Megacities have strong interactions with the surrounding regions through
transport of air pollutants. It has been frequently addressed that the air
quality of Beijing is influenced by the influx of air pollutants from the
North China Plain (NCP). Estimations of air pollutant cross-boundary
transport between Beijing and the NCP are important for air quality
management. However, evaluation of cross-boundary transport using long-term
observations is very limited. Using the observational results of the gaseous
pollutants SO
Megacities are large sources of air pollutants and greatly influence the surrounding areas (Parrish and Zhu, 2009). With a population over 20 million, the city of Beijing is an example of such a megacity. Beijing has faced severe air pollution problems over the past two decades and has intensive interactions with other emission hot spots within the North China Plain (NCP) (Chen et al., 2015; Shao et al., 2006; Zhang et al., 2012). Beijing and the NCP are surrounded by the Yanshan Mountains to the north and the Taihang Mountains to the west. The semi-basin geographical features together with the continental monsoon climate make regional transport of air pollutants between the megacity Beijing and the NCP an important factor affecting air quality in Beijing and the NCP (An et al., 2007; Guo et al., 2010; Lin et al., 2008, 2009; Streets et al., 2007; Wang et al., 2006, 2011, 2015; Wu et al., 2011; Xu et al., 2005, 2011). An improved understanding of the regional transport of air pollutants between Beijing and the NCP is therefore essential for air quality management of the megacity Beijing and establishment of regional-scale emissions control measures.
Previous studies have shed light on the regional transport sources of the
megacity Beijing, and various techniques have been employed, including
rural–urban station observations (Guo et al., 2010; Lin et al., 2008, 2009;
Wang et al., 2006; Xu et al., 2011), mobile laboratory measurements (Wang et
al., 2009a, 2011; Zhu et al., 2016), and modeling studies (An et al., 2007;
Matsui et al., 2009; Wu et al., 2011). A ground-based observation study from
July 2006 to September 2007 at the Gucheng site (Lin et al., 2009), a rural
site southwest of Beijing, found that high concentrations of gaseous
pollutants, including nitric oxide (NO), nitrogen dioxide (NO
Many studies have also attempted to quantify transport fluxes of the main
gaseous pollutants. A mobile laboratory study in Beijing demonstrated
regional transport of SO
In summary, long-term observation of transport flux is necessary to
constrain regional models and to directly evaluate the influence of regional
transport on air quality. Estimations of air pollutant cross-boundary
transport between Beijing and the NCP are important for air quality
management. However, evaluation of cross-boundary transport using long-term
observations is very limited. In this study, we developed a method of
calculating the surface transport flux intensity across a cross-boundary
site based on long-term ground-based measurement and evaluated the regional
transport influence of Beijing and the NCP on the cross-boundary site. The
results showed different transport directions and seasonal variations in the
surface transport flux intensities of the main pollutants, including
SO
The location information of the Yufa site.
The Yufa site is located at the cross-boundary area between Beijing and the
NCP and could be influenced by emissions from the megacity Beijing and
long-range transport from the NCP. The measurements at the Yufa site
(39
The gaseous pollutant species measured included SO
The overview of measurement instruments.
The transport of gaseous pollutants is markedly influenced by meteorological parameters, especially wind speed and wind direction. For local emission sources, wind can facilitate the dilution and diffusion of air pollutants. Strong wind usually has marked diffusion capability, whereas weak wind usually leads to accumulation of air pollutants. For regional sources, strong wind can transport pollutants over long distances and may result in high concentrations of pollutants in downwind areas. Therefore, the relationship between pollutant concentration and wind field is an indicator of regional transport.
The bivariate polar plot graphical technique was used to investigate the
relationships between the concentrations of gaseous pollutants and wind
field and to identify potential emissions sources and transport directions
of air pollutants according to the technique developed by Carslaw et
al. (2006) and Westmoreland et al. (2007). The variables (such as pollutant
concentrations, wind speed, and wind direction) were plotted in polar
coordinates. The procedure was as follows. First, the concentration data were
partitioned into wind speed–wind direction bins, and the mean concentrations
were calculated within each bin. Then, the wind components
Compared to the nonparametric regression used by Henry et al. (2002), the bivariate polar plot involves the dependence of pollutant concentration on both wind speed and wind direction. The nonlinear relationships among the variables (such as concentrations of gaseous pollutants, wind speed, and wind direction) as well as the interactions among these variables can be considered using the GAM method for data smoothing. In addition, the use of polar coordinates makes the graphics more intuitive.
The surface transport fluxes at the Yufa site were calculated with the
following formula (White et al., 1976; Wang et al., 2011):
Figure S1 in the Supplement shows a schematic diagram of the surface flux calculation. The flux intensity here is the product of wind vector and air pollutant concentration measured at the same location. Ideally, we need to use the wind speed and air pollutant concentration with infinite small time resolution to conduct the surface flux calculations. In this study, the hourly data of the pollutants and wind were used, mainly because the pollutant concentration data were converted from the minute data to hourly mean to remove accidental fluctuation and reduce noise. Therefore, we assumed the wind speed and wind direction were constant within 1 h, and hourly wind data were used to match with the hourly air pollutant concentration data to calculate the flux intensity.
