Formaldehyde (HCHO) in the ambient air not only causes cancer but is also an ideal
indicator of volatile organic compounds (VOCs), which are major precursors of
ozone (O3) and secondary organic aerosol (SOA) near the surface. It
is meaningful to differentiate between the direct emission and the secondary
formation of HCHO for HCHO pollution control and sensitivity studies of
O3 production. However, understanding of the sources of HCHO is
still poor in China, due to the scarcity of field measurements (both
spatially and temporally). In this study, tropospheric HCHO vertical column
densities (VCDs) in the Yangtze River Delta (YRD), East China, where HCHO
pollution is serious, were retrieved from the Ozone Mapping and Profiler
Suite (OMPS) onboard the Suomi National Polar-orbiting Partnership
(Suomi-NPP) satellite from 2014 to 2017; these retrievals showed good
agreement with the tropospheric HCHO columns measured using ground-based
high-resolution Fourier transform infrared spectrometry (FTS) with a
correlation coefficient (R) of 0.78. Based on these results, the cancer
risk was estimated both nationwide and in the YRD region. It was calculated
that at least 7840 people in the YRD region would develop cancer in their
lives due to outdoor HCHO exposure, which comprised 23.4 % of total
national cancer risk. Furthermore, the contributions of primary and secondary
sources were apportioned, in addition to primary and secondary tracers from
surface observations. Overall, the HCHO from secondary formation contributed
most to ambient HCHO and can be regarded as the indicator of VOC reactivity
in Hangzhou and in urban areas of Nanjing and Shanghai from 2015 to 2017, due
to the strong correlation between total HCHO and secondary HCHO. At
industrial sites in Nanjing, primary emissions more strongly influenced
ambient HCHO concentrations in 2015 and showed an obvious decreasing trend.
Seasonally, HCHO from secondary formation reached a maximum in summer and a
minimum in winter. In the spring, summer, and autumn, secondary formation had
a significant effect on the variation of ambient HCHO in urban regions of
Nanjing, Hangzhou, and Shanghai, whereas in the winter the contribution from
secondary formation became less significant. A more thorough understanding of
the variation of the primary and secondary contributions of ambient HCHO is
needed to develop a better knowledge regarding the role of HCHO in
atmospheric chemistry and to formulate effective control measures to decrease
HCHO pollution and the associated cancer risk.
Introduction
In recent years, air pollution has become increasingly serious in China
(Tang et al., 2012; Shi et al., 2014; Rohde and Muller, 2015), and it currently
poses a grievous risk to human health (Wang and Mauzerall, 2006). Previous
studies have indicated that air pollution has killed more people worldwide than tuberculosis,
AIDS, breast cancer, and malaria (Yang et al., 2013; World Health
Organization, 2014a, b). According to the US Environmental Protection
Agency (EPA), HCHO has been identified as the major carcinogen
among the 187 hazardous air pollutants (HAPs), and exposure to levels as low as 1 µg m-3
(about 0.7 ppb at standard temperature and pressure)
of HCHO over a human lifetime could cause up to 13 in every million people to develop lung and nasopharyngeal cancer (Zhu et al., 2017a).
HCHO not only directly affects public health but also plays a significant
role in atmospheric photochemistry (Levy, 1971). The photolysis of HCHO leads to
the production of the HOx radical (OH+HO2)
and subsequently affects the oxidative
capacity of the atmosphere (Volkamer et al., 2010). Moreover, HCHO may
contribute to O3 pollution via photochemical reactions
(Haagen-Smit, 1950; Carter, 1994; Russell et al., 1995), which favor
the formation of secondary organic aerosol (SOA) by providing the OH
radical (Yang et al., 2018). Therefore, monitoring and controlling the ambient HCHO
concentration are of great significance for public health.
HCHO can be directly emitted into the atmosphere from biogenic sources, such
as biomass burning and vegetation (Lee et al., 1997; Finlayson-Pitts and
Pitts, 1999; Holzinger et al., 1999; Andreae and Merlet, 2001), and
anthropogenic activities, such as vehicles emissions, industrial emissions,
and coal combustion (Carlier et al., 1986; Williams et al., 1990; Hoekman,
1992; Carter, 1995; Anderson et al., 1996; Kean et al., 2001; Klimont et al.,
2002; Reyes et al., 2006; Wei et al., 2008; Wang et al., 2013; Dong et al.,
2014; Liu et al., 2017). The direct emission of HCHO is mainly due to
incomplete combustion and is closely related to the emission of CO.
Furthermore, HCHO can be formed from the atmospheric oxidation of volatile
organic compounds (VOCs) (Altshuller, 1993; Carter, 1995; Seinfeld and
Pandis, 2016) which also leads to the formation of O3 (Levy, 1971;
Crutzen, 1979; Warneke et al., 2004; Duan et al., 2008). In order to
effectively control HCHO concentrations and improve air quality, it is
necessary to determine the primary emission sources and secondary formation
pathways of ambient HCHO.
In previous studies, the ratio of the Ozone Monitoring Instrument (OMI)
tropospheric column of HCHO and NO2 have been used as indicators of
the sensitivity regime of O3 formation (Duncan et al., 2010; Witte et
al., 2011; Liu et al., 2016; Su et al., 2017), under the assumption that HCHO is
a proxy for the reactivity of VOCs. Secondary HCHO is produced
with the formation of peroxy radicals (RO2), and is therefore positively
correlated with RO2; thus, it can be identified as a proxy for total
VOC reactivity (Carter, 1994). Therefore, strictly speaking, only the HCHO concentration
that stems from secondary formation can be used to analyze the sensitivity
regime of O3 production, combined with the NO2
concentration. As secondary formation is the dominant source of HCHO on a
global scale (De Smedt et al., 2008), total HCHO is usually adopted to
replace secondary HCHO as the indicator of total VOC reactivity. However, in
some regions that are subject to strong human activities, the contribution of primary
emissions to ambient HCHO cannot be ignored (Ma et al., 2016). Hence, the separation of
secondary HCHO from primary HCHO would be favorable to develop a better understanding
of O3 formation sensitivity, especially in urban areas.
