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Exploring the spatiotemporal dynamic evolution of local climate zones (LCZ) associated with changes in land surface temperature (LST) can help urban planners deeply understand urban climate. Firstly, we monitored the evolution of 3D urban spatial form in Chengdu City, Sichuan Province, China from 2010 to 2020, used the ordinary least squares model to fit the dynamic correlation (DR) between the changes in urban spatial patterns and changes in LST, and revealed the changes of urban spatial patterns closely related to the rise in LST. Secondly, the spatiotemporal patterns of LST were examined by the integration of the Space-Time Cube model and emerging hotspot analysis. Finally, a prediction model based on curve fitting and random forest was integrated to simulate the LST of study area in 2025. Results show the following: the evolution of the urban spatial form consists of three stages: initial incremental expansion, midterm incremental expansion and stock renewal, and late stock renewal and ecological transformation. The influence of the built environment on the rise of LST is greater than that of the natural environment, and the building density has a greater effect than the building height. The overall LST shows a warming trend, and the seven identified LST spatiotemporal patterns are dominated by oscillating and new hotspots patterns, accounting for 51.99 and 11.44% of the study area, respectively. The DR between urban spatial form and LST varies across different time periods and built environment types, whereas the natural environment is always positively correlated with LST. The thermal environment of the city will warm up in the future, and the area affected by the heat island will shift to the central of the city.

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Landscape classification methods significantly impact the exploration of the mechanism of the relationship between landscapes and atmospheric particulate matter. This study compared the local climate zones (LCZs) and traditional land use/cover change (LUCC) landscape classification methods in explaining spatial differences in concentrations of atmospheric particulate matter (PM2.5 and PM10) and explored the mechanisms involved in how landscape elements affect atmospheric particulate matter. This was done by establishing a PM2.5 and PM10 land use regression (LUR) model of LCZ and LUCC landscapes under low, typical, and high particle concentration gradients in urban and suburban areas. The results show that under an LCZ classification system, the number of patches in the urban area of Shanghai was 548 times higher than that of a LUCC system. Moreover, LCZs were successfully established for LUR models in 12 scenarios, while only five models were established for LUCC, all of which were suburban models. The R2 of the LUR model based on the LCZ landscape and atmospheric particulate matter was mostly higher than that of the LUCC. For unnatural landscapes, the LUCC demonstrated that an urbanized environment positively affects the concentration of atmospheric particles. However, the LCZ analysis found that areas with high-density buildings have a positive effect on atmospheric particles, while most areas with low-density buildings significantly reduced the number of atmospheric particles present. Generally, compared with the traditional LUCC landscape classification method, LCZ integrates Shanghai's physical structure and classifies the urban landscape more accurately, which is closely related to the urban atmospheric particulate matter, especially in the urban area. Because the low-density building area has the same effect on the particulate matter as the natural landscape, the use of low-density buildings is recommended when planning new towns.

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Atmospheric environment in urban built-up area is severely influenced by the surrounding landscape pattern. Understanding the relationship between air pollution and surrounding landscape pattern at small scale has great significance for mitigating air pollution from the perspective of urban construction. The annual average concentrations of NO2, SO2, PM2.5 and PM10 from 266 air pollution monitoring stations in 30 provincial capitals of China in 2017 were chosen as dependent variables. Ten two-dimensional and three-dimensional landscape pattern indices (number of buildings, building aggregation, building density, impervious water ratio, quantitative density of catering, building footprint area, high building ratio, floor area ratio, total building area and building type Shannon diversity index) within the 3 km area around the monitoring stations were used as independent variables. The effects of landscape pattern on the concentration of four air pollutants were analyzed using the boosted regression trees model. The results showed that the concentration of four air pollutants in the central and northern cities were significantly higher than that in the southeast coastal cities and southwest cities. The most important factor affecting the concentrations of NO2, SO2, PM2.5 and PM10 was the impervious ratio, with relative contribution rates of 40.7%, 36.3%, 51.0% and 51.8% respectively. The results of sub-region analysis showed that the most important influencing factor differed in different regions, including the impervious ratio in the East and Central China; the number and density of buildings in South China; the impervious ratio and diversity of building types in North China; the impervious ratio and the number of buildings in Northeast China, the density of buildings in Northwest China. Such differences were mainly caused by climate, topography, urban planning, and other factors.

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