The effects of using reprocessable material on the durability and mechanical properties of landfill leachate collection HDPE pipes

This paper investigated the durability and mechanical properties of landfill leachate collection HDPE pipes which had been made of different weight percent amounts of virgin and reprocessable HDPE compounds (VC and RC). Durability is reported base on the chemical properties, obtained through oxidative induction time (OIT) and melt flow index (MFI) measurements, at the temperature of 50 °C and over a period of 12 months immersion in a synthetic leachate. Mechanical properties are also described according to tensile and pressure tests which had been conducted on the pipes samples. All of the factors were examined had been affected by the addition of RC, but for the special combination the antioxidant depletion was significantly affected by the experimental aging condition and no important changes had been observed in the other pipe properties. The results from OIT tests indicate that the rate of antioxidant depletion is reduced by an increase in the weight percent amounts of RC, during the experimental aging condition. This reduction is probably attributed to the extraction of antioxidants from RC in their recovery process. Finally, although these results are related to the particular HDPE compound, antioxidant formulation and condition examined, but it can be said that the use of clean own reprocessable material for the production of landfill leachate pipes shall be permitted without limitations.     

Leaching behavior of some demolition wastes

Demolition wastes may be used in different civil engineering applications as road constructions, concrete, and embankments or landfill. Regardless its application, leaching tests of the waste should be carried out to assess concentrations of pollutants. Concrete, brick and mixture of concrete, bricks, tiles and ceramics wastes were subject to percolation test—CEN/TS 14405, and batch test—SR EN 12457. The leachates were analyzed with respect to concentration of inorganic elements—arsenic, barium, cadmium, chromium, copper, mercury, molybdenum, nickel, lead, selenium, zinc, fluoride, chloride and sulfate, and organic compounds (phenol index). The concentrations of elements in leachates were compared with the limit values of European regulation for the acceptance of inert wastes at landfills. Generally, the releases of inorganic species in leachates were below limits values. Some waste leachates obtained by percolation and batch test had high values for phenol index.     

Electro-dissolution of metal scrap anodes for nickel ion removal from metal finishing effluent

This contribution reports a novel and cost efficient strategy for nickel ion removal from metal finishing effluents by electro-dissolution of scrap aluminium and iron sacrificial anodes. Electro-coagulation of effluent was carried out at 30 mA/cm² current density for 60 min. The nickel ion concentration of electroplating effluent was analysed by Atomic Absorption Spectroscopy. SEM images of iron and aluminium scrap anodes were critically analysed. Parameters such as heavy metal removal, anode dissolution rate with respect to heavy metal removal, reaction kinetics and cost estimation have been elaborately studied. Electro-coagulation at 30 mA/cm² for 60 min using iron and aluminium scrap anodes resulted in 95.9 and 94.1 % nickel ion reduction, respectively, with 0.0094 and 0.0053 g/ppm dissolution rates. The energy consumption for scrap aluminium and iron anodes was 0.0547 kWh/L. Loose internal bonding and spongy surface morphology of used metal scrap render high porosity and active surface area, enhancing reaction rate. Low cost and ready availability of waste scrap makes the process of electro-coagulation economically viable. Thus, the findings from this contribution point decisively at the superiority of waste metal scrap-based anodes for economic and environmentally sustainable heavy metal ion removal from metal finishing effluent.     

Characterization and quantification of solid waste generation in the Niger Delta Region of Nigeria: a case study of Ogbe-Ijoh community in Delta State

Journal of Material Cycles and Waste Management - In this study, the composition, characteristics and generation rates of solid waste (SW) in Ogbe-Ijoh community were investigated to provide...     

Films Made by Blending Poly(ε-Caprolactone) with Starch and Flour from Sagu Rhizome Grown at the Venezuelan Amazons

Starch-based composite films have been proposed as food packaging. In this context, the study of non-conventional starch sources (sagu, Canna edulis Kerr) has worldwide special attention, because these materials can impart different properties as carbohydrate polymers. A thorough study of the matrices used (sagu starch and flour) was carried out. In the same way, thermoplastic starch (TPS)/PCL blend and thermoplastic flour (TFS)/PCL blend were obtained by melt mixing followed by compression moulding containing glycerol as plasticizer. In this study, chemical composition of the matrices and their properties were related with the properties of the developed films. Moisture content, water solubility, X-ray diffraction, thermogravimetric analysis and mechanical and microstructural properties were evaluated in the films. Taking into account the results, the sagu flour has great potential as starchy source for food packaging applications. However, concretely the flour had lower compatibility with the PCL compared to the starch/PCL blend.     

Preparation and Fundamental Characterization of Cellulose Nanocrystal from Oil Palm Fronds Biomass

The objective of this work was to isolate cellulose nanocrystal (CNC) from oil palm fronds (Elaeis guineensis) and its subsequent characterization. Isolation involves sodium hydroxide/anthraquinone pulping with mechanical refining followed by total chlorine free bleaching (includes oxygen delignification, hydrogen peroxide oxidation and peracetic acid treatment) before acid hydrolysis. Bleaching significantly decreased kappa number and increased α-cellulose percentage of fibers as confirmed by Technical Association of the Pulp and Paper Industry standards. Transmission electron microscopy (TEM), X-ray diffraction, Fourier transform infrared spectroscopy and thermogravimetric analysis revealed that acid hydrolysis along with bleaching improved crystallinity index and thermal stability of the extracted nanocrystals. It was observed that CNC maintained its cellulose 1 polymorph despite hydrolysis treatment. Mean diameter as observed by TEM and average fiber aspect ratio of obtained CNC was 7.44 ± 0.17 nm and 16.53 ± 3.52, respectively making it suitable as a reinforcing material for nanocomposite.     

