Search Results (2)

Green infrastructure is imperative for efficiently mitigating flood disasters in urban areas. However, inadequate green space planning under rapid urbanization is a critical issue faced by most Chinese cities. Aimed at theoretically understanding the rainwater storage capacity and improvement potential of urban green spaces, a synthetic simulation model was developed to quantify rainfall surface flow accumulation (FA) based on the morphological factors of a flow basin: the area, circumference, maximum basin length, and stream length sum. This model consisted of applying the Urban Forest Effects-Hydrology model (UFORE-Hydro) to simulate the actual precipitation-to-surface runoff ratio through a procedure involving canopy interception, soil infiltration, and evaporation; additionally, a relatively accurate multiple flow direction-maximum downslope (MFD-md) algorithm was applied to distribute the surface flow in a highly realistic manner, and a self-built “extraction algorithm” extracted the surface runoff corresponding to each studied basin alongside four fundamental morphological parameters. The various nonlinear regression functions were assessed from both univariable and multivariable perspectives. We determined that the Gompertz function was optimal for predicting the theoretical quantification of surface FA according to the morphological features of any given basin. This article provides parametric vertical design guidance for improving the rainwater storage capacities of urban green spaces. Full article

Atmospheric particulate matter (PM) is a major air pollutant. PM_2.5 and PM₁₀ pose particularly serious threats to the ecological environment and human health. Vegetation plays an important role in reducing the concentration of particles. Based on a long time series of air quality, meteorological, and vegetation coverage data in the Beijing–Tianjin–Hebei (BTH) region, the present paper evaluated the influence at the overall and built-up area scales and quantified the process involved in the dry settlement of particles on vegetation based on a mathematical model. The experimental results showed that (1) the total amounts of PM₁₀ reduced by vegetation in the BTH area were 505,200 t, 465,500 t, 477,200 t and 396,500 t in 2015, 2016, 2017 and 2018, respectively, and the total amount of PM_2.5 was reduced by 19,400 t, 19,200 t, 16,400 t and 12,700 t, respectively. The annual reduction in PM₁₀ and PM_2.5 from 2015 to 2018 by vegetation in the BTH region showed a downwards trend, and the annual reduction was mainly caused by the significant decrease in PM concentration. (2) More than 80% of the reduction in annual yield was concentrated in May–September, and a large leaf area was the main reason for the largest yield reduction in the growing season. The efficiency of PM reduction in forestland was approximately five–seven times that in grassland, and the deciduous broad-leaved forest was the main driver of this reduction in each forest. (3) The reduction in PM₁₀ by vegetation was approximately 30 times that of PM_2.5. However, the reduction in PM_2.5 by vegetation should not be ignored because PM_2.5 has a stronger correlation with human production and living activities. Increasing the area and density of green space via afforestation, returning farmland to forest and giving full play to the self-purification function of green spaces are very important to reducing and controlling the concentration of PM. Full article