C. S. P. Ojha

NUTRIENT REQUIREMENT FOR UASB PROCESS : A REVIEW

Abstract In literature, numerous studies regarding nutrients dosing are available in UASB reactors. Various nutrients and tracemetals used in UASB studies include nitrogen, phosphorous, yeast extract, magnesium, potassium, calcium, sulphur, iron, aluminium, zinc, nickel, cobalt, molybdenum, manganese, copper, boron, selenium, citrate, resazurine, EDTA, and hydrochloric acid. Often, there is a wide variation in the composition and quantification of nutrients and tracemetals, used to supplement the influent feed in UASB reactors. In this paper, nutrients and tracemetals formulations used in different studies on UASB reactors are compiled and critically analysed. In order to have uniformity, concentrations of nutrients and tracemetals have been converted into equivalent terms as g nutrients or tracemetals/ g of waste COD. The ranges for this ratio for all the constituents of nutrients and tracemetals are given. Besides, the ranges associated with minimum start-up periods are also obtained. It is hoped that the information presented in this study would facilitate the nutrients dosing in UASB reactors.

Air pollution health and environmental impacts

Air Pollution: Health and Environmental Concerns, Bhola R. Gurjar, Luisa T. Molina, and C.S.P. Ojha Air Pollution Monitoring and Modeling Air Pollution Monitoring and Source Characterization, Anita Lakhani, Rajasekhar Balasubramanian, and Bhola R. Gurjar Air Pollution Modeling: Theory and Application, C.S.P. Ojha, Marcelo Mena, Sarath Guttikunda, Bhola R. Gurjar, and Wenfang Lei Air Pollution and Health Effects Indoor Air Pollution and Health Effects, Radha Goyal and Mukesh Khare Effects of Indoor Air Pollution from Biomass Fuel Use on Womens Health in India, Twisha Lahiri and Manas Ranjan Ray Health Effects of Urban Air Pollution in India, Manas Ranjan Ray and Twisha Lahiri Air Pollutants Exposure and Health Effects during the MILAGRO-MCMA 2006 Campaign, Horacio Tovalin, Olf Herbarth, Martha P. Sierra-Vargas, Bo Strandberg, Salvador Blanco, Libia Vega, Constantinos Sioutas, Juan Jose Hicks, Ruben Marroquin, Gustavo Acosta, Marco Guarneros, Vicente Hernandez, Elizabeth Estrada-Muniz, Ivonne Ma Olivares, Dora A. Perez, Yessica D. Torres-Ramos, Frank Ulrich, Robyn Hudson, Ernesto Reyes, Tracy Rodriguez, Guillermo Elizondo, and Eliseo Cantellano Polycyclic Aromatic Hydrocarbons: Sources, Distribution, and Health Implications, Nirat Rajput and Anita Lakhani Cellular Mechanisms behind Particulate Matter Air Pollution-Related Health Effects, Ernesto Alfaro-Moreno, Claudia Garcia-Cuellar, Andrea De-Vizcaya-Ruiz, Leonora Rojas-Bracho, and Alvaro R. Osornio-Vargas Health Risk Assessment and Management Emission of Airborne Particulate Matter in Indoor Environments: Exposure and Risk Assessment, Rajasekhar Balasubramanian, See Siao Wei, and Sathrugnan Karthikeyan Estimation of Health Impacts due to PM10 in Major Indian Cities, P. Nema and S.K. Goyal Health Risk Assessment and Management for Air Toxics in Indian Environment, Manju Mohan and Bhola R. Gurjar Air Quality Management: Techniques and Policy Aspects The Economics of Air Pollution: Theories, Valuation Methods, and Policy Aspects, Dilek Demirbas Elements of Air Quality Management: Atmospheric Science Tools for Developing Effective Policy, Jeffrey R. Brook, Michael D. Moran, William Pennell, and Lorraine Craig Environmental Impacts of Air Pollution Assessing Ground-Level Ozone (O3) Impacts to Crops in Parts of Asia and Southern Africa: The Regional Air Pollution in Developing Countries (RAPIDC) Crops Project, L.D. Emberson, P. Buker, M. Engardt, A.M. van Tienhoven, M. Agrawal, M. Zunckel, K. Hicks, H. Pleijel, N.T. Kim Oanh, L.P. Amgain, T. Islam, S.R.A. Shamsi, G.A.D. Perera, G.H.J. Kruger, and P.R. Smit Impacts of Air Pollution on Ecosystem and Human Health: A Sustainability Perspective, Ioan Manuel Ciumasu and Naela Costica Regional and Global Environmental Issues of Air Pollution, Luisa T. Molina and Bhola R. Gurjar Index

