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Cardiovascular disease (CVD) poses a serious threat to urban health with the development of urbanization. There are multifaceted and comprehensive influencing factors for CVD, so clarifying the spatial distribution characteristics of CVD and multiple environmental influencing factors is conducive to improving the active health intervention of urban environment and promoting the sustainable development of cities The spatial distribution characteristics of CVD deaths in a certain district, Bengbu City, Huaihe River Basin, China, in 2019 were explored, and the correlation between multiple environmental factors and CVD mortality was investigated in this study, to reveal the action mechanism of multiple environmental factors affecting the risk of mortality. Relevant studies have shown that (1) CVD deaths are characterized as follows: male deaths are more than females; the mortality is higher in those of higher age; most of them are unemployed; cardiocerebral infarction is the main cause of death; and the deaths are mainly distributed in the central city and near the old urban area. (2) The increased CVD mortality can be attributed to the increased density of restaurants and cigarette and wine shops around the residential area, the increased traffic volume, the dense residential and spatial forms, the low green space coverage, and the distance from rivers. Therefore, appropriate urban planning and policies can improve the active health interventions in cities and reduce CVD mortality. [ABSTRACT FROM AUTHOR]

As the fine particulate matter (PM 2.5) polluting seriously threat people's health, exploring its mitigation strategies has become an urgent issue to be studied. Urban land use, the carrier of urban functions and human activities, has been widely recognized as an important contributor of PM 2.5 pollution. Taking Wuhan metropolitan area as an example, this study employs a deep learning simulation method to explore the effects of land use types and density on the spatial distribution of PM 2.5 pollutants. The PM 2.5 concentration data, raster-based land use data and meteorological conditions data are analyzed to identify their dynamic spatiotemporal characteristics. The meteorological conditions, including temperature and wind speed, are incorporated into the simulation platform, which improves the precision significantly. The simulation results show that PM 2.5 concentration caused by construction land such as industrial, residential, transportation, logistics and warehousing, commercial, utilities, and public service sequentially decreases. The impact of FAR on PM 2.5 concentration is spatially different. With the increase of FAR, some north construction pixels present PM 2.5 mitigation effects while a few grids in the south appear heavier pollution. By analyzing the results of different simulation scenarios, specific spatial-based PM 2.5 mitigation strategies and control measures are provided to promote healthy and sustainable urban development. This method can be transferred and applied to other metropolitans, so as to provide as a reference for policymakers and urban planners to promote effective air pollution mitigation strategies from the view of spatial planning. • Explore the impact of construction lands on PM 2.5 using a deep learning approach. • Urban meteorological conditions are considered in the model. • Effects order of lands on PM 2.5 : Industrial > residential > transportation. • The increase of FAR alleviates the PM 2.5 pollution in most north pixels. • Spatial-based PM 2.5 mitigation strategies were proposed. [ABSTRACT FROM AUTHOR]