Pandora spectrometers can retrieve nitrogen dioxide
(NO2) vertical column densities (VCDs) via two viewing geometries:
direct Sun and zenith sky. The direct-Sun NO2 VCD measurements have
high quality (0.1 DU accuracy in clear-sky conditions) and do not rely on
any radiative transfer model to calculate air mass factors (AMFs); however,
they are not available when the Sun is obscured by clouds. To perform
NO2 measurements in cloudy conditions, a simple but robust NO2
retrieval algorithm is developed for Pandora zenith-sky measurements. This
algorithm derives empirical zenith-sky NO2 AMFs from coincident
high-quality direct-Sun NO2 observations. Moreover, the retrieved
Pandora zenith-sky NO2 VCD data are converted to surface NO2
concentrations with a scaling algorithm that uses chemical-transport-model
predictions and satellite measurements as inputs. NO2 VCDs and surface
concentrations are retrieved from Pandora zenith-sky measurements made in
Toronto, Canada, from 2015 to 2017. The retrieved Pandora zenith-sky
NO2 data (VCD and surface concentration) show good agreement with both
satellite and in situ measurements. The diurnal and seasonal variations of
derived Pandora zenith-sky surface NO2 data also agree well with in
situ measurements (diurnal difference within ±2 ppbv). Overall, this
work shows that the new Pandora zenith-sky NO2 products have the
potential to be used in various applications such as future satellite
validation in moderate cloudy scenes and air quality monitoring.
Introduction
Nitrogen dioxide (NO2) is an important air pollutant and plays a
critical role in tropospheric photochemistry (e.g., ECCC, 2016;
EPA, 2014). It is primarily emitted from combustion processes such as fossil
fuel combustion (e.g., traffic, electricity generation from power plants) and
biomass burning, as well as from lightning. NO2 is a nitrate aerosol
precursor, and it also contributes to acid deposition and eutrophication
(ECCC, 2016). Exposure to NO2 can lead to adverse health
effects, such as irritation of the lungs, a decrease in lung function, and
an increase in susceptibility to allergens for people with asthma
(EEA, 2017; WHO, 2017).
As surface NO2 concentrations are regulated by many environmental
agencies (e.g., Environment and Climate Change Canada and US Environment
Protection Agency), in situ NO2 measurements are commonly carried out
by many national monitoring networks, such as the National Air Pollution
Surveillance (NAPS;
https://www.canada.ca/en/environment-climate-change/services/air-pollution/monitoring-networks-data/national-air-pollution-program.html, last access: 15 August 2019)
network in Canada, which was established in 1969. The in situ methods used
to measure surface NO2 have evolved over the years; for example,
luminol chemiluminescence
(e.g., Kelly et al., 1990;
Maeda et al., 1980; Wendel et al., 1983), long-path differential optical
absorption spectroscopy (e.g., Platt, 1994), photolytic
conversion/chemiluminescence
(e.g., Gao
et al., 1994; Ryerson et al., 2000), and laser-induced fluorescence
(e.g., Thornton et al., 2000) are all found
to be reliable methods with an uncertainty within 10 % at 1 ppbv and
higher concentration levels (McClenny, 2000). Currently, the in
situ approach used by NAPS for surface NO2 air quality monitoring is
the photolytic conversion/chemiluminescence technique, which converts
NO2 to NO and subsequently detects the NO by chemiluminescence reaction
(McClenny, 2000; NRC, 1992). This in situ monitoring
measurements provides good measurements at ground level (0.4 ppbv accuracy),
but NO2 is not uniformly mixed through the atmosphere, and not even
within the atmospheric boundary layer due to emission and removal processes
taking place at the surface.
Total vertical column NO2 can be measured by many ground-based
UV–visible remote-sensing instruments using direct-Sun, zenith-sky, or
off-axis spectroscopy techniques
(Cede
et al., 2006; Drosoglou et al., 2017; Herman et al., 2009; Lee et al., 1994;
Noxon, 1975; Piters et al., 2012; Roscoe et al., 2010; Tack et al., 2015;
Vaughan et al., 1997). These measurements are of high quality and good
precision, and have been widely used for atmospheric chemistry studies
(e.g.,
Adams et al., 2012; Hendrick et al., 2014) and satellite validations
(e.g.,
Celarier et al., 2008; Drosoglou et al., 2018; Irie et al., 2008; Wenig et
al., 2008). Among all these different viewing geometries, direct-Sun
measurements are of high accuracy and are not dependent on radiative
transfer models (RTMs) to calculate air mass factors (AMFs)
(Herman et
al., 2009) or on knowledge of other atmospheric constituents. Zenith-sky
observations have been widely used for stratospheric ozone and NO2
observations, particularly under cloudy conditions when direct-Sun
measurements are unreliable (note that zenith-sky observations use scattered
sunlight and are less sensitive to clouds, e.g.,
Zhao et al., 2019). Off-axis
measurements have good sensitivity in the boundary layer and could provide
tropospheric trace gas profiles and surface concentrations
(Frieß
et al., 2011; Hendrick et al., 2014; Kramer et al., 2008; Wagner et al.,
2011), but they are more sensitive to cloud cover than zenith-sky
measurements.
The Pandora Sun spectrometer is a new instrument developed to measure
vertical column densities (total columns) of trace gases in the atmosphere
using Sun and sky radiation in the UV–visible part of the spectrum (Herman
et al., 2009). One of its primary data products is NO2 total vertical
column density (VCD) from the direct-Sun viewing mode, where VCD represents
the vertically integrated number of molecules per unit area and is reported
in units of molec cm-2 or Dobson units (1 DU =2.6870×1016 molec cm-2). The Pandora direct-Sun NO2 VCD
products have been validated through many field campaigns
(Flynn
et al., 2014; Lamsal et al., 2017; Martins et al., 2016; Piters et al.,
2012; Reed et al., 2015), ground-based comparisons
(Herman
et al., 2009; Wang et al., 2010), and satellite validations
(Ialongo
et al., 2016; Lamsal et al., 2014).
Since their introduction in 2006, Pandora spectrometers have been deployed
at more than 50 sites globally. The Pandora no. 103 instrument used in this
study has been deployed in Toronto, Canada since 2013 to perform direct-Sun
measurements (Zhao et al., 2016). Since 2015, the observation
schedule of Pandora no. 103 has been modified to perform alternating
direct-Sun and zenith-sky measurements. Knepp et al. (2017) assessed Pandora's
capability to derive stratospheric NO2 using zenith-sky viewing
geometry (in twilight periods), but their study was limited to slant column
densities (SCDs). At this time, there are no standard Pandora zenith-sky
NO2 VCD data products available. As one goal of this work, we have
focused on developing a new NO2 retrieval algorithm for zenith-sky
measurements to expand Pandora NO2 measurements into cloudy scenes.
In addition to retrieval of zenith-sky total column NO2, another goal
of this work is to derive surface NO2 concentration from total column
measurements. Surface NO2 has been a focus of scientific studies due to
its strong correlation with air quality (AQ) and health issues
(ECCC, 2016), with NO2 as one of the three components (along
with ozone and PM2.5) used to compute the Air Quality Health Index
(AQHI; Stieb et al., 2008) in Canada's AQ
public awareness programs. Efforts to link total column NO2 with its
surface concentrations have been made by many researchers
(Flynn
et al., 2014; Knepp et al., 2015; Kollonige et al., 2017; Lamsal et al.,
2008, 2014; McLinden et al., 2014). For example, Knepp et al. (2015) proposed a
method to estimate NO2 surface mixing ratios from Pandora direct-Sun
total column NO2 via application of a planetary boundary layer (PBL)
height correction factor. Kollonige et al. (2017) adapted this
method and compared Pandora direct-Sun surface NO2 and Ozone Monitoring Instrument (OMI) surface
NO2. They concluded that the two main sources of error for the
conversion of the total column NO2 to surface NO2 are (1) poor
weather conditions (e.g., cloud cover and precipitation) and (2) PBL height
estimation, both of which affect the NO2 column–surface relationship
and instrument sensitivities to boundary layer NO2. Thus, in this work,
we present a simple but robust algorithm for deriving surface NO2
concentration from Pandora zenith-sky measurements, which has several
advantages, such as the ability (1) to extend Pandora NO2 measurements
to cloudy conditions and (2) to provide more accurate surface NO2
concentration estimates that are less sensitive to PBL height. This
work also provides reliable total column NO2 measurements in cloudy
conditions and could be used in satellite validations in partially cloudy
scenes.
