The frequent urban floods have seriously affected the regional sustainable development in recent years. It is significant to understand the characteristics of urban flood risk and reasonably predict urban flood risk under different land use scenarios. This study used the random forest and multi-criteria decision analysis models to assess the spatiotemporal characteristics of flood risk in Zhengzhou City, China, from 2005 to 2020, and proposed a robust method coupling Bayesian network and patch-generating land use simulation models to predict future flood risk probability. We found that the flood risk in Zhengzhou City presented an upward trend from 2005 to 2020, and its spatial pattern was “high in the middle and low in the surrounding areas”. In addition, land use patterns under the sustainable development scenario would be more conducive to reducing flood risk. Our results can provide theoretical support for scientifically optimizing land use to improve urban flood risk management.

Fine particulate matter (PM_2.5) is a harmful air pollutant that seriously affects public health and sustainable urban development. Previous studies analyzed the spatial pattern and driving factors of PM_2.5 concentrations in different regions. However, the spatiotemporal heterogeneity of various influencing factors on PM_2.5 was ignored. This study applies the geographically and temporally weighted regression (GTWR) model and geographic information system (GIS) analysis methods to investigate the spatiotemporal heterogeneity of PM_2.5 concentrations and the influencing factors in the middle and lower reaches of the Yellow River from 2000 to 2017. The findings indicate that: (1) the annual average of PM_2.5 concentrations in the middle and lower reaches of the Yellow River show an overall trend of first rising and then decreasing from 2000 to 2017. In addition, there are significant differences in inter-province PM_2.5 pollution in the study area, the PM_2.5 concentrations of Tianjin City, Shandong Province, and Henan Province were far higher than the overall mean value of the study area. (2) PM_2.5 concentrations in western cities showed a declining trend, while it had a gradually rising trend in the middle and eastern cities of the study area. Meanwhile, the PM_2.5 pollution showed the characteristics of path dependence and region locking. (3) the PM_2.5 concentrations had significant spatial agglomeration characteristics from 2000 to 2017. The “High-High (H-H)” clusters were mainly concentrated in the southern Hebei Province and the northern Henan Province, and the “Low-Low (L-L)” clusters were concentrated in northwest marginal cities in the study area. (4) The influencing factors of PM_2.5 have significant spatiotemporal non-stationary characteristics, and there are obvious differences in the direction and intensity of socio-economic and natural factors. Overall, the variable of temperature is one of the most important natural conditions to play a positive impact on PM_2.5, while elevation makes a strong negative impact on PM_2.5. Car ownership and population density are the main socio-economic influencing factors which make a positive effect on PM_2.5, while the variable of foreign direct investment (FDI) plays a strong negative effect on PM_2.5. The results of this study are useful for understanding the spatiotemporal distribution characteristics of PM_2.5 concentrations and formulating policies to alleviate haze pollution by policymakers in the Yellow River Basin.