To investigate the spatiotemporal patterns of fine particulate matter (PM_2.5) under years of control measures in China, a comprehensive analysis including statistical analysis, geographical analysis, and health impact assessment was conducted on millions of hourly PM_2.5 concentrations data during the period of 2017–2020 in six typical major urban agglomerations. During the period of 2017–2020, PM_2.5 concentrations in the Beijing–Tianjin–Hebei urban agglomeration (BTH-UA), Central Plains urban agglomeration (CP-UA), Yangtze River Delta urban agglomeration (YRD-UA), Triangle of Central China urban agglomeration (TC-UA), Chengdu–Chongqing urban agglomeration (CY-UA), and Pearl River Delta urban agglomeration (PRD-UA) decreased at a rate of 6.69, 5.57, 5.45, 3.85, 4.66, and 4.1 µg/m³/year, respectively. PM_2.5 concentration in BTH-UA decreased by 30.5% over four years, with an annual average of 44.6 µg/m³ in 2020. CP-UA showed the lowest reduction ratio (22.1%) among the six regions, making it the most polluted urban agglomeration. In southern BTH-UA, northeastern CP-UA, and northwestern TC-UA, PM_2.5 concentrations with high levels formed a high–high agglomeration, indicating pollution caused by source emission in these areas was high and hard to control. Atmospheric temperature, pressure, and wind speed have important influences on PM_2.5 concentrations. RH has a positive correlation with PM_2.5 concentration in north China but a negative correlation in south China. We estimated that meteorological conditions can explain 16.7–63.9% of the PM_2.5 changes in 129 cities, with an average of 33.4%, indicating other factors including anthropogenic emissions dominated the PM_2.5 changes. Among the six urban agglomerations, PM_2.5 concentrations in the CP-UA were most influenced by the meteorological change. Benefiting from the reduction in PM_2.5 concentration, the total respiratory premature mortalities in six regions decreased by 73.1%, from 2017 to 2020. The CP-UA had the highest respiratory premature mortality in six urban agglomerations. We suggested that the CP-UA needs more attention and stricter pollution control measures.

Theoretical perspective: The function of rural human settlements serves as a crucial perspective for interpreting rural community restructuring, and hybridity is a powerful tool for decoding the function of rural human settlements. Objectives and methodology: Based on rural communities’ sample survey data in typical counties and cities of the Jianghan Plain in 2012 and 2022, an evaluation index system for rural community restructuring was constructed across three dimensions: entity hybridity, network hybridity, and meaning hybridity. This paper aimed to analyze the characteristics of rural community restructuring and human settlement functions evolving in the traditional agricultural plain using the weighted TOPSIS method, the Markov chain, and the synergy model. Results and conclusions: From 2012 to 2022, the level of rural community restructuring in the Jianghan Plain has significantly improved, and the comprehensive index of rural community restructuring has gradually narrowed across different rural communities. Meaning hybridity plays a major role in rural community restructuring in the Jianghan Plain. Social culture is the dominant element of rural community restructuring. Public security situation contributes 21.35% to rural community restructuring as the key indicator. Both macro humanistic environment and micro spatial attributes have significant influences on rural community restructuring. These findings can provide a scientific basis for the optimization of the rural community system in the Jianghan Plain.

Sustainable livelihoods for farmers are crucial to achieving the United Nations Sustainable Development Goals (SDGs). In particular, the sustainability of farmers’ livelihoods in plain agricultural areas is strategically important for poverty alleviation, food security, and the reduction in urban–rural disparities. Drawing on data from a sample survey of farmers and field interviews conducted in the Jianghan Plain, this study evaluates farmers’ sustainable livelihoods in plain agricultural areas. The results indicate the following: (1) In 2022, the sustainable livelihood level of farmers in the Jianghan Plain was relatively low; the average Sustainable Livelihood Index (SLI) of the sampled farmers was 0.3462, with livelihood capital and livelihood outcomes accounting for 50.58% and 49.42% of the SLI, respectively. (2) A significant bidirectional positive interaction existed between farmers’ livelihood capital and livelihood outcomes. Specifically, the coefficient of the promotion effect of livelihood capital on livelihood outcomes was 0.1312, whereas that of the feedback effect of livelihood outcomes on livelihood capital was 1.0128. (3) The coupling coordination degree between farmers’ livelihood capital and livelihood outcomes was moderately unbalanced, with livelihood capital identified as the primary obstacle to farmers’ sustainable livelihood development. The average obstacle degree of livelihood capital was 0.7305, indicating a high degree of constraint. The cumulative obstacle degree of social capital (0.2707), human capital (0.1876), and natural capital (0.1460) reached 60.43%, indicating that these three factors collectively constituted the key obstacles. These findings provide valuable insights into promoting farmers’ livelihood sustainability and rural revitalization in plain agricultural areas.