The impact of human interference on the ecological environment has attracted a significant amount of attention. In this study, hemeroby index (HI) was constructed to quantify the degree of human disturbance, and the relationship between HI and landscape pattern index was explored in a newly metropolitan area. The main objectives of this study were to analyze the temporal and spatial characteristics of landscape pattern and human disturbance in the process of urbanization of county-level cities in China, and to explore the relationship between the landscape pattern index and human disturbance under different disturbance intensity. The conclusions showed that: (1) the degree of human interference in the new area is on the rise, with a slow increase from 1980 to 2010, but since 2010, human interference has increased significantly. The diffusion of human disturbance intensity has obvious spatial directivity, spreading from east to west. (2) The impact of human activities on landscape pattern is shown as increasing fragmentation and increasing number of landscape types. With the enhancement of human activities, the heterogeneity and fragmentation of landscape types in the region reach their highest points. With the continuous increase of human activities, on a certain scale, the landscape types will gradually tend to be the same, and the same type of landscape patches will become one piece and tend to be integrated. This phenomenon is particularly obvious at the patch type level. (3) There is an inflection point value between human disturbance and landscape pattern index in landscape or patch type. On both sides of the inflection point value, the landscape pattern parameters and human disturbance have obvious opposite trends. (4) In the low interference range (1 ≤ HI < 4), with the increase of human interference, more heterogeneous structures (shown in the increase of SHDI) are brought to the landscape, there is more landscape fragmentation (shown in the decrease of LPI, CONTRACT, and AI), and the patch shape and landscape structure tend to be complex (shown in the increase of LSI, SQP, ED, and other indexes); in the high interference range (4 ≤ HI ≤ 7), due to human intervention, the increase of LPI, contract, and AI indicates that intense human activities turn the landscape into a broken and scattered structure and tend to be consistent and homogeneous. LSI, SQP, and ED were negatively correlated with HI at this stage, indicating that with the enhancement of human activities, the complexity of landscape shape decreased.

In recent years, atmospheric PM_2.5 pollution in China has become increasingly severe and exploring the relationships among its influencing factors is important in the prevention and control of air pollution. Although previous studies have identified complexity in variations in PM_2.5 concentrations and recognized the interaction of multiple factors, little quantitative information is available on the evolution of the relationships among these factors, their spatial heterogeneity, and the multiscale interactions between them. In this study, geographical detector and multiscale geographically weighted regression models have been used to explore the multiscale interactions among natural and socioeconomic factors and PM_2.5 concentration in China over the period 2000–2015. The results indicate that the relationship between natural factors and PM_2.5 concentration is stronger than that for socioeconomic factors. The type of interaction between each factor is dominated by bivariate and nonlinear enhancement, exhibiting strong interactions between natural factors and anthropogenic factors. Although the effect of each factor on PM_2.5 is complex, the relative influence of both human activities and social factors is shown to have gradually increased over time and population, agriculture, urbanization, and socioeconomic activities in general make important contributions to PM_2.5. In addition, the scale of effects related to natural factors is smaller and more stable compared to the influence of human activities during the period 2000-2015. There are significant differences in the way natural factors and socioeconomic factors affect PM_2.5, and there is strong non-stationarity of spatial relationships. Factors associated with topography, vegetation (NDVI), climate (temperature), natural sources, and agricultural activity are shown to be important determinants of PM_2.5 across China and warrant significant attention in terms of managing atmospheric pollution. The study demonstrates that spatial differences in the direction, intensity, and scale of each factor should be accounted for to improve prevention and control measures and alleviate regional PM_2.5 pollution.

As a great practice of building a community of shared future for mankind, the Belt and Road Initiative is facing geopolitical risk brought by great power games, regional conflicts and terrorism. It is an important mission of geopolitical research to scientifically deal with the geopolitical risk along the Belt and Road. This study systematically constructs the geopolitical risk assessment index system and analyzes the spatiotemporal evolution, obstacle factors and risk types of geopolitical risk of countries along the Belt and Road by using the entropy weight TOPSIS model, obstacle degree model and minimum variance method. The research results showed that: (1) From 2005 to 2020, the polarization of geopolitical risk in countries along the Belt and Road was very significant, and the overall trend of geopolitical risk tended to deteriorate. (2) The Middle East and Eastern Europe were the most important geopolitical risk zones along the Belt and Road, and Afghanistan, Iraq, Russia and Ukraine were the main high geopolitical risk centers, with significant risk spillover effects from these centers. (3) Terrorism and close relations with the United States were the most important obstacle factors for geopolitical risk in countries along the Belt and Road, and military intervention politics, trade dependence degree and foreign debt burden were important obstacle factors for geopolitical risk in countries along the Belt and Road. (4) Geopolitical risk along the Belt and Road can be divided into sovereign risk dominant type, sovereign and military risk dominant type, sovereign and major power intervention risk dominant type, and sovereign and military and major power intervention risk jointly dominated type, among which sovereign and military and major power intervention risk jointly dominated type was the most important geopolitical risk type. In order to scientifically deal with geopolitical risk in countries along the Belt and Road, it is necessary to strengthen geopolitical risk awareness, pay attention to the dominant geopolitical risk factors, strengthen the control of regional geopolitical risk spillover and formulate reasonable risk prevention and control scheme based on geopolitical risk types.