Naturally occurring radionuclides in drinking water: An exercise in risk benefit analysis

The scientific background information describing the occurrence, measurement, health effects, treatment technology, risk assessment and economic consequences of the presence of naturally occurring radionuclides in drinking water are described for 60,000 public drinking water supplies. The relevant data for the occurrence of radium, uranium and radon in drinking water supplies are discussed and analysed. Radon is of importance because it is released in the process of taking showers and baths and in washing dishes and clothes. Its progeny is then inhaled, leading to the risk of lung cancer. Radium and uranium can both cause bone cancer. The range of average occurrence of natural radioactivity in drinking water is as follows:²²⁶Ra, 0.3 to 0.8 pCi L^–1;²²⁸Ra, 0.4 to 1.0 pCi L^–1; uranium, 0.3 to 2.0 pCi L^–1 and²²²Rn, 500 to 600 pCi L^–1. The estimated lifetime risks due to the mean groundwater concentrations of naturally occurring radionuclides are:²²⁶Ra and^228Ra, 1.0 10^–5; uranium, 2.0 × 10^–6 and radon, 4.0 × 10^–4. The cost to reduce total radium levels to 5.0 pCi L^–1 is about $9 million. An equivalent expenditure would be required to reduce radon levels to about 4,000 pCi L^–1, or uranium levels to about 100 pCi L^–1. The problem of maximizing the total mortality and the reduction per unit dollar outlay per unit dollar cost for the uranium/radon case is examined.The thoughts and ideas expressed in this paper are those of the authors and are not necessarily those of the US Environmental Protection Agency.This paper is published as a contribution to discussion on this problem and not as a paper providing new research data.     

Reproductive ecology of <Emphasis Type="Italic">Fungiacyathus marenzelleri</Emphasis> from 4100 m depth in the northeast Pacific Ocean

Fungiacyathus marenzelleri (Vaughan, 1906) is a deep-water solitary coral, cosmopolitan in distribution that is found at depths of 300–6,328 m. This study examined gametogenesis, inter-annual variability and reproductive periodicity of F. marenzelleri collected from Station M (34°50′N, 123°00′W) in the northeast Pacific at a depth of 4,100 m. Samples were collected (May, June, October 1996; August 1998; February, June 2001; and June 2002) and histologically processed with spermatogenic stage, oocyte size and fecundity measured. Four stages of spermatogenesis were identified and all males contained multiple stages of sperm development in each seasonal sample. Three stages of oocyte development were identified; previtellogenic (<28–150 μm), vitellogenic (150–300 μm) and late vitellogenic (300–400 μm). Comparison of mean oocyte diameters among sampling dates showed there were no inter-annual variations or seasonal differences. Overall, fecundity was 1,290 (±407) oocytes polyp⁻¹, and with no significant differences between sample months. Fecundity was not polyp-size dependent. This study shows a similar quasi-continuous mode of reproduction to this species examined from the Northeast Atlantic Ocean, but the fecundity is reduced by 50%. The reproductive output may fluctuate in relation to the input of organic material at this site, as shown by non-significant trends in the oocyte size-frequency and fecundity data. A quasi-continuous output of gametes would promote successful fertilisation and wide dispersal of the lecithotrophic larvae.     

Using multi-objective classification to model communities of soil microarthropods

In agricultural soil, a suite of anthropogenic events shape the ecosystem processes and populations. However, the impact from anthropogenic sources on the soil environment is almost exclusively assessed for chemicals, although other factors like crop and tillage practices have an important impact as well. Thus, the farming system as a whole should be evaluated and ranked according to its environmental benefits and impacts. Our starting point is a data set describing agricultural events and soil biological parameters. Using machine learning methods for inducing regression and model trees, we produce empirical models able to predict the soil quality from agricultural measures in terms of quantities describing the soil microarthropod community. We are also interested in discovering additional higher level knowledge. In particular, we have identified the most important factors influencing the population densities of springtails and mites and their biodiversity. We also identify to which agricultural actions different microarthropods react distinctly. To obtain this higher level knowledge, we employ multi-objective regression trees.     

