Modeling and optimization of trihalomethanes formation potential of surface water (a drinking water source) using Box–Behnken design

Purpose  

The present research aims to investigate the individual and interactive effects of chlorine dose/dissolved organic carbon ratio, pH, temperature, bromide concentration, and reaction time on trihalomethanes (THMs) formation in surface water (a drinking water source) during disinfection by chlorination in a prototype laboratory-scale simulation and to develop a model for the prediction and optimization of THMs levels in chlorinated water for their effective control.     

Characteristics and treatment of greywater—a review

Safe and sufficient quantity of water is necessary for a healthy growth of human beings. The gap between water demand and available water supply is increasing day by day. Proper sanitation, especially decentralized approach, can solve the problem of water supply and wastewater management and that can be done by reuse of greywater. Typically, from a household, greywater (GW) flow is around 65 % of the total wastewater flow. Further light greywater is around 50 % of the total GW. Hence, GW has a high potential for recycle and reuse. The aim of this article is to reveal the present state of art in GW treatment and to identify the further scope for research. Present article contains a review on per capita GW generation, GW characteristics, and its treatment. Around 22 treatment systems comprising different treatment processes are discussed in detail for removal efficiency of pollutants, effluent concentrations and their compliance with wastewater reuse guidelines and standards. Constructed wetland and filtration were found efficient in the removal of most of the reuse parameters compared to other technologies. Anaerobic followed by aerobic system with post-disinfection unit may be a sustainable option for GW treatment for reuse. There is a need to develop the technologies for GW treatment at household level to increase the reuse practises at grass root level. Further, there is need of development of flow diagram with different technologies by targeting the type of reuse (flushing, gardening, agriculture, etc.).     

Vermicomposting of source-separated human faeces by Eisenia fetida: effect of stocking density on feed consumption rate, growth characteristics and vermicompost production

The main objective of the present study was to determine the optimum stocking density for feed consumption rate, biomass growth and reproduction of earthworm Eisenia fetida as well as determining and characterising vermicompost quantity and product, respectively, during vermicomposting of source-separated human faeces. For this, a number of experiments spanning up to 3 months were conducted using soil and vermicompost as support materials. Stocking density in the range of 0.25-5.00 kg/m² was employed in different tests. The results showed that 0.40-0.45 kg-feed/kg-worm/day was the maximum feed consumption rate by E. fetida in human faeces. The optimum stocking densities were 3.00 kg/m² for bioconversion of human faeces to vermicompost, and 0.50 kg/m² for earthworm biomass growth and reproduction.     

Investigating hydrochemistry of groundwater in Indo-Gangetic alluvial plain using multivariate chemometric approaches

Groundwater hydrochemistry of an urban industrial region in Indo-Gangetic plains of north India was investigated. Groundwater samples were collected both from the industrial and non-industrial areas of Kanpur. The hydrochemical data were analyzed using various water quality indices and nonparametric statistical methods. Principal components analysis (PCA) was performed to identify the factors responsible for groundwater contamination. Ensemble learning-based decision treeboost (DTB) models were constructed to develop discriminating and regression functions to differentiate the groundwater hydrochemistry of the three different areas, to identify the responsible factors, and to predict the groundwater quality using selected measured variables. The results indicated non-normal distribution and wide variability of water quality variables in all the study areas, suggesting for nonhomogenous distribution of sources in the region. PCA results showed contaminants of industrial origin dominating in the region. DBT classification model identified pH, redox potential, total-Cr, and λ ₂₅₄ as the discriminating variables in water quality of the three areas with the average accuracy of 99.51 % in complete data. The regression model predicted the groundwater chemical oxygen demand values exhibiting high correlation with measured values (0.962 in training; 0.918 in test) and the respective low root mean-squared error of 2.24 and 2.01 in training and test arrays. The statistical and chemometric approaches used here suggest that groundwater hydrochemistry differs in the three areas and is dominated by different variables. The proposed methods can be used as effective tools in groundwater management.     

Dicarboxylic acids and related compounds in fine particulate matter aerosols in Huangshi,central China

