Journal of Environmental Engineering

Surface soil contamination is a worldwide problem that is often addressed by the use of regulatory guidance values (RGVs) that establish the maximum amount of contamination that may exist without prompting a regulatory response. Chlorinated benzenes are a family of 12 synthetic aromatic contaminants for which at least 168 jurisdictions have promulgated guidance values. Analysis of the 973 RGVs for the eight most commonly regulated chlorinated benzenes is presented. The RGVs of chlorobenzene, 1,2-, 1,3- and 1,4-diclorobenzene, 1,2,4-trichlorobenzene, 1,2,4,5-tetrachlorobenzene, and hexachlorobenzene vary by over six orders of magnitude. The RGVs for pentachlorobenzene vary by five orders of magnitude. The RGV distributions resemble those of lognormal random variables, but contain nonrandom clusters and extreme values that deviate from the lognormal distribution model. Point clusters appear to reflect the influence of organizations such as the U.S. Environmental Protection Agency (USEPA) and the Canadian Council of Environmental Ministers. Uncertainty analysis applied to the USEPA RGV derivation model indicates that only about 39% of all RGVs fall within easily justified uncertainty ranges. Methods are discussed to help reduce the remaining RGV variability.

The presence of both heavy metals and bacteria is frequently reported in urban storm water runoff. Adsorption and complexation of metals onto bacteria and soil takes place as storm water runoff infiltrates into the subsurface, potentially changing both bacterial and mineral surfaces, and altering the attachment of bacteria onto soil surfaces. Scanning electron microscopy and energy dispersive X-ray spectroscopy analyses were performed on soil samples equilibrated with synthetic storm water amended with copper, lead, and zinc to determine changes in the elemental content of soil. Sets of batch sorption experiments of Escherichia coli onto soil were conducted under different conditions by varying solution composition and soil exposure history. E. coli attachment increases in synthetic storm water with elevated heavy metals concentrations ($K_d=0.0229\mathrm{mL}/\mathrm{mg}$) as opposed to nutrient buffer ($K_d=0.0100\mathrm{mL}/\mathrm{mg}$) for the same untreated soil. For E. coli both suspended in nutrient buffer, $K_d$ is higher when equilibrated with metals-treated soil ($K_d=0.0119\mathrm{mL}/\mathrm{mg}$) in comparison with untreated soil ($K_d=0.0100\mathrm{mL}/\mathrm{mg}$). Results indicate that the presence of heavy metals in solution increases bacterial attachment to soil surfaces.

Mathematical modeling of the contaminants in the subsurface is important to predict the spread of the plume as well as for risk assessment. A three-dimensional subsurface contaminant transport model with an instantaneous input is developed to represent the high dimensionality of the real field. Because of the inherent randomness, heterogeneity of the transport process, macrodispersion, non-Fickian motion, and ergodicity, general assumptions of linearity and Gaussian distribution do not hold for the real field. Therefore, a state-space transport model for the nonlinear and non-Gaussian system is proposed in this study. In this paper, the state variable (concentration vector) and parameter (first-order decay) are updated with the simulated measurements. A particle filter, which is a sequential Monte Carlo method, provides a rigorous general framework for dynamic state estimation problems in the Bayesian scheme. In this paper, the reactive contaminant transport in the subsurface is treated as a dynamic state and parameter estimation problem. A type of particle filter, commonly called sequential importance resampling (SIR) is used for this subsurface transport problem. The model estimation is compared with a true random field, which acts as a reference. A promising improvement of the estimation accuracy is attained with the SIR particle filter when compared with a traditional deterministic approach. The particle filter data assimilation scheme reduces the prediction error by 48% in estimation accuracy. A standard technique to perform parameter estimation consists of extending the state with the parameter to transform the problem into a suboptimal filtering problem. This approach requires the use of special particle filtering techniques which are affected by several drawbacks. An alternative approach in combining parameter estimation with the particle filter method is considered in this study. The concept of the norm has been introduced to address the sequential weight assignment to the parameter estimation. The estimates of the parameter clearly show the efficacy of this innovative approach in this field.

Contaminants have been modeled using numerical models to predict their fate and transport. These models are simplified by introducing approximation that plagues the model with truncation and round-off errors. In order to improve the accuracy and effectiveness of contaminant concentration prediction, three simulation techniques such as particle, Kalman and extended Kalman filters were used in this research as a tool to model the groundwater contaminant using a three-dimensional (3D) subsurface advection-dispersion-reaction model. The 3D model was discretized spatially and temporally using the forward-time-central-space (FTCS) method. A total of 18 observation points were used to run the simulation filters. The dynamical models used by the simulation filters are embedded with random Gaussian noise to mimic a real-life situation. The Kalman and extended Kalman filters also have an advantage of storing only the previous estimate to make a new prediction. The particle filter however, applies sequential Monte Carlo method and probability density functions to make estimates. The filters are capable of providing a better prediction than the numerical method when sparse observation data are used. The algorithms to generate the simulation and the numerical results were run on Matlab 7.1. The effectiveness of the prediction results were assessed using the root mean square error (RMSE), mean absolute error (MAE) and maximum absolute error ($E_{\max }$) equations. The results show that the filters perform better than the numerical method. The filters are capable of reducing the error in the numerical results by approximately 70%.

