The need for quantitative molecular methods is growing in environmental, food, and medical fields but is hindered by low and variable DNA extraction and by co-extraction of PCR inhibitors. DNA extracts from Enterococcus faecium, seawater, and seawater spiked with E. faecium and Vibrio parahaemolyticus were tested by qPCR for target recovery and inhibition. Conventional and novel methods were tested, including Synchronous Coefficient of Drag Alteration (SCODA) and lysis and purification systems used on an automated genetic sensor (the Environmental Sample Processor, ESP). Variable qPCR target recovery and inhibition were measured, significantly affecting target quantification. An aggressive lysis method that utilized chemical, enzymatic, and mechanical disruption enhanced target recovery compared to commercial kit protocols. SCODA purification did not show marked improvement over commercial spin columns. Overall, data suggested a general need to improve sample preparation and to accurately assess and account for DNA recovery and inhibition in qPCR applications.

A two-fluid, small scale numerical ocean model was developed to simulate plume dynamics and increases in water acidity due to leakages of CO2 from potential sub-seabed reservoirs erupting, or pipeline breaching into the North Sea. The location of a leak of such magnitude is unpredictable; therefore, multiple scenarios are modelled with the physiochemical impact measured in terms of the movement and dissolution of the leaked CO2. A correlation for the drag coefficient of bubbles/droplets free rising in seawater is presented and a sub-model to predict the initial bubble/droplet size forming on the seafloor is proposed. With the case studies investigated, the leaked bubbles/droplets fully dissolve before reaching the water surface, where the solution will be dispersed into the larger scale ocean waters. The tools developed can be extended to various locations to model the sudden eruption, which is vital in determining the fate of the CO2 within the local waters.

An estimated 100 million people inhabit coastal areas at risk from flooding and erosion due to climate change. Seagrass meadows, like other coastal ecosystems, attenuate waves. Due to inconsistencies in how wave attenuation is measured results cannot be directly compared. We synthesised data from laboratory and field experiments of seagrass-wave attenuation by converting measurements to drag coefficients (CD). Drag coefficients varied from 0.02–5.12 with CD¯ = 0.74 for studies conducted in turbulent flow in non-storm conditions. A statistical model suggested that seagrass species affects CD although the exact mechanism remains unclear. A wave model using the estimated CD¯ as an input parameter demonstrated that wave attenuation increased with meadow length, shoot density, shoot width and canopy height. Findings can be used to estimate wave attenuation by seagrass, in any given set of conditions.

The present paper focuses on developing a numerical oil spill model that incorporates the full three-dimensional wave–current interactions for a better representation of the spilled oil transport mechanics in complicated coastal environments. The incorporation of surface wave effects is not only imposing a traditional drag coefficient formulation at the free surface, but also the 3D momentum equations are adjusted to include the impact of the vertically dependent radiation stresses on the currents. Based on the current data from SELFE and wave data from SWAN, the oil spill model utilizes oil particle method to predict the trajectory of individual droplets and the oil concentration. Compared with the observations in Dalian New Port oil spill event, the developed model taking into account wave–current coupling administers to giving better conformity than the one without. The comparisons demonstrates that 3D radiation stress impacts the spill dynamics drastically near the sea surface and along the coastline, while having less impact in deeper water.

Predicting sediment transport rate (STR) in the presence of flexible vegetation is a critical task for modelers. Sediment transport modeling methods in the coastal region is equally challenging due to the nonlinearity of the STR–vegetation interaction. In the present study, the kernel extreme learning model (KELM) was integrated with the seagull optimization algorithm (SEOA), the crow optimization algorithm (COA), the firefly algorithm (FFA), and particle swarm optimization (PSO) to estimate the STR in the presence of vegetation cover. The rigidity index, D₅₀/wave height, Newton number, drag coefficient, and cover density were used as inputs to the models. The root mean square error (RMSE), the mean absolute error (MAE), and percentage of bias (PBIAS) were used to evaluate the capability of models. This study applied the novel ensemble model, and the inclusive multiple model (IMM), to assemble the outputs of the KELM models. In addition, the innovations of this study were the introduction of a new IMM model, and the use of new hybrid KELM models for predicting STR and investigating the effects of various parameters on the STR. At the testing level, the MAE of the IMM model was 22, 60, 68, 73, and 76% lower than those of the KELM-SEOA, KELM-COA, KELM-PSO, and KELM models, respectively. The IMM had a PBIAS of 5, whereas the KELM-SEOA, KELM-COA, KELM-PSOA, and KELM had PBIAS of 9, 12, 14, 18, and 21%, respectively. The results indicated that the increasing drag coefficient and D₅₀/wave height had decreased the STR. From the findings, it was revealed that the IMM and KELM-SEOA had higher predictive ability for STR. Since the sediment is one of the most important sources of environmental pollution, therefore, this study is useful for monitoring and controlling environmental pollution.