基于多源数据的大气细颗粒物对北京市城市热岛的影响研究

在土地利用方式改变、能源消耗持续增长、人口膨胀的共同作用下，城市热岛效应日趋显著。大气细颗粒物（PM_2.5）污染不断加剧，对城市热岛强度也产生了一定的影响。利用地面空气质量监测站点的逐小时PM_2.5污染监测数据、气象监测站点的日均数据和MODIS地表温度数据，结合土地利用类型，划分城郊气象站点和地表温度采样点，分别计算北京市日均PM_2.5浓度、冠层城市热岛强度和地表城市热岛强度，并计算地表城市热岛强度指数，得出热岛强度空间分布图。经过对PM_2.5与冠层城市热岛强度、地表城市热岛强度及其空间分布的相关性分析，得出以下结论：（1）北京市地表城市热岛强度的月、季间变化明显，主要受土地覆盖类型影响，夏季高于冬季，冠层城市热岛强度的月、季间变化较小；（2）PM_2.5质量浓度与冠层城市热岛强度、地表城市热岛强度均呈显著负相关，相关系数分别为?0.5199和?0.6115；（3）昼间地表城市热岛强度与PM_2.5质量浓度的相关性高于夜间；（4）PM_2.5质量浓度变化对地表城市热岛的空间分布有着显著的影响。随着PM_2.5质量浓度的增加，强热岛空间范围向城区缩减。

Impacts of atmospheric fine particulate matter (PM2.5) on urban heat island with multi-source data: a case study of Beijing

Background, aim, and scope In recent decades, Beijing, the capital of China, has experienced rapid economic development and urban expansion, which result in serious fine particulate matter (PM_2.5) concentration and urban heat island (UHI). In order to alleviate these environmental problems, an investigation on the correlations between UHI and PM_2.5 is of great significance. Materials and methods With hourly PM_2.5 concentration data, daily air temperature data and 5-day, 1 km MODIS land surface temperature (LST) products, the impacts of PM_2.5 concentration on both SUHI and CUHI in 2015 have been comprehensively evaluated with the Pearson correlation analysis. Results Results discover that SUHI was much larger in the summer, while CUHI almost kept unchanged during the study year; and the highest PM_2.5 concentration happened in December. It is worth noting that SUHI area deceased a lot when PM_2.5 concentration increased, which suggested SUHI was significantly negatively affected by PM_2.5. This is also verified by the Pearson correlation analysis, which demonstrates high level of PM_2.5 concentration could alleviate both daytime SUHI and CHUI to some extent. However, the influence of PM_2.5 concentration on night time SUHI was not significant based on our data and analysis. Discussion Both natural (water, vegetation and so on) and anthropogenic causes (impervious surface, civil heating) had significant impacts on UHI intensity. In December, civil heating and stable air caused highest PM_2.5 concentration. Although the impacts of PM_2.5 on daytime SUHI and CUHI are relative clear in this study, related driving mechanisms are still hard to learn, due to the difficulty in clarifying the opposing impacts aerosols have on the radiative properties and biogeochemistry of urban atmosphere. Conclusions The spatial-temporal dynamics of SUHI were significantly influenced by the type of land surface and human activity, as well as the PM_2.5 concentration. Recommendations and perspectives This study could help local authorities to optimize the policies for sustainable urban development, by exploring the influence of a specific factor on UHI spatial-temporal dynamics. In order to reveal general rules, more data needs to be collected and more analysis methods should be involved in the future study.