Freshwater Mussel,Lamellidens Marginalis (Lamarck) (Mollusca: Bivalvia: Unionidae) As an Indicator of River Pollution

Abstract

Analyses were made of heavy metals, manganese, nickel, copper, zinc and lead in water samples and soft body, shell and different tissues (gills, digestive glands, mantle and viscera) of the Unionid mussel, Lamellidens marginalis collected from two tributaries of the Cauvery river. Water samples from Station I contained higher concentrations of the metals than those from Station II. the concentration of metals in water at both stations were in the descending order: Mn > Zn > Pb > Ni > Cu. However, the concentrations of metals in the soft body were in the descending order: Zn > Mn > Pb > Ni > Cu at both stations in all size groups of mussels tested. the concentration of zinc maintained a linear relationship with the size of the mussels, but manganese showed a reverse trend. Small size (4-5 cm) mussels accumulated more manganese (105.5 μg.g^?1 dry wt.) than larger ones (7-8 cm; 6.5 μg.g^?1 dry wt.). Both young and old is accumulate the same level of lead, copper and nickel in the soft body. the order of concentrations of metals (Mn, Pb, Zn, Ni and Cu) in the shell of mussels from both stations coincided with the order of concentrations of background water except for lead. the accumulation of lead was higher in shell (30.4-36.2 μg.g^?1 dry wt.) than in soft body (6.4-12.0 μg.g^?1 dry wt.). the pattern of concentration of metals in the various tissues reveal that the digestive glands have greater ability than other tissues to concentrate most metals under study. the concentration factors for soft body, shell and different tissues are presented. the advantages in using the common mussel for biomonitoring of contaminants in water is also discussed.     

Photochemical decomposition of tetrachlorovinphos

The photochemical degradation of tetrachlorovinphos in solvents and as a solid has been investigated. Photoproducts have been isolated and characterized by spectroscopic methods and comparison with authentic samples. The rate of phototransformation of this compound has also been studied.     

Supporting conservationists’ mental health through better working conditions

Biodiversity conservation work can be challenging but rewarding, and both aspects have potential consequences for conservationists’ mental health. Yet, little is known about patterns of mental health among conservationists and its associated workplace protective and risk factors. A better understanding might help improve working conditions, supporting conservationists’ job satisfaction, productivity, and engagement, while reducing costs from staff turnover, absenteeism, and presenteeism. We surveyed 2311 conservation professionals working in 122 countries through an internet survey shared via mailing lists, social media, and other channels. We asked them about experiences of psychological distress, working conditions, and personal characteristics. Over half were from and worked in Europe and North America, and most had a university-level education, were in desk-based academic and practitioner roles, and responded in English. Heavy workload, job demands, and organizational instability were linked to higher distress, but job stability and satisfaction with one's contributions to conservation were associated with lower distress. Respondents with low dispositional and conservation-specific optimism, poor physical health, and limited social support, women, and early-career professionals were most at risk of distress in our sample. Our results flag important risk factors that employers could consider, although further research is needed among groups underrepresented in our sample. Drawing on evidence-based occupational health interventions, we suggest measures that could promote better working conditions and thus may improve conservationists’ mental health and abilities to protect nature.     

Current trends in diagnosis and treatment strategies of COVID-19 infection

Environmental Science and Pollution Research - Coronaviruses are terrifically precise and adapted towards specialized respiratory epithelial cells, observed in organ culture and human volunteers...     

Dynamics of oxytetracycline and resistance genes in soil under long-term intensive compost fertilization in Northern China

In the present study, we explored the dynamics of antibiotics (ciprofloxacin, norfloxacin, enrofloxacin, and oxytetracycline), tetracycline resistance genes (TRGs), and bacterial communities over 2013–2015 in soils fertilized conventionally or with two levels (82.5 and 165 t/ha) of compost for 12 years. In the soil receiving 165 t/ha of compost, only oxytetracycline was 46% higher than that in the conventionally fertilized soil. Transient enrichment of both tetM (20% to 9-fold) and tetK (25% to 67-fold) was observed in multiple instances immediately after the application of compost. The majority of genera which positively correlated with tetM or tetK were affiliated to Proteobacteria, Actinobacteria, Firmicutes, and Bacteroidetes. The structural equation model analysis indicated that fertilization regimes directly affected the bacterial composition and antibiotics and had an indirect effect on the abundance of tetK and tetM via these antibiotics. In summary, this study shed light into the complex interactions between fertilization, antibiotics, and antibiotic resistance pollution in greenhouse soil.

     

A novel bagging ensemble approach for predicting summertime ground-level ozone concentration

Ozone pollution appears as a major air quality issue, e.g. for the protection of human health and vegetation. Formation of ground level ozone is a complex photochemical phenomenon and involves numerous intricate factors most of which are interrelated with each other. Machine learning techniques can be adopted to predict the ground level ozone. The main objective of the present study is to develop the state-of-the-art ensemble bagging approach to model the summer time ground level ozone in an industrial area comprising a hazardous waste management facility. In this study, the feasibility of using ensemble model with seven meteorological parameters as input variables to predict the surface level O3 concentration. Multilayer perceptron, RTree, REPTree, and Random forest were employed as the base learners. The error measures used for checking the performance of each model includes IoAd, R2, and PEP. The model results were validated against an independent test data set. Bagged random forest predicted the ground level ozone better with higher Nash-Sutcliffe coefficient 0.93. This study scaffolded the current research gap in big data analysis identified with air pollutant prediction.