Optimization of Pellet Production from Agro-Industrial By-Products: Effect of Plasticizers on Properties of Pellets and Composite Pots

The present study aimed to optimize the pellets formulation (deoiled rice bran, potato peel powder and plasticizers) for the development of the injection molded pots. The maximum hardness and bulk density (desirable responses) were obtained for pellets having 100 g of deoiled rice bran, 100 g potato peel powder and 14 % of cashew nut shell liquid (CNSL) as well as 14 % of glycerol (GL) (on raw material basis). The optimized pellets and the pots developed from them were characterized for their physico-chemical, functional, rheological and morphological properties. Expansion ratio, pellet durability index and hardness of the pellets with 14 % CNSL were found to be 1.097, 98.647 % and 485.551 N, respectively. For pellets with 14 % GL expansion ratio, pellet durability index and hardness were found to be 1.150, 97.747 % and 462.949 N, respectively. The biodegradation analysis of the pots developed from optimized pellets with 14 % CNSL and GL degraded in 11 and 9 weeks, respectively. Porosity, puncture force, density and hardness of ‘AP’ pots were 27.473 %, 495.731 N, 1.549 g/ml and 542.641 N, respectively. However, for ‘BP’ pots, the porosity, puncture force, density and hardness were 32.548 %, 440.149 N, 1.191 g/ml and 507.841 N, respectively. Pots prepared from 14 % CNSL (AP) were better in physical and mechanical properties as compared to pots developed from glycerol.     

Fourth-corner correlation is a score test statistic in a log-linear trait–environment model that is useful in permutation testing

Ecologists wish to understand the role of traits of species in determining where each species occurs in the environment. For this, they wish to detect associations between species traits and environmental variables from three data tables, species count data from sites with associated environmental data and species trait data from data bases. These three tables leave a missing part, the fourth-corner. The fourth-corner correlations between quantitative traits and environmental variables, heuristically proposed 20 years ago, fill this corner. Generalized linear (mixed) models have been proposed more recently as a model-based alternative. This paper shows that the squared fourth-corner correlation times the total count is precisely the score test statistic for testing the linear-by-linear interaction in a Poisson log-linear model that also contains species and sites as main effects. For multiple traits and environmental variables, the score test statistic is proportional to the total inertia of a doubly constrained correspondence analysis. When the count data are over-dispersed compared to the Poisson or when there are other deviations from the model such as unobserved traits or environmental variables that interact with the observed ones, the score test statistic does not have the usual chi-square distribution. For these types of deviations, row- and column-based permutation methods (and their sequential combination) are proposed to control the type I error without undue loss of power (unless no deviation is present), as illustrated in a small simulation study. The issues for valid statistical testing are illustrated using the well-known Dutch Dune Meadow data set.     

Investigating the link between $$\hbox {PM}_{2.5}$$ and atmospheric profile variables via penalized functional quantile regression

Fine particulate matter (\(\hbox {PM}_{2.5}\)) events negatively affect the health of numerous persons globally each year. Previous works have described the association between air pollution and surface-level meteorological conditions; however, there has been less focus on the task of linking air pollution events with meteorological conditions at higher levels of the atmosphere. Working within the functional data framework, we develop a penalized functional quantile regression (PFQR) procedure to model conditional quantiles of a continuous response based on a functional covariate, with the ability to penalize selected derivatives of the estimated coefficient function. Our aim is to investigate the relationship between atmospheric profile variables (APVs), assumed to be functional, and key quantiles of the conditional distribution of surface-level \(\hbox {PM}_{2.5}\). Via a simulation study, we find that the performance of our PFQR procedure compares favorably to other related approaches. We conclude with an analysis of \(\hbox {PM}_{2.5}\) data at two Southeastern US locations, Columbia, SC and Tampa, FL, where we estimate the coefficient functions for the APVs corresponding to both ‘typical’ and ‘high’ \(\hbox {PM}_{2.5}\) events. As we believe that the true coefficient functions are smooth and may be exactly zero over subsets of their domains, we impose penalties on the 0th and 2nd derivatives. Our analysis indicates that the corresponding atmospheric conditions differ between the two locations, and that the conditions differ seasonally within location.     

Estimation bias under model selection for distance sampling detection functions

Many simulation studies have examined the properties of distance sampling estimators of wildlife population size. When assumptions hold, if distances are generated from a detection model and fitted using the same model, they are known to perform well. However, in practice, the true model is unknown. Therefore, standard practice includes model selection, typically using model comparison tools like Akaike Information Criterion. Here we examine the performance of standard distance sampling estimators under model selection. We compare line and point transect estimators with distances simulated from two detection functions, hazard-rate and exponential power series (EPS), over a range of sample sizes. To mimic the real-world context where the true model may not be part of the candidate set, EPS models were not included as candidates, except for the half-normal parameterization. We found median bias depended on sample size (being asymptotically unbiased) and on the form of the true detection function: negative bias (up to 15% for line transects and 30% for point transects) when the shoulder of maximum detectability was narrow, and positive bias (up to 10% for line transects and 15% for point transects) when it was wide. Generating unbiased simulations requires careful choice of detection function or very large datasets. Practitioners should collect data that result in detection functions with a shoulder similar to a half-normal and use the monotonicity constraint. Narrow-shouldered detection functions can be avoided through good field procedures and those with wide shoulder are unlikely to occur, due to heterogeneity in detectability.