Multi-response optimization of Fenton process for applicability assessment in landfill leachate treatment

Fenton process, as a pretreatment method, was found to be effective in the primary treatment of mature/medium landfill leachate. However, the main problem of the process is the large amount of produced sludge that requires an accurate feasibility evaluation for operational applications. In this study, the response surface methodology was applied for the modeling and optimization of Fenton process in three target responses, (1) overall COD removal, (2) sludge to iron ratio (SIR) and (3) organics removal to sludge ratio (ORSR), where the latter two were new self-defined responses for prediction of sludge generation and applicability assessment of the process, respectively. The effective variables included the initial pH, [H₂O₂]/[Fe²⁺] ratio and Fe²⁺ dosage. According to the statistical analysis, all the proposed models were adequate (with adjusted R² of 0.9116–0.9512) and had considerable predictive capability (with prediction R² up to 0.9092 and appropriate adequate precision). It was found that all the variables had significant effects on the responses, specifically by their observed role in dominant oxidation mechanism. The optimum operational conditions obtained by overlay plot, were found to be initial pH of 5.7, [H₂O₂]/[Fe²⁺] ratio of 17.72 and [Fe²⁺] of 195 mM, which led to 69% COD removal, 2.4 (l sludge/consumed mole Fe²⁺) of SIR and 16.5 (gCOD removed/l produced sludge) for ORSR in verification test, in accordance with models-predicted values. Finally, it was observed that [H₂O₂]/[Fe²⁺] ratio and Fe²⁺ dosage had significant influence on COD removal, while Fe²⁺ dosage and [H₂O₂]/[Fe²⁺] ratio had remarkable effects on SIR and ORSR responses, respectively.     

Spatial patterns of heavy metals in soil under different geological structures and land uses for assessing metal enrichments

One hundred and thirty composite soil samples were collected from Hamedan county, Iran to characterize the spatial distribution and trace the sources of heavy metals including As, Cd, Co, Cr, Cu, Ni, Pb, V, Zn, and Fe. The multivariate gap statistical analysis was used; for interrelation of spatial patterns of pollution, the disjunctive kriging and geoenrichment factor (EF_G) techniques were applied. Heavy metals and soil properties were grouped using agglomerative hierarchical clustering and gap statistic. Principal component analysis was used for identification of the source of metals in a set of data. Geostatistics was used for the geospatial data processing. Based on the comparison between the original data and background values of the ten metals, the disjunctive kriging and EF_G techniques were used to quantify their geospatial patterns and assess the contamination levels of the heavy metals. The spatial distribution map combined with the statistical analysis showed that the main source of Cr, Co, Ni, Zn, Pb, and V in group A land use (agriculture, rocky, and urban) was geogenic; the origin of As, Cd, and Cu was industrial and agricultural activities (anthropogenic sources). In group B land use (rangeland and orchards), the origin of metals (Cr, Co, Ni, Zn, and V) was mainly controlled by natural factors and As, Cd, Cu, and Pb had been added by organic factors. In group C land use (water), the origin of most heavy metals is natural without anthropogenic sources. The Cd and As pollution was relatively more serious in different land use. The EF_G technique used confirmed the anthropogenic influence of heavy metal pollution. All metals showed concentrations substantially higher than their background values, suggesting anthropogenic pollution.     

Bio-indicators in cadmium toxicity: Role of HSP27 and HSP70

Environmental Science and Pollution Research - Heat shock proteins (HSPs) are a family of proteins that are expressed by cells in reply to stressors. The changes in concentration of HSPs could be...     

Modelling volatility of growth rate in atmospheric carbon dioxide concentrations in a Bayesian approach

Atmospheric gases, such as carbon dioxide, ozone, methane, nitrous oxide, and etc., create a natural greenhouse effect and cause climate change. Therefore, modelling behavior of these gases could help policy makers to control greenhouse effects. In a Bayesian framework, we analyse and model conditional variance of growth rate in atmospheric carbon dioxide concentrations (ACDC) using monthly data from a subset of the well known Mauna Loa atmosphere carbon dioxide record. The conditional variance of ACDC monthly growth rate is modelled using the autoregressive conditional heteroscedasticity (ARCH), generalized ARCH model (GARCH) and a few variants of stochastic volatility (SV) models. Smooth transition ARCH and GARCH models are shown to be able to capture the dynamics in the conditional variance in ACDC level growth rate and to improve the forecast performance of ACDC growth rate.     

Randomly selected order statistics in ranked set sampling: A less expensive comparable alternative to simple random sampling

Rank-based sampling designs are powerful alternatives to simple random sampling (SRS) and often provide large improvements in the precision of estimators. In many environmental, ecological, agricultural, industrial and/or medical applications the interest lies in sampling designs that are cheaper than SRS and provide comparable estimates. In this paper, we propose a new variation of ranked set sampling (RSS) for estimating the population mean based on the random selection technique to measure a smaller number of observations than RSS design. We study the properties of the population mean estimator using the proposed design and provide conditions under which the mean estimator performs better than SRS and some existing rank-based sampling designs. Theoretical results are augmented with some numerical studies and a real-life example, where we also study the performance of our proposed design under perfect and imperfect ranking situations.     

