@article{af8c72991bf64f5d933b0739af879795,
title = "Increasing Heat Stress in Urban Areas of Eastern China: Acceleration by Urbanization",
abstract = "A combination of hot temperature and high humidity (high heat stress) has severe impacts on environment, society, and public health, especially in urban areas where the majority of the world's population lives. This study investigates the changes of heat stress in urban areas of eastern China and urbanization effects. Data for 242 urban areas and records from a dense network of nearly 2,000 stations are examined. All urban areas have experienced substantial increases in mean heat stress and the frequencies of extreme heat stress days and events during 1971–2014. The increases in human-perceived heat stress are even stronger than air temperature. Urban areas experience more intense heat stress than the surrounding rural areas. We estimate that urbanization accounts for nearly 30% of the increase in mean and extreme heat stress. Urbanization effects are more prominent in the major urban conglomerates such as Beijing-Tianjin-Hebei and the Yangtze and Pearl river deltas.",
keywords = "climate change, heat index, heat stress, long-term trend, urban heat island, urbanization effect",
author = "Ming Luo and Lau, {Ngar Cheung}",
note = "Funding Information: This study is funded by the National Natural Science Foundation of China (41871029 and 41401052), the Fundamental Research Funds for the Central Universities of China (18lgzd04), and the AXA research fund. Daily meteorological observations are collected from the China Meteorological Data Service Center (http://data.cma. cn). The urban areas in shapefile are from the City University of New York (https://www.baruch.cuny.edu/confluence/display/geoportal/ESRI+ International+Data). The MODIS urban extents are provided by Schneider et al. (2009; https://nelson.wisc.edu/sage/ data-and-models/schneider.php). The LandScan population data set is provided by the Oak Ridge National Laboratory (Dobson et al., 2000). The buffering analysis is conducted in ArcGIS Desktop 10.5. The MATLAB codes for computing heat index and identifying heat waves are available at https://drive.google.com/open?id= 1P2qzRhLj_OodIiLgiDAP81d8i1uXnv-S. Funding Information: This study is funded by the National Natural Science Foundation of China (41871029 and 41401052), the Fundamental Research Funds for the Central Universities of China (18lgzd04), and the AXA research fund. Daily meteorological observations are collected from the China Meteorological Data Service Center (http://data.cma.cn). The urban areas in shapefile are from the City University of New York (https://www.baruch.cuny.edu/confluence/display/geoportal/ESRI+International+Data). The MODIS urban extents are provided by Schneider et al. (; https://nelson.wisc.edu/sage/data-and-models/schneider.php). The LandScan population data set is provided by the Oak Ridge National Laboratory (Dobson et al.,). The buffering analysis is conducted in ArcGIS Desktop 10.5. The MATLAB codes for computing heat index and identifying heat waves are available at https://drive.google.com/open?id=1P2qzRhLj_OodIiLgiDAP81d8i1uXnv-S. Publisher Copyright: {\textcopyright}2018. American Geophysical Union. All Rights Reserved.",
year = "2018",
month = dec,
day = "16",
doi = "10.1029/2018GL080306",
language = "English (US)",
volume = "45",
pages = "13,060--13,069",
journal = "Geophysical Research Letters",
issn = "0094-8276",
publisher = "American Geophysical Union",
number = "23",
}