This paper is organized as follows. Section 2 describes the measured and
modelled NO2 data used in this study. In Sect. 3, the empirical AMFs
for Pandora zenith-sky NO2 measurements are derived using high-quality
Pandora direct-Sun total column NO2 data. These empirical AMFs and the
Network for the Detection of Atmospheric Composition Change
(NDACC)
AMFs (Hendrick et al., 2011; Sarkissian et al., 1995; Van Roozendael et al.,
1998; Van Roozendael and Hendrick, 2009; Vaughan et al., 1997) are both
applied to Pandora zenith-sky total column NO2 retrievals to help
evaluate the performance of the empirical AMFs. Also, the retrieved Pandora
zenith-sky total column NO2 data are evaluated by comparison with
satellite measurements. In Sect. 4, the zenith-sky total column NO2
data are converted to surface concentration by using a scaling algorithm.
The zenith-sky surface NO2 concentration data are assessed by
comparison with in situ measurements. Lastly, in Sect. 5, several aspects
of this zenith-sky surface NO2 dataset are discussed, which include
diurnal and seasonal variation, and PBL effect, followed by conclusions in
Sect. 6.
Datasets and modelsMeasurementsPandora direct-Sun total column <inline-formula><mml:math id="M62" display="inline"><mml:mrow class="chem"><mml:msub><mml:mi mathvariant="normal">NO</mml:mi><mml:mn mathvariant="normal">2</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula>
The Pandora instrument records spectra between 280 and 530 nm with
a resolution of 0.6 nm
(Herman
et al., 2009, 2015; Tzortziou et al., 2012). It uses a
temperature-stabilized Czerny–Turner spectrometer, with a 50 µm
entrance slit, 1200 groove mm-1 grating, and a 2048×64
back-thinned Hamamatsu charge-coupled device (CCD) detector. The spectra are
analyzed using a total optical absorption spectroscopy (TOAS) technique
(Cede, 2019), in which absorption cross sections for multiple
atmospheric absorbers, such as ozone, NO2, and sulfur dioxide (SO2), are
fitted to the spectra.
The Pandora direct-Sun total column NO2 data are produced using
Pandora's standard NO2 algorithm implemented in the BlickP software
(Cede, 2019). The measured direct-Sun spectra from 400 to 440 nm
are used in the TOAS analysis. A synthetic reference spectrum is produced by
averaging multiple measured spectra and corrected for the estimated total
optical depth included in it. Cross sections of NO2 at an effective
temperature of 254.5 K
(Vandaele et al., 1998),
ozone at an effective temperature of 225 K
(Brion et al.,
1993, 1998; Daumont et al., 1992), and a fourth-order polynomial are all
fitted. The resulting NO2 SCDs are then converted to total column VCDs
by using direct-Sun geometry AMFs. Herman et al. (2009) show
that Pandora direct-Sun total column NO2 has a clear-sky precision of
0.01 DU (in the slant column) and a nominal accuracy of 0.1 DU (in the vertical
column, 2σ level). Additional information on Pandora calibrations,
operation, and retrieval algorithms can be found in Herman et al. (2009) and
Cede (2019).
The Pandora no. 103 instrument has been deployed in Toronto since September 2013 to perform direct-Sun observations (Zhao et al., 2016).
The instrument is installed on the roof of the Environment and Climate
Change Canada (ECCC) Downsview building (43.7810∘ N,
-79.4680∘ W) in Toronto. The building is located in a suburban
area with multiple roads nearby. Since 2015, the instrument has been employing an
alternating direct-Sun and zenith-sky observation schedule, which consists
of direct-Sun measurements every 90 s and zenith-sky measurements
every 30 min during the sunlit period. About 2.5 years
(February 2015 to September 2017) of continuous alternating measurements are
used in this study.
Pandora zenith-sky total column <inline-formula><mml:math id="M77" display="inline"><mml:mrow class="chem"><mml:msub><mml:mi mathvariant="normal">NO</mml:mi><mml:mn mathvariant="normal">2</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula>
Retrieval of trace gases from Pandora's zenith-sky measurements is not
included in the standard BlickP processing software (Cede, 2019).
The Pandora zenith-sky spectra for this study are processed using the
differential optical absorption spectroscopy (DOAS) technique
(Noxon, 1975; Platt, 1994; Platt
and Stutz, 2008; Solomon et al., 1987) with the QDOAS software
(Danckaert et al., 2015). A single reference spectrum is used,
which was obtained from a zenith-sky measurement at local noon from a day
that had low total column NO2. Following the NDACC recommendations
(Van Roozendael and Hendrick, 2012), NO2 differential slant
column densities (dSCDs) are retrieved in the 425–490 nm window (to retrieve
oxygen collision complex simultaneously). The oxygen collision complex
(O2)2 (referred here as O4), which is created by the
collision of two oxygen molecules, has broadband absorptions from UV to near-IR spectral ranges
(Greenblatt
et al., 1990; Platt and Stutz, 2008; Thalman and Volkamer, 2013). O4 is
widely used as a reference gas by many DOAS applications to infer cloud and
aerosol properties
(e.g.,
Gielen et al., 2014; Wagner et al., 2004, 2014, 2016, 2019; Wang et al., 2015;
Zhao et al., 2019). Cross sections of NO2 at an effective temperature
of 254.5 K (Vandaele et al.,
1998), ozone at an effective temperature of 223 K
(Bogumil et al., 2003),
H2O
(Rothman
et al., 2005), O4 (Hermans et al., 2003), and
ring (Chance and Spurr, 1997) are all fitted; a
fifth-order polynomial and a first-order linear offset are also included in
the DOAS analysis.
The output of QDOAS is NO2 dSCDs, which can be converted to total
column NO2 via the Langley plot method with the use of the NDACC
NO2 AMF look-up table (LUT) (Van Roozendael and Hendrick,
2012). The NDACC AMF LUT is used here only as a reference since it was
primarily developed for retrieval of stratospheric NO2. Other empirical
zenith-sky NO2 AMFs have been developed and are used to convert
NO2 dSCDs to total columns. Details about these two different AMFs are
given in Sect. 3.1.
OMI SPv3 data
OMI is a Dutch–Finnish nadir-viewing
UV–visible spectrometer aboard the National Aeronautics and Space
Administration (NASA)'s Earth Observing System (EOS) Aura satellite that was
launched in July 2004. The OMI instrument measures the solar radiation
backscattered by the Earth's atmosphere and surface between 270 and 500 nm
with resolution of 0.5 nm
(Levelt et al., 2006,
2018). OMI has a 780×576 CCD detector that measures at 60
across-track positions simultaneously and thus does not require
across-track scanning. Due to this approach, the spatial resolution of the
CCD pixels varies significantly along the across-track direction: those
pixels near the track centre have a ground footprint of 13km×24km
(along track × across track), whereas those close to the track edge
(e.g., view zenith angle =56∘) have a ground footprint roughly
of 23km×126km
(de Graaf et al., 2016). Note
that from 2012 onwards the smallest pixels (across-track positions) can no
longer be used and are excluded from the analysis (known as the “row
anomaly”, i.e., Levelt et al., 2018). This means the “smallest” pixels
available for an OMI comparison are larger than 13km×24km.
The OMI NO2 data used in this work are the NASA standard product (SP)
(Bucsela
et al., 2013; Wenig et al., 2008) version 3.0 level 2 (SPv3.0)
(Krotkov
et al., 2017). The NO2 SCDs are derived using the DOAS technique in the
405–465 nm window (Marchenko et al.,
2015). The AMFs used in SPv3.0 are calculated by using 1∘×1.25∘ (latitude × longitude) resolution a priori
NO2 and temperature profiles from the Global Modeling Initiative (GMI)
chemistry–transport model with yearly varying emissions
(Krotkov
et al., 2017).
In situ measurements
The NAPS network was established in
1969 to monitor and assess the quality of ambient (outdoor) air in the
populated regions of Canada. NAPS provides accurate long-term air quality
data (ozone, NO2, SO2, carbon monoxide (CO), fine particulate
matter, etc.) of a uniform standard across
Canada (e.g.,
Dabek-Zlotorzynska et al., 2011; Reid and Aherne, 2016).