Enterococci concentrations in seawater samples collected in 2010 at a Gulf Coast beach in the afternoon were significantly lower ($12\mathrm{MPN}/100\mathrm{mL}$) compared with morning samples ($172\mathrm{MPN}/100\mathrm{mL}$). The factors affecting temporal differences of enterococci concentration in beach waters were studied through five laboratory experiments analyzing beach sands, solar radiation, salinity, and turbidity. Enterococci were found in beach sands at a geometric mean of 43 MPN per 100 g of sand, demonstrated the ability to persist for extended periods of time, and increased when incubated (geometric mean of 54 MPN per 100 g sand). Solar radiation inactivated large enterococci concentrations ($\ge 24,196\mathrm{MPN}/100\mathrm{mL}$) in as little as four hours in salinities ranging from 0 to 25 parts per thousand (ppt). Increased turbidity (70 and 140 NTU) hindered the effect of solar radiation, suggesting that near-shore turbidity may promote higher enterococci concentrations. The results indicate that enterococci replenishment along Gulf coastal waters is not due to reactivation as found in other areas. This work illustrates that beach sands, solar radiation, salinity, and turbidity impact enterococci concentrations in Gulf Coast beach waters.

Excellent adsorptive removal of cadmium was accomplished from a pH 1.0 adjusted acidic aqueous solution by using two physically loaded ionic liquids with various hydrophobicity, [${\mathrm{Emim}}^{+}{\mathrm{Tf}}_2{\mathrm{N}}^{-}$] and [${\mathrm{Omim}}^{+}{\mathrm{Tf}}_2{\mathrm{N}}^{-}$], on nano-silica sorbents, [$\mathrm{Nano}\text{-}\mathrm{Si}\text{-}\mathrm{OH}\text{-}{\mathrm{Emim}}^{+}{\mathrm{Tf}}_2{\mathrm{N}}^{-}$] and [$\mathrm{Nano}\text{-}\mathrm{Si}\text{-}\mathrm{OH}\text{-}{\mathrm{Omim}}^{+}{\mathrm{Tf}}_2{\mathrm{N}}^{-}$], respectively. Adjusted solutions to a pH 1.0 were found to produce the highest cadmium adsorption capacity values ($1.200–1.300\mathrm{mmol}{\mathrm{g}}^{-1}$). An anion exchange mechanism was proposed for such high adsorption of cadmium via the exchange of anionic cadmium species by [${\mathrm{Tf}}_2{\mathrm{N}}^{-}$] anion. Several operational controlling factors were monitored and optimized, including the reaction pH, reaction time, sorbent dose, initial cadmium concentration, interfering ions, and hydrophobicity of ionic liquids. The more hydrophobic ionic liquid, [${\mathrm{Omim}}^{+}{\mathrm{Tf}}_2{\mathrm{N}}^{-}$], was found to enhance the adsorption efficiency of the modified sorbent in all evaluated controlling factors. Adsorption equilibrium modeling data were fitted to Brauner-Emmet-Teller and Langmuir models. The potential implementations of [$\mathrm{Nano}\text{-}\mathrm{Si}\text{-}\mathrm{OH}\text{-}{\mathrm{Emim}}^{+}{\mathrm{Tf}}_2{\mathrm{N}}^{-}$] and [$\mathrm{Nano}\text{-}\mathrm{Si}\text{-}\mathrm{OH}\text{-}{\mathrm{Omim}}^{+}{\mathrm{Tf}}_2{\mathrm{N}}^{-}$] were successfully established for cadmium removal. The percentage extraction values of cadmium from industrial waste water and drinking tap water samples, both adjusted to a pH 1.0, were found to be $97.25–99.30\pm 2.00–4.00\%$. The determined percentage extraction values ($94.11–98.47\pm 2.00–4.00\%$) were also identified for the two water samples adjusted to pH 7.0 after three stages of microcolumn elution.

The present study investigates the electrocoagulation-electro-oxidation (EC-EO) process for the treatment of restaurant wastewater (RWW) loaded with organic and inorganic matter, oil, grease, and suspensions solids. The EC-EO process was evaluated in terms of its capability to simultaneously produce an oxidant and coagulant agents by using either iron or aluminum electrodes arranged in a bipolar configuration or graphite electrodes arranged in a monopolar configuration in the same electrolytic cell. Relatively high concentrations of active chlorine ($9.6\mathrm{mg}/\min$) and aluminum ($20–40\mathrm{mg}\mathrm{Al}/\mathrm{L}$) or iron ($40–60\mathrm{mg}\mathrm{Fe}/\mathrm{L}$) were produced in situ. The best performance for RWW treatment was obtained by using aluminum and graphite plates alternated in the electrode pack and operated at current of 0.4 A during 90 min of treatment with pH adjusted to approximately 7.0. Under these conditions, more than 98% of oil and greases (O&G) were removed, whereas chemical oxygen demand (COD) and biological oxygen demand (BOD) removal reached 90% and 86%, respectively. Likewise, more than 88% of soluble phosphate was removed, and the process was effective in removing turbidity (98%) and suspended solids (98%). The EC-EO process operated under the best conditions involved a total cost of $\mathrm{US}\$1.56\pm 0.01/{\mathrm{m}}^3$ of treated restaurant effluent. This cost includes energy and electrode consumption, chemicals, and sludge disposal.

This paper studied the removal of the herbicide Glyphosate in aqueous solution using ions capable of forming an insoluble complex Glyphosate salt (ferric chloride). A maximum Glyphosate removal of 67.4% was achieved using $300\mathrm{mg}/\mathrm{L}$ of ferric chloride. The precipitation of the insoluble Glyphosate salt was affected by rapid mixing velocity, rapid mixing time, dosage of flocculant, and types of impellers. An artificial neural networks (ANN) model was used to predict the removal of Glyphosate. The results showed good agreement over the range of experimental and predicted data. Increasing the velocity gradient increased the Glyphosate removal. The Glyphosate removal decreased with further increase in shear stress. At higher flocculant dosage, the effect of impeller shear is less as the flocs are stronger. The results showed that the effect of impeller type was highly dependent on the rapid mixing velocity and rapid mixing time.