Extra-pair paternity in the facultatively polygynous spotless starling,<Emphasis Type="Italic"> Sturnus unicolor</Emphasis>

A potential cost of polygyny that may have restrained its evolution in some avian species is the presence of extra-pair offspring in the nests of males mated with several females. However, this relationship is not frequently found and an experimental approach investigating the association between extra-pair offspring and polygyny, while controlling for male traits related to polygyny, has not been undertaken. In this study, we manipulated the testosterone levels of facultatively polygynous spotless starlings, Sturnus unicolor, to establish experimentally different groups of polygyny and analyse its influence on levels of extra-pair offspring in males for which other traits were randomised. During two consecutive breeding seasons, we examined the effects of harem size on the ability of males to assure genetic paternity, assessed by DNA fingerprinting, and on reproductive success in their own nests. The frequency of extra-pair fertilisations varied between 10% (14/140) in 1996 and 20% (39/194) in 1997. Year-to-year analyses of extra-pair paternity variables with treatment as factor and harem size (number of simultaneous mated females) as covariate, were significant only in relation to harem size. Males with few simultaneously mated females were less cuckolded than more polygynous males in 1997 but not in 1996, indicating that mating costs of polygyny occurred, at least in the year with the higher rate of extra-pair paternity. Because polygynous males have more social mates, they may accrue higher reproductive success irrespective of their risk of being cuckolded. However, our results suggest that to be polygynous is costly in terms of paternity loss when we experimentally induce mating status controlling for individual male traits. However, the low values of our power tests for the statistically non-significant results of hormone on cuckoldry do not allow us to discard this hormone effect altogether.     

Variation in secondary metabolite and aragonite concentrations in the tropical green seaweed Neomeris annulata: effects on herbivory by fishes

The tropical alga Neomeris annulata (Dickie) (Dasycladaceae: Chlorophyta) produces brominated sesquiterpenes and deposits aragonite throughout the thallus. This study, conducted throughout 1990–1991, showed that the fleshy, apical portions of the thalli (tips) were high in secondary metabolite concentrations (1.5%, mean combined secondary metabolites based on dry mass) and relatively low in calcium carbonate (aragonite form) (65.2% ash). The basal portions were lower in combined secondary metabolites (0.2% dry mass), and higher in aragonite (90.0% ash). The crude organic extract of the alga deterred fish feeding in the field at concentrations of 5, 10 and 15% dry mass, but not at a lower concentration of 1.5%. Natural concentrations of crude organic extract ranged from 1.5 to 15.3% in whole individuals and averaged 5.1% based on dry mass. Two brominated sesquiterpenes were isolated as major metabolites from the crude extract, but only one deterred feeding at natural concentrations. Ash concentrations in N. annulata were 60% dry mass in both the tips and bases. Aragonite strongly deterred feeding at concentrations of 65 and 90% dry mass. When a naturally occurring combination of organic extract and aragonite in the tips (10% crude extract and 65% aragonite) was compared with that of the bases (0.8% crude extract and 90% aragonite), no significant difference in grazing was observed. Combinations of secondary metabolites and aragonite were also tested against one or the other single defense. The combination of defenses proved a more effective deterrent than either secondary metabolites or aragonite alone.     

Secondary metabolites of the cyanobacterium Microcoleus lyngbyaceus and the sea hare Stylocheilus longicauda: palatability and toxicity

