Vertical Distribution Characteristics of PM2.5 Observed by a Mobile Vehicle Lidar in Tianjin, China in 2016

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  • Corresponding author: Yunsheng DONG, ysdong@aiofm.ac.cn
  • Funds:

    Supported by the National Key Project of Ministry of Science and Technology of China (2016YFC0203302), National 863 Program for High Technology Research and Development (2014AA06A512), National (Key) Basic Research and Development (973) Program of China (2014CB447900), National Natural Science Foundation of China (91544232, 41305126, and 41605020), and Strategic Priority Research Program of the Chinese Academy of Sciences (XDB05040300)

  • doi: 10.1007/s13351-018-7068-z

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  • We present mobile vehicle lidar observations in Tianjin, China during the spring, summer, and winter of 2016. Mobile observations were carried out along the city border road of Tianjin to obtain the vertical distribution characteristics of PM2.5. Hygroscopic growth was not considered since relative humidity was less than 60% during the observation experiments. PM2.5 profile was obtained with the linear regression equation between the particle extinction coefficient and PM2.5 mass concentration. In spring, the vertical distribution of PM2.5 exhibited a hierarchical structure. In addition to a layer of particles that gathered near the ground, a portion of particles floated at 0.6–2.5-km height. In summer and winter, the fine particles basically gathered below 1 km near the ground. In spring and summer, the concentration of fine particles in the south was higher than that in the north because of the influence of south wind. In winter, the distribution of fine particles was opposite to that measured during spring and summer. High concentrations of PM2.5 were observed in the rural areas of North Tianjin with a maximum of 350 μg m–3 on 13 December 2016. It is shown that industrial and ship emissions in spring and summer and coal combustion in winter were the major sources of fine particles that polluted Tianjin. The results provide insights into the mechanisms of haze formation and the effects of meteorological conditions during haze–fog pollution episodes in the Tianjin area.
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  • Fig. 1.  Mobile lidar measurement path (black line) in Tianjin.

    Fig. 2.  Time series of particle extinction coefficient profiles on 26 April 2016.

    Fig. 3.  Profiles of (a) particle extinction coefficient and (b) depolarization ratio at 0806 local time (LT) 26 April 2016.

    Fig. 4.  Linear fitting of the extinction coefficient and mass concentration of PM2.5.

    Fig. 5.  (a) Spatial distribution of PM2.5 mass concentration along the experiment path measured by the mobile lidar and (b) backward trajectories at 500-m height determined by the HYSPLIT model on 26 April 2016.

    Fig. 6.  (a) Spatial distribution of PM2.5 mass concentration along the experiment path measured by the mobile lidar, (b) distributions of SO2, NO2, and PM2.5 obtained by monitoring sites along the path of the mobile lidar, and (c) backward trajectories at 500-m height determined by the HYSPLIT model on 3 July 2016.

    Fig. 7.  (a) Spatial distribution of PM2.5 mass concentration along the path measured by the mobile lidar, (b) distributions of SO2, NO2, and PM2.5 obtained by monitoring sites along the measurement path of the mobile lidar, and (c) backward trajectories at 500-m height determined by the HYSPLIT model on 13 December 2016.

    Fig. 8.  (a) Spatial distribution of PM2.5 mass concentration along the path measured by the mobile lidar and (b) backward trajectories at 500-m height determined by the HYSPLIT model on 14 December 2016.

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Vertical Distribution Characteristics of PM2.5 Observed by a Mobile Vehicle Lidar in Tianjin, China in 2016

    Corresponding author: Yunsheng DONG, ysdong@aiofm.ac.cn
  • 1. Key Laboratory of Environmental Optics and Technology, Anhui Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Hefei 230031
  • 2. University of Science and Technology of China, Hefei 230026
  • 3. Tianjin Academy of Environmental Sciences, Tianjin 300191
  • 4. Chinese Academy of Sciences Wuxi Photonics Co.,Ltd., Wuxi 214135
  • 5. The Air Force Military Representative's Office in Beijing, Beijing 100086
Funds: Supported by the National Key Project of Ministry of Science and Technology of China (2016YFC0203302), National 863 Program for High Technology Research and Development (2014AA06A512), National (Key) Basic Research and Development (973) Program of China (2014CB447900), National Natural Science Foundation of China (91544232, 41305126, and 41605020), and Strategic Priority Research Program of the Chinese Academy of Sciences (XDB05040300)