Implications: The main focus of this paper is to model the summer time ground level O₃ concentration in an Industrial area comprising of hazardous waste management facility. Comparison study was made between the base classifiers and the ensemble classifiers. Most of the conventional models can well predict the average concentrations. In this case the peak concentrations are of importance as it has serious effect on human health and environment. The models developed should also be homoscedastic.     

Mercury in the sediments of Vembanad Lake,western coast of India

Mercury, a global pollutant, is popping up in places where it was never expected before and it burdens in sediments and other non-biological materials. It is estimated to have increased up to five times the pre-human level due to anthropogenic activities. Vembanad backwaters, one of the largest Ramsar site in India, which have extraordinary importance for its hydrological function, are now considered as one of the mercury hot spots in India. In this study, surface sediment samples of Vembanad Lake and nearshore areas have been seasonally analysed for total mercury and methyl mercury concentrations while the core sediment samples were analysed for total mercury. The results showed that the northern part of the lake was more contaminated with mercury than the southern part. The mercury concentration was relatively high in the subsurface sediment samples, indicating the possibility of historic industrial mercury deposition. A decreasing trend in the mercury level towards the surface in the core sediment was also observed. The geochemical parameters were also analysed to understand the sediment mercury chemistry. Anoxic conditions, pH and organic carbon, sulphur and Fe determined the presence of various species of mercury in the sediments of Vembanad Lake. The prevailing physical and geochemical conditions in Vembanad Lake have indicated the chances of chemical transformation of mercury and the potential hazard if the deposited mercury fractions are remobilised.     

Breakthrough Curves Characterization and Identification of an Unknown Pollution Source in Groundwater System Using an Artificial Neural Network (ANN)

Contamination of groundwater constrains its uses and poses a serious threat to the environment. Once groundwater is contaminated, the cleanup may be difficult and expensive. Identification of unknown pollution sources is the first step toward adopting any remediation strategy. The proposed methodology exploits the capability of a universal function approximation by a feed-forward multilayer artificial neural network (ANN) to identify the sources in terms of its location, magnitudes, and duration of activity. The back-propagation algorithm is utilized for training the ANN to identify the source characteristics based on simulated concentration data at specified observation locations in the aquifer. Uniform random generation and the Latin hypercube sampling method of random generation are used to generate temporal varying source fluxes. These source fluxes are used in groundwater flow and the transport simulation model to generate necessary data for the ANN model-building processes. Breakthrough curves obtained for the specified pollution scenario are characterized by different methods. The characterized breakthrough curves parameters serve as inputs to ANN model. Unknown pollution source characteristics are outputs for ANN model. Experimentation is also performed with different number of training and testing patterns. In addition, the effects of measurement errors in concentration measurements values are used to show the robustness of ANN based methodology for source identification in case of erroneous data.     

共价键型有机硅铝复合絮凝剂的混凝效果

利用正硅酸乙酯(TEOS)、二乙氧基二甲基硅烷(DEDMS)和γ-氨丙基二乙氧基甲基硅烷(APDES)3种硅烷偶联剂作为有机硅源,制备了共价键型有机硅铝复合絮凝剂,研究了共价键型有机硅铝复合絮凝剂处理浊度水、色度水、含油废水以及垃圾渗滤液膜浓缩液的效果。结果表明,3种共价键型有机硅铝复合絮凝剂具有优良的除浊脱色性能,其除油效果远远优于无机高分子絮凝剂。对于垃圾渗滤液膜浓缩液,共价键型有机硅铝复合絮凝剂可以去除常规絮凝剂无法去除的物质,显著降低出水COD。3种共价键型絮凝剂中,以TEOS为硅源的共价键型絮凝剂除浊效果最佳,以APDES为硅源的共价键型絮凝剂除油和去除COD效果最佳。共价键型有机硅铝复合絮凝剂在含油废水和高浓度有机废水的深度处理方面有很好的应用前景。     

Solar oxidation and removal of arsenic from water: An experimental study

Arsenic poses a significant threat to both human health and the environment. Arsenic removal through solar oxidation has been investigated in a batch process. Arsenic was artificially added to both deionized and tap water to conduct the experiments. Clean, colorless, transparent, Polyethylene Terephthalate (PET) bottles were used for Solar Oxidation and Removal of Arsenic (SORAS) experiments. Various parameters including concentration of arsenic, iron, and photo-catalyst were varied during the experiments. The maximum arsenic removal efficiency obtained was 94% and 88% for deionized water and tap water respectively when ferrous ammonium sulphate and lemon juice were used. Maximum efficiency of 88% and 82% was obtained for deionized and tap water respectively when locally available ferrous alum and glacial acetic acid were used. The change in volume of the photo-catalyst (lemon juice and glacial acetic acid) also did not affect the SORAS process significantly. Therefore, the recommended volume for the photo-catalyst was 1–2 ml/L. SORAS can very well be used for areas contaminated with arsenic having concentrations less than 100 μg/L.