As a core karst region in Southwest China, Guanling County plays a crucial role in regional ecological governance. This study integrates the InVEST model, landscape pattern index analysis, and the MGWR spatial model to systematically explore the dynamic mechanisms of habitat quality in Guanling’s karst mountains. Key findings include: (1) Landscape pattern alterations exhibit significant impacts on habitat quality, characterized by strong spatial heterogeneity; (2) Expansion of forest and grassland effectively buffers the negative effects of construction land expansion, forming an ecological compensation mechanism through enhanced landscape connectivity; (3) Between 2000 and 2020, the proportion of high-importance habitat quality zones increased from 54.79% to 56.16%, with moderate-importance zones stabilizing at approximately 7.80% and general-importance zones growing to 2.46%. The results provide a multi-scale analytical framework for habitat protection and land use optimization in fragile karst ecosystems.

Given the complex terrain and economic development status of Guizhou Province, research on tropospheric NO₂ column concentration using satellite remote sensing is still insufficient. Observing the spatial–temporal evolution characteristics of tropospheric NO₂ column concentration can ensure the stable development of air quality. Based on the Google Earth Engine (GEE) platform, NO₂ column concentration data retrieved from Sentinel-5P TROPOMI were analyzed using spatial autocorrelation, hotspot analysis, and geographic detector methods (Geodetector). The results show that NO₂ column concentration in Guizhou Province exhibits seasonal variation, characterized by higher levels in winter and lower levels in summer, with transitional values in spring and autumn. The annual average concentration was highest in 2021 at 3.47 × 10⁻⁵ mol/m² and lowest in 2022 at 2.85 × 10⁻⁵ mol/m². Spatially, NO₂ column concentration displays a distribution pattern of “high in the west, low in the east; high in the north, low in the south”, with significant spatial clustering. The distribution of cold and hot spots aligns with areas of high and low values. NO₂ column concentration is primarily influenced by socio-economic factors, with the interaction between any two factors enhancing the explanatory power of individual factors on NO₂ column concentration.