Abstract: We present mobile vehicle lidar observations in Tianjin, China during the spring, summer, and winter of 2016. Mobile observations were carried out along the city border road of Tianjin to obtain the vertical distribution characteristics of PM2.5. Hygroscopic growth was not considered since relative humidity was less than 60% during the observation experiments. PM2.5 profile was obtained with the linear regression equation between the particle extinction coefficient and PM2.5 mass concentration. In spring, the vertical distribution of PM2.5 exhibited a hierarchical structure. In addition to a layer of particles that gathered near the ground, a portion of particles floated at 0.6–2.5-km height. In summer and winter, the fine particles basically gathered below 1 km near the ground. In spring and summer, the concentration of fine particles in the south was higher than that in the north because of the influence of south wind. In winter, the distribution of fine particles was opposite to that measured during spring and summer. High concentrations of PM2.5 were observed in the rural areas of North Tianjin with a maximum of 350 μg m–3 on 13 December 2016. It is shown that industrial and ship emissions in spring and summer and coal combustion in winter were the major sources of fine particles that polluted Tianjin. The results provide insights into the mechanisms of haze formation and the effects of meteorological conditions during haze–fog pollution episodes in the Tianjin area.

1.   Introduction
  • China has been suffering from severe haze pollution in recent years, especially in megacity clusters such as the Beijing–Tianjin–Hebei area [called Jing–Jin–Ji (JJJ) in Chinese], Yangtze River Delta (YRD), and Pearl River Delta (PRD) (Parrish and Zhu, 2009), because of the ra-pid economic growth and urbanization. Fine particles (PM2.5), which are the main contributor of haze pollution, have attracted worldwide attention. Fine particles in the boundary layer exert significant direct and indirect effects on human lives and activities. Therefore, determining the distribution characteristics of fine particles is essential. Extensive studies were conducted to detect the concentration of fine particles near the ground using particulate matter samplers (Pu et al., 2015; Zhang et al., 2017). However, particulate matter samplers do not effectively measure the vertical distribution of particles in the boundary layer. Only a few researchers obtained the vertical profile of fine particles. Yang et al. (2005) obtained the PM2.5 profiles in Beijing by putting some PM2.5 monitors on different altitudes of a 325 m–high meteorological tower. Tao et al. (2016) profiled the vertical distribution of the PM2.5 mass concentration in the boundary layer with a charge-coupled device (CCD) side-scattering lidar. The feasibility of the observation of fine particle profile based on lidar was suggested. However, most haze pollution occurs on a regional scale, and point measurements based on a lidar site or a lidar network cannot be adequate representatives. To conduct regional observations on this phenomenon, a vehicle-based mobile lidar system was developed and applied in atmospheric monitoring (Lyu et al., 2016). The mobile lidar can obtain the vertical distribution of fine particles quickly on a regional scale.

    Most previous studies focused on the haze pollution in Beijing, China (Che et al., 2015a; Jing et al., 2015; Wu et al., 2016; Ma et al., 2017), while some concerned about the haze pollution in the JJJ area (Che et al., 2014, 2015b; Sun et al., 2014; Wu et al., 2017). Considerable results have shown that urban or industrial aerosols and regionally transported pollutants from the southwest pathway under some particular meteorological conditions were significant factors in haze formation in Beijing (Chen et al., 2015; Zhang et al., 2017). However, relevant observations in Tianjin are rare, and the mechanisms of haze formation and the effect of meteorological conditions are not well understood there. Thus, observations in Tianjin are strongly needed to access the distribution characteristics of fine particles, the formation mechanism of haze, and the effect of meteorological conditions. In this paper, fine particles profile were observed by using a vehicle-based mobile lidar system during different seasons in Tianjin in 2016. In that same period, the SO2, NO2, and PM2.5 data were obtained by local monitoring stations. Furthermore, an in-depth study [composite analysis with Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model] was also performed to discuss the source and characteristic of fine particles in Tianjin.

