Predicting natural arsenic contamination of bedrock groundwater for a local region in Korea and its application

A logistic regression model for the probability of arsenic exceeding the drinking water guidelines (10 μg/L) in bedrock groundwater was developed for a selected county in Korea, where arsenic occurrence and release reactions have been investigated. Arsenic was enriched naturally by the oxidation of sulfide minerals in metasedimentary rocks and mineralized zones, and due to high mobility in alkaline pH conditions, concentrations were high in groundwater of the county. When considering these reactions of arsenic release and water quality characteristics, several geological and geochemical factors were selected as influencing variables in the model. In the final logistic regression model, geological units of limestone and metasedimentary rocks, the concentrations of nitrate and sulfate, and distances to closed mines and adjacent granite were retained as statistically significant variables. Predicted areas of high probability agreed well with known spatial contamination patterns in the county. The model was also applied to an adjacent county, where the groundwater has not previously been tested for the presence of arsenic, and a probability map for arsenic contamination was then produced. Through the analysis of arsenic concentrations at the wells of high probability, it was determined that the applied model accurately indicated the arsenic contamination of groundwater. The logistic regression approach of this study can be applied to predict arsenic contamination in areas of similar geological and geochemical conditions to the county used in this model.     

A multimodel assessment of the climate change effect on the drought severity–duration–frequency relationship

In this study, the patterns of past and future drought occurrences in the Seoul region were analysed using observed historical data from the Seoul weather station located in the Korean Peninsula and four different types of general circulation models (GCMs), namely, GFDL:CM2_1, CONS:ECHO‐G, MRI:CGCM2_3_2 and UKMO:HADGEM1. To analyse statistical properties such as drought frequency duration and return period, the Standardized Precipitation Index was used to derive the severity–duration–frequency (SDF) curve from the drought frequency analysis. In addition, a drought spell analysis was conducted to estimate the frequency and change of drought duration for each drought classification. The results of the analysis suggested a decrease in the frequency of mild droughts and an increase in the frequency of severe and extreme droughts in the future. Furthermore, the average duration of droughts is expected to increase. A comparison of the SDF relationship derived from the observed data with that derived via the GCMs indicated that the drought severity for each return period was reduced as drought duration increased and that the drought severity derived from the GCMs was severer than the severity obtained using the observed data for the same duration and return period. Furthermore, among the four types of GCMs used in this study, the MRI model predicted the most severe future drought for the Seoul region, and the SDF curve derived using the MRI model also resulted in the highest degree of drought severity compared with the other GCMs. Copyright © 2012 John Wiley & Sons, Ltd.     

Towards estimating root‐zone soil moisture using surface multispectral and thermal sensing: A spectral and hydrometeorological dataset from the Dookie experiment site,Victoria, Australia

This paper describes surface hydrometeorological and spectral datasets collected from two tower sites located in the University of Melbourne's Dookie experimental farm, Victoria, Australia. The datasets were collected from different vegetation types including wheat, canola, and grazed pasture during the 2012, 2013, and 2014 cropping seasons. The dataset includes 30‐min frequency latent and sensible heat flux measurements and layer‐average soil moisture data at profile depths of 0–5, 0–30, 30–60, 60–90, and 90–120 cm. Air temperature, wind speed, wind direction, relative humidity, precipitation, and incoming and outgoing longwave and shortwave radiation data were also collected from two locations in the study area. The dataset described in this paper is available online.     

Effect of phenol on embryo development and expression of metallothionein in the sea urchin Hemicentrotus pulcherrimus

In this study, we identified and cloned the sea urchin Hemicentrotus pulcherrimus MT (Hp-MT) mRNA. We examined the gameto- and embryo-toxic effects and the expression of Hp-MT mRNA at various concentrations of phenol in H. pulcherrimus. We found that the normal embryogenesis rate was significantly inhibited when H. pulcherrimus was exposed to phenol (EC₅₀ = 1565.86 ppb, 95% Cl = 1183.47-2037.84 ppb). The no observed effective concentration (NOEC) and the lowest observed effective concentration (LOEC) of the normal embryogenesis rate were < 10 ppb and 100 ppb, respectively. Hp-MT cDNA is 651 bp in length and encodes a protein of 64 amino acids. We found that the expression of Hp-MT mRNA was significantly increased with phenol treatment in a concentrationdependent manner. These results suggest that phenol at greater than 100 ppb has a toxic effect during the early embryonic stages of H. pulcherrimus, and MT mRNA may be used as a biomarker for risk assessment of phenol contamination.     

Under-ice measurements of suspended particulate matters using ADCP and LISST-Holo

Ocean Science Journal - Using a mooring package comprising an acoustic Doppler current profiler (ADCP) and holographic imaging system, a 1-day ice camp study was performed under the Arctic sea ice...     

Observational and numerical analysis of the characteristics of MCS development associated with trigger effect of isolated islands

In order to clarify the characteristics of Mesoscale Convective System (MCS) development and understand the impact of the trigger effect of isolated islands, observational and numerical analysis of the heavy rainfall were carried out over the southwestern part of the Korean Peninsula on July 14, 2004. Satellite based remote sensing data and numerical model MM5 with observational data adjustment were used in this study. The MCS development, in this case, was accompanied not by directional wind shear, but by speed shear which was strongly associated with development of the updraft cloud. An inversion layer at a 750 hPa level is one of the fundamental factors in increasing instability. Effective separation of the upper and lower level atmospheric structure due to an inversion layer at a 750 hPa level creates a suitable condition to develop a MCS. According to numerical analysis it has been found that isolated islands located off the southwestern part of the Korean Peninsula are not a principal factor in causing the heavy rainfall due to the evolution of MCS in this case. Transferable topographic forcing of the downwind side can often induce the variation of MCS intensity, while associated with the precipitation amount over the lee side of the isolated islands at a mature stage of MCS development.     

