Exploring the spatial differentiation of PM2.5 concentrations in typical urban agglomerations and analyzing their atmospheric health patterns are necessary for building high-quality urban agglomerations. Taking the Xiamen-Zhangzhou-Quanzhou urban agglomeration as an example, and based on exploratory data analysis and mathematical statistics, we explore the PM2.5 spatial distribution patterns and characteristics and use hierarchical analysis to construct an atmospheric health evaluation system consisting of exposure–response degree, regional vulnerability, and regional adaptation, and then identify the spatial differentiation characteristics and critical causes of the atmospheric health pattern. This study shows the following: (1) The average annual PM2.5 value of the area in 2020 was 19.16 μg/m3, which was lower than China’s mean annual quality concentration limit, and the overall performance was clean. (2) The spatial distribution patterns of the components of the atmospheric health evaluation system are different, with the overall cleanliness benefit showing a “north-central-south depression, the rest of the region is mixed,” the regional vulnerability showing a coastal to inland decay, and the regional adaptability showing a “high north, low south, high east, low west” spatial divergence pattern. (3) The high-value area of the air health pattern of the area is an “F-shaped” spatial distribution; the low-value area shows a pattern of “north-middle-south” peaks standing side by side. The assessment of health patterns in the aforementioned areas can provide theoretical references for pollution prevention and control and the construction of healthy cities.

IntroductionThe shallow mountainous area in Hebei province is a crucial part of the ecological security barrier and regional ecological conservation construction in the Beijing-Tianjin-Hebei (BTH) region. In recent years, the contradictions in the development of the rural “production-living-ecological” function (PLEF) in shallow mountainous areas are prominent, so optimize its spatial pattern is beneficial to rural sustainable development. But there are significant problems in the existing research, such as the lack of fine-scale research and effective guidance for rural PLEF. Based on this, this study takes Quyang County as an example, starts from the perspective of PLEF coordinated development, finally puts forward the optimization strategy of rural production-living-ecological space (PLES) pattern by evaluating rural PLEF and its coupling co-scheduling.MethodsThis study first fused multi-source data such as POI and remote sensing images to build a comprehensive evaluation system of rural PLES, combined with entropy weight method and analytic hierarchy process to give weight to various indicators, and calculated the PLEF distribution of Quyang County on the 300 × 300m grid scale. Then the collaborative development of PLEF is measured by coupling coordination degree model. Finally, according to PLEF and its coupling and coordination, the functional space types are divided according to the principles of coordinated development and ecological optimization, and the optimization strategy of PLES pattern is proposed on the village scale.Results(1) The spatial distribution of PLEF in Quyang County is significantly different, and the order of functional intensity is: ecological space (ES) > production space (PS) > living space (LS). (2) The PLEF coupling coordination degree generally presents the spatial distribution characteristics of “low in the north and high in the south”, which is highly related to its topographic features. The high-value areas are mainly spread over southern plains with developed economy and rich ecological resources, while the low-value areas are located in the northern mountains and the central hills. (3) On the grid scale, the PLES pattern is identified as six types: production-living-ecological balance space (PLEBS), production-living space (PLS), production-ecological space (PES), living-ecological space (LES), ES and PS. Among them, the proportion of PLEBS and ES is larger. (4) On the village scale, it is suggested that PLEBS villages further emphasize high-quality coordinated development; ecological function leading optimization type (EFLOT) villages adhere to ecological priorities and ensure the development of ecological space functions; villages with composite functions should combine their own advantages and the spatial characteristics of the surrounding countryside, optimize and control infrastructure configuration, industrial structure, ecological protection and other aspects of classification, overcome shortcomings and improve the coordination of the PLEF.DiscussionBased on previous studies, this paper explored and improved the research scale, analysis methods, evaluation indexes and optimization ideas in the field of rural PLEF. Therefore, the results can guide for the high-quality coordinated development of territorial space and rural revitalization construction of counties in shallow mountainous areas.

Shrinking cities are a global issue with regional characteristics. This paper focuses on the county-level administrative units in the Three Northeastern Provinces in China to identify and classify shrinking cities using a two-step identification method and explores their spatial-temporal evolution. The paper utilizes the panel threshold regression model for empirical testing. The results indicate the following: (1) The number of shrinking cities in the region is large and deep. Quantitatively, the shrinking cities account for about 50% of the whole; spatially, there are six major shrinking city “groups”, showing the distribution trend around the “Ha-Da” urban corridor. (2) The threshold effect test reveals that GDP is a critical threshold variable influencing the formation of shrinking cities. Moreover, cities are classified into three types based on the threshold values: Type I (GDP > 2,270,731 yuan), Type II (434,832 < GDP ≤ 2,270,731), and Type III (GDP < 434,832). (3) The results of the dual-threshold and grouped regression models show significant variations in the dominant factors of shrinking cities of different scales. Variables such as impervious area, fiscal revenue, and grass area demonstrate relatively stable promoting effects.