2.   Methodology
  • Mobile lidar measurements were conducted in Tianjin in 2016 to obtain the distribution characteristics of fine particles under different seasons. Tianjin, one of the metropolises in North China, is located east of the Bohai Sea and north of the Yanshan Mountain. It has an approximate population of 15 million and an area of around 12,000 km2. The city is characterized by high population density, heavy traffic, and enhanced industrial activities. Thus, it has a variety of fine particle sources, e.g., marine particles from the Bohai Sea, anthropogenic particles (transportation emissions, industrial emissions, and coal burning emissions) from highly populated urban centers, industrial areas, and rural areas, etc. In addition, regional transportation is also an important source of pollution particles in Tianjin. In this paper, observations in different seasons, spring, summer, and winter, are presented and discussed. Figure 1 shows the mobile lidar measurement path in Tianjin during the experiment.

    Figure 1.  Mobile lidar measurement path (black line) in Tianjin.

  • The vehicle-based mobile lidar system was developed at the Anhui Institute of Optics and Fine Mechanics. The sub-units of the mobile lidar system include a laser emitting unit, a receiving unit, a signal acquisition unit, and a global positioning system (GPS). The laser source is a pulsed Nd:YAG laser that emits short pulses at 532 and 355 nm in 20 Hz. The receiving telescope of the lidar system is based on a Cassegrain design. The range resolution of the lidar is 7.5 m. The full overlap height of the lidar is about 100 m. Detailed specifications of the mobile lidar were provided in a previous study (Lyu et al., 2016). The mobile lidar can detect atmospheric aerosols and indicate the stereoscopic distribution properties of aerosols.

    The formulas for aerosol extinction coefficient (Fernald, 1984) and depolarization ratio are:

    α a (z)= S a S m α m (z)+ P(z) z 2 exp[2( S a S m 1) z z C α m (z) dz] P( z c ) z 2 α a ( z c )+ S a S m α m ( z c ) +2 z z c P(z) z 2 exp[2( S a S m 1 ) z z c α m (z) dz]dz , ((1))
    δ=k P P // , ((2))

    where αa and αm are the particle extinction and molecular extinction coefficients, respectively. The particle extinction-to-backscatter ratio Sa = 50 Sr, the corresponding ratio for the molecular Sm = 8π/3, and P(z) is the received power at height z in Formula (1). δ is the linear depolarization ratio, k is a calibration factor, and P and $P_{//} $ are cross-polarized and co-polarized signals, respectively, in Formula (2). The vertical profiles of extinction coefficient are retrieved by using Formula (1). The profiles of fine particles can also be derived from particle extinction coefficient with the following formula:

    M PM 2.5 =ξα, ((3))

    where M P M 2.5 is the PM2.5 mass concentration, α is the particle extinction coefficient, and ξ is a specific coefficient independent of altitude, which can be considered a constant when aerosol hydrophilic growth is negligible (Tao et al., 2016). Considering the unstable factors in the measurement, constant c is included in the formula, as shown in Formula (4). Parameters ξ and c can be obtained by use of linear regression analysis (Han et al., 2006).

    M PM 2.5 =ξα+c. ((4))
  • The paths of air masses were determined by the National Oceanic and Atmospheric Administration Hybrid Signal Particle Lagrangian Integrated Trajectory model (http://ready.arl.noaa.gov/HYSPLIT_traj.php). In the following analysis, we set 500 m as the starting height to obtain information on particle sources and types. The backward trajectories of air masses at Tianjin were calculated to study the effect of meteorological conditions and regional transport.

