R. Bruce Robinson

EXTRACTION OF CESIUM NITRATE FROM CONCENTRATED SODIUM NITRATE SOLUTIONS WITH 21-CROWN-7 ETHERS: SELECnVITY AND EQUILIBRIUM MODELING

ABSTRACT The extraction of cesium nitrate from a high concentration of sodium nitrate by a family of 21-crown-7 ethers in 1,2-dichloroethane has been investigated. Dibenzo-21-crown-7 (DB21C7) and bis[4(5),4′(5′)-rm-butylbenzo]-21-crown-7 (BtBB21C7) ethers have higher selectivity for cesium but lower extraction efficiency than dicyclohexano-21-crown-7 (DC21C7) ether. As measured by the distribution coefficient ratio Dcs/DNa, the cesium selectivity averaged 78 and 93 for DB21C7 and BtBB21C7, respectively. However, in the case of DC21C7, the cesium selectivity was lower and decreased approximately three-fold from 10 to 3 as cesium loading increased. Alkyl substitution on the benzo group has only a small effect on the extraction behavior. It was shown by use of the equilibrium modeling program SXLSQI that the extraction of sodium and cesium could be adequately modeled by augmenting the previously determined cesium nitrate equilibrium model with a 1:1 Na+:crown organic-phase complex. No evidence for a mixed-me...

Uncertainty and Sensitivity Analysis of Activated Sludge Model No.1 by Monte Carlo Simulation for Single CSTR with Universal Distribution Parameters

Uncertainty in activated sludge design parameters can result in unacceptable levels of uncertainty in plant performance. We conducted Monte Carlo simulations of the Activated Sludge Model No. 1 (ASM1) to quantify the extent to which uncertainty in the model parameters, as determined from a survey of the literature, leads to uncertainty in plant performance. The results demonstrate that the levels of uncertainty may, depending on the effluent permit, be unacceptably high. We use Spearman rank correlation analysis of the Monte Carlo results to identify the model parameters to which the model is most sensitive as an aid in selecting model parameters to be measured as part of the design process. As a result, engineers and regulators can have a high degree of confidence that the plant will perform as required, without resorting to overly conservative assumptions or large safety factors.

Application of Two-Directional Time Series Models to Replace Missing Data

Missing data commonly exist in operational records of wastewater treatment plants, such as influent and effluent water quality data. To deal with missing data, time series models that characterize trend, lag, and seasonality may be applied. In this paper, two-time series model-based methods, i.e., the two-directional exponential smoothing (TES) and TES with white noise (TESWN) added methods, are developed to replace missing data. Comparisons with traditional missing-data-replacement methods are also evaluated in the context of predicting missing values from influent data and the subsequent effect when the resulting influent time series are used as an input to process simulation models. The TES method is shown to be most appropriate when the goal is to minimize the average error associated with the missing value. The TESWN method is shown to be better suited for characterizing the amount of uncertainty that may be associated with the missing values.

Statistically based design of wastewater treatment plants (WWTPs) using Monte Carlo simulation of Activated Sludge Model No.1 (ASM1)

To deal with uncertainties in wastewater treatment plants (WWTPs), high safety factors are commonly adopted in the traditional plant design procedure, which may lead to an over-designed plant. In this paper, the Monte Carlo method is applied in both steady-state and dynamic simulations of the activated sludge model No.1 (ASM1) to quantify the uncertainty of the plant performance. Statistical analysis of the plant effluent indicates that it has a wider distribution, which demonstrates the motivation for using conservative design assumptions in the first place. However, we suggest that a better design approach is proposed based on an acceptable risk of effluent violations. To support this design approach, we will present methodologies for estimating the frequency of occurrence effluent violation based on 7-day and 30day average effluent permit levels. Armed with this type of information, engineers, regulators and the community can more realistically evaluate the trade-off between risk and cost.

Application of Time Series Models to Analyze and Forecast the Influent Components of Wastewater Treatment Plants (WWTPs)

Time series models were developed to describe the statistical characteristics of the influent components of a wastewater treatment plant (WWTP) in Oak Ridge, TN. The data used to generate the models consisted of measurements of flow, temperature, BOD5, suspended solids, and ammonia nitrogen over nearly a 3-year period. The data set was characterized by periodically missing values during weekends and holidays. A two-directional exponential smoothing method was developed to estimate the values of those missing data points, prior to time series modeling. Several commonly used time series models, including the exponential smoothing model, ARIMA model, and the dynamic regression model, were applied to the time series of the five plant influent variables. The best models for each influent variable were selected based on various statistics and the ability of the models to forecast future values in the time series. The time series models were then used to simulate random time series of the influent variables with the same statistical characteristics as the original data. The original and randomly generated time series were characterized by similar means, standard deviations, cross-correlations and autocorrelation functions. These randomly generated time series can be used in conjunction with dynamic process models to evaluate the ability of a given design to effectively treat effluent flows under conditions of variability different than those present in the historical data.

