Steroid estrogens at sub-micrograms per liter levels are frequently detected in surface water, and increasingly cause public concern of their potential impacts on ecosystems and human health. Assessing the environmental fate and risks of steroid estrogens requires accurate characterization of various physicochemical and biological processes involving these chemicals in aquatic systems. This paper reports sorption of three estrogens, 17beta-estradiol (estradiol), estrone, and 17alpha-ethinyl estradiol (EE2), by seven soil and sediment samples at both equilibrium and rate-limiting conditions. The results indicated that attainment of sorption equilibrium needs about 2 d when aqueous estrogen concentrations (C(t)s) are 25 to 50% of their solubility limits (S(W)S), but equilibrium requires 10 to 14 d when the Ct is 20 times lower than the S(W). The measured sorption isotherms are all nonlinear, with the Freundlich model parameter n ranging from 0.475 to 0.893. The observed isotherm nonlinearity correlates to a gradual increase of single-point organic carbon-normalized sorption distribution coefficient (capacity) (K(OC)) as the equilibrium estrogen concentration (Ce) decreases. At Ce = 0.5S(W), all three estrogens have log K(OC) values of 3.14 to 3.49, whereas at Ce = 0.02S(W), the log K(OC) values for estrone, EE2, and estradiol are within ranges of 3.40 to 3.81, 3.45 to 3.85, and 3.71 to 4.12, respectively. This study suggests that, when at sub-micrograms per liter levels, these estrogenic chemicals may exhibit even slower rates and greater capacities of sorption by soils and sediments.

The sorption of microcystins (MCs) to fifteen lake sediments and four clay minerals was studied as a function of sediment/clay properties, temperature, and pH through well−controlled batch sorption experiments. All sorption data for both sediments and clays are well described by a nonlinear Freundlich model (n f varies between 0.49 and 1.03). The sorption process for MCs exhibited different adsorptive mechanisms in different lake sediments mainly dependent on the sediment organic matter (OM). For sediments with lower OM (i.e., less than 8%), the sorption of MCs decreases with increasing OM and is dominated by the competition for adsorption sites between MCs and OM. In contrast, MC sorption to organic-rich (i.e., more than 8%) sediments increases with increasing OM and is dominated by the interaction between MCs and adsorbed OM. The sorption thermodynamics of MCs onto sediments showed that MC sorption is a spontaneous physisorption process with two different mechanisms. One mechanism is an exothermic process for sediment with lower OM, and the other is an endothermic process for sediment with higher OM. Furthermore, the sorption of MCs onto sediments is pH dependent (sorption decreased with increasing pH). These results provide valuable informations for a better understanding of the natural abiotic attenuation mechanisms for MCs in aquatic ecosystems.

Scene text recognition has inspired great interests from the computer vision community in recent years. In this paper, we propose a novel scene text recognition method using part-based tree-structured character detection. Different from conventional multi-scale sliding window character detection strategy, which does not make use of the character-specific structure information, we use part-based tree-structure to model each type of character so as to detect and recognize the characters at the same time. While for word recognition, we build a Conditional Random Field model on the potential character locations to incorporate the detection scores, spatial constraints and linguistic knowledge into one framework. The final word recognition result is obtained by minimizing the cost function defined on the random field. Experimental results on a range of challenging public datasets (ICDAR 2003, ICDAR 2011 demonstrate that the proposed method outperforms stateof-the-art methods significantly both for character detection and word recognition.

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The first paper of this series reported that soil/sediment organic matter (SOM) can be fractionated into four fractions with a combined wet chemical procedure and that kerogen and black carbon (BC) are major SOM components in soil/sediment samples collected from the industrialized suburban areas of Guangzhou, China. The goal of this study was to determine the sorptive properties forthe four SOM fractions for organic contaminants. Sorption isotherms were measured with a batch technique using phenanthrene and naphthalene as the sorbates and four original and four Soxhlet-extracted soil/sediment samples, 15 isolated SOM fractions, and a char as the sorbents. The results showed that the sorption isotherms measured for all the sorbents were variously nonlinear. The isolated humic acid (HA) exhibited significantly nonlinear sorption, but its contribution to the overall isotherm nonlinearity and sorption capacity of the original soil was insignificant because of its low content in the tested soils and sediments. The particulate kerogen and black carbon (KB) fractions exhibited more nonlinear sorption with much higher organic carbon-normalized capacities for both sorbates. They dominate the observed overall sorption by the tested soils and sediments and are expected to be the most important soil components affecting bioavailability and ultimate fate of hydrophobic organic contaminants (HOCs). The fact that the isolated KB fractions exhibited much higher sorption capacities than when they were associated with soil/sediment matrixes suggested that a large fraction of the particulate kerogen and BC was not accessible to sorbing HOCs. Encapsulation within soil aggregates and surface coverage by inorganic and organic coatings may have caused large variations in the accessibility of fine kerogen and BC particles to HOCs and hence lowered the sorption capacity of the soil. This variability posts an ultimate challenge for precisely predicting HOC sorption by soils from the contents of different types of SOM.

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