Design of Filtration System for Aerated Sewage Water

The study investigated the potential of sand and activated charcoal filtration systems to enhance water quality for irrigation by treating aerated sewage effluent from. Setup involved a 60 cm deep sand filter connected as the inlet to another 30 cm deep sand filter and this filter linked as the inlet to a 30 cm deep charcoal filter. These filters were operated in series at hydraulic loading rates (HLR) of 60 m/h and 10 m/h. Notably, operating the filters in series at an HLR of 10 m/h yielded superior effluent water quality compared to an HLR of 60 m/h. System achieved significant removal efficiencies for turbidity, BOD5, COD, Total Nitrogen (Total-N), Total Phosphorous (Total-P) with 71.9%, 54.4%, 71.9%, 44.4%, 39.1%, and 42.9% with a 90 cm deep sand filter at an HLR of 10 m/h, and also with a combination of sand and charcoal filters at an HLR of 25 m/h system achieved 81.6%, 80.3%, 63.5%, 47.5%, and 64.3% respectively. We also examined the chemical characteristics of both untreated and treated sewage water samples, revealing a hierarchy of cation and anion prevalence as follows: Na+ > Ca2+ > Mg2+ > K+ for cations, and Cl- > HCO3- > SO42- > CO32- for anions. Our study demonstrates that the combination of aeration and sand filtration effectively ensures safety by preventing water body pollution and unpleasant odours with high-quality treated wastewater suitable for sustainable agricultural use.

Assessing Land Use Dynamics of Lower Bhavani Basin Using Multiple GIS Platforms

Land use describes the actual form of land, such as a forest or open water and classification based on human utilization. Land use map provides the information about the current landscape of an area. In this study, the Lower Bhavani basin's land use and land cover were classified using GIS platforms and data from the Landsat 8 satellite. The platform utilized in this study were Semi-Automated Plugin (SAP) in QGIS and Random forest method in Google Earth Engine (GEE). The findings suggested that both platforms performed efficiently and displayed comparable percentages of land covered by various land use features. The accuracy of the resulting land use map was evaluated using a Google Earth image, and it was discovered that SAP and GEE hold 91.8% and 92.6% of the total accuracy. This study aids in evaluating and classifying the various Geographic Information System platforms land use trends.