Use of an artificial neural network for analysis of the susceptibility to landslides at Boun,Korea

The purpose of this study was to develop landslide susceptibility analysis techniques using artificial neural networks and to apply the resulting techniques to the study area of Boun in Korea. Landslide locations were identified in the study area from interpretation of aerial photographs and field survey data. A spatial database of the topography, soil type, timber cover, geology, and land cover was constructed and the landslide-related factors were extracted from the spatial database. Using these factors, the susceptibility to landslides was analyzed by artificial neural network methods. The results of the landslide susceptibility maps were compared and verified using known landslide locations at another area, Yongin, in Korea. A Geographic Information System (GIS) was used to analyze efficiently the vast amount of data and an artificial neural network turned out to be an effective tool to analyze the landslide susceptibility.     

Remotely sensed ET for streamflow modelling in catchments with contrasting flow characteristics: an attempt to improve efficiency

An efficient calibration with remotely sensed (RS) data is important for accurate predictions at ungauged catchments. This study investigates the advantages of streamflow-sensitive regionalization on calibration with RS evapotranspiration (ET). Regionalization experiments are performed at 28 catchments in Australia. The catchments are classified into three groups based on annual rainfall and runoff coefficients. Streamflow, RS ET, and a multi-objective RS ET-streamflow calibration are performed using the DiffeRential Evolution Adaptive Metropolis algorithm in each catchment. Simplified Australian Water Resource Assessment-Landscape model is calibrated for a selection of five parameters. Posterior probability distributions of parameters from three calibrations performed at donor catchments in each group are inspected to find the parameter for regionalization in the individual group. In group 1 of wetter catchments, regionalization of parameter FsoilEmax (soil evaporation scaling factor) helps to simplify the calibration without any deterioration in ET, soil moisture (SM) and streamflow predictions. Regionalization of parameter Beta (coefficient describing rate of hydraulic conductivity increase with water content) in group 2 assists to improve the streamflow predictions with no decrement in ET and SM predictions. However, regionalization is not able to provide satisfactory results in group 3. Group 3 includes low-yielding catchments, with average annual rainfall below 1000 mm/year and runoff coefficient less than 0.1, where traditional streamflow calibration also fails to produce accurate results. This study concludes that streamflow-sensitive regionalization is effective for improving the efficacy of RS ET calibration in wetter catchments.     

Influence of environmental disturbances and reproductive strategy on genetic diversity and differentiation of Zostera marina populations on the southern coast of Korea

The genetic diversity and differentiation of four Zostera marina populations along the southern coast of Korea were estimated using random amplified polymorphic DNA (RAPD) markers to determine the effects of natural and anthropogenic stresses and reproductive strategy on within‐population genetic diversity. The mean number of alleles and gene diversities, indicating population genetic diversity, was highest in the Z. marina population that was exposed to repeated environmental disturbances, and lowest in the most undisturbed population. The higher genetic diversity in the disturbed population was associated with a higher contribution of sexual reproduction to population persistence. This suggests that both the level of disturbances and the reproductive strategy for population persistence contributed significantly to population genetic diversity at the study sites. According to the analysis of molecular variance (AMOVA), 76% genetic variation was attributable to differences among individuals within populations. The observed genetic differentiation (F_ST = 0.241) among Z. marina populations at the study sites appeared to result from reduced meadow size, increased genetic drift, and a high incidence of asexual reproduction. Increased population genetic diversity can enhance resistance and resilience to environmental disturbances; thus, this investigation of seagrass population genetics provides valuable new insights for the conservation, management, and restoration of seagrass habitats.     

Physiological Characteristics and Related Biochemical Parameters of Snow Algae from King George Island,Antarctica

Red and green snow caused by snow algal blooms is common on glaciers and snowfields worldwide. Reddish and greenish snow samples containing algae were collected at the vicinity of penguin rockeries on King George Island (62°13′S, 58°47′W, near the King Sejong Station), Antarctica in February 2017 to investigate their physiology. Eight pigments and six fatty acids were detected from the samples. No difference in pigment and fatty acid (FA) composition was found between reddish and greenish snow samples. In contrast, spectral profiling and mycosporine-like amino acids (MAAs) were different between reddish and greenish snow. Particularly in greenish snow, a high absorbance between 450–600 nm was observed. The average MAA concentration was 316.0 μg g^-1 in greenish snow, which was higher than that of reddish snow (278.2 μg g^-1). The MAA to Particulate organic carbon (POC) ratio (mg (g C)^-1) for reddish snow (6.2 mg (g C)^-1) was higher than that of greenish snow (2.6 mg (g C)^-1). These results suggest that reddish and greenish snow are considered to be the same species based on pigment and FA composition. Compared with photoprotective pigments, MAAs offer snow algae a more effective photoprotection strategy to promote tolerance of natural levels of ultraviolet radiation (UVR).