Substituting missing data in compositional analysis

Multivariate analysis of environmental data sets requires the absence of missing values or their substitution by small values. However, if the data is transformed logarithmically prior to the analysis, this solution cannot be applied because the logarithm of a small value might become an outlier. Several methods for substituting the missing values can be found in the literature although none of them guarantees that no distortion of the structure of the data set is produced. We propose a method for the assessment of these distortions which can be used for deciding whether to retain or not the samples or variables containing missing values and for the investigation of the performance of different substitution techniques. The method analyzes the structure of the distances among samples using Mantel tests. We present an application of the method to PCDD/F data measured in samples of terrestrial moss as part of a biomonitoring study.     

Heavy metals in sandy sediments of the Rías Baixas (NW Spain)

We studied the concentrations of certain heavy metals in the total and labile fractions of sandy sediments from four embayments situated on the northwestern coast of Spain. In orderto compensate for the effects of the heterogeneity in texture ofthe different samples, data were normalized. Organic carbon was the most suitable normalizing factor for copper, lead and zinc inthe total fraction and copper in the labile fraction, and iron was the most suitable for the other metals in both fractions.Enrichment of copper, lead and zinc in these sediments may have been caused by sewage contamination. The percentages of extractability for chromiun were remarkable.     

Concentration of arsenic in water, sediments and fish species from naturally contaminated rivers

Arsenic (As) may occur in surface freshwater ecosystems as a consequence of both natural contamination and anthropogenic activities. In this paper, As concentrations in muscle samples of 10 fish species, sediments and surface water from three naturally contaminated rivers in a central region of Argentina are reported. The study area is one of the largest regions in the world with high As concentrations in groundwater. However, information of As in freshwater ecosystems and associated biota is scarce. An extensive spatial variability of As concentrations in water and sediments of sampled ecosystems was observed. Geochemical indices indicated that sediments ranged from mostly unpolluted to strongly polluted. The concentration of As in sediments averaged 6.58 μg/g ranging from 0.23 to 59.53 μg/g. Arsenic in sediments barely followed (r = 0.361; p = 0.118) the level of contamination of water. All rivers showed high concentrations of As in surface waters, ranging from 55 to 195 μg/L. The average concentration of As in fish was 1.76 μg/g. The level of contamination with As differed significantly between species. Moreover, the level of bioaccumulation of As in fish species related to the concentration of As in water and sediments also differed between species. Whilst some fish species seemed to be able to regulate the uptake of this metalloid, the concentration of As in the large catfish Rhamdia quelen mostly followed the concentration of As in abiotic compartments. The erratic pattern of As concentrations in fish and sediments regardless of the invariable high levels in surface waters suggests the existence of complex biogeochemical processes behind the distribution patterns of As in these naturally contaminated ecosystems.     

Trace-metal contamination in the glacierized Rio Santa watershed,Peru

The objective of this research is to characterize the variability of trace metals in the Rio Santa watershed based on synoptic sampling applied at a large scale. To that end, we propose a combination of methods based on the collection of water, suspended sediments, and riverbed sediments at different points of the watershed within a very limited period. Forty points within the Rio Santa watershed were sampled between June 21 and July 8, 2013. Forty water samples, 36 suspended sediments, and 34 riverbed sediments were analyzed for seven trace metals. The results, which were normalized using the USEPA guideline for water and sediments, show that the Rio Santa water exhibits Mn concentrations higher than the guideline at more than 50% of the sampling points. As is the second highest contaminating element in the water, with approximately 10% of the samples containing concentrations above the guideline. Sediments collected in the Rio Santa riverbed were heavily contaminated by at least four of the tested elements at nearly 85% of the sample points, with As presenting the highest normalized concentration, at more than ten times the guideline. As, Cd, Fe, Pb, and Zn present similar concentration trends in the sediment all along the Rio Santa.The findings indicate that care should be taken in using the Rio Santa water and sediments for purposes that could affect the health of humans or the ecosystem. The situation is worse in some tributaries in the southern part of the watershed that host both active and abandoned mines and ore-processing plants.     

