The Impact of Urban Green Patches on Air Pollutant Concentration: A Case Study of Hangzhou

Abstract

To explore the impact of landscape pattern index on air pollutant, this study takes the annual average concentrations of PM_2.5, O₃, PM₁₀, and NO₂ at 14 national air quality monitoring stations in Hangzhou from 2014 to 2021 as the dependent variables, and selects five landscape pattern indices of green patches within 500m of the monitoring stations as independent variables. An enhanced regression tree model was used to study the influence of landscape patterns on the concentrations of the four air pollutants. The results show that the most significant influencing factors for the concentrations of PM_2.5, O₃, PM₁₀, and NO₂ are the aggregation index, Shannon's diversity index, aggregation index, and largest patch index respectively, with relative influence rates of 29.27%, 25.06%, 31.28%, and 28.58%, respectively. The aggregation index has a significant impact on all types of air pollutants and plays a good role in reducing air pollution. With higher regional patch aggregation index, the concentration of air particulate matter and nitrogen oxides is greatly alleviated. The largest patch index is significantly negatively correlated with air particulate matter and ozone concentration, and an increase in green areas has a good mitigating effect on these two types of air pollutants. As the Shannon diversity index increases, there is a general trend of decreasing particulate matter concentration, while the concentrations of nitrogen dioxide and ozone show a decrease as well. This suggests that the complexity of landscape shape and boundaries is conducive to the reduction of nitrogen dioxide and ozone concentrations to a certain extent.