Mechanisms of nickel sorption by a bacteriogenic birnessite

A synergistic experimental-computational approach was used to study the molecular-scale mechanisms of Ni sorption at varying loadings and at pH 6-8 on the biogenic hexagonal birnessite produced by Pseudomonas putida GB-1. We found that Ni is scavenged effectively by bacterial biomass-birnessite assemblages. At surface excess values below 0.18 mol Ni kg⁻¹ sorbent (0.13 mol Ni mol⁻¹ Mn), the biomass component of the sorbent did not interfere with Ni sorption on mineral sites. Extended X-ray absorption fine structure (EXAFS) spectra showed two dominant coordination environments: Ni bound as a triple-corner-sharing (Ni-TCS) complex at vacancy sites and Ni incorporated (Ni-inc) into the MnO₂ sheet, with the latter form of Ni favored at high sorptive concentrations and decreased proton activity. In parallel to our spectral analysis, first-principles geometry optimizations based on density functional theory (DFT) were performed to investigate the structure of Ni surface complexes at vacancy sites. Excellent agreement was achieved between EXAFS- and DFT-derived structural parameters for Ni-TCS and Ni-inc. Reaction-path calculations revealed a pH-dependent energy barrier associated with the transition from Ni-TCS to Ni-inc. Our results are consistent with the rate-limited incorporation of Ni at vacancy sites in our sorption samples, but near-equilibrium state of Ni in birnessite phases found in nodule samples. This study thus provides direct and quantitative evidence of the factors governing the occurrence of Ni adsorption versus Ni incorporation in biogenic hexagonal birnessite, a key mineral in the terrestrial manganese cycle.     

中国东北地区中生代火山岩的大地构造意义

中国东北地区中生代火山岩可划分为西部大兴安岭环状火山岩带、南部火山岩带和东部火山岩带,它们是古亚洲洋构造域向太平洋构造域转换时期不同构造环境的产物。西部大兴安岭环状火山岩带的形成与古亚洲洋闭合过程中壳幔相互作用引起的深部热地幔柱的上升有关,南部火山岩带与构造域转换时期走滑拉伸构造有关,而东部火山岩带则是太平洋板块斜向俯冲作用的产物。     

A scattering-based over-land rainfall retrieval algorithm for South Korea using GCOM-W1/AMSR-2 data

Heavy summer rainfall is a primary natural disaster affecting lives and properties in the Korean Peninsula. This study presents a satellite-based rainfall rate retrieval algorithm for the South Korea combining polarization-corrected temperature (PCT) and scattering index (SI) data from the 36.5 and 89.0 GHz channels of the Advanced microwave Scanning Radiometer 2 (AMSR-2) onboard the Global Change Observation Mission (GCOM)-W1 satellite. The coefficients for the algorithm were obtained from spatial and temporal collocation data from the AMSR-2 and groundbased automatic weather station rain gauges from 1 July - 30 August during the years, 2012-2015. There were time delays of about 25 minutes between the AMSR-2 observations and the ground raingauge measurements. A new linearly-combined rainfall retrieval algorithm focused on heavy rain for the PCT and SI was validated using ground-based rainfall observations for the South Korea from 1 July - 30 August, 2016. The validation presented PCT and SI methods showed slightly improved results for rainfall > 5 mm h^-1 compared to the current ASMR-2 level 2 data. The best bias and root mean square error (RMSE) for the PCT method at AMSR-2 36.5 GHz were 2.09 mm h^-1 and 7.29 mm h^-1, respectively, while the current official AMSR-2 rainfall rates show a larger bias and RMSE (4.80 mm h^-1 and 9.35 mm h^-1, respectively). This study provides a scatteringbased over-land rainfall retrieval algorithm for South Korea affected by stationary front rain and typhoons with the advantages of the previous PCT and SI methods to be applied to a variety of spaceborne passive microwave radiometers.     

