Characteristics of PM2.5, CO2 and particle-number concentration in mass transit railway carriages in Hong Kong

被引:20
|
作者
Zheng, Hai-Long [1 ]
Deng, Wen-Jing [1 ]
Cheng, Yan [2 ]
Guo, Wei [2 ]
机构
[1] Educ Univ Hong Kong, Dept Sci & Environm Studies, Tai Po, Hong Kong, Peoples R China
[2] Xi An Jiao Tong Univ, Sch Human Settlements & Civil Engn, Xian, Peoples R China
关键词
CO2; PM2.5; Particle-number concentration (PNC); Mass transit railway (MTR); Hong Kong; PUBLIC TRANSPORTATION MODES; SEOUL METROPOLITAN SUBWAY; PARTICULATE MATTER; AIR-QUALITY; ULTRAFINE PARTICLES; HOSPITAL ADMISSIONS; COMMUTERS EXPOSURE; CARBON-MONOXIDE; GROUND-LEVEL; INDOOR;
D O I
10.1007/s10653-016-9844-y
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Fine particulate matter (PM2.5) levels, carbon dioxide (CO2) levels and particle-number concentrations (PNC) were monitored in train carriages on seven routes of the mass transit railway in Hong Kong between March and May 2014, using real-time monitoring instruments. The 8-h average PM2.5 levels in carriages on the seven routes ranged from 24.1 to 49.8 A mu g/m(3), higher than levels in Finland and similar to those in New York, and in most cases exceeding the standard set by the World Health Organisation (25 A mu g/m(3)). The CO2 concentration ranged from 714 to 1801 ppm on four of the routes, generally exceeding indoor air quality guidelines (1000 ppm over 8 h) and reaching levels as high as those in Beijing. PNC ranged from 1506 to 11,570 particles/cm(3), lower than readings in Sydney and higher than readings in Taipei. Correlation analysis indicated that the number of passengers in a given carriage did not affect the PM2.5 concentration or PNC in the carriage. However, a significant positive correlation (p < 0.001, R-2 = 0.834) was observed between passenger numbers and CO2 levels, with each passenger contributing approximately 7.7-9.8 ppm of CO2. The real-time measurements of PM2.5 and PNC varied considerably, rising when carriage doors opened on arrival at a station and when passengers inside the carriage were more active. This suggests that air pollutants outside the train and passenger movements may contribute to PM2.5 levels and PNC. Assessment of the risk associated with PM2.5 exposure revealed that children are most severely affected by PM2.5 pollution, followed in order by juveniles, adults and the elderly. In addition, females were found to be more vulnerable to PM2.5 pollution than males (p < 0.001), and different subway lines were associated with different levels of risk.
引用
收藏
页码:739 / 750
页数:12
相关论文
共 50 条
  • [31] Source apportionment of PM2.5 chemically speciated mass and particle number concentrations in New York City
    Masiol, M.
    Hopke, P. K.
    Felton, H. D.
    Frank, B. P.
    Rattigan, O. V.
    Wurth, M. J.
    LaDuke, G. H.
    ATMOSPHERIC ENVIRONMENT, 2017, 148 : 215 - 229
  • [32] Assessing Effect of Targeting Reduction of PM2.5 Concentration on Human Exposure and Health Burden in Hong Kong Using Satellite Observation
    Lin, Changqing
    Lau, Alexis K. H.
    Lu, Xingcheng
    Fung, Jimmy C. H.
    Li, Zhiyuan
    Li, Chengcai
    Wong, Andromeda H. S.
    REMOTE SENSING, 2018, 10 (12):
  • [33] Using daily excessive concentration hours to explore the short-term mortality effects of ambient PM2.5 in Hong Kong
    Lin, Hualiang
    Ma, Wenjun
    Qiu, Hong
    Wang, Xiaojie
    Trevathan, Edwin
    Yao, Zhenjiang
    Dong, Guang-Hui
    Vaughn, Michael G.
    Qian, Zhengmin
    Tian, Linwei
    ENVIRONMENTAL POLLUTION, 2017, 229 : 896 - 901
  • [34] Spatial and temporal variations Of PM1, PM2.5, PM10 and particle number concentration during the AUPHEP-project
    Gomiscek, B
    Hauck, H
    Stopper, S
    Preining, O
    ATMOSPHERIC ENVIRONMENT, 2004, 38 (24) : 3917 - 3934
  • [35] Quantifying PM2.5 mass concentration and particle radius using satellite data and an optical-mass conversion algorithm
    Liu, Ming
    Zhou, Gaoxiang
    Saari, Rebecca K.
    Li, Sabrina
    Liu, Xiangnan
    Li, Jonathan
    ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING, 2019, 158 : 90 - 98
  • [36] Quantifying PM2.5 mass concentration and particle radius using satellite data and an optical-mass conversion algorithm
    Liu M.
    Zhou G.
    Saari R.K.
    Li S.
    Liu X.
    Li J.
    ISPRS Journal of Photogrammetry and Remote Sensing, 2019, 158 : 90 - 98
  • [38] Mass Concentration, Chemical Composition, and Source Characteristics of PM2.5 in a Plateau Slope City in Southwest China
    Shi, Jianwu
    Feng, Yinchuan
    Ren, Liang
    Lu, Xiuqing
    Zhong, Yaoqian
    Han, Xinyu
    Ning, Ping
    ATMOSPHERE, 2021, 12 (05)
  • [39] Remote Sensing Model for Estimating Atmospheric PM2.5 Concentration in the Guangdong-Hong Kong-Macao Greater Bay Area
    Dai Y.-Y.
    Gong S.-Q.
    Zhang C.-J.
    Min A.-L.
    Wang H.-J.
    Huanjing Kexue/Environmental Science, 2024, 45 (01): : 8 - 22
  • [40] Measurement of PM2.5 Mass Concentration Using an Electrostatic Particle Concentrator-Based Quartz Crystal Microbalance
    Ngo, Nhan Dinh
    Lee, Jaegil
    Kim, Myeong-Woo
    Jang, Jaesung
    IEEE ACCESS, 2019, 7 : 170640 - 170647