NERC Open Research Archive
Data and models in the reporting of the GHG fluxes from soils disturbed by land use and climate change
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Geoscience after IT: table of contents
Get PDFTable of Contents, with links to full-text of articles in NORA that were published as a Special Issue of Computers & Geosciences, 'Geoscience after IT: a view of the present and future impact of information technology on geoscience
Immunomodulatory arsenal of nymphal ticks
Get PDFTicks have developed their own immunomodulatory mechanisms to inhibit the host inflammatory response. One of them involves the ability to subvert the cytokine network at the site of tick feeding by secreting cytokine binding molecules. Most studies have focused on the immunomodulatory prowess of adult female ticks. Here we describe anti-cytokine activity in salivary gland extracts (SGEs) prepared from 2-day-fed nymphs of Dermacentor reticulatus Fabricius, Ixodes ricinus L., Rhipicephalus appendiculatus Neumann and Amblyomma variegatum Fabricius. Anti-CXCL8 activity was detected in nymphs of all species. Relatively high activity against CCL2, CCL3 and CCL11 was observed in SGEs of R. appendiculatus and A. variegatum nymphs, whereas SGEs of I. ricinus nymphs showed comparatively high anti-interleukin-2 (-IL-2) and anti-IL-4 activities. These data show that nymphs, which epidemiologically are usually more important than adults as disease vectors, possess a range of anti-cytokine activities that may facilitate pathogen transmission
Controls over N2O, NOx and CO2 fluxes in a calcareous mountain forest soil
Get PDFWe measured nitrogen oxides (N2O and NOx), dinitrogen (N2) and carbon dioxide (CO2) emissions from a spruce-fir-beech forest soil in the North Tyrolean limestone Alps in Austria. The site received 10.6–11.9 kg N ha−1 y−1 nitrogen as bulk deposition. Fluxes of nitric oxide (NO) were measured by an automatic dynamic chamber system on an hourly basis over a two year period. Daily N2O emissions were obtained by a semi-automatic gas measuring system. In order to cover spatial variability biweekly manual measurements of N2O and CO2 emissions were carried out in addition. For acquiring information on the effects of soil and meteorological conditions and of N-deposition on N-emissions we chose the auto-regression procedure (time-series analysis) as our means of investigation. Hence, we could exclude the data's autocorrelation in the course of the time. We found that soil temperature, soil moisture and bulk N-deposition followed by air temperature and precipitation were the most powerful influencing parameters effecting N-emissions. With these variables, up to 89% of observed temporal variations of N-emissions could be explained. During the two-year investigation period between 2.5 and 3.5% of deposited N was reemitted in form of N2O whereas only 0.2% were emitted as NO. At our mountain forest site the main end-product of microbial activity processes was N2 and trace gases (N2O and NO) were only of minor importance
Prediction and analysis of long-term variability of temperature and salinity in the Irish Sea
Get PDFThe variability of temperature and salinity in the Irish Sea over the 40 year period 1960 - 1999 is investigated using a free-running fine-resolution local area model. The skill of the model to represent observed temperature and salinity variability is assessed using conductivity-temperature-depth survey data ( 3397 profiles) and a long time series of measurements from Cypris station (southwest of Isle of Man). This clearly demonstrates that the model can reproduce the observed seasonal and longer-term cycles in temperature, with mean and RMS errors of - 0.01 degrees C and 0.78 degrees C. Particularly apparent is the long-term warming trend at Cypris station and throughout the model domain. Model estimates of salinity are less accurate and are generally too saline (mean and RMS errors are 0.79 and 0.98 practical salinity units). Inaccuracies are likely to arise from boundary conditions and forcing (riverine and surface). However, while absolute values are not particularly well represented, the model reproduces many of the trends in the salinity variability observed at Cypris station, suggesting that the dominant physical processes in the Irish Sea, with timescales up to similar to 3 years, are well represented. The model is also used to investigate the variability in temperature stratification. While stratification is confined to approximately the same geographical area in each year of the simulation, there is significant variability in the timing of the onset and breakdown of stratification and in the peak surface to bed temperature difference. Together, these results suggest that a local area model with limited boundary conditions may be sufficiently accurate for climatic investigation of some (locally forced) parameter
Catchment Risk Assessment of Steroid Oestrogens from Sewage Treatment Works
Get PDFThis project has developed a regional catchment-based risk assessment for steroid
oestrogens in England and Wales. Using the Low Flows 2000 water quality (LF2000-WQX)
model, which can predict river concentrations of contaminants discharged through sewage
treatment plants (STPs), the project has focussed on predicting concentrations of the three
most potent steroid oestrogens in rivers and the associated risk of endocrine disruption in
fish. The model could equally well be applied to any other chemical of concern found in
treated sewage effluent, where loading can be estimated on a per capita basis.
The model, believed to be the first of its kind, covers the inland waters of England and
Wales in unprecedented detail. It includes 357 catchments covering 122,000km2 and
incorporates more than 2,000 STPs serving over 29 million people (STPs discharging to
estuaries and coastal waters and those serving very small communities are excluded). The
model predicts the concentrations of three steroid oestrogens – oestradiol (E2), oestrone
(E1) and ethinyloestradiol (EE2) – and the associated risk of endocrine disruption for
10,313 individual river reaches (21,452 km). The scale of this assessment underlines the
usefulness of computer-based risk assessment methods. Developing this regional model
was only possible due to the remarkable cooperation of different groups within the
Environment Agency and the UK water industry in establishing the underlying database.