Pre- and post-earthquake regional loss assessment using deep learning

As urban systems become more highly sophisticated and interdependent, their vulnerability to earthquake events exhibits a significant level of uncertainties. Thus, community-level seismic risk assessments are indispensable to facilitate decision making for effective hazard mitigation and disaster responses. To this end, new frameworks for pre- and post-earthquake regional loss assessments are proposed using deep learning methods. First, to improve the accuracy of the response prediction of individual structures during the pre-earthquake loss assessment, a widely used nonlinear static procedure is replaced by the recently developed probabilistic deep neural network model. The variabilities of the nonlinear responses of a structural system given the seismic intensity can be quantified during the loss assessment process. Second, to facilitate near-real-time post-earthquake loss assessments, an adaptive algorithm, which identifies the optimal number and locations of sensors in a given urban area, is proposed. Using a deep neural network that estimates area-wide structural damage given the spatial distribution of the seismic intensity levels as a surrogate model, the algorithm adaptively places additional sensors at property lots at which errors from surrogate estimations of the structural damage are the greatest. Note that the surrogate model is constructed before earthquake events using simulated datasets. To test and demonstrate the proposed frameworks, the paper introduces thorough numerical investigations of two hypothetical urban communities. The proposed frameworks using the deep learning methods are expected to make critical advances in pre- and post-earthquake regional loss assessments.     

Assessment of change in design flood frequency under climate change using a multivariate downscaling model and a precipitation-runoff model

Precipitation and runoff are key elements in the hydrologic cycle because of their important roles in water supply, flood prevention, river restoration, and ecosystem management. Global climate change, widely accepted to be happening, is anticipated to have enormous consequences on future hydrologic patterns. Studies on the potential changes in global, regional, and local hydrologic patterns under global climate change scenarios have been an intense area of research in recent years. The present study contributes to this research topic through evaluation of design flood under climate change. The study utilizes a weather state-based, stochastic multivariate model as a conditional probability model for simulating the precipitation field. An important premise of this study is that large-scale climatic patterns serve as a major driver of persistent year-to-year changes in precipitation probabilities. Since uncertainty estimation in the study of climate change is needed to examine the reliability of the outcomes, this study also applies a Bayesian Markov chain Monte Carlo scheme to the widely used SAC-SMA (Sacramento soil moisture accounting) precipitation-runoff model. A case study is also performed with the Soyang Dam watershed in South Korea as the study basin. Finally, a comprehensive discussion on design flood under climate change is made.     

Effects of environmental and biological conditions on the recruitment and growth of the Manila clam <Emphasis Type="Italic">Ruditapes philippinarum</Emphasis> on the west coast of Korea

The distribution of the Manila clam Ruditapes philippinarum, which is often dominant in intertidal zones, is influenced by both environmental and biological conditions. However, there have been few comprehensive studies on the interactive effects of these two groups of factors. The present study examined the environmental and biological parameters determining the population dynamics of the clams that is a dominant component of the intertidal communities of Euhangri and Padori on the west coast of Korean peninsula. We collected R. philippinarum and other members of the macrobenthos (> 1 mm long) monthly from 0.25 m² quadrats deployed in the intertidal zones at Euhangri, Taean, and Padori during the period from August 2013 to January 2015. Physicochemical parameters of the water and sediment were measured at the same time. Water temperature and salinity is high and low in the summer to winter, respectively. While mean grain size of the sediment was higher at Euhangri than at Padori, total mean density of R. philippinarum was higher at Euhangri (325 ind./ 0.25 m² at Padori vs. 194 ind./0.25 m² at Euhangri). Settled spat (< 10 mm in length) density was much higher at Euhangri than at Padori (132 vs. 12 individuals/0.25m², respectively). R. philippinarum spats settled down on the sediment at Euhangri in October and grew continually until the following May, when they reached adult size. Spats that settled down at Padori between March and April were not able to reach the adult stage. As the density of the adult population increased, the condition index of individual clams decreased, but as the population density of the spat increased the body condition index increased. The chlorophyll a content of the sediments at Padori exceeded that at Euhangri and decreased as the population of R. philippinarum increased. The shapes of R. philippinarum shells at Euhangri were more prolate than those at Padori, and the condition index at Euhangri exceeded that at Padori, indicating better growth conditions at Euhangri. The condition index and density of R. philippinarum were affected by the amount of chlorophyll a in the water column and in the sediment. The recruitment success of spats was negatively influenced by spat density. We suggest that regulation of habitat conditions for R. philippinarum, including the food supply, will optimize production of these commercial clams.