Simultaneous spectrophotometric determination of crystal violet and malachite green in water samples using partial least squares regression and central composite design after preconcentration by dispersive solid-phase extraction

In this paper, a simple, fast, and inexpensive method is introduced for the simultaneous spectrophotometric determination of crystal violet (CV) and malachite green (MG) contents in aquatic samples using partial least squares regression (PLS) as a multivariate calibration technique after preconcentration by graphene oxide (GO). The method was based on the sorption and desorption of analytes onto GO and direct determination by ultraviolet–visible spectrophotometric techniques. GO was synthesized according to Hummers method. To characterize the shape and structure of GO, FT-IR, SEM, and XRD were used. The effective factors on the extraction efficiency such as pH, extraction time, and the amount of adsorbent were optimized using central composite design. The optimum values of these factors were 6, 15 min, and 12 mg, respectively. The maximum capacity of GO for the adsorption of CV and MG was 63.17 and 77.02 mg g^?1, respectively. Preconcentration factors and extraction recoveries were obtained and were 19.6, 98% for CV and 20, 100% for MG, respectively. LOD and linear dynamic ranges for CV and MG were 0.009, 0.03–0.3, 0.015, and 0.05–0.5 (μg mL^?1), respectively. The intra-day and inter-day relative standard deviations were 1.99 and 0.58 for CV and 1.69 and 3.13 for MG at the concentration level of 50 ng mL^?1, respectively. Finally, the proposed DSPE/PLS method was successfully applied for the simultaneous determination of the trace amount of CV and MG in the real water samples.     

Examining Relationships Between Socioeconomic Factors and Landscape Metrics in the Southern Basin of the Caspian Sea

Socioeconomic forces are not only among the main drivers of landscape dynamics; they are also influenced by landscape patterns. Landscape structure and functions are closely related to natural and social factors. The objective of this study was to investigate the relationships among some human-related factors and landscape ecological metrics as landscape pattern indicators and to identify suitable metrics for modeling these relationships. To this goal, landscape ecological metrics were calculated for each of the 32 counties of Mazandaran and Guilan provinces located in the southern basin of the Caspian Sea using land use/cover maps in class level. Stream network metrics were calculated using a digital elevation model, road density metrics were calculated using map of main roads separately, and significant metrics were selected according to results of correlation tests and factor analysis. The correlations between these metrics and socioeconomic factors were tested, and their relationships were modeled with multiple linear regressions. Significant relationships were found among socioeconomic factors and landscape ecological metrics, and land use/cover data are applicable for modeling socioeconomic factors, especially demographic and employment structure factors. Among the landscape metrics applied in this study, road density, mean patch size, mean nearest neighbor distance, and percentage of a land use/cover class in landscape were important metrics for predicting socioeconomic factors. Our findings indicated that road density metric and percentages of urban class are useful for predicting urban socioeconomic factors and percentage of agriculture and forest classes in the landscape are suitable metrics for predicting rural socioeconomic factors.     

Synthesis and optimization of spherical nZVI (20–60 nm) immobilized in bio-apatite-based material for efficient removal of phosphate: Box-Behnken design in a fixed-bed column

In the present study, bio-apatite/nZVI composite was synthesized through Fe(III) reduction with sodium borohydride and was fully characterized by FTIR, XRD, SEM–EDX, TEM, BET, BJH, and pH_PZC. Column experiments were carried out for the removal of phosphate as a function of four operational parameters including initial phosphate concentration (100–200 mg L^?1), initial solution pH (2–9), bed height (2–6 cm), and influent flow rate (2.5–7.5 mL min^?1) using a response surface methodology (RSM) coupled with Box-Behnken design (BBD). 2D contour and 3D surface plots were employed to analyze the interactive effects of the four operating parameters on the column performance (e.g., uptake capacity and saturation time). According to ANOVA analysis, the influent flow rate and bed height are the most important factor on phosphate uptake capacity and saturation time, respectively. A quadratic polynomial model was excellently fitted to experimental data with a high coefficient of determination (>?0.96). The RSM-BBD model predicted maximum phosphate adsorption capacity of 85.71 mg g^?1 with the desirability of 0.995 under the optimal conditions of 135.35 mg L^?1, 2, 2 cm, and 7.5 mL min^?1 for initial phosphate concentration, initial solution pH, bed height, and influent flow rate, respectively. The XRD analysis demonstrated that the reaction product between bio-apatite/nZVI composite and phosphate anions was Fe₃ (PO₄)₂. 8H₂O (vivianite). The suggested adsorbent can be effectively employed up to five fixed-bed adsorption–desorption cycles and was also implemented to adsorb phosphate from real samples.