A multi-axis differential optical absorption spectroscopy (MAX-DOAS)
instrument was deployed in May and June 2016 at a monitoring station
(37.18
The North China Plain (NCP) is one of the most populated, industrialized and
economically developed regions in China. The NCP region is located in the
northern part of eastern China with an area of about 3 % of the total
area of China and with about 20 % of the Chinese population, covering
major parts of the provinces Hebei, Henan, Shandong, the northern parts of
Anhui and Jiangsu, and the megacities of Beijing and Tianjing. The NCP region
is between the Bohai and Huanghai seas to the east and the Taihang Mountains
to the west. The Yan Mountains and the Dabie Mountains and Yangtze River
delineate northern and southern borders. The main part of the NCP region is
shown in Fig. 1. With rapid economic growth and urbanization, air pollution
in the NCP region has become severe. The NCP has suffered from the most
frequent and severe haze events in China based on the reports from the
Ministry of Environmental Protection (MEP, 2017). Previous studies
characterized the composition of aerosol particles (e.g. Huang et al., 2014;
L. Wang et al., 2012, 2015) and their gaseous precursors (e.g. Ma et al.,
2012; Hendrick et al., 2014; Zhu et al., 2016; Jin et al., 2016) to better
understand haze events (e.g. Fu et al., 2014). The role of regional transport
(e.g. Ding et al., 2009) in haze events has been studied with chemical
transport modelling (e.g. L. Wang et al., 2012, 2015) and observations such
as ground-based stations, mobile platforms (e.g. Zhu et al., 2016), aircraft
(e.g. Ding et al., 2009) and satellites (e.g. Tao et al., 2012). Previous
studies demonstrated that secondary aerosols formed through photochemical
reactions from trace gas precursors, e.g. nitrogen dioxide (
Topography maps
(
The multi-axis differential optical absorption spectroscopy (MAX-DOAS) technique, invented about 15 years ago, allows one to derive vertical profiles of trace gases and aerosols in the troposphere from the observation of scattered sunlight at multiple elevation angles (Hönninger and Platt, 2002; Bobrowski et al., 2003; Van Roozendael et al., 2003; Hönninger et al., 2004; Wagner et al., 2004; Wittrock et al., 2004). The existing profile inversion approaches for MAX-DOAS can be sorted into two groups: inversion algorithms based on the optimal estimation (OE) method and iterative approaches, e.g. the Gauß–Newton or Levenberg–Marquardt methods (Rodgers, 2000; Frieß et al., 2006, 2011; Wittrock, 2006; Irie et al., 2008, 2011; Clémer et al., 2010; Yilmaz, 2012; Hartl and Wenig, 2013; Y. Wang et al., 2013a, b), and the so-called parameterized approaches using look-up tables (X. Li et al., 2010, 2013; Vlemmix et al., 2010, 2011; Wagner et al., 2011; Irie et al., 2008, 2011). The MAX-DOAS technique is suitable for long-term observations of trace gases and aerosols with a relative high time resolution of several minutes due to its simple instrument concept, low cost and automatic operation. Several networks of MAX-DOAS instruments have been built to record long-term measurements (e.g. Kanaya et al., 2014). Such measurements, and also data from short-term measurement campaigns, have been used for environmental studies as well as for the validation of satellite observations and model simulations (e.g. Irie et al., 2008; Roscoe et al., 2010; Ma et al., 2013; Kanaya et al., 2014; Vlemmix et al., 2015a; T. Wang et al., 2014; Y. Wang et al., 2017a, b; Hendrick et al., 2014).
Previous studies have reported MAX-DOAS measurements of
The Atmosphere-Aerosol-Boundary Layer-Cloud (A
This paper is structured as follows. Section 2 gives an overview of the topography and pollution conditions in the area around the measurement station and the MAX-DOAS measurements. Section 3 introduces other independent measurements and the trajectory simulations. Section 4 presents comparisons of MAX-DOAS results with independent measurements. Effects of regional and local transport of pollutants are discussed in Sect. 5. The conclusions are presented in Sect. 6.