Modeling of Suspended Sediment Concentration at Kasol in India Using ANN, Fuzzy Logic, and Decision Tree Algorithms

The prediction of the sediment loading generated within a watershed is an important input in the design and management of water resources projects. High variability of hydro-climatic factors with sediment generation makes the modelling of the sediment process cum- bersome and tedious. The methods for the estimation of sediment concentration based on the properties of flow and sediment have limitations attributed to the simplification of important parameters and boundary conditions. Under such circumstances, soft computing approaches have proven to be an efficient tool in modelling the sediment concentration. The focus of this paper is to present the development of models using Artificial Neural Network (ANN) with back propagation and Levenberg-Maquardt algorithms, radial basis function (RBF), Fuzzy Logic, and decision tree algorithms such as M5 and REPTree for predicting the suspended sediment concentration at Kasol, upstream of the Bhakra reservoir, located in the Sutlej basin in northern India. The input vector to the various models using different algorithms was derived con- sidering the statistical properties such as auto-correlation function, partial auto-correlation, and cross-correlation function of the time series. It was found that the M5 model performed well compared to other soft computing techniques such as ANN, fuzzy logic, radial basis function, and REPTree investigated in this study, and results of the M5 model indicate that all ranges of sediment concentration values were simulated fairly well. This study also suggests that M5 model trees, which are analogous to piecewise linear functions, have certain advantages over other soft computing techniques because they offer more insight into the generated model, are acceptable to decision makers, and always converge. Further, the M5 model tree offers explicit expressions for use by field engineers. DOI: 10.1061/(ASCE)HE.1943-5584.0000445.

Application of ANN, Fuzzy Logic and Decision Tree Algorithms for the Development of Reservoir Operating Rules

Optimal use of scarce water resources is the prime objective for water resources development projects in the developing country like India. Optimal releases have been generally expressed as a function of reservoir state variables and hydrologic inputs by a relationship which ultimately allows the policy/water managers to determine the water to be released as a function of available information. Optimal releases were obtained by using optimal control theory with inflow series and revised reservoir characteristics such as elevation area capacity table, zero elevation level as input in this study. Operating rules for reservoir were developed as a function of demand, water level and inflow. Artificial Neural Network (ANN) with back propagation algorithm, Fuzzy Logic and decision tree algorithms such as M5 and REPTree were used for deriving the operating rules using the optimal releases for an irrigation and power supply reservoir, located in northern India. It was found that fuzzy logic model performed well compared to other soft computing techniques such as ANN, M5P and REPTree investigated in this study.

Techno-economic evaluation of soil-aquifer treatment using primary effluent at Ahmedabad, India.

A pilot study was carried out in Sabarmati River bed at Ahmedabad, India for renovation of primary treated municipal wastewater through soil aquifer treatment (SAT) system. The infrastructure for the pilot SAT system comprised of two primary settling basins, two infiltration basins and two production wells located in the centre of infiltration basins for pumping out renovated wastewater. The performance data indicated that SAT has a very good potential for removal of organic pollutants, nutrients as well as bacteria and viruses. The SAT system was found to be more efficient and economical than the conventional wastewater treatment systems and hence recommended for adoption. A salient feature of the study is the introduction of biomat concept and its contribution in the overall treatment process.

Model for Nonlinear Root Water Uptake Parameter

AbstractAn empirical relationship is developed for the nonlinear root water uptake parameter in the O-R moisture uptake model from easily measurable plant physiological parameters, such as maximum daily transpiration, maximum root depth, and time to attain the maximum transpiration. A nondimensional parameter, termed specific transpiration, that involves the plant physiological parameters is used in this empirical relationship. Data for determining this relationship are obtained by minimizing the deviations between the field observed moisture depletions of 28 crops reported in the literature, and the Richards equation–based numerically simulated soil moisture depletions combined with the moisture uptake model accounting for root water uptake. In addition to cross-validation, field experiments on three Indian crops (maize, Indian mustard, and wheat) are conducted to further validate the proposed empirical relationship. Comparisons of model predictions with field observations of soil moisture profiles and m...

Downscaling of precipitation for lake catchment in arid region in India using linear multiple regression and neural networks.

