Do green spaces affect the spatiotemporal changes of PM in Nanjing?

Introduction: Among the most dangerous pollutants is PM, 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 PM concentration. Our study objective was to understand the importance of green spaces in pollution reduction.

Methods: Daily PM 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 PM concentration and its empirical relationship with vegetation and landscape structure in Nanjing.

Results: The daily average, minimum, and maximum PM concentrations from the nine stations were 74.0, 14.2, and 332.0 μg m, 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 PM 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 PM concentration was accepted at specific seasons and scales. The PM concentration appeared very highly correlated ( > 0.85) with green cover in spring at 1-2 km scales, highly correlated ( > 0.6) in autumn and winter at 4 km scale, and moderately correlated in summer ( > 0.4) at 2-, 5-, and 6-km scales. However, a non-significant correlation between green cover and PM concentration was found when its level was >75 μg m. Across the Nanjing urban landscape, the east and southwest parts had high pollution levels.

Conclusions: 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.