Scholars at home and abroad have studied the concentrations of Se in coal and the relationship between Se and sulfur assignment. However, little attention has been paid to the study of Se in floating coal, especially the distribution pattern of Se in floating coal of densimetric fractions and the influencing factors. A total of 138 coal samples were collected from 14 provinces and cities in China to test the Se and sulfur concentrations in coal, and we carried out the comparison of Se with forms of sulfur with 81 of them. A total of 10 coal samples were selected for float-and-sink analyses to investigate the distribution pattern of Se in floating coal. The results showed that the average Se concentration of Chinese coal was 2.26 μg/g, which is identical with the results of previous research studies, which found an average Se concentration of 2~3 μg/g. Selenium was not uniformly distributed in floating coals of densimetric fractions but it had some regularity. The main controlling factor was the sulfur content in the coal. The Se concentrations of the high-sulfur coal showed a “linear growth” distribution with an increasing density, the Se concentrations of the floating coal in each densimetric fraction increased slowly and the Se concentrations of the floating coal of a densimetric fraction > 2.00 g/cm³ increased sharply. The other controlling factor was the ash yield of the coal. The Se concentrations of the low–medium-sulfur floating coal showed a “parabolic-like” distribution, i.e., low Se concentrations at both ends and high Se concentrations at the middle densimetric fraction. With the increase in the ash content of the coal from 18.23% to 51.92%, the densimetric fraction with the highest Se concentration gradually progressed from 1.40 g/cm³ to 1.50 g/cm³, 1.60 g/cm³ and 1.80 g/cm³ in order. The correlation between the selenium of the raw coal from different regions and the total sulfur, pyrite sulfur and organic sulfur was not significant. However, there existed a significant correlation between Se, total sulfur, pyrite sulfur and organic sulfur in floating coals from the same mining area and depositional environment, among which pyrite sulfur was the most correlated with Se, followed by total sulfur and organic sulfur.

The hydrocarbon source rocks of the marine carbonates of the Ordovician Majiagou Formation in the Ordos Basin are generally in the high-overmature stage and are, therefore, not suitable for hydrocarbon thermal simulation experiments. Their hydrocarbon generation potential and hydrocarbon generation characteristics are not clearly understood. Meanwhile, Nordic Cambrian carbonates are similar in lithology, parent material type, and sedimentary age, and are in the low evolution stage, which is suitable for hydrocarbon thermal simulation experiments. Therefore, in this study, we selected the Nordic carbonates for the gold tube thermal simulation experiment to analyze the content and geochemical characteristics of the thermal simulation products. The experimental results are also compared and analyzed with the characteristics of thermal simulation products of Pingliang Formation mud shale (contemporaneous with the Majiagou Formation) and Shanxi Formation coal (in the upper part of the Majiagou Formation), which are similar to the Majiagou Formation in the Ordos Basin. The results showed that the Nordic carbonate has different hydrocarbon production characteristics from the mud shale of the Pingliang Formation of the same parent material type, and although the hydrocarbon production yields of the two are not very different, the carbonate still produces methane at 600 °C. The hydrocarbon production yield of the Nordic carbonates is almost equivalent to that of type-II₂ kerogen, indicating that the hydrocarbon production yield is not related to lithology and only to the organic matter type; however, the Nordic carbonate can produce a large amount of H₂S. The alkane carbon isotope changes are mainly controlled by the degree of thermal evolution, showing gradual heaviness with increasing temperature. No carbon isotope sequence reversal occurred during the thermal simulation, and its distribution range is roughly the same as that of the alkane carbon isotope composition of the mud shale of the Pingliang Formation. The ethane carbon isotope composition is as heavy as −21.2‰ at the high-temperature stage, showing similar coal-type gas characteristics. The addition of calcium sulphate (CaSO₄) causes the TSR reaction to occur, which has a significant impact on the methane content under high maturity conditions, reducing its content by more than 50% at 600 °C. However, the addition of CaSO₄ increases the yield of heavy hydrocarbon gases, such as ethane, and promotes the production of C_6-14 hydrocarbons and C₁₄₊ hydrocarbons at high-temperature stages, and the addition of CaSO₄ substantially increases the yield of H₂, CO₂, and H₂S. The thermal simulation results have implications for the hydrocarbon formation mechanism of the early Paleozoic marine carbonate formation system in the stacked basins of the Ordos Basin and the Tarim Basin in China.

