地球信息科学学报 ›› 2020, Vol. 22 ›› Issue (12): 2348-2357.doi: 10.12082/dqxxkx.2020.190624

• 地理空间分析综合应用 • 上一篇    下一篇

2019年北京市PM2.5人群暴露剂量特征分析

林金煌1(), 陈文惠2, 张岸3,*()   

  1. 1.南京大学地理与海洋科学学院,南京 210023
    2.福建师范大学地理科学学院,福州 350007
    3.中国科学院地理科学与资源研究所 资源与环境信息系统国家重点实验室,北京 100101
  • 收稿日期:2019-10-24 修回日期:2020-10-16 出版日期:2020-12-25 发布日期:2021-02-25
  • 通讯作者: 张岸 E-mail:linjinhuang93@163.com;zhangan@igsnrr.ac.cn
  • 作者简介:林金煌(1993— ),男,福建漳州人,博士生,主要从事地理信息系统应用与环境健康研究。 E-mail: linjinhuang93@163.com
  • 基金资助:
    国家重点研发计划项目(2017YFB0503500);国家自然科学基金创新群体项目(41421001);福建省科技厅公益类科研院所专项(2017R1034-1)

Analysis of PM2.5 Population Exposure Doses Characteristics in Beijing in 2019

LIN Jinhuang1(), CHEN Wenhui2, ZHANG An3,*()   

  1. 1. School of Geography and Ocean Science, Nanjing University, Nanjing 210023, China
    2. College of Geographical Science, Fujian Normal University, Fuzhou 350007, China
    3. State Key Laboratory of Resources and Environmental Information System, Institute of Geographica Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
  • Received:2019-10-24 Revised:2020-10-16 Online:2020-12-25 Published:2021-02-25
  • Contact: ZHANG An E-mail:linjinhuang93@163.com;zhangan@igsnrr.ac.cn
  • Supported by:
    National Key Research and Development Program of China(2017YFB0503500);Science Fund for Creative Research Groups of the National Natural Science Foundation of China(41421001);Special Projects for Public Welfare Research Institutes of Fujian Science and Technology Departmenta(2017R1034-1)

摘要:

近年来,PM2.5已成为雾霾爆发的主要污染物之一,人口长期暴露在高浓度的PM2.5中可能会大大的提高居民患病的几率,危害居民身心健康。本研究以空气污染严重且人口高度集中的北京市作为研究区,以2019年北京市的PM2.5浓度监测数据、人口空间分布栅格数据及不同人群的长期呼吸量等为数据基础,构建了“污染物浓度—暴露人口—呼吸量”的PM2.5人口暴露剂量评估模型,进而对北京市2019年的PM2.5人口暴露强度空间分异特征及不同人群的暴露剂量差异进行分析。结果表明:① 2019年北京市的PM2.5浓度在冬季时最高,日均浓度达48.89 μg/m3,并均呈现出北低南高的整体态势;② PM2.5人口暴露量存在显著的空间分异特征,不同人群的PM2.5暴露量均呈现出由城中心向周边减弱的整体态势,高暴露区主要集中于城区地带;③ 不同性别、年龄组人群的PM2.5人口暴露强度存在明显的空间分异特征,且城市内部不同人群的PM2.5暴露剂量也存在明显差异;④ PM2.5的暴露风险并非完全取决于污染物浓度的大小,而是由污染源浓度和暴露受体的空间分布特征等多方面共同决定,北京城区的高PM2.5人口暴露区才是高风险区,是未来政府有效防控污染物危害的核心区。

关键词: PM2.5, 人口暴露, 暴露剂量, 普通克里格, 暴露受体, 时空演变, 人群响应, 北京市

Abstract:

In recent years, PM2.5 has become one of the main pollutants in the haze outbreak. The risk of long-term exposure to PM2.5 of high concentration may greatly increase the risk of disease and endanger the physical and mental health of residents. In this study, Beijing was taken as the research area where air pollution was serious and the population was highly concentrated. Based on the data of PM2.5 concentration, the grid data of population spatial distribution, and the long-term respiratory volume of different populations in Beijing in 2019, an assessment model of PM2.5 population exposure was established. Furthermore, the spatial distribution characteristics of PM2.5 population exposure intensity and the differences of exposure-response among different populations in Beijing in 2019 were analyzed. The results show that: (1) In 2019, the PM2.5 concentration in Beijing is the highest in winter, and the daily average concentration is 48.89 μg/m3, which shows an overall trend of low in the north and high in the south; (2) There are significant spatial differences in PM2.5 population exposure, and the PM2.5 exposure of different populations shows an overall trend of weakening from the center of the city to the surrounding areas, and the high exposure areas were mainly concentrated in urban areas; (3) There are obvious spatial differences in the exposure intensity of PM2.5 population in different gender and age groups, and there were also significant differences in the response of PM2.5 exposure among different populations in the city; (4) The exposure risk of PM2.5 was not entirely determined by the concentration of pollutants, but by the concentration of pollution sources and exposure receptors, the high-risk area of population exposure to PM2.5 in Beijing urban area was the high-risk area, and it was the core area for the government to effectively prevent and control pollution hazards in the future.

Key words: PM2.5, population exposure, exposure doses, ordinary Kriging, exposure receptor, spatio-temporal evolution, populations response, Beijing