北京市区人为热输入估算及其对局地温度的影响研究
文献类型：学位论文
| 作者 | 王业宁 |
| 学位类别 | 硕士 |
| 答辩日期 | 2016-05 |
| 授予单位 | 中国科学院研究生院 |
| 授予地点 | 北京 |
| 导师 | 陈利顶 ; 孙然好 |
| 关键词 | 人为热，源清单法，交通指数，时空变化，温度 Anthropogenic heat, inventory approach, transportation index, spatiotemporal scales, temp |
| 其他题名 | Estimation of anthropogenic heat and the effect on local temperature in Beijing |
| 学位专业 | 生物工程 |
| 中文摘要 | 随着我国社会经济的快速发展，人类生产生活日益增加的排热量会显著改变城市局地热环境，加剧热岛效应强度，而人为热对城市气候的影响越来越受到重视。由于不同气候区、不同时空尺度的人为热排放特征不同，人为热的计算异常复杂和不确定。本文基于能源、人口、社会经济等统计数据，结合meta分析和DeST软件模拟得到的数据，利用自上而下源清单法对北京市区的人为热输入量进行详细计算，得出其不同热源排放总量与不同行政单元内的时空分布特征，可适用于气候模型模块的开发应用；同时选择典型位点对其小气候特征进行流动与连续监测，构建空间与时间序列上人为排热与小气候特征的定量关系。研究得到如下结论： （1）主城区的人为热年排放总量为1.11×1018 J，为太阳辐射总量的8.1%，其中建筑排热最高，占总人为热排放的45.3%，其次为交通和工业部分，分别占30.1%、20.2%。建筑排热在不同季节不同时刻均有明显差别，一般出现“双峰”现象，同人们作息规律相一致；交通排热的月变化不显著，日变化在09:00、18:00时左右强度很高；人体新陈代谢和工业排热没有明显变化。 （2）人为排热总量最大的为朝阳和海淀区，占研究区总排放量的52.2%，最少的是东城和大兴区，两者均占7.7%。主城区平均排放强度为14.55 W·m-2，最大为西城区的82.30 W·m-2，而大兴区仅为2.61 W·m-2。人为热排放高值区多集中于北二环与北四环内，约为60～100 W·m-2，少数街道和地区排热在150 W·m-2以上，最高排热强度272～501 W·m-2为北京CBD（Central Business District）区域。 （3）交通排热强度呈现辐射状空间分布，市区平均排热强度为8.6~10.8 W·m-2，三环内地区达32.2~53.9 W·m-2，白天平均排热强度约为夜间的2~10倍，且早晚高峰期排热强度最大，非工作日的排热空间特征同工作日并无明显差异。实验道路的温差同8:00 a.m.的交通排热相关性最显著，温差增幅为0.91 ℃/10 W·m-2，排热对其温差有约10~20分钟的滞后效应。 人为热研究时需首先考虑时空尺度，基于源清单法的人为热计算有助于更好地理解研究区的热环境状况。车辆排热的时空动态研究可指导交通道路的合理布局及规划管理，以评价并改善城市热环境。下一步研究中需要加强空间数据与统计数据的集成，进一步完善空间格局与动态分析。 |
| 英文摘要 | The rapid economic development promotes huge demands of urban expansion and energy consumption in big cities of China. The emission of anthropogenic heat (AH) from human activities poses significant impacts on urban which enhance the urban heat island effect. The causes and consequences of AH vary in different areas according to the natural background and human activities. Therefore, the calculation methods of AH are not normalized due to the temporal and spatial variations. Based on statistical data of energy, demographic, economic, etc., combined with meta analysis and DeST simulated data, the AH in the Beijing metropolis is calculated using the top-down inventory approach in this study. We used different methods to capture the AH sources and spatiotemporal characteristics. The microclimate data were collected through field sampling to verify the results of AH calculation and explore the quantitative relationship with AH. The results of AH have the potential to be used in other models for simulating climate change. The main conclusions are: (1) The total amount of AH in the research region is 1.11×1018 J/a, account for 8.1% of the solar radiation. The building heat emission contributes to 45.3%, followed by transportation and industrial sectors for 30.1% and 20.2%. The AH of building heat emission varies in different seasons, posing a ‘double peak’ pattern in a typical day. The monthly variation of transportation heat emission is not significant and the peak of transportation heat emission is at 9:00 and 18:00. The AH of human metabolism and industrial activities are stable in different seasons. (2) The spatial variation of AH is significant in the region. The AH of Chaoyang and Haidian District accounts for 52.2% of the total amount while that of Dongcheng and Daxing District contributes to 7.7% only. The average AH of the research region is 14.55 W·m-2, with a maximum of 82.30 W·m-2 in the Xicheng District and a minimum of 2.61 W·m-2 in the Daxing District. The high AH (60~100 W·m-2) are found in the region between the northern 4th and 2nd ring-road. Some areas are found to have the highest AH (272～501 W·m-2) in the Beijing CBD. The correlation coefficients (0.4~0.6) between the AH and land surface temperature exhibit the positive effect of AH on temperature. (3) The spatial pattern of vehicle heat is radially distributed in Beijing. The mean heat emission ranges from 8.6 to 10.8 W ·m-2 and reaches the maximum of 53.9 W·m-2 inside the 3rd ring road. The average vehicle heat emission at daytime is 2~10 times than the values at nighttime. The maximum values of vehicle emission appear in the morning and evening. The spatial patterns of vehicle heat are similar between weekends and weekdays. The vehicle heat emission at 8:00 a.m. shows a significant correlation with its temperature difference on experiment roads. The temperature variation reaches 0.91 ℃/10 W·m-2 and presents a lag effect (10~20 min) of vehicle heat on temperature amplification. We suggest that the AH research should pay more focus to look for a reasonable calculation method based on spatial and temporal scales. The accurate results could provide useful information to better understand the AH emissions of a specific area. The spatiotemporal characteristics of building heat and vehicle heat emission with field experiment may give guidelines for the scientific planning of traffic roads and urban landscapes. The understanding of AH calculation brings more potentials to assess and improve the living environment through rational urban planning. Further researches about integration of geospatial and statistical data should be reinforced to refine the spatiotemporal patterns and dynamic analysis. |
| 源URL | [http://ir.rcees.ac.cn/handle/311016/37023]  |
| 专题 | 生态环境研究中心_城市与区域生态国家重点实验室 |
| 推荐引用方式 GB/T 7714 | 王业宁. 北京市区人为热输入估算及其对局地温度的影响研究[D]. 北京. 中国科学院研究生院. 2016. |
入库方式： OAI收割
来源：生态环境研究中心
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