S Mohan

In this paper a novel approach for effective utilization of river assimilative capacity has been proposed. The method, referred to as waste load scheduling (WLS) is based on the principle that by restricting the effluent discharge into the river to only one polluter at any given day will allow us to utilize the available river assimilative capacity in a more efficient manner. This is achieved by scheduling the dischargeable waste load among the polluters, such that a waste load schedule once developed will specify two things: (1) which polluter has to discharge his/her effluent on a given day; and (2) what is the quantity of effluent that he/she can discharge. By scheduling the waste load discharge into the river thus, will considerably reduce the total effluent discharge into the river and hence a greater degree of water quality level can be achieved when compared to traditional waste load allocation methods. For the mathematical development of the model, the WLS problem was envisaged as analogous to a machine scheduling problem. In a simple single MS problem n number of jobs are required to be scheduled on a single machine to minimize/maximize a pre-defined performance measure. In a WLS problem, the river can be treated as a machine and the polluters discharging effluent directly into the river are analogous to the jobs to be scheduled. Treating the waste load scheduling problem in an analogous way to a MS problem enables us to apply the solution methods used for solving standard sequencing and scheduling problems to the proposed waste load scheduling problem. Although the present paper discusses the special case of waste load scheduling in which only one polluter can discharge effluent at any given day (suitable when the number of point load sources is small), it is however, possible to extend it to a more general case involving a large number of polluters as easily. In the accompanying paper, the application of the developed model to a case study has been explained in detail. The proposed model and its application proved that the model is highly efficient in solving the waste load allocation problem in a more comprehensive way.

Nitrate sorption potentials of three surface soils (soils-1-3) were evaluated under different solute concentrations, i.e. 1-100mgL-1. Batch and diffusion-cell adsorption experiments were conducted to delineate the diffusion property and maximum specific nitrate adsorption capacity (MSNAC) of the soils. Ho's pseudo-second order model well fitted the batch adsorption kinetics data (R2>0.99). Subsequently, the MSNAC was estimated using Langmuir and Freundlich isotherms; however, the best-fit was obtained with Langmuir isotherm. Interestingly, the batch adsorption experiments over-estimated the MSNAC of the soils compared with the diffusion-cell tests. On the other hand, a proportionate increase in the MSNAC was observed with the increase in soil organic matter content (OM) under the batch and diffusion-cell tests. Therefore, increasing the soil OM by the application of natural compost could stop nitrate leaching from agricultural fields and also increase the fertility of soil. © 2010 Elsevier Ltd.

Slum displacement and resettlement have embedded the prospective of livelihood problems of the inhabitants got displaced. Rehabilitation programme have ensured the livelihood and also enable them to competent to explore the opportunities and utilized the same for their betterment. Especially in Cities Training imparted by Slum Clearance Board have unleash positive outcome on livelihood. The present study made an attempt to understand the effectiveness of the training programme given to selected slum dwellers in Chennai. The study has undertaken survey with 201 samples who got training for tailoring and beautician courses imparted by TNSCB. The study found that training components have strong influence over skill development of the selected trainees undergone training imparted by TNSCB.

Plastic products in municipal solid waste result in the extraction of phthalates in leachate that also contains large amounts of organic matter, such as humic substances, ammonia, metals, chlorinated organics, phenolic compounds, and pesticide residues. Phthalate esters are endocrine disruptors, categorized as a priority pollutant by the US Environmental Protection Agency (USEPA). Biological processes are inefficient at degrading phthalates due to their stability and toxic characteristics. In this study, the peroxone (ozone/hydrogen peroxide) process (O3/H2O2), an O3-based advanced oxidation process (AOP), was demonstrated for the removal of diethyl phthalate (DEP) in synthetic leachate simulating solid-waste leachate from an open dump. The impact of the O3 dose during DEP degradation. the formation of ozonation intermediate by-products. and the effects of H2O2 dose, pH, and ultraviolet absorbance at 254 nm (UVC) were determined during ozonation. Removal of 99.9% of an initial 20 mg/L DEP was obtained via 120 min of ozonation (transferred O3 dose = 4971 mg/L) with 40 mg/L H2O2 in a semi-batch O3 system. Degradation mechanisms of DEP along with its intermediate products were also determined for the AOP treatment. Indirect OH radical exposure was determined by using a radical probe compound (pCBA) in the O3 treatment.

The persistent problem in reservoir operation is that the derived optimal releases fail to incorporate the decision maker or reservoir operators’ knowledge into reservoir operation models. The reservoir operators’ knowledge is specific to that particular reservoir and incorporating such an experienced knowledge will help to derive field reality based operation rules. The available historical reservoir operation databases are the representative samples of reservoir operators’ knowledge or experience. Thus, an attempt has been made that deals with the development of a methodological framework to recover or explore the historical reservoir operation database to derive the reservoir operators’ knowledge as operational rules. The developed methodological framework utilizes the strength and capability of recently developed predictive datamining algorithms to recover the knowledge from large historical database. Predictive data-mining algorithms such as a) classifier: Artificial Neural Network (ANN), and b) regression: Support Vector Regression (SVR) have been used for single reservoir operation data-mining (SROD) modelling framework to explore the temporal dependence between different variables of reservoir operation. The rules of operation or knowledge learned from the training database have been used as guiding rules for predicting the future reservoir operators’ decision on operating the reservoir for the given condition on the inflow, initial storage, and demand requirements. The developed SROD model was found to be efficient in exploring the hidden relationships that exist in a single reservoir system.