The in situ NO2 data used in this study were collected at the NAPS
Toronto north station (located 100 m away from the Pandora instrument). The
site is 186 m above sea level, and the height of the air intake is 4 m above
the ground.
The in situ NO2 concentration is measured using a photolytic NO2
instrument (Thermo 42i) that is also sensitive to other gaseous inorganic
nitrogen compounds (e.g., nitric acid (HNO3) and peroxyacetyl nitrate
(PAN)) (McLinden
et al., 2014). Thus, in areas where direct NOx (nitrogen oxides)
emission sources are limited and other nitrogen compounds are present,
NO2 may be overestimated (e.g., in rural areas). For the current site,
however, this positive bias has been found to be only about 5 %, except
for very low NO2 concentrations (< 5 ppbv) (Yushan Su, Ontario
Ministry of the Environment, Conservation and Parks, personal communication,
October 2018).
Numerical models
Predicted NO2 fields from three atmospheric chemistry models are used
in the algorithm described in Sect. 4.1 to derive surface NO2
concentration from Pandora zenith-sky total column NO2 data. Following
McLinden et al. (2014), this work
uses the Global Environmental Multi-scale Modelling Air quality and
CHemistry (GEM-MACH) regional chemical transport model (CTM) and the
GEOS-Chem global CTM to simulate total columns and vertical profiles of
tropospheric NO2 and surface NO2 concentration. The stratospheric
NO2 partial columns are estimated using OMI satellite data and the
Pratmo box model.
GEM-MACH
GEM-MACH is ECCC's regional air quality forecast model. It is run
operationally twice per day to predict hourly surface pollutant
concentrations over North America for the next 48 h
(Moran
et al., 2009; Pavlovic et al., 2016; Pendlebury et al., 2018). The model
consists of an online tropospheric chemistry module
(Akingunola
et al., 2018; Pavlovic et al., 2016) embedded within the ECCC Global
Environmental Multi-scale (GEM) numerical weather prediction model
(Côté et al., 1998). Physical and
chemical processes represented in GEM-MACH include emissions, dispersion,
gas- and aqueous-phase chemistry, inorganic heterogeneous chemistry, aerosol
dynamics, and wet and dry removal. The model uses gridded hourly emission
fields based on US and Mexican national inventories from the US
Environmental Protection Agency (EPA) Air Emissions Modeling Platform and on
Canada's national Air Pollutant Emission Inventory (APEI; https://pollution-waste.canada.ca/air-emission-inventory, last access: 23 November 2018) (Zhang
et al., 2018). Currently, only NOx emissions in the PBL are included in
the operational model; free-tropospheric NOx emissions from lightning
and in-flight aircraft are not considered. In this work, the GEM-MACH hourly
NO2 vertical profiles from 0 to 1.5 km and surface concentrations are
retrieved from archived operational forecasts on the native model grid
covering North America at 10km×10km horizontal resolution for
the period April 2016 to December 2017. The corresponding grid box closest
to the Pandora location was used in this study.
GEOS-Chem
The GEOS-Chem chemical transport model
(Bey et al., 2001) has been
used extensively in the retrieval of tropospheric columns and has been
shown to be capable of reasonably simulating the vertical distributions of
NO2
(Lamsal
et al., 2008; Martin et al., 2002; McLinden et al., 2014). The model has a
detailed representation of tropospheric chemistry, including aerosols and
their precursors (Park
et al., 2004). In the simulation used in this study, a global lightning
NOx source of 6 Tg N yr-1 (Martin et al., 2002) was imposed.
Lightning NOx emissions are computed as a function of cloud-top height
and are scaled globally as described by Sauvage et al. (2007) to match Optical
Transient Detector/Lightning Imaging Sensor (OTD/LIS) climatological
observations of lightning flashes. The model was run on a 1/2∘×2/3∘ (latitude × longitude) grid in nested
mode over North America and was driven by assimilated meteorology from the
Goddard Earth Observing System (GEOS-5). The modelled NO2 profiles were
used to calculate monthly mean NO2 partial columns in the free
troposphere (1.5 to 12 km), as the GEM-MACH model does not include
free-tropospheric NO2 sources (lightning, in-flight aircraft
emissions).
Pratmo box model
Pratmo is a stratospheric photochemical box model
(Brohede
et al., 2008; Lindenmaier et al., 2011; McLinden et al., 2000). The model
has detailed stratospheric chemistry that includes long-lived species
(nitrous oxide (N2O), methane (CH4), and water vapor (H2O))
and halogen families (NOy, Cly, and Bry) that are based on a
combination of three-dimensional model output and tracer correlations
(Adams et al., 2017). Heterogeneous
chemistry of background stratospheric sulfate aerosols is also included. The
model is constrained with climatological profiles of ozone and temperature.
Stratospheric NO2 has a strong diurnal variation; therefore, diurnal
corrections must be applied when OMI stratospheric NO2 measurements
(around local noon) are interpolated to Pandora measurement times. Ratios of
modelled stratospheric NO2 columns are calculated at OMI overpass time
and Pandora measurement time. These ratios are multiplied by the OMI
measured stratospheric NO2 to produce stratospheric NO2 columns
corresponding to the time of Pandora measurements. Details about the use of
the Pratmo box model and the calculation of stratospheric NO2 partial
columns are provided in Sect. 4.1.
Total column <inline-formula><mml:math id="M145" display="inline"><mml:mrow class="chem"><mml:msub><mml:mi mathvariant="normal">NO</mml:mi><mml:mn mathvariant="normal">2</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula> retrievalZenith-sky air mass factor
The NDACC UV–visible network uses zenith-sky AMFs in its total column
NO2 retrievals. To improve the overall homogeneity of the UV–visible
NO2 column measurements, NDACC recommended using the NO2 AMF LUT
(Van Roozendael and Hendrick, 2012). This LUT is based on
climatological NO2 profiles that are composed of (1) 20–60 km NO2
profiles developed by Lambert et al. (1999, 2000) and (2) 12–20 km
NO2 profiles derived from SAOZ (Système D'Analyse par Observations Zénithales) balloon observations (Van
Roozendael and Hendrick, 2012). The NO2 concentration is set to zero
below 12 km altitude. The NO2 AMFs have been calculated using the
UVSPEC/DISORT RTM
(Hendrick
et al., 2006; Wagner et al., 2007). The parameters used in building the LUT
are wavelength, ground albedo, altitude of the station, and solar zenith
angle (SZA). Aerosol extinction, ozone, and temperature profiles come from
an aerosol model (Shettle, 1989), the US Standard
Atmosphere, and the TOMS V8 climatology, respectively.
The NDACC LUT is designed for stratospheric NO2 retrievals. Note that
the absence of tropospheric NO2 in the NDACC LUT construction will lead
to an underestimation of the total column NO2 in urban areas. For
example, from 2015 to 2017, tropospheric NO2 accounted for 73±11 % (1σ) of the total column amounts in Toronto (OMI SPv3.0
data). To account for this significant tropospheric NO2 in urban areas,
new empirical AMFs were developed in this study and the NDACC AMF LUT is
used for comparison purposes only. In Tack et al. (2015),
a more sophisticated four-step approach to derive total and tropospheric
NO2 columns from zenith-sky measurements was proposed, which involved
using a RTM to calculate appropriate tropospheric AMFs. However, due to
benefits from using the high-quality Pandora direct-Sun total column
NO2 measurements, this work took a different but simple and robust
approach to derive zenith-sky total column NO2.
Empirical AMFs are calculated for Pandora zenith-sky NO2 measurements
in such a way that they can be used to retrieve zenith-sky total column
NO2 values that match the high-quality Pandora direct-Sun total column
NO2 values. Inferring total columns from zenith-sky observations
through comparisons with accurate direct-Sun observations is a common
approach for Brewer and Dobson zenith-sky total ozone measurements
(Kerr et al., 1988). For example, in the Brewer
instrument zenith-sky ozone algorithm, weighted zenith-sky light intensities
measured at four wavelengths (F) are expressed as a function of the slant
path (μ) and total column ozone (Kerr et al., 1981).
The nine semi-empirical coefficients used to derive total column ozone from
measured F in the equation are estimated from a set of direct-Sun and
zenith-sky observations made nearly simultaneously
(Fioletov et al., 2011). Instead of finding the link
between zenith-sky spectral intensity and total column values (i.e.,
following the Brewer and Dobson zenith-sky total ozone retrieval method),
deriving empirical zenith-sky AMFs for Pandora zenith-sky measurements is
more straightforward since Pandora zenith-sky spectra can be analyzed to
produce NO2 dSCDs.
