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The NERC and CEH trade marks and logos ('the Trademarks') are registered trademarks of NERC in the UK and other countries, and may not be used without the prior written consent of the Trademark owner. . 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 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.

The objective of this study was to investigate the associations between potassium and obesity/metabolic syndrome. We identified eight relevant studies and applied meta-analysis, and nonlinear dose-response analysis to obtain the available evidence. The results of the pooled analysis and systematic review indicated that high potassium intake could not reduce the risk of obesity (pooled OR = 0.78; 95% CI: 0.61–1.01), while serum potassium and urinary sodium-to-potassium ratio was associated with obesity. Potassium intake was associated with metabolic syndrome (pooled OR = 0.75; 95% CI: 0.50–0.97). Nonlinear analysis also demonstrated a protective effect of adequate potassium intake on obesity and metabolic syndrome. Adequate intake of fruits and vegetables, which were the major sources of potassium, was highly recommended. However, additional pertinent studies are needed to examine the underlying mechanism.

Many statistical models are available for spatial data but the vast majority of these assume that spatial separation can be measured by Euclidean distance. Data which are collected over river networks constitute a notable and commonly occurring exception, where distance must be measured along complex paths and, in addition, account must be taken of the relative flows of water into and out of confluences. Suitable models for this type of data have been constructed based on covariance functions. The aim of the paper is to place the focus on underlying spatial trends by adopting a regression formulation and using methods which allow smooth but flexible patterns. Specifically, kernel methods and penalized splines are investigated, with the latter proving more suitable from both computational and modelling perspectives. In addition to their use in a purely spatial setting, penalized splines also offer a convenient route to the construction of spatiotemporal models, where data are available over time as well as over space. Models which include main effects and spatiotemporal interactions, as well as seasonal terms and interactions, are constructed for data on nitrate pollution in the River Tweed. The results give valuable insight into the changes in water quality in both space and time.

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