Browsing by Author "Arijit Barman"

Now showing 1 - 2 of 2

Spatial variability of arsenic in Indo-Gangetic basin of Varanasi and its cancer risk assessment
(Elsevier Ltd, 2020) Arghya Chattopadhyay; Anand Prakash Singh; Satish Kumar Singh; Arijit Barman; Abhik Patra; Bhabani Prasad Mondal; Koushik Banerjee
The Indo-Gangetic alluvium is prime region for intensive agricultural. In some areas of this region, groundwater is now becoming progressively polluted by contamination with poisonous substances like arsenic. Intensive irrigation with arsenic contaminated ground water in dry spell results in the formation of As(III) which is more toxic. Thus groundwater quality assessment of Gangetic basin has become essential for its safer use. Therefore we under took study on the spatial variability of arsenic by collecting georeferred groundwater samples on grid basis from various water sources like dug well, bore and hand pumps covering the river bank region of Ganga basin. Water quality was investigated through determination pH, EC, TDS, salinity, Na, K, Ca, Mg, SAR, SSP, CO3, HCO3, RSC, Cl, As, Fe, Zn, Mn and Cu, etc. Results pointed severe As contamination in ground water of three sites of the study area. ARC GIS software is now able to process maps along with tabular data and compare them well, to provide the spatial visualization of information and using this tool, the Geographical Information System (GIS) of arsenic was developed. It was noticed from spatial maps that concentration of arsenic was more near the meandering points of Ganga. © 2019 Elsevier Ltd
VIS-NIR reflectance spectroscopy as an alternative method for rapid estimation of soil available potassium
(Indian journals, 2020) Bhabani Prasad Mondal; Bharpoor S. Sekhon; Priya Paul; Arijit Barman; Arghya Chattopadhyay; Nilimesh Mridha
Potassium (K) is an important macronutrient for crop plant and plays a crucial role in crop production. Therefore, accurate and rapid estimation of soil available K is necessary for judicious application of available K in an intensively cropped region. However, traditional soil chemical analysis for assessing soil available K is very much laborious, expensive and time consuming. The visible near-infrared (VIS-NIR) reflectance spectroscopy is considered as a promising alternative technique for rapid, non-destructive and ecofriendly estimation of available K and other soil properties. An experiment was carried out in an intensively cultivated region of Ludhiana district of Punjab to investigate the potential of VIS-NIR technique for accurate prediction of available K using multivariate model. A total of 170 georeferenced surface soil samples (0-15 cm) were collected from the study site for both chemical and spectral analysis of available K. A popular statistical technique namely, partial least square regression (PLSR) was employed to develop spectral model for K prediction. Important statistical diagnostics like coefficient of determination (R2), root mean square error (RMSE) and residual prediction deviation (RPD) were used to evaluate the efficacy of prediction model. The results showed that the R2 and RMSE and RPD values were 0.41, 0.09 and 1.44, respectively for independent validation dataset of PLSR model. The RPD value indicated acceptable prediction accuracy for soil available K with PLSR model. Comparatively lower performance of the studied prediction model could be ascribed to the less variation in the collected spectra of soil samples and the use of linear multivariate model. Therefore, the study suggested to explore advanced non-linear data mining techniques for achieving better prediction accuracy for soil available K. © 2020, Indian journals. All rights reserved.