It must also be made clear that the surface flux intensity calculated in
this study is the per unit area flux across the Yufa site, which is
different from the flux across a large area reported in other studies (e.g.,
Wang et al., 2011). Our results could only be extrapolated if the
concentrations of all the pollutants, and wind speed and direction were
homogenously distributed, vertically and horizontally. Otherwise, vertical
profiles of air pollutant concentration and wind are needed to calculate
the cross-section transport flux of two adjacent regions for the whole
boundary layer with the integrating formula below:
The 12 h air mass back trajectories arriving at the Yufa site at 500 m
above ground level were calculated using the National Oceanic and
Atmospheric Administration (NOAA) Hybrid Single-Particle Lagrangian
Integrated Trajectory Version 4 model (HYSPLIT-4 model)
(
Time series of hourly mean (black line) and 24 h smoothing
concentrations (red line) of SO
The potential source contribution function (PSCF) analysis was performed with
the GIS-based software TrajStat
(
The study domain was divided into
To reduce the effect of small values of
The time series of hourly average and 24 h smoothing concentrations of
SO
Meteorological parameters such as WS, WD, RH, T, and BP were also measured at
the Yufa site; the monthly statistics are shown in Fig. 3. Northern (usually in
winter) or southern wind (usually in summer) prevailed at the Yufa site, with
monthly average wind speed mostly below 2 m s
Monthly statistics of wind speed (WS) for northern wind
Wind rose plots based on frequencies of hourly data in Autumn 2006, Autumn 2007, Winter 2006/07, Winter 2007/08, Spring 2007, Spring 2008, Summer 2007, Summer 2008. Spring (MAM): March, April, and May; Summer (JJA): June, July, and August; Autumn (SON): September, October, and November; Winter (DJF): December, January, and February.
Bivariate polar plots for SO
The seasonal variations in gaseous pollutants and meteorological parameters could be linked in certain ways. For example, the high temperature and low pressure in summer suggested a high boundary layer and diluted gaseous pollutants to some extent. The high temperature, light intensity, and relative humidity also favored the chemical transformation of these primary pollutants and the formation of secondary pollutants. The high wind speeds in spring and winter also affected regional transport, and therefore the concentrations of gaseous pollutants, as discussed below.
As shown in Fig. 1, the Yufa site is located in the boundary area of Beijing
city and the NCP. Prevalent south-southwestern or north-northeastern wind would
bring in polluted or clean air masses to the site. Air masses from both
directions would pass over the Yufa site. Regional transport from the
megacity Beijing and the NCP could therefore be observed at the Yufa site.
The transport directions for gaseous pollutants, including SO
Figure 5a–g show the bivariate polar plots for SO
The high concentrations of NO, NO
Spatial distribution of seasonal NO
Seasonal bivariate polar plots for SO
The different patterns of the bivariate polar plots reflected the differences in local emission and regional transport for different species. The emissions, the meteorological conditions, the chemical reaction rate, and the species lifetime, which have essential influence on the regional transport, vary greatly by seasons. Thus the seasonal variations of the bivariate polar plots and the corresponding causes were discussed in this section.
Figure 7b–d show seasonal variations of the bivariate polar plots for NO,
NO
Figure 7e is the seasonal bivariate polar plots of CO, which clearly show
the relatively higher mean concentration of CO (> 1 ppm) with
winds at low wind speed (< 2 m s
The total and seasonal net surface flux intensities (mean
Figure 7a clearly shows the relatively higher mean concentration of SO
Finally, the bivariate polar plots in Fig. 7f and g show the dependence of
O
In conclusion, the emissions in the Beijing area are closely related to the
observed concentrations of NO, NO
To evaluate the surface transport of the main air pollutants from Beijing and
the NCP to the Yufa site, the surface flux intensities were calculated with
Eq. (3) based on observations at the Yufa site. The mean net surface flux
intensities in each season were also calculated for the 2-year observation
period (Table 2). The overall net surface flux intensities (mean
(a) The total and seasonal surface influx intensities (mean
To understand the transport fluxes reported here, it is necessary to discuss
the affecting factors. First, the prevalent wind is a dominant factor
affecting the surface fluxes. Figure 8 shows the time series of daily average
surface flux intensity, i.e., the per unit cell flux
(
Time series of surface flux intensity (i.e., flux per unit cell,
Second, the transport flux is determined not only by the wind field but also
by the emissions of pollutants in the upwind area. Various pollutants showed
different patterns of seasonal variations in flux as a result of relatively
high emission intensities in the upwind area compared to local emissions. For
example, the seasonal surface flux intensities of SO
The influence of emissions on transport flux could also be inferred from an emissions-reduction scenario. For example, the 29th Olympic Games were held in Beijing during the period from 8 August 2008 to 20 September 2008. The Beijing government implemented aggressive long- and short-term air quality control measures in Beijing and its surrounding areas before and during the Olympic period to maintain good air quality during the Olympic Games (Wang et al., 2010, 2011). The control measures included moving heavily polluted factories out of Beijing, reducing the traffic emission through an odd/even plate number rule, and freezing construction activities (Wang et al., 2009a). The concentrations of pollutants and the surface flux intensities during the 2008 Olympic Games were substantially reduced compared to the corresponding period of 2007 (Table 4). Besides the favored meteorological conditions (Fig. S2), the significant emission reduction both in the Beijing area and the NCP during the 2008 Beijing Olympic Games played a key role in the decrease of the transport flux intensities (Zhou et al., 2010).