Previous studies have attempted to identify contributions to ambient HCHO
from direct emission and secondary formation via the used of linear multiple
regression analysis. Because of the strong relation between primary HCHO and
CO emission and the formation of HCHO and O3 from the oxidation of
VOCs, CO and O3 can be used as the respective tracers for primary
emission and secondary formation of HCHO. Several studies have been carried
out on primary and secondary sources of HCHO using the CO–O3
tracer pair. For instance, contributions from primary and secondary sources
to ambient HCHO were characterized by statistical analogy to CO and
O3 in Houston, TX in the summer of 2000 (Friedfeld et al., 2002), in Beijing during the 2008 Olympic Games (Li et al., 2010), and in Hong Kong
from winter of 2012 to autumn of 2013 (Lui et al., 2017). Hong et al. (2018) estimated contributions from primary emission and secondary formation to HCHO measured by MAX-DOAS using the
CO–Ox (O3+NO2) tracer pair which was measured
by the Sensor Networks for Air Quality (SNAQ) in the winter of 2015 in
Yangtze River Delta. Garcia et al. (2006) indicated that using the CO–CHOCHO
tracer pair to separate different sources of HCHO was more reasonable than
using CO–O3 because CHOCHO has a similar atmospheric lifetime to
HCHO. However, continuous observations of CHOCHO are unfortunately not
available.
Previous studies have often concentrated on contributions from different
sources at one specific site and within a short time period; this is due to
the constraints of available measurements of HCHO and its tracers with
respect to primary emissions and secondary formation. In China, CO and
O3 measurements can be obtained from the China National
Environmental Monitoring Center (CNEMC) network. However, HCHO is not
measured at these stations. There are various analytical methods available to
measure HCHO concentrations, including the following: Fourier transform
infrared spectroscopy (FTIR; Lawson et al., 1990); differential optical
absorption spectroscopy (DOAS; Platt and Perner, 1980); tunable diode laser
absorption spectroscopy (TDLAS); the 2,4-dinitrophenylhydrazine (DNPH)
method, in which HCHO is captured using DNPH and then analyzed by
high-performance liquid chromatography (HPLC; Fung and Grosjean, 1981); and
proton transfer reaction mass spectrometry (PTR-MS; Karl et al., 2003).
Nevertheless, it is difficult to perform long-term observations of the HCHO
concentration over a wide area using ground-based measurements. However,
long-term information regarding ambient HCHO on a global scale can be
accessibly obtained from space-based remote sensors such as Global Ozone
Monitoring Experiment (GOME; Martin et al., 2004), the Scanning Imaging
Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY; Bovensmann
et al., 1999), the GOME-2 instruments (De Smedt et al., 2012, 2015), the
Ozone Monitoring Instrument (OMI; González Abad et al., 2015), the Ozone
Mapping and Profiler Suite (OMPS; C. Li et al., 2015; González Abad et
al., 2016) and the TROPOspheric Monitoring Instrument (TROPOMI; De Smedt et
al., 2018). However, HCHO observations from SCIAMACHY and TROPOMI are only
available until 2011 (Shah et al., 2018) and after October 2017,
respectively; the GOME-2A instrument suffers degradation issues (De Smedt et
al., 2012); OMI observations are easily affected by the instrumental “row
anomaly” (González Abad et al., 2015); and the spatial (80 km× 40 km) and temporal (within 1.5 d) resolution of GOME-2B is even
lower than OMPS (Munro et al., 2016). After considering the long-term record
and data quality, HCHO observations from OMPS were adopted in this study. Due
to the low global coverage of the Tropospheric Emission Spectrometer
(Aura-TES; Luo et al., 2002, 2007) and the increasing noise of the
Atmospheric InfraRed Sounder (Aqua-AIRS) after 2004 (Pagano et al., 2012),
which both measure tropospheric CO in close temporal proximity to the OMPS
observations, obtaining a precise CO concentration in the YRD after 2013 from
satellite data was difficult. Therefore, CO and O3 concentrations
at the surface, simultaneously monitored by the China National Environmental
Monitoring Center (CNEMC) network, were used in this study.
In this paper, we focus on contributions from primary and secondary sources
to ambient HCHO from 2015 to 2017 in Nanjing (the capital of Jiangsu
Province), Hangzhou (the capital of Zhejiang Province) and Shanghai; these
are all megacities in the YRD region and have populations of more than
8 million people. The YRD region is located on the alluvial plains where the
Yangtze River drains into the East China Sea, and consists of Jiangsu and
Zhejiang provinces and the Shanghai municipality. The YRD region is one of
the most important economic areas in China and is also a rapidly developing
industrial sector. Along with the very rapid development of economy and
industry, the YRD region is currently suffering from serious air pollution
(H. Li et al., 2015; Gao et al., 2016; T. Wang et al., 2017; Wang et al.,
2018). Details regarding the measurement of specific air pollutants are
described in Sect. 2, whereas the spatiotemporal distribution of HCHO VCDs in
the YRD region, as well as the contributions from primary and secondary
sources to ambient HCHO are shown in Sect. 3.