The cyanobacterium Microcoleus lyngbyaceus (Kütz.) Crouan (Lyngbya majuscula Gomont) produces a variety of nitrogen-containing secondary metabolites. M. lyngbyaceus is eaten by the specialist sea hare Stylocheilus longicauda, which sequesters secondary metabolites from its diet and transforms some sequestered metabolites into related compounds. We examined the palatability and toxicity of the metabolites malyngamide A, malyngamide B, and a mixture of majusculamides A and B (from M. lyngbyaceus), and malyngamide B acetate (from S. longicauda, derived from malyngamide B), in order to explore a series of hypotheses about why M. lyngbyaceus produces secondary metabolites and why S. longicauda sequesters and transforms them. All three M. lyngbyaceus metabolites significantly reduced feeding by omnivorous pufferfish (Canthigaster solandri) and crabs (Leptodius spp.). Direct comparisons indicated that neither malyngamide A nor the majusculamide mixture differed significantly in palatability from malyngamide B. The S. longicauda metabolite malyngamide B acetate did not significantly deter feeding by either consumer. Direct comparisons indicated that pufferfish found malyngamide B acetate more palatable than malyngamide B, and that crabs showed a non-significant trend in this direction. Palatability of the metabolites did not correlate with toxicity. Although no significant differences were observed among the M. lyngbyaceus metabolites in their palatability, two toxicity assays consistently ranked malyngamide A more toxic than malyngamide B, which was more toxic than the majusculamide mixture. The majusculamide mixture did not significantly differ in toxicity from malyngamide B acetate, even though the two differed strongly in palatability.     

Determination of fluoroquinolone antibiotics in wastewater effluents by liquid chromatography-mass spectrometry and fluorescence detection

The occurrence of quinolone antibiotics (QAs) was investigated in wastewater effluents and surface river/lake waters in the US and Canada by using solid-phase extraction with mixed phase cation exchange disk cartridge and liquid chromatography-mass spectrometry (LC-MS) and liquid chromatography fluorescence detection (LC-FLD). Ofloxacin (OFL) was detected in secondary and final effluents of a wastewater treatment plant (WWTP) in East Lansing, Michigan, at concentrations of 204 and 100 ng/l, respectively. The mass flow calculation, estimated by multiplying the OFL concentration in the final effluent by the average influent volume of the WWTP, showed that the discharge of OFL to the river was 4.8 g/day. The OFL concentrations in wastewater effluents measured in this study are comparable to or less than those observed in several European countries. QAs were not detected in river and lake waters analyzed in this study, which may due to dilution effects and to the higher detection limits, relative to those reported previously. OFL concentrations were approximately 1-2 orders of magnitude lower than the EC50 concentrations for environmental bacterium. However, greater concentrations of other QAs in sewage sludge from WWTPs may result in cumulative effects. Considering that the sewage sludge is applied to the land as fertilizers, soil-dwelling organisms could experience greater exposures to such antibiotics. Monitoring studies of QAs in sewage from WWTPs and in sediment/soil near aquaculture facilities and livestock farms will be necessary for the evaluation of the environmental distribution and risk of these compounds.     

Application of rough sets analysis to identify polluted aquatic sites based on a battery of biomarkers: a comparison with classical methods

The evaluation of toxicological effects at the cellular and molecular levels in organisms are often used to determine sites subjected to contamination problems that pose a threat to the long-term survival of organisms. However, the integration of multiple measurements on the health status of organisms into a model for site discrimination is challenging. This study compares two discrimination methods which are based on rule inference: rough sets (RS) analysis and classification trees (CT) with classical multivariate discriminant analysis (DA). Site classification was attempted with six biomarkers of effects: metallothionein levels, lipid peroxidation, DNA damage, levels of lipophosphoproteins (i.e., vitellins), phagocytosis activity and haemocyte cell viability on clam (Mya arenaria) populations from the Saguenay River fjord (Quebec, Canada). Rule based methods have the advantage of complete independence from data distribution constraints in contrast to the classification methods from multivariate analysis that are more commonly used in ecotoxicology. Results show that RS and CT gave better classifications than DA because they do not require strong distributional assumptions. Moreover, RS provided classification rules that could identify the most important biomarker(s) for site discrimination. RS and CT were shown to be simple and efficient methods for classifying multivariable ecotoxicological data. This methodology would be especially useful when freedom from distributional assumptions is required.     

Prediction of SO2 levels using neural networks

In this paper, we present an adaptation of the air pollution control help system in the neighborhood of a power plant in As Pontes (A Coru?a, Spain), property of Endesa Generación S.A., to the European Council Directive 1999/30/CE. This system contains a statistic prediction made half an hour before the measurement, and it helps the staff in the power plant prevent air quality level episodes. The prediction is made using neural network models. This prediction is compared with one made by a semiparametric model.