Characterization of a mercury-reducing Bacillus cereus strain isolated from the Pulicat Lake sediments, south east coast of India

Pulicat Lake sediments are often severely polluted with the toxic heavy metal mercury. Several mercury-resistant strains of Bacillus species were isolated from the sediments and all the isolates exhibited broad spectrum resistance (resistance to both organic and inorganic mercuric compounds). Plasmid curing assay showed that all the isolated Bacillus strains carry chromosomally borne mercury resistance. Polymerase chain reaction and southern hybridization analyses using merA and merB3 gene primers/probes showed that five of the isolated Bacillus strains carry sequences similar to known merA and merB3 genes. Results of multiple sequence alignment revealed 99% similarity with merA and merB3 of TnMERI1 (class II transposons). Other mercury resistant Bacillus species lacking homology to these genes were not able to volatilize mercuric chloride, indicating the presence of other modes of resistance to mercuric compounds.

Characterization of a mercury-reducing Bacillus cereus strain isolated from the Pulicat Lake sediments, south east coast of India

Pulicat Lake sediments are often severely polluted with the toxic heavy metal mercury. Several mercury-resistant strains of Bacillus species were isolated from the sediments and all the isolates exhibited broad spectrum resistance (resistance to both organic and inorganic mercuric compounds). Plasmid curing assay showed that all the isolated Bacillus strains carry chromosomally borne mercury resistance. Polymerase chain reaction and southern hybridization analyses using merA and merB3 gene primers/probes showed that five of the isolated Bacillus strains carry sequences similar to known merA and merB3 genes. Results of multiple sequence alignment revealed 99% similarity with merA and merB3 of TnMERI1 (class II transposons). Other mercury resistant Bacillus species lacking homology to these genes were not able to volatilize mercuric chloride, indicating the presence of other modes of resistance to mercuric compounds.

Nitrogen and carbon flows through Belandur lake - Role of Bellandur lake as a natural wetland treating Bangalore wastewater

Much of the Bangalore sewage is treated in three streams namely Bellandur (K&C Valley),Vrishabhavati and Hebbal-Nagavara stream systems. Among these it is estimated that out of a total of about 500MLD of partially treated sewage is let into the Bellandur tank. We estimate that a total of about 77t N non-industrial anthropogenic nitrogen efflux (mainly urine and excreta) in Bangalore city. This is distributed between that handled by the three sewage streams, soak-pits and land deposition. About 17-24.5t N enters the Bellandur tank daily. This has been happening over few decades and our observations suggest that this approximately 380ha tank is functioning as a C and N removal system with reasonable efficiency. The ammoniacal and nitrate nitrogen content of the water at the discharge points were estimated and found that over 80% of the nitrogen influx and over 75% of the C influx is removed by this tank system. We observed that there are three nitrogen sinks namely bacterial, micro-algal and macrophytes. The micro-algal fraction is dominated by Microcystis and Euglenophyceae members and they appear to constitute a significant fraction. Water hyacinth represents the single largest representative of the macrophytes. This tank has been functioning in this manner for over three decades. We attempt to study this phenomenon from a material balance approach and show that it is functioning with a reasonable degree of satisfaction as a natural wetland. As the population served and concomitant influx into this wetland increases, there is a potential for the system to be overloaded and to collapse. Therefore a better understanding of its function and the need for maintenance is discussed in the paper.

Spatial Analysis and Characterisation of Lentic Ecosystems: A Case Study of Varthur Lake, Bangalore

Lentic ecosystems vital functions such as recycling of nutrients, purification of water, recharge of groundwater,augmenting and maintenance of stream flow and habitat provision for a wide variety of flora and fauna along with their recreation values necessitates their sustainable management through appropriate conservation mechanisms. Failure to restore these ecosystems will result in extinction of species or ecosystem types and cause permanent ecological damage. In Bangalore, lentic ecosystems (for example lakes) have played a prominent role serving the needs of agriculture and drinking water. But the burgeoning population accompanied by unplanned developmental activities has led to the drastic reduction in their numbers (from 262 in 1976 to 81). The existing water bodies are contaminated by residential, agricultural, commercial and industrial wastes/effluents. In order to restore the ecosystem, assessment of the level of contamination is crucial. This paper focuses on characterisation and restoration aspects of Varthur lake based on hydrological, morphometric, physical-chemical and socio-economic investigations for a period of six months covering post monsoon seasons. The results of the water quality analysis show that the lake is eutrophic with high concentrations of phosphorous and organic matter. The morphometric analysis indicates that the lake is shallow in relation to its surface area. Socio-economic analyses show dependence of local residents for irrigation, fodder, etc. These analyses highlight the need and urgency to restore the physical, chemical and biological integrity through viable restoration and sustainable watershed management strategies, which include pollution abatement, catchment treatment, desilting of the lake and educating all stakeholders on the conservation and restoration of lake ecosystems.

C:N ratio of Sediments in a sewage fed Urban Lake

C:N ratio of lake sediments provide valuable information about the source and proportions of terrestrial, phytogenic and phycogenic carbon and nitrogen. This study has been carried out in Varthur lake which is receiving sewage since many decades apart from large scale land cover changes. C:N profile of the surficial sediment layer collected in the rainy and the dry seasons revealed higher C:N values[43] due to the accumulation of autochthonous organic material mostly at the deeper portions of the lake. This also highlights N limitation in the sludge either due to uptake by micro and macro-biota or rapid volatilization, denitrification and possible leaching in water. Organic Carbon was lower towards the inlets and higher near the deeper zones. This pattern of Organic C deposition was aided by gusty winds and high flow conditions together with impacts by the land use land cover changes in the watershed. Spatial variability of C:N in surficial sediments is significant compared to its seasonal variability. This communication provides an insight to the pattern in which nutrients are distributed in the sludge/sediment and its variation across seasons and space impacted by the biotic process accompanied by the hydrodynamic changes in the lake.