MAX-DOAS measurements were performed during the A
Figure 1b indicates the downtown area of Xingtai, with about 7 million
inhabitants, located
Averaged maps of
Maps of average tropospheric VCDs of
A Tube MAX-DOAS instrument (Donner, 2016) developed by MPIC, Mainz,
Germany, was operated at the measurement station during the period from 8 May
to 10 June 2016. More details about the instrument can be found in
Donner (2016). Spectra of scattered sunlight were routinely recorded by the
MAX-DOAS instrument at 11 elevation angles (1, 2, 3, 4, 6, 8, 10, 15, 20, 30
and 90
Differential slant column densities (dSCDs; the integrated trace gas number
density along the effective light path) of
Settings used for the
Examples of typical DOAS fits of
Tropospheric vertical profiles of aerosol extinction and volume mixing ratios
(VMRs) of
Retrieved aerosol extinction profiles at 360 nm are converted into those at
313, 339 and 354 nm for RTM simulations of air mass factors of
Examples of results derived from MAX-DOAS measurements on
11 May 2016 (with high pollution levels).
One indicator for the confidence of the profile inversion is the consistency
of the measured and modelled dSCDs (dSCDs simulated by the RTM SCIATRAN for
the retrieved profiles). For a systematic analysis, we screened the
suspicious profile results with larger differences of measured and modelled
dSCDs than the thresholds listed in Table 2. After the filtering, the scatter
plots, correlation coefficients (
Different filters and corresponding thresholds applied to the MAX-DOAS results. The thresholds are experientially determined to exclude most outliers. Also the corresponding fractions of remaining data are indicated (SZA is solar zenith angle; RIO is relative intensity offset in the DOAS fit; RMS is root-mean square of the residual in the DOAS fit).
On 11 May 2016, a typical day with high pollution was selected to show
MAX-DOAS results for polluted conditions. Time series of retrieved profiles
from the MAX-DOAS measurements on 11 May 2016 are shown in Fig. 4b, and
selected profiles at around noon are plotted in Fig. 4d. Note that profiles
shown in Fig. 4b are not screened based on the differences of modelled and
measured dSCDs. However, the black dots at the top of each panel of Fig. 4b
indicate the confident results that remain after filtering). In Fig. 4b a
large variability in profile shapes and absolute values can be seen,
especially at altitudes below 1 km. The vertical resolution and
sensitivities of the retrievals at different altitudes can be quantified by
the so-called averaging kernel matrix
Since clouds can strongly impact the MAX-DOAS results, different sky
conditions are identified from the MAX-DOAS observations of the colour index,
which is ratio of intensities of sunlight at 330 nm against to at 390nm and
its temporal variations and elevation angle dependences using the cloud
classification scheme with certain thresholds. The scheme is developed in
Wagner et al. (2014, 2016) and verified in Y. Wang et al. (2015) by comparing
with coincident independent ground-based and satellite measurements of clouds
and aerosols. The scheme assigns individual MAX-DOAS measurements to one of
the five dominant sky condition categories, “cloud-free and low aerosol
load”, “cloud-free and high aerosol load”, “cloud holes”, “broken
clouds” and “continuous clouds”, based on measurements of the colour
index. Additionally, some of measurements are assigned to a secondary
category of “optically thick clouds” based on MAX-DOAS measurements of
Since certain thresholds are used for the identification of cloud scenarios,
two sky conditions might be interchanged because the derived quantities are
close to the chosen thresholds. The problem occurs relatively often between
the categories of cloud-free and high aerosol load and continuous clouds
because they are only distinguished by the absolute value of the colour index.
The issue can impact the MAX-DOAS results of aerosol profiles and AODs due to
the remaining cloud contamination. Fortunately the problem can be easily
solved if an additional filter is applied, which is the convergence between
measured and modelled
In addition the issue can also impact the comparisons of MAX-DOAS results with coincident independent measurements under different sky conditions in Sect. 4.1. However since the cases close to the thresholds do not dominate in each category, the general conclusions on the effects of clouds and aerosols are not significantly impacted.
