Destruction of Aspergillus versicolor,Penicillium chrysogenum,Stachybotrys chartarum,and Cladosporium cladosporioides spores using chemical oxidation treatment process

The survival of aqueous suspensions of Penicillium chrysogenum, Stachybotrys chartarum, Aspergillus versicolor, and Cladosporium cladosporioides spores was evaluated using various combinations of hydrogen peroxide and Fe²⁺ as catalyst. Spore concentrations of 10⁶–10⁷ colony forming units per milliliter (CFU/mL) were suspended in water and treated with initial hydrogen peroxide and iron concentrations ranging from 0.05 to 10 percent and 100 to 200 ppm, respectively. After four hours of reaction time, samples were plated on agar plates, and the viable fraction of spores was determined by the number of colonies formed. Hydrogen peroxide concentrations above 50,000 ppm resulted in greater than 6‐log₁₀ reduction of viable spores for both catalyzed and noncatalyzed reactions. Iron had a strong catalytic effect when added to solutions with hydrogen peroxide concentration above 5,000 ppm and resulted in two to three orders of magnitude greater reduction compared to hydrogen peroxide alone. Additional samples taken after 24 hours of reaction time showed that the effect of the addition of 100 and 200 ppm of Fe²⁺ catalyst was mostly kinetic, and noncatalyzed hydrogen peroxide had sporicidal effects similar to catalyzed hydrogen peroxide. This study identified initial reagent concentrations of hydrogen peroxide and Fe²⁺ that accomplish a 6‐log₁₀ reduction of viable mold spores within reaction times of 4 and 24 hours. © 2007 Wiley Periodicals, Inc.     

Efficient search procedures for extreme pollutant values

Extreme pollutant values are of great interest in water quality monitoring because of their frequent toxicological significance. The principal barrier to the detection of these values, however, is the cost of extensive and comprehensive monitoring. This paper demonstrates an efficient method to determine the maximum sample measurement from a finite set of sequential samples without explicitly testing them all. It is assumed that the process of sample measurement is distinct from collection and has higher costs. It is further assumed that the measurements have high positive autocorrelation.A methodology is presented based on a common industrial testing procedure referred to as composite sampling—the physical pooling or compositing of a set of sequential samples before measurement. A method known as primary first order compositing (PFOC) was found to be superior to the traditional technique of random sampling, particularly if small composite sizes are utilized.The authors are, respectively, operations research analyst, Vancouver, B. C.; Associate Professor and Chairman, Policy Analysis Division, Faculty of Commerce and Business Administration, University of British Columbia; and Associate Professor, Management Science Division, Faculty of Commerce, U.B.C.     

支持西印度洋地区海洋和海岸带资源可持续管理的海洋研究

西印度洋(WIO)地区以其迷人的海岸带、丰富的海洋生物多样性以及富饶的海洋和海岸带资源而著称.但是地处WIO地区的许多国家像肯尼亚、莫桑比克、索马里、南非、坦桑尼亚、科摩罗、马达加斯加、毛里求斯、留尼旺、塞舌尔等却极度贫困,特别是近二三十年以来,该地区的环境退化现象以及自然资源和生物多样性的下降越来越明显.     

Buchbesprechungen

The Science of Nature -     

Ternäre Verbindungen von Cadmiumhalogeniden mit Arsen und Antimon

The Science of Nature -     

Scale,context, and decision making in agricultural adaptation to climate variability and change

This work presents a framework for viewing agricultural adaptation, emphasizing the multiple spatial and temporal scales on which individuals and institutions process information on changes in their environment. The framework is offered as a means to gain perspective on the role of climate variability and change in agricultural adaptation, and developed for a case study of Australian agriculture. To study adaptation issues at the scale of individual farms we developed a simple modelling framework. The model highlights the decision making element of adaptation in light of uncertainty, and underscores the importance of decision information related to climate variability. Model results show that the assumption of perfect information for farmers systematically overpredicts adaptive performance. The results also suggest that farmers who make tactical planting decisions on the basis of historical climate information are outperformed by those who use even moderately successful seasonal forecast information. Analysis at continental scales highlights the prominent role of the decline in economic operating conditions on Australian agriculture. Examples from segments of the agricultural industry in Australia are given to illustrate the importance of appropriate scale attribution in adapting to environmental changes. In particular, adaptations oriented toward short time scale changes in the farming environment (droughts, market fluctuations) can be limited in their efficacy by constraints imposed by broad changes in the soil/water base and economic environment occuring over longer time scales. The case study also makes the point that adaptation must be defined in reference to some goal, which is ultimately a social and political exercise. Overall, this study highlights the importance of allowing more complexity (limited information, risk aversion, cross-scale interactions, mis-attribution of cause and effect, background context, identification of goals) in representing adaptation processes in climate change studies.     

Estimating the economic burden from illnesses associated with recreational coastal water pollution--a case study in Orange County, California

A cost-of-illness framework was applied to health and income data to quantify the health burden from illnesses associated with exposure to polluted recreational marine waters. Using data on illness severity due to exposure to polluted coastal water and estimates of mean annual salaries and medical costs (adjusted to 2001 values) for residents of Orange County, California, we estimated that the economic burden per gastrointestinal illness (GI) amounts to 36.58 dollars, the burden per acute respiratory disease is 76.76 dollars, the burden per ear ailment is 37.86 dollars, and the burden per eye ailment is 27.31 dollars. These costs can become a substantial public health burden when millions of exposures per year to polluted coastal waters result in hundreds of thousands of illnesses. For example, exposures to polluted waters at Orange County's Newport and Huntington Beaches were estimated to generate an average of 36,778 GI episodes per year. At this GI illness rate, one can also expect that approximately 38,000 more illness episodes occurred per year of other types, including respiratory, eye, and ear infections. The combination of excess illnesses associated with coastal water pollution resulted in a cumulative public health burden of 3.3 million dollars per year for these two beaches. This paper introduces a public health cost variable that can be applied in cost-benefit analyses when evaluating pollution abatement strategies.     

A hierarchical Bayesian model for forecasting state-level corn yield

Historically, the National Agricultural Statistics Service crop forecasts and estimates have been determined by a group of commodity experts called the Agricultural Statistics Board (ASB). The corn yield forecasts for the “speculative region,” ten states that account for approximately 85 % of corn production, are based on two sets of monthly surveys, a farmer interview survey and a field measurement survey. The members of the ASB subjectively determine a forecast on the basis of a discussion of the survey data and auxiliary information about weather, average planting dates, and crop maturity. The ASB uses an iterative procedure, where initial state estimates are adjusted so that the weighted sum of the final state estimates is equal to a previously-determined estimate for the speculative region. Deficiencies of the highly subjective ASB process are lack of reproducibility and a measure of uncertainty. This paper describes the use of Bayesian methods to model the ASB process in a way that leads to objective forecasts and estimates of the corn yield. First, we use small area estimation techniques to obtain state-level forecasts. Second, we describe a way to adjust the state forecasts so that the weighted sum of the state forecasts is equal to a previously-determined regional forecast. We use several diagnostic techniques to assess the goodness of fit of various models and their competitors. We use Markov chain Monte Carlo methods to fit the models to both historic and current data from the two monthly surveys. Our results show that our methodology can provide reasonable and objective forecasts of corn yields for states in the speculative region.