The wetlands are unique ecological sites which play a substantial role in the environment relating to its unique characteristics of biology, hydrology, and ecology. Wetlands provide a transition between the aquatic and terrestrial life forms, hence providing huge carrying capacity for all kinds of organisms. Soil plays an important role in any water body right from storage of the pollutant to the release of those pollutants. There are many characteristics of the soil which governs the behaviour of pollutants among them are micro-organisms. The wetland soil is highly saturated with low oxygen content hence it becomes a perfect habitat for diverse bacterial communities. The rhizobacteria are mainly responsible for the leaching of heavy metals from the soil. The low molecular organic acids which were released by the rhizobacteria in the soil helps in leaching the heavy metals, and this leads to one of the characters of bioavailability to the plants. In this study, the soil bacteria have been classified for the wetland in south Chennai, India, regarding its abilities to enhance the phytoremediation process. DNA extraction technique was adopted followed by 16srna in identifying the bacteria. The results yielded eight different bacterial species of Proteobacteria, Firmicutes, and Actinobacteria phylum. The bacterial diversity helps to understand the bioavailability phenomena to the plants and also in finding out the best effective bacteria to use in the bacterial assisted phytoremediation studies.

Indoor air pollution deteriorates the quality of air present in the household or enclosed areas. The polluted air present contains hazardous pollutants like carbon monoxide (CO), nitrogen oxides, volatile organic compounds and particulate matter which could be respired easily. When inhaled, these pollutants can cause negative health impacts leading to various pulmonary and cardiac problems and in extreme conditions to mortality. Enclosed parking garages are one of the major sources for indoor air pollution. The reason for buildup of combustion pollutants is a lack of proper ventilation system in enclosed parking areas. Therefore, proper planning is required during construction design rather than post-construction addition of this necessary facility. In this study, the micro-environment of India Habitat Centre (IHC) parking area and their pollutants concentrations was examined; the pollutants concentrations were compared with ambient air concentrations. The pollutants found were in higher concentrations than the ambient; e.g., CO level ranged from 12 ppm to a maximum level of 164 ppm, exceeding the American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) and WHO guidelines; similarly PM2.5 concentration averaged more than 100 μg·m-3 with a maximum concentration of 234 μg·m-3 and NO2 concentration were 25 to 56 μg·m-3, thereby leading to indoor air pollution and main sufferers are the guards and employees of IHC. Mitigation measures have been suggested for ameliorating the problem. © 2013 The Author(s).

Ozone pollution appears as a major air quality issue, e.g. for the protection of human health and vegetation. Formation of ground level ozone is a complex photochemical phenomenon and involves numerous intricate factors most of which are interrelated with each other. Machine learning techniques can be adopted to predict the ground level ozone. The main objective of the present study is to develop the state-of-the-art ensemble bagging approach to model the summer time ground level ozone in an industrial area comprising a hazardous waste management facility. In this study, the feasibility of using ensemble model with seven meteorological parameters as input variables to predict the surface level O3 concentration. Multilayer perceptron, RTree, REPTree, and Random forest were employed as the base learners. The error measures used for checking the performance of each model includes IoAd, R2, and PEP. The model results were validated against an independent test data set. Bagged random forest predicted the ground level ozone better with higher Nash-Sutcliffe coefficient 0.93. This study scaffolded the current research gap in big data analysis identified with air pollutant prediction. Implications: The main focus of this paper is to model the summer time ground level O3 concentration in an Industrial area comprising of hazardous waste management facility. Comparison study was made between the base classifiers and the ensemble classifiers. Most of the conventional models can well predict the average concentrations. In this case the peak concentrations are of importance as it has serious effect on human health and environment. The models developed should also be homoscedastic.

The main aim of the project is to develop a new Database Information and Management System (DIMS) which is available and accessible online. Success of any modeling is embedded in using the perfect and adequate length of data. This is vital for studying or developing a scientific model for natural processes such as climate change and geo-hazards. Thus a geo-referenced meteorological, coastal and hydrological database for decision-making and policy formulation according to climate change impact has been developed. The objective of this study is to provide the DIMS that will allow sharing of climate change parameters that has impacted on the coast of selected countries. The methodology has online hosting of database combined with rapid data retrieval for both analytical and modeling functions. The outcome of the Web-GIS based DIMS would serve as a decision-support tool and aids for development of an integrated and sustainable management strategies for climate change and geo-hazards. The project has currently a database relevant to selected stations along the coasts of Malaysia, Singapore and India available on the project webpage www.globalclimate-engine.org. The project could be extended to cover the entire database pertaining to the in-land areas of these regions.

Triclosan (TCS) is a potential endocrine-disrupting compound (EDC), which produces an adverse impact on aquatic life and human beings. Wastewater discharge is considered as the primary source of triclosan in water bodies. The study is aimed to investigate the occurrence and environmental risk of triclosan released by municipal wastewater treatment plants (WWTP). An analytical protocol was developed and validated to determine the presence of TCS in the samples through offline solid-phase extraction (SPE) and liquid chromatography - electron spray ionization (ESI)—quadrupole mass spectrum (LC/ESI/MS). The limit of detection and quantification of protocol was estimated as 2.8 ng/L and 6.25 ng/L, respectively. The season-wise influent and effluent samples from two WWTP in Chennai, India, were monitored. The TCS concentrations in samples were found in the range of 443 to 1757 ng/L. The Risk Quotient (RQ) method was performed to evaluate the environmental (ecotoxicological and human health) risk associated with the exposure of TCS-containing wastewater. The results of the study revealed that primary producer (algae) was highly vulnerable to exposure of TCS in the aquatic environment. The estimated daily intake of TCS was much lower than the reference dosage, and this indicates that TCS did not produce any considerable risk to human health. Also, it suggested that additional treatment was required for complete removal of triclosan residues.