Adsorption-desorption and leaching potential of glyphosate and aminomethylphosphonic acid in acidic Malaysian soil amended with cow dung and rice husk ash

This study investigates adsorption-desorption and the leaching potential of glyphosate and aminomethylphosphonic acid (AMPA) in control and amended—addition of cow dung or rice husk ash—acidic Malaysian soil with high oxide mineral content. The addition of cow dung or rice husk ash increased the adsorptive removal of AMPA. The isotherm data of glyphosate and AMPA best fitted the Freundlich model. The constant K_f for glyphosate was high in the control soil (544.873 mg g^?1) followed by soil with cow dung (482.451 mg g^?1) then soil with rice husk ash (418.539 mg g^?1). However, for AMPA, soil with cow dung was high (166.636 mg g^?1) followed by soil with rice husk ash (137.570 mg g^?1) then the control soil (48.446 mg g^?1). The 1/n values for both glyphosate and AMPA adsorptions were <?1 indicating their strong affinity for adsorbents. Desorption of both glyphosate and AMPA occurred only in the control soil. The compounds were not detected in soils with added cow dung or rice husk ash. The addition of cow dung or rice husk ash increased glyphosate mobility. However, ground water ubiquity scores for both control and amended soils were <?2.8. This indicated glyphosate is a transitional herbicide; therefore, its leaching potential in the soil is low, despite the addition of cow dung or rice husk ash. Addition of these wastes decreased the mobility and leaching potential of AMPA. The addition of cow dung or rice husk ash could be beneficial in increasing adsorption and enhancing degradation of these compounds.     

Estimating sampling frequency in pollen exposure assessment over time

A time series model was fitted to the pollen concentration data collected in the Greater Cincinnati area for the Cincinnati Childhood Allergy and Air Pollution Study (CCAAPS). A traditional time series analysis and temporal variogram approach were applied to the regularly spaced databases (collected in 2003) and irregularly spaced ones (collected in 2002), respectively. The aim was to evaluate the effect of the sampling frequency on the sampling precision in terms of inverse of standard error of the overall level of mean value across time. The presence of high autocorrelation in the data was confirmed and indicated some degree of temporal redundancy in the pollen concentration data. Therefore, it was suggested that sampling frequency could be reduced from once a day to once every several days without a major loss of sampling precision of the overall mean over time. Considering the trade-offs between sampling frequency and the possibility of sampling bias increasing with larger sampling interval, we recommend that the sampling interval should take values from 3 to 5 days for the pollen monitoring program, if the goal is to track the long-term average.     

The Metallic Composition of Aerosols at Three Monitoring Sites in Korea During Winter 2002

In the present study, the distribution patterns of various metals were analyzed and compared using PM samples collected concurrently from three monitoring sites located in Korea (Seoul, Busan, and Jeju island) in December 2002. As these sites can represent metal pollution with different degrees of anthropogenic activities, their concentration levels were distinguished in a systematic manner in the order of Jeju, Busan, and Seoul. By comparing the present data sets with those measured previously from other locations in Korea and around the world, we attempted to diagnose the general status of elemental pollution on the Korean peninsula. Through an application of different statistical approaches, the major processes controlling elemental levels were assessed for each of the three study sites. The results indicated the importance of both crustal and anthropogenic sources in all sites with their relative roles varying significantly from each other. The results of the metal analysis data, when examined in relation to back trajectory analysis, confirmed that their concentration changes are affected quite sensitively with air mass movement patterns. The overall results of this study consistently indicated the contribution of a strong anthropogenic source area (e.g., China) to the observed metal concentration levels in the study area, but the strengths of such signals vary considerably across the Korean peninsula.     

The Spatiotemporal Pattern of Fires in Northern Taiga Larch Forests of Central Siberia

The periodicity of fires in larch forests of Evenkia and their relationship with landscape elements have been studied. Cross-sections with “burns” in them caused by past fires have been analyzed in 72 test plots; the fire chronology encompassed the period from the 15th to the 20th century. The between-fire intervals (BFIs) have been calculated by two methods: (I) on the basis of burns alone and (II) on the basis of burns and the start of growth of the new generation of larch after the earliest fire. The BFI depends on local orographic features; it is 86 ± 11 (105 ± 12), 61 ± 8 (73 ± 8), 139 ± 17 (138 ± 18), and 68 ± 14 (70 ± 13) years for northeastern slopes, southwestern slopes, bogs, and flatlands, respectively. The mean BFIs calculated by methods I and II are 82 ± 7 and 95 ± 7 years, respectively. The permafrost horizon rises at a mean rate of 0.3 cm per year after a forest fire. It has been shown that the number of fires regularly peaks at periods of 36 and 82 years. There is also a temporal trend in fire frequency: the mean BFI was approximately 100 years in the 19th century and 65 years in the 20th century.     

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’.