Measurements and methodologyOMPS HCHO observation
The Ozone Mapping and Profiling Suite Nadir Mapper (OMPS-NM), which is one of
the OMPS suite of instruments, was launched on 28 October 2011 onboard the
Suomi National Polar-orbiting Partnership (Suomi-NPP) satellite. The
Suomi-NPP is a polar sun-synchronous satellite with an average altitude of
824 km (Flynn et al., 2014). It crosses the Equator each afternoon at about
13:30 LT (local time) on the ascending node. The OMPS-NM combines a single
grating and a 340×740 pixel charge-coupled device (CCD) detector to
measure UV radiation every 0.42 nm from 300 to 380 nm with a 1.0 nm full
width at half maximum (FWHM) resolution. A recent analysis showed that the
OMPS-NM sensor has a signal-to-noise ratio of 2000 : 1 or better within the
wavelength range of 320 to 370 nm (Seftor et al., 2014). It has a
110∘ cross-track field of view (FOV) providing high temporal (daily
global coverage) resolution, and measurements are combined into 35
cross-track bins giving a spatial resolution of 50 km × 50 km in OMPS-NM standard Earth science mode. The OMPS-NPP Nadir Mapper
Earth View Level 1B data, available at
https://search.earthdata.nasa.gov (last access: 8 May 2019), were
downloaded to retrieve the HCHO slant column density (SCD). Details regarding
the fitting settings for the HCHO SCD retrieval are given in González
Abad et al. (2016). In this study, the vertical profiles of HCHO from
WRF-Chem modeling were used to calculate the air-mass factor (AMF). The model
configuration was described in detail in our previous study (Su et al.,
2017). Finally, the HCHO VCD was calculated using the AMF as follows:
VCD=SCDAMF
FTS tropospheric HCHO column measurement
The ground-based FTS instrument, located west of Hefei (117.17∘ E,
31.9∘ N), is a candidate station for the Network for Detection of
Atmospheric Composition Change (NDACC, http://www.ndacc.org/, last
access: 8 May 2019). The global NDACC can perform simultaneous retrieval of
mixing ratio profiles for trace gases, such as O3, CO,
CH4, NO2, and HCHO, using FTS (Kurylo, 1991; Notholt et
al., 1995; Vigouroux et al., 2009), and the retrieval results have been
widely used for atmospheric chemistry research and the validation of
satellite observations (Yuan et al., 2015; Sun et al., 2017; W. Wang et al.,
2017; Tian et al., 2018; Sun et al., 2018). The observation system consists
of a high-resolution Fourier transform spectrometer (IFS 125HR), a solar
tracker (Tracker-A solar 547), and a weather station (ZENO-3200). The FTS
solar spectra measurements were performed over a broad spectral range from
600 to 4500 cm-1 with a spectral resolution of 0.005 cm-1, and
the HCHO spectra were recorded in the range from 2400 to 3310 cm-1. The
vertical profile of HCHO was retrieved using the SFIT4 algorithm
(version 0.9.4.4). Detailed retrieval settings for HCHO are listed in Sun et
al. (2018). Daily a priori profiles of pressure, temperature, and H2O
are obtained from the National Centers for Environmental Protection and the
National Center for Atmospheric Research (NCEP/NCAR) reanalysis. And a priori
profiles of HCHO and its interfering gases were taken from a dedicated Whole
Atmosphere Community Climate Model (WACCM). For HCHO observation, the four
microwindows (MW) which are centered at around 2770 cm-1 were selected
for satellite validation, and a de-weighting signal-to-noise ratio (SNR) of
600 was used. The instrument line shape (ILS) is described by analyzing HBr
cell spectra using LINEFIT14.5 software. Finally the tropospheric HCHO
partial column was calculated by integrating the retrieved profiles with the
air-mass profile within 15 km of the retrievals as follows:
PCtrop=∫015Xr×Am,
where Xr is the retrieved profile of HCHO, Am is the
air-mass profile, and PCtrop is the tropospheric HCHO
column. The FTS total systematic error of the HCHO retrieval is less than
3.3 %, and the total random error is less than 9.6 % (Sun et
al., 2018). Due to the high accuracy, the trace gas concentrations measured
using FTS have been widely used to validate corresponding satellite products
(Yamamori et al., 2006; Jones et al., 2009; Yurganov et al., 2010; Reuter et
al., 2011). Here we used the FTS HCHO measurement to validate the OMPS HCHO
product.
The China National Environmental Monitoring Center (CNEMC) network
Surface air pollutants monitored on an hourly basis by CNEMC were provided by
the Ministry of Environment Protection of the People's Republic of China
(http://106.37.208.233:20035/, last access: 8 May 2019). The national
air quality monitoring network consisted of about 950 sites in 2013, and had
extended to 1597 sites in 454 major cities by 2017. Three trace gases,
including CO, O3, and SO2, inhalable particles
(PM10), and fine particulate matter (PM2.5) are simultaneously
measured by all sites, and the data have been widely used in various research
(W.-N. Wang et al., 2017; Li et al., 2018; Liu et al., 2018; X. Lu et al.,
2018). In this study, CO and O3 data from 2104 to 2017 were used.
Data quality controls similar to those in previous studies (Barrero et al.,
2015; He et al., 2017; Li et al., 2018) were applied to remove data outliers.
Briefly, all hourly data at a specific monitoring site were transformed into
z scores, and then the transformed data (Zi) were removed if they
met one of the following conditions: (1) the absolute Zi was larger
than 4 (Zi>4), (2) the increment of Zi from the
previous hourly value was larger than 9 (Zi-Zi-1>9), or (3) the ratio of the z score to its centered
moving average of order 3 (MA3) was larger than 2
(3ZiZi-1+Zi+Zi+1>2).
ResultsComparison with FTS tropospheric HCHO
In order to validate the OMPS observations using FTS measurements, the HCHO
tropospheric column measured using FTS was averaged around the OMPS satellite
overpass time. The OMPS tropospheric HCHO column was selected for satellite
pixels within 50 km of the Hefei site and with cloud fraction less than
20 %, and these pixels were averaged to minimize the random noise
(Y. Wang et al., 2017). Figure 1a shows that tropospheric HCHO VCDs observed
by OMPS and FTS are in good agreement (R=0.78). The coincident time series
of both data indicate that OMPS HCHO observations successfully capture the
maximum HCHO concentration in summer and the minimum HCHO concentration in
winter (Fig. 1b). However, it is important to note that the OMPS HCHO
observations underestimate the tropospheric HCHO column by 0 %–60 %
compared with the FTS results, especially in summer; these results are
consistent with a previous study by De Smedt et al. (2015). The
underestimation from OMPS was attributed to the spatial average from
satellite observations, errors from HCHO SCDs affected by spectral fitting, and AMF calculation error which were affected by scattering weights calculated by the radiative transfer model and HCHO vertical profiles modeled by WRF-Chem (Zhu et al., 2016).
Correlation analysis (a) and time series (b) of
tropospheric HCHO VCDs measured by OMPS and FTS from 1 July 2015 to
20 November 2017.
(a) The monthly averaged HCHO vertical profiles in the
troposphere measured by FTS. (b) The monthly averaged
tropospheric HCHO column (black line), the HCHO column below 1 km (red line)
measured by FTS, and the ratio of the HCHO column below 1km in the tropospheric
HCHO column (blue line).
Most of the atmosphere HCHO was concentrated in the troposphere. The monthly
averaged vertical profiles of HCHO shown in Fig. 2a indicate that the
structure of the HCHO vertical profiles in different months was consistent.
