Understanding the driving mechanisms of vegetation development is critical for maintaining terrestrial ecosystem function in mountain areas, especially under the background of climate change. The Qinba Mountains (QBM), a critical north–south transition zone in China, is an environmentally fragile area that is vulnerable to climate change. It is essential to characterize how its ecological environment has changed. Currently, such a characterization remains unclear in the spatiotemporal patterns of the nonlinear effects and interactions between environmental factors and vegetation changes in the QBM. Here, we utilized the Normalized Difference Vegetation Index (NDVI), obtained from Google Earth Engine (GEE) platform, as an indicator of terrestrial ecosystem conditions. Then, we measured the spatiotemporal heterogeneity for vegetation variation in the QBM from 2003 to 2018. Specifically, the Geodetector method, a new geographically statistical method without linear assumptions, was employed to detect the interaction between vegetation and environmental driving factors. The results indicated that there is a trend of a general increase in vegetation growth amplitude (the average NDVI increased from 0.810 to 0.858). The areas with an NDVI greater than 0.8 are mainly distributed in the Qinling Mountains and the Daba Mountains, which account for more than 76.39% of the QBM area. For the entire region, the global Moran’s index of the NDVI is greater than 0.95, indicating that vegetation is highly concentrated in the spatial domain. The Geodetector identified that landform type was the primary factor in controlling vegetation changes, contributing 24.19% to the total variation, while the explanatory powers of the aridity index and the wetness index for vegetation changes were 22.49% and 21.47%, respectively. Furthermore, the interaction effects between any two factors outperformed the influence of a single environmental variable. The interaction between air temperature and the aridity index was the most significant element, contributing to 47.10% of the vegetation variation. These findings can not only improve our understanding in the interactive effects of environmental forces on vegetation change, but also be a valuable reference for ecosystem management in the QBM area, such as ecological conservation planning and the assessment of ecosystem functions.

Background Most studies on air pollution exposure and its associations with human health in China have focused on the heavily polluted industrial areas and/or mega-cities, and studies on cities with comparatively low air pollutant concentrations are still rare. Only a few studies have attempted to analyse particulate matter (PM) for the vibrant economic centre Shenzhen in the Pearl River Delta. So far no systematic investigation of PM spatiotemporal patterns in Shenzhen has been undertaken and the understanding of pollution exposure in urban agglomerations with comparatively low pollution is still limited. Methods We analyze daily and hourly particulate matter concentrations and all-cause mortality during 2013 in Shenzhen, China. Temporal patterns of PM (PM_2.5 and PM₁₀) with aerodynamic diameters of 2.5 (10) μm or less (or less (including particles with a diameter that equals to 2.5 (10) μm) are studied, along with the ratio of PM_2.5 to PM₁₀. Spatial distributions of PM₁₀ and PM_2.5 are addressed and associations of PM₁₀ or PM_2.5 and all-cause mortality are analyzed. Results Annual average PM₁₀ and PM_2.5 concentrations were 61.3 and 39.6 μg/m³ in 2013. PM_2.5 failed to meet the Class 2 annual limit of the National Ambient Air Quality Standard. PM_2.5 was the primary air pollutant, with 8.8 % of days having heavy PM_2.5 pollution. The daily PM_2.5/PM₁₀ ratios were high. Hourly PM_2.5 concentrations in the tourist area were lower than downtown throughout the day. PM₁₀ and PM_2.5 concentrations were higher in western parts of Shenzhen than in eastern parts. Excess risks in the number of all-cause mortality with a 10 μg/m³ increase of PM were 0.61 % (95 % confidence interval [CI]: 0.50–0.72) for PM₁₀, and 0.69 % (95 % CI: 0.55–0.83) for PM_2.5, respectively. The greatest ERs of PM₁₀ and PM_2.5 were in 2-day cumulative measures for the all-cause mortality, 2-day lag for females and the young (0–65 years), and L02 for males and the elder (>65 years). PM_2.5 had higher risks on all-cause mortality than PM₁₀. Effects of high PM pollution on mortality were stronger in the elder and male. Conclusions Our findings provide additional relevant information on air quality monitoring and associations of PM and human health, valuable data for further scientific research in Shenzhen and for the on-going discourse on improving environmental policies.