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.     

Observations and analytical modeling of freshwater and rainwater lenses in coastal dune systems

Observations are reported on (i) groundwater recharge rates under various types of vegetation as measured with megalysimeters in the dunes, (ii) freshwater lenses along the Dutch North Sea coast in the early 1900s, and (iii) rainwater lenses that develop on top of laterally migrating, artificially recharged riverwater. Subsequently analytical methods are presented to estimate annual natural groundwater recharge as function of rainfall and vegetation, and to calculate the size, shape and transition zone of freshwater lenses on saline groundwater and rainwater lenses on infiltrated riverwater. An empirical correction factor, based on the hydraulic resistance of an aquitard within the freshwater lens, is proposed to account for the frequently observed reduction of the Ghyben-Herzberg ratio of 40. This factor raises the groundwater table, reduces the depth of the fresh/salt interface and increases the lens formation time. The suite of methods offers a tool box for knowledge based water management of dune systems, by rapidly predicting: (i) more or less autonomous changes due to sealevel rise, climate change and vegetation development; and (ii) the potential (side) effects of interventions. Knowing what happened or will happen to the fresh water lens or a rainwater lens is important, because changes impact on important natural habitat parameters such as salinity, depth to groundwater table, decalcification rate (and thus on pH, Ca/Al, PO₄, NH₄) and nutrient availability, and on drinking water supply. The analytical models are applied to predict effects of sealevel rise, coastal progradation, vegetation changes, and increased temperature of coastal air and river water to be infiltrated.     

2D brine sewage after impinging on a shallow sea free surface

We consider the problem of the vertically upwards disposal of heavy brine sewage from a two-dimensional diffuser in a lighter, homogeneous, motionless and shallow ambient sea. The rejected high salinity water of seawater desalination plants for urban and agricultural uses is such a case of a two dimensional fountain. The disposal of brine sewage produces a negative buoyant jet due to its initial momentum, which impinges on the free surface, spreads laterally on it and then sinks downwards, because of the negative buoyancy. Laboratory experiments and dimensional considerations are used in this paper in order to investigate the spreading behavior (width) of the vertical fountain which impinges on the free surface of the shallow ambient fluid. The experimental results have been used to derive an equation relating the width at the free surface with the initial parameters of the flow. In addition, the experimentally measured dilution of the heavier brine sewage on the recipient’s surface is compared with the dilution which was calculated by a numerical simulation of a well-known commercial software package, CORJET (a CORMIX sub model).     

Modeling and data assessment of longitudinal salinity in a low-gradient estuarine river

Continuous data of vertical-profile salinity were analyzed for four stations located successively upriver in a macrotidal estuary, the lower St. Johns River (Northeast Florida, USA). The data analysis confirmed well-mixed salinity conditions in the river with at most 1.3 ppt of vertical variability at Dames Point (river km 20), where the main variations of salinity are along the longitudinal axis of the river. Given the well-mixed salinity conditions and dominant horizontal structure of salinity variations in the river, we present and apply a barotropic, two-dimensional modeling approach for hydrodynamic-salinity transport simulation in the lower St. Johns River. When properly forced by offshore surge, high-resolution wind fields and freshwater river inflows, the model replicated the salinity measurements remarkably well, including the separation into tidal and sub-tidal components. The data and model results show that, at times, offshore winds and surge can be more influential on longitudinal salinity variations than local winds over the river. We demonstrate the importance of using proper boundary conditions to force the model relative to the minimal sensitivity of the model to parameter adjustment of horizontal mixing and uncertainty-based perturbation of wind and inflow forcings.