A new approach is presented in this paper for the dynamic modeling of the chemical and isotopic evolution of C_1–3 during the hydrocarbon generation process. Based on systematic data obtained from published papers for the pyrolysis of various hydrocarbon sources (type I kerogen/source rock, type II kerogen/source rock, type III kerogen/source rock, crude oil, and asphalt, etc.), the empirical evolution framework of the chemical and isotopic composition of C_1–3 during the hydrocarbon generation process was built. Although the empirical framework was built only by fitting a large amount of pyrolysis data, the chemical and isotopic composition of C_1–3 derived from the pyrolysis experiments all follow evolution laws, convincing us that it is applicable to the thermal evolution process of various hydrocarbon sources. Based on the simplified formula of the isotopic composition of mixed natural gas at different maturities (δ¹³C_mixed), δ¹³C_mixed = $\frac{\mathrm{X}{\times \mathrm{n}}_{\mathrm{i}\mathrm{A}}{\times \delta }^{13}{\mathrm{C}}_{\mathrm{i}\mathrm{A}}+\mathrm{Y}{\times \mathrm{n}}_{\mathrm{i}\mathrm{B}}{\times \delta }^{13}{\mathrm{C}}_{\mathrm{i}\mathrm{B}}}{\mathrm{X}{\times \mathrm{n}}_{\mathrm{i}\mathrm{A}}+\mathrm{Y}{\times \mathrm{n}}_{\mathrm{i}\mathrm{B}}}$, it can be derived that the cumulative isotopic composition of alkane generated in a certain maturity interval can be expressed by the integral of the product of the instantaneous isotopic composition and instantaneous yield at a certain maturity point, and then divided by the cumulative yield of alkane generated in the corresponding maturity interval. Thus, the cumulative isotopic composition (A(X)), cumulative yield (B(X)), instantaneous isotope (C(X)), and instantaneous yield (D(x)) in the dynamic model, comply with the following formula during the maturity interval of (X₀~X). A(X) = $\frac{{\int }_{{\mathrm{X}}_0}^{\mathrm{X}}\mathrm{C}\left(\mathrm{X}\right)\times \mathrm{D}\left(\mathrm{X}\right)\mathrm{d}\mathrm{x}}{\mathrm{B}(\mathrm{X})}$, where A(X) and B(X) can be obtained by the fitting of pyrolysis data, and D(x) can also be obtained from the derivation of B(X). The dynamic model was applied on the pyrolysis data of Pingliang Shale to illustrate the quantitative evolution of the cumulative yield, instantaneous yield, cumulative isotope, and instantaneous isotope of C_1–3 with increasing maturity. The dynamic model can quantify the yield of methane, ethane, and propane, as well as δ¹³C₁, δ¹³C₂, and δ¹³C₃, respectively, during the hydrocarbon generation process. This model is of great significance for evaluating the natural gas resources of hydrocarbon source rock of different maturities and for identifying the origin and evolutionary process of hydrocarbons by chemical and isotopic data. Moreover, this model provides an approach to study the dynamic evolution of the isotope series of C_1–3 (including reversed isotopic series), which is promising for revealing the mechanism responsible for isotopic reversal when combined with post-generation studies.

A new approach is presented for dynamic modeling of chemical and isotopic evolution of C1-3 during the hydrocarbon generation process. Based on systematic data obtained from published papers for pyrolysis of various hydrocarbon sources(type I kerogen/source rock, type II kerogen/source rock, type III kerogen/source rock, crude oil, asphalt, etc), the empirical evolution framework of chemical and isotopic composition of C1-3 during the hydrocarbon generation process was built. Although the empirical framework was built only by fitting large amount of pyrolysis data, the chemical and isotopic composition of C1-3 derived from the pyrolysis experiments all follow the evolution laws, convincing us that it is applicable to the thermal evolution process of various hydrocarbon sources. Based on the simplified formula of the isotopic composition of mixed natural gas at different maturities (δ13Cmixed) δ13Cmixed=(X〖*n〗_iA 〖*δ〗^13 C_iA+Y〖*n〗_iB 〖*δ〗^13 C_iB)/(X〖*n〗_iA+Y〖*n〗_iB ), it can be derived that the cumulative isotopic composition of alkane generated in certain maturity interval can be expressed by the integral of the product of instantaneous isotopic composition and instantaneous yield at certain maturity point, and then divided by the cumulative yield of alkane generated in corresponding maturity interval. Thus, the cumulative isotopic composition(A(X)), cumulative yield(B(X)), instantaneous isotope(C(X)),and instantaneous yield(D(x)) in the dynamic model comply with the following formula during the maturity interval of (X0-X) A(X)=(∫_(X_0)^X▒〖C(X)*D(X)dx〗)/(B(X)), where A(X) and B(X) can be obtained by fitting of pyrolysis data, and D(x) can also be obtained from the derivation of B(X). The dynamic model was applied on the pyrolysis data of Pingliang Shale to illustrate the quantitative evolution of cumulative yield ,instantaneous yield, cumulative isotope and instantaneous isotope of C1-3 with increasing maturity.The dynamic model can quantify the yield of methane, ethane and propane as well as δ13C1, δ13C2 and δ13C3 respectively during the hydrocarbon generation process, which is of great significance to evaluate the natural gas resources of hydrocarbon source rock with different maturity and to identify the origin and evolutionary process of hydrocarbons by chemical and isotopic data. Moreover, this model provides an approach to study the dynamic evolution of isotope series of C1-3 (including reversed isotopic series), which is hopeful to reveal the mechanism responsible for isotopic reversal when combined with post-generation studies.

This study characterizes the physicochemical properties of coal gangue in Huainan, a typical coal resource-based city in China, and evaluates variations in its chemical composition and associated ecological risks. The results show that the coal gangue in the Huainan mining area is composed mainly of quartz and clay minerals, with SiO₂ and Al₂O₃ together accounting for over 86% of the total composition. Rare earth element concentrations are generally higher than background levels, whereas heavy metal concentrations are generally below the risk screening values for soil contamination of agricultural land. Complex associations are observed among the elements in coal gangue. The correlation coefficients between SiO₂ and the other oxides or heavy metals range from −0.750 to −0.993, indicating significant negative correlations and suggesting that the silicate mineral phase occurs independently of other element-enriched phases. The potential ecological risk index (RI) for heavy metals ranges from 33.75 to 300.71 and is driven primarily by Hg and Cd. The RI for rare earth elements ranges from 98.9 to 220.3, with Lu as the key influencing element. The predicted probability of adverse biological effects is 14–15%. Overall, classified management of coal gangue in the Huainan mining area is recommended, together with strengthened continuous monitoring of Hg and Cd and optimization of ecological disposal strategies by integrating potential ecological risk assessment with analysis of adverse biological effects, thereby further supporting the green transition of resource-depleted cities.