Identification of Sources and Mobilization of Trace Elements in Shallow Groundwater of the Upper Ganges River Basin

Abstract

The concentration of trace metals in groundwater is still not within the usual standards established by the national and international monitoring authorities. This study aims to evaluate the sources and distribution of trace metals in the shallow aquifer water of the Ganges River basin in the Bangladesh area. A total of 40 groundwater samples were collected and investigated for 11 trace elements and some selected water parameters followed by sophisticated methods. The results presented that three metals, viz. Fe, Mn, and Pb crossed the limit of WHO standard in most of the samples (70-100%), representing severe human health hazards. Besides, the samples are slightly acidic to basic and highly mineralized by divalent cations and bicarbonate because total hardness was found very high. To assess factors affecting the trace metal loads in groundwater samples and determine their possible sources and mobility, multivariate statistical techniques, including Pearson’s correlation coefficient, principal components analysis, and dendrogram cluster analysis are performed. Results revealed that sources of trace elements were identified to be geologic and little from human activities, i.e., application of agrochemicals, excess water mining, and industrial wastewater. It was recognized that the elevated concentration of trace elements in samples was caused by geogenic factors rather than manmade activities. Groundwater in this area poses a large threat due to high levels of trace metals, without any purification, it is essential to avoid this water for drinking. It advised that the groundwater quality should be constantly monitored for sustainable water quality management in the study zone.

Hardness recognition of robotic forearm based on visual–tactile fusion

“…The rapid development of vision technology has promoted the hardness recognition [6] of objects, but with the rapid development, its vision technology also possesses certain limitations, when recognizing objects with similar appearance and shape, such as plasticine and sand with the same color, it is more difficult to recognize objects through vision technology, Althoefer K et al [7] proposed to use a miniature tactile sensor, which is based on multiple gradients of force, and the change of force and shape of the object to obtain tactile information, and input the collected tactile information into a deep learning network to process the collected feature information. Luu Q K et al [8] proposed a new tactile algorithm network, which simulates the data set to train the tactile neural network to extract deep tactile information through the tactile network.…”