LAUSR

Abstract Atmospheric particulate matter (PM) pollution is becoming a growing global problem with the rapid process of urbanization. Urban green space (UGS) can effectively alleviate PM; however, few studies have investigated the effects of the UGS morphological pattern on PM, especially from a spatial strategy perspective. This study probed the contribution and strength of UGS on variation of PM2.5 concentration based on morphological spatial pattern analysis (MSPA). Three relative indicators (range, duration, and rate) were used to represent PM2.5 changes, and seven MSPA classes (core, islet, perforation, edge, loop, bridge, and branch) were performed to measure UGS morphological patterns. Stepwise regression analysis was used to build the PM2.5 estimation models and partial correlation analysis was used to further analyze how well different MSPA classes influence PM2.5. Results showed that MSPA classes and meteorological factors combined can explain more of PM2.5 increase variance at a high PM2.5 level, and 40.7–81.4% for PM2.5 reduction variance, and meteorological factors contributed more to PM2.5 increase and reduction. Higher proportions of the core and bridge were conducive to restrict the growth and promote the reduction of PM2.5 concentration, however, a higher proportion of perforation, islet, and edge showed opposite results. The effects of loop and branch were complex. In addition, higher air temperature and lower relative humidity were effective in reducing PM2.5. Wind speed, also a significant factor, had an unstable influence. The study results may provide important insights and effective spatial strategies for urban managers to mitigate PM2.5.