Geochemical disturbance of soil cover in the nonferrous mining centers of the Selenga River basin

The anthropogenic geochemical transformation of soil cover in large nonferrous mining centers of the Selenga River basin was assessed. The results of the geochemical survey of 2010–2012 revealed the spatial distribution patterns and abundances of 18 hazardous heavy metals and metalloids in the soils of Erdenet (Mongolia) and Zakamensk (Buryat republic, Russian Federation). In both cities, mining activities disturbed soil cover which accumulates Mo, Cu, As, Sb, W in Erdenet and Bi, W, Cd, Be, Pb, Mo, Sb in Zakamensk. Maximum accumulation of elements in Erdenet is restricted to the industrial zone. In Zakamensk, it has spread on ½ of the territory with the degree of multielemental pollution exceeding the extremely dangerous level by 16 times. The effect of mining centers on the state of the river system is local and does not spread to the Selenga River. Downstream from Erdenet, an artificial pool intercepts heavy metal and metalloid flows of the Erdenetii-Gol River. By contrast, downstream from the tailing dumps of the Dzhida tungsten–molybdenum plant the concentrations of ore elements W and Mo and their accessories Bi and Cd in the Modonkul River exceed background values by 146, 20, 57, and 21 times, respectively, decreasing by an order of magnitude 30 km downstream.     

Atrazine contamination in agricultural soils from the Yangtze River Delta of China and associated health risks

Atrazine is one of the most widely applied and persistent herbicides in the world. In view of limited information on the regional contamination of atrazine in soils in China, this study investigated the spatial distribution and environmental impacts of atrazine in agricultural soils collected from the Yangtze River Delta (YRD) as an illustrative analysis of rapidly developing regions in the country. The results showed that the concentrations of atrazine in the YRD agricultural soils ranged from <1.0 to 113 ng/g dry weight, with a mean of 5.7 ng/g, and a detection rate of 57.7 % in soils. Pesticide factory might be a major source for the elevated levels of atrazine in Zhejiang Province. The contamination of atrazine was closely associated with land use types. The concentrations and detection rates of atrazine were higher in corn fields and mulberry fields than in rice paddy fields. There was no significant difference in compositions of soil microbial phospholipids fatty acids among the areas with different atrazine levels. Positive relationship (R = 0.417, p < 0.05, n = 30) was observed between atrazine and total microbial biomass. However, other factors, such as soil type and land management practice, might have stronger influences on soil microbial communities. Human health risks via exposure to atrazine in soils were estimated according to the methods recommended by the US EPA. Atrazine by itself in all the soil samples imposed very low carcinogenic risks (<10^?6) and minimal non-cancer risks (hazard index <1) to adults and children.     

Physicochemical fingerprinting of thermal waters of Beira Interior region of Portugal

Mineral natural waters and spas have been used for therapeutic purposes for centuries, with Portugal being a very rich country in thermal waters and spas that are mainly distributed by northern and central regions where Beira Interior region is located. The use of thermal waters for therapeutic purposes has always been aroused a continuous interest, being dependent on physicochemical fingerprinting of this type of waters the indication for a treatment in a specific pathological condition. In the present work, besides a literature review about the physicochemical composition of the thermal waters of the Beira Interior region and its therapeutic indications, it was carried out an exhaustive multivariate analysis—principal component analysis and cluster analysis—to assess the correlation between different physicochemical parameters and the therapeutic indications claims described for these spas and thermal waters. These statistical methods used for data analysis enables classification of thermal waters compositions into different groups, regarding to the different variable selected, making possible an interpretation of variables affecting water compositions. Actually, Monfortinho and Longroiva are clearly quite different of the others, and Cró and Fonte Santa de Almeida appear together in all analysis, suggesting a strong resemblance between these waters. Thereafter, the results obtained allow us to demonstrate the role of major components of the studied thermal waters on a particular therapeutic purpose/indication and hence based on compositional and physicochemical properties partially explain their therapeutic qualities and beneficial effects on human health. This classification agreed with the results obtained for the therapeutic indications approved by the Portuguese National Health Authority and proved to be a valuable tool for the regional typology of mineral medicinal waters, constituting an important guide of the therapeutic armamentarium for well and specific-oriented pathological disturbs.     

