Comparison of PM2.5 and PM10 in A Suburban Area in Korea During April, 2003

Airborne particulate matter (PM) concentrations were measured in Iksan, a suburban area in South Korea during April, 2003. PM_2.5 (particles with an aerodynamic diameter less than 2.5 μm) and PM₁₀ (particles with an aerodynamic diameter less than 10 μm) samples were collected, and the chemical characteristics of particles were examined for diurnal patterns, yellow dust/rainfall influences, and scavenging effects. Average concentrations of PM_2.5 and PM₁₀ mass measured were 37.3 ± 16.2 μg m⁻³ and 60.8 ± 29.5 μg m⁻³, respectively. The sum of ionic chemical species concentrations for PM_2.5 and PM₁₀ was 16.9 ± 7.3 and 23.1 ± 10.1 μg/m³, respectively. A significant reduction in PM mass concentrations during rainfall days was observed for coarse mode (PM_{2.5 − 10}) particles, but less reduction was found for fine (PM_2.5) mass concentration. SO₄ ²⁻, NH₄ ⁺, and K⁺ predominated in fine particulate mode, NO₃ ⁻ and Cl⁻ predominated in fine particle mode and coarse particle mode, but Na⁺, Mg²⁺, and Ca²⁺ mostly existed in coarse mode. The high concentration of ammonium due to local emissions and long-range transport neutralized sulfate and nitrate to ammonium sulfate and ammonium nitrate, which were major forms of airborne PM in Iksan. Average mass concentrations of PM₁₀ in daytime and at night were 57.6 and 70.0 μg m⁻³, and those of PM_2.5 were 35.4 and 42.5 μg m⁻³, respectively. NO₃ ⁻ and Cl⁻ in both PM_2.5 and PM₁₀ were about double at night than in the daytime, while the rest of the chemical species were equal or a little higher at night than in the daytime. The results suggest the formation of ammonium nitrate and chloride when high ammonia concentration and low air temperature are allowed. Backward air trajectory analyses showed that air masses arriving at the site during yellow dust period were transported from arid Chinese regions, which resulted in high concentrations of airborne PM mass concentrations. In the meantime, air mass trajectories during a rainfall period were mostly from the Pacific Ocean or the East China Sea, along with a relatively low PM concentration.     

The effects of type A behavior dimensions and optimism on coping strategy,health, and performance

Type A behavior dimensions and optimism were examined as predictors of health and performance. In addition, this research also explored the ways that Type As and optimists cope with stressful situations. The achievement striving dimension of the Type A behavior pattern and optimism were positively related to class performance, while the anger/hostility dimension was positively related to the health symptom of anxiety. Optimism, on the other hand, was negatively related to anxiety. The interaction of achievement striving and optimism was negatively related to anxiety. Additionally, while achievement striving was positively related to problem-focused coping, irritability showed a negative association with problem-focused coping strategy. The results of this study provide insights for both individuals and organizations regarding how to cope with daily stresses in order to lower health risks and improve performance.     

有机化合物生物富集因子的计算机预测研究

有机化合物在生物体内的富集,通常用生物富集因子(bioconcentration factor,简称BCF)来表达,这是化合物生态环境毒性评估的重要指标。为合理预测有机化合物是否易于生物富集,首先从美国环保局网站收集了624个具有不同BCF值的化合物,然后采用7种分子指纹结合5种机器学习方法(包括支持向量机、C4.5决策树、k最近邻法、随机森林法和朴素贝叶斯法),构建了化合物BCF的分类预测模型,所有模型均采用独立外部验证集进行验证。其中,使用Chemo Typer分子指纹结合支持向量机方法得到的二分类模型,整体预测准确度最好,达到了85.4%。通过采用信息增益、频率分析等方法,进一步确定了化合物中易于引起生物富集的关键子结构,包括芳基氯、二芳基醚、氯代烷烃等。研究中所用到的方法为有毒化学品的生态风险评价提供了良好可靠的预测工具。     

The trends of composite environmental indices in Korea

In this paper we construct for the first time an annual Composite Environmental Index from 1986 to 1995 in Korea, which is useful for evaluating the efficiency of environmental policies. Nine types of environmental problems are examined. On average, the composite index has increased annually by 5.3% over the 1986-1995 period, which implies that overall environmental quality has deteriorated gradually during this period. Problems such as the greenhouse effect, loss of biodiversity, natural resource depletion, and ecotoxication led to deterioration of environmental quality in the 1990s.     

