Temporal Variations and Spatial Distribution of Air Pollutants in Shaoxing, a City in Yangtze Delta, China Based on Mobile Monitoring Using a Sensor Package

被引:3
|
作者
Zhao, Gaohan [1 ]
Pang, Xiaobing [2 ]
Li, Jingjing [3 ]
Xing, Bo [3 ]
Sun, Songhua [3 ]
Chen, Lang [2 ]
Lu, Youhao [2 ]
Sun, Qianqian [2 ]
Shang, Qianqian [2 ]
Wu, Zhentao [2 ]
Yuan, Kaibin [2 ]
Wu, Hai [4 ]
Ding, Shimin [5 ]
Li, Haiyan [1 ]
Liu, Yi [6 ]
机构
[1] Beijing Univ Civil Engn & Architecture, Sch Environm & Energy Engn, Beijing 100044, Peoples R China
[2] Zhejiang Univ Technol, Coll Environm, Hangzhou 310014, Peoples R China
[3] Shaoxing Ecol & Environm Monitoring Ctr Zhejiang P, Shaoxing 312000, Peoples R China
[4] Natl Inst Metrol, Beijing 102200, Peoples R China
[5] Yangtze Normal Univ, Green Intelligence Environm Sch, Chongqing 408100, Peoples R China
[6] Chinese Acad Sci, Xinjiang Inst Ecol & Geog, State Key Lab Desert & Oasis Ecol, Key Lab Ecol Safety & Sustainable Dev Arid Lands, Urumqi 830011, Peoples R China
基金
中国国家自然科学基金;
关键词
mobile monitoring; NO2; spatial distribution; temporal variability; personal exposure; LONG-TERM EXPOSURE; BLACK CARBON; POLLUTION; METHODOLOGY; LONDON;
D O I
10.3390/atmos14071093
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Currently, traffic-related sources are considered to be one of the major contributors to air pollutants in urban areas. As the number of motor vehicles increases, the impact of traffic-related air pollutants (TRAPs) on human health has also increased in recent years. People are easily exposed to TRAPs in their daily lives. However, long-term exposure to TRAPs can have adverse health effects. Mobile monitoring is more flexible compared to traditional urban monitoring stations and can effectively obtain the spatial variation characteristics of air pollutants. We mounted a sensor package on an electric bicycle and conducted mobile measurements of CO, NO2 and SO2 on a circular road in the center of Shaoxing, a city in the center of the Yangtze Delta, China. The CO, NO2 and SO2 concentrations were observed to be higher in the morning and evening rush hours, and the three pollutants show different seasonal and spatial variation characteristics. CO concentration was higher in urban arterial and crossroads. NO2 concentration was variable, alternating between high and low concentrations. SO2 concentration was relatively stable and aggregated. This study provides important information on the spatial and temporal variations of TRAPs, which helps commuters understand how to effectively reduce pollutant exposure during personal travel.
引用
收藏
页数:15
相关论文
共 34 条
  • [11] Ambient Air Particulate Matter in the Yangtze River Delta Region, China: Spatial, Annual, and Seasonal Variations and Health Risks
    Zhao, Wenchang
    Cheng, Jinping
    Guo, Meixiu
    Cao, Qingfeng
    Yin, Yongwen
    Wang, Wenhua
    ENVIRONMENTAL ENGINEERING SCIENCE, 2011, 28 (11) : 795 - 802
  • [12] Spatial and temporal variations in criteria air pollutants in three typical terrain regions in Shaanxi, China, during 2015
    Xu, Yong
    Ying, Qi
    Hu, Jianlin
    Gao, Yuan
    Yang, Yang
    Wang, Dexiang
    Zhang, Hongliang
    AIR QUALITY ATMOSPHERE AND HEALTH, 2018, 11 (01) : 95 - 109
  • [13] High Resolution Spatio-temporal Monitoring of Air Pollutants Using Wireless Sensor Networks
    Rajasegarar, Sutharshan
    Zhang, Peng
    Zhou, Yang
    Karunasekera, Shanika
    Leckie, Christopher
    Palaniswami, Marimuthu
    2014 IEEE NINTH INTERNATIONAL CONFERENCE ON INTELLIGENT SENSORS, SENSOR NETWORKS AND INFORMATION PROCESSING (IEEE ISSNIP 2014), 2014,
  • [14] Spatio-temporal variation of the relationship between air pollutants and land surface temperature in the Yangtze River Delta Urban Agglomeration, China
    Jiang, Yue
    Lin, Wenpeng
    Xu, Di
    Xu, Dan
    SUSTAINABLE CITIES AND SOCIETY, 2023, 91
  • [15] A novel mobile monitoring approach to characterize spatial and temporal variation in traffic-related air pollutants in an urban community
    Yu, Chang Ho
    Fan, Zhihua
    Lioy, Paul J.
    Baptista, Ana
    Greenberg, Molly
    Laumbach, Robert J.
    ATMOSPHERIC ENVIRONMENT, 2016, 141 : 161 - 173
  • [16] Spatial-temporal Analysis of Daily Air Quality Index in the Yangtze River Delta Region of China During 2014 and 2016
    Ye Lei
    Ou Xiangjun
    CHINESE GEOGRAPHICAL SCIENCE, 2019, 29 (03) : 382 - 393
  • [17] Mobile monitoring of air pollution using low cost sensors to visualize spatio-temporal variation of pollutants at urban hotspots
    Nagendra, Shiva S. M.
    Yasa, Pavan Reddy
    Narayana, M., V
    Khadirnaikar, Seema
    Rani, Pooja
    SUSTAINABLE CITIES AND SOCIETY, 2019, 44 : 520 - 535
  • [18] High-resolution spatial and spatiotemporal modelling of air pollution using fixed site and mobile monitoring in a Canadian city
    Clark, Sierra Nicole
    Kulka, Ryan
    Buteau, Stephane
    Lavigne, Eric
    Zhang, Joyce J. Y.
    Riel-Roberge, Christian
    Smargiassi, Audrey
    Weichenthal, Scott
    Van Ryswyk, Keith
    ENVIRONMENTAL POLLUTION, 2024, 356
  • [19] Spatial and temporal variations of six criteria air pollutants in 31 provincial capital cities in China during 2013-2014
    Wang, Yungang
    Ying, Qi
    Hu, Jianlin
    Zhang, Hongliang
    ENVIRONMENT INTERNATIONAL, 2014, 73 : 413 - 422
  • [20] A Study for Spatial Distribution of Principal Pollutants in Daegu Area Using Air Pollution Monitoring Network Data
    Ju, Jae-Hee
    Hwang, InJo
    JOURNAL OF KOREAN SOCIETY FOR ATMOSPHERIC ENVIRONMENT, 2011, 27 (05) : 545 - 557