Biomedical subjects

Leo Posthuma

Publications and source records attributed to Leo Posthuma.

7 recordsLinked to original sources

PubMed2006

Estimating the impact of high-production-volume chemicals on remote ecosystems by toxic pressure calculation.

Although many chemicals are in use, the environmental impacts of only a few have been established, usually on per-chemical basis. Uncertainty remains about the overall impact of chemicals. This paper estimates combined toxic pressure on coastal North Sea ecosystems from 343 high-production-volume chemicals used within the catchment of rivers Rhine, Meuse, and Scheldt. Multimedia fate modeling and species sensitivity distribution-based effects estimation are applied. Calculations start from production volumes and emission rates and use physicochemical substance properties and aquatic ecotoxicity data. Parameter uncertainty is addressed by Monte Carlo simulations. Results suggest that the procedure is technically feasible. Combined toxic pressure of all 343 chemicals in coastal North Seawater is 0.025 (2.5% of the species are exposed to concentration levels above EC50 values), with a wide confidence interval of nearly 0-1. This uncertainty appears to be largely due to uncertainties in interspecies variances of aquatic toxicities and, to a lesser extent, to uncertainties in emissions and degradation rates. Due to these uncertainties, the results support gross ranking of chemicals in categories: negligible and possibly relevant contributions only. With 95% confidence, 283 of the 343 chemicals (83%) contribute negligibly (less than 0.1%) to overall toxic pressure, and only 60 (17%) need further consideration.

Ecosystem↗

PubMed2006

Predictive models attribute effects on fish assemblages to toxicity and habitat alteration.

Biological assessments should both estimate the condition of a biological resource (magnitude of alteration) and provide environmental managers with a diagnosis of the potential causes of impairment. Although methods of quantifying condition are well developed, identifying and proportionately attributing impairment to probable causes remain problematic. Furthermore, analyses of both condition and cause have often been difficult to communicate. We developed an approach that (1) links fish, habitat, and chemistry data collected from hundreds of sites in Ohio (USA) streams, (2) assesses the biological condition at each site, (3) attributes impairment to multiple probable causes, and (4) provides the results of the analyses in simple-to-interpret pie charts. The data set was managed using a geographic information system. Biological condition was assessed using a RIVPACS (river invertebrate prediction and classification system)-like predictive model. The model provided probabilities of capture for 117 fish species based on the geographic location of sites and local habitat descriptors. Impaired biological condition was defined as the proportion of those native species predicted to occur at a site that were observed. The potential toxic effects of exposure to mixtures of contaminants were estimated using species sensitivity distributions and mixture toxicity principles. Generalized linear regression models described species abundance as a function of habitat characteristics. Statistically linking biological condition, habitat characteristics including mixture risks, and species abundance allowed us to evaluate the losses of species with environmental conditions. Results were mapped as simple effect and probable-cause pie charts (EPC pie diagrams), with pie sizes corresponding to magnitude of local impairment, and slice sizes to the relative probable contributions of different stressors. The types of models we used have been successfully applied in ecology and ecotoxicology, but they have not previously been used in concert to quantify impairment and its likely causes. Although data limitations constrained our ability to examine complex interactions between stressors and species, the direct relationships we detected likely represent conservative estimates of stressor contributions to local impairment. Future refinements of the general approach and specific methods described here should yield even more promising results.

Animals↗

PubMed2006

Predicted effects of toxicant mixtures are confirmed by changes in fish species assemblages in Ohio, USA, rivers.

The purposes of this study were to investigate whether exposure to toxicant mixtures is associated with fish assemblage characteristics in the field and to describe the relationships between predicted chronic and acute mixture risks and observed impacts. Fish abundance and abiotic monitoring data from Ohio, USA, surface waters were compiled and analyzed. Variability of biotic and abiotic parameters was large. Exposure assessment, risk assessment with species-sensitivity distributions, and mixture toxicity rules were used to calculate a relative risk predictor: The multisubstance potentially affected fraction of species (msPAF). Predicted acute and chronic risks ranged from low values to more than 10 and 50% of species potentially affected, respectively. Pearson correlations between predicted risk and observed assemblage characteristics were nonsignificant for total abundance, number of species, Shannon-Weaver index, and evenness. Moderately significant correlations were found between predicted risk and abundance for 23% of individual species. Both abundance increases and decreases were observed. Generalized linear model (GLM) regressions revealed significant nonlinear associations between predicted risk and the abundance for 50% (metals and ammonia) and 55% (household product ingredients) of the species. Local ecological impact was expressed as the fraction of species expected but not observed, both with and without attribution of impact to mixture exposure. The association between predicted impacted fraction and the fraction of species expected but not observed was not significant. Predicted acute and chronic impacted fractions were associated significantly with the observed fraction of species likely lost by the action of toxicant mixtures under field conditions, with wide confidence bounds. These findings confirm the view that higher mixture impacts are expected in the field at higher msPAF.

Animals↗

PubMed2005

Short-term ecological risks of depositing contaminated sediment on arable soil.