In this paper, downscaling models are developed using a Linear Multiple Regression (LMR) and Artificial Neural Networks (ANNs) for obtaining projections of mean monthly precipitation to lake-basin scale in an arid region in India. The effectiveness of these techniques is demonstrated through application to downscale the predictand (precipita- tion) for the Pichola lake region in Rajasthan state in India, which is considered to be a climatically sensitive region. The predictor variables are extracted from (1) the National Centers for Environmental Prediction (NCEP) reanalysis dataset for the period 1948-2000, and (2) the simulations from the third-generation Canadian Coupled Global Climate Model (CGCM3) for emission scenarios A1B, A2, B1 and COMMIT for the period 2001-2100. The scatter plots and cross- correlations are used for verifying the reliability of the simulation of the predictor variables by the CGCM3. The perform- ance of the linear multiple regression and ANN models was evaluated based on several statistical performance indicators. The ANN based models is found to be superior to LMR based models and subsequently, the ANN based model is applied to obtain future climate projections of the predictand (i.e precipitation). The precipitation is projected to increase in future for A2 and A1B scenarios, whereas it is least for B1 and COMMIT scenarios using predictors. In the COMMIT scenario, where the emissions are held the same as in the year 2000.

Evaluation of machine learning tools as a statistical downscaling tool : temperatures projections for multi-stations for Thames River Basin, Canada

Many impact studies require climate change information at a finer resolution than that provided by global climate models (GCMs). This paper investigates the performances of existing state-of-the-art rule induction and tree algorithms, namely single conjunctive rule learner, decision table, M5 model tree, and REPTree, and explores the impact of climate change on maximum and minimum temperatures (i.e., predictands) of 14 meteorological stations in the Upper Thames River Basin, Ontario, Canada. The data used for evaluation were large-scale predictor variables, extracted from National Centers for Environmental Prediction/National Center for Atmospheric Research reanalysis dataset and the simulations from third generation Canadian coupled global climate model. Data for four grid points covering the study region were used for developing the downscaling model. M5 model tree algorithm was found to yield better performance among all other learning techniques explored in the present study. Hence, this technique was applied to project predictands generated from GCM using three scenarios (A1B, A2, and B1) for the periods (2046–2065 and 2081–2100). A simple multiplicative shift was used for correcting predictand values. The potential of the downscaling models in simulating predictands was evaluated, and downscaling results reveal that the proposed downscaling model can reproduce local daily predictands from large-scale weather variables. Trend of projected maximum and minimum temperatures was studied for historical as well as downscaled values using GCM and scenario uncertainty. There is likely an increasing trend for Tmax and Tmin for A1B, A2, and B1 scenarios while decreasing trend has been observed for B1 scenarios during 2081–2100.

Evaluation of a Nonlinear Root-Water Uptake Model

Soil-water movement due to root-water uptake, is a key process for plant growth and transport of water in the soil plant system. There are different root-water uptake models to determine the extraction rate of soil moisture by roots. The present study examines the performance of different root-water extraction models using available data as well as data generated under controlled conditions. Data pertaining to moisture uptake in respect to two crops: wheat (Triticum aestivum L.) and maize (Zea mays L.) along with soil-water characteristics have been monitored at the Indian Institute of Technology Roorkee, agricultural farm. For this purpose, a numerical model is also formulated by incorporating different moisture extraction terms as sink terms in the Richards equation. A nonlinear root-water uptake model selected as the base model was evaluated for its moisture uptake efficiency. The work establishes the merits of the base model over other extraction terms considered, particularly constant and linear extr...

A CRITIQUE ON OPERATIONAL STRATEGIES FOR START-UP OF UASB REACTORS : EFFECTS OF SLUDGE LOADING RATE AND SEED/BIOMASS CONCENTRATION

Abstract In order to develop a proper start-up strategy for UASB reactors, the effect of sludge loading rate (SLR) and seed/biomass concentration ( X ) on the granulation time ( G t ) has been studied by using the available experimental data on certain types of waste. The observed variation of SLR and X with G t , indicate a definite trend. Based on these trends, it is postulated that SLR and X are important variables to be controlled during granulation. It is found that the granulation time increases whenever the SLR or X value departs from an optimum value corresponding to lowest G t value. Keeping in view the experimental inaccuracy of the data points, the operating limits of SLR and X have been suggested on the basis of 1.25 × the lowest attainable G t for as many as six types of waste. It is expected that these proposed limits are more rational because of their dependence on a larger database. A comparative evaluation of proposed and existing guidelines suggests that the proposed limits, in general, are quite close to some of the available limits in the literature for few waste types only. On the basis of these derived limits, a systematic start-up strategy for UASB reactor has also been evolved, which may prove useful in overcoming the most serious start-up problems in such reactors. However, these conclusions need experimental validation in future studies.