Applying Spatial Regression Model to Explore the Relationship among Urban Green Areas, PM2.5 and Land Surface Temperature

• Main Authors: CHANG, YEN-CHING, 張晏菁
• Other Authors: SHIU, YI-SHIANG
• Format: Others
• Language: zh-TW
• Published: 2018
• Online Access: http://ndltd.ncl.edu.tw/handle/c2fj38

碩士 === 逢甲大學 === 景觀與遊憩碩士學位學程 === 106 === Previous studies have discussed the problems of PM2.5 with different aspects. Many studies showed that PM2.5 causes significant damage to human health, such as respiratory and cardiovascular diseases. We used MODIS satellite imagery and government open data to discuss the relationships among urban green areas, PM2.5 and land surface temperature in the former Taichung city in winter and summer in 2017. We tried to quantify the relationships with 5 different grid sizes in general spatial model, ranging from 250 m to 1250 m. With local indicators of spatial association, we confirmed that spatial distribution of PM2.5 is significantly different in different seasons. According to the results of the general spatial model, there was a certain relationship between land surface temperature and PM2.5 concentration and also a positive effect on each other. However, we found that the positive and negative effects of NDVI on PM2.5 concentrations were different in the winter and summer seasons. It is speculated that dry riverbed during the low flow season and barren land in land consolidation area in winter caused raised dust pollution, which is the reason why the dust retention with plants could not be effectively exerted. Finally, we expected the results can help evaluate the current urban green areas configuration in former Taichung City to reduce land surface temperature and purify the air quality. The public sector can also use the results for future decision-making to improve the habitability of the current urban environment.