The relation between VCD and dSCD can be expressed as
VCD=dSCD+RCDAMF,
where RCD is the reference column density that shows the slant column
amount of the trace gas in the reference spectrum (Sect. 2.1.2). If we
make an assumption that the coincident direct-Sun (DS) and zenith-sky (ZS)
measurements sampled the same air mass, then the empirical zenith-sky AMFs
(referred to here as AMFZS-Emp) can be calculated by assuming
VCDDS= VCDZS, which gives
VCDDSSZA=dSCDZSSZA+RCDZSAMFZS-Emp(SZA).
Next, we can use nearly coincident VCDDS and dSCDZS in a
multi-non-linear regression to retrieve AMFZS-Emp and RCDZS
together. To ensure the quality of the retrieved AMFZS-Emp, only high-quality
direct-Sun total column NO2 data are used with SZA < 75∘.
Details about the empirical zenith-sky AMF calculation are
shown in Appendix A.
Figure 1 shows a comparison of the empirical zenith-sky AMFs and NDACC AMFs
(calculated for the Toronto measurements). Total column NO2 can then be
retrieved using Eq. (1) and these two sets of AMFs, where the one based on
empirical AMFs is referred to as VCDZS-Emp and the one based on NDACC
AMFs is referred to as VCDZS-NDACC. The RCD value used in the
retrievals is 0.39±0.01 DU, which is retrieved along with
AMFZS-Emp (Appendix A). Figure 2 shows the comparisons of the NO2
columns measured by zenith-sky and direct-Sun methods. The regression
analyses were performed by using the following coincidence criteria: (1) nearest
Pandora direct-Sun measurement that was within ±5 min of
Pandora zenith-sky measurement, (2) SZA < 75∘, and (3) Pandora
direct-Sun total column NO2 data have assured high quality
(BlickP L2 data quality flag for nitrogen dioxide is 0). In general, the
VCDZS-Emp and VCDZS-NDACC performed as expected. Compared with
VCDDS, the VCDZS-NDACC shows a -25 % bias, while the
VCDZS-Emp only shows a -4 % bias (indicated by the red lines on each
panel and their slopes). In addition, VCDZS-Emp shows less SZA
dependence than VCDZS-NDACC (see the increased bias for measurements
made in larger SZA conditions in Fig. 2b). These results confirm that, for
urban sites, the tropospheric NO2 profile should be included when
calculating empirical zenith-sky AMFs. In the rest of the paper, only the
zenith-sky NO2 retrieved using empirical AMFs will be discussed. The
derived zenith-sky total column NO2 values are affected by both clouds
and aerosols due to their impact on the light path. The presence of clouds
and aerosols contributes to the uncertainty of the measurements. However,
the impact of aerosols is expected to be moderate in most cases compared to
that of clouds
(e.g.,
Hendrick et al., 2011; Tack et al., 2015). Thus, this work has focused on
evaluating the impact from clouds. Note that the Pandora zenith-sky total
column NO2 data discussed in Sect. 3 are a “clear-sky subset” of
Pandora zenith-sky measurements. The assessment of Pandora zenith-sky
NO2 measurements in cloudy conditions is provided in Sect. 4.
Comparison of zenith-sky NO2 air mass factors. Blue and red
squares with error bars (standard error) represent the empirical discrete
zenith-sky NO2 AMFs in each SZA bin for Toronto for the period
February 2015 to September 2017. Blue and red lines show the fitted empirical zenith-sky
NO2 AMFs. NDACC AMFs calculated using the NDACC look-up table and
assuming no NO2 in the troposphere are shown in yellow.
Comparisons of NO2 total columns (2015–2017): (a) zenith-sky
total column NO2 retrieved using empirical AMFs vs. direct-Sun total
column NO2, (b) zenith-sky total column NO2 retrieved using NDACC
AMFs vs. direct-Sun total column NO2. On each scatter plot, the red
line is the linear fit with intercept set to 0, and the black line is the
one-to-one line. The scatter plot is colour-coded by solar zenith angle
(SZA).
Comparison with satellite measurements
To illustrate the NO2 variability over Toronto, Fig. 3 shows the time
series (2015–2017) from Pandora direct-Sun, zenith-sky, and OMI SPv3.0 total
column NO2. In general, the NO2 datasets from the ground-based
Pandora instrument and the satellite follow the same pattern. However, the
satellite data are likely to miss the peak NO2 values in the morning
since OMI only passes over Toronto once per day around 13:30 LT (local
time).
Annual time series of Pandora direct-Sun (DS), Pandora zenith-sky
(ZS), and OMI SPv3 total column NO2 in Toronto from 2015 to 2017.
We also performed regression analyses by using the following coincidence
criteria: (1) nearest (in time) measurement that was within ±30 min
of OMI overpass time, (2) closest OMI ground pixel (having a distance from
the ground pixel centre to the location of the Pandora instrument less than
20 km), and (3) cloud fraction < =0.3 (the effective geometric
cloud fraction, as determined by the OMCLDO2 algorithm; Celarier et al.,
2016). In this comparison, only high-quality OMI data are used
(VcdQualityFlags =0) (Celarier et al., 2016).
Figure 4a and b show the scatter plots of OMI vs. Pandora direct-Sun and
OMI vs. Pandora zenith-sky total column NO2, respectively. Figure 4c
and d show similar comparisons but only use OMI NO2 measured by
“small pixels” (i.e., having viewing zenith angle of less than
35∘). The better correlation and lower bias for zenith-sky vs.
direct-Sun measurements might be a case of coincident errors; i.e., compared to Pandora
direct-Sun total column NO2, both OMI and Pandora zenith-sky total
column NO2 underestimate the local NO2 at Toronto (see Fig. 2).
When taking into account the standard error of the fitting and the
confidence level of R, the difference between zenith-sky and direct-Sun data
is not significant (i.e., in Fig. 4 from panels a to d, the slopes with
standard error are 0.64±0.02, 0.67±0.02, 0.70±0.04,
and 0.71±0.03; the 95 % confidence intervals for R values are 0.45
to 0.63, 0.61 to 0.75, 0.43 to 0.77, and 0.60 to 0.86). The comparison
results indicate that, at the Toronto site, OMI underestimates the total
column by about 30 %. This underestimation is qualitatively consistent
with the fact that the Pandora location is near the northern edge of peak
Toronto NO2, and the relatively large OMI pixels are also generally
sampling areas of less NO2 in the vicinity. The use of the relatively
coarse (1∘) GMI model for profile shapes (Sect. 2.1.3) will
also lead to a low bias considering the peak NOx emissions span roughly
0.5∘×0.5∘. Similar results have been found
elsewhere.
OMI vs. Pandora total column NO2 (2015–2017).
Panels (a) and (c) show OMI vs. Pandora direct-Sun
NO2, and (b) and (d) show OMI vs.
Pandora zenith-sky NO2. Panels (a) and (b) show all available OMI
measurements, while panels (c) and (d) show OMI data from small pixels only. On
each scatter plot, the red line is the linear fit with intercept set to 0
and the black line is the one-to-one line. All scatter plots are
colour-coded by the distance from the centre of an OMI ground pixel to the
location of Pandora.
Ialongo et al. (2016)
reported a similar negative bias using OMI SPv3.0 and Pandora direct-Sun
total column NO2 in Helsinki (-32 % bias and R=0.51), and they
suggested this was due to the difference between the OMI pixel and the
relatively small Pandora field of view. In Reed et al. (2015), Pandora measurements at
11 sites were evaluated; the authors found that the best correlation between
OMI SPv3.0 and Pandora direct-Sun total column NO2 data is for rural
sites. They concluded this could be due to smaller atmospheric variability
in the rural region. Other studies such as Goldberg et al. (2017) found an even worse
OMI–Pandora comparison between these two data products with striking
negative bias at high values and poor correlation (R=0.3). The authors
attributed the poor agreement to the coarse resolution of OMI and its AMFs
computed with GMI a priori NO2 profiles. In general, our comparison results
show that (1) the Pandora direct-Sun total column NO2 data measured in
Toronto have a reasonable agreement with OMI, and (2) the Pandora zenith-sky
total column NO2 data show results similar to those for direct-Sun
total column when compared with OMI SPv3.0.
