Effects of Green space landscape patterns on particulate matter in Zhejiang Province, China

Abstract

Particulate matter (PM) pollution and its health effects are receiving more attention. Green space can provide critical ecosystem services, and increasing the supply of green space can reduce PM pollution, but effects of green spaces on PM at different scales are not clear. Based on the traffic and meteorological factors from 50 monitoring stations, principal composition cluster analysis (PCA) and hierarchical cluster analysis (HCA) were implemented. Daily PMs (PM10 and PM2.5) concentrations were measured at 50 monitoring stations in Zhejiang (1 Feb 2015 to 28 Feb 2017) to quantify the spatiotemporal change of PM concentration and its empirical relationship with green spaces and landscape structure. The result shows: (1) At 5-km, or smaller, scale, the correlation between green space and PM2.5 is stronger than PM10; (2) at 2-km scale or less, the total edge length has more impact on PMs than green cover area, while at 3–5-km scale, the influence of green cover is more dominated; (3) In the mountain and hilly area in Zhejiang that are dominated by meteorological factors, we can establish the PM2.5 forecast model by 3–4-km scale green spaces, while in those basins or low lands, we can build the PM2.5 forecast model by using 1–2-km scale green spaces. The results are of great importance for urban green space planning, especially when it comes to the size and shape of the green space. In addition, it can provide guidance to the future application of LUR model in Zhejiang area.