Figure 5 presents an overview of the near-surface values and column densities
derived from the MAX-DOAS measurements during the campaign from 8 May to
10 June. To provide some information about the diurnal variation, daily
averages for three time intervals of 06:00–10:00 (morning), 10:00–14:00
(noon) and 14:00–18:00 (afternoon) are shown. The
corresponding full time series of the MAX-DOAS results for individual days
are shown in Fig. S3 in the Supplement. To remove measurements of reduced
quality, filters for the solar zenith angle (SZA), relative intensity offset,
the RMS of the residuals of the DOAS fits, differences of modelled and
measured dSCDs, and sky conditions are applied to the results. The details of
the filtering process and thresholds for different species are shown in
Table 2. Here it needs to be noted that a lower SZA threshold is set for the
filtering of the
Daily averaged (for three different periods of the day) near-surface
aerosol extinction and trace gas VMRs (left), and AODs and trace gas VCDs
(right) for the whole campaign. The blue, red and green colours indicate
results for the time periods of 06:00–10:00, 10:00–14:00 and 14:00–18:00,
respectively. The ratios of HONO versus
Total averaged near-surface aerosol extinctions (0.43 km
Consistent day-to-day variations can be found between
The correlation between the near-surface and column values is also
investigated for the different species. Higher correlations (
Ratios of HONO and
Ratios of CHOCHO and HCHO have been used in a number of previous studies to identify sources of VOCs, since CHOCHO and HCHO have different precursors or different formation pathways (e.g. Vrekoussis et al., 2010; Li et al., 2013). The day-to-day variations in the ratios CHOCHO/HCHO (VCDs and VMRs) in the morning, at around noon and in the afternoon are shown in Fig. 5. They are between 2 % to 2.5 % on average. Similar ratios have been observed at rural sites, e.g. 1.7 % in Nashville, USA (Lee et al., 1998); 3.6 % in Cabauw, the Netherlands (Irie et al., 2011); and at urban sites, e.g. 3.6 % in Mexico City, Mexico (Lei et al., 2009). However, considerably higher ratios of up to 10 % were also reported in previous studies. For instance, averaged ratios of 6 % to 8 % were derived from the MAX-DOAS measurements in July 2006 in the suburban area of the city of Guangzhou in southern China (Li et al., 2013). The lower ratios derived from our measurements could be (at least partly) related to anthropogenic primary emissions of HCHO. Measurements shown in Benish et al. (2019) also indicate that isoprene, a dominant nature source of secondary HCHO, is not high in the measurement area.
The MAX-DOAS results are compared to several independent ground-based measurements at the measurement station as well as aircraft measurements over the station. The independent measurements are introduced in Sect. 3.1. For the interpretation of the MAX-DOAS results with respect to transport, we performed backward trajectory simulations and also used the meteorology data from a local weather station. Both data sets are introduced in Sect. 3.2.
An ECOTECH-manufactured in situ gas analyser system measured VMRs of CO, NO,
Surface HCHO VMRs were monitored during the period from 18 to 23 May 2016, using the formaldehyde analyser (Aero-Laser, Germany, Model 4021) based on fluorometric Hantzsch reactions (Gilpin et al., 1997; Rappenglück et al., 2010) with a time resolution of about 1 min. For the comparison with the MAX-DOAS HCHO results, the in situ measurements are averaged over the individual time intervals of the MAX-DOAS measurements.
A sun photometer operated by the Institute of Remote Sensing and Digital
Earth, Chinese Academy of Sciences, measures the AODs at eight wavelengths
between 340 and 1634 nm. The AODs at 340 and 380 nm are averaged for the
comparison with the AODs retrieved from MAX-DOAS measurements at
A forward-scattering visibility meter (550 nm) was also operated at the
measurement station during the entire measurement period. Aerosol extinction
at 360 nm is derived from the visibility at 550 nm using an
Ångström exponent of 1, which is the average value derived from the
sun-photometer measurements during the whole campaign period. The conversion
could contribute a typical uncertainty of up to 20 % due to variability
and uncertainties in the Ångström exponent. F. Wang et al. (2018)
reported a much higher value for the Ångström exponent: from 0.49 to
2.53 (median 1.53) for the same campaign. These values are based on
observations made from the aircraft equipped with an aerosol inlet with a
reported 50 % cut-off at 5
A three-wavelength Raman polarization lidar system (Tao et al., 2012; Liu et al., 2013) developed by the Key Laboratory of Atmospheric Composition and Optical Radiation, Hefei Institutes of Physical Science, Chinese Academy of Sciences (CAS), was operated on several days during the campaign. Profiles of aerosol extinctions at 355 nm above 500 m are retrieved from the lidar measurements with a vertical resolution of 7.5 m. There are two mainly cloud-free days (16 and 17 May) on which the lidar measurements overlapped with the MAX-DOAS measurements. Therefore comparisons between MAX-DOAS and lidar are done for these days (Sect. 4.2).
