Structure of an urban green space indirectly affects the distribution of airborne particulate matter: A study based on structural equation modelling

Urban green spaces can effectively regulate microclimate and improve air quality. However, the relationship between the structure of an urban green space, meteorological conditions, and the concentration distribution of different sized particles inside and outside of green spaces is unclear. In this study, a field survey was conducted on the structures of 188 green spaces in Beijing. The temperature and relative humidity were also monitored, and the concentrations of different sized particles were measured both inside and outside of the selected green spaces to comprehensively analyse the relationships between these variables and the structure of a green space. The results of structural equation modelling showed that the structure of a green space indirectly affected the concentration distribution of particulate matter (PM) through the effects of cooling and humidification, while the direct effect was not obvious. In addition, the impact mechanisms of the structure of a green space on the concentration distribution of particles differed between PM₁, PM_1–5, and PM_5–25, primarily due to the influence of particle size and meteorological factors on the movement of PM. This study deepens the understanding of the influencing mechanisms of the structure of a green space on the concentration distribution of PM, thus providing an important reference for subsequent related field and numerical simulation research.     

Establishing a "dynamic two-step floating catchment area method" to assess the accessibility of urban green space in Shenyang based on dynamic population data and multiple modes of transportation

As an integral part of the urban environment, urban green space (UGS) is of great significance in maintaining urban ecosystem balance and biodiversity. Spatial accessibility is an important indicator of UGS distribution and can be calculated by the two-step floating catchment area method (2SFCA). However, problems exist in previous studies using 2SFCA: (1) the dynamics in population distributions are ignored when measuring UGS demand; and (2) travel costs are calculated for only a single mode of transportation. To address these problems, this study proposes a dynamic two-step floating catchment area method (D2SFCA) based on the Baidu heatmap and direction application programming interface and compares it with the traditional 2SFCA to investigate the characteristics of and differences in UGS accessibility in the first-ring built-up area of Shenyang, China. The results show the following: (1) the dynamic population distribution data calculated by the heatmap yielded the highest population density raster (581.0–1342.0 p/hm²) in areas with dense road networks and the lowest population density raster (2.0–91.0 p/hm²) in areas with railways and rivers, thereby more accurately reflecting reality and better quantifying the UGS demand than the static population distribution data; (2) the D2SFCA and 2SFCA findings had a slightly different distribution, and the D2SFCA assessment results more accurately reflected actual patterns, especially when the road data were inaccurate. In summary, the D2SFCA is more suitable for assessing accessibility and can identify specific areas that lack UGS. This study provides a scientific basis and methodological support for improving the services level and equity of UGS.