Surface sediment diatoms from the western Pacific marginal seas and their correlation to environmental variables

Diatom data of 192 surface sediment samples from the marginal seas in the western Pacific together with modern summer and winter sea surface temperature and salinity data were analyzed. The results of canonical correspondence analysis show that summer sea-surface salinity (SSS) is highly positively correlated with winter SSS and so is summer sea-surface temperature (SST) with winter SST. The correlations between SSSs and SSTs are less positively correlated, which may be due to interactions of regional current pattern and monsoon climate. The correlations between diatom species, sample sites and environmental variables concur with known diatom ecology and regional oceanographic characters. The results of forward selection of the environmental variables and associated Monte Carlo permutation tests of the statistical significance of each variable suggest that summer SSS and winter SST are the main environmental factors affecting the diatom distribution in the area and therefore preserved diatom data from down core could be used for reconstructions of summer SSS and winter SST in the region.

Quartz crystal microbalance study of the kinetics of surface initiated polymerization

Surface initiated polymerization (SIP) is a valuable tool in synthesizing functional polymer brushes, yet the kinetic understanding of SIP lags behind the development of its application. We apply quartz crystal microbalance (QCM) to address two issues that are not fully addressed yet play a central role in the rational design of functional polymer brushes, namely quantitative determination of the kinetics and the initiator efficiency (IE) of SIP. SIP are monitored online using QCM. Two quantitative frequency-thickness (f-T) relations make the direct determination and comparison of the rate of polymerization possible even for different monomers. Based on the bi-termination model, the kinetics of SIP is simply described by two variables, which are related to two polymerization constants, namely a = 1/(k (p,s,app)-[M][R center dot](0)) and b = k (t,s,app)/(k (p,s,app)[M]). Factors that could alter the kinetics of SIP are studied, including (i) the molecular weight of monomers, (ii) the solvent used, (iii) the initial density of the initiator, (iv) the concentration of monomer, [M], and (v) the catalyst system (ratio among the ingredients, metal, ligands, and additives). The dynamic nature of IE is also described by these two variables, IE = a/(a + bt). Instead of the molecular weight and the polydispersity, we suggest that film thickness, the two kinetic parameters (a and b), and the initial density of the initiator and IE be the parameters that characterize ultra-thin polymer brushes. Besides the kinetics study of SIP, the reported method has many other applications, for example, in the fast screening of catalyst system for SIP and other polymerization systems.

Surface modelling of soil pH

In addition to classical methods, namely kriging, Inverse Distance Weighting (IDW) and splines, which have been frequently used for interpolating the spatial patterns of soil properties, a relatively more accurate surface modelling technique is being developed in recent years, namely high accuracy surface modelling (HASM). It has been used in the numerical tests, DEM construction and the interpolation of climate and ecosystem changes. In this paper, HASM was applied to interpolate soil pH for assessing its feasibility of soil property interpolation in a red soil region of Jiangxi Province, China. Soil pH was measured on 150 samples of topsoil (0-20 cm) for the interpolation and comparing the performance of HASM, kriging. IDW and splines. The mean errors (MEs) of interpolations indicate little bias of interpolation for soil pH by the four techniques. HASM has less mean absolute error (MAE) and root mean square error (RMSE) than kriging, IDW and splines. HASM is still the most accurate one when we use the mean rank and the standard deviation of the ranks to avoid the outlier effects in assessing the prediction performance of the four methods. Therefore, HASM can be considered as an alternative and accurate method for interpolating soil properties. Further researches of HASM are needed to combine HASM with ancillary variables to improve the interpolation performance and develop a user-friendly algorithm that can be implemented in a GIS package. (C) 2009 Elsevier B.V. All rights reserved.

Neural network retrieval of ocean surface parameters from SSM/I data

A new algorithm based on the multiparameter neural network is proposed to retrieve wind speed (WS), sea surface temperature (SST), sea surface air temperature, and relative humidity ( RH) simultaneously over the global oceans from Special Sensor Microwave Imager (SSM/I) observations. The retrieved geophysical parameters are used to estimate the surface latent heat flux and sensible heat flux using a bulk method over the global oceans. The neural network is trained and validated with the matchups of SSM/I overpasses and National Data Buoy Center buoys under both clear and cloudy weather conditions. In addition, the data acquired by the 85.5-GHz channels of SSM/I are used as the input variables of the neural network to improve its performance. The root-mean-square (rms) errors between the estimated WS, SST, sea surface air temperature, and RH from SSM/I observations and the buoy measurements are 1.48 m s(-1), 1.54 degrees C, 1.47 degrees C, and 7.85, respectively. The rms errors between the estimated latent and sensible heat fluxes from SSM/I observations and the Xisha Island ( in the South China Sea) measurements are 3.21 and 30.54 W m(-2), whereas those between the SSM/ I estimates and the buoy data are 4.9 and 37.85 W m(-2), respectively. Both of these errors ( those for WS, SST, and sea surface air temperature, in particular) are smaller than those by previous retrieval algorithms of SSM/ I observations over the global oceans. Unlike previous methods, the present algorithm is capable of producing near-real-time estimates of surface latent and sensible heat fluxes for the global oceans from SSM/I data.