Uncertainty and sensitivity analysis of revised leachate pollution index.

Composite indicators (CIs) are being utilized more frequently to assess and monitor environmental systems. The revised leachate pollution index (r-LPI) is one such composite indicator used to quantify the pollution potential of landfill leachate on a scale of 5-100. The development of CIs involves several steps, and each of these steps has various methodological choices, each of which could lead to different results. Thereby, the reliability of the quantified pollution potential of leachate may be questioned. This study investigated the techniques for developing the r-LPI, examining decisions related to parameter selection, normalization technique, weighting approach, sub-indicator weights, and their aggregation. As the index developer made the decisions, each of these stages was fraught with uncertainty. The uncertainty in the various stages of the development of r-LPI was quantified using the Monte Carlo-based uncertainty analysis and the sensitivity analysis approach. Uncertainty analysis is a helpful but seldom-used step of index development that identifies the model's most dependable sections. Sensitivity analysis was carried out to ascertain the degree of impact the input parameters have on the r-LPI values. The combined use of sensitivity and uncertainty analysis in this study for the formulation of r-LPI affirmed the transparency, credibility, and accuracy of the index.

A critical review of inverse fluidized bed reactors-start-up optimization strategies and wastewater treatment.

A critical evaluation of strategies used for reducing start-up time and biological wastewater treatment using an inverse fluidized bed reactor (IFBR) was done. The start-up of an IFBR is one of the most important, time-consuming, and limiting steps in wastewater treatment using biofilm reactors. Evaluation of different strategies used by various researchers is helpful in future research works with this reactor. Different types of treated wastewater, the effect of wastewater characteristics, carriers used, and reactor hydrodynamics on the reactor performance were reviewed in detail in the first part. The second part of this review covers the use of an IFBR in the biological treatment of different wastewaters through multiple biochemical pathways and how it helped improve performance compared to other reactors. This will enable the researchers to understand the novelty of an IFBR for wastewater treatment and allow them to use it as a potential reactor.

Selection of optimal aggregation function for the revised leachate pollution index (r-LPI).

A tool to quantify the pollution potential of leachate, termed the revised leachate pollution index (r-LPI), has been developed. It was developed using the fuzzy Delphi analytic hierarchy process (FDAHP). The formulation entails four major steps: parameter selection, weight calculation, normalization of parameters, and aggregation of the parameters. Eleven leachate parameters categorized into three criteria were selected using the fuzzy Delphi method (FDM). The relative weights of the parameters and the criteria were computed using the fuzzy analytic hierarchy process (FAHP), and rating curves were used for normalization of the parameters. The selection of an aggregation function is one of the most critical steps in the development of a composite indicator. In this study, the concept of r-LPI was briefly discussed and 14 different aggregation functions were examined to estimate the pollution potential of landfill leachate. Based on accountability and non-accountability of weights of the parameters, ambiguity, eclipsing, and constant functional behavior, 8 aggregation functions were eliminated. The remaining six aggregation functions were subjected to sensitivity analysis. Furthermore, information lost due to aggregation of parameters was quantified. Based on the findings, it was concluded that the weighted additive function effectively quantifies the pollution potential of landfill leachate and thus recommended for the r-LPI.

Effect of nickel on the comparative performance of inverse fluidized bed and continuously stirred tank reactors for biogenic sulphur-driven autotrophic denitrification.

The comparative performance of an inverse fluidized bed reactor (IFBR) having high density polyethylene beads as carrier materials for biofilm formation and a continuous stirred tank reactor (CSTR), both maintaining autotrophic denitrification using biogenic sulphur (ADBIOS) in the absence and presence of nickel (Ni2+), was studied. The reactors were compared in terms of NO3--N and NO2--N removal and SO42--S production throughout the study. A simulated wastewater with an inlet NO3--N concentration of 225 mg/L and a decreasing concentration of biogenic sulphur (bio-S) from 1.5 to 0.375 g/L was used. Both reactors were operated at a hydraulic retention time (HRT) of 48 h for 140 days and at an HRT of 42 h for the following 68 days. A more efficient ADBIOS was observed in the CSTR than IFBR throughout the study due to a better mixing of the feed wastewater in the bulk liquid and a higher availability of bio-S to the suspended cells. The NO3--N removal efficiency in the IFBR decreased by approximately 41% when the feed bio-S was reduced to 0.375 g/L, while it remained unaffected in the CSTR. Conversely, the presence of Ni2+ did not significantly affect NO3--N removal in both reactors even at a feed Ni2+ concentration of 120 mg/L. The highest NO3--N removal rates achieved were 86 and 108 mg NO3--N/(L·day) in the IFBR and CSTR, respectively, in the presence of 120 mg/L of feed Ni2+ at an HRT of 42 h. Batch studies conducted with acclimatized biomass showed that the continuous-flow operation mode in both reactors played a major role in helping the autotrophic denitrifiers to tolerate Ni2+ toxicity.

Exploring the Feasibility of Adsorptive Removal of ZnO Nanoparticles from Wastewater.

This study tested the feasibility of adsorptive removal of ZnO nanoparticles using activated carbon from wastewater. The effects of pH (5, 6, 7, 8, and 9) and nanoparticle/activated carbon concentration ratios (10, 1, 0.1, 0.01, and 0.001) on the removal were studied in batch kinetic studies in both deionized water and wastewater. The adsorption capacity of activated carbon for ZnO nanoparticles were found to be 9.3 ± 0.8 mg/g (93% removal) and 8.2 ± 3.7 mg/g (85% removal) at 640 and 780 min in deionized and wastewater respectively at optimum conditions (pH 8; 10g/L activated carbon concentration, 100mg/L ZnO nanoparticle concentration). The rate constant was lowest at ratio 0.01 and pH 8, and highest at ratio 10 and 1 and pH 5 and 6. Pseudo-second-order and the Weber-Morris model best described the adsorption process. Characterization analysis also confirmed the deposition of ZnO nanoparticles onto activated carbon. Overall activated carbon successfully removed ZnO nanoparticles using the adsorptive process.

