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Rajput P.,Physical Research Laboratory | Sarin M.,Physical Research Laboratory | Kundu S.S.,North Eastern Space Applications Center
Atmospheric Pollution Research | Year: 2013

tmospheric concentrations of elemental, organic and water-soluble organic carbon (EC, OC and WSOC) and polycyclic aromatic hydrocarbons (PAHs) have been studied in PM2.5 (particulate matter of aerodynamic diameter ≤2.5 μm) from a site (Barapani: 25.7 °N; 91.9 °E; 1 064 m amsl) in the foot-hills of NE-Himalaya (NE-H). Under favorable wind-regimes, during the wintertime (January-March), study region is influenced by the long-range transport of aerosols from the Indo-Gangetic Plain (IGP). For rest of the year, ambient atmosphere over the NE-H is relatively clean due to frequent precipitation events associated with the SW- and NE-monsoon. The concentration of PM2.5 over NE-H, during the wintertime, varied from 39-348 μg m-3, with average contribution of OC and EC as 36±8% (AVG±SD) and 6±3%, respectively. For the OC/EC ratio as high as 10-15 (relatively high compared to fossil-fuel source) associated with WSOC/OC ratio exceeding 0.5 in NE-H, it can be inferred that dominant source of carbonaceous aerosols is attributable to biomass burning emissions and/or contributions from secondary organic aerosols (SOA). The OC/PM2.5 ratio from NE-H is somewhat higher compared to upwind regions in the IGP (Range: 0.16-0.24). The abundance of ΣPAHs show large variability, ranging from 4-46 ng m-3, and the ratio of sum of 4- to 6-ring PAHs (Σ(4- to 6-)PAHs) to EC is 2.4 mg g-1; similar to that in the upwind IGP and is about a factor of two higher than that from the fossil-fuel combustion sources. The cross-plot of PAH isomers [FLA/(FLA+PYR) vs. ANTH/(ANTH+PHEN), BaA/(BaA+CHRY+TRIPH), BaP/(BaP+B[b,j,k]FLA) and IcdP/(IcdP+BghiP)] reaffirms the dominant impact of biomass burning emissions. These results have implications to large temporal variability in aerosol radiative forcing and environmental change over the NE-Himalaya. © Author(s) 2012. Source


Gupta S.K.,Durban University of Technology | Chabukdhara M.,North Eastern Space Applications Center | Kumar P.,Durban University of Technology | Singh J.,Dr adh University Faizabad | Bux F.,Durban University of Technology
Ecotoxicology and Environmental Safety | Year: 2014

The aim of this study was to evaluate the extent of heavy metal pollution in river Gomti and associated ecological risk. River water, sediments and locally abundant mollusk (Viviparus (V.) bengalensis) were sampled from six different sites and analyzed for seven metals: Cadmium (Cd), Chromium (Cr), Copper (Cu), Manganese (Mn), Nickel (Ni), Lead (Pb) and Zinc (Zn). Mean metal concentrations (mg/l) in river water were 0.024 for Cd, 0.063 for Cr, 0.022 for Cr, 0.029 for Mn, 0.044 for Ni, 0.018 for Pb and 0.067 for Zn. In river sediments, the concentrations (mg/kg dry wt) were 5.0 for Cd, 16.2 for Cr, 23.2 for Cr, 203.2 for Mn, 23.9 for Ni, 46.2 for Pb and 76.3 for Zn, while in V. bengalensis mean metal concentrations (mg/kg, dry wt) were 0.57 for Cd, 12.0 for Cr, 30.7 for Cu, 29.9 for Mn, 8.8 for Ni, 3.6 for Pb and 48.3 for Zn. Results indicated elevated concentrations of Cu, Zn and Mn in V. bengalensis as compared to other non-essential elements. Potential ecological risk (RI) in sediments showed high to very high metal contamination. Cluster analysis indicated that Pb, Zn, Cd and Ni in sediments may have anthropogenic sources. The findings thus suggest heavy metal contamination of river water and sediments have reached alarming levels, which is well corroborated by elevated level of metal accumulation in V. bengalensis. © 2014 Elsevier Inc. Source


