Non-invasive matrices in human biomonitoring: a review

Humans and other living organisms are exposed to a variety of chemical pollutants that are released into the environment as a consequence of anthropogenic activities. Environmental pollutants are incorporated into the organism by different routes and can then be stored and distributed in different tissues, which leads to an internal concentration that can induce different alterations, adverse effects and/or diseases. Control measures should be taken to avoid these effects and human biomonitoring is a very useful tool that can contribute to this aim. Human biomonitoring uses different matrices to measure the target chemicals depending on the chemical, the amount of matrix necessary for the analysis and the detection limit (LOD) of the analytical technique. Blood is the ideal matrix for most chemicals due to its contact with the whole organism and its equilibrium with organs and tissues where chemicals are stored. However, it has an important disadvantage of being an invasive matrix. The development of new methodology and modern analytical techniques has allowed the use of other matrices that are less or non-invasive, such as saliva, urine, meconium, nails, hair, and semen or breast milk. The presence of a chemical in these matrices reflects an exposure, but correlations between levels in non-invasive matrices and blood must be established to ensure that these levels are related to the total body burden. The development of new biomarkers that are measurable in these matrices will improve non-invasive biomonitoring. This paper reviews studies that measure Cd, Pb, Hg, polychlorinated biphenyls (PCBs), polychlorinated dibenzo-p-dioxins (PCDDs), polychlorinated dibenzofurans (PCDFs), polycyclic aromatic hydrocarbons (PAHs), polybrominated diphenyl ethers (PBDEs), organochlorine pesticides and phthalates in non-invasive matrices, the most used techniques for measurements and what alternative techniques are available.     

Assessing spatial variability of SO2 field as detected by an air quality network using Self-Organizing Maps,cluster, and Principal Component Analysis

In Bilbao (Spain), an air quality network measures sulphur dioxide levels at 4 locations. The objective of this paper is to develop a practical methodology to identify redundant sensors and evaluate a network's capability to correctly follow and represent SO₂ fields in Bilbao, in the frame of a continuous network optimization process.The methodology is developed and tested at this particular location, but it is general enough to be useable at other places as well, since it is not tied neither to the particular geographical characteristics of the place nor to the phenomenology of the air quality over the area.To assess the spatial variability of SO₂ measured at 4 locations in the area, three different techniques have been used: Self-Organizing Maps (SOMs), cluster analysis (CA) and Principal Component Analysis (PCA). The results show that the three techniques yield the same results, but the information obtained via PCA can be helpful not only for that purpose but also to throw light on the major mechanisms involved. This might be used in future network optimization stages. The main advantage of cluster analysis and SOMs is that they provide readily interpretable results. All the calculations have been carried out using the freely available software R.     

Epiphytische Flechten im Wandel von Immissionen und Klima ? Ergebnisse einer Vergleichskartierung 1989/2007 in Nordwestdeutschland

Zusammenfassung   Hintergrund, Ziel und Zweck Borkenbewohnende (epiphytische) Flechten reagieren sehr empfindlich auf verschiedene Umwelteinflüsse wie Stoffgehalte in der Atmosph?re und Temperaturen. Sie haben als Bioindikatoren für Luftverunreinigungen einen hohen Stellenwert bei Umweltuntersuchungen. Basierend auf einer 1989 durchgeführten Kartierung borkenbewohnender (epiphytischer) Flechten in einem landwirtschaftlich intensiv genutzten Raum Nordwestdeutschlands erfolgte 2007 eine Wiederholungsuntersuchung. Dabei galt es, m?gliche Ver?nderungen der Immissionssituation und des Klimas zu erkennen. Material und Methoden Die Studie basiert auf einer vergleichenden Kartierung epiphytischer Flechten von 355 Tr?gerb?umen an 45 Monitoringpunkten. Die angewandte Methode orientiert sich an der 1989 angewandten halb-quantitativen Erfassung. Ergebnisse Es stellten sich gravierende Ver?nderungen in der Vegetation rindenbewohnender Flechten heraus. Insgesamt war eine Erh?hung der Artenzahl an nahezu allen Monitoringpunkten festzustellen. Es wurde eine sehr starke Abnahme s?uretoleranter Arten und eine deutliche Zunahme basen- und n?hrstofffordernder Flechtenarten festgestellt. Darüber hinaus zeigte sich eine deutliche Zunahme w?rmeliebender Flechtenarten mit einem Verbreitungsschwerpunkt in Südeuropa bei gleichzeitigem Rückgang von Arten mit einem boreal-montanem Verbreitungsschwerpunkt. Diskussion Die festgestellte Entwicklung steht im Einklang mit überregionalen Beobachtungen. Sie wird im Wesentlichen auf eine gro?r?umig ver?nderte Immissionssituation, d. h. einer Abnahme der atmosph?rischen SO₂-Belastung und Zunahme der Ammoniakbelastung zurückgeführt. Ebenso treten deutliche Auswirkungen der Klimaerw?rmung hervor. Schlussfolgerungen Mit relativ geringem Untersuchungsaufwand lie?en sich Ver?nderungen bei den epiphytischen Flechten in den letzten 18 Jahren aufzeigen. Diese sind relevant für die Beurteilung regional und kleinr?umig ver?nderter Umweltbedingungen, die auch für andere Organismen und ?kosysteme l?ngerfristig von gro?er Bedeutung sind. Empfehlungen und Ausblick Unter Verwendung standardisierter Methoden vermitteln epiphytische Flechten ein differenziertes Bild der Luftbelastungssituation in Ballungsr?umen und in landwirtschaftlich genutzten Regionen. Darüber hinaus sind sie offensichtlich gute Indikatoren für Temperatur?nderungen ihrer Umgebung. Weiterer Forschungsbedarf besteht für die Verwendung epiphytischer Flechten zum Biomonitoring von Klimaver?nderungen.      

