The scientific promotion of rural revitalization is an important issue in the context of global poverty reduction and sustainable development. For China, the largest developing country in the world, the construction of rural revitalization model villages has become an important measure to achieve agricultural and rural modernization and the coordinated development of urban and rural areas. Research on the rural revitalization model villages in China can provide guidance for the rural transformation development in other developing countries. In this paper, the Yangtze River Delta (YRD) was used as the study area, and the spatial differentiation characteristics and driving factors of 1621 rural revitalization model villages were analyzed using ArcGIS software and the geographical detector method. The results are as follows: (1) The multiscale spatial distributions of rural revitalization model villages in the YRD showed a weak agglomeration and disequilibrium characteristic. Anhui Province has the highest imbalanced distribution of model villages among different provinces in the YRD. (2) The model villages are the most densely distributed along the Yangtze River. Extending to the north and south from areas along the Yangtze River, the distribution of the model villages is first sparse then dense. Model villages agglomerate mainly along rivers and lakes, areas close to traffic arteries and the middle areas of suburban. (3) The spatial differentiation of rural revitalization model villages is the result of the combined effect of multiple factors, and the driving factors also showed significant spatial heterogeneity. The most important driving factors of the spatial differentiation of the model villages of the YRD, Jiangsu, Zhejiang, and Anhui are social development and government intervention, transportation accessibility and economic development, resource endowment and natural conditions, and transportation accessibility and government intervention, respectively. This study has practical significance for optimizing the spatial pattern of rural revitalization model villages in the YRD and facilitating high-quality rural revitalization.

Air pollution has recently gained much attention from the general population. Despite pollution control being an issue in both urban and rural regions, most of the available research has concentrated on urban districts. Hence, investigations into how urban–rural transition affects PM_2.5 are warranted within the framework of urban–rural integration. Using the Yellow River Basin as a case study, this study employed the entropy method and Analytic Hierarchy Process (AHP) to uncover the extent of urban–rural transformation. It then used the spatial autocorrelation method to investigate the spatiotemporal features of PM_2.5 and the spatial econometric model to investigate the mechanisms that influence the relationship between urban–rural transformation and PM_2.5. The results are as follows: (1) The level of urban–rural transformation shows an obvious upward trend with time. The development has progressed from asymmetrical north-east and south-west elevations to a more balanced pattern of north-east, middle-east, and west-west elevations. (2) The PM_2.5 concentration increased steadily, then fluctuated, and finally decreased. Notably, the general pattern has not changed much, and it is high in the east and low in the west. (3) Different subsystems of the urban–rural transformation have different impacts on air pollution at different stages. The influence of industrial transformation (IT) on PM_2.5 showed an inverted “N-shaped” curve of negative–negative–changes, and the industrial structure played a leading role in the spatiotemporal evolution of PM_2.5. An inverted “U-shaped” curve forms the left side of the impact of population transition (PT) on PM_2.5. Land transformation (LT) has a “U-shaped” curve for its effect on PM_2.5. This study provides a new perspective on the topic of PM_2.5 and its connection to urban–rural integration, which is crucial to understanding the dynamics of this shift. To achieve the goal of high-quality development, this study supports regional initiatives to reduce PM_2.5 emissions in the Yellow River Basin. Moreover, the results of this study can provide a reference for decision-makers in the world’s densely populated areas that suffer from serious air pollution.

China’s industrialization and urbanization processes have recently accelerated, leading to the rapid expansion of urban built-up areas. Fossil fuels, such as natural gas, oil, and coal, are consumed in large quantities, resulting in the accumulation of atmospheric pollutants. Severe PM2.5 and O₃ pollution poses significant human health risks, including respiratory diseases, cardiovascular and cerebrovascular diseases, and lung cancer. This study utilized data from various observation stations in Jiangsu Province, the annual statistical yearbook data, and statistical data, such as baseline mortality and socioeconomic indicators, to quantitatively analyze the concentration characteristics of PM2.5 and O₃, premature deaths related to pollutant exposure, and negative health effects in Jiangsu Province from 2018 to 2023. The study examined the spatiotemporal evolution patterns of pollutant concentrations, related premature deaths, and negative health effects in various cities within Jiangsu Province under policy-driven conditions. The results show that (1) the annual average concentration of PM2.5 in Jiangsu Province decreased from 105.88 μg/m³ in 2018 to 55.04 μg/m³ in 2023, marking a reduction of 48.01%. (2) The total number of premature deaths due to long-term exposure to PM2.5 decreased by approximately 87%, whereas the total number of premature deaths due to long-term exposure to O₃ increased by approximately 216%. (3) City level (2.377**) and population structure (1.068**) play an important role in the health effects of air pollution. (4) Short-term exposure to high concentrations of pollutants has a significant negative impact on the health of individuals with underlying diseases.

Air pollution has recently gained much attention from the general population. Despite pollution control being an issue in both urban and rural regions, most of the available research has concentrated on urban districts. Hence, investigations into how urban-rural transition affects PM2.5 are warranted within the framework of urban-rural integration. Using the Yellow River Basin as a case study, this study employed the entropy method and Analytic Hierarchy Process (AHP) to uncover the extent of urban-rural transformation. It then used the spatial autocorrelation method to investigate the spatiotemporal features of PM2.5 and the spatial econometric model to investigate the mechanisms that influence the relationship between urban-rural transformation and PM2.5. The results are as follows: (1) Over time, there was a discernible upward tendency in the change in urban-rural areas. The development has progressed from asymmetrical north-east and south-west elevations to a more balanced pattern of north-east, middle-east, and west-west elevations. (2) The PM2.5 concentration increased steadily, then fluctuated, and finally decreased. Notably, the general pattern has not changed much, and it is high in the east and low in the west. (3) Different subsystems of the urban-rural transformation have different impacts on air pollution at different stages. The influence of industrial transformation (IT) on PM2.5 showed an inverted “N-shaped” curve of negative-positive-negative changes, and the industrial structure played a leading role in the spatiotemporal evolution of PM2.5. Currently, an inverted "U-shaped" curve forms the left side of the impact of population transition (PT) on PM2.5. Land development (LT) has a "U-shaped" curve for its effect on PM2.5. This research provides a new perspective on the topic of PM2.5 and its connection to urban-rural integration, which is crucial to understanding the dynamics of this shift. To achieve its goal of high-quality development, it supports regional initiatives to reduce PM2.5 emissions in the Yellow River Basin. Moreover, it can provide a reference for decision-makers in the world’s densely populated areas that suffer from serious air pollution.