On several days during the measurement period, a Y-12 airplane (twin-engine
multipurpose transport aircraft, Harbin Aircraft Manufacturing Corporation)
from the Weather Modification Office of the Hebei Meteorological Bureau flew
spirals down to about 200 m above the measurement station and other sites to
obtain atmospheric profiles of aerosol optical properties and trace gas
concentrations (F. Wang et al., 2018; Benish et al., 2019). The diameters of
the spiraling circles were about 10 km. The aircraft measurements overlapped
with the MAX-DOAS measurements on 8 and 21 May 2016. On the Y-12 airplane,
To interpret the measurement results, we derived meteorological parameters,
including ambient temperature (
Comparisons of MAX-DOAS aerosol results with co-located independent measurements are done under three different sky condition categories, including “clear sky with low aerosol load”, “clear sky with high aerosol load” and“cloudy sky” (optically thick clouds are excluded). The sky conditions are characterized based on MAX-DOAS measurements (see Sect. 2.2.4). The comparison results are shown in Fig. 6 with colours to indicate the time of day.
Correlation plots of
AODs
AODs retrieved by MAX-DOAS measurements are compared with those retrieved
from the sun-photometer measurements in Fig. 6a. Because for MAX-DOAS
measurements, AOD results are skipped for cloudy conditions (and also sun-photometer measurements are only available for clear sky), no comparison
results for cloudy situations are shown in Fig. 6a. For clear-sky
observations, better agreement is found for the category low aerosol (
The near-surface
The correlation plot of the near-surface HCHO VMRs retrieved from the
MAX-DOAS measurements versus those from the in situ HCHO analyser is shown in
Fig. 6e. Note that only 1 week of in situ data is available for this
comparison. And the category clear sky with low aerosols did not occur in
this week. The averaging values are comparable between the two measurements.
However, the slopes of
Note that the visibility meter and the in situ measurements of
In this section profiles of aerosol extinction retrieved from the MAX-DOAS measurements are compared with those derived from the lidar measurements on 2 mostly cloud-free days. Aerosol loads on 16 May are lower than those on 17 May. The comparison of the time series of both selected days is shown in Fig. 7, also including the AODs derived from the MAX-DOAS and the sun-photometer measurements. Note that no aerosol profiles below 500 m are derived from the lidar measurements due to missing overlap between the outgoing beam and the field of view (FOV) of the telescope. In general reasonable agreement of the aerosol profiles from both techniques is found above 500 m. The remaining differences can probably be explained by the fact that different air masses are observed by both techniques, while horizontal inhomogeneities of aerosols and cloud cover could appear. For example, different clouds and aerosols could be observed by both instruments. Note that the MAX-DOAS telescope was pointed towards the north, while the lidar measured the atmosphere directly above the station. The sun photometer measured the air masses in the direction of the sun.
Vertical profiles of aerosol extinctions derived from MAX-DOAS and
lidar measurements on 16 May 2016
Vertical profiles of aerosol extinction, and VMRs of
The MAX-DOAS results of vertical profiles of aerosol extinction and
In this section, effects of regional and local transport of pollutants in the measurement area are discussed based on a case study in Sect. 5.1 and a systematic analysis in Sect. 5.2. Possible cleaning effects of pollutants caused by precipitation are also discussed in Sect. 5.1.
Photos taken by a camera along the line of sight of the MAX-DOAS instrument in the period from 11 to 16 May 2016 (left to right).
