本研究旨在从景观学的角度出发，分析城市绿地景观格局对PM2.5/10时空分布的影响，并在此基础上探讨基于大气颗粒物缓解为导向的城市绿地景观格局优化措施。论文分析了南京市2018年3月-2020年2月PM2.5/10的时空动态变化特征，采用线性回归分析了绿地斑块面积比、绿地最大斑块指数、面积加权平均板块形状指数、景观分割指数四种景观格局指标对颗粒物浓度的影响，研究结果表明（1）南京市大气颗粒物污染冬季污染程度高，其中PM2.5 和PM10浓度最高分别为2019年1月的草场门80ug/m3和奥体中心129ug/m3。夏季污染程度低，其中PM2.5 和PM10浓度最低分别为2019年8月的浦口12ug/m3和玄武湖30ug/m3。（2）PLAND、LPI、SHAPE_AM在春、秋、冬三季与PM2.5/10浓度成负相关关系，在夏季或夏季的大尺度域中与PM2.5/10浓度成正相关关系； DIVISION在春、秋、冬三季与PM2.5/10浓度成正相关关系，在夏季或夏季的大尺度域中与PM2.5/10浓度成负相关关系。（3）春、秋、冬三季均表现为在大尺度域中四种景观格局指标与PM2.5浓度呈现出更好的拟合关系，其中四种景观格局指标与PM2.5浓度拟合关系最好都出现在冬季的9×9km2缓冲区内, R2分别为0.3958、0.3370、0.5552、0.4626；在中小尺度域中与PM10呈现出更好的拟合关系。研究结果可为城市绿地景观格局优化配置提供数据支撑。
目录
摘要 Ⅲ
关键词 Ⅲ
Abstract Ⅲ
引言
1 绪论 1
1.1 研究背景 1
1.2 研究目的与意义 1
1.3 国内外研究进展 2
1.3.1 空气悬浮颗粒物相关研究 2
1.3.2 城市绿地景观格局相关研究 2
1.3.3 城市绿地景观格局对大气悬浮颗粒物的影响 2
1.3.4 研究不足与展望 2
1.4 研究内容与方案设计 3
1.4.1 研究内容 3
1.4.2 方案设计 3
2 材料与方法 4
2.1 研究区现状 4
2.1.1 地理位置 4
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/> 2.1.2 自然环境 5
2.1.3 社会经济 5
2.2 数据来源 5
2.2.1 颗粒物数据 5
2.2.2 影像数据 6
2.3 研究方法 6
2.3.1 数据处理 6
2.3.2 影像处理 7
2.3.3 景观格局指数 7
2.3.4 线性回归分析 72.4 技术路线 7
3 结果与分析 8
3.1 PM2.5/10时间分布特征 8
3.1.1 PM2.5时间动态变化特征 8
3.1.2 PM10时间动态变化特征 10
3.2 PM2.5/10空间分布特征 12
3.2.1 PM2.5空间分布特征 12
3.2.2 PM10空间分布特征 14
3.3 城市绿地景观格局对PM2.5/10时空分布的影响 15
3.3.1 城市绿地景观格局对PM2.5时空分布的影响 15
3.3.2 城市绿地景观格局对PM10时空分布的影响 20
4 讨论与结论 26
4.1 讨论 26
4.1.1 南京市PM2.5/10的时空分布特征 26
4.1.2 南京市绿地景观格局对PM2.5/10时空分布的影响 26
4.1.3 基于缓解南京市PM2.5/10污染的景观格局优化措施 26
4.2 结论 27
4.3 不足之处 27
4.4 创新点 28
致谢 28
参考文献 28
城市绿地景观格局对PM2.5/10时空分布的影响
Impact of urban green space landscape pattern on the spatial and temporal distribution of PM2.5 / 10
Student majoring in Landscape WANG Peilin
Tutor GUO Min
Abstract: This study aims to analyze the impact of urban green space landscape pattern on the spatial and temporal distribution of PM2.5 / 10 from the perspective of landscape, and on this basis, explore the optimization measures of urban green space landscape pattern based on mitigation of atmospheric particulate matter. The thesis analyzes the spatiotemporal dynamics of PM2.5 / 10 in Nanjing from March 2018 to February 2020, and uses linear regression to analyze the green patch area ratio, green patch maximum patch index, area weighted average plate shape index, landscape The impact of the four landscape pattern indicators of the segmentation index on the concentration of particulate matter. The research results show that: (1) Nanjing has a high degree of atmospheric particulate pollution in winter, and the highest concentrations of PM2.5 and PM10 are the grass field gate 80ug /m3 in January 2019. and Olympic Sports Center 129ug / m3. Summer pollution is low, and the lowest concentrations of PM2.5 and PM10 are Pukou 12ug / m3 and Xuanwu Lake 30ug / m3 in August 2019, respectively. (2) PLAND, LPI, and SHAPE_AM have a negative correlation with PM2.5 / 10 concentration in spring, autumn, and winter, and a positive correlation with PM2.5 / 10 concentration in the largescale domain in summer or summer; DIVISION in spring , Autumn and winter have a positive correlation with PM2.5 / 10 concentration, and have a negative correlation with PM2.5 / 10 concentration in the largescale domain in summer or summer. (3) The three seasons of spring, autumn and winter show that the four landscape pattern indicators and PM2.5 concentration show a better fitting relationship in the largescale domain, of which the four landscape pattern indicators and PM2.5 concentration fitting relationship It is best to appear in the 9 × 9km2 buffer zone in winter, R2 is 0.3958, 0.3370, 0.5552, 0.4626 respectively; in the small and medium scale domain, it shows a better fitting relationship with PM10. The research results can provide data support for the optimal allocation of urban green space landscape pattern.

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