Sociodemographic, reproductive and dietary predictors of organochlorine compounds levels in pregnant women in Spain

Organochlorine pesticides and polychlorinated biphenyls (PCBs) are consistently found in human tissues. Serum levels of organochlorine compounds (OC) in pregnant women in particular have raised concern about possible harm to humans in the early phases of physical and behavioural development. The objective of the present study was to evaluate the association between concentration of OCs in serum of two cohorts of pregnant women from Gipuzkoa and Sabadell in Spain and socioeconomic, reproductive and dietary variables. Concentration of polychlorinated biphenyls (PCBs: 28, 52, 101, 118, 138, 153 and 180), hexachlorobenzene (HCB), beta and gamma-hexachlorocyclohexane (β-HCH and γ-HCH), heptachlor epoxide, dichlorodiphenyl dichloroethylene (p,p'-DDE) and dichlorodiphenyl trichloroethane (p,p'-DDT) were measured in the serum of 1259 pregnant women. Associations between OCs and potential predictor variables were assessed using linear regression models adjusted for potential confounders. The compounds most commonly found in the serum were p,p'-DDE (99% of the samples) and PCB-153 (95% of the samples). Geometric means of serum concentrations (ng g?1 lipid) of organochlorine pesticides were 110.0, 19.1, and 33.5 for p,p'-DDE, β-HCH, and HCB respectively, while the geometric means of PCBs were 21.8, 38.9 and 26.9 for PCB 138, 153, and 180 respectively. The levels of all OCs increased with age. BMI was positively associated with the concentration of organochlorine pesticides but inversely related to PCB concentrations. The serum levels of OCs fell only after a cumulative period of breastfeeding of over a year. Levels of PCBs were related to fish intake, but in general dietary factors did not improve the explained variability of the concentrations of OCs. Overall, the levels of OCs found in the study are at the lower end of the range reported in Spain and other countries.     

Integrated biological responses of zebrafish (Danio rerio) to analyze water quality in regions under anthropogenic influence

This study analyzed water quality in regions around Patos lagoon (Southern Brazil) that are under anthropogenic pressure. Water samples were collected from five different sites, including one used as a source for human consumption (COR) and others known to be influenced by human activities (IP). Danio rerio (Teleostei, Cyprinidae) organisms were exposed for 24 h to these water samples, plus a control group. It was observed that: (1) reactive oxygen species levels were lower in COR and IP than in the control group; (2) glutamate-cysteine ligase (catalytic subunit) expression was higher in COR than in other sites; (3) exposure to all water samples affected long-term memory (LTM) when compared to control group. Thus, some water samples possess the ability to modulate the antioxidant system and to induce a decline in cognitive functions, as measured by LTM. The obtained results indicate that a combination of variables of different organization level (molecular, biochemical and behavioral) can be employed to analyze water quality in impacted regions.     

Implications of geographic variability on Comparative Toxicity Potentials of Cu, Ni and Zn in freshwaters of Canadian ecoregions

Current methods of estimating potential environmental impacts of metals in hazard and Life Cycle Impact Assessment (LCIA) do not consider differences in chemistry and landscape properties between geographic sites. Here, we developed and applied a model for regional aquatic impact characterization of metals using an updated method for estimating environmental fate factor (FF), bioavailability factor (BF) and aquatic ecotoxicity factor (EF). We applied the model to analyze differences in Comparative Toxicity Potentials (CTPs) of Cu, Ni and Zn for 24 Canadian ecoregions. The combined impacts of regional variability in ambient chemistry (in particular DOC, pH and hardness) and landscape properties (water residence time) can change the CTPs of these metals for freshwater by up to three orders of magnitude and change the relative ranking of metal hazard between ecoregions. Variation among Canadian freshwater chemistries and landscape characteristics influence the FFs within two orders of magnitude, BFs within two orders of magnitude for Ni and Zn and four orders of magnitude for Cu, and EFs within one order of magnitude. Sensitivity of metal FFs to environmental parameters alone spans three orders of magnitude when a constant water chemistry was used for all ecoregions. These results indicate that application of regionalised metal CTPs can have a significant influence in the analysis of ecotoxicological impacts in the life cycle assessment of products and processes.     

Effects of C60 nanoparticle exposure on earthworms (Lumbricus rubellus) and implications for population dynamics

Effects of C₆₀ nanoparticles (nominal concentrations 0, 15.4 and 154 mg/kg soil) on mortality, growth and reproduction of Lumbricus rubellus earthworms were assessed. C₆₀ exposure had a significant effect on cocoon production, juvenile growth rate and mortality. These endpoints were used to model effects on the population level. This demonstrated reduced population growth rate with increasing C₆₀ concentrations. Furthermore, a shift in stage structure was shown for C₆₀ exposed populations, i.e. a larger proportion of juveniles. This result implies that the lower juvenile growth rate due to exposure to C₆₀ resulted in a larger proportion of juveniles, despite increased mortality among juveniles. Overall, this study indicates that C₆₀ exposure may seriously affect earthworm populations. Furthermore, it was demonstrated that juveniles were more sensitive to C₆₀ exposure than adults.     

