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In India, water contamination is a serious environmental issue. Untreated sewage is typically the main cause of water contamination in India. Protecting water quality for sustainable livelihoods and poverty reduction becomes a greatest challenge with the technological change and population growth. Measurement of Water Quantity and Real Time Monitoring becomes mandate to advance the water stewardship. In this paper, to keep track of the quality, smart water quality monitoring system(Ph, temperature, turbidity) and analysing of water quantity (flow or level) based on IOT is proposed for the study area of Okkiyam Maduvu, watershed in Chennai. This system is implemented using various sensors to measure the water quality like Ph, temperature, turbidity sensor and for analysing water water quantity used flow or level sensors. All these sensors are controlled by using the microcontroller - Arduino ESP32. This proposed system is placed in the study area Okkiyam Maduvu watershed to monitor water quality and quantity and visualized those sensed data using the IOT platform through Blynk App. This helps to know the water level and its quality of Okkiyam Maduvu watershed and it helps to take necessary precaution if the water quality is reduced and its level raised beyond the level. [ABSTRACT FROM AUTHOR]

Air is one of the most fundamental constituents for the sustenance of life on earth. The meteorological, traffic factors, consumption of nonrenewable energy sources, and industrial parameters are steadily increasing air pollution. These factors affect the welfare and prosperity of life on earth; therefore, the nature of air quality in our environment needs to be monitored continuously. The Air Quality Index (AQI), which indicates air quality, is influenced by several individual factors such as the accumulation of NO2, CO, O3, PM2.5, SO2, and PM10. This research paper aims to predict and forecast the AQI with Machine Learning (ML) techniques, namely linear regression and time series analysis. Primarily, Multilinear Regression (MLR) model, supervised machine learning, is developed to predict AQI. NO2, Ozone(O2), PM 2.5, and SO2 sensor output collected from Central Pollution Control Board (CPCB), Chennai region, India, fed as input features and optimized AQI calculated from sensor's output set as a target to train the regression model. The obtained model parameters are validated with new and unseen sensor output. The Key Performance Indices (KPI) like coefficient of determination, root mean square error, and mean absolute error were calculated to validate the model accuracy. The K-cross-fold validation for testing data of MLR was obtained as around 92%. Secondly, the Auto-Regressive Integrated Moving Average (ARIMA) time series model is applied to forecast the AQI. The obtained model parameters were validated with unseen data with a timestamp. The forecasted AQI value of the next 15 days lies in a 95 % confidence interval zone. The model accuracy of test data was obtained as more than 80%. [ABSTRACT FROM AUTHOR]

This paper reports the development of a public transport trip planner to help the urban traveller in planning and preparing for his commute using public transportation in the city. A Genetic Algorithm (GA) approach that handles realtime Global Positioning Systems (GPS) data from buses of the Metropolitan Transport Corporation (MTC) in Chennai City (India) has been used to develop the planner. The GA has been shown to provide good solutions within the problem's computation time constraints. The developed trip planner has been implemented for static network data first and subsequently extended to use real-time data. The "walk mode" and Chennai Mass Rapid Transit System (MRTS) have also been included in the geospatial database to extend the route-planner's capabilities. The algorithm has subsequently been segmented to speed up the prediction process. In addition, a temporal cache has also been introduced during implementation, to handle multiple queries generated simultaneously. The results showed that there is promise for scalability and citywide implementation for the proposed real-time route-planner. The uncertainty and poor service quality perceived with public transport bus services in India could potentially be mitigated by further developments in the route-planner introduced in this paper. [ABSTRACT FROM AUTHOR]

This paper delineates the process of activation of soot or black carbon particles into cloud droplets over Chennai, India (an Asian megacity). Archaic wood-fired cooking stove emissions from crowded shantytown settlements dispense copious amounts of carbonaceous particles into the city's atmosphere during morning and evening cooking periods. Traffic emissions also add considerably to the city's particulate matter loads. At other times, when these sources are reduced (e.g. during non-cooking periods), a pall of residual pollution hovers above the city. This study assesses this problem definitively through process modelling studies undertaken during a typical pre-monsoonal month (October) when the levels of pollution are very high. The novelty of this paper includes a clear quantification of the percentage of activated nuclei resulting from these sources using a sophisticated cloud chemical parcel model. The results show that 85% of the soot and black carbon particles are activated into very small low-lying cloud droplets within Chennai's boundary layer. We further show for the first time that the presence of these particles results in a slowing down of the auto-conversion process (cloud water to rain droplets). This study could serve as a pointer for other Asian Cities with a sizeable shantytown population in India and elsewhere. [ABSTRACT FROM AUTHOR]

Energy is an efficient source in the present world and it is very significant. Huge building systems such as luxury hotels, commercial complex, malls and educational institutes require chiller plants for air-conditioning purpose in their premises. In this paper, a case analysis on energy savings and efficiency improvement of a chiller plant is done for a luxury hotel building located at Chennai, Tamil Nadu, India. The building consists of carrier make 2 Nos. 633 kW/180 TR and 1 No. 1185 kW/337 TR water cooled chillers. The chiller plant system at the site consumes up to 43% of the facility's annual energy cost. To increase the energy savings and efficiency of existing system and to achieve a lower number on the Specific Power Consumption (SPC), the existing chiller plant is retrofitted with 1 No. 879 kW/250 TR Premium efficiency chiller and an energy analysis is done by simulating the proposed chiller using Chiller Plant Automation (CPA) software and by understanding the energy usage of the chiller as per the site operating load and weather conditions. With the change in chiller, the system efficiency of the hotel increased by 40% and the energy consumption reduced to 38%. [ABSTRACT FROM AUTHOR]