Bhusan K.,North Eastern Space Applications Center | Goswami D.C.,Gauhati University
Landslide Science and Practice: Global Environmental Change | Year: 2013

The North Eastern Region of India because of its relatively immature topography, fragile geologic base and active tectonics is vulnerable to landslide activities and the scenario is further accentuated due to various developmental activities. Almost one fifth of India's landslide prone areas are located in this region. Guwahati, a major city in North East India is one such fast developing city that falls under medium to high category of the Global Landslide Susceptibility Map. The hills of the city have slopes between 15° and 25° where numbers of landslide affected sites are scattered. Almost 50 % of the soil samples analyzed from landslide affected sites showed low strength of the soils. Compared to the global threshold, Guwahati needs less intensity of rainfall (I = 28.7 D-0.890) for landsliding. Moreover, change in land use over a period of 30 years shows correlation between hill slope alteration and increment in landslide incidences. © Springer-Verlag Berlin Heidelberg 2013. Source


Chutia D.,North Eastern Space Applications Center | Bhattacharyya D.K.,Tezpur University | Sudhakar S.,North Eastern Space Applications Center
Applied Geomatics | Year: 2012

This work presents an effective hybrid classification approach for feature extraction from fused images of two different satellite sensors. Wavelet transform function was used to fuse the panchromatic Cartosat-I and multispectral Landsat ETM+ sensor's images which could preserve both the spatial and spectral components of the original images. Multi-resolution segmentations based on homogeneity criterion formed the basis for the hybrid approach which uses supervised fuzzy NN approach of classifier in conjunction with knowledge classification system. Gaussian fuzzy membership function was defined on an optimal set of object features such as the Normalized Difference Vegetation Index, band mean, area, shape index and brightness derived from the segmented image objects for class description. Based on our kappa index analysis evaluation, the hybrid approach provides significantly better performance than its other counterparts such as artificial neural network, maximum likelihood classifier and support vector machine in terms of classification accuracy. © Società Italiana di Fotogrammetria e Topografia (SIFET) 2012. Source


Chutia D.,North Eastern Space Applications Center | Bhattacharyya D.K.,Tezpur University | Kalita R.,North Eastern Space Applications Center | Goswami J.,North Eastern Space Applications Center | And 2 more authors.
Applied Geomatics | Year: 2014

Hyperspectral remote sensing data is characterized by the large number of contiguous spectral bands with narrow bandwidth. Enormous information available in hyperspectral data is quite challenging for classification as compared to multispectral remote sensing data. Most of the widely used conventional 'hard' classifiers are producing inconsistent classification results while employed in classification of hyperspectral data. In this paper, we present an effective hyperspectral classification model for achieving higher accuracy. The proposed model is characterized by three major components: dimensionality reduction using principal component analysis (PCA), multiresolution segmentation, and fuzzy membership-based nearest neighbor (NN)-classification. Here, the bands of the dimensionality-reduced images are represented by the first principal component (PC) of each of the spectral region covered by the hyperspectral sensor. Then, multiresolution segmentation is carried out on these PC composite images based on color and shape homogeneity criterion. The conventional NN-classifier is effectively used by appropriate utilization of fuzzy membership function defined on a set of optimal features derived from the segmented image objects. We demonstrate a case study on Hyperion sensor data of Earth Observing-1 (EO-1) satellite. A comparative assessment is carried out with other competing techniques such as spectral angle mapper (SAM), artificial neural network (ANN), and support vector machine (SVM) on a set of images with different land cover surfaces. The proposed classification model outperforms the existing classification approaches investigated here. © 2014 Società Italiana di Fotogrammetria e Topografia (SIFET). Source

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