Modelling the potential mobility of Cd,Cu, Ni,Pb and Zn in Mollic Fluvisols

European floodplain soils are frequently contaminated with potentially toxic inorganic substances. We used a multi-surface model to estimate the aqueous concentrations of Cd, Cu, Ni, Pb and Zn in three Mollic Fluvisols from the Central Elbe River (Germany). The model considered complexation in solution and interactions with soil organic matter (SOM), a clay mineral and hydrous Al, Fe and Mn oxides. The amounts of reactive metals were derived from extraction with 0.43 M HNO₃. Modelling was carried out as a function of pH (soil pH ± 1.4) because it varies in floodplain soils owing to redox processes that consume or release protons. The fraction of reactive metals, which were dissolved according to the modelling, was predominantly <1%. Depending on soil properties, especially pH and contents of SOM and minerals of the clay fraction, the modelled concentrations partially exceeded the trigger values for the soil–groundwater pathway of the German soil legislation. This differentiation by soil properties was given for Ni, Pb and Zn. On the other hand, Cd was more mobile, i.e., the trigger values were mostly exceeded. Copper represented the opposite, as the modelling did not predict exceeding the trigger values in any horizon. Except for Pb and partially Zn (where oxides were more important), SOM was the most important adsorbent for metals. However, given the special composition and dynamics of SOM in mollic horizons, we suggest further quantitative and qualitative investigations on SOM and on its interaction with metals to improve the prediction of contaminant dynamics.     

Characterization of hard- and softwood biochars pyrolyzed at high temperature

A wide range of waste biomass/waste wood feedstocks abundantly available at mine sites provide the opportunity to produce biochars for cost-effective improvement of mine tailings and contaminated land at metal mines. In the present study, soft- and hardwood biochars derived from pine and jarrah woods at high temperature (700 °C) were characterized for their physiochemical properties including chemical components, electrical conductivity, pH, zeta potential, cation-exchange capacity (CEC), alkalinity, BET surface area and surface morphology. Evaluating and comparing these characteristics with available data from the literature have affirmed the strong dictation of precursor type on the physiochemical properties of the biochars. The pine and jarrah wood feedstocks are mainly different in their proportions of cellulose, hemicellulose and lignin, resulting in biochars with heterogeneous physiochemical properties. The hardwood jarrah biochar exhibits much higher microporosity, alkalinity and electrostatic capacity than the softwood pine. Correlation analysis and principal component analysis also show a good correlation between CEC–BET–alkalinity, and alkalinity–ash content. These comprehensive characterization and analysis results on biochars’ properties from feedstocks of hardwood (from forest land clearance at mine construction) and waste pine wood (from mining operations) will provide a good guide for tailoring biochar functionalities for remediating metal mine tailings. The relatively inert high-temperature biochars can be stored for a long term at mine closure after decades of operations.     

The evolution of pollution profile and health risk assessment for three groups SVOCs pollutants along with Beijiang River,China

Three important groups of semi-volatile organic compounds (SVOCs), polycyclic aromatic hydrocarbons (PAHs), organic chlorinated pesticides (OCPs) and phthalate esters (PAEs), were produced by various human activities and entered the water body. In this study, the pollution profiles of three species including 16 PAHs, 20 OCPs and 15 PAEs in water along the Beijiang River, China were investigated. The concentrations of Σ₁₆PAHs in the dissolved and particulate phases were obtained as 69–1.5 × 10² ng L^?1 and 2.3 × 10³–8.6 × 10⁴ ng g^?1, respectively. The levels of Σ₂₀OCPs were 23–66 ng L^?1 (dissolved phase) and 19–1.7 × 10³ ng g^?1 (particulate phase). Nevertheless, higher levels of PAEs were found both in the dissolved and particulate phases due to abuse use of plastic products. Furthermore, non-cancer and cancer risks caused by these SVOCs through the ingestion absorption and dermal absorption were also assessed. There was no non-cancer risk existed through two kinds of exposure of them at current levels, whereas certain cancer risk existed through dermal absorption of PAHs in the particulate phase in some sampling sites. The results will show scientific insights into the evaluation of the status of combined pollution in river basins, and the determination of strategies for incident control and pollutant remediation.     

