Impact of tidal-stream arrays in relation to the natural variability of sedimentary processes

AbstractTidal Energy Converter (TEC) arrays are expected to reduce tidal current speeds locally, thus impacting sediment processes, even when positioned above bedrock, as well as having potential impacts to nearby offshore sand banks. Furthermore, the tidal dissipation at potential TEC sites can produce high suspended sediment concentrations (turbidity maxima) which are important for biological productivity. Yet few impact assessments of potential TEC sites have looked closely at sediment dynamics beyond local scouring issues. It is therefore important to understand to what extent exploitation of the tidal energy resource will affect sedimentary processes, and the scale of this impact is here assessed in relation to natural variability. At one such site in the Irish Sea that is highly attractive for the deployment of TEC arrays, we collect measurements of sediment type and bathymetry, apply a high resolution unstructured morphodynamic model, and a spectral wave model in order to quantify natural variability due to tidal and wave conditions. We then simulate the impacts of tidal-stream energy extraction using the morphodynamic model. Our results suggest that the sedimentary impacts of ‘first generation’ TEC arrays (i.e. less than 50 MW), at this site, are within the bounds of natural variability and are, therefore, not considered detrimental to the local environment. Yet we highlight potential environmental issues and demonstrate how impact assessments at other sites could be investigated

Estimating treatment importance in multidrug-resistant tuberculosis using Targeted Learning : an observational individual patient data network meta-analysis

Persons with multidrug‐resistant tuberculosis (MDR‐TB) have a disease resulting from a strain of tuberculosis (TB) that does not respond to at least isoniazid and rifampicin, the two most effective anti‐TB drugs. MDR‐TB is always treated with multiple antimicrobial agents. Our data consist of individual patient data from 31 international observational studies with varying prescription practices, access to medications, and distributions of antibiotic resistance. In this study, we develop identifiability criteria for the estimation of a global treatment importance metric in the context where not all medications are observed in all studies. With stronger causal assumptions, this treatment importance metric can be interpreted as the effect of adding a medication to the existing treatments. We then use this metric to rank 15 observed antimicrobial agents in terms of their estimated add‐on value. Using the concept of transportability, we propose an implementation of targeted maximum likelihood estimation, a doubly robust and locally efficient plug‐in estimator, to estimate the treatment importance metric. A clustered sandwich estimator is adopted to compute variance estimates and produce confidence intervals. Simulation studies are conducted to assess the performance of our estimator, verify the double robustness property, and assess the appropriateness of the variance estimation approach

Characterising the spatial and temporal variability of the tidal-stream energy resource over the northwest European shelf seas

As devices move from full-scale prototype to commercial installations, it is important that developers have detailed knowledge of the tidal energy resource. Therefore, the spatial distribution of the tidal currents over the northwest European shelf seas has been examined to improve understanding of the tidal-stream energy resource. Using a three-dimensional hydrodynamic model (ROMS) at �1 km spatial resolution, and applying device characteristics of the Seagen-S turbine, we show that the ratio of the amplitudes of the M2 and S2 tidal currents can lead to significant variability in annual practical power generation � variability that is not accounted for when considering only the mean peak spring tidal velocities, as is generally the case in resource feasibility studies. In addition, we show that diurnal inequalities (governed by K1 and O1 tidal constituents) and tidal asymmetries (governed by the relationship between M2 and its compound tide M4) over the northwest European shelf seas can further affect power generation at potential high-energy sites. Based on these variabilities, the spatial distribution of the tidal-stream �capacity factor� has been calculated. We find that mean peak spring tidal velocities can under-estimate the resource by up to 25%, and that annual practical power generation can vary by �15% for regions experiencing similar mean peak spring tidal velocities, due to the influence of other tidal constituents. Therefore, even preliminary resource assessments should be based on annual average power density, rather than peak spring tidal velocity

Semiparametric theory and empirical processes in causal inference

In this paper we review important aspects of semiparametric theory and empirical processes that arise in causal inference problems. We begin with a brief introduction to the general problem of causal inference, and go on to discuss estimation and inference for causal effects under semiparametric models, which allow parts of the data-generating process to be unrestricted if they are not of particular interest (i.e., nuisance functions). These models are very useful in causal problems because the outcome process is often complex and difficult to model, and there may only be information available about the treatment process (at best). Semiparametric theory gives a framework for benchmarking efficiency and constructing estimators in such settings. In the second part of the paper we discuss empirical process theory, which provides powerful tools for understanding the asymptotic behavior of semiparametric estimators that depend on flexible nonparametric estimators of nuisance functions. These tools are crucial for incorporating machine learning and other modern methods into causal inference analyses. We conclude by examining related extensions and future directions for work in semiparametric causal inference

