The Influence Of Landscape Structure On The Spatiotemporal Changes Of PM_2.5

With increasing intensity and frequency of air pollutions in urban landscapes, PM2.5 has become one of the key species for the livelihood of urban systems. Consequently, the effects of meteorological conditions, green vegetation, and landscape structure on the magnitude and dynamics of PM2.5 over time and across the space have become a research focus in urban studies. In this paper, I examined the empirical relationships between PM2.5 concentration and vegetation in Guangzhou, Nanjing and Beijing. Daily PM2.5 concentration in three cities were manually collected from the air quality report webpage of the Ministry of Environmental Protection, P.R. China, from July 11,2013 through May 31,2015 to analyze the relationships at different temporal scales. I quantified the spatiotemporal changes of PM2.5 concentration and its empirical relationships with vegetation and the landscape structure. In addition, I used stepwise regression to construct the optimal models to explain the relationships between landscape factors and PM2.5 concentrations to understand the importance of green spaces in pollution reduction. The major discoveries of my study include:(1) PM2.5 in Guangzhou, Nanjing and Beijing had clear seasonal changes. The highest concentration was found in winter and the lowest in the summer. The average PM2.5 of the three cities was 47.6ug·m-3,74.0ug·m-3,80.6ug·m-3, respectively, for the three cities. Natural meteorological factors and human factors had great influences on the concentration of PM2.5.(2) Over the 23-month study period, winter is the most polluted season. The number of pollution days in Beijing was the highest, with 40.8% exceeded the national standard and 36.3% in the winter; the number of pollution days in Nanjing was 36.5%, with 47.2% in the winter; the number of pollution days in Guangzhou was only 13.8%, with 69.1% in the winter.(3) Landscape structure and its interactive terms can affect the PM2.5 concentration at different scales. When the climate and landscape are different, the range and extent of PM2.5 are different; precipitation has a significant effect on the PM2.5 concentration in summer. Meteorological condition in Guangzhou played significant roles in all seasons, but only in the autumn in Nanjing. The forest cover, total green cover, and edge length around the green covers showed significantly high correlations with PM2.5 concentration in Guangzhou. Green cover can reduce PM2.5 concentration (R2>0.86) maximally in autumn at 5-6 km scales. In Nanjing, the forest cover, grassland cover, total green cover, and total edge length around the green covers were highly correlated with PM2.5 concentration. Green cover and forest cover can reduce PM2.5 concentration (R2>0.90) maximally in spring at 1-2 km scales. PM2.5 concentration in all seasons were dependent of relative humidity and wind speed in Beijing. Rainy and windy summer can effectively reduce the PM2.5 concentration. The forest cover, grassland cover, total green cover, and total edge length were correlated with PM2.5 concentration. Green cover, total edge length and forest cover can reduce PM2.5 concentration (R2>0.90) maximally in summer at 3-4 km scales (R2>0.90).(4) When the concentration of pollution is relatively low, the landscape factor had a stronger effect on PM2.5 reduction. When PM2.5 concentrations are greater than or equal to 75ug·m-3, the correlation between the green space and the PM2.5 concentration is lower than below 75ug·m-3.