The performance of the clear-sky Pandora zenith-sky total column NO2
data has been assessed by using OMI and Pandora direct-Sun data as described
in Sect. 3.2. However, the validation of cloudy-scene Pandora zenith-sky
total column data is not simple, since near-simultaneous good-quality
direct-Sun or satellite measurements in most cloudy conditions are not
available. This cloudy-scene validation can be done by comparison with in
situ NO2 measurements that are not affected by weather. In general, the
comparison between total column and surface concentrations can be done by
two approaches: (1) convert Pandora zenith-sky total columns to surface
concentrations; and (2) convert in situ surface concentrations to total
column values. For example, Spinei et al. (2018)
calculated “ground-up” VCDs from in situ surface concentrations by using
additional measurements of PBL height or assuming trace gas profiles. In
this work, the first approach is employed since the surface NO2 data
products from Pandora remote-sensing measurements have direct applications
in areas such as air quality monitoring.
Column-to-surface conversion algorithm
A simple but robust scaling method is adapted to derive surface NO2
concentration from Pandora zenith-sky total column NO2 measurements.
Following Lamsal et al. (2008) and
McLinden et al. (2014), the
surface NO2 concentration is estimated using the modelled profile and
surface concentration:
Cpan=Vpan-Vstrat-Vftrop×CVPBLG-M,
where Cpan is the surface NO2 volume mixing ratio (VMR) to be
estimated, C is the surface NO2 VMR from GEM-MACH (or G-M),
Vpan is the total column NO2 measured by Pandora,
Vstrat is the stratospheric NO2 partial column, Vftrop is the
NO2 partial column in the free troposphere, and VPBL is the
NO2 partial column in the PBL. This equation assumes the chemical
transport models can effectively capture the spatial and temporal behaviour
of the concentration-to-partial-column ratio.
In this work, VPBL (0–1.5 km) is integrated from the GEM-MACH NO2
profile and Vftrop (1.5–12 km) is integrated from the GEOS-Chem NO2
profile. Both GEM-MACH and GEOS-Chem have an hourly temporal resolution.
Thus, the integrated VPBL and Vftrop can account for NO2
diurnal variation. However, Vstrat is from OMI monthly mean
stratospheric NO2, which does not have diurnal variation. Thus, the
Pratmo box model is used to calculate stratospheric NO2 diurnal ratios.
The OMI stratospheric NO2 columns are interpolated to morning and
evening hours by multiplying by the box-model diurnal ratios. Details about
the calculation of Vstrat as well as references are provided in Appendix B.
The (C/VPBL)G-M ratio in Eq. (3) is provided by GEM-MACH, and has
hourly temporal resolution. This modelled (C/VPBL)G-M ratio is
referred to here as a conversion ratio RCV. Besides the hourly modelled
conversion ratio, a simple monthly look-up table is built using an average
of the 1.5 years of GEM-MACH model outputs (April 2016 to
December 2017) that were available. The look-up table (referred to here as
the Pandora surface-concentration look-up table, or PSC-LUT) is composed of
monthly conversion ratios with hourly resolution as shown in Fig. 5. For
example, assuming that a Pandora NO2 total column measurement is made
on a day in December at 15:00 LST, then the corresponding conversion ratio
from the PSC-LUT is 28 ppbv DU-1 (see the black arrow). Our
results in Fig. 5 show that the conversion ratio changes throughout the
day as well as with season: 0.1 DU (partial column NO2 in the PBL)
corresponds to 5–8 pptv of surface NO2 in the morning (08:00 LST),
2–3 pptv around local noon (13:00 LST), and 2–4 pptv in the evening (18:00 LST).
In general, the variation of conversion ratios demonstrates that the surface
NO2 concentration is controlled not only by PBL height but also by
both boundary layer dynamics and photochemistry. The surface NO2
derived using the hourly modelled RCV ratio is referred to here as
Cpan-model, while the surface NO2 derived using the monthly mean
PSC-LUT is referred to here as Cpan-LUT. In general, Cpan-model is a
data product that depends on daily model outputs, but Cpan-LUT only
needs the pre-calculated PSC-LUT and is thus less dependent on the model. In
general, the look-up table approach (Cpan-LUT) is aiming for a quick and
near-real-time data delivery. Thus, to minimize year-to-year variation
(e.g., from changing meteorological conditions or changing local emission
patterns), for a given year, we recommend using a mean PSC-LUT that is
calculated from model simulations of previous years. On the other hand, the
Cpan-model is the offline, high-quality, year-specific data product
that will be delivered for air quality research and other applications.
Details of these two different surface NO2 data products are discussed
in the next section.
Dependence of the Pandora surface NO2 concentration look-up
table (PSC-LUT) on month of year and hour of day. The PSC-LUT is constructed
using the GEM-MACH modelled NO2 conversion ratios. Solid lines are
monthly mean conversion ratios colour-coded by month. The shaded envelopes
are the standard error of the mean.
Modelled and Pandora zenith-sky surface NO2 vs. in situ
NO2 (2016–2017). Panel (a) shows the GEM-MACH modelled surface NO2
data vs. in situ NO2; panels (b) and (c) show the Pandora ZS
surface NO2 data vs. in situ NO2. The Pandora ZS surface NO2
data in panels (b) and (c) are derived using the hourly modelled conversion ratio
and the monthly PSC-LUT, respectively. Panels (d) to (f) are histograms
corresponding to the data in panels (a) to (c). On each scatter plot, the red line
is the linear fit with intercept set to 0 and the black line is the
one-to-one line. The scatter plots are colour-coded by the normalized
density of the points.
Comparison with measurements and model
Figure 6 shows the evaluation of modelled and Pandora zenith-sky surface
NO2 concentrations, both using in situ NO2 measurements as the
reference. The Pandora data have been filtered for heavy clouds (details are
given in Sect. 4.3). The GEM-MACH modelled surface concentrations in
Toronto reproduce the in situ measurements very well with the comparison
showing high correlation (R=0.78) and moderate positive bias (37 %,
Fig. 6a). The Pandora zenith-sky surface NO2 data, Cpan-model,
shows almost the same correlation (R=0.77), with only -7 % bias
(Fig. 6b). The better performance of Cpan-model is expected since the
conversion method for Pandora zenith-sky measurements relies on the GEM-MACH
modelled NO2 profile (see Eq. 3); in other words, the Pandora
zenith-sky surface NO2 has at least one more piece of information
(i.e., NO2 total column) than GEM-MACH surface NO2
concentrations. The Cpan-LUT shows a similar correlation coefficient
(R=0.73) and has improved bias (-3 %, Fig. 6c). This result
(slightly lower correlation) is also reasonable and acceptable since
Cpan-LUT is derived with the monthly PSC-LUT, which has less accurate
information than the hourly modelled data.
Besides the improved bias, Pandora zenith-sky surface NO2
concentrations, Cpan-model and Cpan-LUT (Fig. 6e and f), also have
better frequency distributions than the GEM-MACH (Fig. 6d). Figure 6d
shows that the NO2 surface concentrations peaks (ambient background
concentrations) from model and in situ data are misaligned. This indicates
that the GEM-MACH NO2 background surface concentrations have a 1 ppbv
low bias at this site. In contrast, the zenith-sky surface NO2 at
peak frequency matches the in situ data (Fig. 6e and f), indicating that
the low bias of the background surface NO2 value has been corrected
with this additional information from Pandora zenith-sky total column
measurements. In addition, in high NO2 concentration conditions
(> 20 ppbv), the zenith-sky surface NO2 also shows better
agreement with the in situ NO2 than do the modelled data. The mean of
the top 10 % of the in situ data is 26±1 ppbv (uncertainty of the
mean), whereas the corresponding values for GEM-MACH, Cpan-model, and
Cpan-LUT are 39±1, 26±1, and 27±1 ppbv, respectively.
Modelled and Pandora direct-Sun surface NO2 vs. in situ
NO2 (2016–2017). Panel (a) shows the GEM-MACH modelled surface NO2 data
vs. in situ NO2; panels (b) and (c) show the Pandora DS surface
NO2 data vs. in situ NO2. The Pandora DS surface NO2
data in panels (b) and (c) are derived using the hourly modelled conversion ratio and the
monthly PSC-LUT, respectively. Panels (d) to (f) are histograms corresponding to
the data in panels (a) to (c). On each scatter plot, the red line is the linear fit
with intercept set to 0 and the black line is the one-to-one line. The
scatter plots are colour-coded by the normalized density of the points.