Effect of waves on the tidal energy resource at a planned tidal stream array

Wave�current interaction (WCI) processes can potentially alter tidal currents, and consequently affect the tidal stream resource at wave exposed sites. In this research, a high resolution coupled wave-tide model of a proposed tidal stream array has been developed. We investigated the effect of WCI processes on the tidal resource of the site for typical dominant wave scenarios of the region. We have implemented a simplified method to include the effect of waves on bottom friction. The results show that as a consequence of the combined effects of the wave radiation stresses and enhanced bottom friction, the tidal energy resource can be reduced by up to 20% and 15%, for extreme and mean winter wave scenarios, respectively. Whilst this study assessed the impact for a site relatively exposed to waves, the magnitude of this effect is variable depending on the wave climate of a region, and is expected to be different, particularly, in sites which are more exposed to waves. Such effects can be investigated in detail in future studies using a similar procedure to that presented here. It was also shown that the wind generated currents due to wind shear stress can alter the distribution of this effect

Future variability of solute transport in a macrotidal estuary

AbstractThe physical controls on salt distribution and river-sourced conservative solutes, including the potential implications of climate change, are investigated referring to model simulations of a macrotidal estuary. In the UK, such estuaries typically react rapidly to rainfall events and, as such, are often in a state of non-equilibrium in terms of solute transport; hence are particularly sensitive to climate extremes. Sea levels are projected to rise over the 21st century, extending the salinity maximum upstream in estuaries, which will also affect downstream solute transport, promoting estuarine trapping and reducing offshore dispersal of material. Predicted ‘drier summers’ and ‘wetter winters’ in the UK will influence solute transport further still; we found that projected river flow climate changes were more influential than sea-level rise, especially for low flow conditions. Our simulations show that projected climate change for the UK is likely to increase variability in estuarine solute transport and, specifically, increase the likelihood of estuarine trapping during summer, mainly due to drier weather conditions. Future changes in solute transport were less certain during winter, since increased river flow will to some extent counter-act the effects of sea-level rise. Our results have important implications for non-conservative nutrient transport, water quality, coastal management and ecosystem resilience

Bose-Einstein condensate collapse: a comparison between theory and experiment

We solve the Gross-Pitaevskii equation numerically for the collapse induced by a switch from positive to negative scattering lengths. We compare our results with experiments performed at JILA with Bose-Einstein condensates of Rb-85, in which the scattering length was controlled using a Feshbach resonance. Building on previous theoretical work we identify quantitative differences between the predictions of mean-field theory and the results of the experiments. Besides the previously reported difference between the predicted and observed critical atom number for collapse, we also find that the predicted collapse times systematically exceed those observed experimentally. Quantum field effects, such as fragmentation, that might account for these discrepancies are discussed.Comment: 4 pages, 2 figure

Research needs for optimising wastewater-based epidemiology monitoring for public health protection

Wastewater-based epidemiology (WBE) is an unobtrusive method used to observe patterns in illicit drug use, poliovirus, and severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). The pandemic and need for surveillance measures have led to the rapid acceleration of WBE research and development globally. With the infrastructure available to monitor SARS-CoV-2 from wastewater in 58 countries globally, there is potential to expand targets and applications for public health protection, such as other viral pathogens, antimicrobial resistance (AMR), pharmaceutical consumption, or exposure to chemical pollutants. Some applications have been explored in academic research but are not used to inform public health decision-making. We reflect on the current knowledge of WBE for these applications and identify barriers and opportunities for expanding beyond SARS-CoV-2. This paper critically reviews the applications of WBE for public health and identifies the important research gaps for WBE to be a useful tool in public health. It considers possible uses for pathogenic viruses, AMR, and chemicals. It summarises the current evidence on the following: (1) the presence of markers in stool and urine; (2) environmental factors influencing persistence of markers in wastewater; (3) methods for sample collection and storage; (4) prospective methods for detection and quantification; (5) reducing uncertainties; and (6) further considerations for public health use