The HCHO concentration decreased by 70 % with an increase in the height
from 0.7 to 2 km and continued to decrease slowly in the troposphere above
2 km. The ratio of the HCHO column below 1 km in the tropospheric HCHO
column remained at ∼67 % and did not show seasonal variation
(Fig. 2b). The HCHO column below 1 km measured by FTS and the OMPS
tropospheric HCHO column also showed good agreement (R=0.78, Fig. S1 in the
Supplement). The slope of 0.83 for the linear regression analysis between
the FTS HCHO column below 1 km and the OMPS tropospheric column was higher than the slope of 0.51 for the regression between OMPS data and the tropospheric HCHO VCD
from FTS, indicating that the OMPS tropospheric HCHO column better represents
the HCHO column below 1km. Furthermore, the mean of the relative differences
between the HCHO column below 1 km measured by FTS and the tropospheric HCHO
column measured by OMPS is zero. In previous studies, a uniform and constant
correction factor has been applied to account for the bias between the
tropospheric HCHO column observed by satellite and that measured by aircraft
(e.g., Anderson et al., 2016). In this study, we regard the tropospheric HCHO
column observed by OMPS as that below 1 km measured by FTS in terms of their
numerical value. Assuming that HCHO mixes well within the 1 km above the
surface, the mixing ratio of HCHO can be calculated as follows:
Mppbv=1.25×VCDmoleculescm-2B×Δpatm,
where M is the mixing ratio of HCHO, B is unit conversion factor of DU to
molecules cm-2 which is 2.688×1016, VCD is the OMPS tropospheric HCHO VCDs, and Δp is the pressure difference between the surface and 1 km (Ziemke et
al., 2001; Lee et al., 2008).
The spatial distribution of monthly averaged tropospheric HCHO VCDs
observed by OMPS and the cancer risk in August 2017.
Spatiotemporal distribution of HCHO VCDs in the Yangtze River
Delta
The average HCHO VCD over China in August 2017 was 6.50×1015 molecules cm-2, which would lead up to 50 people in every million to
develop cancer. The total national cancer risk was estimated by combining
the averaged HCHO VCDs with the population, and it was estimated that at least 33 500 people in China would
develop lung and nasopharyngeal cancer in their lifetime due to outdoor HCHO
exposure using Eq. (4):
4T=∑iPi×Ci5Y=TL,
where T is the total number of people in China who may develop cancer due
to outdoor HCHO exposure, Pi and Ci represent the respective
population and average cancer risk of provincial level administrative region
i, Y is the number of cancer cases per year, and L is the life
expectancy. Considering the life expectancy of 76.4 years (2015) in China
(http://data.stats.gov.cn/easyquery.htm?cn=C01&zb=A0304&sj=2016, last
access: 8 May 2019, in Chinese), 439 cancer cases per year in China are
caused by high outdoor HCHO concentrations (Eq. 5). The HCHO VCDs in most of
western China, e.g., Yunnan Province, Tibet, Gansu Province, Qinghai
Province, the Xinjiang Uyghur Autonomous Region, Ningxia Province, and the
Inner Mongolia Autonomous Region, were lower than the national average
(Fig. 3). The HCHO VCDs in central China were higher than the national
average, and the distribution of HCHO VCDs was homogenous. The highest HCHO
VCDs occurred in eastern China, specifically in the Beijing–Tianjin–Hebei
region, the northern Henan Province, the western Shandong Province, the YRD
region, and the Pearl River Delta (PRD) region, with the most severe HCHO
pollution occurring in the YRD. The high outdoor HCHO concentration in the
YRD increases the cancer risk for inhabitants, with up to 155 people in every
million suffering cancer, which is about four times the cancer risk reported
in the US (Zhu et al., 2017a). The total cancer risk in the YRD comprises
23.4 % of total national cancer risk in China, and about 7840 people in
the YRD may develop cancer during their lifetime due to severe HCHO
pollution. Therefore, effectively controlling the ambient HCHO concentration
in the YRD is an urgent issue. In addition, the YRD has been identified as
the region with the highest emissions of VOCs (Qiu et al., 2014; Wu et al.,
2015); therefore, the determination of the contribution from primary sources
and secondary formation to ambient HCHO is needed to formulate appropriate
control measures.
(a) The spatial distribution of annual mean tropospheric
HCHO column observed by OMPS in the YRD in 2015, and (b) changes in
the HCHO VCDs between 2016 and 2015 and (c) between 2017 and 2016.
In the YRD region, the highest HCHO VCDs occurred in southwestern Jiangsu
Province (e.g., Nanjing, Changzhou, Wuxi, and Suzhou cities), northern
Zhejiang Province (e.g., Hangzhou, Jiaxing, Huzhou, and Ningbo cities), and
Shanghai in 2015 (Fig. 4a). The spatial distribution of annual mean HCHO
VCDs in 2016 and 2017 was similar to that in 2015. The HCHO VCDs in the YRD
region showed a fluctuating trend from 2015 to 2017 (Fig. 4b, c). The HCHO
concentration in Jiangsu Province rose from 2015 to 2016, whereas only HCHO
VCDs in southwestern Jiangsu Province showed a decreasing trend from 2016 to
2017. The HCHO concentration in Shanghai and Hangzhou showed a similar trend: they
clearly decreased from 2015 to 2016 and then increased from 2016 to 2017. An
opposite trend was observed for HCHO VCDs in other cities in Zhejiang
Province.
Determination of primary and secondary contributions to ambient
HCHO
The statistical analysis of simultaneous real-time measurements of HCHO, CO,
and O3 can be described using a multiple regression model:
[CHCHO]=β0+β1×[CCO]+β2×[CO3],
where β0, β1, and β2 are the coefficients
fitted by the model (Garcia et al., 2006), and CHCHO, [CCO], and
[CO3] represent the
concentrations or the transformations of concentrations for HCHO, CO, and
O3, respectively. Similar to Garcia et al. (2006), we applied
10 different transformations on the time series of HCHO, CO, and O3: linear (no transformation), natural log,
square root, second power, third power, inverse, inverse of natural log,
inverse of square root, inverse of second power, and inverse of third power. Linear regression without
transformation was identified as the best model (Table S1). The relative
contributions of background concentration, primary emissions, and secondary
formation to the ambient HCHO can be calculated using the following
equations:
7RPrimary=β1×[CCO]β0+β1×[CCO]+β2×[CO3]×100%8RSecondary=β2×[CO3]β0+β1×[CCO]+β2×[CO3]×100%9RBackground=β0β0+β1×[CCO]+β2×[CO3]×100%,
where RPrimary represents the contribution to ambient
HCHO from primary sources (e.g., industrial and vehicle emissions),
RSecondary represents the contribution to ambient
HCHO from secondary sources (i.e., photochemical VOC oxidation), and
RBackground represents background contributions to
the ambient HCHO which can not be classified as primary or secondary
contributions. In previous studies in the YRD (Wang et al., 2015; Ma et al.,
2016), the background HCHO concentration of 1 ppbv has been selected to represent
the HCHO concentration under regional conditions. Therefore, the β0 coefficient is fixed at 1 ppbv in the multiple linear
regression in this study.
