Modeling groundwater quality over a humid subtropical region using numerical indices, earth observation datasets, and X-ray diffraction technique: a case study of Allahabad district, India

Abstract

Water is undoubtedly the vital commodity for all living creatures and required for well-being of the human society. The present work is based on the surveys and chemical analyses performed on the collected groundwater samples in a part of the Ganga basin in order to understand the sources and evolution of the water quality in the region. The two standard indices such as water quality index and synthetic pollution index for the classification of water in the region are computed. The soil and sediment analysis are carried out with the help of X-ray diffractometer (XRD) for the identification of possible source of ions in water from rock and soil weathering. The dominant minerals which include quartz, muscovite, plagioclase, and orthoclase are reported in the area. The study further utilizes the multivariate statistical techniques for handling large and complex datasets in order to get better information about the groundwater quality. The following statistical methods such as cluster analysis (CA), factor analysis (FA), and principal component analysis (PCA) are applied to handle the large datasets and to understand the latent structure of the data. Through FA/PCAs, we have identified a total of 3 factors in pre-monsoon and 4 factors in post-monsoon season, which are responsible for the whole data structure. These factors explain 77.62 and 82.39% of the total variance of the pre- and post-monsoon datasets. On the other hand, CA depicted the regions that have similar pollutants origin. The average value of synthetic pollution index of groundwater during pre-monsoon is 9.27, while during post-monsoon, it has been recorded as 8.74. On the other hand, the average values of water quality index of groundwater during pre-monsoon and post-monsoon seasons are found as 217.59 and 233.02, respectively. The study indicates that there occurs an extensive urbanization with gradual vast development of various small- and large-scale industries, which is responsible for degradation in water quality. The overall analysis reveals that the agricultural runoff, waste disposal, leaching, and irrigation with wastewater are the main causes of groundwater pollution followed by some degree of pollution from geogenic sources such as rock and soil weathering, confirmed through XRD analysis.

Predicting Spatial and Decadal LULC Changes Through Cellular Automata Markov Chain Models Using Earth Observation Datasets and Geo-information

“…The results of LULC category achieved through classification are presented in Table 4. The historical land use pattern of the district has been changing steadily at a slow pace which also brings adverse effects on natural resources of the region (Singh et al 2013b, c;Singh et al 2014). The area has witnessed increase in urbanization which brought on the fundamental land use change during the last two decades; this is proved by the rapid increase of urban and rural population.…”

“…This seemed to be a good projection, although a slight transformation of this class is predicted to become waste land (6577 pixels) and other fallow land (5558 pixels), as seen in Table 8. This may be due to the migration of population from their old habitat to a new habitat (Singh et al 2014). The class agriculture, which remains the major class of the study area with 3,484,049 pixels, seems to be highly transformed to other classes of the study area with major transformation towards built up class (530,864 pixels).…”

Estimation of Surface Runoff from Semi-arid Ungauged Agricultural Watershed Using SCS-CN Method and Earth Observation Data Sets

“…Many studies have proved the use of EO data namely groundwater [27,46,48,51,52], river water quality [55], coastal water [22], lake and wetlands [5,47,53,59,60], land use/land cover mapping [45,46,48,50,51], land use change trajectories [56], land use/land cover modeling [28,49], urban land use dynamics [4], hydrological modeling [31], forest mapping [44], cyclone tracking [16], soil characterization [37], climate change [54], slope estimation [57], landscape ecology [47,53], ocean studies [35,36] and watershed management [67], watershed prioritization [68]. GIS processing has become a critical step in hydrologic modeling since it contributes to generate model parameters in a spatially distributed manner.…”

Appraisal of urban lake water quality through numerical index, multivariate statistics and earth observation data sets

“…According to Singh et al (2015), the concept of water quality to categorize water according to its degree of purity or pollution dated back to year 1848. Around the same time, the importance of water quality to public health was recognized in the UK (Snow 1856).…”

“…Around the same time, the importance of water quality to public health was recognized in the UK (Snow 1856). Water Quality Index (WQI) methodologies have been developed to provide single number that expresses the overall water quality at a certain location and time, based on several water quality parameters (Parmar and Bhardwaj 2013;Vasanthavigar et al 2010;Avvannavar and Shrihari 2008;Singh et al 2015) and can be used to provide the overall summaries of water quality on a scientific basis. Parmer and Bhardwaj (2013) have applied WQI and fractal dimension approach to study the water of Harike lake on the confluence of Beas and Sutlej rivers of Punjab (India).…”