Grab and passive sampling applied to pesticide analysis in the São Lourenço river headwater in Campo Verde – MT,Brazil

In this study, the quality of surface water in the headwaters of São Lourenço River in Mato Grosso, Brazil, was evaluated in relation to contamination by pesticides. For this purpose, samples were collected between December 2015 and June 2016 by grab sampling and by passive sampling using an integrative polar organic compound sampler installed in the field during four 14-day cycles between March and June 2016. The analyses were performed by gas chromatography (CG/MS) and by liquid chromatography (UPLC-MS/MS). The results showed the detection of two pesticides (atrazine and pyraclostrobin) of the five analyzed by passive sampling and eight active principles among the 20 analyzed (malathion, diuron, carbofuran, carbendazim, trifluralin, imidacloprid, metolachlor, and acetamiprid) by grab sampling. The detection of 10 pesticides, even almost a decade after the beginning of a recovery process of the ciliary forest, confirms the headwaters' vulnerability to these contaminants and passive sampling proved to be an important tool in capturing small concentrations of pesticides constituting an interesting complement to grab sampling.     

Removal of heavy metal ions from wastewaters using dendrimer-functionalized multi-walled carbon nanotubes

Dendrimer-functionalized multi-walled carbon nanotubes (MWCNT) for heavy metal ion removal from wastewaters were developed. Triazole dendrimers (TD) were built directly onto the carbon nanotube surface by successive click chemistry reactions affording the zero- and first-generation dendrimer-functionalized MWCNT (MWCNT-TD1 and MWCNT-TD2). The Moedritzer-Irani reaction carried out on the amino groups present on the MWCNT-TD2 sample gave the corresponding α-aminophosphonate nanosystem MWCNT-TD2P. Both MWCNT-TD2 and MWCNT-TD2P nanosystems have been characterized by physical, chemical, and morphological analyses. Their chelating abilities towards the toxic metal ions Pb²⁺, Hg²⁺, and Ni²⁺ and the harmless Ca²⁺ ion have been experimentally evaluated in the two different sets of experiments and at the salt concentrations of 1 mg/mL or 1 μg/mL by inductively coupled plasma mass spectrometry (ICP-MS). The results of these studies pointed out the interesting chelating behavior for the phosphonated nanosystem towards the Hg²⁺ ion. The complexation mode of the best chelating system MWCNT-TD2P with mercury was investigated through density functional theory (DFT) calculations, suggesting a chelation mechanism involving the two oxygen atoms of the phosphate group. The synthesized dendrimers, supported on the multi-walled carbon nanotubes, have shown the potential to be used for the selective toxic metal ion removal and recovery.     

Prediction of membrane fouling using artificial neural networks for wastewater treated by membrane bioreactor technologies: bottlenecks and possibilities

Membrane fouling is a major concern for the optimization of membrane bioreactor (MBR) technologies. Numerous studies have been led in the field of membrane fouling control in order to assess with precision the fouling mechanisms which affect membrane resistance to filtration, such as the wastewater characteristics, the mixed liquor constituents, or the operational conditions, for example. Worldwide applications of MBRs in wastewater treatment plants treating all kinds of influents require new methods to predict membrane fouling and thus optimize operating MBRs. That is why new models capable of simulating membrane fouling phenomenon were progressively developed, using mainly a mathematical or numerical approach. Faced with the limits of such models, artificial neural networks (ANNs) were progressively considered to predict membrane fouling in MBRs and showed great potential. This review summarizes fouling control methods used in MBRs and models built in order to predict membrane fouling. A critical study of the application of ANNs in the prediction of membrane fouling in MBRs was carried out with the aim of presenting the bottlenecks associated with this method and the possibilities for further investigation on the subject.     

Fir decline and mortality in the southern Siberian Mountains

Regional Environmental Change - Increased dieback and mortality of “dark needle conifer” (DNC) stands (composed of fir (Abies sibirica), Siberian pine (Pinus sibirica) and spruce (Picea...     

Dynamic trends of heavy metal contents in plants and soil under different industrial air pollution regimes

Based on 30-year monitoring of Ni²⁺ and Cu²⁺ concentrations in the organic horizon of Albic Rustic Podzols and the foliage of six plant species, a dynamic trend in the level of heavy metal accumulation in the components of forest ecosystems of the Kola Peninsula has been revealed against the background of five- to eightfold reduction of pollutant emissions. The direction of the trend has been found to differ: the size of polluted area and pollution level increase with time, while the concentrations of heavy metals in plants decrease due to reduction in their input from the polluted air.     

Siberian musk deer in the diets of tiger and bears in the Sikhote-Alin

The role of the Siberian musk deer in the diets of the Amur tiger, brown bear, and Asian black bear has been studied in the Sikhote-Alin State Biosphere Reserve in 1992 to 2016. Information has been collected from 763 tiger feeding sites, and the contents of tiger feces have been analyzed. Radiotelemetry has been used to evaluate tiger–musk deer relationships. The musk deer accounted for 0.26% of all animal species found to be eaten by the tiger. Musk deer remains in feces of the tiger, brown bear, and Asian black bear occurred with frequencies of 1.3, 0.2, and 0.3%, respectively. A case of musk deer avoiding the presence of brown bear is described.