The development characteristics of nanopores (with pore sizes <200 nm) in coal are a key factor affecting the accumulation and migration of coalbed methane (CBM). Thus, an appropriate determination method and calculation model are essential for accurate nanopore representation. Based on the experiments of low-pressure CO2 adsorption (LP-CO2GA) at 273 K and low-pressure N2 adsorption (LP-N2GA) at 77 K on four coals with different ranks, the abilities of different models (e.g., Langmuir, Dubinin-Radushkevich (D-R), Dubinin-Astakhov (D-A), Brunauer-Emmett-Teller (BET) and nonlocal density functional theory (NLDFT)) to accurately predict the pore parameters were analyzed. The results showed that (1) for LP-N2GA, the Langmuir model is only suitable for gas adsorptions at low relative pressure conditions (P/P0 < 0.01), and its error value increased with the relative adsorption pressure. The fitting results of the D-R model showed good agreement with the D-A model under low relative pressure of LP-CO2GA (P/P0 < 0.01), and the D-A model had more accurate fitting results. The BET model is more accurate than the other models (φ = −1.2733%) only in the interval of LP-N2GA with 0.05 < P/P0 < 0.35. The data also showed that the NLDFT model can maintain a higher fitting accuracy for LPCO2/N2GA processes at relative adsorption pressures from 0.001–0.9996. (2) Using LP-CO2GA with the Langmuir, D-R, D-A, and NLDFT models, the micropore specific surface area (SSA; 66.9570–248.6736 m2/g) and pore volume (0.0201– 0.0997 cm3/g) were obtained, while the values of meso-/macropore SSA (0.0007–2.3398 m2/g) and pore volume (0.0036–0.04 cm3/g) were calculated by LP-N2GA with the BET and NLDFT models. The results showed that the fitting accuracy in descending order was the D-R, D-A, Langmuir and NLDFT models. (3) In combination with the applicable model range, LP-CO2GA with the NLDFT model was recommended for micropore analysis of the coal pore sizes from 0.36–1.1 nm, while LP-N2GA combined with the NLDFT model was recommended for nanopore analysis of pore sizes from 1.1–200 nm. (4) The characteristics of pore development in the Beiloutian coal were analyzed using LP-CO2/N2GA combined with the NLDFT model. It was found that a pore volume and SSA less than 1.0 nm accounted for 88.82% of the total pore volume and 98.05% of the total SSA, indicating that micropores in coal are the main space for CBM storage and are key physical factors for the occurrence and migration of coalbed methane. The conclusions of this article will provide a basis for the accurate calculation of nanopores in coal.

Mine gas disaster prediction and prevention are based on gas content measurement, which results in initial stage loss when determining coal gas desorption contents in engineering applications. We propose a Bayesian probability statistical method in the coal gas desorption model on the basis of constrained prior information. First, we use a self-made coal sample gas desorption device to test initial stage gas desorption data of tectonic coal and undeformed coal. Second, we calculate the initial stage loss of different coal samples with the power exponential function parameters by using Bayesian probability statistics and least squares estimation. Results show that Bayesian probability statistics and least squares estimation can be used to obtain regression and desorption coefficients, thereby illustrating the Bayesian estimation method's validity and reliability. Given that the Bayesian probability method can apply prior information to constrain the model's posterior parameters, it provides results that are statistically significant in the initial stage loss of coal gas desorption by connecting observation data and prior information.Keywords Tectonic coal gas • Bayes method • Loss of desorption • Coal and gas outburst • Methane

Various measures for reducing air pollution have been promulgated since 2013 in China. To investigate the synergistic results of emission control and meteorological environment, PM2.5 samples collected from October 2013 to July 2016 and November 2018 to October 2019 in Jiaozuo city were analyzed for their compositions, secondary species (Ss) variations, and factors changing for Ss formation. The results showed that the concentrations of sulfate, nitrate, ammonium, and secondary organic aerosols (SOAs) generally decreased over the same seasonal period during these years. In addition, the concentrations and proportions of each Ss increased with the increase in the PM2.5 level in these years, implying that although PM2.5 levels have been reduced by various control policies, Ss formation would remain the major contributor to PM elevations. The enhanced effects of gas-phase reactions on intensification of sulfate, SOA, and PM were observed in 2018–2019, which was consistent with the elevation of nitrate and SOA at PM levels of >150 μg/m3. Only sulfate in all PM levels sharply decreased after 2015, showing the fine effect of coal-related pollution control and the importance of collaborative control of NO x , volatile organic compounds, and organic aerosol emissions with SO2 emissions in the future.

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