Estimation of the Spatio-Temporal Characteristics of Anthropogenic Heat Emission in the Qinhuai District of Nanjing Using the Inventory Survey Method

被引:10
|
作者
Zhang, Guixin [1 ]
Luo, Yanghuan [2 ]
Zhu, Shanyou [3 ]
机构
[1] Nanjing Univ Informat Sci & Technol, Sch Geog Sci, Nanjing 210044, Peoples R China
[2] Climate Ctr Guizhou Prov, Guiyang 550002, Peoples R China
[3] Nanjing Univ Informat Sci & Technol, Sch Remote Sensing & Geomat Engn, 219 Ningliu Rd, Nanjing 210044, Peoples R China
基金
国家重点研发计划; 中国国家自然科学基金;
关键词
Anthropogenic heat emission; Bottom-up energy inventory method; Remote sensing; Spatio-temporal distribution; URBAN CLIMATE; ISLAND; RELEASE; SUMMER; CITY; BUILDINGS; POLLUTION; TAIPEI; IMPACT; CHINA;
D O I
10.1007/s13143-019-00142-9
中图分类号
P4 [大气科学(气象学)];
学科分类号
0706 ; 070601 ;
摘要
Taking the Qinhuai District of Nanjing, China, as the study area, this research adopted the bottom-up energy inventory method to estimate the anthropogenic heat emission at the spatial resolution of 100 m during the daytime and nighttime. Land use data derived by the visual interpretation from high resolution imagery was combined with the field investigation as well as statistical population data to estimate the spatial distribution of the population, which was then used to calculate the human metabolism. The traffic heat emission estimation was mainly based on the interpretation of different levels of roads and the statistical vehicle volume from field video recordings. The spatialized population, the collected energy consumption statistical data, the corresponding function and the number of floors in the buildings were combined to compute the industrial and the building heat emissions, respectively. The results illustrate the detailed spatio-temporal distribution variances of each type of anthropogenic heat emission during the daytime and the nighttime, which show a higher reasonability and precision. During the daytime, the high intensity of anthropogenic heat emissions is mainly distributed in the southwest of the study area, while the heat intensity is uniformly distributed during the nighttime. The average anthropogenic heat flux densities are 33.45 W/m(2)and 15.34 W/m(2)in the daytime and the nighttime, respectively. The highest heat flux density with the value of 14.93 W/m(2)is released by commercial buildings during the daytime, while the traffic heat is the highest with the average value of 5.17 W/m(2)during the nighttime.
引用
收藏
页码:367 / 380
页数:14
相关论文
共 22 条
  • [1] Estimation of the Spatio-Temporal Characteristics of Anthropogenic Heat Emission in the Qinhuai District of Nanjing Using the Inventory Survey Method
    Guixin Zhang
    Yanghuan Luo
    Shanyou Zhu
    [J]. Asia-Pacific Journal of Atmospheric Sciences, 2020, 56 : 367 - 380
  • [2] Novel Approach to Estimate Missing Data Using Spatio-Temporal Estimation Method
    Shelotkar, Aniruddha D.
    Ingole, P. V.
    [J]. INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS, 2016, 7 (04) : 167 - 174
  • [3] Estimation of siltation in Tuirial dam: a spatio-temporal analysis using GIS technique and bathymetry survey
    Lawmchullova, Imanuel
    Rao, Ch. Udaya Bhaskara
    [J]. JOURNAL OF SEDIMENTARY ENVIRONMENTS, 2024, 9 (01) : 81 - 97
  • [4] Estimation of siltation in Tuirial dam: a spatio-temporal analysis using GIS technique and bathymetry survey
    Imanuel Lawmchullova
    Ch. Udaya Bhaskara Rao
    [J]. Journal of Sedimentary Environments, 2024, 9 : 81 - 97
  • [5] Natural and Anthropogenic Ground Deformation Monitored Using High Spatio-Temporal Resolution MSBAS Time Series Method
    Samsonov, Sergey
    d'Oreye, Nicolas
    Smets, Benoit
    [J]. MULTITEMP 2013: 7TH INTERNATIONAL WORKSHOP ON THE ANALYSIS OF MULTI-TEMPORAL REMOTE SENSING IMAGES, 2013,
  • [6] Estimation of anthropogenic heat emission over South Korea using a statistical regression method
    Lee, Sang-Hyun
    Kim, Soon-Tae
    [J]. ASIA-PACIFIC JOURNAL OF ATMOSPHERIC SCIENCES, 2015, 51 (02) : 157 - 166
  • [7] Estimation of anthropogenic heat emission over South Korea using a statistical regression method
    Sang-Hyun Lee
    Soon-Tae Kim
    [J]. Asia-Pacific Journal of Atmospheric Sciences, 2015, 51 : 157 - 166
  • [8] Long-term Wind Speed Series Estimation Method for Islands Using Spatio-temporal Correlation
    Li, Hongzhong
    Liu, Guodong
    Mi, Yang
    [J]. Gaodianya Jishu/High Voltage Engineering, 2023, 49 (08): : 3185 - 3198
  • [9] A Method of Spatio-temporal Characteristics Analysis of Chemical Enterprises Using Big Data and Their Potential Impacts on Waterbodies
    Wang, Ziwei
    Cai, Hongyan
    Chen, Mulin
    [J]. Journal of Geo-Information Science, 2022, 24 (04) : 673 - 683
  • [10] Exploring driving force factors of building energy use and GHG emission using a spatio-temporal regression method
    Zhang, Yan
    Teoh, Bak Koon
    Zhang, Limao
    [J]. ENERGY, 2023, 269