Removal of mixture of ZnO and CuO nanoparticles (NPs) from water using activated carbon in batch kinetic studies.

The aim of this study was to understand the effects of pH (5, 6, 7, 8, 9) and activated carbon (AC) concentration (0.01, 0.1, 1, 10, 100 g/L) on the removal of a colloidal solution of nanoparticles (NPs) (ZnO + CuO) using AC in batch kinetic studies. Results revealed that adsorption capacities of AC for Zn and Cu (individually) were 0.9 ± 0.028 mg/g (91.3% removal) and 0.95 ± 0.036 mg/g (95.6% removal) in deionized water and 0.6 ± 0.038 mg/g (78% removal) and 0.75 ± 0.078 mg/g (83% removal) in wastewater at pH 8 (Zn) and pH 6 (Cu) respectively. In a colloidal solution mixture of zinc oxide nanoparticles (ZnONPs) and copper oxide nanoparticles (CuONPs), adsorption capacity of AC for Zn in deionized water was 0.71 ± 0.051 mg/g (74.7% removal) and in wastewater was 0.576 ± 0.019 mg/g (69% removal) and for Cu in deionized water was 0.82 ± 0.017 mg/g (81.2% removal) and in wastewater was 0.6 ± 0.032 mg/g (71.5% removal). Overall, this study provided a detailed analysis of the removal capacity of AC and indicated that AC can be used as an efficient adsorbent filter for removing engineered NPs like ZnONPs and CuONPs (single and mixture) from water. As there is a need for removing NPs from wastewater, removal of NPs using an AC-based adsorptive-filter might become a promising method.

Evaluation of coarse and fine particles in diverse Indian environments.

The estimates of airborne fine particle (PM2.5) concentrations are possible through rigorous empirical correlations based on the monitored PM10 data. However, such correlations change depending on the nature of sources in diverse ambient environments and, therefore, have to be environment specific. Studies presenting such correlations are limited but needed, especially for those areas, where PM2.5 is not routinely monitored. Moreover, there are a number of studies focusing on urban environments but very limited for coal mines and coastal areas. The aim of this study is to comprehensively analyze the concentrations of both PM10 and PM2.5 and develop empirical correlations between them. Data from 26 different sites spread over three distinct environments, which are a relatively clean coastal area, two coal mining areas, and a highly urbanized area in Delhi were used for the study. Distributions of PM in the 0.43-10-µm size range were measured using eight-stage cascade impactors. Regression analysis was used to estimate the percentage of PM2.5 in PM10 across distinct environments for source identification. Relatively low percentage of PM2.5 concentrations (21, 28, and 32%) in PM10 were found in clean coastal and two mining areas, respectively. Percentage of PM2.5 concentrations in PM10 in the highly urbanized area of Delhi was 51%, indicating a presence of a much higher percentage of fine particles due to vehicular combustion in Delhi. The findings of this work are important in estimating concentrations of much harmful fine particles from coarse particles across distinct environments. The results are also useful in source identification of particulates as differences in the percentage of PM2.5 concentrations in PM10 can be attributed to characteristics of sources in the diverse ambient environments.

Formulation of a landfill pollution potential index to compare pollution potential of uncontrolled landfills.

Generally speaking, landfilling is one of the prominent methods of waste disposal around the globe, but some under-developed and developing countries still continue to practice uncontrolled open dumping of waste. These uncontrolled landfills pose a relatively high threat to the various elements of the environment in comparison with the conventional engineered landfills that are used in many developed countries. However, some closed, un-engineered landfills do exist in developing countries. This paper presents a novel approach to compare the pollution potential of uncontrolled landfills using an index. The landfill pollution potential index (LPPI) has been developed using the Delphi technique and is an aggregation of six pollution indices that have already been developed for the quantification of different environmental elements. The LPPI is an increasing scale index, in which a higher index value indicates a higher pollution threat. The LPPI of a landfill in Delhi was calculated and the high LPPI value indicates that the respective landfill poses a significant threat to the environment. The LPPI can be used as an aid to diagnose a landfill's pollution potential relative to other landfills and therefore also to rank remediation investments.

Analysis of leachate pollution index and formulation of sub-leachate pollution indices.

An index known as leachate pollution index (LPI) for quantifying the leachate contamination potential of municipal landfills had been developed and reported by the authors. It is a quantitative tool by which the leachate pollution data of landfill sites can be reported uniformly. LPI is an increasing scale index and has been formulated based on the Delphi technique. It provides a convenient means of summarizing complex leachate pollution data and facilitates its communication to the general public, field professionals and policy makers. However, it is observed that the LPI, like any other environmental index, fails to effectively communicate the details about the strength of various pollutants/pollutant groups present. In an effort to make the LPI more informative and useful, it is proposed to divide the LPI into three sub-indices. The aggregation of these three sub-LPIs will result in the overall LPI. The formulation and the application of LPI and its three sub-indices are presented in this paper. It has been concluded that the splitting of LPI into three sub-indices provides a better insight on the strength of various pollutants and can be useful to the experts in deciding various management issues regarding leachate treatment. The leachate characteristics of a UK landfill have been used as a case study to demonstrate the calculation of three sub-LPIs and the overall LPI.