Green spaces can effectively reduce atmospheric particulate matter (PM) pollution and regulate negative air ions (NAI), this ecological function of green spaces is directly determined by their types and structures. However, the mechanism of the interactions among the types and structures of green spaces, PM, and NAI are still unclear. In the present study, we selected Dongling Park in Shenyang, China as an example of a typical green space. We ran field experiments to analyze the influences of various green space types and structures on PM10, PM2.5, and NAI. The results showed that green spaces had significantly lower PM10 and PM2.5 and higher NAI concentrations than non-green spaces. Among the four vegetation types (tree-shrub-herb, tree-herb, shrub-herb, and tree-shrub), the tree-shrub-herb type most effectively decreased atmospheric PM10 and PM2.5 concentrations (32.54 ± 8.14% and 21.26 ± 4.07%, respectively) and increased NAI concentrations (115.43 ± 10.00%) to a significantly greater extent than the other vegetation types (p < 0.05). Canopy density was strongly and significantly positively correlated with PM2.5 and PM10 concentrations (r = 0.84* and r = 0.86**, respectively). By contrast, shrub layer richness was significantly negatively correlated with PM2.5 and PM10 concentrations (r = −0.68* and r = −0.74*, respectively). Furthermore, significant positive correlations with NAI concentrations were detected in the canopy density and proportion of evergreen tree species significantly (r = 0.40* and r = 0.46*, respectively). The results illustrate that the green space vegetation types and structures, especially the density of tree canopy, the richness of shrub layer, and the proportion of evergreen tree species, can influence the airborne PM10, PM2.5 and NAI concentrations. Therefore, vegetation types and structures should be considered in the process of planning urban green spaces that perform the ecological function of improving air quality.