Sediments act as sinks of suspended material from surface water. Dredging of regional waters and subsequent disposal of the sediment on soil may lead to contamination of the soil, in some cases resulting in exceedance of soil quality standards. Soil quality standards are based on total concentrations. Total levels, however, do not always give an indication of adverse effects in soil ecosystems. Instead, truly bioavailable concentrations should be used as indicators. In this study we aim to test a set of suited indicators. We carried out partition and accumulation assays with metals and polycyclic aromatic hydrocarbons (PAHs) in soils and mixtures of soil and sediment, as well as a limited number of toxicity bioassays. We also investigated the rate of disappearance of PAHs from mixtures of sediments and soils. The experiments confirm that total levels indeed are not indicative of truly occurring toxic effects: mixing of highly contaminated sediments with soil hardly gave rise to either additional accumulation of metals and PAHs or excess toxicity. This indicates that the bioavailability of the metals and PAHs present in the sediment is limited. This general finding is confirmed by the low rate of disappearance of PAHs from the mixtures. It is concluded that inclusion of the aspects of bioavailability, mixture toxicity, and degradation, in the way described in this report, will solve the major limitations of the current methodology of classification of contaminated sediments.

Agriculture↗

PubMed2005

Complex mixture toxicity for single and multiple species: proposed methodologies.

Methods for the assessment of ecological risks associated with exposure to defined mixtures of toxicants are reviewed and formalized for single-species toxicity. Depending on the modes of action of toxicants in a mixture, these methods apply either the model for concentration additivity (CA) or the model for response additivity (RA). For complex mixtures, the present paper advocates the use of a new, two-step, mixed-model approach as a logical extension of model selection: Mixture toxicity for individual modes of action is evaluated with the CA model, and the toxicities of different modes of action are combined using the RA model. Using comparable mixture toxicity strategies in combination with the concept of species-sensitivity distributions, we develop a method to address and predict the risk for direct effects on the composition of species assemblages and biodiversity. The data needed for modeling can be obtained from existing databases, and lack of data can, in part, be addressed by the use of toxicity patterns in those databases. Both single- and multiple-species methods of mixture risk prediction are useful for risk management, because they allow ranking of polluted sites and affected species as well as identification of the most hazardous contaminants, at least in a comparative way. Validation of the proposed methods is feasible but currently limited because of a lack of appropriate data.

Animals↗

PubMed2004

Location-specific ecotoxicological risk assessment of metal-polluted soils.

When chemical analysis indicates metal pollution, a second-tier method is needed to evaluate whether toxic effects occur at the polluted sites. A method based on pollution-induced community tolerance (PICT) was developed using samples taken from locations polluted with sewage more than 20 years ago. Microorganisms extracted from soil samples were exposed to a concentration range of zinc, nickel, copper, chromium (III), or chromium (VI) salts in a buffer suspension. The remaining activity of the intoxicated microorganisms was determined by color formation with 31 different organic substrates in microtiter plates. Microorganisms from moderately Zn-polluted sites (>45 mg/kg) showed an increased tolerance for zinc. Nickel tolerance was observed at 51 mg Ni/kg soil, chromium (VI) tolerance at 923 mg Cr/kg. In most cases, tolerance also was observed at higher concentrations. High concentrations of 1,494 mg Cu/kg or 3,935 mg Cr/kg did not show PICT, indicating a limited bioavailability of Cu and Cr at these sites. The benefits of our method are its greater sensitivity compared to other tests used at these sites, and its specificity for those metals that exceed allowable levels.

Chromogenic Compounds↗

PubMed2003

Metal uptake from soils and soil-sediment mixtures by larvae of Tenebrio molitor (L.) (Coleoptera).

Bioassays were performed to evaluate the impact of soil characteristics on Cd, Cu, Pb, and Zn uptake by larvae of Tenebrio molitor. Metal accumulation was determined in 13 natural field soils, one metal-spiked field soil, four soil-sediment mixtures, and Cd- or Zn-spiked OECD artificial soil. Statistical analyses were used to investigate covariation of accumulation patterns with various soil metal pools and soil properties. Body concentrations of Cu and Zn in Zn-spiked OECD soils, field soils, and soil-sediment mixtures mostly remained constant. Considerable variation was noted for all Cd and Pb steady-state body concentrations among field soils and soil-sediment mixtures. For the spiked field soil and in the Cd-spiked OECD soil, body concentrations increased almost linearly with time. For the nonessential metals Cd and Pb, larval body concentrations correlated mainly to the total metal pool of the soil. Cd uptake at similar total Cd concentrations was within the same range among spiked OECD soils, field soils, and mixtures. A comparison of the findings with studies on other soil-inhabiting species shows that metal uptake patterns depend on metal type, soil type, and exposed species. It is suggested that soil organisms can be categorized according to gross divergence in ecophysiological characteristics, determined by, for instance, (non)permeability of the outer integument. These characteristics appear as similarities among multivariate functions as derived for the beetle.

Animals↗