In a previous study, Ma et al. (2016) analyzed the primary emissions and
secondary formation of HCHO from 15 April to 1 May 2015 at an
industrial zone in Nanjing, which is located less than 15 km from one of
our research site, Maigaoqiao (MGQ) station, using the in situ measurement
of primary tracers (i.e., benzene, toluene, and CO) and a secondary tracer
(i.e., O3). Ma et al. (2016) found that the average relative
contributions of industry-related emissions, secondary formation, and
background were 59.2 %, 13.8 %, and 27 %, respectively. In this
study, the average relative contributions from primary sources, secondary
formation, and background to HCHO at MGQ station during the same period were
50.5 %, 30.8 %, and 18.7 %, respectively. We note that the relative
contribution from primary emissions was similar to that reported by in Ma et
al. (2016), with a difference of 8.7 %. However, the relative contribution
from secondary formation was 17 % larger and the
relative contribution from background was 8.3 % smaller than the values reported in Ma et al. (2016).
It should be noted that the contribution of
secondary formation we calculate is the maximum daily value, as the
overpass time of the OMPS is 13:30 LT every day. Therefore, it is reasonable to assume that
the contribution of secondary formation we calculate is also larger due to
the favorable conditions for photochemical reactions at noon (Hong et al., 2018). In conclusion,
the tropospheric HCHO column observed by OMPS can be used to analyze primary and
secondary contributions to ambient HCHO via a multiple linear regression
fit, and CO and O3 concentrations measured by CNEMC Network can be used
as indicators of primary emission sources and secondary formation sources,
respectively.
Primary and secondary sources of ambient HCHO in Nanjing
The distribution of the three stations selected to explore the primary and
secondary sources of HCHO is shown in Fig. 5a, and the heavy industrial zones are
delineated using red lines (Zheng et al., 2015). In order to ensure the
representativeness of the regression model, only data fulfilling the criteria in
the analysis (correlation coefficient (R) larger than 0.6 and
significance value lower than 0.05) were kept; the same data quality control was carried out for
the Hangzhou and Shanghai analyses. We selected the MGQ site,
located southwest of the industrial zones, the Ruijin Road site (RJR),
located in the center of Nanjing, and the Xianlin University Town site
(XLUT), located southeast the of industrial zones, to analyze the HCHO concentration
from different sources in Nanjing. As presented in Table 1, the annual
average HCHO concentrations at industrial sites were larger than those at an
urban site in 2015 and 2017. The seasonal average HCHO concentration adheres
to the following seasonal order in both the industrial zones and center of Nanjing
(Fig. 6 and Table 3): summer > autumn > spring > winter.
Maps of Nanjing (a), Hangzhou (b), and
Shanghai (c). The suburban sites are marked using white, the
downtown sites are marked using red, and the industrial zones are denoted
using red lines.
The time series of absolute (a, c, e) and relative (b, d, f) contributions of primary
sources, secondary sources, and background sources to the HCHO concentration
from December 2014 to November 2017 at the RJR site (a, b), the MGQ
site (c, d), and the XLUT site (e, f) in Nanjing. Black
dashed lines represent the threshold value for HCHO pollution.
Annual average of the measured HCHO concentrations (ppbv) and
absolute and relative contributions from different sources in Nanjing,
Hangzhou, and Shanghai. The values in parentheses represent the contributions from different sources; lines 1–2 in each year refer to primary HCHO,
lines 3–4 refer to secondary HCHO, and line 5 refers to measured HCHO.
The Pearson correlation coefficient (R) between the measured HCHO
and primary HCHO emissions and secondary HCHO formation in Nanjing, Hangzhou,
and Shanghai from 2015 to 2017. Line 1 in each year refers to primary HCHO and line 2 refers to secondary HCHO.
Year Nanjing Hangzhou Shanghai MGQRJRXLUTXSCXTFDCHHKPDNADSL(industrial(urban(industrial(suburban)(suburban)(urban(urban(urban(suburban)zone)area)zone)area)area)area)2015Primary0.140.210.840.69-0.10.060.180.20.83Secondary0.580.750.470.90.820.810.550.650.612016Primary-0.430.27-0.3-0.40.310.510.420.060.28Secondary0.820.710.730.770.840.680.770.690.812017Primary0.070.50.510.840.420.380.260.060.58Secondary0.70.770.740.680.760.790.770.750.74
On average, secondary formation was the dominant source of ambient HCHO at
all the three sites in Nanjing (Table 1). However, primary emission was found
to be the most significant source in winter at the RJR site (Fig. 6a, b), in
winter of 2015 at the MGQ site (Fig. 6c, d), and from August to December in
2015 at the XLUT site (Fig. 6e, f). At the industrial sites, MGQ and XLUT,
the annual average HCHO concentration from primary emissions decreased by
64 % and 63.44 % from 2015 to 2017 in comparison with 2015,
respectively. As coal combustion is an important primary source of ambient
HCHO (Wang et al., 2013; Liu et al., 2017), energy saving methods and the
reduction of energy consumption, especially the reduction of coal
consumption, caused the decrease of primary HCHO emission, according to the
Nanjing municipal government
(http://www.nanjing.gov.cn/xxgk/szf/201403/t20140321_2544036.html, last
access: 15 August 2018, in Chinese). The variation of the HCHO concentration
from different sources at MGQ was similar to that at XLUT due to the short
distance between the two sites. However, the annual average HCHO
concentration from primary emissions at XLUT was larger than that at MGQ.
Primary emissions of HCHO at the urban site were smaller than those at
industrial sites in 2015 and larger than those at suburban sites in 2016 and
2017. The correlation coefficients between primary HCHO and total HCHO or
secondary HCHO and total HCHO can be used to compare the contribution from
the two sources to the variation of HCHO: a larger correlation coefficient
means a higher contribution to the variation of HCHO. At the urban site,
secondary formation contributed more to the variation of HCHO concentration
from 2015 to 2017 (R=0.76, Fig. 7a) than primary emission (R=0.35,
Fig. S2a). While at the industrial sites, the contribution from secondary
formation to the variation of the HCHO concentration was less evident (R≤0.67, Fig. 7b, c). At the XLUT site, primary HCHO emission was more
significant to the variation of the HCHO concentration than secondary
formation in 2015 (Table 2). With respect to the seasonal average, secondary
formation contributed most to the ambient HCHO concentration, and reached a
maximum in summer, due to the improvement of photochemical reaction (Wang et
al., 2016), and a minimum in winter (Table 3). In spring, summer, and autumn,
the variation of the ambient HCHO was affected more by secondary formation,
whereas in winter, it was influenced more by primary emission in the urban
region (Table 4).
