Road traffic accidents pose significant challenges to sustainable urban safety and intelligent transportation management. The effective hazard identification of crash hotspots is crucial in implementing targeted safety measures. A severity-weighted system was adopted to quantify crash hazard levels. Using 1059 valid crash records of the City of London, the spatial correlations of crash points were first examined via average nearest neighbor analysis. Then, the optimal KDE bandwidth was determined via ArcGIS’s automatic extraction method, multi-distance spatial cluster analysis, and incremental spatial autocorrelation (ISA) analysis. The predictive accuracy index (PAI) was used to evaluate the accuracy of KDE results at various bandwidths. The results revealed a clustered spatial distribution of crash points. The optimized KDE bandwidth obtained via ISA analysis was 134 m, and the yielded PAI was 4.381, indicating better predictive accuracies and balanced hotspot distributions and reflecting both local concentrations and the overall continuity of crash hazard hotspots. Applying this bandwidth to the validation data allowed the successful identification of most high-risk areas and potential crash hazard hotspots attributed to traffic environmental factors; this method exhibits reliability, accuracy, and robustness over medium to long time scales. This workflow can serve as an analytical template for assisting planners in improving the identification accuracy of hazard hotspots, thereby reducing crash occurrences, actively promoting sustainable traffic safety development, and providing valuable insights for targeted crash prevention and intelligent traffic safety management in urban areas.

The urban heat island (UHI) effect and its associated extreme weather events have adverse impacts on human environment-coupled systems. However, the spatiotemporal variations in the UHI effect, as well as potential influencing factors, across climate zones remain poorly understood. This study explored how climate zones influenced the spatiotemporal variation in, trends in, and drivers of summer daytime surface UHI intensity (SUHII) in 220 Chinese cities located in five climate zones from 2000 to 2020. SUHII was quantified using MODIS land surface temperature (LST) data and remote sensing-derived urban built-up area masks were used to quantify SUHII. The Mann–Kendall test was applied to detect long-term SUHII trends, while Pearson correlation and stepwise multiple regression analyses were performed to identify key climatic and geographic drivers across different climate zones. The results indicated summer daytime SUHII values of 1.75 °C ± 1.19 °C, 1.74 °C ± 0.81 °C, 2.37 °C ± 0.75 °C, 2.14 °C ± 1.00 °C, and 2.36 °C ± 0.91 °C for the middle temperate zone (MTZ), south temperate zone (STZ), north subtropical zone (NSZ), middle subtropical zone (MSZ), and south subtropical zone (SSZ), respectively. In most cities, the SUHII increased significantly over time (p < 0.05). Pearson’s correlation analysis indicated that the enhanced vegetation index (EVI) and net radiation (NR) were moderately correlated with the SUHII in the MTZ, with correlation coefficients (r) of 0.465 and 0.42 (p < 0.05). Using a multivariate stepwise regression model, the relative contributions of various influencing factors to the UHI effect were quantified, explaining 27.1% to 57.2% of the variation across different climate zones. In particular, the economic vulnerability index and population density were the main factors affecting the SUHII in the MTZ and SSZ. Our findings support the development of policies aimed at mitigating the UHI effect by addressing the specific requirements of different climate zones to reduce.

The analysis of the small-scale spatial and temporal evolution characteristics of particulate matter (PM), especially Particulate Matter 2.5 (PM _2.5 ) and Particulate Matter 10 (PM ₁₀ ), and the influence of meteorological factors is crucial for understanding the mechanisms of large-scale PM formation and transport, and further to develop precise dust pollution prevention measures. In this study, Zhenjiang Port was analyzed first time, which was based on hourly data of PM _2.5 and PM ₁₀ concentrations from 15 air quality monitoring stations from January 2019 to December 2020. With respect to evolution characteristics of PM _2.5 and PM ₁₀ concentrations within the study area, the results showed that: (1) Daily averages exhibit varying degrees of exceedances, with PM _2.5 exceeding Level 3 standards accounting for 13.68%. (2) Spatially, both PM _2.5 and PM ₁₀ display a pattern of higher concentrations in the western areas and lower concentrations in the eastern areas. (3) Diurnal fluctuations are relatively stable during the day, with peak values occurring around 06:00 in the morning. (4) PM _2.5 has a long retention time in the air with hysteresis. (5) For the relationship of concentration and temperature, it shows positive correlation for below 6°C while negative correlation for above 6°C. (6) For the relationship of concentration and relative humidity, it shows positive correlation for below 80% and above 90%, while negative correlation for at 80–90%. The research findings can provide a theoretical basis for formulating atmospheric pollution prevention and control measures for Zhenjiang Port, while also serving as a reference for studying the evolution mechanisms of PM in Zhenjiang City and even larger-scale regions.