| 面向二/三维城市形态指标的PM2.5浓度调控模拟 |
| 摘要点击 2048 全文点击 688 投稿时间：2021-11-29 修订日期：2022-01-24 |
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| 中文关键词 空气污染 城市形态 国土空间规划 优化调控 地理探测器 |
| 英文关键词 air pollution urban form national territory spatial planning optimized adjustment geographical detector |
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| 中文摘要 |
| 城市形态可影响空气污染的源分布与扩散过程，但如何通过优化城市形态指标缓解城市空气污染一直缺少实用的定量方法与模型.故以长沙市为例，定量分析了二/三维城市形态指标对主城区PM2.5浓度分布的影响，提出了一种关键因子指标驱动下的城市空气质量优化调控模型（UFR-AQOM），开展了指标阈值与空气质量目标双约束下的城市PM2.5浓度情景模拟实验.结果表明，长沙市主城区PM2.5浓度高值区呈现"一轴两区四节点"的空间格局，商业服务用地与道路交通用地、高斑块密度与高用地容积率网格PM2.5浓度相对较高.三类城市形态因子指标中，开发强度对城市PM2.5浓度空间变化的影响程度最大，景观格局、用地功能次之，多因子共同作用比单因子影响更加显著.顾及因子指标贡献差异与交互作用的UFR-AQOM模型可有效用于城市PM2.5浓度的优化调控模拟（R2=0.65，RMSE=1.40 μg·m-3）.面向PM2.5浓度达标约束要求，长沙市主城区宜加强周长面积分维数与斑块密度等景观格局指标的全面管控，同时应考虑对工业占比与水域占比等城市用地功能指标和用地容积率等城市开发强度指标的分区优化调控.研究成果将为面向空气质量改善的国土空间规划指标调控提供决策支持. |
| 英文摘要 |
| It is well known that urban forms can affect the source distribution and diffusion process of air pollution; however, practical quantitative methods and models on alleviating urban air pollution by optimizing urban form indexes are lacking. Using Chang Sha city as an example, we quantitatively analyzed the PM2.5 concentration distribution in terms of 2D/3D urban form indexes (e.g., land use functionality, landscape pattern, and development intensity). Based on this, the urban form regulation-aided air quality optimization model (UFR-AQOM) was proposed and consequently employed to simulate the scene-dependent PM2.5 concentrations under double constraints from both the index threshold and air quality objectives. The results showed that the high value area of PM2.5 concentration in Chang Sha featured a "one axis and four nodes" spatial pattern. PM2.5 concentrations in grids with commercial or road land applications, high patch density or high Shannon index, and high land plot ratio or low sky openness were shown to be relatively higher. The development intensity indexes had the greatest impact on the spatial variation in PM2.5 concentration, followed by landscape pattern and land use functionality, and the interaction of factor indexes could significantly strengthen their own single contributions. The UFR-AQOM model, taking into account the contribution differences and interactions among different factors, could effectively simulate the spatial variation in PM2.5 concentration in urban areas (R2=0.65,RMSE=1.40 μg·m-3). In order to meet the regulations of PM2.5 standards, the overall management of landscape pattern indexes, such as the integral dimension of the perimeter surface and patch density, should be strengthened in the main urban area of Changsha. Further, the zoned optimization of PM2.5 concentrations could be implemented by controlling the urban land use indexes, such as the industrial land use ratio and water area ratio, as well as the development intensity indexes such as the land use area ratio. These research results provided support for decisions in the optimization of national territory spatial planning indexes targeting air quality improvement. |
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