Assessing of Environmental Quality in Six Areas in India,Sri Lanka and Bangladesh

Environmental quality for environmental health has been examined visually by describing general conditions and drinking water supply, sanitation and solid waste treatment conditions and practices in six South-East Asian villages with a dense population. The environmental review was supplemented by discussions with local people and political and administrative decision makers. Some drinking water analyses were done in order to show the water quality to the local people. The quality of the general conditions and the conditions and practices of drinking water supply, excreta disposal and solid waste treatment practices have been graded with the grading system presented below. The grading could be done in 1 or 2 days in each area by two persons, of which one was an environmental scientist and the other a social scientist, who also knew the local culture and worked as a translator.     

Base-neutral extractable organic pollutants in biota and sediments from Lake Pontchartrain

A study was conducted to screen the three passes that link Lake Pontchartrain to the Gulf of Mexico via Lake Borne for the presence of EPA base-neutral (BN) priority pollutants and any other pollutants detected in significant concentration. Biota and sediment samples were collected and analytical procedures were developed for the trace analysis of BN organics in these matrices. Compounds identified include alkanes (normal, branched, cyclic), alkenes, aromatics, alkylated aromatics, polynuclear aromatic hydrocarbons and their alkylated derivatives, phthalates, ketones, furans, thiophenes, phenols, amines, nitriles, thiazoles, amides, aldehydes, alcohols, free fatty acids, fatty acid methyl and ethyl esters, phosphates, sterols. Concentrations were in the parts-per-billion range.     

The impact of coal utilization in Texas under the National energy plan

The paper describes the results of a study of the impact of the National Energy on the trend towards increased utilization of coal and lignite in Texas with forecasts of increased coal and lignite utilization for the electric utility and industrial sectors. Environmental impacts of this increased coal and lignite use are projected in terms of increased air pollutant emissions and air quality impacts. Economic costs of compliance with alternative source emission regulations are also projected for the electric utility industry.Lignite consumption in Texas under the National Energy Plan is projected to increase from the present 13 million metric tons in 1976 to 57 million metric tons annually by 1985. Sub-bituminous coal consumption in Texas is projected to increase from 1 million metric per year in 1976 to 49 million metric tons per year in 1985. Bituminous coal consumption in Texas is expected to increase from less than one million metric tons per year in 1976 to about 3 million metric tons per year in 1985.Major increases in sulfur oxides emissions from coal and lignite combustion in Texas can be expected by 1985 of up to 1.5 × 10⁹ kg per year without controls and 0.2 × 10⁹ kg per year with controls. Increases in acid precipitation formation will result in north-east Texas from extensive lignite usage for electric power generation as a detriment to agriculture. The photochemical air pollution problem in the Houston area will probably worsen primarily because of increased nitrogen oxides and sulfur oxides emissions because of industrial coal combustion. Capital costs of air pollution controls in Texas for coal-fired utility boilers are estimated as up to U.S. $3.9 billion by 1985, with total operating costs of up to U.S. $1.2 billion per year.     

Prediction of effluent concentration in a wastewater treatment plant using machine learning models

Of growing amount of food waste, the integrated food waste and waste water treatment was regarded as one of the efficient modeling method. However, the load of food waste to the conventional waste treatment process might lead to the high concentration of total nitrogen(T-N) impact on the effluent water quality. The objective of this study is to establish two machine learning models—artificial neural networks(ANNs) and support vector machines(SVMs), in order to predict 1-day interval T-N concentration of effluent from a wastewater treatment plant in Ulsan, Korea. Daily water quality data and meteorological data were used and the performance of both models was evaluated in terms of the coefficient of determination(R~2), Nash–Sutcliff efficiency(NSE), relative efficiency criteria(d rel). Additionally, Latin-Hypercube one-factor-at-a-time(LH-OAT) and a pattern search algorithm were applied to sensitivity analysis and model parameter optimization, respectively. Results showed that both models could be effectively applied to the 1-day interval prediction of T-N concentration of effluent. SVM model showed a higher prediction accuracy in the training stage and similar result in the validation stage.However, the sensitivity analysis demonstrated that the ANN model was a superior model for 1-day interval T-N concentration prediction in terms of the cause-and-effect relationship between T-N concentration and modeling input values to integrated food waste and waste water treatment. This study suggested the efficient and robust nonlinear time-series modeling method for an early prediction of the water quality of integrated food waste and waste water treatment process.