Xiangtan, South China, is characterized by year-round high relative humidity and very low wind speeds. To assess levels of PM_2.5, daily samples were collected from 2016 to 2017 at two urban sites. The mass concentrations of PM_2.5 were in the range of 30–217 µg/m³, with the highest concentrations in winter and the lowest in spring. Major water-soluble ions (WSIIs) and total carbon (TC) accounted for 58–59% and 21–24% of the PM_2.5 mass, respectively. Secondary inorganic ions (SO₄²⁻, NO₃⁻, and NH₄⁺) dominated the WSIIs and accounted for 73% and 74% at the two sites. The concentrations of K, Fe, Al, Sb, Ca, Zn, Mg, Pb, Ba, As, and Mn in the PM_2.5 at the two sites were higher than 40 ng/m³, and decreased in the order of winter > autumn > spring. Enrichment factor analysis indicates that Co, Cu, Zn, As, Se, Cd, Sb, Tl, and Pb mainly originates from anthropogenic sources. Source apportionment analysis showed that secondary inorganic aerosols, vehicle exhaust, coal combustion and secondary aerosols, fugitive dust, industrial emissions, steel industry are the major sources of PM_2.5, contributing 25–27%, 21–22%, 19–21%, 16–18%, 6–9%, and 8–9% to PM_2.5 mass.

The lower Cambrian Niutitang Formation in northwestern Hunan, South China, has already reached its high or over matured stage, and is formed with hydrothermal activity and deposition. Thus, it is extreme difficult to predict the total organic carbon (TOC) content accurately by common methods with well logging data. In order to solve this problem, we try to use artificial neural networks for predicting the TOC of the black shales in the study case. We got the input vectors through the principal component analysis, and based on the relationships and the logging response mechanism between TOC and logging data. In the BP algorithm, some important parameters including the sample databases, the number of hidden layer nodes, transfer function and weight value adjustment etc, were all optimized correctly in the networks. Then we built the mathematical model through a large number of learning sample datum and the error function between the actual and expected outputs, and found that the results are good by many indicators which judge the performance of the model. In testing samples, mean absolute and relative errors are all reduced probably due to the datum ranges and features are focused, but the accuracy is also dropping when the numbers of participating samples are reduced. Through redefining the TOC ≥ 1.0% samples database, we gained more accurately value for high TOC source rock. Finally, we think the results suggest that the method is suitable for the shale gas resource exploration under similar geological conditions and data characteristics.

We report on the effects of magmatic hydrothermal fluids on the mineralogical and geochemical compositions of 12U and 12L Coals from the Meitian Mine in the Meitian Coalfield, southern China. The minerals in 12U Coal are predominantly chlorite, quartz, and calcite, while the minerals in 12L Coal consist mainly of illite, quartz, chlorite, kaolinite, and mixed-layer illite/smectite (I/S). The vesicle- and fracture-filling illite, chlorite, I/S, pyrite, and fluorite, cleat- and fracture-filling carbonate minerals (i.e., calcite, and dolomite), and cleat-filling tremolite, diopside, and talc have epigenetic hydrothermal origins. Tremolite, diopside, and talc were probably formed from the reaction between dolomite and Si-rich magmatic hydrothermal fluids. Elevated Pb–Zn–Sn–Cd assemblages are characteristic for the 12U Coal, while 12L Coal is enriched in W, Rb, Cs, Th, V, Zn, and Zr, most notably W, Rb, and Cs. REY (Rare Earth Elements and Yttrium) plots for almost all coals, partings and host rocks are similar, showing an M-type REY distribution, Gd-maximum, positive Y anomalies, and negative Ce anomalies, suggesting acid hydrothermal circulation in the coal-bearing strata. Rubidium and cesium in the coal is clearly associated with K-rich clay minerals (illite + I/S), and to a lesser extent with silicate minerals that were precipitated from hydrothermal solutions. W in the coals mainly occurs in the inorganic constituents of illite and pyrite, especially illite. Enrichment of W, Rb, and Cs in the coal and host rocks is genetically associated with magmatic hydrothermal fluids. Specifically, magmatic hydrothermal fluids of relatively high temperatures that are rich in volatile matter can extract abundant W, Rb, and Cs from granitic melts. The enrichment of these rare metals in the coal is mainly related to illitization. Our study results suggest that, for coal intruded by magmatic rocks, the type of hydrothermal alteration may greatly influence the enrichment of elements.

Coal texture is important for predicting coal seam permeability and selecting favorable blocks for coalbed methane (CBM) exploration. Drilled cores and mining seam observations are the most direct and effective methods of identifying coal texture; however, they are expensive and cannot be used in unexplored coal seams. Geophysical logging has become a common method of coal texture identification, particularly during the CBM mining stage. However, quantitative methods for identifying coal texture based on geophysical logging data require further study. The support vector machine (SVM), a machine-learning method, has received great interest due to its remarkable generalization performance, and it has been used to quantitatively identify hard and soft coal using geophysical logging data. In this study, four well-logging curves, the acoustic time difference (AC), caliper log (CAL), density (DEN), and natural gamma (GR), were used for coal texture analysis. Hard coal (undeformed and cataclastic coal) exhibited higher DEN, GR, lower CAL, and lower AC than soft coal. The accuracy rate of coal texture identification was highest (97%) when the linear kernel function was applied, and the maximum training accuracy rate was achieved when the penalty parameter value of the linear kernel increased to 1. The results of verification with a newly cored CBM exploration well indicated that the SVM-based identification method was effective for coal texture analysis. With the increasing availability of data, this method can be used to distinguish hard and soft coal in a coal-bearing basin under numerous sample learning conditions.