A study on genotoxicity of textile dyeing industry effluents from Rajshahi,Bangladesh, by the Allium cepa test

Textile dye effluents are believed to be toxic as they might exert various harmful effects on living organisms including genotoxicity. These effluents are the main source of direct and continuous input of pollutants into the aquatic ecosystems. In this study, dye effluents from a local silk dyeing industry were analysed for their genotoxic potential by the Allium cepa genotoxicity test. The A. cepa test is characterised as a genotoxicity test where the roots of A. cepa are grown in different concentrations of the test material. The macroscopic results clearly showed that the toxicity of the dye effluents prompted A. cepa root growth inhibition, and this effect increased with higher concentrations of the effluents. At the cellular level, no dividing cells were found at higher concentrations such as 60%, 80% and 100% of the effluents. However, at a lower concentration of 20%, dividing cells were identified, although the mitotic index was much lower than that of the control. Microscopic analysis showed that the dye effluents induced chromosomal aberrations at significant levels. Taken together, these results revealed that the textile dyeing industry effluents are toxic to eukaryotic cells and these dyes have genotoxic properties that can potentially lead to cancer development and adverse health conditions.     

Nearest neighbor methods for testing reflexivity

Nearest neighbor (NN) methods are widely employed for drawing inferences about spatial point patterns of two or more classes. We introduce a method for testing reflexivity in the NN structure (i.e., NN reflexivity) based on a contingency table which will be called reflexivity contingency table (RCT) henceforth. The RCT is based on the NN relationships among the data points and was used for testing niche specificity in literature, but we demonstrate that it is actually more appropriate for testing the NN reflexivity pattern. We derive the asymptotic distribution of the entries of the RCT under random labeling and introduce tests of reflexivity based on these entries. We also consider Pielou’s approach on RCT and show that it is not appropriate for completely mapped spatial data. We determine the appropriate null hypotheses and the underlying conditions/assumptions required for all tests considered. We investigate the finite sample performance of the tests in terms of empirical size and power by extensive Monte Carlo simulations and illustrate the methods on two real-life ecological data sets.     

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.     

A graphical assessment and spatial clustering of the Deepwater Horizon oil spill impact on Laughing Gulls

The Deepwater Horizon oil spill occurred in the Gulf of Mexico on April 20, 2010. Considered the largest accidental marine oil spill in history, oil flowed for three months and approximately five million barrels of oil spilled through by mid-July 2010. In this article, we analyze bird data to assess the impact of the oil spill on the Gulf wildlife. Particularly, we want to determine in which regions Laughing Gulls were mostly affected by the oil spill, and whether those regions spatially shifted throughout the year 2010. Though our data sets have some limitations to apply statistical analysis methods, we obtained very interesting results. Our analyses showed the general consistency of the results based on two population data sets (from the 2011 Data Expo and from the eBird community) and justified the use of both publicly available data sets. We showed that the closer the surface oil spill area approached to the Laughing Gulls habitats, the more significant clusters of bird cases were observed.     

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.     

Comparing canopy leaf temperature of three Central European tree species based on simultaneous confidence bands for penalized splines

Temperature is an important physical factor that is known to strongly affect biodiversity as well as ecosystems and their functioning. However, research in this area is still relatively limited; this may also be attributed to the multitude of influencing factors and the complexity of the statistics involved. This study analyzes the differences between the surface temperature of three Central European broadleaf tree species. A better understanding of these differences may help to elucidate the role of microclimate in biodiversity. We consider a time series of high-resolution thermal images taken from a meteorological observation tower and calculate mean canopy leaf temperatures for beech, ash and maple (Fagus silvatica, Fraxinus excelsior and Acer pseudoplatanus). In a first step, comparable image areas are extracted from the thermal image sections of the crown of each tree species avoiding shadow areas, branches, etc. We used an automatic segmentation technique, the Otsu thresholding. Extracted canopy leaf temperature values were then processed and the resulting temperature profiles estimated by O’Sullivan penalized splines. For comparing the differences in canopy leaf temperature over time, we propose the construction of simultaneous confidence bands. The analyses show that there are significant—though small—differences in canopy surface temperature between the three tree species.     

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.