Photos taken by a camera along the line of sight of the MAX-DOAS instrument at around noon on the days from 11 to 16 May 2016 are shown in Fig. 9, indicating different pollution conditions. What happened on these days to result in either blue skies or low visibility (high pollution)? The question will be answered based on regional and local meteorological data and the temporal evolution of the pollutants. The regional transport related to the weather system of cold and warm front from the north-west and south could be the dominant driver for the air quality in the region.
All available data on pollutants and meteorological parameters during the period from 11 to 16 May 2016 are shown in Fig. 10. The corresponding plots for the other days of the campaign are shown in Fig. S3 in the Supplement. Backward trajectories of 12 h ending at 100 m and 1 km over the measurement site in intervals of 1 h are also shown in Fig. 10. The trajectories indicate the regions from which regional transport of air mass will originate at the measurement site. Also surface wind directions and wind speeds are provided in Fig. 10, indicating the origin of local (short-range) transport of air. The mountain-plain topography causes a daily cycle with downslope (north-east winds) and upslope (south-east winds) winds. Hourly accumulated precipitation rates, ambient humidity and temperatures are also shown in Fig. 10. Note that trajectories, local winds, precipitation, humidity and temperatures are shown for the full 24 h of each day, while visibility, AOD at 360 nm, surface trace gas VMRs and MAX-DOAS results are only shown for daytime (06:00 to 18:00 BT). The results derived from the MAX-DOAS measurements in Fig. 10 include cloud classification results and vertical profiles of aerosols and trace gases (including cloud-contaminated results). Note that the profiles shown in Fig. 10 are not screened based on the differences of modelled and measured dSCDs (see Table 2) and clouds, but the black dots at the top of each panel of profiles indicate the confident results which have passed the filters described in Table 2. In addition, daily total numbers of fire points derived from the MODIS satellite observations in the NCP area are shown in Fig. 10 in order to show the potential influence of biomass burning.
Results from MAX-DOAS measurements, trajectories, meteorological data and independent measurements of pollutants during the period from 11 to 16 May 2016. The figures surrounded by the red and blue dashed boxes show the values for 24 h periods (red) or 12 h (blue) daylight periods (06:00–18:00). The total fire points in the NCP area are derived from MODIS observations and the hourly accumulated precipitation were measured at the station (third row). In the fifth row hourly averaged local winds are given by arrows moving with the wind. The colour bars of the MAX-DOAS profiles are given at the bottom. The black rhombuses plotted above the individual subfigures of the time series of MAX-DOAS profiles mark those that are most reliable (thresholds are given in Table 2).
The results shown in Fig. 10 indicate that aerosols and trace gases steeply decrease from 11 to 12 May. Figure 9 indicates that also the visibility significantly increased from 11 to 12 May. The backward trajectories in Fig. 10 indicate that the origin of air masses arriving at the measurement site changes from the south and south-west to north-west during the night from 11 to 12 May. As shown in Figs. 1 and 2, the area around Wu'an about 50 km south-west of the measurement site is significantly polluted due to emissions from many iron and coal coking factories. The high amounts of pollutants observed in the morning on 11 May can be attributed to night-time transport of pollutants from the Wu'an area based on the dominant south-westerly trajectories before sunrise. In contrast, the areas in the other directions, especially in the north-west, are relatively clean. Therefore the cleaning event can be attributed to regional transport of clean air from the north-west.
The 14th was a day of steady, stratiform rain associated with a warm front
followed by cold front passage from the north-west. Frontal passage was on the
14th with south-east (SE) winds ahead of the front with north-west (NW) winds
and high pressure behind it. The dominant NW and SE trajectories control the
measurement area on the day before and after the weather event on 14 May.
Therefore a slightly higher pollution level was observed on 13 May than that
on 12 May due to the transports of pollutants from the south-east. A clean sky
appeared on 15 May due to the transports of clean air mass from north-west.
