Land surface temperature (LST) is an important driving factor in the land-atmosphere energy cycle. To examine the spatiotemporal patterns of LST changes and the internal mechanisms driven by multiple factors, we used a trend analysis method on TRIMS LST data from 2000 to 2020 in the Qingling-Daba Mountains. The optimal parameter geographic detector (OPGD) model was used to detect the influence of twelve factors, including elevation, precipitation, albedo, relative humidity (RH) and normalized difference vegetation index (NDVI), on the spatial distribution of LST, as well as to explore the dominant factors affecting LST differentiation in the study area. The results showed that: (1) From 2000 to 2020, the average annual LST of the Qinling-Daba Mountains was 18.17 °C. The warming trend was obvious (0.034 °C/a), and the warming effect at nighttime (0.066 °C/a) was stronger than that during daytime (0.0004 °C/a). The difference between day and night temperature (DIF) was decreasing. (2) The seasonal changes in LST and DIF in the Qinling-Daba Mountains were significant, and the spatial distribution of their average values in the summer was slightly larger and fluctuated more than in the other seasons. (3) Elevation was the main driving factor affecting the spatial distribution of LST, with the contribution scores of 62.9% in the daytime and 92.7% in the nighttime. The controlling effects of these factors were generally stronger in the nighttime than in the daytime. (4) Nighttime elevation had the strongest interaction with precipitation (contribution score of 95%), while daytime elevation had the strongest interaction with albedo (contribution rate of 83%). We revealed the temporal and spatial variation in LST in the Qinling-Daba Mountains since 2000 and explored the main driving factors involved, thereby improving our understanding of LST changes in the Qinling-Daba Mountains. This study can provide a scientific basis for distinguishing dominant drivers of LST dynamics in the Qinling-Daba Mountains.

Surface fine particulate matter (PM) with a diameter of less than 2.5 microns (PM2.5) negatively impacts human health and the economy. However, due to data and model limitations, obtaining high-quality, high-spatial-resolution surface PM2.5 concentration data is a challenging task, and it is difficult to accurately assess the temporal and spatial changes in PM2.5 levels at a small regional scale. Here, we combined multi-angle implementation of atmospheric correction (MAIAC) aerosol products, ERA5 reanalysis data, etc., to construct an STW-LightGBM model that considers the spatiotemporal characteristics of air pollution and estimate the PM2.5 concentration of China’s surface at 1 km resolution from 2015 to 2020. Our model performed well, and the fitting accuracy of the 10-fold cross-validation between years was 0.877–0.917. The fitting accuracy of the model was >0.85 at different time scales (month, season, and year). The average slope of the regression prediction was 0.9 annually. The results showed that PM2.5 pollution improved from 2015 to 2020. The average PM2.5 concentration decreased by 4.55 μg/m³, and the maximum decrease reached 90.51 μg/m³. The areas with high PM2.5 concentrations were predominantly in the North China Plain, Sichuan Basin, and Xinjiang in the west, and the levels in areas with elevated PM2.5 levels were consistent across most study years. The standard deviation ellipse for PM2.5 in China showed a ‘northeast–southwest’ spatial distribution. From an interannual perspective, the average values of the four seasonal stations in the country showed a downward trend from 2015 to 2020, with the most obvious decline in winter, from 70.67 μg/m³ in 2015 to 46.75 μg/m³ in 2020. Compared to earlier inversion studies, this work provides a more stable and accurate method for obtaining high-resolution PM2.5 data, which is necessary for local air governance and environmental ecological construction at a fine scale.