Urban greening based on the supply and demand of atmospheric PM2.5 removal

Abstract

Faced with various urban environmental problems, cities are implementing greening plans to satisfy the demands of residents for a more habitable environment. Because the relationship between the supply and demand of ecosystem services (ESs) often changes spatially and seasonally, identifying the priority space and suitable species for greening are critical. Taking the removal of particulate matter less than 2.5 µm (PM2.5) in Beijing, China, as an example, a Greening Demand Index was proposed by combining seasonal nongreen coverage, PM2.5 concentration, and population density. The results show that, the greening demand (GD) increases along the suburban-urban gradient and is higher in the impervious areas than in other land-cover types. Without considering the seasonal variation in ES supply and demand, the GD will be underestimated in areas with deciduous vegetation coverage. On the one hand, demand comes from scarcity, and on the other hand, demand also comes from inequity. To alleviate the urban-suburban greening demand difference (GDD), greening in impervious areas is the key. To alleviate the seasonal GDD, evergreen greening in the forest and impervious areas is crucial. Forest evergreen greening, which has often been overlooked in the past, should also be considered. Three tree planting scenarios with different species compositions were simulated to evaluate the effects on PM2.5 and green distribution. The results indicate that evergreen trees are more efficient in removing atmospheric PM2.5 and are indispensable in alleviating the seasonal variation in PM2.5 concentration and the spatiotemporally uneven distribution of green. Therefore, they are recommended for greening. This research will provide help for establishing tree planting schemes in urban areas.

Keywords

• Urban forestry
• Scenario analysis
• Ecosystem service
• Evergreen greening