The total column-to-surface concentration conversion algorithm has also been
applied to the Pandora direct-Sun total column NO2 (see Fig. 7).
Figure 7b shows that the direct-Sun surface NO2 data have a similar
agreement with the in situ data (-8 % bias and R=0.80) as the
zenith-sky surface NO2. In high NO2 concentration conditions,
direct-Sun data have a similarly good agreement with the in situ
measurements. For this direct-Sun based dataset, the mean of the top 10 %
of the in situ data is 27±1 ppbv, whereas the corresponding values
for GEM-MACH, Cpan-model, and Cpan-LUT are 40±1, 27±1, and 27±1 ppbv, respectively.
Thus, in general, both Pandora zenith-sky and direct-Sun surface NO2
datasets can be used reliably to obtain surface concentrations. The good
consistency between Cpan-model and Cpan-LUT implies that two
versions of Pandora surface NO2 data can be delivered in the future,
i.e., an offline version that relies on the inputs from hourly model and a
near-real-time version that only needs a pre-calculated LUT.
Measurements in different sky conditions
Although zenith-sky observations are less sensitive to cloud conditions than
direct-Sun observations, we still need to be cautious about the derived
zenith-sky surface NO2 in heavy cloud conditions. Due to enhanced
scattering, heavy clouds could lead to a significant overestimation of
surface NO2 derived from zenith-sky measurements. A cloud filtering
method based on retrieved O4 dSCDs is used to identify these
conditions. High retrieved O4 values correspond to long optical path
lengths, and therefore it is expected that corresponding NO2 values are
overestimated as discussed in Appendix C.
Example of surface NO2 concentration time series in all
conditions (April 2017). The in situ, Pandora DS, and Pandora
ZS surface NO2 concentrations are shown by different
coloured dots. The TSI relative strength of direct-Sun data is
plotted as a colour-coded horizontal dotted line in the top area of each panel.
For Pandora zenith-sky data, the measurements with enhanced O4 (heavy
cloud indicator) are also labelled by green squares. Dates are in mm/dd format.
The effectiveness of the zenith-sky NO2 in cloudy scenes is
demonstrated by the time series plots (Fig. 8) of in situ and Pandora
direct-Sun and zenith-sky data (in their original temporal resolutions).
Under clear-sky conditions (for example, 8–14 April), both Pandora
direct-Sun and zenith-sky-based surface concentrations correlate well with
the in situ measurements. Under moderately cloudy conditions, when Pandora
direct-Sun observations cannot provide high-quality data, Pandora zenith-sky
observation still can yield good measurements that compare well with in situ
data (for example, 26–29 April). Under heavy cloud conditions, however,
which are identified by enhanced O4 (Appendix C), Pandora
zenith-sky-derived surface NO2 yielded higher than in situ measurements
(for example, 4 and 6 April; see the green squares). This feature is due to
the enhanced multi-scattering in heavy cloud conditions, which leads to
enhanced NO2 absorption in the measured spectra.
Sensitivity tests (Appendix C) show that only 10 % of all zenith-sky
measurements are strongly affected by this enhanced absorption, indicating
the zenith-sky NO2 algorithm is applicable to most measurements made in
thin and moderate cloud conditions (Toronto has about 44 % of daylight
hours with clear-sky conditions per year). The relative strength of
direct Sun measured by a collocated total sky imager (model TSI-880) is
plotted at the top of each panel in Fig. 8 as an additional indicator of sky
conditions. The relative strength of the direct Sun is from the integration of
blocking-strip luminance. In general, when the relative strength of
direct-Sun is high (> 60), good-quality direct-Sun and zenith-sky
NO2 data can both be produced. However, when Sun strength is moderate
(30–60), only zenith-sky NO2 data are reliable. When Sun strength is
low (< 30), zenith-sky NO2 has increased bias and needs to be
filtered out.
Discussion
This study evaluated the performance of Pandora zenith-sky measurements with
Pandora direct-Sun measurements, satellite measurements, and in situ
measurements. In general, the quality of zenith-sky data is affected by
three main factors: (1) quality of empirical zenith-sky AMFs; (2) cloud
conditions (heavy clouds or moderate/thin clouds); and (3) quality of
modelled NO2 profile (this factor only applies to Pandora surface
NO2 data). The quality of empirical zenith-sky AMFs and the cloud
effect have been addressed in Appendices A and C, respectively. The third
factor is discussed in Sect. 5.1 and 5.2. The uncertainty estimations for
Pandora zenith-sky and direct-Sun data products are provided in Appendix D.
Diurnal and seasonal variation
From the Pandora zenith-sky and direct-Sun measurements, and modelled
NO2 profiles, surface NO2 concentrations were obtained that agree
well with in situ measurements collected at the same location. The Pandora
surface NO2 data were also analyzed in more detail with a focus on
temporal variations. Figure 9 shows the averaged surface NO2 diurnal
variations of four different datasets. The in situ instrument produces
continuous measurements 24 h d-1, whereas Pandora only has
measurements when sunlight is available. The diurnal variation of surface
NO2 concentration is controlled by dynamics (e.g., vertical mixing,
wind direction), photochemistry, and local emissions. Thus, the diurnal
variations are calculated using only the hours when in situ, direct-Sun, and
zenith-sky data are all available.
Diurnal variation of surface NO2 concentration (2016–2017).
The x axis is the local standard time (LST). Lines with dot/square symbols
represent the hourly mean of corresponding data indicated by the legend. The
shaded area represents the 1σ envelope.
Figure 9 shows that all four datasets/curves captured the enhanced morning
surface NO2 and the decreasing trend afterwards. However, the model has
a positive offset (6–9 ppbv) in the morning (due in part to the use of older
emission inventories; Moran et al., 2018) and a
negative offset (1–3 ppbv) in the evening relative to the in situ data. For
example, at 07:00 LST, in situ NO2 is 14.9±9.3 ppbv, while
GEM-MACH, Pandora DS, and Pandora ZS NO2 are 23.5±15.0,
15.6±10.5, and 15.2±6.8 ppbv, respectively. At 17:00 LST,
in situ NO2 is 7.3±5.8 ppbv, while GEM-MACH, Pandora DS,
and Pandora ZS NO2 are 5.6±5.0, 3.6±2.6, and
5.2±3.4 ppbv, respectively. The larger standard deviations in the
morning are due to the datasets not being divided into workdays and
weekends. Compared to the modelled data, the Pandora direct-Sun and
zenith-sky data show improvements in the morning but almost no changes for
the evening. This feature is investigated and found to be correlated with
the GEM-MACH modelled PBL height (details in Sect. 5.2).
Diurnal variation of surface NO2 concentration by season
(2016–2017). The x axis is the local standard time (LST). Each panel
represents data collected in one season (spring, summer, autumn, or winter).
Solid lines represent mean of corresponding data indicated by the legend.
The shaded area represents the 1σ envelope.
The diurnal variation is also examined by grouping the data by seasons.
Figure 10 shows that the surface NO2 concentrations in winter
(December, January, and February) are higher than the corresponding values
in summer (June, July, and August). This difference is mainly due to short
sunlit periods and less solar radiation (e.g., increased lifetime of
NO2 and decreased PBL height) in winter. The model has better agreement
with the in situ data in summer than in the colder seasons. The best
performance of the model is found around local noon, and this feature is not
dependent on seasons. Figure 10 also shows that the quality of Pandora
zenith-sky and direct-Sun surface NO2 estimates is affected by the
quality of GEM-MACH modelled data. For example, Fig. 10c shows that in
autumn (September, October, and November), GEM-MACH has the largest offset
in the morning. This error is thus propagated to the Pandora direct-Sun
surface data, and leads to a larger offset in the morning (than any other
season). On the other hand, when GEM-MACH shows a better agreement with in
situ measurements (e.g., in spring and summer), Pandora zenith-sky and
direct-Sun estimates also show better agreement with in situ observations.
In general, both Pandora direct-Sun and zenith-sky surface NO2 data
show good agreement with in situ measurements in all seasons; the hourly
mean values of Pandora surface NO2 are all well within the 1σ
envelope of the in situ measurements.
