Faults, as unique geological structures, disrupt the mechanical connections between rock masses. During coal mining, faults in the overlying strata can disturb the original stress balance, leading to fault activation and altering the typical subsidence patterns. This can result in abnormal ground deformation and significant damage to surface structures, posing a serious geological hazard in mining areas. This study examines the influence of a known fault (F13 fault) on ground subsidence in the Wannian Mine of the Fengfeng Mining Area. We utilized 12 Sentinel-1A images and applied SBAS-InSAR, StaMPS-InSAR, and DS-InSAR time-series InSAR methods, alongside the D-InSAR method, to investigate surface deformations caused by the F13 fault. The monitoring accuracy of these methods was evaluated using leveling measurements from 28 surface movement observation stations. In addition, the density of effective monitoring points and the relative strengths and limitations of the three time-series methods were compared. The findings indicate that, in low deformation areas, DS-InSAR has a monitoring accuracy of 7.7 mm, StaMPS-InSAR has a monitoring accuracy of 16.4 mm, and SBAS-InSAR has an accuracy of 19.3 mm.

Despite significant improvements in air quality during and after COVID-19 restrictions, haze continued to occur in Zhengzhou afterwards. This paper compares ionic compositions and sources of PM_2.5 before (2019), during (2020), and after (2021) the restrictions to explore the reasons for the haze. The average concentration of PM_2.5 decreased by 28.5% in 2020 and 27.9% in 2021, respectively, from 102.49 μg m⁻³ in 2019. The concentration of secondary inorganic aerosols (SIAs) was 51.87 μg m⁻³ in 2019, which decreased by 3.1% in 2020 and 12.8% in 2021. In contrast, the contributions of SIAs to PM_2.5 increased from 50.61% (2019) to 68.6% (2020) and 61.2% (2021). SIAs contributed significantly to PM_2.5 levels in 2020–2021. Despite a 22~62% decline in NO_x levels in 2020–2021, the increased O₃ caused a similar NO₃⁻ concentration (20.69~23.00 μg m⁻³) in 2020–2021 to that (22.93 μg m⁻³) in 2019, hindering PM_2.5 reduction in Zhengzhou. Six PM_2.5 sources, including secondary inorganic aerosols, industrial emissions, coal combustion, biomass burning, soil dust, and traffic emissions, were identified by the positive matrix factorization model in 2019–2021. Compared to 2019, the reduction in PM_2.5 from the secondary aerosol source in 2020 and 2021 was small, and the contribution of secondary aerosol to PM_2.5 increased by 13.32% in 2020 and 12.94% in 2021. In comparison, the primary emissions, including biomass burning, traffic, and dust, were reduced by 29.71% in 2020 and 27.7% in 2021. The results indicated that the secondary production did not significantly contribute to the PM_2.5 decrease during and after the COVID-19 restrictions. Therefore, it is essential to understand the formation of secondary aerosols under high O₃ and low precursor gases to mitigate air pollution in the future.