Correlation analysis of total HCHO and secondary HCHO from 2015 to
2017 at the RJR site (a), the MGQ site (b), and the XLUT
site (c) in Nanjing; at the XS site (d), the CXT site (e),
and the FDCH site (f) in Hangzhou; and at the HK site (g), the PDNA
site (h), and the DSL site (i) in Shanghai (significance values
are equal to zero).
Seasonal average of the measured HCHO concentrations (ppbv) and absolute
and relative contributions from different sources from 2015 to 2017 in
Nanjing, Hangzhou, and Shanghai.The values in parentheses represent the contributions from different sources; lines 1–2 in each season refer
to primary HCHO, lines 3–4 refer to secondary HCHO, and line 5 refers to measured HCHO.
Season Nanjing Hangzhou Shanghai MGQRJRXLUTXSCXTFDCHHKPDNADSL(industrial(urban(industrial(suburban)(suburban)(urban(urban(urban(suburban)zone)area)zone)area)area)area)WinterPrimary1.3981.1511.0130.8660.460.8761.0322.3830.741(29.45%)(32.01%)(25.15%)(25.58%)(16.50%)(25.77%)(22.25%)(50.13%)(20.72%)Secondary2.0111.4691.9931.2091.3321.0612.1971.2121.874(47.15%)(39.54%)(48.73%)(40.75%)(46.82%)(38.48%)(53.13%)(27.54%)(51.17%)Measured4.4033.3943.6942.6272.3422.4913.8414.0983.338SpringPrimary1.510.9810.9220.6060.440.6450.7281.5980.589(27.04%)(20.00%)(18.14%)(15.89%)(10.71%)(16.59)(14.30%)(35.00%)(12.70%)Secondary3.0172.9963.3652.3522.7442.2883.3992.0433.166(54.37%)(59.04%)(62.45%)(57.94%)(64.27%)(57.21%)(65.71%)(42.88%)(65.83%)Measured5.6195.1615.5754.2594.4734.1935.3344.9464.975SummerPrimary1.0471.4141.9641.8780.6951.1391.0511.5262.013(13.89%)(20.64%)(25.53%)(25.59%)(10.26%)(16.86%)(13.75%)(19.98%)(24.31%)Secondary5.5034.4844.5424.3895.1434.5125.8755.2735.361(72.46%)(64.54%)(60.70%)(60.45%)(74.66%)(67.79%)(72.97%)(66.66%)(63.41%)Measured7.7647.2777.9197.4517.1896.9368.368.1968.602AutumnPrimary1.1031.5371.991.4850.7750.9821.5381.5292.231(18.36%)(26.40%)(28.31%)(20.11%)(14.86%)(17.48%)(23.46%)(22.93%)(30.91%)Secondary3.9053.4623.9214.4784.1633.8764.2414.1633.87(63.95%)(55.73%)(56.39%)(64.87%)(66.62%)(64.08%)(61.15%)(61.65%)(54.57%)Measured5.8515.4636.3526.6545.4965.4776.1835.9376.789
The Pearson correlation coefficient (R) between the measured HCHO
and primary HCHO emission and secondary HCHO formation in Nanjing, Hangzhou,
and Shanghai in winter, spring, summer, and autumn. Line
1 in each season refers to primary
HCHO and line 2 refers to
secondary HCHO.
Season Nanjing Hangzhou Shanghai MGQRJRXLUTXSCXTFDCHHKPDNADSL(industrial(urban(industrial(suburban)(suburban)(urban(urban(urban(suburban)zone)area)zone)area)area)area)WinterPrimary0.670.24-0.050.420.010.470.610.610.5Secondary-0.41-0.230.12-0.240.03-0.25-0.42-0.42-0.44SpringPrimary0.110.210.170.040.290.130.120.16-0.21Secondary0.250.60.430.430.470.260.560.390.62SummerPrimary0.170.540.220.08-0.020.20.10.340.08Secondary0.450.590.260.330.690.350.60.620.35AutumnPrimary0.420.260.540.710.05-0.030.260.360.31Secondary0.710.680.370.120.480.550.410.480.19Primary and secondary sources of ambient HCHO in Hangzhou
Three sites, including Xiasha (XS), Chengxiang Town (CXT), and
the “Fifth District of Chaohui” (FDCH) which are located in the
northeastern suburbs, the eastern suburbs, and the center of Hangzhou,
respectively, were selected to explore the sources of HCHO in Hangzhou, as
shown in Fig. 5b. There was no significant difference between the HCHO
concentrations at the suburban sites (Table 1). The HCHO concentration at these
three sites reached a maximum in summer and a minimum in winter,
which was in agreement with a previous study by De Smedt et al. (2015). The
average HCHO concentration in 2016 was larger than the average concentrations in 2015 and 2017 at
all three sites.
The time series of absolute (a, c, e) and relative (b, d, f) contributions of primary sources, secondary sources, and background sources to the HCHO concentration from December 2014
to November 2017 at the XS (a, b), CXT (c, d), and
FDCH (e, f) sites in Hangzhou. Black dashed lines represent the
threshold value for HCHO pollution.
As shown in Table 1, secondary formation, with respect to the annual average, contributed
more to ambient HCHO than primary emissions from 2015 to 2017 in Hangzhou. It
is interesting that primary emissions of HCHO showed a small increasing trend
from 2015 to 2017 at suburban sites (Fig. 8a, c, Table 1). According to the environment bulletin
released by the Hangzhou government, this may have been caused by the significant increase in the number of vehicles and the less effective
control measures regarding ambient HCHO. Moreover, secondary HCHO also showed an important
growing tendency from 2015 to 2016 and then began to decrease in 2017. The
variation of the HCHO concentration was mainly affected by secondary formation
from 2015 to 2017, due to the strong correlation (R>0.7; Fig. 7d, e, f).
In all seasons, the seasonal average of secondary formation from
2015 to 2017 was larger than primary emissions in Hangzhou (Table 3). In
contrast, primary emission were seen to exceed secondary formation and
become the main source on individual days in winter of 2016 and in summer
of 2017 at the XS site (Fig. 8a, b), in winter
of 2016 at the CXT site (Fig. 8c, d), and in winter of 2016 and 2017 at urban
site (Fig. 8e, f). In winter, the impact of primary
emission on the variation of HCHO was more significant than secondary
formation, whereas in spring, summer, and autumn, secondary formation became
more important to the variation of ambient HCHO, except in the northeastern suburbs
in autumn (Table 4).