Modelling spatial patterns of correlations between concentrations of heavy metals in mosses and atmospheric deposition in 2010 across Europe

Background

This paper aims to investigate the correlations between the concentrations of nine heavy metals in moss and atmospheric deposition within ecological land classes covering Europe. Additionally, it is examined to what extent the statistical relations are affected by the land use around the moss sampling sites. Based on moss data collected in 2010/2011 throughout Europe and data on total atmospheric deposition modelled by two chemical transport models (EMEP MSC-E, LOTOS-EUROS), correlation coefficients between concentrations of heavy metals in moss and in modelled atmospheric deposition were specified for spatial subsamples defined by ecological land classes of Europe (ELCE) as a spatial reference system. Linear discriminant analysis (LDA) and logistic regression (LR) were then used to separate moss sampling sites regarding their contribution to the strength of correlation considering the areal percentage of urban, agricultural and forestry land use around the sampling location. After verification LDA models by LR, LDA models were used to transform spatial information on the land use to maps of potential correlation levels, applicable for future network planning in the European Moss Survey.

Results

Correlations between concentrations of heavy metals in moss and in modelled atmospheric deposition were found to be specific for elements and ELCE units. Land use around the sampling sites mainly influences the correlation level. Small radiuses around the sampling sites examined (5 km) are more relevant for Cd, Cu, Ni, and Zn, while the areal percentage of urban and agricultural land use within large radiuses (75–100 km) is more relevant for As, Cr, Hg, Pb, and V. Most valid LDA models pattern with error rates of <?40% were found for As, Cr, Cu, Hg, Pb, and V. Land use-dependent predictions of spatial patterns split up Europe into investigation areas revealing potentially high (=?above-average) or low (=?below-average) correlation coefficients.

Conclusions

LDA is an eligible method identifying and ranking boundary conditions of correlations between atmospheric deposition and respective concentrations of heavy metals in moss and related mapping considering the influence of the land use around moss sampling sites.

     

Environmental effects of shipping imports from China and their economic valuation: the case of metallic valve components

This paper aims to consider the total cost, including polluting environmental emissions, of imports from China to Europe of five valve components in aluminium, steel and iron. These are compared with locally produced alternatives to investigate whether internalising such external costs affects their price competitiveness. Seaborne trade represents 90% of international trade and is growing faster than the world’s economy, but marine pollution emissions are not reflected in market prices. Chinese aluminium, iron and steel production are about 2.5 times more polluting than their European counterparts, but this is also not reflected in prices. In this paper both types of extra emissions are calculated, valued and added to the cost of the components analysed, for comparison with locally produced alternatives. The locally produced components generally remain uncompetitive with Chinese imports for all likely values of external cost, except for heavy aluminium components under high external cost assumptions. The Chinese iron components analysed are simply too low in cost for European (in this case Spanish) equivalents to be competitive even allowing for environmental costs; while the stainless steel ones are so small and light that transport cost, even including externalities, is not significant. While these specific results are of limited generalizability, they provide insight into an issue of considerable and growing importance. Some suggestions are offered for further research.     

Assessing the effects of heavy metal contamination on the proteome of the moss Pseudoscleropodium purum cross-transplanted between different areas

Protein expression was assessed in samples of Pseudoscleropodium purum cross-transplanted between one unpolluted (UNP) and two polluted (POLL) sites. Firstly, the level of expression (LE) of 17 proteins differed between native mosses from both types of sites, but differences were only maintained throughout the experiment for 5 of them. The LE of these five proteins changed over time in mosses transplanted from UNP to POLL and vice versa, becoming similar to that in autotransplants. However, these changes occurred slower than changes in the heavy metal concentrations measured in the same samples, and therefore they were not related to atmospheric pollution. Although the proteins identified were associated with moss metabolism, the expected growth reduction in samples autotransplanted within POLL (as a result of the down-regulation of photosynthesis-related proteins), did not occur. This supports the hypothesis that mosses growing in polluted areas adapt to heavy metal pollution and are able to reduce/overcome their toxic effects (i.e., reduced growth). Nevertheless, further specific research must be carried out to identify the proteins involved in this type of response, as lack of information on the bryophyte genome precludes us from reaching further conclusions.     

