Analysis of the Temporal Distribution Characteristics of PM2.5 Concentration and Risk Evaluation of Its Inhalation Exposure

被引:0
|
作者
Wang, Xiaoxia [1 ]
Liu, Xuezhen [2 ]
Wang, Luqi [1 ]
Dong, Zhongzhen [3 ]
Han, Xiaowei [4 ]
机构
[1] Guangdong Univ Technol, Sch Civil & Transportat Engn, Guangzhou 510006, Guangdong, Peoples R China
[2] Cangzhou Air Pollut Control Ctr, Cangzhou 061000, Hebei, Peoples R China
[3] Rizhao City Ecol Environm Protect Serv Ctr, Rizhao 276800, Shandong, Peoples R China
[4] Weifang Med Univ, Sch Basic Med, Weifang 261053, Shandong, Peoples R China
基金
中国国家自然科学基金;
关键词
Air quality monitoring; PM2; 5; pollution; Temporal distribution characteristics; Human activity patterns; Exposure risk assessment; PARTICULATE MATTER; DISPERSION; TUNNEL;
D O I
10.1007/s11356-022-20511-8
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
PM2.5 poses a threat to human health. It is important to evaluate the potential risk of PM2.5 inhalation exposure when people are located in different spatiotemporal activity locations. In this study, the PM2.5 concentration was detected by the atmospheric cruise monitoring system (ACMS), a new detection technology used for city-wide PM2.5 concentration monitoring. People were divided into eight categories of five typical activity patterns, including rest (R), sedentary behavior (SB), light physical activity (LPA), moderate physical activity (MPA), and vigorous physical activity (VPA). The PM2.5 inhalation exposure risk was then estimated for these typical activities. The research results showed that the time sequence data of the ACMS had a similar tendency to change as those of the traditional air quality monitoring stations (AQMS). Although both passed the stationarity test, the relative error (RE) of the monthly average PM2.5 concentration between the ACMS and AQMS was 7.5-14%. RE was usually lower when the individual air quality index (IAQI) of PM2.5 was higher. Otherwise, RE was higher. The research results also showed that PM2.5 exposure was positively correlated with PM2.5 concentration, respiration rate, and human activity patterns. Because adults had a higher monthly average potential exposure (MAPE) than minors and that males had a higher MAPE than females. The potential exposure generated by LPA and MPA reached 50.76% of the total potential exposure (TPE). VPA brought about a 14.7% increase in the TPE. The research findings are helpful to understand the temporal distribution characteristics of PM2.5 concentrations and guide the potential risk evaluation of PM2.5 inhalation exposure.
引用
收藏
页码:71460 / 71473
页数:14
相关论文
共 50 条
  • [21] Temporal characteristics and forecasting of PM2.5 concentration based on historical data in Houston, USA
    Du, Jianbang
    Qiao, Fengxiang
    Yu, Lei
    RESOURCES CONSERVATION AND RECYCLING, 2019, 147 : 145 - 156
  • [22] Analysis of Spatio-temporal Distribution Characteristics and Influencing Factors of PM2.5 Concentration in Urban Agglomerations on the Northern Slope of Tianshan Mountains
    Wang X.-N.
    Zhang Z.
    Liu F.-Q.
    Huanjing Kexue/Environmental Science, 2024, 45 (03): : 1315 - 1327
  • [23] Fine Simulation and Analysis of Temporal and Spatial Characteristics of PM2.5 Concentration Distribution in Different Urban Scenarios based on Mobile Monitoring Data
    Xie X.
    Li D.
    Lu J.
    Wu S.
    Xu F.
    Journal of Geo-Information Science, 2022, 24 (08) : 1459 - 1474
  • [24] Distribution of PM2.5 concentration based on bezier surface
    Zhao, Bingchen
    Huang, Junying
    Zhang, Bin
    Xia, Shaofang
    Zhang, Xiaojing
    International Journal of Earth Sciences and Engineering, 2014, 7 (06): : 2315 - 2319
  • [25] Concentration Distribution and Control strategy of Indoor PM2.5
    Qu, Yunxia
    Wang, Huanhuan
    Zhu, Linlin
    Ji, Jiayan
    10TH INTERNATIONAL SYMPOSIUM ON HEATING, VENTILATION AND AIR CONDITIONING, ISHVAC2017, 2017, 205 : 1606 - 1611
  • [26] Analysis on mass concentration of PM2.5 in Kunshan
    Yu Liangmin
    Yu Lei
    Chen Feng
    Gu Haidong
    PROCEEDINGS OF THE 2016 5TH INTERNATIONAL CONFERENCE ON CIVIL, ARCHITECTURAL AND HYDRAULIC ENGINEERING (ICCAHE 2016), 2016, 95 : 236 - 241
  • [27] Definition and characteristics of PM2.5 background concentration in Beijing
    Ma, Zhi-Qiang
    Xu, Jing
    Zhang, Xiao-Ling
    Yin, Xiao-Hui
    He, Yun
    Shi, Xue-Feng
    Zhongguo Huanjing Kexue/China Environmental Science, 2015, 35 (01): : 7 - 12
  • [28] Spatio-temporal Evolution and Population Exposure Risk to PM2.5 in the Guanzhong Area
    Huang X.-J.
    Qi M.-Y.
    Li Y.-Y.
    Wang S.
    Huang X.
    Huanjing Kexue/Environmental Science, 2020, 41 (12): : 5245 - 5255
  • [29] Spatial and temporal characteristics analysis and prediction model of PM2.5 concentration based on SpatioTemporal-Informer model
    Ma, Zhanfei
    Luo, Wenli
    Jiang, Jing
    Wang, Bisheng
    Ma, Ziyuan
    Lin, Jixiang
    Liu, Dongxiang
    PLOS ONE, 2023, 18 (06):
  • [30] Temporal variations of PM2.5 and PM10 concentration over Hyderabad
    Ajay Kumar M.C.
    Vinay Kumar P.
    Venkateswara Rao P.
    Nature Environment and Pollution Technology, 2020, 19 (05) : 1871 - 1878