精细监测城市人居活动场景PM2.5浓度是新形势下国家全面降低空气污染健康损害的新需求。但国内外现广泛采用的高级统计建模监测手段在城市PM2.5浓度空间分布精细模拟中仍普遍受到监测站点数量与建模理论假设缺陷限制。依托迁移学习思想，项目拟通过在城市内部典型场景（源领域）开展加密监测，提出与实证全新的PM2.5浓度时空分异理论假设，揭示城市微环境场景PM2.5浓度时空分异的地理规律；研究建立面向场景配置的PM2.5浓度深度学习智能模型，阐明场景配置相似下的源领域PM2.5浓度智能模型向稀疏监测目标领域迁移的内在机制。作为“以在源领域小范围补充加密建模样本、实现目标领域大范围PM2.5浓度空间分布精细模拟”的创新尝试，项目提出的微环境场景PM2.5浓度时空分异理论假设、研发的空间分布智能建模与迁移方法，可为城市空气质量时空精细模拟制图提供新的理论与方法支撑，提升我国大气污染空间监管与防控技术水平。

Monitoring urban PM2.5 concentrations at a fine spatial scale in which individuals live and work has been becoming a new demand for the decrease of the national public health risk. However, the widely used advanced statistical modeling at home and aboard for fine PM2.5 concentration monitoring is still problematic because of the sparse monitoring stations and the defective modeling theory hypothesis. In view of this, based on the transfer learning idea, this project will for the first propose and validate a new spatial-temporal heterogeneity theory hypothesis through the densified observation in the source regions (i.e. typical microenvironment scenarios in urban area), and consequently reveal the geographical law of spatial-temporally differentiated PM2.5 concentrations at the urban microenvironment scenarios. Meanwhile, scenario configuration-oriented deep learning intelligence model for PM2.5 concentration modeling will be developed. And the inner mechanism for PM2.5 concentration intelligence model transferring between source regions and target regions will be claimed under the conditions of similar scenario configurations. In summary, as an innovative attempt, this project will realize the PM2.5 concentration mapping at fine-resolution in large target regions with sparse monitoring stations through the densified observation samples in source regions. The newly proposed spatial-temporal heterogeneity theory hypothesis, as well as the consequently developed deep learning intelligence modeling and transferring methods in this project will provide a new theoretical and method supports for fine scale mapping of urban air quality, and enhance the levels of national techniques for microenvironment scenario-oriented air pollution monitoring and prevention.