Do green spaces affect the spatiotemporal changes of PM2.5 in Nanjing?

Among the most dangerous pollutants is PM2.5, which can directly pass through human lungs and move into the blood system. The use of nature-based solutions, such as increased vegetation cover in an urban landscape, is one of the possible solutions for reducing PM2.5 concentration. Our study objective was to understand the importance of green spaces in pollution reduction. Daily PM2.5 concentrations were manually collected at nine monitoring stations in Nanjing over a 534-day period from the air quality report of the China National Environmental Monitoring Center (CNEMC) to quantify the spatiotemporal change of PM2.5 concentration and its empirical relationship with vegetation and landscape structure in Nanjing. The daily average, minimum, and maximum PM2.5 concentrations from the nine stations were 74.0, 14.2, and 332.0 μg m−3, respectively. Out of the 534 days, the days recorded as “excellent” and “good” conditions were found mostly in the spring (30.7 %), autumn (25.6 %), and summer (24.5 %), with only 19.2 % of the days in the winter. High PM2.5 concentrations exceeding the safe standards of the CNEMC were recorded predominately during the winter (39.3–100.0 %). Our hypothesis that green vegetation had the potential to reduce PM2.5 concentration was accepted at specific seasons and scales. The PM2.5 concentration appeared very highly correlated (R 2 > 0.85) with green cover in spring at 1–2 km scales, highly correlated (R 2 > 0.6) in autumn and winter at 4 km scale, and moderately correlated in summer (R 2 > 0.4) at 2-, 5-, and 6-km scales. However, a non-significant correlation between green cover and PM2.5 concentration was found when its level was >75 μg m−3. Across the Nanjing urban landscape, the east and southwest parts had high pollution levels. Although the empirical models seemed significant for spring only, one should not devalue the importance of green vegetation in other seasons because the regulations are often complicated by vegetation, meteorological conditions, and human activities.