In addition, it should be noted that the heavy rain that happened on 14 May could
remove the
On 16 May transport of pollutants from the south-west direction can be seen
after 14:00 BT. However the concentrations of
In summary we conclude that high pollution levels at the measurement site typically occur if air is transported from the south. Low pollution is associated with other wind directions, especially if air is transported from the north-west briskly behind a cold front. In addition, Y. Wang et al. (2018) demonstrated that particle formations from gas precursors and growths significantly contribute to aerosols and impact the visibility during the campaign. This effect is combined with transports of air mass because activities of particle formations depend on amounts of gas pollutants.
In order to reveal the effects of transports from different areas on the
variations in pollutants observed by MAX-DOAS, we applied a novel procedure
based on the backward trajectories. In the procedure, first a grid map of the
region around the measurement site within the latitude and longitude ranges
of 4
Average reproduced maps (inside the red dashed square) of column
densities of
For
In order to quantify the differences of pollutants under different dominant
transport conditions, we sort the measurement days of the whole campaign into
three groups based on the synoptic situation and the dominant directions of
the night-time trajectories, including southerly, north-westerly and easterly
trajectories. The sorting is also related to synoptic situations. The
southerly and north-westerly trajectories are related to the warm sectors
ahead of the front and cold sectors behind the front, respectively. The
easterly trajectories are for the site controlled by a maritime tropical air
mass. Considering that the lifetimes of the observed trace gases are
typically longer during night-time than daytime (because of lower OH radical
concentrations), the measurement data are sorted mainly based on the
night-time trajectories. Nine days (9, 11, 18, 19, 23, 30 and 31 May and 7
and 9 June), 8 days (12, 13, 15, 16, 17, 24, 25 and 26 May) and 8 days (20,
21, 22 and 27 May and 1, 2, 6 and 8 June) fall into these three categories
with dominant southerly, north-westerly and easterly trajectories,
respectively. Figure S5 in the Supplement presents contributions
(percentages) of air mass from different locations in the area (
Averaged diurnal variations in tropospheric columns and near-surface values
of aerosols,
Averaged MAX-DOAS results for the three groups of days with
different dominant directions of night-time trajectories (different colours).
In the left two columns of panels
In order to analyse the influence of local winds, bivariate plots of AOD, and
the VCDs of
Bivariate figures of AOD
Different characteristics are found for the different species in Figs. 12 and 13 and are discussed in the following.
Similar patterns of diurnal variations in
For days with dominant southerly trajectories, high values of HONO were
observed, especially in the morning (Fig. 12d). These enhancements are
consistent with high levels of
Significantly higher HCHO and CHOCHO values for southerly trajectories than
for the other trajectories are found in Fig. 12e and f, indicating that large
proportions of HCHO and CHOCHO sources are related to the transport of
anthropogenic emissions from the south-west industrial area. Because of the
short lifetime of HCHO and CHOCHO of the order of hours, daytime regional
transport of HCHO and CHOCHO are probably not significant, but long-lived
VOCs can be expected to be transported and oxidized to HCHO and CHOCHO in the
measurement area. In addition, regional transport of HCHO and CHOCHO during
night-time could still be possible and explain the high values of HCHO and
CHOCHO in the morning. The oxidation of VOCs to HCHO and CHOCHO is probably
the dominant source, because the peak values of HCHO and CHOCHO are observed
in the late morning and at around noon. Correlation coefficients of the
near-surface HCHO and CHOCHO VMR with the
Low values of aerosols were observed only for north-westerly trajectories in
Fig. 12a. The phenomenon is similar to HCHO and CHOCHO and also
consistent with the similar patterns in the satellite maps of AOD and HCHO
shown in Fig. 2. Peak values of aerosols at around 10:00 BT for southerly
trajectories are probably due to photochemical formations of secondary
aerosols and effects of systematic variations in local winds (as for
In general, regional transport, especially during night-time, is the dominant factor which determines the amounts of all pollutants in the measurement area. Local winds and photochemistries play considerable roles in the corresponding diurnal variations. Y. Wang et al. (2018) demonstrated the same conclusion about aerosols regarding the significant effect of regional transport based on aircraft measurements operated in the same campaign region and period.
There are extensive farmlands in the NCP region, and farmers normally burn
residuals of plants after harvest, especially wheat straw in May and June.