Episodic Stream Acidification in the Great Smoky Mountains National Park: An Investigation into the Mechanisms of Acidification and Impacts on Native Brook Trout

In 2006, 67-km of 12 streams in the Great Smoky Mountains National Park (GRSM) were listed on the 303d list as impaired due to low pH from atmospheric deposition and unknown sources, requiring a TMDL to be developed. The GRSM receives some of the highest rates of atmospheric acid deposition in the U.S. in the form of sulfur and nitrogen oxides, which can cause stream pH to drop below 5.0 (minimum of 4.0) for 2-days or longer. Acids enter poorly buffered streams through wet deposition and from naturally occurring organic acids and accumulated dry deposition flushed from watersheds, temporarily reducing pH and ANC in streams. Stream acidification has been shown to have damaging effects on the health of aquatic ecosystems and biota, and is suspected to be a primary cause of the extirpation of native brook trout ( Salvelinus fontinalis ) in six headwater streams in the GRSM. To develop appropriate TMDLs, it is imperative to understand the environmental processes associated with stream acidification, determine system responses to atmospheric deposition, and evaluate impacts to biota. The objectives of the current research are to 1) characterize the chemical constituents in stream water during baseflow and stormflow in three forested watersheds in the GRSM, 2) identify potential mechanisms responsible for episodic acidification, and 3) evaluate physiological distress in native brook trout during episodes of stream acidification. Conductivity, pH, turbidity, stage height and temperature were monitored continuously (15-minute intervals) at three study sites using multi-parameter data sondes. Baseflow grab samples and precipitation samples were collected; automatic water samplers captured stormflow samples. Stormflow, baseflow, and precipitation samples were analyzed for pH, ANC, trace metals, and major cations and anions. To provide evidence that native brook trout are impacted by stream acidification, in situ bioassay experiments were conducted. Changes in native brook trout physiology were determined during two acid runoff episodes. Brook trout were put in cages at the three sites and fish were sampled before and after stream acidification events. To assess physiological stress in brook trout as a response to acid conditions, whole-body sodium concentrations of individual fish were evaluated. ANC and pH depressions were observed during all stormflows at the three study sites. Sulfate, nitrate, and organic acid concentrations increased during runoff episodes. Base cation concentrations generally increased during stormflow at two stream sites, but diluted occasionally at the third site. The relative changes in ion concentrations were used to determine which ions (acids) were most responsible for ANC depression. ANC contribution analysis indicates acid deposition may be the primary cause of episodic stream acidification, but it appears organic acids and cation dilution may also contribute. Results of the in situ bioassay demonstrate that stream acidification can negatively affect native southern brook trout physiology in the GRSM under actual field conditions. Trout lose the ability to regulate critical blood ions, as exemplified by a loss of whole-body sodium, when stream pH dropped below 5.1.

Understanding water quality responses to long-term acidic deposition in a high-elevation southern appalachian watershed: A focus on soil watershed processes

Noland Divide Watershed (NDW), locating in the Great Smoky Mountains National Park, is characterized as one of the watersheds to receive some of the highest acidic deposition in the US. It has been continuously monitored for deposition, soil water and stream water chemistry weekly and biweekly, since 1991. The long-term trend analysis over past 17 years (1991–2007) found that decreasing sulfate and proton in the precipitation, however stream sulfate concentration, pH and ANC did not show the same significant trends. In contrast, atmospheric deposition of nitrogen was found to be increasing over time, but stream nitrate concentration was observed to be declining, apparently due to increasing overstory vegetation uptake. This long-term study has found mean annual detention of sulfate, nitrate and ammonium of 60%, 3% and 95% respectively in the watershed. Sulfate is accumulated in soil matrix by soil adsorption. Ammonium was mainly converted to nitrate by soil mineralization and nitrification at surface soil layer, and combined with deposited nitrate to be uptaken by plant. Despite the decreasing nitrate concentration and stable sulfate concentration, stream recovery from acidification is not observed in the NDW, due to the depletion of base cations with decreasing stream base cation concentrations.