Estimation of water quality profiles in deep lakes based on easily measurable constituents at the water surface using artificial neural networks coupled with stationary wavelet transform.

This study proposes a novel framework to accurately estimate water quality profiles in deep lakes based on parameters measured at the water surface, considering Boulder Basin of Lake Mead as a case study. Hourly-measured meteorological data were used to compute heat exchange between lake and atmosphere. Heat fluxes combined with every 6-hour measured water temperature, conductivity, and dissolved oxygen (DO) profiles, from the water surface to a depth of 100 m over a 48-month period, were used to train seven different artificial neural network-based methods for estimating water quality profiles. Effects of different factors influencing lake water quality, including lake-atmosphere interactions, wind-induced mixing, thermocline depth, winter turnover, oxygen depletion and other factors were investigated in different methods. A method employing stationary wavelet transform with a depth-progressive estimation of temperature, conductivity, and DO generated the smallest average relative errors of 0.52%, 0.22%, and 0.62%, respectively in the water column over a 48-month period. Abrupt changes in temperature, conductivity, and DO profiles due to thermal stratification, winter turnover, and oxygen hypoxia increased estimation errors. The largest errors occurred near the interface between the epilimnion and metalimnion, where vertical mixing intensity significantly decreased.

Nonlinear programming technique for analyzing flocculent settling data.

The traditional graphical approach for drawing iso-concentration curves to analyze flocculent settling data and design sedimentation basins poses difficulties for computer-based design methods. Thus, researchers have developed empirical approaches to analyze settling data. In this study, the ability of five empirical approaches to fit flocculent settling test data is compared. Particular emphasis is given to compare rule-based SETTLE and rule-based nonlinear programming (NLP) techniques as a viable alternative to the modeling methods of Berthouex and Stevens (1982), San (1989), and Ozer (1994). Published flocculent settling data are used to test the suitability of these empirical approaches. The primary objective, however, is to determine if the results of a NLP optimization technique are more reliable than those of other approaches. For this, mathematical curve fitting is conducted and the modeled concentration data are graphically compared to the observed data. The design results in terms of average solid removal efficiency as a function of detention times are also compared. Finally, the sum of squared errors values from these approaches are compared. The results indicate a strong correlation between observed and NLP modeled concentration data. The SETTLE and NLP approaches tend to be more conservative at lower retention times and less conservative at longer retention times. The SETTLE approach appears to be the most conservative. In terms of sum of squared errors values, NLP appears to be rank number one (i.e., best model) for eight data sets and number two for six data sets among 15 data sets. Therefore, NLP is recommended for analyzing flocculent settling data as a logical extension of other approaches. The NLP approach is further recommended as it is an optimization technique and uses conventional mathematical algorithms that can be solved using widely available software such as EXCEL and LINGO.

Needs-based sewerage prioritization: alternative to conventional cost-benefit analysis.

This paper presents an empirical approach to select and prioritize sewerage projects within set budgetary limitations. The methodology includes a model which quantifies benefits of a sewerage project as an index or dimensionless number. The index considers need and urgency of sewerage and other project goals. Benefit is defined as the difference in anticipated impact between the current condition (without the project) and the expected condition with the project. Anticipated benefits primarily include reduction in environmental pollution, reduction of human diseases and morbidity, and other tangible and intangible improvement. This approach is a powerful decision tool for sewerage prioritization and an effective alternative to conventional cost-benefit analysis. Unlike conventional analysis, this approach makes no attempt to convert project benefits and other impacts into a monetary measure. This work recognizes that the decision to provide sewerage based solely on net benefits is not practical. Instead, benefit-cost ratios (B/C) are calculated utilizing cost-effectiveness approach. Using these ratios, 16 unserviced areas of Ensenada, Mexico are ranked. The prioritization rankings produced by this method must be further scrutinized and carefully reviewed for logic, accuracy of input data, and practicality of implementation. A similar framework may also be useful for prioritizing other public works projects.

Pressure-assisted ozonation of PCB and PAH contaminated sediments.

Sediment contamination by recalcitrant organics such as polychlorinated biphenyls (PCBs) and polycyclic aromatic hydrocarbons (PAHs) is prevalent and of a great concern. Remediation efforts are hampered by the hydrophobic nature of the contaminants that limits their availability as well as by the sediment matrix that limits their exposure to treatment agents. Using contaminated sediment samples from the Passaic River, St. Louis River, Waukegan Harbor, and Wells National Estuarine Research Reserve, this research demonstrated a new ozonation technique that incorporates rapid, successive cycles of pressurization (690 kPa) and depressurization, enabling more effective treatment than conventional ozonation would. Conventional ozonation reached maximum 60% and 40% removal of PAHs from the Passaic River (40 mg kg(-1) initially) and St. Louis River sediment (520 mg kg(-1) initially), respectively, in 1h; however, removals ceased despite prolonged treatment for 2h. The pressure-assisted technique removed 96% of PAHs from both river sediments within 1h; it completely removed both PAHs (16 mg kg(-1) initially) and PCBs (5.1 mg kg(-1) initially) from the Waukegan Harbor sediment in 0.5 h. The heightened treatment is explained by soil aggregate fracturing upon pressure cycles that exposes the contaminants as well as by the confluence of hydrophobic contaminants and O(3) at the gas-liquid interface in the presence of microbubbles. The technique is expected to accelerate O(3) treatment of a wide range of organic contaminants, and it may provide treatment to dredged and stored contaminated sediment.

Simulation of resuspended sediments resulting from dredging operations by a numerical flocculent transport model.

Environmental remediations such as dredging operations cause contaminated sediments from the bottom of water bodies to become suspended into the water column. These resuspended particles are significant water quality concerns and cause adverse effects to aquatic organisms. In this paper, we present a vertically integrated two-dimensional flocculent sediment transport model to better model concentration changes of resuspended bottom sediments. The flocculent transport model has been applied to the Savannah River cutterhead dredge field study involving the resuspension of bottom sediments. The results showed that the model predictions correlate reasonably well with field data. These comparisons suggest that the flocculent sediment transport model can be used to predict the concentration profiles of a plume of toxic compounds resulting from cutterhead dredge operation.

Development of a two-dimensional analytical model for predicting toxic sediment plumes due to environmental dredging operations.

A two-dimensional analytical transport model is developed to predict the dredge induced plume concentration in the horizontal plane under steady-state for simple hydraulic conditions. The derivation of the analytical solution is based on the solution to the advection-diffusion equation. The application of this analytical model is limited to mechanical dredge operations, bucket dredge, which has continuous point source in the water column. The analytical model developed herein is used to simulate sediment concentration distributions from a hypothetical dredging site and compared to results from a similar model using Stokes' settling. The result shows that Stokes' settling underestimates settling in the near-field and overestimates settling in the far-field plume. In summary, the model provides the ability to account for the rapid settling of larger particles in the suspended sediment near the dredging operations while simulating the slower settling and diffusive transport of smaller particles moving away from the dredging sites.