Using fuzzy set theory to address the uncertainty of susceptibility to drought

This paper presents the technical aspects of a new methodology for assessing the susceptibility of society to drought. The methodology consists of a combination of inference modelling and fuzzy logic applications. Four steps are followed: (1) model input variables are selected—these variables reflect the main factors influencing susceptibility in a social group, population or region, (2) fuzzification—the uncertainties of the input variables are made explicit by representing them as ‘fuzzy membership functions’, (3) inference modelling—the input variables are used to construct a model made up of linguistic rules, and (4) defuzzification—results from the model in linguistic form are translated into numerical form, also through the use of fuzzy membership functions. The disadvantages and advantages of this methodology became apparent when it was applied to the assessment of susceptibility from three disciplinary perspectives: Disadvantages include the difficulty in validating results and the subjectivity involved with specifying fuzzy membership functions and the rules of the inference model. Advantages of the methodology are its transparency, because all model assumptions have to be made explicit in the form of inference rules; its flexibility, in that informal and expert knowledge can be incorporated through ‘fuzzy membership functions’ and through the rules in the inference model; and its versatility, since numerical data can be converted to linguistic statements and vice versa through the procedures of ‘fuzzification’ and ‘defuzzification’.     

“Applicability of the t-Test for Detecting Trends in Water Quality Variables,” by Robert H. Montgomery and Jim C. Loftis2

Montgomery and Loftis (1987) have listed several situations for which the t-test does not accurately reproduce Type I errors, and should therefore be avoided. Characteristics common to water quality data (skewness or other non-normality, presence of outliers and less-thans) also reduce the power of the t-test, in relation to nonparametric alternatives. Thus if one is interested in reaching correct decisions when trends or differences exist, and not just when they do not, the t-test should not be considered “robust” (in the sense of being generally applicable) when its assumptions are violated. Further, t-tests assume that differences in means are relevant (the mean is a good measure of central tendency), and that data groups differ by some additive amount. When all of these assumptions are recognized, and in light of the availability of truly robust and comparatively powerful non-parametric alternatives, we believe there is little applicability of the t-test for detecting trends or differences in water quality variables.     

A novel role of computers in the natural products field

We describe several computer programs developed in our laboratory as aids in the determination of unknown molecular structures. These programs include CONGEN [1], for determination of all plausible structural candidates; REACT [2, 3], for simulation of sequences of chemical reactions; and STRUCC, for evaluating candidates and planning new experiments. We stress the importance of symbiosis between chemist and computer, the combination building on their respective strengths to increase the speed and accuracy of structural assignments.     

Near-field dispersion modeling for regulatory applications

This paper evaluates the application of dispersion models to estimate near-field pollutant concentrations in two case studies. The Industrial Source Complex Short-Term Model (ISCST3) was evaluated with hexavalent chromium measurements collected within 100 m of two facilities in Barrio Logan, San Diego, CA. ISCST3 provided reasonable estimates for higher pollutant concentrations but underestimated lower concentrations. To understand the observed distribution of concentrations in Barrio Logan, a recently conducted tracer experiment was analyzed. The tracer, sulfur hexafluoride, was released at ambient temperature from an urban facility at the University of California at Riverside, and concentrations were measured within 20 m of the source. Modeling results indicated that Industrial Source Complex-Plume Rise Model Enhancement and American Meteorological Society/U.S. Environmental Protection Agency Regulatory Model-Plume Rise Model Enhancement overestimated high concentrations and underestimated low concentrations. A diagnostic study with a simple Gaussian dispersion model that incorporated site-specific meteorology was used to evaluate model results. This study found that incorporating lateral meandering for nonbuoyant urban plumes in Gaussian dispersion models could improve concentration estimates even when downwash is not considered. Incorporating a meandering component in ISCST3 resulted in improvements in estimating hexavalent chromium concentrations in Barrio Logan. Credible near-source concentration estimates depend on accurate characterization of emissions, onsite micrometeorology, and a method to account for lateral meandering in the near field.     

Weekday/weekend ozone differences: what can we learn from them?

A national analysis of weekday/weekend ozone (O3) differences demonstrates significant variation across the country. Weekend 1-hr or 8-hr maximum O3 varies from 15% lower than weekday levels to 30% higher. The weekend O3 increases are primarily found in and around large coastal cities in California and large cities in the Midwest and Northeast Corridor. Both the average and the 95th percentile of the daily 1-hr and 8-hr maxima exhibit the same general pattern. Many sites that have elevated O3 also have higher O3 on weekends even though traffic and O3 precursor levels are substantially reduced on weekends. Detailed studies of this phenomenon indicate that the primary cause of the higher O3 on weekends is the reduction in oxides of nitrogen (NOx) emissions on weekends in a volatile organic compound (VOC)-limited chemical regime. In contrast, the lower O3 on weekends in other locations is probably a result of NOx reductions in a NOx-limited regime. The NOx reduction explanation is supported by a wide range of ambient analyses and several photochemical modeling studies. Changes in the timing and location of emissions and meteorological factors play smaller roles in weekend O3 behavior. Weekday/weekend temperature differences do not explain the weekend effect but may modify it.     

Comparative toxicity of Solvent Yellow 33 [2-(2′-quinolinyl)-1,3-indandione] and Solvent Green 3 (1,4-di-p-toluidinoanthraquinone) dyes to freshwater organisms

Solvent Yellow 33 and a Solvent Green 3 mixture (30:70 mixture of Solvent Yellow 33 and Solvent Green 3) were not acutely toxic to seven of nine freshwater species when tested at the solubility limits of the dyes in freshwater. A solubility limit solution of the Solvent Green 3 mixture killed 50% of the rainbow trout tested for 96 h but was non-toxic when diluted by 50%. Both dyes caused a reduction in green algal growth at solubility limits. The Solvent Green 3 mixture was the most detrimental causing a 98–99% reduction in growth after 5 days of exposure.