Metal-contaminated potato crops and potential human health risk in Bolivian mining highlands

This study assessed metals in irrigation water, soil and potato crops impacted by mining discharges, as well as potential human health risk in the high desert near the historic mining center of Potosí, Bolivia. Metal concentrations were compared with international concentration limit guidelines. In addition, an ingested average daily dose and minimum risk level were used to determine the hazard quotient from potato consumption for adults and children. Irrigation water maximum concentrations of Cd, Pb and Zn in mining-impacted sites were elevated 20- to 1100-fold above international concentration limit guidelines. Agricultural soils contained total metal concentrations of As, Cd, Pb and Zn that exceeded concentration limits in agricultural soil guidelines by 22-, 9-, 3- and 12-fold, respectively. Potato tubers in mining-impacted sites had maximum concentrations of As, Cd, Pb and Zn that exceeded concentration limits in commercially sold vegetables by 9-, 10-, 16- and fourfold, respectively. Using conservative assumptions, hazard quotients (HQ) for potatoes alone were elevated for As, Cd and Pb among children (range 1.1–71.8), in nearly all of the mining-impacted areas; and for As and Cd among adults (range 1.2–34.2) in nearly all of the mining-impacted areas. Only one mining-impacted area had a Pb adult HQ for potatoes above 1 for adults. Toxic trace elements in a major regional dietary staple may be a greater concern than previously appreciated. Considering the multitude of other metal exposure routes in this region, it is likely that total HQ values for these metals may be substantially higher than our estimates.     

Trace elements in surface sediments of the Hooghly (Ganges) estuary: distribution and contamination risk assessment

Our objective was to evaluate distribution and accumulation of trace elements (TEs) in surface sediments along the Hooghly (Ganges) River Estuary, India, and to assess the potential risk with view to human health. The TE concentrations (mg kg^?1 dry weight) exhibited a wide range in the following order: Al (31.801 ± 15.943) > Fe (23.337 ± 7584) > Mn (461 ± 147) > S (381 ± 235) > Zn (54 ± 18) > V (43 ± 14) > Cr (39 ± 15) > As (34 ± 15) > Cu (27 ± 11) > Ni (24 ± 9) > Se (17 ± 8) > Co (11 ± 3) > Mo (10 ± 2) > Hg (0.02 ± 0.01). Clay, silt, iron, manganese and sulphur were important for the accumulation of TE in the sediments as confirmed by factor analysis and Pearson correlation. The accumulation and dispersal of TEs were most likely to be governed by both tide-induced processes and anthropogenic inputs from point and non-point sources. Enrichment factor analysis and geoaccumulation index revealed serious contamination of the sediments with Se and As, while comparing the consensus-based sediment quality guidelines (SQGs), adverse biological effects to benthic fauna might be caused by As, Cu, Ni and Cr. This investigation may serve as a model study and recommends continuous monitoring of As, Se, Cu, Ni and Cr to ascertain that SQGs with respect to acceptable levels of TEs to safeguard geochemical health and ecology in the vicinity of this estuary.     

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.     

Effect of deforestation on fertility attributes of Mollisols in the NW of Iran

The impacts of deforestation on soil fertility indices – are still not well understood in the forest lands of Iran characterised by Mediterranean type climate. Consistent with this, 8 soil pedons and 32 soil cores were described and sampled from four different soil types of forest and adjacent cultivated soil along a Mollisols transect. The results revealed a considerable depletion in the values of soil organic carbon (by 60–88%), total N (by 67–88%), available K (by 20–45%), cation exchange capacity (by 9–21%), and the diethylene-triamine pentaacetic acid (DTPA) fraction of Fe (by 40–72%), Mn (by 10–60%), and Zn (by 49–80%) after deforestation. In contrast, soil pH (by 0.36–0.9 units), C:N ratio (by 3–84%), available P (22–139%), and DTPA Cu (by 4–55%) tended to increase due to deforestation. Cultivated soils showed a drop of 70–82% in the values of the soil productivity index than to those of the forest soil, indicating a degrading and declining effect of deforestation on soil productivity capacity. It was found that the majority of soil fertility indices were affected negatively by deforestation and more than half of the organic matter was lost to deforestation, which, in turn, could lead to deterioration in soil quality or land productivity capacity.     

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.