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Climate change and rapid urbanization are two global processes that have significantly aggravated natural disasters, such as drought and flooding. Urbanization without resilient and sustainable planning and execution could lead to undesirable changes in landscapes and stormwater regulation capacity. These changes have exacerbated the effects of extreme climatic events with disastrous consequences in many cities worldwide. Unfortunately, the major storm in Zhengzhou, China on 20 July 2021 was one of these examples. This event provided a rare opportunity to study the key roles of green infrastructures (GI) in mitigating flooding risks in a major urban center after a devasting flood event. Using the data from high-resolution images collected via two satellites, a comprehensive study of the Jialu System in Greater Zhengzhou was conducted to systematically compare how far the river water had reached before and after the 20 July 2021 major storm in order to identify the main weak links in the city's GI and stormwater management system. A flood inundation intensity index (FI) in the Upper (UJLR), Middle (MJLR), and Lower (LJLR) Regions of the Jialu River System was generated. Bivariate Moran's I, a correlation coefficient between FI and landscape characteristics, was calculated and used to identify problem areas for future improvements. Our results showed that the MJLR had the severest flooding impacts. LJLR had the biggest change in how far the river water reached after flooding, ranging from 4.59 m to 706.28 m. In UJLR, the percentages of mine, crop land, and green space had the highest global bivariate Moran's I correlation coefficients. In MJLR, the percentages of vacant land, impervious surfaces, and water body had the highest global bivariate Moran's I correlation coefficients. In LJLR, the percentages of vacant land, water body, and crop land had the highest global bivariate Moran's I correlation coefficients. The total percentages of both high landscape characteristics indices-high flood inundation intensity indices and low landscape characteristics indices-high flood inundation intensity indices areas are 12.96%, 13.47%, and 13.80% in UJLR, MJLR, and LJLR, respectively. These land cover composition types identified for each region can be treated as areas of primary focus. However, GeoDector Model (GDM) analyses showed that our eight variables of landscape characteristics were not independent. Hence, a more comprehensive approach integrating all eight variables is still necessary in future flood mitigation efforts. [ABSTRACT FROM AUTHOR]

• PM 10 and PM 2.5 concentrations showed significant seasonal cycling but only PM 10 concentrations showed any relationship with greenspace layout. • Increasing the overall abundance of greenspace and percentage of large greenspace patches can reduce the PM 10 concentration significantly. • Increasing the amount of contact between greenspace patches and the surrounding area can reduce PM 10 concentrations at larger spatial scales. Atmospheric particulate matter (PM) pollution has become a major global public health concern, particularly in urbanizing and rapidly developing nations such as China. Health effects from PM pollution include increases in cardiovascular and respiratory illnesses and problems during pregnancy and child development. Developing methods to mitigate PM pollution is a major priority for urban planning. Urban forests and other greenspaces are known to have both direct and indirect effects on PM concentrations, but uncertainty remains about how best to design greenspaces to reduce PM loads. This is partly because of contrasting effects that have been observed at different spatial scales. We used high-resolution satellite imagery and two years of daily PM concentration data from nine national air pollution monitoring stations in Zhengzhou, China, to examine the relative importance of seasonal cycling and greenspace effects on PM concentrations. We described patterns of urban greenspace at multiple spatial scales (buffer radii of 0.5 km – 3 km) using 15 different landscape pattern metrics. Multivariate methods (cluster analysis, principal component analysis) were used to process these into a smaller number of uncorrelated descriptors that described major patterns of variation in greenspaces. Linear mixed models were used to model seasonal cycling and greenspace effects on PM 10 and PM 2.5 concentrations for each spatial scale. We found that: 1) monthly mean, maximum, and standard deviations of concentrations of both PM 2.5 and PM 10 showed stationarity across seasons and months, being greatest in winter and smallest in summer; 2) urban greenspace patterns did not significantly affect PM 2.5 concentrations; 3) increasing the overall abundance of greenspace, and percentage of large greenspace patches, at almost all scales significantly reduced PM 10 concentrations; and 4) at larger spatial scales, increasing the amount of contact between the edges of greenspace patches and the surrounding urban area could reduce PM 10 concentrations. In the context of urban planning and renewal, it may be possible to reduce PM 10 pollution by carefully planning the shape and layout of greenspace networks across scales. [ABSTRACT FROM AUTHOR]

In the context of global warming, more and more cities are experiencing extreme Urban Heat Island (UHI) effects and extreme weather phenomena, but urban green spaces are proven to mitigate UHI. Most of UHI's research focuses on the large scale and uses remote sensing methods, which do not reflect the dynamic characteristics in detail and do not detect internal influencing factors of the green space cooling effect. Therefore, this study focused on Small Green Spaces (SGS), carrying out the measurement of the meteorological parameters (temperature, relative humidity, wind direction, wind speed, photosynthetic radiation) of the 16 sites in four types of coverage (Impervious surface; Shrub-grass; Tree-grass; Tree-shrub-grass) in a university campus. At the same time, the coverage characteristic parameters, such as Canopy Density (CD), Leaf Area Index (LAI), Photosynthetically Active Radiation (PAR), Mean Leaf Angle (MLA), of each plot were analyzed and compared. The results showed that there were significant differences in temperature among different coverage types in SGS. The biggest difference was concentrated in the noon period when solar radiation is strongest during the day. The difference between the four types of coverage with vegetation at night was small. The maximum air temperature difference among the four types could reach 8.9 ℃ and the maximum relative humidity difference was 28.5%. The cooling effect of the multi-layer vegetation-covered (Tree-shrub-grass) area was the largest compared to the impervious surface, indicating that tree cover was the core factor affecting the temperature. Temperature and relative humidity had a close correlation with surface coverage types and some plant community characteristics (such as CD and LAI). The cooling and humidifying effects of plants were also related to PAR and leaf angle. The results provide suggestions for green space management and landscape design. [ABSTRACT FROM AUTHOR]