Claire Miller

Cyanobacterial blooms: Statistical models describing risk factors for national-scale lake assessment and lake management

Cyanobacterial toxins constitute one of the most high risk categories of waterborne toxic biological substances. For this reason there is a clear need to know which freshwater environments are most susceptible to the development of large populations of cyanobacteria. Phytoplankton data from 134 UK lakes were used to develop a series of Generalised Additive Models and Generalised Additive Mixed Models to describe which kinds of lakes may be susceptible to cyanobacterial blooms using widely available explanatory variables. Models were developed for log cyanobacterial biovolume. Water colour and alkalinity are significant explanatory variables and retention time and TP borderline significant (R2-adj=21.9%). Surprisingly, the models developed reveal that nutrient concentrations are not the primary explanatory variable; water colour and alkalinity were more important. However, given suitable environments (low colour, neutral-alkaline waters), cyanobacteria do increase with both increasing retention time and increasing TP concentrations, supporting the observations that cyanobacteria are one of the most visible symptoms of eutrophication, particularly in warm, dry summers. The models can contribute to the assessment of risks to public health, at a regional to national level, helping target lake monitoring and management more cost-effectively at those lakes at the highest risk of breaching World Health Organisation guideline levels for cyanobacteria in recreational waters. The models also inform restoration options available for reducing cyanobacterial blooms, indicating that, in the highest risk lakes (alkaline, low colour lakes), risks can generally be lessened through management aimed at reducing nutrient loads and increasing flushing during summer.

Water quality of Loch Leven: responses to enrichment, restoration and climate change

It is usually assumed that climate change will have negative impacts on water quality and hinder restoration efforts. The long-term monitoring at Loch Leven shows, however, that seasonal changes in temperature and rainfall may have positive and negative impacts on water quality. In response to reductions in external nutrient loading, there have been significant reductions in in-lake phosphorus concentrations. Annual measures of chlorophyll a have, however, shown little response to these reductions. Warmer spring temperatures appear to be having a positive effect on Daphnia densities and this may be the cause of reduced chlorophyll a concentrations in spring and an associated improvement in water clarity in May and June. The clearest climate impact was the negative relationship between summer rainfall and chlorophyll a concentrations. This is highlighted in extreme weather years, with the three wettest summers having very low chlorophyll a concentrations and the driest summers having high concentrations. To predict water quality impacts of future climate change, there is a need for more seasonal predictions from climate models and a greater recognition that water quality is the outcome of seasonal responses in different functional groups of phytoplankton and zooplankton to a range of environmental drivers.

Septo-Optic Dysplasia: Antenatal Risk Factors and Clinical Features in a Regional Study

Background: Septo-optic dysplasia (SOD) is a disorder with postulated environmental and genetic aetiology. This study delineates clinical features and potential perinatal environmental factors along with epidemiology in SOD children. Methods: Assessment of patients with SOD triad features in the UK West Midlands region. Results: Of 227 patients identified between 1998 and 2009 with 1 or more feature of the triad, 55 had midline defects, 149 had optic nerve hypoplasia and 132 had hypopituitarism. Eighty-eight children (52% males; incidence 8.3/100,000 live births) had SOD defined as 2 out of 3 features and 21 (24%) had all 3. Sixty-one percent had anterior pituitary deficiency and 21.5% had diabetes insipidus. Median maternal/paternal ages in SOD were 21 and 23.5 years, compared to UK means of 29.3 and 32.4 years (p < 0.001). First trimester bleeding was markedly increased at 12/48 (25%) compared to 0.07% in the UK (p < 0.001). Ethnicity showed a non-significant higher prevalence in Afro-Caribbean and mixed race groups, and significantly lower prevalence (p = 0.004) in South Asian groups compared to West Midland and Birmingham city data: 8% versus 2.5 and 6.7%, 9% versus 1.8 and 3.2% and 3% versus 8.4 and 21%, respectively. Conclusions: SOD is associated with younger maternal and paternal age, primigravida births and ethnic differences. Increased first trimester bleeding may indicate that SOD is a vascular disruption sequence.

A comparison of clustering approaches for the study of the temporal coherence of multiple time series

Two approaches for clustering of time series have been considered. The first is a novel approach based on a modification of classic state-space modelling while the second is based on functional clustering. For the latter, both k-means and complete-linkage hierarchical clustering algorithms are adopted. The two approaches are compared using a simulation study, and are applied to lake surface water temperature for 256 lakes globally for 5 years of data, to investigate information obtained from each approach.

Spatiotemporal statistical modelling of long-term change in river nutrient concentrations in England & Wales.

Concentrations of nutrient nitrogen (N) and phosphorus (P) are elevated in rivers across large areas of Europe (European Nitrogen Assessment (ENA), Sutton et al., 2011). Environmental policies have been implemented over the past 20 years with the aim of reducing nitrogen inputs to surface waters. However, environmental and ecological status is still below set targets (ENA, Sutton et al., 2011). Identification of patterns in long-term change for nutrient trends in hydrological catchments in England & Wales is required to assess impacts of nutrient management policy and provide better evidence for future policy. Such information could provide essential evidence for supporting policy by combining information from the wider catchment, rather than relying on the analysis of data from individual sites. Surface water quality is subject to considerable spatial and short-period temporal variability, reflecting variability in loading and dilution. This makes it difficult to determine temporal trends at individual monitoring sites with relatively sparse sampling. Here we apply spatiotemporal statistical additive models for both nitrogen and phosphorus in river networks across England & Wales to investigate the overall pattern of nutrient concentrations in these river surface waters over the past 20-40 years. Concentrations of Orthophosphate (OP) have generally decreased over time for many of the Large Hydrological Areas with a seasonal pattern highlighting one peak in the summer months. Over the past ten years, Total Oxidised Nitrogen (Nitrate+Nitrite, TON) concentrations have generally been slowly decreasing or fairly constant. However, prior to 2000, concentrations were generally on an upward trend. The seasonal pattern highlights one trough in the summer months. The highest levels for OP and TON broadly occur in the same general areas across England & Wales. On average, over time, the lowest values are evident in the north-west and south-west (particularly for OP) and highest values are evident in the Midlands, Anglian and Southern regions.

Spatially weighted functional clustering of river network data

Incorporating spatial covariance into clustering has previously been considered for functional data to identify groups of functions which are similar across space. However, in the majority of situations that have been considered until now the most appropriate metric has been Euclidean distance. Directed networks present additional challenges in terms of estimating spatial covariance due to their complex structure. Although suitable river network covariance models have been proposed for use with stream distance, where distance is computed along the stream network, these models have not been extended for contexts where the data are functional, as is often the case with environmental data. The paper develops a method of calculating spatial covariance between functions from sites along a river network and applies the measure as a weight within functional hierarchical clustering. Levels of nitrate pollution on the River Tweed in Scotland are considered with the aim of identifying groups of monitoring stations which display similar spatiotemporal characteristics.

Challenges in modeling detailed and complex environmental data sets: a case study modeling the excess partial pressure of fluvial \hbox {CO}_2

Advances in sensor technology enable environmental monitoring programmes to record and store measurements at a high temporal resolution, enhancing the capacity to detect and understand short duration changes that would not have been apparent in the past with monthly, fortnightly or even daily sampling. However, there are various challenges in terms of the processing and analysis of these environmental high-frequency data due to their complex behavior over the different timescales and the strong correlation structure that persists over a large number of lags. Here, we explore the complexities of modeling high-frequency data which arise from environmental applications. With increasing understanding of the importance of surface waters as a source of atmospheric

Complications following incident stroke resulting in readmissions: an analysis of data from three Scottish health surveys

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