The establishment of the road green belt (RGB) is an effective means to reduce particle matter (PM_2.5) emissions from road traffic. This study tested the ability of 23 common tree species in Shenzhen to reduce PM_2.5 concentrations using field investigations and wind tunnel tests. The association between leaf microstructure and individual reduction ability was also analyzed. Finally, the impact of three RGB configurations (i.e., arbor, shrub, arbor + shrub) on road PM_2.5 dispersion and deposition was simulated using the ENVI-met three-dimensional aerodynamic model, based on which an optimal RGB configuration was proposed. There were three key findings of the tests. First, the wind speed was the main factor affecting the PM_2.5 concentration (54.2%), followed by vehicle flow (27.7%), temperature (14.2%), and time factor (7.6%). Second, the range of dry deposition velocity (V_d) was 0.04-6.4 m/s, and the dominant dust-retaining plant species were the evergreen trees, Ficus microcarpa and Ficus altissima, and the evergreen shrubs, Codiaeum variegatum and Fagraea ceilanica. A higher proportion of grooves or larger stomata would increase the probability that the blade would capture PM_2.5. Third, the shrub RGB demonstrated the best performance in terms of pollutant dispersion; its PM_2.5 concentration at the respiratory height (RH, 1.5 m) on the pedestrian crossing was 15-20% lower than the other RGB configurations. In terms of pollutant deposition, the arbor + shrub composite RGB was two-fold better than the other RGB configurations. Moreover, it was more advantageous to plant shrub RGBs in street canyons to achieve a balance between the lowest concentration and the largest deposition of PM_2.5 pollutants. The findings of this study will facilitate the RGB configurations with good dust retention ability.

Land suitability assessment is fundamental in space control planning and land development because of its effects on land use and urban layout. Rainstorms and waterlogging have become one of the most common natural disasters in the coastal areas of China. As a result, the concept of an ecological sponge city was incorporated into the construction of cities in the future. Taking Shenzhen–Shantou special cooperation zone (SSCZ), we constructed a storm flooding model based on the SCS flow generation model and GIS to explore the spatial distribution characteristics of the flooding risk in a rainstorm of 100-year lasting 1 h. Combined with population and economic indicators, a radial basis function (RBF) network was utilized to evaluate the environmental risk, the vulnerability of disaster-bearing bodies, and the rain–flood resilience of sponge cities. The self-organizing feature mapping (SOFM) model was used for cluster analysis. Spatial differences were found in the construction suitability of the study area. A suitable construction area (73.59% of the entire area) was located downtown. The construction of the artificial spongy body in the highest vulnerable area (3.25%) needs to be strengthened. The control construction area (3.3%) is located along the banks of the river, with relatively high risk and low resilience of flood control engineering. Ecological construction (19.85%) serves as the sponge body of ecological buffer. The factors of waterlogging, ecology, population, and economy could be integrated comprehensively by applying neural network methods for urban planning and construction.