From land cover change to land function dynamics: a major challenge to improve land characterization

Land cover change has always had a central role in land change science. This central role is largely the result of the possibilities to map and characterize land cover based on observations and remote sensing. This paper argues that more attention should be given to land use and land functions and linkages between these. Consideration of land functions that provide a wide range of goods and services makes more integrated assessments of land change possible. The increasing attention to multifunctional land use is another incentive to develop methods to assess changes in land functions. A number of methods to quantify and map the spatial extent of land use and land functions are discussed and the implications for modeling are identified based on recent model approaches in land change science. The mixed use of land cover, land use and land function in maps and models leads to inconsistencies in land change assessments. Explicit attention to the non-linear relations between land cover, land use and land function is essential to consistently address land change. New methods to map and quantify land function dynamics will enhance our ability to understand and model land system change and adequately inform policies and planning.     

Anoxic-aerobic treatment of the liquid fraction of cattle manure

Cattle manure from a dairy farm was treated in order to reduce its pollution potential. The manure came from a farm with 120 cows kept in stables in a free stall barn. As pretreatment, the manure is usually filtered on the farm using a screw press separator with a 0.5 mm mesh. Approximately 70% of the total filtered volume passes through the screen, thus constituting the liquid fraction. This fraction, with a composition of around 64,500 mg COD/l, 5770 mg total-N/l and 800 mg total-P/l, was subjected to centrifugation followed by a two-step biological treatment (anoxic-aerobic) to reduce organic matter (COD), nitrogen and phosphorus compounds. Centrifugation led to the following removal efficiencies: 35% total solids, 60% COD, 75% total phosphorus and 20% total nitrogen (mainly organic nitrogen). With the subsequent anoxic-aerobic treatment, average removal efficiencies of 85% for COD, 90% for total phosphorus and 75% for total nitrogen were achieved.     

Injury and loss concentration by sinkings in fishing fleets

Sinkings of fishing vessels, when accompanied by human loss, are accidents which lead to a great social upheaval, especially in the local area where they occur. In order to avoid or reduce these accidents, national governments must include in their legislation for this sector the appropriate safety measures. These resources and their distribution must be assigned in accordance with the needs of the accidentality, this requiring a detailed knowledge of the concentration of accidentality of a country’s fishing sector. In this context, the aim of the present work is twofold: first, to formulate a methodology for the fishing sector of a country, allowing the inequalities in the concentration of sinking accidents to be analysed; and second, to apply this methodology to the Spanish fishing fleet. Thus, two indices are constructed per region and type of fishing, for two variables: fishermen and vessels. Results are obtained both for the concentration of accidents and the associated Lorenz curves. An increase in the inequality in the spatial and functional distribution of these accidents is verified.     

Application of fuzzy sets and cognitive maps to incorporate social science scenarios in integrated assessment modelsA case study of urbanization in Ujung Pandang, Indonesia

Decision–support systems in the field of integrated water management could benefit considerably from social science knowledge, as many environmental changes are human-induced. Unfortunately the adequate incorporation of qualitative social science concepts in a quantitative modeling framework is not straightforward. The applicability of fuzzy set theory and fuzzy cognitive maps for the integration of qualitative scenarios in a decision–support system was examined for the urbanization of the coastal city of Ujung Pandang, Indonesia. The results indicate that both techniques are useful tools for the design of integrated models based on a combination of concepts from the natural and social sciences. This revised version was published online in July 2006 with corrections to the Cover Date.     

Finding threatened forest areas in the central volcanic mountain range conservation area in Costa Rica

The degree at which tropical forests are exposed to human pressure is spatially dependent. Population density, proximity to roads, terrain slope, logging activities and land distribution projects are well known factors inducing deforestation and forest degradation in Latin America. Using expert knowledge to weight these threat factors and a Geographical Information System for spatial modeling, a multi-criteria analysis procedure is presented, that allows stratifying a study region in categories of deforestation threat. The procedure was implemented in the Central Volcanic Mountain Range Conservation Area (CVMRCA) in Costa Rica with the purpose of finding areas with a combination of physical and socioeconomic characteristics that is particularly predisposing to a high probability of deforestation. To validate the map, the CVMRCA was stratified in categories of deforestation risk, and the result was superposed to historical deforestation data of the period 1986–1996. The good correlation between risk category and historical deforestation (r = 0.91, p < 0.001) indicates that the map can be used as a decision support tool for defining priority areas for conservation action.     

Principal response curves technique for the analysis of multivariate biomonitoring time series

Although chemical and biological monitoring is often used to evaluate the quality of surface waters for regulatory purposes and/or to evaluate environmental status and trends, the resulting biological and chemical data sets are large and difficult to evaluate. Multivariate techniques have long been used to analyse complex data sets. This paper discusses the methods currently in use and introduces the principal response curves method, which overcomes the problem of cluttered graphical results representation that is a great drawback of most conventional methods. To illustrate this, two example data sets are analysed using two ordination techniques, principal component analysis and principal response curves. Whereas PCA results in a difficult-to-interpret diagram, principal response curves related methods are able to show changes in community composition in a diagram that is easy to read. The principal response curves method is used to show trends over time with an internal reference (overall mean or reference year) or external reference (e.g. preferred water quality or reference site). Advantages and disadvantages of both methods are discussed and illustrated.