Planetary boundary layer effect
The larger morning offset in modelled surface NO2 may indicate that the
GEM-MACH modelled PBL heights are biased in the morning when the boundary
layer is shallow. Figure 11 (left column) shows the modelled PBL height
plotted as a function of the difference between modelled and in situ surface
NO2. Figure 11a shows that, in general, the difference between modelled
and in situ NO2 decreases with an increase of PBL height. When the
modelled PBL height is less than 100 m, the mean difference is 18±12 ppbv (1σ), while when the modelled PBL height is 1 km, the mean
difference is only 2.9±6.4 ppbv.
Illustration of planetary boundary layer (PBL) effect
(2016–2017). The y axis is planetary boundary layer height in kilometers. The x axes
for the left column are the difference between GEM-MACH and in situ surface
NO2 concentrations; the x axes for the right column are the difference
between Pandora zenith-sky (Cpan-model) and in situ surface NO2
concentration. Panels (a) and (b) show all available data, panels (c) and (d) show
the morning data (before 09:00 LST), panels (e) and (f) show the
noon data (from 11:00 to 13:59 LST), and panels (g) and (h) show the evening data
(after 15:00 LST).
Even though the modelled surface concentrations are significantly impacted
by the PBL, the modelled conversion ratio (from column to surface
concentrations) seems unaffected since the surface NO2 concentrations
derived from Pandora zenith-sky data (Cpan-model) show much less
dependence on the PBL height (Fig. 11b). When the modelled PBL height is
less than 100 m, the mean difference is 0.9±8.9 ppbv. When the
modelled PBL height is 1 km, the mean difference is slightly improved to 0.1±4.4 ppbv. Figure 11c and h show similar plots to Fig. 11a and b,
but the dataset has been divided into three time bins (before 09:00,
11:00 to 13:59, and after 15:00 LST). Figure 11c, e, and f confirm that
whenever the modelled PBL height is low, the relative difference between the
model and in situ data is high. However, in general, most of these shallow
PBL height conditions occur in the morning, and thus the modelled surface
NO2 has larger bias compared to in situ data in the morning. Figure 11d,
f, and h show that Pandora zenith-sky surface NO2 data have
similar performance for all these three time bins, which indicates that the
data have less PBL height dependency than the modelled data. In other words,
the model is able to capture the ratio between the boundary layer partial
column and surface NO2, although the PBL height may not be correct in
the model. When this ratio is applied to both Pandora direct-Sun and
zenith-sky data, the estimated surface concentrations agree better with the
in situ measurements.
Conclusions
The Pandora spectrometer was originally designed to retrieve total columns
of trace gases such as ozone and NO2 from direct-Sun spectral
measurements in the UV–visible spectrum. In this work, a new zenith-sky
total column NO2 retrieval algorithm has been developed. The algorithm
is based on empirical AMFs derived from nearly simultaneous direct-Sun and
zenith-sky measurements. It is demonstrated that this algorithm can retrieve
total columns in thin and moderate cloud conditions when direct-Sun
measurements are not available: only 10 % of the measurements affected by
heavy cloud have to be filtered out due to large systematic biases (68 %). The new Pandora zenith-sky total column NO2 data shows only
-4 % bias compared to the standard Pandora direct-Sun data product. In
addition, OMI NO2 SPv3.0 data demonstrate similar biases (-30 % and
-29 %, respectively) when compared to direct-Sun and zenith-sky Pandora
total column NO2 data.
Surface NO2 concentrations were calculated from Pandora direct-Sun and
zenith-sky total column NO2 using column-to-surface ratios derived from
GEM-MACH regional chemical transport model. The bias between Pandora-based
direct-Sun and zenith-sky NO2 surface concentration estimates and in
situ measurements is only -8 % and -7 % (with correlation coefficients
0.80 and 0.77), respectively, while the bias between the modelled
concentrations and in situ measurements is up to 37 %. The Pandora-based
surface NO2 concentrations also show good diurnal and seasonal
variation when compared to the in situ data. High surface NO2
concentrations in the morning (from 06:00 to 09:00 LST) are
present in all measured and modelled datasets, while, on average, the model
overestimates surface NO2 in the morning by 8.6 ppbv (at 07:00 LST). It
appears that the bias in modelled surface NO2 is related at least in
part to an incorrectly diagnosed PBL height. In contrast, the difference
between Pandora-based and in situ NO2 does not show any significant
dependence on the PBL height. Thus, to enable a fast and practical Pandora
surface NO2 data production, the use of a pre-calculated conversion
ratio PSC-LUT is recommended.
The new retrieval algorithm for Pandora zenith-sky NO2 measurements can
provide high-quality NO2 data (both total column and surface
concentration) not only in clear-sky conditions but also in thin and
moderate cloud conditions, when direct-Sun observations are not available.
Long-term Pandora zenith-sky NO2 data could be used in future satellite
validation for the medium cloudy scenes. Moreover, a column-to-surface
conversion look-up table was produced for the Pandora instruments deployed
in Toronto; therefore, quick and practical Pandora-based surface NO2
concentration data can be obtained for air quality monitoring purposes. The
variation of conversion ratios in the PSC-LUT demonstrates that the surface
NO2 concentration is controlled not only by the PBL height but also by
both boundary layer dynamics and photochemistry. This conversion approach
can also be used to derive surface concentrations from satellite VCD
measurements and thus can be particularly useful for the new generation of
geostationary satellite instruments for air quality monitoring such as the
Tropospheric Emissions: Monitoring of Pollution (TEMPO;
Zoogman et al.,
2014). Currently, the standard Pandora observation schedule includes
direct-Sun, zenith-sky, and multi-axis scanning measurements (i.e.,
measuring at multiple viewing angles). At present, multi-axis measurement
algorithms are still under development, but in the future, by using the
multi-axis measurements and optimal estimation techniques
(e.g., Rodgers, 2000) or the five-angle O2O2 ratio algorithm
(Cede, 2019), it may be possible for Pandora measurements to be
used to derive NO2 tropospheric profiles and columns.
Data availability
Pandora data are available from the Pandonia network (http://pandonia.net/media/documents/BlickSoftwareSuite_Manual_v11.pdf, Cede, 2019). In situ surface NO2 data are available
from the National Air Pollution Surveillance (NAPS) program
(http://maps-cartes.ec.gc.ca/rnspa-naps/data.aspx, last access: 15 August 2019). OMI NO2 SPv3.0 data
are available from 10.5067/Aura/OMI/DATA2017 (Krotkov et al., 2019). Any additional data may be
obtained from Xiaoyi Zhao (xiaoyi.zhao@canada.ca).
Empirical zenith-sky AMF
Before calculating the empirical zenith-sky AMF, the VCDDS and
dSCDZS have both been strictly filtered to ensure any measurements used
in this calculation have the highest quality. For VCDDS, data are
filtered following Cede (2019) with several factors being
considered, such as wavelength shift and residual in spectra fitting,
direct-Sun AMF, and estimated uncertainties for the vertical column. For
dSCDZS, data are filtered using similar criteria as for VCDDS,
with adjustments for zenith-sky observations.
The VCDDS and dSCDZS data are merged and divided into several SZA
bins. Each bin covers 5∘. A multi-non-linear regression is
performed by using the following equation:
VCD1⋮VCDn=dSCD1⋯0⋮⋱⋮0⋯dSCDnb1⋮bn+RCDI1⋯0⋮⋱⋮0⋯Inb1⋮bn,
where VCDn is not a single direct-Sun VCD data point but is an m×1 matrix (m is the total number of measurements in SZA bin number
n); the VCDn represents all direct-Sun VCDs in a 5∘ SZA bin, and
each element of the m×1 matrix is a single VCD in that SZA bin.
Similarly, dSCDn is also an m×1 matrix, with each element
representing a single coincident zenith-sky dSCD in SZA bin number n.
In is an m×1 indicator function, where the elements of
In are set to 1. The RCD and b1 to bn are the parameters to be
retrieved. In short, the design of this regression is based on Eq. (2)
(Sect. 3.1). The idea is to retrieve zenith-sky AMFs in several SZA bins,
and, at the same time, all these regressions in different SZA bins are
constrained to share a common predictor (RCD). The regression model can be
solved by using an iterative procedure (Seber and Wild, 2003)
to yield the estimated coefficients, b1 to bn and RCD. The bn is
the reciprocal of zenith-sky AMF in SZA bin n.
