Improvements in the two-dimensional nuclear magnetic resonance spectroscopy of humic substances

Understanding pollutant sorption, bioremediation of these pollutants, and their interactions with humic substances requires knowledge of molecular-level processes. New developments with nuclear magnetic resonance (NMR) experiments and labeled compounds have improved the overall understanding of these mechanisms. The advancements made with two-dimensional NMR show great promise, as structural information and hydrogen-carbon bond connectivity can be discerned. This communication presents the application of improved two-dimensional NMR methods, the double quantum filtered (DQF) correlation spectroscopy (COSY) and echo/anti-echo heteronuclear single quantum coherence (HSQC) experiments, for use in structural studies of humic substances. Both experiments were found to produce significant improvements over the conventional COSY and heteronuclear multiple quantum coherence (HMQC) experiments that have been previously employed in similar studies. The more sensitive echo/anti-echo HSQC experiment produced more cross-peaks with higher resolution when compared with the HMQC spectra. The DQF-COSY significantly suppressed the diagonal signals and allowed numerous signals previously hidden in the standard COSY experiment to be observed. These improvements will aid current characterization strategies of humic substances from soils, sediments, and water and their subsequent reactions with pollutants and microorganisms.     

Simulating endosulfan transport in runoff from cotton fields in Australia with the GLEAMS model

Endosulfan (6,7,8,9,10,10-hexachloro-1,5,5a,6,9,9a-hexahydro-6,9methano-2,4,3-benzodioxathiepin 3-oxide), a pesticide that is highly toxic to aquatic organisms, is widely used in the cotton (Gossypium hirsutum L.) industry in Australia and is a risk to the downstream riverine environment. We used the GLEAMS model to evaluate the effectiveness of a range of management scenarios aimed at minimizing endosulfan transport in runoff at the field scale. The field management scenarios simulated were (i) Conventional, bare soil at the beginning of the cotton season and seven irrigations per season; (ii) Improved Irrigation, irrigation amounts reduced and frequency increased to reduce runoff from excess irrigation; (iii) Dryland, no irrigation; (iv) Stubble Retained, increased soil cover created by retaining residue from the previous crop or a specially planted winter cover crop; and (v) Reduced Sprays, a fewer number of sprays. Stubble Retained was the most effective scenario for minimizing endosulfan transport because infiltration was increased and erosion reduced, and the stubble intercepted and neutralized a proportion of the applied endosulfan. Reducing excess irrigation reduced annual export rates by 80 to 90%, but transport in larger storm events was still high. Reducing the number of pesticide applications only reduced transport when three or fewer sprays were applied. We conclude that endosulfan transport from cotton farms can be minimized with a combination of field management practices that reduce excess irrigation and concentration of pesticide on the soil at any point in time; however, discharges, probably with endosulfan concentrations exceeding guideline values, will still occur in storm events.     

Comparing computing formulas for estimating concentration ratios

The purpose of this paper is to provide guidance on the choice of computing formulas (estimators) for estimating average concentration ratios and other ratio-type measures of radionuclides and other environmental contaminant transfers between ecosystem components. Mathematical expressions for the expected value of three commonly used estimators (arithmetic mean of ratios, geometric mean of ratios, and the ratio of means) are obtained when the multivariate lognormal distribution is assumed. These expressions are used to explain why these estimators will not in general give the same estimate of the average concentration ratio. They illustrate that the magnitude of the discrepancies depends on the magnitude of measurement biases, and on the variance and correlations associated with spatial heterogeneity and measurement errors. This paper also reports on a computer simulation study that compares the accuracy of eight computing formulas for estimating a ratio relationship that is constant over time and/or space. Statistical models appropriate for both controlled spiking experiments and observational field studies for either normal or lognormal distributions are considered. Our results indicate that for either type of study the geometric mean is generally preferred if the lognormal distribution applies. However, the geometric mean has the disadvantage that its expected value depends on n, the number of measurements taken. Ricker's estimator, $\text{R}\text{?}{}_{\mathrm{rt}}$, appears to perform worse than the other estimators studied when the observations are lognormal. All eight estimators appear to be equally accurate for the controlled spiking study when data are normally distributed. For observational field studies when data are normally distributed the ratio of means or slight modifications thereof are preferred to other estimators investigated. Before one chooses a computing formula for estimating a concentration ratio, thought should be given to what target value needs to be estimated to satisfy study objectives, and to whether the normal or lognormal distribution is a more realistic model. The geometric mean performs well for lognormal distributions, but comparison of geometric means or of a geometric mean with environmental limits can be misleading if n is small. The arithmetic mean of ratios is a conservative choice in that it will always give a larger estimate than will the geometric mean. It may also be severely biased when data are lognormal and the variances of measurement errors are large. The ratio of the means is a reasonable choice if the distribution is normal. The median of the observed ratios, $\text{R}\text{?}{}_{\mathrm{<mtext>md</mtext>}}$, is useful estimate since it is easily obtained and has an easily understood interpretation as the point above which and below which 50% of the observed ratios lie. Also, it is appropriate no matter what the distribution of the observed ratios may be. Confidence limits on the median are also easily obtained. Finally, while this paper emphasizes applications in radionuclide research, our results should be applicable to a wide range of environmental contaminants since many contaminants have approximately lognormal distributions.     

A feasibility and cost comparison of perchloroethylene dry cleaning to professional wet cleaning: case study of Silver Hanger Cleaners, Bellingham, Massachusetts

The Toxics Use Reduction Institute (TURI) has been working with the dry cleaning sector for over 10 years - focusing on the ultimate goal of eliminating the use of perchloroethylene in this sector due to the availability of less toxic, feasible alternatives. Professional wet cleaning has been identified as one of these alternatives and has been a focus of the Institute in recent years. In 2008, the Institute provided a matching grant to Silver Hanger Cleaners in Bellingham, Massachusetts to convert their operations from perchloroethylene-based to water-based processes. Two years of data have been collected from the facility, reflecting one year of solvent use and one year of dedicated professional wet cleaning. The analysis of that data is presented here, including capital costs, performance data and associated costs, operational costs, and resource use and associated costs.