3.   Results and discussion
  • During the experiment, the atmosphere environment monitoring vehicle, which is equipped with mobile lidar, moved clockwise along the border road of Tianjin under different seasons. The main route of the monitoring vehicle passed over seven districts of Tianjin, from as far north as Jizhou District to as far south as Binhai District. The route had a length of approximately 400 km, which took approximately eight hours to finish. In the following analysis, four examples are presented to show the comparative results in different seasons. In the following experimental cases, the relative humidity was less than 60%. Therefore, hygroscopic growth was negligible (Chen et al., 2014).

    On 26 April 2016, a clear day in spring, the vehicle-based mobile lidar system operated from 0700 to 1600 local time (LT). Figure 2 shows the time series of particle extinction coefficient observed by lidar for this case. The particle was accumulated almost below 2.5 km from the ground. The vertical distribution of fine particles exhibited a hierarchical structure with a particle layer concentrated near the ground and a thick particle layer suspended at the height of 0.6–2.5 km. A relatively clean layer existed between the two pollution layers. Figure 3 presents a particle extinction profile at 0806 LT and the corresponding depolarization ratio profile. Figure 3 shows that the major difference between the two layers was the depolarization ratio. The depolarization ratio of the floating layer was significantly higher than that of the surface layer below 0.5 km. The result indicated that the particles of the two layers may come from different sources.

    Figure 2.  Time series of particle extinction coefficient profiles on 26 April 2016.

    Figure 3.  Profiles of (a) particle extinction coefficient and (b) depolarization ratio at 0806 local time (LT) 26 April 2016.

    The combination of in-situ PM2.5 sampler data and particle extinction coefficient retrieved by lidar was found to be a valid method to obtain PM2.5 profile with lidar. In Fig. 4, the in-situ measured PM2.5 data were compared with the particle extinction coefficient near the ground. The correlation coefficient between the particle extinction and PM2.5 data was roughly 0.85. A linear regression equation was established to convert lidar data to PM2.5 data.

    Figure 4.  Linear fitting of the extinction coefficient and mass concentration of PM2.5.

  • Using the real-time latitude and longitude data obtained by GPS, we overlaid the profiles of PM2.5 mass concentration on Google Earth map, as shown in Fig. 5a. The maximum value of PM2.5 appeared near the port in the southeast of Tianjin. The fine particles mainly concentrated at heights lower than 0.5 km at the southeast of the route. The PM2.5 mass concentrations ranged from 10 to 200 μg m–3. The air mass at 500 m was mainly from the southeast in spring, as indicated in Fig. 5b. The air mass came from the southeast, passed through the port, and arrived at Tianjin. The air mass was likely to bring pollutants from the port (emissions from ships), particularly from high-sulfur fuel. Yao and Wang (2016) determined that the Bohai gulf area was one of the main potential source areas of SO2 during spring based on potential source contribution function analysis. Liu et al. (2017) demonstrated that ship emissions substantially contributed to the air pollution in Shanghai. The port city in North China is busy, and ship emissions are deciding factors that cannot be neglected in Tianjin. Therefore, the southeast part near the port had a high concentration of PM2.5 during the observational experiment.

    Figure 5.  (a) Spatial distribution of PM2.5 mass concentration along the experiment path measured by the mobile lidar and (b) backward trajectories at 500-m height determined by the HYSPLIT model on 26 April 2016.

  • To obtain the distribution characteristics, potential sources, and meteorological conditions effects of fine particles in other seasons, more observations based on the mobile lidar were conducted on the same route. Figures 68 show the typical examples in the summer (3 July 2016) and winter (13–14 December 2016) to indicate the spatial distributions of PM2.5 mass concentration and corresponding backward trajectories, respectively.

    Figure 6.  (a) Spatial distribution of PM2.5 mass concentration along the experiment path measured by the mobile lidar, (b) distributions of SO2, NO2, and PM2.5 obtained by monitoring sites along the path of the mobile lidar, and (c) backward trajectories at 500-m height determined by the HYSPLIT model on 3 July 2016.

    Figure 7.  (a) Spatial distribution of PM2.5 mass concentration along the path measured by the mobile lidar, (b) distributions of SO2, NO2, and PM2.5 obtained by monitoring sites along the measurement path of the mobile lidar, and (c) backward trajectories at 500-m height determined by the HYSPLIT model on 13 December 2016.

