The outdoor wind comfort is important for evaluations of the urban wind environment. The existing wind comfort criteria are based on the correlations between wind parameters, human activity levels, and wind comfort level categories. These criteria vary among different climates and regions, and they are influenced by meteorological parameters and human wind adaptation. In this study, an outdoor wind comfort questionnaire survey was conducted and meteorological measurements were collected in Xi’an, which is located in the cold region of China. The square and lake side were chosen as the test sites. The mean wind sensation vote (MWSV) and wind speed of neutral wind sensation were used as indexes to analyze the correlation between outdoor wind comfort and human wind adaptation. The results showed that the wind sensation level was generally moderate. At −1 < MWSV < 1, the fluctuations in the wind speed had little effect on the wind sensation, but when MWSV > 1 or MWSV < −1, the degree of discomfort increased as the absolute value of MWSV increased. When the wind speed exceeded 1 m/s, the amount of clothing insulation was greatly increased. The metabolic rate fluctuated between 1.5 met and 2 met. Expectations that the wind speed in the winter would be less than 1.05 m/s were greater than expectations that the wind speed would be more than 1.05 m/s in the summer, and people generally adapted to a low wind environment with wind speeds below 1 m/s. The acceptable wind ranges for more than 90% of participants at the square and lake side were (0 m/s, 2.02 m/s) and (0.01 m/s, 2.86 m/s). This study provides support for the revision of outdoor wind comfort standards and a methodological basis for outdoor wind comfort research at the same latitude throughout the world.

Existing national park zoning studies exhibits significant gaps in integrating ecological sensitivity, tourism resource potential, and particularly the spatiotemporal dynamics of human activities. Against the backdrop of the Qinghai Lake National Park, which encompasses China’s largest inland saltwater lake and serves as the core tourism region of Qinghai Province, this study develops a tri-dimensional evaluation framework of “place, people and things”, where “place”—a multi-factors ecological sensitivity analysis, “people”—Mobile-data-based Active Population Estimation (MAPE) for spatiotemporal behavioral analysis and “things”—a comprehensive tourism resource potential assessment, and ultimately achieve functional zoning through multi-dimensional superposition. Key findings indicate that the overall ecological sensitivity of the basin is relatively high, with highly sensitive and extremely sensitive areas account for 46.24% of the total study area. Tourism potential has a highly uneven distribution, with limited-potential zones covering 42.90% of the basin, while top-level potential areas constitute merely 0.60%. Based on the integrated analysis, a refined five-zone functional system is proposed: strict protection zone, harmonious transition zone, innovative development zone, rational development zone, and stable maintenance zone, while the latter three account for less than 6%, revealing an extreme scarcity of “low ecological sensitivity, high tourism potential, and high tourist flow” areas. Each zone is coupled with differentiated control measures. This research provide quantitative support for the delineation of ecological protection redlines and sustainable tourism planning in Qinghai Lake National Park.

This study analyzed the spatiotemporal dynamics of the vegetation ecological quality under climate change. Focusing on the vegetation conditions, a vegetation ecological quality index was constructed, expressing as the product of vegetation fraction cover (VFC), net primary productivity (NPP), and geographic coverage area. The results of trend and significance analysis showed that from 2000 to 2023, the VEQI in the Qinling Mountains exhibited a significant improvement, with an average slope of 4.91 gC·a⁻¹ and 96.2% of the area showing high stable improvement. Partial correlation analysis revealed that precipitation had a stronger positive influence on VEQI than temperature, with over 98% of the area showing a positive correlation with precipitation, while temperature was positively correlated in 95.0% of the area but negatively correlated in high-altitude mountain zones. Therefore, four climate-driven patterns were identified: precipitation-driven (31.2%), temperature-driven (2.3%), co-driven (54.2%), and climate-stable (12.3%), suggesting that vegetation ecological quality in most regions is co-driven by both temperature and precipitation. Based on the results of trend and significance analysis and climate-driven patterns, the Qinling Mountains were divided into three ecological risk zones: low-risk (36.1%), middle-risk (56.9%), and high-risk (7.0%), with corresponding differentiated control measures proposed.