The model calculates how much of each of the three oestrogens would be discharged into
the receiving waters from data on the flow of the STPs and the populations they serve. The
in-stream concentrations are then calculated based on dilutions down through the river
network, including the effects of the natural attenuation rate. During the development of the
model, refinements were added to allow one of the oestrogens, oestradiol, to be converted
in-stream into its first degradation product, oestrone, which is another oestrogen. This
simulates what has been observed in the field and allows a more accurate prediction of
overall oestrogenicity. In addition, an approach has been developed that allows users to
identify and calculate what additional levels of improvement are required for the most
polluting STPs in order for there to be no predicted risk of endocrine disruption in their
catchment.
Three specific tasks were required to generate the factors underpinning the model. First,
the most recent literature and data on STP oestrogen removal efficiencies were reviewed.
Primary treatment plants, activated sludge plants (with and without tertiary treatment) and
biological filter plants (with and without tertiary treatment) were all considered. The latest
UK data indicated that the removal efficiency for E1 in biological filter plants without tertiary
treatment was significantly different to that previously determined, being reduced by around
30 per cent. For all other types of STP, the new data indicated that a slightly improved
removal efficiency of 69 per cent should be used. In the cases of E2 and EE2, only a slight
modification was necessary, increasing the removal efficiency to 83 per cent for all
treatment types.
Second, recent scientific studies measuring the effects of steroid oestrogens were
reviewed. This allowed PNECs (predicted no effect concentrations) of 0.1ngL-1, 1ngL-1 and
3ngL-1 to be established for EE2, E2 and E1 respectively. A method for calculating the E2
equivalent concentrations was also developed. This divides each steroid by its respective
PNEC to produce a measure of relative potency and these values are then summed, as the
effects of steroids have been shown to be additive. Thus the [E2 equivalent] = [EE2]/0.1 +
[E2]/1 + [E1]/3 (with the square brackets denoting concentrations).
Finally, the risk class boundaries were also reviewed and it was established that the
currently proposed total steroid oestrogen PNEC (1ngL-1 E2 equivalent) remained valid for
distinguishing ‘no risk’ sites from ‘at risk’ sites. The review also determined that the
boundary between ‘at risk’ and ‘high risk’ sites should be set at 10ngL-1 E2 equivalent. This
Science Report – Catchment Risk Assessment of Steroid Oestrogens from Sewage Treatment Work v
was estimated to be equivalent to the lowest measured population effect end-point for E2 in
published literature.
Overall, the majority of the reaches in England and Wales (61 per cent using mean
concentrations and expressed as a percentage of the total river length modelled) are
predicted not to be ‘at risk’ from endocrine disruptive effects in fish (< 1ngL-1 E2 equivalent).
However, a significant proportion remains ‘at risk’ (>1 ngL-1 E2 equivalent; 39 per cent of
length of the modelled reaches under mean conditions). These risk proportions are not
evenly distributed throughout England and Wales. The lowest proportions predicted to be
‘at risk’ are in Wales and the South West (5 per cent and 16 per cent respectively). In the
Southern, North East, and North West regions, 34 per cent, 38 per cent, and 34 per cent of
the reach lengths are predicted to be ‘at risk’ respectively. The highest proportions of
reaches predicted to be ‘at risk’ are in the Thames, Midlands and Anglian regions, with 67
per cent, 55 per cent, and 50 per cent respectively. Key factors influencing the proportion of
river reaches classified as being ‘at risk’ are the location of densely populated areas and
the available dilution (which is a function of rainfall, evaporation and upstream water use).
The proportion of lengths predicted to be ‘at risk’ seems rather high, but the high proportion
of intersex individuals reported in wild roach in two national surveys (Environment Agency
1995 and 2003) suggests the predicted risk is reasonable, at least for this species.
A very small proportion of reaches, around 1–2 per cent, were predicted to be at ‘high risk’
(>10ngL-1 E2 equivalent). However, many of these ‘high risk’ reaches were short stretches
of headwaters or ditches composed almost entirely of sewage effluent. For this reason,
consideration will need to be given to the most appropriate use of this model in determining
which options for improving the removal of oestrogens from the environment will provide the
greatest benefit for fish populations and their habitats.
A more detailed risk assessment was carried out using these same methods for 12 sites
defined as Special Areas of Conservation (SACs). A simpler ‘no risk’ (<1ngL-1 E2
equivalent) or ‘at risk’ (E2 equivalent >1ngL-1) assessment was used, which incorporated a
lower predicted no effect concentration for EE2 of 0.06ngL-1. Four of these sites were
predicted to have at least one reach ‘at risk’ under mean concentrations, rising to seven
sites under 90th percentile concentrations (Chapter 8).
This risk assessment was based on readily available data sets and due diligence has been
taken in the quality control of these data. However, there are limitations associated with the
data and certain outstanding issues that will need to be addressed in the longer term, which
both contribute uncertainty to the model. For instance, a correct association needs to be
made between each STP and its receiving water course and it would be advantageous to
use measured dry weather flows rather than estimated values. Further improvements would
include having more detailed estimates of STP steroid removal efficiencies, or even
measured values for individual STPs, and refining the PNEC, which may alter the risk
category thresholds and the calculation of E2 equivalent concentrations. Also, it is
recommended that the predicted environmental concentrations produced by the model
should be compared with measured data in water bodies. Furthermore, the predicted risk
for fish should be compared to observed effect data, so that the risk assessment can be
refined accordingly.
This model gives a detailed and comprehensive picture of the likely levels of exposure of
freshwater fish populations to steroid oestrogens. It should therefore help in the
development of a rational and cost effective strategy to reduce the risk of population
decline, by targeting areas where steroid oestrogen reduction would prove of greatest
benefit to fish stocks and to the wider environment