Suchergebnisse

The term "natural cities" refers to human settlements or human activities in general that are naturally or objectively defined. In this study, we utilized the head/tail breaks method to identify natural cities and characterize their living structures in mainland China from 2000 to 2020. This identification was based on nighttime light (NTL) and gridded population distribution (GPD) data, which, respectively, indicate urban areas and residential settlements. Furthermore, we explicitly identified the evolutions of two distinct categories of natural cities and the dynamics of their inherent living structures. Our objective was to verify that the head/tail breaks method is a powerful approach to deriving natural cities that signify various types of human settlements from diverse data sources, while also effectively characterizing their living structures. Also, this article contributes a novel perspective to examine the inequality between the urban-area expansion and the evolution of residential settlements in urban areas (or urban settlements). Our findings reveal a substantial increase in both the number and sizes of urban areas over time. However, there was an intriguing trend observed in urban settlements, where an increasing number corresponded to a gradual decrease in size. Additionally, the inequality exhibited regional disparities and rapidly developing regions showed a higher potential to enter a turning year of urban areas surpassing urban settlements. The dynamics of living structures show that urban settlements always had more hierarchical levels, more substructures, a much higher degree of order, and a much more living (orderly or beautiful) structure than urban areas. The urban-area expansion in mainland China was extremely rapid over the past decades, but the evolution of urban settlements was more "natural" than that (i.e., it consistently generated a living structure characterized by a higher degree of order).

Abstract
Background
Up until now, evidence pertaining to the short-term effects of fine particulate matter (PM2.5) in cardiovascular diseases (CVD) is scarce in China. In this study, we aim to estimate the association between short-term exposure to PM2.5 and hospitalizations for total and cause-specific CVD in Lanzhou of China.

Methods
Daily counts of cardiovascular admissions were obtained from three large general hospitals in Lanzhou, China between 2014 and 2019. Air quality and meteorological data were obtained from the monitoring stations nearest to the admitting hospitals. We utilized Quasi-Poisson time-series regressions with distributed lag nonlinear models (DLNM) to assess the association between PM2.5 and CVD admitted in the three general hospitals. A stratified analysis was also conducted for age, sex, and disease subcategories.

Results
PM2.5 was positively correlated with daily admissions for total or other cause-specific CVD under different lag patterns. For every 10 μg/m3 increase in the PM2.5 concentration, the relative risk of daily admissions for total CVD, ischemic heart disease (IHD), heart rhythm disturbances (HRD), heart failure (HF), and cerebrovascular disease (CD) was: 1.011 [95% confidence interval (CI), 1.001–1.020] in lag01; 1.020 (95% CI 1.004–1.036) in lag07; 1.013 (95% CI 1.001–1.026) in lag7; 1.018 (95% CI 1.005–1.038) in lag1; and 1.007 (95% CI 1.001–1.018) in lag1. Both low and high temperatures increased the risk of cardiovascular hospitalization. No differences were found after stratification by gender and age. We found an almost linear relationship between the exposure to PM2.5 and cause-specific CVD admissions with no threshold effect. Males as well as the elderly, aged ≥ 65 years, were more vulnerable to PM 2.5 exposure.

Conclusions
Our results have demonstrated that PM2.5 has adverse impacts on cardiovascular hospitalizations in Lanzhou, especially on IHD.