Primary and secondary sources of ambient HCHO in Shanghai
The “Pudong New Area” site (PDNA), the Hongkou site (HK), and the
“Dianshan Lake in Qingpu” site (DSL) were selected to represent east of the
city center, north of the city center, and the suburbs, respectively
(Fig. 5c). The annual average HCHO concentration decreased by 0.342 ppbv
from 2015 to 2016 and increased by 0.529 ppbv from 2016 to 2017 at the HK
site (Table 1). The annual average HCHO concentration at the
PDNA site increased by 0.15 ppbv in 2016 and decreased by 0.056 ppbv in
2017, compared with that from the previous year. At the DSL site, the HCHO
concentration was less than that at HK, except in 2016. The HCHO
concentration at DSL initially increased by 0.179 ppbv from 2015 to 2016 and
then decreased by 0.143 ppbv from 2016 to 2017. We can conclude that the
ambient HCHO concentration has not been strictly controlled in recent years.
With respect to the seasonal average, the HCHO concentration reached a
maximum in summer and a minimum in winter in Shanghai, which concurs with
findings from Nanjing and Hangzhou (Table 3). Moreover, the HCHO
concentration in Shanghai in summer was larger than that in Nanjing and
Hangzhou.
The time series of absolute (a, c, e) and relative (b, d, f) contributions of primary
sources, secondary sources, and background sources to the HCHO concentration from December 2014 to November 2017 at the HK (a, b), PDNA (c, d), and
DSL (e, f) sites in Shanghai. Black dashed lines represent the
threshold value for HCHO pollution.
On average, secondary formation was the most significant contribution to
ambient HCHO in Shanghai, except at the PDNA site in 2016 (Table 1 and Fig. 9).
The contribution from secondary formation developed, showing a rising trend, and
primary HCHO emission showed a decreasing trend from 2015 to 2017, at the
suburban site (Fig. 9e, f, Table 1). The variation of the HCHO concentration was
more strongly affected by secondary formation (R>0.70, Fig. 7g, h, i)
than primary emissions (R<0.43, Fig. S2g, h, i) from 2015
to 2017. In summer and autumn, secondary formation was the largest source of
ambient HCHO (Fig. 9a, b, c, d) and was the main factor influencing the variation
of the HCHO concentration in urban regions of Shanghai. At PDNA site, in comparison, primary
emissions (R=0.61, Table 4) had a more significant influence on the variation of ambient HCHO than secondary
formation (R=-0.42, Table 4) in winter, and it
became the most important source of ambient HCHO in spring and winter of
2016. The secondary sources of HCHO in the three megacities showed a similar
seasonal variation, and showed a similar order of influence to the seasonal variation of
HCHO concentration: summer > autumn > spring > winter. The HCHO concentration from secondary formation in Shanghai in
summer was larger than that in Nanjing and Hangzhou.
DiscussionReconsidering the proxy for VOC reactivity using the HCHO concentration
Duncan et al. (2010) determined the indicator of VOC and NOx controls on
surface O3 formation using HCHO VCDs observed by OMI as the proxy for
VOC reactivity in August between 2005 and 2007 in the US. This is
reasonable, as the ambient HCHO concentration mainly stemmed from the atmospheric
oxidation of isoprene in summer over North America (Palmer et al., 2003; Zhu
et al., 2017b; Marvin et al., 2017); furthermore, the HCHO concentration from secondary
formation contributed 87 % during daytime in Los Angeles in the 1980s
(Kawamura et al., 2000), and over 80 % of the ambient HCHO concentration is
from secondary formation over the eastern US (Luecken et al., 2006).
However, the contribution from secondary formation in the YRD is smaller than
that in the US, accounting for about 70 % in summer and 40 %–54 %
in winter. The good correlation between total HCHO and secondary
HCHO results in a good correlation between total HCHO and RO2, which
means that total HCHO is an appropriate proxy for VOC reactivity in this case. Total HCHO can be regarded as
the proxy for VOC reactivity over a 3-year study period from 2015 to
2017 due to the good correlation between total HCHO and secondary HCHO
in Nanjing, Hangzhou, and Shanghai (R≥0.59). If the study
period was restricted to 2015, using the ambient HCHO concentration to indicate VOC
reactivity would cause significant errors, as ambient HCHO showed a
stronger correlation with primary emission than secondary formation during this period
(e.g., in the industrial zone of Nanjing and in the suburbs of Shanghai).
In addition, the correlation between total HCHO and secondary HCHO depends on
season. In winter, the ambient HCHO concentration cannot be represented by
the concentration from secondary formation due to the poor correlation (R<0.12,
P>0.05). However, in spring (at RJR and DSL), in summer (at RJR,
CXT, HK, and PDNA), and in autumn (at RJR and MGQ) total HCHO can
be used as a proxy for VOC reactivity (R>0.59, P<0.01).
HCHO control measures adapted to local conditions
Sources of ambient HCHO at different sites varied largely and were closely
related to the types of industries present (Zheng et al.,
2016). In order to better adapt HCHO control measures to local
conditions, it is meaningful to estimate the different sources of ambient
HCHO. The results in Sect. 3.3 indicate that the main influencing factor for
the ambient HCHO concentration depends on the season. Here we define HCHO
pollution using a HCHO concentration threshold value of 10 ppbv, which is
the 95th percentile of all of the observation data. HCHO pollution events
mostly occurred in summer, accounting for 81.0 %, 81.3 %, and
95.2 % of all observed pollution events in Nanjing, Hangzhou, and
Shanghai, respectively. In summer, HCHO pollution mostly occurred during the periods from
24 to 27 July 2016 and 22 to 27 July 2017 in Nanjing and from 23 to 27 July
2016 and from 20 to 27 July 2017 in Hangzhou. HCHO pollution in Shanghai
lasted longer than episodes in Nanjing and Hangzhou: from 10 to 27 July of
2016 and 2017. Moreover, HCHO pollution events were also observed during the
period from 23 to 27 August 2015 and from 5 to 24 August 2017 north of the
center of Shanghai. In the Shanghai suburbs, three HCHO pollution periods
were also observed: (1) from 19 June to 5 July 2015, (2) from 19 to 23 August
2016, and (3) from 4 to 7 August 2017. During HCHO pollution events,
secondary formation contributed most to the ambient HCHO concentration and
increased more significantly than the primary HCHO contribution. Compared
with the concentrations before pollution days, secondary HCHO and primary
HCHO increased by 0.90 and -0.12 ppbv on average in Nanjing, respectively,
by 1.33 and 0.21 ppbv in Hangzhou, respectively, and by 1.26 and 0.14 ppbv
in Shanghai, respectively. Therefore decreasing VOC emissions, the source of
secondary HCHO, would have a more significant effect on controlling HCHO
pollution in Nanjing, Hangzhou, and Shanghai.
