Morphology and cultural behavior of Botryococcus protuberans with notes on the genus

The green alga Botryococcus protuberans was isolated from its natural environment and its morphology under different cultural conditions was examined. The alga was characterized by a high starch content and reddish oil drops as the assimilatory products. Photosynthetic pigments, Chl a, Chl b, carotenoids and xanthophylls are present. Modification of environmental conditions in modified Chu-10 medium resulted in optimum growth of the alga. Fatty acid composition revealed palmitic acid being the major component, while lauric acid, myristic acid and stearic acid were found in less quantity.     

Pollution tolerance and distribution pattern of plants in surrounding area of coal-fired industries

Higher concentration of SO2 and particulate matters was reported in surrounding areas of coal-fired industries which influences the distribution pattern of plants. Sensitive plant species are abolished from such areas, however, only pollution tolerant species survive under stress conditions. The present study was designed to investigate the vegetation composition around coal-fired industries i.e. brick industries. To categorise plants as sensitive or resistant air pollution tolerance index (APTI) value was calculated. Out of 99 plants studied, Ricinus communis with APTI 81.10 was found to be the most resistant wild plant showing uniform distribution at all the polluted sites. On the other hand, Lepidium sativum with APTI 5.27 was recorded as the most sensitive plant and found to be present only at the less polluted sites.     

Biochemical parameters of plants as indicators of air pollution

In the present study species like Mangifera indica, Linn., Cassia fistula, Linn., and Eucalyptus hybrid were exposed to different air pollution load for short duration (active biomonitoring). Variation in biochemical parameters like chlorophyll, protein, soluble sugar free amino acid, ascorbic acid, nitrate reductase, superoxide dismutase and peroxidase in the leaves were found to be pollution load dependent. These variations can be used as indicators of air pollution for early diagnosis of stress or as a marker for physiological damage to trees prior to the onset of visible injury symptoms. Just by analyzing these biochemical indicators air quality can also be assessed.     

Changes in cardiac rhythm and calcium levels in guinea pigs treated chronically with fenitrothion

Fenitrothion, an acetylcholinesterase inhibitor, causes bradycardia following long‐term exposure. To analyse the mechanism underlying these cardiac changes, ginea pigs were exposed to fenitrothion 5 and 10 mg/kg for 30 consecutive days. Electrocardiogram (ECG) showed increase in PR and RR intervals. These changes were more prominent in the animals receiving 10 mg/kg fenitrothion which occasionally showed extrasystoles. In isolated atria preparation heart rate was decreased at higher dose. Amplitude of cardiac rhythm decreased with respect to increased ACh concentration. Hypocalcemia was recorded in fenitrothion‐treated animals. The residual content of fenitrothion was increased in both groups of animals following a pattern: brain> spleen> heart> liver> kidney.     

Socioeconomic backwardness and vulnerability to climate change: evidence from Uttar Pradesh state in India

This study tests the thesis that ‘vulnerability to climate change is not only a result of biophysical events alone but also influenced by the socioeconomic conditions in which climate change occurs’. The study chose Uttar Pradesh (UP), a state in India, for its importance in the nation's food and nutrition security programme and its high sensitivity to climate change. It uses an indicator approach to see which districts of UP are the most vulnerable to climate change, and attempts to identify the factors on a set of explanatory variables. The study finds that infrastructurally and economically developed districts are less vulnerable to climate change; in other words, vulnerability to climate change and variability is linked with social and economic development. This observation is corroborated by the findings of relational analysis wherein livestock, forestry, consumption of fertilizer, per capita income, and infant mortality rate are observed to be important correlates of vulnerability to climate change.     

Identification of sources affecting fog formation using receptor modeling approaches and inventory estimates of sectoral emissions

Positive matrix factorization (PMF) was used to identify factors affecting fog formation in Kanpur during the ISRO-GBP land campaign-II (LC-II) in December 2004. PMF predicted factors were validated by contrasting the emission strength of sources in the foggy and clear periods, using a combination of potential source contribution function (PSCF) analysis and quantitative emission inventory information. A time series aerosol chemical data set of 29 days and 12 species was decomposed to identify 4-factors: Secondary species, Biomass burning, Dust and Sea salt. PMF predicted particle mass with a satisfactory goodness-of-fit (slope of 0.83 ± 0.17 and R² of 0.8), and strong species within 11–12% relative standard deviation. Mean contributions of anthropogenic factors were significantly higher during the foggy period for secondary species (2.9 ± 0.3) and biomass burning (1.2 ± 0.09) compared to the clear period. Local sources contributing to aerosols that mediated fog events at Kanpur, based on emissions in a 200 km × 200 km area around Kanpur city were thermal power plants and transportation (SO₂) and biofuel combustion (BC and OM). Regional scale sources influencing emissions during the foggy period, in probable source regions identified by PSCF included thermal power plants, transportation, brick kilns and biofuel combustion. While biofuel combustion and transportation are distributed area sources, individual point sources include coal-fired thermal power plants located in Aligarh, Delhi, Ghaziabad, Jhansi, Kanpur, Rae Bareli and Rupnagar and brick kilns located in Allahabad, Agra, Farrukhabad, Ghaziabad, Kanpur, Ludhiana, Lucknow and Rae Bareli. Additionally, in the foggy period, large areas of probable source regions lay outside India, implying the significance of aerosol incursion from outside India.     

