A Simplified Consistent Nonlinear Mild-Slope Equation Model for Random Waves Propagation and Dissipation.

The model for ocean surface wave propagation can be formulated either in the form of deterministic models or stochastic models. The stochastic models appear to be particularly attractive in the global domain due to their computational efficiency. However, in the nearshore region, the phase becomes highly correlated, and the phase information therefore becomes critical. Therefore, a simplified consistent nonlinear mild-slope equation model has been developed in order to take advantage of the deterministic model for handling phase information, as well as the stochastic model for numerical simplicity. We demonstrate the advanced performance of the present model for random waves by comparing it with laboratory data and previous models.

A Modified Frequency Distribution Function of Wave-Breaking-Induced Energy Dissipation.

A nonlinear frequency-domain model and a probabilistic wave breaking model have been employed together to simulate the propagation of nearshore wave breaking and to provide estimates of related statistical quantities such as skewness and asymmetry. This combination of models requires a pre-specification of the frequency dependence of dissipation. Prior work has suggested that a frequency-squared weighting for the dissipation term is most appropriate via physical arguments. However, the original frequency distribution function significantly underpredicts the higher-order moments, particularly the accuracy of asymmetry predictions is in need of further improvement. An intensity of frequency dependence for the breaking-induced damping coefficient is introduced here to further adjust the dissipation function in order to increase the accuracy of asymmetry predictions. By correcting the frequency dependence function with a new form of frequency dependence in the breaking coefficient, the model results are in better agreement with the measurements of the spectrum and higher-order statistics, as well as with the free surface elevation measurements. It is also seen from testing the model with three different cases that the more evident the influence of the breaking mechanism is on the wave transformation process, the more pronounced the contribution of this modification is.

Potential Human Health Hazard of Post-Hurricane Harvey Sediments in Galveston Bay and Houston Ship Channel: A Case Study of Using In Vitro Bioactivity Data to Inform Risk Management Decisions.

Natural and anthropogenic disasters may be associated with redistribution of chemical contaminants in the environment; however, current methods for assessing hazards and risks of complex mixtures are not suitable for disaster response. This study investigated the suitability of in vitro toxicity testing methods as a rapid means of identifying areas of potential human health concern. We used sediment samples (n = 46) from Galveston Bay and the Houston Ship Channel (GB/HSC) areas after hurricane Harvey, a disaster event that led to broad redistribution of chemically-contaminated sediments, including deposition of the sediment on shore due to flooding. Samples were extracted with cyclohexane and dimethyl sulfoxide and screened in a compendium of human primary or induced pluripotent stem cell (iPSC)-derived cell lines from different tissues (hepatocytes, neuronal, cardiomyocytes, and endothelial) to test for concentration-dependent effects on various functional and cytotoxicity phenotypes (n = 34). Bioactivity data were used to map areas of potential concern and the results compared to the data on concentrations of polycyclic aromatic hydrocarbons (PAHs) in the same samples. We found that setting remediation goals based on reducing bioactivity is protective of both "known" risks associated with PAHs and "unknown" risks associated with bioactivity, but the converse was not true for remediation based on PAH risks alone. Overall, we found that in vitro bioactivity can be used as a comprehensive indicator of potential hazards and is an example of a new approach method (NAM) to inform risk management decisions on site cleanup.

A Consistent Nonlinear Mild-Slope Equation Model.

A new nonlinear frequency-domain model based on the mild-slope equation is outlined. The model is an enhancement over previous work in that a closer correspondence between scaling of nonlinearity and horizontal variation of bathymetry is made relative to earlier models. This results in additional terms in the nonlinear summation terms of the model, as amplitude gradient terms are required in order to formulate a consistent model. From the resulting elliptic model, a parabolic approximation is developed in order to efficiently model the equations. Comparisons between the present model, previously-formulated models, and experimental data show that the present model does evidence improvement in performance over previous models.

Temporal and spatial analysis of per and polyfluoroalkyl substances in surface waters of Houston ship channel following a large-scale industrial fire incident.

Firefighting foams contain per- and polyfluoroalkyl substances (PFAS) - a class of compounds widely used as surfactants. PFAS are persistent organic pollutants that have been reported in waterways and drinking water systems across the United States. These substances are of interest to both regulatory agencies and the general public because of their persistence in the environment and association with adverse health effects. PFAS can be released in large quantities during industrial incidents because they are present in most firefighting foams used to suppress chemical fires; however, little is known about persistence of PFAS in public waterways after such events. In response to large-scale fires at Intercontinental Terminal Company (ITC) in Houston, Texas in March 2019, almost 5 million liters of class B firefighting foams were used. Much of this material flowed into the Houston Ship Channel and Galveston Bay (HSC/GB) and concerns were raised about the levels of PFAS in these water bodies that have commercial and recreational uses. To evaluate the impact of the ITC incident response on PFAS levels in HSC/GB, we collected 52 surface water samples from 12 locations over a 6-month period after the incident. Samples were analyzed using liquid chromatography-mass spectrometry to evaluate 27 PFAS, including perfluorocarboxylic acids, perfluorosulfonates and fluorotelomers. Among PFAS that were evaluated, 6:2 FTS and PFOS were detected at highest concentrations. Temporal and spatial profiles of PFAS were established; we found a major peak in the level of many PFAS in the days and weeks after the incident and a gradual decline over several months with patterns consistent with the tide- and wave-associated water movements. This work documents the impact of a large-scale industrial fire, on the environmental levels of PFAS, establishes a baseline concentration of PFAS in HSC/GB, and highlights the critical need for development of PFAS water quality standards.

Optimization of Bathymetry Estimates for Nearshore Hydrodynamic Models Using Bayesian Methods.

A Bayesian inverse framework is developed to optimize the skill of a predictive numerical model via interpolation of bathymetric measurements to provide the most probable bathymetric surface. The nu cmerical model is a coupled wave flow model and predicts wave and hydrodynamic information (e.g., significant wave height and longshore velocity). The Bayesian method, coupled with Markov chain Monte Carlo (MCMC) optimization, is used to find the bathymetric field, which serves to minimize the residual errors between measured data and the corresponding numerical model results. By using a Bayesian approach, the range of probable model parameters is inferred from the observed data. Monte Carlo simulation is also applied to this numerical model to perform the uncertainty analysis of the model output fields (wave height and flow velocity). This analysis is performed by taking random samples from the probability distribution function (PDF) of inputs and running the model as required until the desired precision (±0.05 m for significant wave height) in output fields is achieved. The case study used in this analysis is the DUCK94 experiment, which was conducted at the US Army Field Research Facility at Duck, North Carolina, in the fall of 1994. The unknown model parameters for the hydrodynamic model involve those controlling bathymetric resolution. Furthermore, the ability of the statistical model to estimate the observed data is tested by running the forward model for two sets of input parameters: the estimated input parameters updated by the previously mentioned statistical model and the prior (noninformative) parameters. Using the model parameters estimated from the Bayesian analysis leads to improved comparisons to data. Using the presented method, the relative errors between the model outputs and the observed data for significant wave height at nearshore gauges is reduced by 30%.