PM_2.5 (particulate matter with an aerodynamic diameter <2.5 μm) samples were collected in Huangshi, central China, from March 2012 to February 2013 and were analyzed for dicarboxylic acids (diacids) and related compounds (DARCs). Oxalic acid (C₂; 416 ng m^?3) was the most abundant species, followed by phthalic (Ph; 122 ng m^?3), terephthalic (tPh; 116 ng m^?3), succinic (C₄; 70.4 ng m^?3), azelaic (C₉; 67.9 ng m^?3), and adipic (C₆; 57.8 ng m^?3) acids. Relatively high abundances of Ph and tPh differed from the distribution in urban and marine aerosols, indicating contributions from nearby anthropogenic sources. Glyoxylic acid (ωC₂; 41.4 ng m^?3) was the dominant oxoacid, followed by 9-oxononanoic (ωC₉; 40.8 ng m^?3) and pyruvic (Pyr; 24.1 ng m^?3) acids. Glyoxal (Gly; 35.5 ng m^?3) was the dominant α-dicarbonyl. Highest average concentrations were found for C₂, ωC₂, and C₉ in autumn, for C₄, for Pyr and C₆ in spring, for Ph, ωC₉, and Gly in summer, whereas the lowest values were observed in winter. Seasonal variations and correlation coefficients of DARCs demonstrate that both primary emissions and secondary production are important sources. Principal component analysis of selected DARCs species suggests that a mixing of air masses from anthropogenic and biogenic sources contribute to the Huangshi aerosols.

Implications: Both primary emissions and secondary production are important sources of diacids and related compounds in PM_2.5 from Huangshi, central China. Principal component analysis of selected diacids in Huangshi aerosols suggests that mixing of air masses from anthropogenic and biogenic sources contribute to ambient aerosols in central China.     

Vertical characterization of soil contamination using multi-way modeling – A case study

This study describes application of chemometric multi-way modeling approach to analyze the dataset pertaining to soils of industrial area with a view to assess the soil/sub-soil contamination, accumulation pathways and mobility of contaminants in the soil profiles. The three-way (sampling depths, chemical variables, sampling sites) dataset on heavy metals in soil samples collected from three different sites in an industrial area, up to a depth of 60 m each was analyzed using three-way Tucker3 model validated for stability and goodness of fit. A two component Tucker3 model, explaining 66.6% of data variance, allowed interpretation of the data information in all the three modes. The interpretation of core elements revealing interactions among the components of different modes (depth, variables, sites) allowed inferring more realistic information about the contamination pattern of soils both along the horizontal and vertical coordinates, contamination pathways, and mobility of contaminants through soil profiles, as compared to the traditional data analysis techniques. It concluded that soils at site-1 and site-2 are relatively more contaminated with heavy metals of both the natural as well as anthropogenic origins, as compared to the soil of site-3. Moreover, the accumulation pathways of metals for upper shallow layers and deeper layers of soils in the area were differentiated. The information generated would be helpful in developing strategies for remediation of the contaminated soils for reducing the subsequent risk of ground-water contamination in the study region.     

Artificial neural network modeling of the river water quality—A case study

The paper describes the training, validation and application of artificial neural network (ANN) models for computing the dissolved oxygen (DO) and biochemical oxygen demand (BOD) levels in the Gomti river (India). Two ANN models were identified, validated and tested for the computation of DO and BOD concentrations in the Gomti river water. Both the models employed eleven input water quality variables measured in river water over a period of 10 years each month at eight different sites. The performance of the ANN models was assessed through the coefficient of determination (R²) (square of the correlation coefficient), root mean square error (RMSE) and bias computed from the measured and model computed values of the dependent variables. Goodness of the model fit to the data was also evaluated through the relationship between the residuals and model computed values of DO and BOD. The model computed values of DO and BOD by both the ANN models were in close agreement with their respective measured values in the river water. Relative importance and contribution of the input variables to the model output was evaluated through the partitioning approach. The identified ANN models can be used as tools for the computation of water quality parameters.     

Optimization of nitrate reduction by EDTA catalyzed zero-valent bimetallic nanoparticles in aqueous medium

The present study aims to investigate the EDTA catalyzed reduction of nitrate (NO ₃ ^? ) by zero-valent bimetallic (Fe?CAg) nanoparticles (ZVBMNPs) in aqueous medium and to enumerate the effect of temperature, solution pH, ZVBMNPs dose and EDTA concentration on NO ₃ ^? reduction. Batch experimental data were generated using a four-factor Box?CBehnken design. Optimization modeling was performed using the response surface method for maximizing the reduction of NO ₃ ^? by ZVBMNPs. Significance of the independent variables and their interactions were tested by the analysis of variance and t test statistics. The model predicted maximum reduction capacity (340.15?mg?g^?1 NO ₃ ^? ) under the optimum conditions of temperature, 60?°C; pH?4; dose, 1.0?g?l^?1; and EDTA concentration, 2.0?mmol?l^?1 was very close to the experimental value (338.62?mg?g^?1) and about 16?% higher than the experimentally determined capacity (291.32?mg?g^?1). Study demonstrated that ZVBMNPs had higher reduction efficiency than Fe⁰ nanoparticles for NO ₃ ^? . EDTA significantly enhanced the NO ₃ ^? reduction by ZVBMNPs. The EDTA catalyzed reduction of NO ₃ ^? by ZVBMNPs can be employed for the effective decontamination of water.