Characterization of different road dusts in opencast coal mining areas of India

Dust from haul and transport roads are the major source of air pollution in opencast coal mining areas. Dust generated during mining operations pollutes air which causes different health problems. Various available techniques are implemented in the field to minimize and control dust in mining areas. However, they are not very effective because dust deposited on road surfaces are not removed by these techniques. For effective control of dust in opencast mining areas, it has to be regularly collected from road surfaces and may be converted into solid form, and subsequently can be used as a domestic fuel considering its physicochemical properties. The present paper describes a comparative study of qualitative and quantitative aspects of road dust samples of four coalfields of India. The pH of the dust was found to be in the range of 5.1–7.7. Moisture, ash, volatile matter, fixed carbon, water-holding capacity, bulk density, and specific gravity of dust samples were found to be in the range of 0.5–3.0%, 45–76%, 12.6–20.0%, 10.2–45.3%, 21.17–31.71%, 1.15–1.70, and 1.73–2.30 g cm⁻³, respectively. Observing the overall generation and characteristics of coal dust, it is suggested that coal dust from haul and transport roads of mining areas can be effectively collected and used as domestic fuel.     

Process parameters for decolorization and biodegradation of orange II (Acid Orange 7) in dye-simulated minimal salt medium and subsequent textile effluent treatment by Bacillus cereus (MTCC 9777) RMLAU1

In this study, Bacillus cereus isolate from tannery effluent was employed for orange II dye decolorization in simulated minimal salt broth and textile effluent. Most of the physicochemical parameters of textile effluent were above the permissible limits. The strain was highly tolerant to dye up to 500 mg l^?1. Increasing dye concentration exerted inhibitory effect on the bacterial growth and decolorization. The maximum decolorization of initial 100 mg dye l^?1 was achieved at optimum pH 8.0 and 33 °C under static culture conditions during 96-h incubation. Supplementation with optimized glucose (0.4 %, w/v) and ammonium sulfate (0.1 %, w/v) with 3.0 % B. cereus inoculum further enhanced dye decolorization to highest 68.5 % within 96-h incubation. A direct correlation was evident between bacterial growth and dye decolorization. Under above optimized conditions, 24.3 % decolorization of unsterilized real textile effluent by native microflora was achieved. The effluent decolorization enhanced substantially to 37.1 % with B. cereus augmentation and to 40.5 % when supplemented with glucose and ammonium sulfate without augmentation. The maximum decolorization of 52.5 % occurred when textile effluent was supplemented with optimized exogenous carbon and nitrogen sources along with B. cereus augmentation. Gas chromatography–mass spectrometry identified sulfanilic acid as orange II degradation product. Fourier transform infra red spectroscopy of metabolic products indicated the presence of amino and hydroxyl functional groups. This strain may be suitably employed for in situ decolorization of textile industrial effluent under broad environmental conditions.     

Ecosystem evaluation (1989–2012) of Ramsar wetland Deepor Beel using satellite-derived indices

The unprecedented urban growth especially in developing countries has laid immense pressure on wetlands, finally threatening their existence altogether. A long-term monitoring of wetland ecosystems is the basis of planning conservation measures for a sustainable development. Deepor Beel, a Ramsar wetland and major storm water basin of the River Brahmaputra in the northeastern region of India, needs particular attention due to its constant degradation over the past decades. A rule-based classification algorithm was developed using Landsat (2011)-derived indices, namely Normalised Difference Water Index (NDWI), Modified Normalised Difference Water Index (MNDWI), Normalised Difference Pond Index (NDPI), Normalised Difference Vegetation Index (NDVI) and field data as ancillary information. Field data, ALOS AVNIR and Google Earth images were used for accuracy assessment. A fuzzy accuracy assessment of the classified data sets showed an overall accuracy of 82 % for MAX criteria and 90 % for RIGHT criteria. The rules were used to classify major wetland cover types during low water season (January) in 1989, 2001 and 2012. The statistical analysis of the classified wetland showed heavy manifestation in aquatic vegetation and other features indicating severe eutrophication over the past 23 years. This degradation was closely related to major contributing anthropogenic factors, such as a railway line construction, growing croplands, waste disposal and illegal human settlements in the wetland catchment. In addition, the landscape development index (LDI) indicated a rapid increase in the impact of the surrounding land use on the wetland from 1989 to 2012. The techniques and results from this study may prove useful for top-down landscape analyses of this and other freshwater wetlands.