    Figure 8.  (a) Spatial distribution of PM2.5 mass concentration along the path measured by the mobile lidar and (b) backward trajectories at 500-m height determined by the HYSPLIT model on 14 December 2016.

    On 3 July, the distribution of fine particles was the same as that in spring. Figure 6a shows that the fine particles mainly concentrated at the heights lower than 0.5 km in southeast of the measurement path. The north area had lower PM2.5 data (with a mean of 35 μg m–3), and the southeast sections had a relatively high value with an average of 104 μg m–3 near the ground. Figure 6b presents the density distributions of SO2, NO2, and PM2.5 obtained by monitoring sites along the measurement path of the mobile lidar. The sampling data obtained by the monitoring sites indicate that the concentration of PM2.5 in the south was higher than that in the north. The distributing characteristics of PM2.5 (data obtained by mobile lidar and sampling data obtained by monitoring sites) were consistent. On 3 July, the air mass was mainly from the south instead of the southeast, as shown in Fig. 6c. Thus, the ship emission effect was not apparent under the southeast wind field. Hence, in addition to ship emission effects from the southeast, pollution sources existed in the south of the high-value region.

  • As shown in the previous examples, when the wind mainly came from the southeast or south in spring and summer, higher concentrations of PM2.5 were detected in the south. In the following two typical examples in winter, as shown in Figs. 7, 8, the air mass was mainly from the north and higher concentrations of PM2.5 always appeared in the north of Tianjin. On 13 December 2016, the wind blew from the northwest with wind speed of 2 m s–1 near the ground. The maximum value of PM2.5 obtained by lidar was 350 μg m–3, indicating heavy pollution in the north of Tianjin. The fine particles mainly concentrated at the heights lower than 1 km in the northern part of the route. Figure 7b presents the distributions of SO2, NO2, and PM2.5 obtained at the monitoring sites along the measurement path of the mobile lidar. As mentioned above, the concentrations of PM2.5 during summer were higher in the south than that in the north. By contrast, the concentrations of PM2.5 during winter in the north were higher than that in the south. Figure 7b also shows that both SO2 and NO2 were higher than that in summer, particularly in the north of Tianjin. The phenomenon was always attributed to coal combustion in winter. In addition, a strong positive correlation existed between the gaseous pollutants (SO2 and NO2) and PM2.5 in winter. Thus, the heavy haze in the north of Tianjin was caused by coal combustion. The results of the observations in winter showed that coal combustion was the chief source in the north of Tianjin.

4.   Conclusion
  • Observations of pollutants based on a mobile lidar were carried out in spring, summer, and winter seasons to study the distribution characteristics of PM2.5 in Tianjin. According to the measurement results, the fine particles mainly concentrated near the ground. The distribution characteristics of PM2.5 obtained by the mobile lidar and from the monitoring sites were consistent. The combined results obtained by mobile lidar, sampling data obtained by monitoring sites, and air mass trajectories in the observation period, indicated that the distribution of PM2.5 and wind direction were connected. The concentration of fine particles in the south was higher than that in the north because of the influence of the south (south and southeast) wind in spring and summer, suggesting the existence of emission sources in the south and southeast of Tianjin. The fine particles were probably from the local sources (such as industrial emissions and ship emissions) in the upwind region. In winter, the concentration of fine particles in the north was higher than that in the south because of the prevailing north wind. High concentrations of PM2.5 were observed in the rural areas of North Tianjin. The high concentrations of PM2.5 in the north were mainly from coal combustion during the heating season in Tianjin, suggesting the substantial contribution of coal combustion in North China.

Acknowledgments
  • The authors gratefully acknowledge the NOAA Air Resources Laboratory for providing the HYSPLIT mo-del and the Google Earth for providing the map used in this study. We also thank Chinese Academy of Sciences Wuxi Photonics Co.,Ltd. for providing part of the lidar data measured in April 2016 and their help during the experiment.

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