Integrated analysis of the effects of agricultural management on nitrogen fluxes at landscape scale

The integrated modelling system INITIATOR was applied to a landscape in the northern part of the Netherlands to assess current nitrogen fluxes to air and water and the impact of various agricultural measures on these fluxes, using spatially explicit input data on animal numbers, land use, agricultural management, meteorology and soil. Average model results on NH₃ deposition and N concentrations in surface water appear to be comparable to observations, but the deviation can be large at local scale, despite the use of high resolution data. Evaluated measures include: air scrubbers reducing NH₃ emissions from poultry and pig housing systems, low protein feeding, reduced fertilizer amounts and low-emission stables for cattle. Low protein feeding and restrictive fertilizer application had the largest effect on both N inputs and N losses, resulting in N deposition reductions on Natura 2000 sites of 10% and 12%, respectively.     

Assessing the recovery potential of alpine moss-sedge heath: reciprocal transplants along a nitrogen deposition gradient

The potential of alpine moss-sedge heath to recover from elevated nitrogen (N) deposition was assessed by transplanting Racomitrium lanuginosum shoots and vegetation turfs between 10 elevated N deposition sites (8.2-32.9 kg ha⁻¹ yr⁻¹) and a low N deposition site, Ben Wyvis (7.2 kg ha⁻¹ yr⁻¹). After two years, tissue N of Racomitrium shoots transplanted from higher N sites to Ben Wyvis only partially equilibrated to reduced N deposition whereas reciprocal transplants almost matched the tissue N of indigenous moss. Unexpectedly, moss shoot growth was stimulated at higher N deposition sites. However, moss depth and biomass increased in turfs transplanted to Ben Wyvis, apparently due to slower shoot turnover (suggested to result partly from decreased tissue C:N slowing decomposition), whilst abundance of vascular species declined. Racomitrium heath has the potential to recover from the impacts of N deposition; however, this is constrained by the persistence of enhanced moss tissue N contents.     

Stepwise calibration of the activated sludge model no. 1 at a partially denitrifying large wastewater treatment plant

Activated sludge modeling technology is maturing; however, currently, there exists a great need to increase its use in daily engineering practice worldwide. A good way for building the capacities of the practitioners is to promote good modeling practices and standardize the protocols. In this study, a systematic procedure was proposed to calibrate the Activated Sludge Model No. 1 (ASM1) at a large wastewater treatment plant, by which the model adequately predicted the quality of the effluent and the sludge quantities. A hydraulics model was set up and validated through a tracer test. The Vesilind settling constants were measured and combined with the default value of the flocculent zone settling parameter, to calibrate the clarifiers. A virtual anoxic tank was installed in the return activated sludge to mimic the denitrification occurring in the settlers. In ASM1, the calibrated parameters were only two influent chemical oxygen demand fractions and one kinetic constant (oxygen half-saturation coefficient).     

Modeling transit bus fuel consumption on the basis of cycle properties

A method exists to predict heavy-duty vehicle fuel economy and emissions over an "unseen" cycle or during unseen on-road activity on the basis of fuel consumption and emissions data from measured chassis dynamometer test cycles and properties (statistical parameters) of those cycles. No regression is required for the method, which relies solely on the linear association of vehicle performance with cycle properties. This method has been advanced and examined using previously published heavy-duty truck data gathered using the West Virginia University heavy-duty chassis dynamometer with the trucks exercised over limited test cycles. In this study, data were available from a Washington Metropolitan Area Transit Authority emission testing program conducted in 2006. Chassis dynamometer data from two conventional diesel buses, two compressed natural gas buses, and one hybrid diesel bus were evaluated using an expanded driving cycle set of 16 or 17 different driving cycles. Cycle properties and vehicle fuel consumption measurements from three baseline cycles were selected to generate a linear model and then to predict unseen fuel consumption over the remaining 13 or 14 cycles. Average velocity, average positive acceleration, and number of stops per distance were found to be the desired cycle properties for use in the model. The methodology allowed for the prediction of fuel consumption with an average error of 8.5% from vehicles operating on a diverse set of chassis dynamometer cycles on the basis of relatively few experimental measurements. It was found that the data used for prediction should be acquired from a set that must include an idle cycle along with a relatively slow transient cycle and a relatively high speed cycle. The method was also applied to oxides of nitrogen prediction and was found to have less predictive capability than for fuel consumption with an average error of 20.4%.