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

Abstract

The earth observation data sets were employed to study the land use/land cover change in study area from year 2000-2010. Vegetation, built-up area and agriculture classes had shown maximum changes. The lake water samples were analyzed, and further, Water Quality Index (WQI) was computed to categorize the lake water. The average value of WQI is 64.52, 52.23 and 42.45 in premonsoon, monsoon and post-monsoon seasons, respectively. Generally, pre-monsoon samples have higher number of polluted samples. Moreover, we applied the multivariate statistical techniques for handling large and complex data sets in order to get better information about the lake water quality. Factor analysis and principal component analysis are applied to understand the latent structure of the data sets, and we have identified a total of four factors in pre-monsoon, three factors in monsoon and three factors in post-monsoon season, which are responsible for the whole data structure. These factors have explained that 90.908, 89.078 and 85.456 % of the cumulative percentage variance of the pre-monsoon, monsoon and post-monsoon data sets. Overall analysis reveals that the agricultural runoff, waste disposal, leaching and irrigation with wastewater, land transformation in the surrounding areas are the main causes of lake water pollution followed by some degree of pollution from geogenic sources such as rock weathering. Hence, there is an urgent need of proper attention and management of resources.

Assessment of heavy metal contamination in the sediment of the River Ghaghara, a major tributary of the River Ganga in Northern India

“…Many activities such as land use/land cover change Narsimlu et al 2015;Kumar et al 2017), sewage sludge, mine waste, industrial waste, waste water (Gautam et al 2013), atmospheric pollutants, pesticides, and fertilizer applications are the main activities controlling the flow of river and contributors of heavy metals discharged into rivers, lakes, estuaries, and marines (Chen et al 2004;Adaikpoh et al 2005; Lepane and Heonsalu 2007;Amin et al 2014;Singh et al 2016Singh et al , 2017. Beside these wastes, rivers also carry large quantities of solid wastes, including thousands of animal carcasses and hundreds of human corpses are dumped into the active channel of the rivers and on its banks every day in recent years.…”

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.…”

Water quality and ecological role of urban lake: a case study of Słoneczne Lake in Szczecin (NW-Poland)

“…Prior to statistical analysis, standardization of measurement data was carried out to avoid discrepancies between different units of individual water quality indices. To test the usage of collected data for multivariate statistical techniques, the Kaiser-Meyer-Olkin measure of sample adequacy test was performed and Bartlett's test of sphericity was performed (Chow et al, 2016;Gao et al, 2015;Taoufim et al, 2017;Singh et al, 2016).…”