The 3D spatiotemporal distribution (spatial and annual-month-daily temporal) features of the air quality index (AQI), air pollutants (PM_2.5, PM₁₀, SO₂, NO₂, CO, and O₃-8h), and air pollution risks (R) from 2003 to 2018 were investigated to understand the severity of air pollution in Henan province. The multiyear ascending trends for AQI and R values, with a peak in 2015, were observed in 2003 to 2018 since the annual population-weighted average concentrations of PM_2.5, PM₁₀, NO₂, and O₃-8h were always higher than the CAAQs II from 2013 to 2017 in Henan province. Changes in the monthly moving average AQI values in Henan province conformed to a U-shaped pattern, with the highest values in the winter (from December to February) and the lowest in the summer (from June to August). Triple peaks for AQI values of 8:00‒10:00 a.m., 6:00‒8:00 p.m., and 2:00‒4:00 p.m. in the representative municipalities corresponded with the morning and evening traffic tendencies and photochemical process. A spatial analysis indicated that there were decreasing trends for air pollution from northwest to southeast in Henan province. Data on 48 concrete parameters were collected from 2003 to 2017 to construct a driving force‒pressure‒state‒influence‒response (DPSIR) model for assessing the socioeconomic forces driving air pollution in this province. It was the too-rapid growth of the driving force index (DFI), induced by urban development and population growth (UDPG), economic growth and change of industrial structure (EGCIS), and energy consumption growth and structure change (ECGSC), that led to a direct increase in the atmospheric pollution burden, i.e., total emissions from air pollution and industrial emissions, which are linearly correlated to values of UDPG and ECGSC, respectively (p < 0.05). Furthermore, the prediction models for AQI and R values in Henan province, with the growth rates being 4.251 DFI⁻¹ and 0.0816 DFI⁻¹, respectively, were simulated by multiple linear regression analysis. Therefore, the integrated risks of air pollution in Henan province were originally driven by DFI.

The coordinated development of atmospheric pollution and socio-economic growth plays a core role in the sustainable development of cities and regions. The relationship between socio-economic growth and air pollution can be described using decoupling analysis. The seven key regions of China (168 cities), including Beijing–Tianjin–Hebei and its surrounding areas (BTHSR), the Yangtze River Delta region (YRDR), the Fen-Wei Plain (FWP), the Chengdu–Chongqing region (CCR), the urban agglomeration of the middle reaches of the Yangtze River (MLRYR), the Pearl River Delta region (PRDR), and other provincial capitals and municipalities with specialized plans (OPCCSP) were taken as targets to investigate the spatiotemporal evolution characteristics of AQI values and PM_2.5 concentrations from 2014 to 2022. Then, the decoupling relationship between the AQI/PM_2.5 and the socio-economic growth index (SEGI) in these key regions was deeply researched by the Tapio decoupling model. The main results were as follows: (1) Although the continuous improvement in air quality was observed in these seven key regions in China, the PM_2.5 concentration in the BTHSR and FWP was still higher than 35 μg·m⁻³. The AQI showed a spatial pattern of high in the north and low in the south, and the distribution of PM_2.5 in China was high in the east and low in the west. (2) The decoupling degree between air pollution and socio-economic growth was relatively high in the PRDR and YRDR. In contrast, the degree of decoupling was poor in the FWP and OPCCSP. The decoupling states were primarily influenced by industrial structure, energy consumption, and urbanization. (3) The decoupling of air pollution from socio-economic growth was in a strong decoupling state throughout the majority of the study period, achieving a comparatively ideal decoupling state in 2018. However, the overall decoupling states of the seven regions were not sustainable, and the decoupling stability was relatively poor. During the research period, the decoupling state between socio-economic growth and air pollution changed and was unstable.

Icing and galloping disasters threaten the safe operation of power transmission lines, and effective response depends on multi-agent collaboration. To address the insufficient attention paid in existing studies to grassroots execution constraints, this paper constructs a tripartite evolutionary game model involving local governments, power grid enterprise O&M management, and grassroots O&M teams. The model integrates collaborative investment, agency costs, benefit sharing, and multi-layer reward–punishment mechanisms into a unified framework. Replicator dynamics and numerical simulations are then used to analyze the evolution of collaborative strategies; the parameters are non-dimensional benchmark values rather than empirically calibrated estimates. The results show that the system exhibits multi-stability and path dependence, with fully non-collaborative and fully collaborative equilibria possibly remaining stable simultaneously. The combination of strong government regulation and incentives with high-level enterprise collaborative management is the key mechanism for overcoming the low-collaboration trap, and strict accountability has higher marginal policy efficiency than equivalent subsidies. Reducing grassroots execution costs and moderately increasing their share of disaster mitigation benefits can accelerate collaborative convergence and expand the attraction basin of the high-collaboration equilibrium. This study provides theoretical support and mechanism-design implications for enhancing the resilience of collaborative emergency response for power transmission lines under extreme weather conditions.

Drought and flood catastrophes threaten water security in the Yellow River Basin, yet prediction is hampered by sparse extreme event records. This study couples catastrophe series extraction via GM(1,1), Weibull extreme value modeling, and bootstrap uncertainty quantification, applying it to 74 years (1951–2024) of annual precipitation in Zhengzhou, China. Percentile thresholds of 410 mm (drought) and 864.6 mm (extreme precipitation) yield five drought and seven extreme precipitation catastrophe years. Both Weibull fits pass three goodness-of-fit tests, whereas the GM(1,1) benchmark fails for the flood series (mean error 21.5% vs. 3.31% for drought). The 0.9 quantile recurrence times (17.8 and 22.9 years) project the next drought catastrophe around 2031 (90% bootstrap window 2025–2033) and the next extreme precipitation catastrophe around 2044 (2034–2047); conditional residual life forecasts shift these by only 1–2 years (to 2033 and 2045, respectively). Two truncated record hindcasts (2013 drought; 2003 flood) fell within the predicted windows, and threshold perturbation and alternative distribution analyses support their robustness. Divergent shape parameters (k = 2.47 vs. 1.29) reveal accelerating drought but decelerating extreme precipitation hazard, providing probabilistic lead times for disaster risk management in data-scarce regions.