Seasonal trends of atmospheric nitrogen dioxide and sulfur dioxide over North Santa Clara, Cuba

Atmospheric nitrogen dioxide (NO₂) and sulfur dioxide (SO₂) levels were monitored simultaneously by means of Radiello passive samplers at six sites of Santa Clara city, Cuba, in the cold and the warm seasons in 2010. The dissolved ionic forms of NO₂ and SO₂ as nitrate and sulfite plus sulfate, respectively, were determined by means of ion chromatography. Analysis of NO₂ as nitrite was also performed by UV–Vis spectrophotometry. For NO₂, significant t tests show good agreement between the results of IC and UV–Vis methods. The NO₂ and SO₂ concentrations peaked in the cold season, while their minimum levels were experienced in the warm season. The pollutant levels do not exceed the maximum allowable limit of the Cuban Standard 39:1999, i.e., 40 μg/m³ and 50 μg/m³ for NO₂ and SO₂, respectively. The lowest pollutant concentrations obtained in the warm season can be attributed to an increase in their removal via precipitation (scavenging) while to the decreased traffic density and industrial emission during the summer holidays (e.g., July and August).     

The analysis of pollutant profiles by means of compositional analysis. An application to PCDD/F data

Compositional analysis consists of a group of techniques used to manipulate closed (=compositional) data, i.e. multivariate data summing to a fixed quantity (proportions, percentages). It is based on the analysis of the relations among variables and the use of logarithmic transformations. It has been claimed that this group of techniques should be used to analyse profiles of pollutant sources because profiles themselves are proportions. We show in this paper that, for the exploratory analysis of these data, a good strategy is to combine the analysis done with and without transformation because they give different and complementary insights on the structure of the data. We discuss in particular the study of processes such as the mixing of pollutants produced by different sources and the exponential decay of concentrations with distance to the source found in many studies. The clr transformation is also appropriate for the study of the variables having small proportions, which remain concealed by the abundant variables when analysed without transformation. We present simulations to illustrate these ideas and we also apply these techniques to two data sets of PCDD/F content in moss tissues.     

Spatially valid data of atmospheric deposition of heavy metals and nitrogen derived by moss surveys for pollution risk assessments of ecosystems

For analysing element input into ecosystems and associated risks due to atmospheric deposition, element concentrations in moss provide complementary and time-integrated data at high spatial resolution every 5 years since 1990. The paper reviews (1) minimum sample sizes needed for reliable, statistical estimation of mean values at four different spatial scales (European and national level as well as landscape-specific level covering Europe and single countries); (2) trends of heavy metal (HM) and nitrogen (N) concentrations in moss in Europe (1990–2010); (3) correlations between concentrations of HM in moss and soil specimens collected across Norway (1990–2010); and (4) canopy drip-induced site-specific variation of N concentration in moss sampled in seven European countries (1990–2013). While the minimum sample sizes on the European and national level were achieved without exception, for some ecological land classes and elements, the coverage with sampling sites should be improved. The decline in emission and subsequent atmospheric deposition of HM across Europe has resulted in decreasing HM concentrations in moss between 1990 and 2010. In contrast, hardly any changes were observed for N in moss between 2005, when N was included into the survey for the first time, and 2010. In Norway, both, the moss and the soil survey data sets, were correlated, indicating a decrease of HM concentrations in moss and soil. At the site level, the average N deposition inside of forests was almost three times higher than the average N deposition outside of forests.