Assessment of treatment capabilities of Varthur Lake, Bangalore, India

Manmade waterbodies have traditionally been used for domestic and irrigation purposes. Unplanned urbanization and ad-hoc approaches have led to these waterbodies receiving untreated sewage. This enriches and eutrophies the waterbody. A physicochemical and biological analysis of sewage-fed Varthur Lake in Bangalore was carried out and its treatment capabilities in terms of BOD removal, nutrient assimilation and self-remediation were assessed. Anaerobic conditions (0 mg/L) prevail at the inlet which improves towards the outlets due to algal aeration. This removed > 50% BOD in the monsoon season but was inhibited by floating macrophytes in all other seasons. Alkalinity, TDS, conductivity and hardness values were higher when compared to earlier studies. This study shows the lake behaves as an anaerobic~aerobic lagoon with a residence time of 4.8 d treating the wastewater to a considerable extent. Further research is required to optimise the system performance.

Dynamic multi-relational Chinese restaurant process for analyzing influences on users in social media

We study the problem of analyzing influence of various factors affecting individual messages posted in social media. The problem is challenging because of various types of influences propagating through the social media network that act simultaneously on any user. Additionally, the topic composition of the influencing factors and the susceptibility of users to these influences evolve over time. This problem has not been studied before, and off-the-shelf models are unsuitable for this purpose. To capture the complex interplay of these various factors, we propose a new non-parametric model called the Dynamic Multi-Relational Chinese Restaurant Process. This accounts for the user network for data generation and also allows the parameters to evolve over time. Designing inference algorithms for this model suited for large scale social-media data is another challenge. To this end, we propose a scalable and multi-threaded inference algorithm based on online Gibbs Sampling. Extensive evaluations on large-scale Twitter and Face book data show that the extracted topics when applied to authorship and commenting prediction outperform state-of-the-art baselines. More importantly, our model produces valuable insights on topic trends and user personality trends beyond the capability of existing approaches.

Evaluation of TRMM PR Sampling Error Over a Subtropical Basin Using Bootstrap Technique

Quantitative use of satellite-derived rainfall products for various scientific applications often requires them to be accompanied with an error estimate. Rainfall estimates inferred from low earth orbiting satellites like the Tropical Rainfall Measuring Mission (TRMM) will be subjected to sampling errors of nonnegligible proportions owing to the narrow swath of satellite sensors coupled with a lack of continuous coverage due to infrequent satellite visits. The authors investigate sampling uncertainty of seasonal rainfall estimates from the active sensor of TRMM, namely, Precipitation Radar (PR), based on 11 years of PR 2A25 data product over the Indian subcontinent. In this paper, a statistical bootstrap technique is investigated to estimate the relative sampling errors using the PR data themselves. Results verify power law scaling characteristics of relative sampling errors with respect to space-time scale of measurement. Sampling uncertainty estimates for mean seasonal rainfall were found to exhibit seasonal variations. To give a practical example of the implications of the bootstrap technique, PR relative sampling errors over a subtropical river basin of Mahanadi, India, are examined. Results reveal that the bootstrap technique incurs relative sampling errors < 33% (for the 2 degrees grid), < 36% (for the 1 degrees grid), < 45% (for the 0.5 degrees grid), and < 57% (for the 0.25 degrees grid). With respect to rainfall type, overall sampling uncertainty was found to be dominated by sampling uncertainty due to stratiform rainfall over the basin. The study compares resulting error estimates to those obtained from latin hypercube sampling. Based on this study, the authors conclude that the bootstrap approach can be successfully used for ascertaining relative sampling errors offered by TRMM-like satellites over gauged or ungauged basins lacking in situ validation data. This technique has wider implications for decision making before incorporating microwave orbital data products in basin-scale hydrologic modeling.

Evaluation of Precipitation Retrievals From Orbital Data Products of TRMM Over a Subtropical Basin in India

The spatial error structure of daily precipitation derived from the latest version 7 (v7) tropical rainfall measuring mission (TRMM) level 2 data products are studied through comparison with the Asian precipitation highly resolved observational data integration toward evaluation of the water resources (APHRODITE) data over a subtropical region of the Indian subcontinent for the seasonal rainfall over 6 years from June 2002 to September 2007. The data products examined include v7 data from the TRMM radiometer Microwave Imager (TMI) and radar precipitation radar (PR), namely, 2A12, 2A25, and 2B31 (combined data from PR and TMI). The spatial distribution of uncertainty from these data products were quantified based on performance metrics derived from the contingency table. For the seasonal daily precipitation over a subtropical basin in India, the data product of 2A12 showed greater skill in detecting and quantifying the volume of rainfall when compared with the 2A25 and 2B31 data products. Error characterization using various error models revealed that random errors from multiplicative error models were homoscedastic and that they better represented rainfall estimates from 2A12 algorithm. Error decomposition techniques performed to disentangle systematic and random errors verify that the multiplicative error model representing rainfall from 2A12 algorithm successfully estimated a greater percentage of systematic error than 2A25 or 2B31 algorithms. Results verify that although the radiometer derived 2A12 rainfall data is known to suffer from many sources of uncertainties, spatial analysis over the case study region of India testifies that the 2A12 rainfall estimates are in a very good agreement with the reference estimates for the data period considered.