Abstract:Rapid urbanization in China has exacerbated atmospheric pollution, particularly in urban areas.Urban lakes played a crucial role in moderating local climate and air quality by influencing atmospheric boundary layer circulation and pollutant transport mechanisms.However, previous studies have relied primarily on mesoscale models, where the influence of additional coupled mechanisms may impact the accuracy of sensitivity results.To address this, we utilized a conceptual urban land surface model to conduct suburban-scale sensitivity experiments, isolating the primary factors and mechanisms by which urban lakes influence the distribution and transport of near-surface pollutants.Focusing on the diurnal patterns of pollutant mass concentrations at lakeside and non-lakeside urban stations in Nanjing during the summer, we found notable differences in pollutant behavior.For NO2, a ground-emitted pollutant, lakeside stations recorded a daytime average concentration of (1.64 ±0.29) μg·m-3 higher than non-lakeside, while nighttime concentrations were (0.51 ±1.39) μg·m-3 lower.In contrast, O3, which forms at mid-and upper-boundary layers, exhibited lower daytime concentrations by (9.57 ±2.19) μg·m-3 at lakeside stations, with nighttime levels (1.24 ±4.68) μg·m-3 higher.No significant differences were found for PM2.5 concentrations.Using a two-dimensional land surface model, we conducted sensitivity experiments to examine the effects of lake presence and lake-to-urban land distribution under different emission scenarios.Simulations indicated that the model accurately reproduced key temperature and pollutant mass concentrations patterns, comparable to more complex mesoscale model results.Thermal property differences between lake and urban land surfaces significantly impacted low-level atmospheric circulation and vertical stability, altering pollutant diffusion and transport.Daytime thermal stability and limited vertical diffusion over lakeside areas led to a higher concentrations of surface-emitted pollutants near lakes, while concentrations of pollutants formed at mid-levels were lower compared to non-lakeside areas;this pattern reversed at night.Simulation outcomes aligned well with observed data trends and were qualitatively consistent with WRF-Chem results.While emphasizing rigorous monitoring and data processing methods, spatial heterogeneity of urban structures and emissions, as well as observational data limitations, introduced some uncertainties.Future research should incorporate more extensive data from diverse urban regions to better generalize these patterns and strengthen the theoretical foundation for air quality management in urban lakeside environments.