The mean net surface flux intensities (i.e., Flux 2007 and Flux 2008), the influx intensities (positive; from the NCP to Yufa; In 2007 and In 2008), the outflux intensities (negative; from Beijing to Yufa; Out 2007 and Out 2008), and the mean concentrations (i.e., Cont. 2007 and Cont. 2008) during the 2008 Beijing Olympic period (from 8 August 2008 to 20 September 2008) and the same corresponding period of 2007 (from 8 August 2007 to 20 September 2007).
The PSCF maps for the SO
The PSCF maps for the SO
Finally, the chemical properties of these species could also affect the flux.
Take O
Overall, the flux intensities are influenced by at least the wind field, emissions inventory in both the megacity Beijing and the NCP, and the chemical fates of these pollutants in the atmosphere. These observations provide insight for the analysis of projected transport flux under various emissions-reduction scenarios in the future. On the other hand, the dependence of the fluxes on these factors, which can vary, suggests that the fluxes reported here should not be compared with other reports under different conditions.
The discussion above suggested that the regional transport from both Beijing and the NCP have important influence on the air quality of the Yufa site. However, both the bivariate polar plots and surface flux intensity calculation were based on the observation data at a ground measurement site. Considering the limitation of spatial representation of the Yufa site, the PSCF analysis based on the HYSPLIT-4 model was used to demonstrate the regional transport influence of the megacity Beijing and the NCP on Yufa in this section.
PSCF analysis was used in this study by combining backward trajectories and
the corresponding surface transport flux intensities of pollutants. PSCF
results of SO
Uncertainty in calculation of the surface flux intensities in this study
mainly comes from the measurement of the pollutants and the wind. Based on
the instruments used, the uncertainty of the measurement of the
concentrations of SO
In this study, we did not intend to extrapolate from the Yufa site to the entire region. We focus on the method developing and evaluation of the regional transport influence of Beijing and the NCP on the cross-boundary site based on the ground-based observation data. Bivariate polar plots analysis and surface flux intensity calculations were conducted, and we obtained clear evidence of surface pollutant transport from Beijing to the Yufa site and from the NCP to the Yufa site. Considering the variations in the vertical and horizontal distributions of the air pollutants and meteorological parameters, and the influence of the boundary layer on regional transport, three-dimensional observations with high precision and high resolution are needed for further comprehensive discussion of regional transport between Beijing and the NCP.
We used 2-year continuous observation data at a cross-boundary rural site
between the megacity Beijing and the NCP to investigate regional transport
influence on the Yufa site as part of the “Campaigns of Air Quality Research
in Beijing and Surrounding Region 2006–2008” (CAREBeijing 2006–2008). The
gaseous pollutants SO
Through bivariate polar plots, we found that the southern wind, at relatively
high wind speed, was essential for the inflow of SO
The surface flux intensities showed strong net surface transport from
the NCP to Yufa in summer and net surface transport from Beijing to Yufa in
winter, mainly varied with the prevailing wind. The positive net influxes of
SO
PSCF analysis demonstrated that regional transport from Beijing and the NCP to Yufa can be evaluated by the surface flux intensity calculation based on the ground-based measurement data. As a cross-boundary site between the megacity Beijing and the NCP, the surface transport flux intensities at the Yufa site may also indicate transport between the megacity Beijing and the NCP.
Our results again suggest that Beijing and the NCP have tight interactions through regional transport of air pollutants. Factors affecting the transport flux, such as meteorological parameters, especially wind speed and wind direction, emissions inventory, and photochemical reactions, are essential for the regional transport fluxes and thus the air quality of the megacity Beijing and its surrounding areas. Therefore, both local emissions reduction and regional cooperative control should be considered in air quality management of Beijing.
The observation data of the Yufa site used in this paper is available on request.
Tong Zhu designed the experiments and Limin Zeng and the staff of the Yufa site carried out the experiment. Yingruo Li conducted the data analysis with contributions from all co-authors. Jun Liu provided the emission maps. Junxia Wang managed the observation data of the program. Yingruo Li prepared the paper with the help of Tong Zhu, Chunxiang Ye, Jun Liu, and Yi Zhu.
The authors express their sincere thanks to the staff of the Yufa site for carrying out the measurements. This work as part of CAREBeijing (Campaigns of Air Quality Research in Beijing and Surrounding Regions) was supported by Beijing Council of Science and Technology. This study was also supported by the National Natural Science Foundation Committee of China (21190051, 41121004, 41421064), the European 7th Framework Programme Project PURGE (265325), and the Collaborative Innovation Center for Regional Environmental Quality. Edited by: Z. Li Reviewed by: four anonymous referees