A Two-Stage Method to Estimate the Contribution of Road Traffic to PM 2.5 Concentrations in Beijing, China

Xin Fang, Runkui Li, Qun Xu, Matteo Bottai, Fang Fang and Yang Cao
Additional contact information
Xin Fang: Unit of Biostatistics, Institute of Environmental Medicine, Karolinska Institutet, Stockholm 17177, Sweden
Runkui Li: College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
Qun Xu: Department of Epidemiology and Biostatistics, Institute of Basic Medicine Sciences, Chinese Academy of Medical Sciences & School of Basic Medicine, Peking Union Medical College, Beijing 100005, China
Matteo Bottai: Unit of Biostatistics, Institute of Environmental Medicine, Karolinska Institutet, Stockholm 17177, Sweden
Fang Fang: Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm 17177, Sweden
Yang Cao: Unit of Biostatistics, Institute of Environmental Medicine, Karolinska Institutet, Stockholm 17177, Sweden

IJERPH, 2016, vol. 13, issue 1, 1-19

Abstract: Background : Fine particulate matters with aerodynamic diameters smaller than 2.5 micrometers (PM 2.5 ) have been a critical environmental problem in China due to the rapid road vehicle growth in recent years. To date, most methods available to estimate traffic contributions to ambient PM 2.5 concentration are often hampered by the need for collecting data on traffic volume, vehicle type and emission profile. Objective : To develop a simplified and indirect method to estimate the contribution of traffic to PM 2.5 concentration in Beijing, China. Methods : Hourly PM 2.5 concentration data, daily meteorological data and geographic information were collected at 35 air quality monitoring (AQM) stations in Beijing between 2013 and 2014. Based on the PM 2.5 concentrations of different AQM station types, a two-stage method comprising a dispersion model and generalized additive mixed model (GAMM) was developed to estimate separately the traffic and non-traffic contributions to daily PM 2.5 concentration. The geographical trend of PM 2.5 concentrations was investigated using generalized linear mixed model. The temporal trend of PM 2.5 and non-linear relationship between PM 2.5 and meteorological conditions were assessed using GAMM. Results : The medians of daily PM 2.5 concentrations during 2013–2014 at 35 AQM stations in Beijing ranged from 40 to 92 ?g/m 3 . There was a significant increasing trend of PM 2.5 concentration from north to south. The contributions of road traffic to daily PM 2.5 concentrations ranged from 17.2% to 37.3% with an average 30%. The greatest contribution was found at AQM stations near busy roads. On average, the contribution of road traffic at urban stations was 14% higher than that at rural stations. Conclusions : Traffic emissions account for a substantial share of daily total PM 2.5 concentrations in Beijing. Our two-stage method is a useful and convenient tool in ecological and epidemiological studies to estimate the traffic contribution to PM 2.5 concentrations when there is limited information on vehicle number and types and emission profile.

Keywords: PM 2.5 concentration; road traffic contribution; atmospheric dispersion model; generalized additive mixed model (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/1660-4601/13/1/124/pdf (application/pdf)
https://www.mdpi.com/1660-4601/13/1/124/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jijerp:v:13:y:2016:i:1:p:124-:d:62151

Access Statistics for this article

IJERPH is currently edited by Ms. Jenna Liu

More articles in IJERPH from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().