The industries of Nanjing include the chemical industry, steel plants, and
the cement industry (Zheng et al., 2016). In the Nanjing industrial zone,
secondary sources of HCHO were about 4 times larger than primary sources in
2017. Therefore, at industrial sites, decreasing VOC emissions from
chemical-related activities is an efficient way to control the ambient HCHO
concentration. In Hangzhou, labor-intensive industries are the most important
parts of industry economics (Zheng et al., 2016). Secondary HCHO
concentrations in the eastern suburbs of Hangzhou were highest from 2016 to
2017 (Table 1), corresponding to a previous study by B. Lu et al. (2018)
which indicated that the largest amount of VOC emissions were measured in the
eastern suburbs of Hangzhou and that VOC emissions from furniture
manufacturing were the largest source. Secondary HCHO
contributed most to ambient HCHO during the period when it was not
effectively controlled (from 2015 to 2017). Considering that industrial
manufacturing is developing rather rapidly and that VOC emissions occur from
many types of industries, i.e., textile industry, printing, leather
manufacturing, and shoemaking (Liu et al., 2008; Khan and Malik, 2014),
controlling VOC emissions in Hangzhou will be a huge challenge. Unlike
Nanjing and Hangzhou, tertiary industry has played a leading role in
Shanghai's economy since 2013, and primary and secondary industries have
decreased since 1999 (Chen et al., 2016). However, the HCHO concentration
from secondary sources has not been controlled effectively. Decreasing
private cars on the road, developing evaporative control regulations, and
improving traffic management, which needs to be better suited to local
conditions, would help reduce not only VOC emissions but also HCHO emissions,
especially in the downtown area (Wang and Zhao, 2008; Liu et al., 2015,
2017).
Summary
Tropospheric HCHO VCDs derived from OMPS observations were validated using
ground-based FTS measurements in Hefei, from 2015 to 2017. The HCHO VCDs
observed by OMPS and FTS were in good agreement (R=0.78). Serious HCHO
pollution was observed in the YRD region, especially in southwestern Jiangsu
Province (e.g., Nanjing, Changzhou, Wuxi, and Suzhou cities), in northern
Zhejiang Province (e.g., Hangzhou, Jiaxing, Huzhou, and Ningbo cities), and
in Shanghai. Estimating different sources of ambient HCHO and then formulating
control measures adapted to local conditions are effective ways of
controlling HCHO pollution. However, measurements of HCHO are scarce, both spatially and
temporally. In this study, we analyzed primary and secondary contributions
to ambient HCHO using a multiple linear regression based on HCHO observed by
OMPS. At the MGQ site in Nanjing, the average relative contributions from
primary emissions, secondary formation, and background sources to HCHO from 15 April
to 1 May 2015 were 50.5 %, 30.8 %, and 18.7 %,
respectively, which was in good agreement with previous research. Therefore, tropospheric
HCHO columns observed by OMPS can be used to estimate different sources of
ambient HCHO.
Primary and secondary contributions to ambient HCHO were determined in Nanjing, Hangzhou,
and Shanghai from 2015 to 2017. Overall, the HCHO concentration
from secondary formation contributed most to ambient HCHO in the megacities
in the YRD region and influenced the variation of the ambient HCHO concentration in
Hangzhou, Shanghai, and urban regions of Nanjing. In comparison, at industrial sites
of Nanjing, the annual average HCHO concentration from primary emissions
decreased significantly from 2015 to 2016 due to energy saving methods and the reduction of energy
consumption, and the contribution from secondary formation to the variation
of the HCHO concentration was less significant. At suburban regions in Hangzhou,
primary emissions showed a small increasing trend from 2015 to 2017, whereas at
urban sites, primary emissions of HCHO in Shanghai were larger than those in
Hangzhou. Seasonally, secondary HCHO reached a maximum in summer and a
minimum in winter. In spring, summer, and autumn, secondary formation played
a crucial role in the variation of the HCHO concentration in urban areas of Nanjing,
Hangzhou, and Shanghai, whereas in winter, primary emission contributed more to
variation of ambient HCHO in Nanjing, Shanghai, and urban areas of Hangzhou.
These findings contribute to formulating effective HCHO pollution control
measures. Furthermore, the usability of total HCHO as a proxy of VOC
reactivity depends on the timescale utilized. Total HCHO can be regarded as a proxy
for VOC reactivity over a 3-year study period, whereas only secondary
HCHO can be used as a proxy for VOC reactivity over a shorter study period,
e.g., in winter. Therefore separating the HCHO contributions from different sources is of great
significance in sensitivity studies of O3 production.
Data availability
The data used in this paper is available on request from
the corresponding author (chliu81@ustc.edu.cn and qhhu@aiofm.ac.cn).
The supplement related to this article is available online at: https://doi.org/10.5194/acp-19-6717-2019-supplement.
Author contributions
WS and CL contributed equally to this study. CL and QH designed and
supervised the study, and CL also helped retrieve the satellite data. WS
wrote the paper. SZ, YS, and WW retrieved the FTS data, and YZ ran the
WRF-Chem model. JL supported the project, and JK contributed to discussions regarding the
results.
Competing interests
The authors declare that they have no conflict of
interest.
Acknowledgements
This work was supported by National Key Research and Development Program of
China (grant nos. 2017YFC0210002, 2018YFC0213104, 2018YFC0213100, and 2016YFC0203302),
the National Natural Science Foundation of China (grant nos. 41722501, 91544212,
51778596, and 41575021), and the National High-Resolution Earth Observation
Project of China (grant no. 05-Y20A16-9001-15/17-2). The authors acknowledge
the OMPS project for making the OMPS-NPP Nadir Mapper Earth View Level 1B data
product available online. We also thank the Ministry of Environment
Protection of the People's Republic of China for making data measured by the
atmospheric environment automatic monitoring stations available online.
Review statement
This paper was edited by Min Shao and reviewed by two
anonymous referees.
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