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Humic acids obtained from a Chinese lignite via alkali treatment were analyzed using Fourier transform infrared spectroscopy and Orbitrap mass spectrometry coupled with an electrospray ion source (ESIOrbitrap-MS). Raw coal and the corresponding residue were characterized via scanning electron microscopy and energy dispersive spectrometry. Over 4700 heteroatom-containing compounds with wide distributions of molecular mass and unsaturation degree were detected via the ESI-Orbitrap-MS, and around 60 percent of the detected species were found to be oxygen-containing compounds. In addition, van Krevelen diagram and double-bond equivalent (DBE) plot were introduced to provide more structural details of the compounds. For the species only containing C, H, and O (HA CHO ), condensed aromatic compounds with a DBE value over 20 only contained 1 or 2 oxygen atoms. Carboxyl-and hydroxylcontaining aliphatic compounds (CHCACs) were predominant in HA CHO with 5 or 6 oxygen atoms. Both the CHCACs and aromatic carboxylic acids or phenols were grouped into clusters in the van Krevelen diagram to be recognized. The introduction of a nitrogen atom to the HA CHO species was based on the structures of the HA CHO species, which is also indicated by the van Krevelen diagram.

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A Bayesian dynamic linear model (BDLM) framework for data modeling and forecasting is proposed to evaluate the performance of an operational cable-stayed bridge, that is, Ting Kau Bridge in Hong Kong, by using SHM strain field data acquired. One of the major challenges in dealing with the existing in-service bridge under extreme typhoon loads is to forecast structural behavior using the typhoon response exhibiting non-stationarity, large data fluctuations and strong randomness. The first attempt for SHM data modeling during extreme events, that is, typhoons, using BDLM framework, was conducted in this study. The data from multiple sensors are analyzed for one-step, multi-step ahead forecasting and missing data imputation. The overall bridge behavior is incorporated into a forecasting model by superposition of forecasting results of trend (representing the structural baseline response), periodic component (response component evolving regularly over time), and autoregressive component (time-dependent error) through BDLM algorithm. The results demonstrate that the BDLM framework yielded more accurate calculations compared with Gaussian process and Variational Heteroscedasticity Gaussian Process methods with respect to one-step ahead forecasting for strain data under typhoons. Multi-step ahead forecasting was successfully carried out both for non-typhoon and typhoon responses within an acceptable precision range. The correlation between periodic component and temperature was also investigated. Regarding missing data imputation, BLDM algorithm can generate robust results due to making full use of the monitoring data both before and after the missing segments.

Direct analysis in real time ionization technique coupled with a quadrupole time-of-flight mass spectrometry (Q-TOF MS) was applied to characterize raw coal and coal derivatives in the solid state.