Management of fresh water weeds (macrophytes) by vermicomposting using Eisenia fetida

In the present study, potential of Eisenia fetida to recycle the different types of fresh water weeds (macrophytes) used as substrate in different reactors (Azolla pinnata reactor, Trapa natans reactor, Ceratophyllum demersum reactor, free-floating macrophytes mixture reactor, and submerged macrophytes mixture reactor) during 2 months experiment is investigated. E. fetida showed significant variation in number and weight among the reactors and during the different fortnights (P <0.05) with maximum in A. pinnata reactor (number 343.3?±?10.23 %; weight 98.62?±?4.23 % ) and minimum in submerged macrophytes mixture reactor (number 105?±?5.77 %; weight 41.07?±?3.97 % ). ANOVA showed significant variation in cocoon production (F₄?=?15.67, P <0.05) and mean body weight (F₄?=?13.49, P <0.05) among different reactors whereas growth rate (F₃?=?23.62, P <0.05) and relative growth rate (F₃?=?4.91, P <0.05) exhibited significant variation during different fortnights. Reactors showed significant variation (P <0.05) in pH, Electrical conductivity (EC), Organic carbon (OC), Organic nitrogen (ON), and C/N ratio during different fortnights with increase in pH, EC, N, and K whereas decrease in OC and C/N ratio. Hierarchical cluster analysis grouped five substrates (weeds) into three clusters—poor vermicompost substrates, moderate vermicompost substrate, and excellent vermicompost substrate. Two principal components (PCs) have been identified by factor analysis with a cumulative variance of 90.43 %. PC1 accounts for 47.17 % of the total variance represents “reproduction factor” and PC2 explaining 43.26 % variance representing “growth factor.” Thus, the nature of macrophyte affects the growth and reproduction pattern of E. fetida among the different reactors, further the addition of A. pinnata in other macrophytes reactors can improve their recycling by E. fetida.     

Brood-care experience, nursing performance, and neural development in the ant Pheidole dentata

Social insect workers mature behaviorally and physiologically with increasing age, generally transitioning from or adding new tasks to their existing repertoire of within-nest nursing tasks. As adult minor workers of the ant Pheidole dentata age, they attend to brood more frequently and nurse more efficiently, perform a broader array of tasks, and undergo myological and neural development. Because these factors covary, the causal relationships among age, task experience, and neural and physiological maturation are not understood. We compared brood-care performance and efficiency by 10-day-old P. dentata minors that had acquired nursing experience to that of equal-age minors experimentally deprived of brood contact. We found the frequency and efficiency of nursing did not significantly differ between experimental and control worker groups, suggesting experience is not required for age-related improvement in nursing efficiency. Workers with and without prior nursing experience did not significantly differ macroscopically in brain anatomy or in brain serotonin content, although workers from the two treatments had slightly, but significantly, different levels of brain dopamine. These results suggest experience with brood is not required for P. dentata minor workers to develop nursing proficiency or undergo a substantial degree of the age-related neural development identifiable by our assessments, which could underscore the ontogeny of brood-care efficiency.     

Dispersion modeling approach for quantification of methane emission rates from natural gas fugitive leaks detected by infrared imaging technique

Recently, infrared optical imaging has been applied in the oil and gas industry as a method to detect potential leaks in pipelines, components and equipment. The EPA suggested that this impending technique is considered as a smart gas LDAR (leak detection, monitoring and repair) for its rapid recognition of leaks, accuracy and robustness. In addition, compared to the conventional method using Total Vapor Analyzer (TVA) or gas sniffer, it has several other advantages, such as the ability to perform real-time scanning and remote sensing, ability to provide area measurement instead of point measurement, and provide an image of the gas which is not visible to naked eye. However, there is still some limitation in the application of optical imaging techniques; it does not give any measurement of gas emissions rates or concentrations of the leaking gas. Infrared cameras can recognize a target gas and distinguish the gas from its surrounding up to a certain concentration, namely the minimum detectable concentration. The value of the minimum detectable concentration depends on the camera design, environmental conditions and surface characteristics when the measurement is taken. This paper proposed a methodology to predict gas emissions rates from the size of the dispersed gas plume or cloud to the minimum detectable concentration. The gas emissions rate is predicted from the downwind distance and the height of the cloud at the minimum detectable concentration for different meteorological conditions. Gas release and dispersion from leaks in natural gas pipeline systems is simulated, and the results are presented.     

Identification of mangrove water quality by multivariate statistical analysis methods in Pondicherry coast,India

Different multivariate statistical analysis such as, cluster analysis, principal component analysis, and multidimensional scale plot were employed to evaluate the trophic status of water quality for four monitoring stations. The present study was carried out to determine the physicochemical parameters of water and sediment characteristics of Pondicherry mangroves—southeast coast of India, during September 2008–December 2010. Seasonal variations of different parameters investigated were as follows: salinity (10.26–35.20 psu), dissolved oxygen (3.71–5.33 mg/L), pH (7.05–8.36), electrical conductivity (26.41–41.33 ms⁻¹), sulfide (1.98–40.43 mg/L), sediment texture sand (39.54–87.31%), silt (9.89–32.97%), clay (3.06–31.20%), and organic matter (0.94–4.64%). pH, temperature, salinity, sand, silt, clay, and organic matter indicated a correlation at P < 0.01. CA grouped the four seasons in to four groups (pre-monsoon, monsoon, post-monsoon, summer) and the sampling sites in to three groups. PCA identified the spatial and temporal characteristics of trophic stations and showed that the water quality was worse in stations 3 and 4 in the Pondicherry mangroves.