂ injectivity and migration, and provide a first feasibility study of pilot-scale CO₂ injection into a multilayered saline aquifer system in southwest Scania, Sweden. Four main scenarios are developed, corresponding to different possible interpretations of available site data. Simulation results show that, on the one hand, stratigraphic uncertainty (presence/absence of a thin mudstone/claystone layer above the target storage formation) leads to large differences in predicted CO₂ storage in the target formation at the end of the test (ranging between 11% and 98% of injected CO₂ remaining), whereas other parameter uncertainty (in formation and cap rock permeabilities) has small impact. On the other hand, the latter has large impact on predicted injectivity, on which stratigraphic uncertainty has small impact. Salt precipitation at the border of the target storage formation affects CO₂ injectivity for all considered scenarios and injection rates. At low injection rates, salt is deposited also within the formation, considerably reducing its availability for CO₂ storage.     

Assessing the Impact of Areal Precipitation Input on Streamflow Simulations Using the SWAT Model1

Masih Ilyas, Shreedhar Maskey, Stefan Uhlenbrook, and Vladimir Smakhtin, 2011. Assessing the Impact of Areal Precipitation Input on Streamflow Simulations Using the SWAT Model. Journal of the American Water Resources Association (JAWRA) 47(1):179‐195. DOI: 10.1111/j.1752‐1688.2010.00502.x Abstract: Reduction of input uncertainty is a challenge in hydrological modeling. The widely used model Soil Water Assessment Tool (SWAT) uses the data of a precipitation gauge nearest to the centroid of each subcatchment as an input for that subcatchment. This may not represent overall catchment precipitation conditions well. This paper suggests an alternative – using areal precipitation obtained through interpolation. The effectiveness of this alternative is evaluated by comparing its simulations with those based on the standard SWAT precipitation input procedure. The model is applied to mountainous semiarid catchments in the Karkheh River basin, Iran. The model performance is evaluated at daily, monthly, and annual scales by using a number of performance indicators at 15 streamflow gauging stations each draining an area in the range of 590‐42,620 km². The comparison suggests that the use of areal precipitation improves model performance particularly in small subcatchments in the range of 600‐1,600 km². The modified areal precipitation input results in increased reliability of simulated streamflows in the areas of low rain gauge density. Both precipitation input methods result in reasonably good simulations for larger catchments (over 5,000 km²). The use of areal precipitation input improves the accuracy of simulated streamflows with spatial resolution and density of rain gauges having significant impact on results.     

Algorithms for Increasing the Efficiency of Precision Production Operator’s Activities

To assess and plan future risk-analysis research projects, 275 documents describing methods and tools for assessing the risks associated with industrial machines or with other sectors such as the military, and the nuclear and aeronautics industries, etc., were collected. These documents were in the format of published books or papers, standards, technical guides and company procedures collected throughout industry. From the collected documents, 112 documents were selected for analysis; 108 methods applied or potentially applicable for assessing the risks associated with industrial machines were analyzed and classified. This paper presents the main quantitative results of the analysis of the methods and tools.     

Extensions of stochastic multiscenario models for long-range planning under uncertainty

In the twenty-first century, the goals of mankind are evolving from rising prosperity to attaining long-range sustainable survival in an acceptable state. Such change will lead to a paradigm shift predicted by Thomas Kuhn. This situation motivates two novel generalized systems, Risk-Constrained Optimization (RCO) and its derivative, Risk-Constrained Optimization/Decision Network (RCO/DN). RCO aims for flexible and robust strategies across large ranges of scenarios and risks. It assumes that for any “serious” (complex and long-range) planning and decision-making problem, externalities are important and uncertainty is radical. Therefore, it rejects the very concept of the correct or the best strategy, replacing it with a strategy that is the most acceptable to decision-maker(s). RCO considers decision-support systems, models, and algorithms to be tools of analysis, rather than methods of selection of the best. It replaces the artificial and risky paradigm of maximization by a cautious natural and evolutionary paradigm of catastrophe avoidance. RCO filters out the worst and riskiest candidate strategies, leaving for judgmental selection a small set of flexible, robust, and reasonably safe strategies. For that purpose, RCO employs enhanced stochastic multiscenario (ESMS) models, transforming them into optimizing filters. It is important that this operation is achieved in accordance with a new principle—changing the overall solution by affecting the values of scenario-specific, rather than general, outcome variables. Furthermore, RCO screens decisions and strategies by several synthetic criteria in a framework of novel strategic frontiers. Thus, RCO embeds ESMS into an ensemble of mutually supporting risk management techniques, legitimizing high-level analytical use of a computer plus optimization model combination.     

Reductive dechlorination of polychlorinated dibenzo-p-dioxins by zerovalent iron in subcritical water

A new method for reductive dechlorination of polychlorinated dibenzo-p-dioxins (PCDDs) and remediation of contaminated soils is described that uses zerovalent iron as the dechlorination agent and subcritical water as reaction medium and extractive solvent. It is found that the zerovalent iron can be applied for stepwise dechlorination of octachlorinated dibenzo-p-dioxin (OCDD) on various matrixes in subcritical water. By using iron powder as matrix higher chlorinated congeners were practically completely reduced to less than tetra-substituted homologues. A significant part of residual OCDD, when it was spiked in to soils, and formed less chlorinated congeners are extracted with water in the given conditions. The solubility of OCDD was increased by a 4–6 orders over its solubility at ambient conditions. The new method of contentious-flow extraction is described.     

Persistent organochlorine contaminants in human tissues of the Czech and Slovak populations

The persistence of lipophilic organochlorine substances leads to their incorporation into the food chain and subsequent uptake by humans. Due to their use in the past, Czech and Slovak countries belong to the countries with a relatively high body burden of organochlorines. Levels of PCBs in human tissues based on the congener‐specific analysis are reported in this paper. Samples were analysed for the most abundant PCB congeners. Prevalent persistent organochlorine pesticides and their isomers/metabolites have also been included. The present study is focused on following areas: i) distribution of analysed substances in the various human tissue samples from selected regions of the Czech and Slovak Republic and ii) comparison with the results from other foreign and local studies.     

Instream and offstream environmental conditions and stream biotic integrity: Importance of scale and site similarities for learning and prediction

Two different methods to predict biotic integrity were tested and compared in the present paper. The first one tries to predict the fish indices of biotic integrity (IBI) at the state or regional scale based on the most similar observations to a specific target site of interest using the simple to implement k-nearest neighbors (or kNN) method. Two different distance functions were considered to find the k-nearest neighbors: the Euclidean and the Mahalanobis. The second method was applied on the same datasets and consisted of a step-wise multiple regression. The two modeling approaches yielded similar results but kNN proved to be more time-efficient and very fast computationally for the given dataset sizes, which allowed applying a leave-one-out cross validation.In an attempt to reveal the importance of scale in the prediction of IBI, regression models were constructed at the state (or regional) scale and at the more refined cluster of sampling sites scale. Clusters of sites were extracted using Kohonen's self-organizing maps (SOM) followed by k-means clustering of the SOM neurons. Cluster-level regression models, constructed after site patterning, performed better in IBI prediction than global regression models constructed without any previous site patterning. The importance of identifying groups of sites with similar environmental characteristics affecting the IBI was revealed. The combined use of site patterning and regression modeling for IBI prediction also helped identifying important variables acting at the local scale which remain latent at the global scale.