Daily Archives: September 9, 2025

Authors: Mohamed Asiq .A, Santhosh M, Vishal.S, D.Jeevanantham,B.E

Abstract: Wastewater treatment is essential for safeguarding public health, protecting ecosystems, and supporting sustainable urban development. This report presents insights from anacademic field visit to the Government Wastewater Treatment Plant (WWTP) at Pollachi, Tamil Nadu. The plant is based on Sequential Batch Reactor (SBR) technology, which provides an efficient and compact solution for secondary treatment of municipal sewage. During the visit, students observed the general layout of the facility, including preliminary units (receiving chamber, screens, grit chambers), secondary biological treatment (SBR reactors, decanters), tertiary treatment (chlorination chambers, contact tanks), and sludge handling units (sludge well, centrifuge building). The plant also houses supporting infrastructure such as laboratory facilities, blower rooms, and landscaped green belts that enhance both aesthetics and environmental protection. The visit provided practical exposure to treatment operations, sludge management, effluent quality monitoring, and safety protocols. It also highlighted the broader significance of WWTPs in ensuring sustainable sanitation, preventing water pollution, and promoting wastewater reuse. This report connects classroom knowledge of environmental engineering with real-world field practice, emphasizing the critical role of wastewater treatment plants in urban infrastructure.

Published by: vikaspatanker

Authors: Sangeeth Kumar.J, Janarthanan.V, Logeshwaran.S, D.Jeevanantham,B.E

Abstract: Water treatment plants (WTPs) play a fundamental role in delivering safe and reliable drinking water to urban and rural populations. This journal paper documents a field visit to the Pollachi Water Treatment Plant (WTP) located at Kolathur Village, Pollachi Taluk, Coimbatore District, which is part of the Combined Water Supply Scheme (CWSS) supplying Pollachi North, Pollachi South, Kinathukadavu, Gudimangalam, and adjoining habitations. The scheme sources water from the Aliyar River, with an intake well and raw water pump house that lifts water for treatment. The plant consists of headworks, aerator, stilling chamber, flash mixers, dividing chambers, clariflocculators, filter beds, clear water sump, and chemical treatment units for coagulation, flocculation, and disinfection. During the visit, the operation of raw water pumping mains, filter media layers, chlorination arrangements, laboratory facilities, booster pumping stations, and service reservoirs were observed. With a designed treatment capacity of 26.38 MLD, the scheme ensures reliable water supply to urban wards and more than 200 rural habitations. This field exposure enabled students to understand the engineering design and operational aspects of drinking water treatment and distribution, bridging theoretical knowledge with field practice

Published by: vikaspatanker

Authors: Manoj Kumar Yadav, Deepak Kumar Singh

Abstract: Air pollution has emerged as one of the most serious environmental and public health challenges in South Asia, with fine particulate matter (PM2.5) identified as the most pernicious pollutant due to its ability to penetrate deep into the human respiratory system. Uttar Pradesh, the most populous state in India, frequently records PM2.5 concentrations that exceed national and international standards. This study presents an integrated framework that combines geostatistical interpolation and machine learning regression to predict PM2.5 levels across ten non-attainment cities in Uttar Pradesh. Daily PM2.5 data for the period 2021–2024 were obtained from continuous monitoring stations and subjected to rigorous preprocessing. Spatial interpolation using Ordinary Kriging was implemented to generate high-resolution exposure surfaces, while machine learning algorithms including Random Forest, Gradient Boosting Regressor, Extreme Gradient Boosting, Support Vector Regression, and K-Nearest Neighbour were trained to capture temporal and spatial variability. Results demonstrate that PM2.5 concentrations consistently exceeded permissible limits, with pronounced seasonal peaks in winter and relative minima during monsoon months. Kriging revealed spatial clustering of pollution hotspots in Ghaziabad, Kanpur, and Lucknow, while peripheral cities exhibited lower but still concerning levels. Among machine learning models, XGBoost achieved the highest predictive performance with R² values above 0.74, followed by Gradient Boosting. Integration of Kriging-derived features into machine learning workflows improved prediction accuracy by 8–12%. The study demonstrates that hybrid geostatistical–machine learning approaches provide reliable and high-resolution PM2.5 predictions, enabling early-warning systems, spatially targeted interventions, and evidence-based policy planning.

Published by: vikaspatanker

Authors: Anish Kumar, Sourav ray, Ambrose Henrey Mwikwabe, Shreya Gandh, Rohit Kumar Singh

Abstract: The New Technology File System (NTFS) is the default file system for modern Windows and contains rich metadata (journaling, security descriptors, etc.) that aids forensic investigations. Its Master File Table (MFT) holds records for every file (even deleted ones), while transactional logs ($LogFile and $UsnJrnl) record detailed changes . However, NTFS also offers covert storage (alternate data streams, directory $DATA, and boot record slack) and exhibits known integrity flaws. This paper reviews current NTFS forensic methods – including MFT parsing, journal analysis, and hidden-data detection 3 4 – and identifies weaknesses (e.g. limited $MFTMirror backup, unexamined boot sector areas 6). We propose novel recovery techniques: an enhanced boot-sector reconstruction algorithm (combining backup boot data with $LogFile-derived geometry) and an improved metadata restoration process that leverages $LogFile and signature scanning when the MFT is damaged. We demonstrate these on synthetic NTFS images and show improved recovery of system structures and hidden content compared to baseline tools. The contributions include new forensic workflows and illustrative diagrams of NTFS layout and analysis steps.

DOI: http://doi.org/10.5281/zenodo.17083346

Published by: vikaspatanker

Authors: Praveen Kumar Yadav, Kundan Bhushan, Er. Manoj Kumar Yadav

Abstract: Rapid urbanization is a primary driver of local climate change, leading to the formation of the Surface Urban Heat Island (SUHI) effect, which poses significant environmental and public health challenges. This study presents a comprehensive spatiotemporal analysis of the SUHI phenomenon in Lucknow, India, over a decade (2014–2024) by leveraging the analytical power of the Google Earth Engine (GEE) platform and ArcGIS. Using annual mean Land Surface Temperature (LST) derived from Landsat 8 thermal imagery, we employed two distinct metrics to quantify the SUHI effect: statistical Urban Hot Spot (UHS) analysis and the Urban Thermal Field Variance Index (UTFVI). SUHI hotspots were identified as areas with LST exceeding two times standard deviations above the regional mean (LST > μ + 2σ), while the UTFVI was used to classify the urban environment into six levels of thermal comfort. The results reveal a significant intensification and spatial expansion of the SUHI effect over the study period. The total area identified as a Urban hotspot increased from 25 km² in 2014 to 26 km² in 2024, a growth of over 4%. Concurrently, the area experiencing the worst ecological conditions ("Worst" UTFVI zone) expanded from 1,038 km² to 1,050 km² a growth of 1.16% . These high-temperature zones are predominantly concentrated in the city's central commercial core and newly developed residential areas, correlating with the expansion of impervious surfaces. This research provides quantitative evidence of Lucknow's escalating thermal risk and underscores the utility of GEE and geospatial indices for monitoring urban environmental health. The findings offer critical insights for policymakers and urban planners to develop targeted heat mitigation strategies, such as the strategic implementation of green infrastructure.

Published by: vikaspatanker