Multivariate Analysis of the Ecoregion Delineation for Aquatic Systems

The ecoregion concept is a popular method of understanding the spatial distribution of the environment', however, it has yet to be adequately demonstrated that the environment is distributed in accordance with these bounded units. In this paper, we generated a testable hypothesis based on the current usage of ecoregions: the ecoregion classification will allow for discrimination between lakes of different water quality. The ecoregion classification should also be more effective better than a comparably scaled classification based on political boundaries, land-use class, or random grouping. To test this hypothesis we used the Environmental Monitoring and Assessment Program (EMAP) lake water chemistry data from the northeast United States. The water chemistry data were reduced to four components using principal component analysis. For comparison to an optimal grouping of these data we used K-means cluster analysis to define the extent at which these lakes could be segregated into distinct classes. Jackknifed discriminant analysis was used to determine the classification rate of ecoregions, the three alternative spatial classification methods, and the clustering algorithm. The classification based on ecoregions was successful for 35% of the lakes included in this study, in comparison to the clustered groups accuracy of 98%. These results suggest that the large scale spatial distribution of ecosystem types is more complicated than that suggested by the present ecoregion boundaries. Further tests of ecoregion delineations are needed and alternative large-scale management strategies should be investigated.     

Rhamnolipid morphology and phenanthrene solubility at different pH values

The effect of pH and rhamnolipids on the solubility of phenanthrene was investigated in a sand-water system. Batch phenanthrene solubilization experiments in this system showed that the highest phenanthrene solubility occurred at pH 5 in the presence of 240 and 150 mg L(-1) rhamnolipids. As the pH was increased from 5 to 7, the apparent solubility of phenanthrene decreased and then stabilized from pH 7 to 8. At pH 4, a dramatic decrease in phenanthrene solubility was observed. This result is in contrast to previous findings obtained in an aqueous system without soil particles. To investigate the reason for this decrease, the critical micelle concentrations (CMCs) were measured in the presence or absence of sand particles, and the maximum amount of sorbed biosurfactant at each pH was calculated based on the differences of the two CMC values. More rhamnolipid molecules were lost by the sorption into sand particles at pH 4 than at other pH values; this explains the dramatic decrease of solubility at pH 4 in the sand-water system. To confirm the explanation for the different solubilizing capacity that results from the structural change of biosurfactant aggregate, cryo-transmission electron microscopy was used. Micrographs showed that the rhamnolipid morphology changed from large lamellar sheets, to vesicle, and then to micelle as the pH increased. The large and multilamellar vesicles at pH 5 were considered to be the most effective structure for the solubilization of phenanthrene.     

Modeling pesticide volatilization from turf

Pesticide volatilization models are typically based on equilibrium partitioning of the chemical into solid, liquid, and gaseous phases in the soil environment. In turf systems direct vaporization from vegetation surfaces is a more likely source, and it is difficult to apply equilibrium methods to plant material due to the uncertainties of solid-liquid-gas partitioning. An alternative approach is to assume that pesticide volatilization is governed by the same processes that affect water evaporation. A model was developed in which evapotranspiration values, as determined by the Penman equation, were adjusted to chemical vaporization using ratios of water and chemical saturated vapor pressures and latent heats of vaporization. The model also assumes first-order degradation of pesticide on turf vegetation over time. The model was tested by comparisons of predictions with measurements of volatilization for eight pesticides measured during 3 to 7 d in 11 field experiments. Measured volatilization fluxes ranged from 0.1 to 22% of applied chemical. Pesticides were divided into two groups based on saturated vapor pressures and organic C partition coefficients. One pesticide was selected from each group to calibrate the model's volatilization constant for the group, and the remaining pesticides were used for model testing. Testing results indicated that the model provides relatively conservative estimates of pesticide volatilization. Predicted mean losses exceeded observations by 20%, and the model explained 67% of the observed variation in volatilization fluxes. The model was most accurate for those chemicals that exhibited the largest volatilization losses.