Burning events during the measurement period are identified from the Fire
Information for Resource Management System (FIRMS) based on MODIS satellite
observations
(
Indeed, high values of HCHO, CHOCHO and aerosols can be seen in Fig. 5 on
most days with high numbers of burning events and also 1 or 2 days after
the events. In contrast, on 15 and 24 May, when the trajectories originated
mainly from north-westerly directions, no enhanced levels of HCHO, CHOCHO and
aerosols are found. Therefore we can expect that VOCs and aerosols emitted
from the burning plants were transported to the measurement area and
considerably impacted the abundances of HCHO, CHOCHO and aerosols (aerosols
might also be additionally formed from the photochemical degradation of the
VOCs) for southerly and easterly trajectories. VOCs and aerosols emitted from
burning events could still impact the measurement area in the 2 days after the
events because of their long atmospheric lifetimes. Here it is important to
note that although
One interesting example is found on 6 June for dominant south-easterly
trajectories. Peak values of HCHO, CHOCHO, aerosols and ozone are found in
the afternoon on 6 June (see Figs. 5 and S3 in the Supplement). However the
The local emissions (including the contributions of local transport from the
downtown area of city of Xingtai) can be treated as the dominant sources of
pollutants for situations with transport from the north-west (the group of
days with north-westerly trajectories). The contributions of regional
transport to the observed pollutants for the two other groups of days can be
roughly estimated based on the relative differences of the pollutant values
compared to those for north-westerly trajectories. Tropospheric columns are
used for the estimation because regional transport often occurs not directly
above the surface. According to this simple calculation, for the days with
mainly southerly trajectories, about 47 %, 45 %, 47 %, 34 %,
46 % and 65 % of the observed amounts of
Vertical profiles and near-surface and column densities of aerosol extinction,
AOD (
We analysed the effects of regional and local transport of pollutants based on case studies and a systematic analysis using the MAX-DOAS measurements, backward trajectories, synoptic situations and local winds. In general, the regional transport, especially during night-time, is found to be the dominant factor in local air quality. For surface values, local winds, photochemistry and PBL dynamics all exert a strong influence on the diurnal variation in the pollutants. The regional transport of gas pollutants plays a more significant role during night-time than daytime due to longer lifetimes at night. We document regular episodes of regional transport of clean air masses from the north-west (often associated with a cold front) and polluted air masses from the southern industrialized areas around the city of Wu'an with many steel and coal coking facilities. Burning events of crop residuals in the NCP region can considerably impact HCHO, CHOCHO and aerosols. Contributions of regional transport to the total amounts of pollutants in the measurement area during the entire measurement period were 20 % to 30 % for trace gases and about 50 % for aerosols.
The data used for this study are available from the authors upon request.
The supplement related to this article is available online at:
YW (affiliation 1) analysed vertical profiles and regional transports of
pollutants by combing different data sets and trajectory simulations and
prepared the paper with contributions from all co-authors. YW
(affiliation 1), StD, SeD and Thomas Wagner designed, operated and analysed
the MAX-DOAS measurements. SB contributed to the analysis of regional
transports in Sect. 5.2.1. YW (affiliation 4) contributed to the operation of
MAX-DOAS measurements. HH, XR and RRD operated and analysed the aircraft
measurements. ZD, DLiu, ZW and JX operated and analysed lidar measurements.
ZL, DLi and HX operated and analysed sun-photometer measurements. YuW
operated in situ measurements. IDS and NT contributed the OMI satellite data
of HCHO and
The authors declare that they have no conflict of interest.
This article is part of the special issue “Regional transport and transformation of air pollution in eastern China”. It is not associated with a conference.
We thank the Institute of Remote Sensing and Institute of Environmental
Physics at the University of Bremen, Bremen, Germany, for their freely accessible radiative
transfer model SCIATRAN. We thank the Belgian Institute for Space Aeronomy
(BIRA-IASB), Brussels, Belgium, for their freely accessible QDOAS software and
for generating the mean map of
This paper was edited by Yuanhang Zhang and reviewed by three anonymous referees.