Even if urban catchments are adequately drained by sewer infrastructures, flooding hotspots develop where ongoing development and poor coordination among utilities conspire with land use and land cover, drainage, and rainfall. We combined spatially explicit land use/land cover data from Luohe City (central China) with soil hydrology (as measured, green space hydraulic conductivity), topography, and observed chronic flooding to analyze the relationships between spatial patterns in pervious surface and flooding. When compared to spatial–structural metrics of land use/cover where flooding was commonly observed, we found that some areas expected to remain dry (given soil and elevation characteristics) still experienced localized flooding, indicating hotspots with overwhelmed sewer infrastructure and a lack of pervious surfaces to effectively infiltrate and drain rainfall. Next, we used curve numbers to represent the composite hydrology of different land use/covers within both chronic flooding and dry (non-flooding) circles of 750 m diameter, and local design storms to determine the anticipated average proportion of runoff. We found that dry circles were more permeable (curve number (mean ± std. error) = 74 ± 2, n = 25) than wetter, flooded circles (curve number = 87 ± 1). Given design storm forcing (20, 50, 100 years’ recurrence interval, and maximum anticipated storm depths), dry points would produce runoff of 26 to 35 percent rainfall, and wet points of 52 to 61 percent of applied rainfall. However, we estimate by simulation that runoff reduction benefits would decline once infiltration-excess (Hortonian) runoff mechanisms activate for storms with precipitation rates in excess of an average of 21 mm/h, contingent on antecedent moisture conditions. Our spatial metrics indicate that larger amounts and patches of dispersed green space mitigate flooding risk, while aggregating buildings (roofs) and green space into larger, separate areas exacerbates risk.

This case study was conducted to quantify the effects of urban greenspace patterns on particle matter (PM) concentration in Zhengzhou, China by using redundancy and variation partitioning analysis. Nine air-quality monitoring stations (AQMS) were selected as the central points. Six distances of 1 km, 2 km, 3 km, 4 km, 5 km, and 6 km were selected as the side lengths of the squares with each AQMS serving as the central point, respectively. We found: (1) the fine size of PM (PM_2.5) and coarse size of PM (PM₁₀) among four seasons showed significant differences; during winter, the concentration of PM_2.5 and PM₁₀ were both highest, and PM_2.5 and PM₁₀ concentration in summer were lowest. (2) To effectively reduce the PM_2.5 pollution, the percentage of greenspace, the differences in areas among greenspace patches, and the edge complexity of greenspace patches should be increased at distances of 2 km and 3 km. To effectively reduce PM₁₀, the percentage of greenspace at a distance of 4 km, the edge density at distances of 2 km and 4 km, and the average area of greenspace patches at a distance of 1 km should be increased. (3) Greenspace pattern significantly affected PM_2.5 at a distance of 3 km, and PM₁₀ at a distance of 4 km. From shorter distance to longer distance, the proportion of variance explained by greenspace showed a decline–increase–decline–increase trend for PM_2.5, and a decline–increase–decline trend for PM₁₀. At shorter distances, the composition of greenspace was more effective in reducing the PM pollution, and the configuration of greenspace played a more important role at longer distances. The results should lead to specific guidelines for more cost-effective and environmentally sound greenspace planning.