William J. Niblock (WJN), an Assistant Surgeon at the Madras General Hospital (MGH), published a paper 'Cancer in India' (2 pages of text and 3 pages of tables) in the Indian Medical Gazette in 1902. He appears to have been a popular surgeon in Madras who surgically treated mouth cancers, testicular filariasis, and calcareous stones in the liver, gall bladder, and kidney. His 'Cancer in India' article is a compilation of numerical data of different cancers recorded in MGH from 1892 to 1901 mostly, and from 1896 to 1901 occasionally. In this article, WJN refers to cancers of different internal organs and external parts. He attributes the mouth cancers to constant chewing of 'betel' (the betel quid), which he explains as giving rise to leukoplakia, forerunner of buccal carcinoma. He supplies many, easily comparable, paired tables. These tables are made of raw numbers about the diverse human populations living in Madras, such as the Indians (Hindus and Muslims), Europeans, and Eurasians (Anglo-Indians) extracted from the 10-year records of MGH. None of the tables has been analyzed statistically. Nevertheless, he supplies the total numbers of admissions into MGH, which serve usefully as denominators in this study; occasionally, he presents data as percentages. Despite the lack of parametric statistical analysis, WJN's article, written in 1902, impresses as a useful contribution, because it provides a cross-sectional view of cancer incidence in Madras, particularly in defiance of Saldanha's supposition that cancers do not manifest in dark-skinned people, such as Indians. [ABSTRACT FROM AUTHOR]

This paper discusses about the results of a short term monitoring study of sea coast contaminated with oil spill. Collision of two oil-cargo ships, namely BW Maple and MT Dawn Kanchipuram near the Ennore Port, Chennai had released an approximately 200 tonnes of heavy fuel oil. We analyzed four different oil spill samples collected from Chennai beaches for different time intervals (1st, 3rd, 6th, 10th day of oil spill) from 1 km away from the source of the off shore spill location. The physicochemical characteristics of spilled oil sludge, concentration of total petroleum hydrocarbons (TPH) and polycyclic aromatic hydrocarbons (PAHs) present in the spilled oil were investigated. The variations in pH, moisture content and volatile hydrocarbon content, viscosity and density of the spilled oil were in the range of 7 ± 0.2, 255–575 g/kg, 118 to 850 PaS and 1.04 to 0.98 g/cm3 respectively. Elemental analysis showed that C, H, N and S were in the range of 9.9–13.3 (%), 0.63 to 1.76 (%), 1.88 to 1.02 (%) and 0.29 to 0.27 (%) respectively. Analysis of oil spill samples using gas chromatography and mass spectrometry (GCMS) shows the presence of recalcitrant and hazardous PAHs such as fluoranthene and pyrene. Ocean sediments were collected at different locations along the coast line from Ennore to Cooum after 60 days of oil spill and analyzed for TPH in GCMS. The results showed that the residual concentrations of TPH varied from 0.19 to 3.43 mg/g. The presence of heavy PAHs, which are highly toxic and recalcitrant, could pose a severe threat to the marine environment and ecology.The long term effects of these compounds were unexplored on marine environment. This environmental monitoring study gives an overview about the post oil spill activities and steps taken to remediate the sludge along the coastline. Image 1 • This study highlights the immediate fate and characteristics of the Chennai oil spill over a short period. • Total petroleum hydrocarbon concentration increased from third day to sixth day and then stabilized. • Viscosity of the spilled oil increased eight times from day1 to day 10 after the oil spill. • Priority pollutants such as Fluoranthene and pyrene were identified in beach sediments using GC-MS. [ABSTRACT FROM AUTHOR]

Cancer is known to be one of the leading causes of mortality in the world. There were about 14.1 million incidences and 8.2 million deaths due to cancer globally. In terms of mouth cancer Age Standardized Rate is 4.0 per 100000 populations worldwide and 7.2 per 100000 populations in India. In Chennai, mouth cancer burden has significantly increased over the past decade irrespective of geographical region. In this paper, the mouth cancer incidence is used to analyze the spatial distribution for high risk and low risk areas of different zones in Chennai by gender for the period of 2004-2013. The aim of this study is to fit a Poisson Gamma model and to explore the Empirical Bayesian and frequentist approach for disease mapping of mouth cancer incidence for Chennai zones by sex. The results of the estimates reveal that the empirical Bayesian estimate is more stable than the conventional frequentist estimates. [ABSTRACT FROM AUTHOR]

This paper explores beauty ideals prevalent among young women in Chennai, India. It is part of a larger project that contributes to the emerging scholarship around women's experiences in the rapidly changing landscape of urban India, with a focus on young female rural to urban migrants living in the city of Chennai (their destination). Drawing on findings from an ethnographic study of women living in hostel accommodation conducted in 2015 and 2016, I consider the talk, observations, and beauty practices of the research participants. In particular, I explore the ways in which 'lighter' skin colour is privileged as more desirable and how participants both reproduce and contest this construction. The discussion here delves into the obsessive aspiration for lighter skin, its gendered dimensions, and its reflection in online spaces. [ABSTRACT FROM AUTHOR]