This regression model has been evaluated by using different sizes for the
SZA bins. A 5∘ SZA bin is selected because the SZA bin must be
small enough to capture the SZA dependency on zenith-sky AMFs, and, at the
same time, it must also be large enough to ensure a sufficient number of
measurements in each SZA bin (to perform reliable regressions). In order to
deal with the diurnal variation of NO2 concentration and changing of
profile shape (e.g., due to changing of boundary layer heights), the dataset
has been divided into morning and evening sets, and discrete AMFs are
retrieved for morning and evening hours separately (see the blue and red squares with
error bars in Fig. 1).
Next, these discrete AMF values are used to fit an empirical zenith-sky
NO2 AMF function, which has the expression
AMF=a1+1.02-a1/cosSZA.
The fitted empirical zenith-sky AMFs are shown in Fig. 1 as blue and red
lines (data regression period from February 2015 to September 2017). Several
sensitivity tests have been performed to assess the quality of the empirical
zenith-sky AMFs, including fitting the AMFs with/without a diurnal
difference, fitting the AMFs with different empirical functions (e.g.,
exponential and simple geometry approximation), and fitting the AMFs by
seasons. All these different choices of empirical AMFs fitting functions or
methods only introduce less than 5 % difference in the retrieved
empirical AMFs. Thus, to make the empirical AMFs simple and robust, we
selected to fit with a diurnal difference (Eq. 5). In addition, the current
empirical AMFs are limited to high- and intermediate-Sun conditions (i.e.,
SZA < 75∘). For low-Sun conditions, the total AMF for
zenith-sky measurements is expected to be a strong function of not only the
SZA but also the tropospheric column itself. Thus, for future work to
derive low-Sun empirical zenith-sky AMFs, the stronger influence of PBL
NO2 has to be accounted (i.e., the geometry-form AMFs are not enough).
Several stratospheric NO2 column values were tested and used in the
surface NO2 concentration algorithm (Eq. 3). Figure B1a shows the OMI
monthly mean (referred to as OMI) and Pratmo box-model stratospheric column
NO2
(Adams
et al., 2016; McLinden et al., 2000) (referred to as box). Since the
satellite only samples Toronto once per day, the OMI stratospheric NO2
lacks diurnal variation. To account for the diurnal variation, diurnal
ratios of NO2 VCD have been calculated and applied to OMI monthly mean
data. The stratospheric NO2 columns are calculated using
VOMIt=Vbox(t)Vboxt0×VOMIt0,
where VOMI(t0) is the OMI-measured stratospheric column, t0 is
OMI overpass time, Vbox(t0) is the modelled stratospheric column at
OMI overpass time, Vbox(t) is the modelled stratospheric column at time
t, and VOMI(t) is the interpolated stratospheric column at time t. The
interpolated OMI stratospheric columns are referred to as OMI box. The grey
dots in Fig. B1b are OMI-box stratospheric NO2 columns. The monthly
mean of the box model (blue line) and OMI box (black line) show that the
amplitude of OMI box is larger than the amplitude of the box model.
Time series of measured and modelled NO2 columns:
(a) stratospheric columns from the box model (hourly) and OMI (monthly),
(b) stratospheric columns from OMI box (hourly), box (monthly) and OMI box
(monthly), and (c) total columns from Pandora zenith sky and OMI.
To justify why this diurnal variation has to be included, Fig. B1c shows
the total column NO2 time series. The diurnal stratospheric NO2
variation is about 0.1 DU in the summer (see grey dots in Fig. A1b) when
Pandora measured monthly mean total column is about 0.5 DU (Fig. B1c).
Thus, neglecting this diurnal variation will lead to diurnal biases in the
derived surface NO2 data (e.g., in the morning, this will lead to the
overestimation of the stratospheric NO2 and thus the underestimation of
surface NO2). Please note that the strength of this bias is related to
(1) the NO2 profile (weights between stratospheric and tropospheric
NO2), and (2) the observation geometry (direct Sun or zenith sky). In
general, an urban site with direct-Sun observation should have smaller
impact from the stratospheric diurnal variation. On the other hand, a rural
site with zenith-sky observation should have a significant impact.
Cloud effect and heavy cloud filtration
Direct-Sun measurements need an unobscured Sun. Even thin clouds could
decrease the quality of retrieved NO2 total columns, especially for
low-altitude clouds. Unlike direct-Sun measurements, zenith-sky observations are
made with scattered sunlight and have limited sensitivity to cloud cover.
For example, Hendrick et al. (2011) calculated
that, for NDACC UV–visible zenith-sky ozone measurements, clouds only
contribute 3.3 % to the total random error. This is because a trace gas
that is mostly distributed in the stratosphere has the mean scattering layer
located at a higher altitude than the cloud layer. However, this assumption
may not be valid for NO2. Depending on the properties of the clouds and
the NO2 profile, the clouds could have non-negligible impacts on
zenith-sky NO2 observations.
A typical method of removing zenith-sky measurements affected by heavy
clouds is to eliminate measurements with large enhancements of O4
and/or H2O (Van Roozendael and Hendrick, 2012). In the
Pandora zenith-sky NO2 retrieval, we use the O4 dSCDs. Since the
measured O4 dSCDs has SZA dependency, all measured O4 dSCDs are
plotted against SZA, and a second-order quantile regression
(Koenker and Hallock, 2001) is applied to select the
top few percentiles of the measured O4 dSCDs.
Illustration of cloud effect and heavy cloud data filtration:
panel (a) shows measured O4 differential slant column densities vs. solar
zenith angle; the grey dots represent the top 0–10th percentile range
of O4. Panel (b) shows the scatter plot of zenith-sky vs. in situ surface
NO2 data that has O4 values within the 0–10th percentile range
(as identified in panel a); panel (c) is similar to (a) but the grey dots represent
the 40th–50th percentile range of O4; panel (d) is similar to (b) but uses
the data that has O4 value within the 40th–50th percentile range. On the scatter plots in panels (b) and (d), the red line is the linear fit with intercept set to 0 and the black line is the one-to-one line. All plots are colour-coded by the normalized density of the
points.
Correlation coefficient and bias (slope) between zenith-sky and
in situ surface NO2 data in different O4 dSCD percentile bins.
Panel (a) shows the correlation coefficients; panel (b) shows the slopes of linear fit
with intercept set to 0.
Example of surface NO2 concentration time series in all
conditions. The in situ, Pandora DS, and Pandora ZS surface NO2 concentrations are shown by different coloured dots.
The TSI relative strength of direct-Sun data is plotted as a
colour-coded horizontal dotted line in the top area of each panel. For Pandora
zenith-sky data, the measurements with enhanced O4 (heavy cloud
indicator) are also labelled by green squares. Dates are in mm/dd format.
Figure C1 shows examples of selected Pandora zenith-sky NO2 data and
their corresponding O4 dSCD values. For example, Fig. C1a shows the
O4 dSCDs vs. SZA, and the top 10 percentiles of the data with enhanced
O4 are marked in grey. The corresponding Pandora zenith-sky data are
plotted against in situ data in Fig. C1b, which shows low correlation (R=0.34)
and high bias (68 %). This result indicates that the enhanced
scattering due to heavy clouds caused a positive bias in the Pandora
zenith-sky NO2 retrieval. Figures C1c and d are similar to Fig. C1a
and b but for selected Pandora zenith-sky NO2 data that have
O4 values within the 40th to 50th percentile range. Figure C1d
shows that when O4 is not enhanced, the derived zenith-sky NO2
has good agreement with in situ data (R=0.8 and bias =-5 %). To
summarize how the retrieved O4 dSCDs can indicate the quality of the
Pandora zenith-sky NO2, the results from the other percentile bins are
shown in Fig. C2. In general, besides the top 10th percentile of data, the
results from all the other bins show good correlation (above 0.6) and low
bias. Thus, in this study, the Pandora zenith-sky NO2 data that have
O4 values within only the top 10th percentile are considered to be
affected by heavy clouds and are removed. Some examples of this heavy cloud
effect are shown in Figs. C3 and 8 in Sect. 4.3.
Uncertainty estimation
The uncertainties of retrieved Pandora zenith-sky NO2 data products
(total column and surface concentration) are estimated and discussed here to
assess the quality of the data products. The uncertainties of total column
and surface concentrations are estimated first using the uncertainty
propagation method (referred to here as the UP method) based on Eqs. (2) and (3).
The combined uncertainties of total column can be calculated using
σVCDZS=σdSCDAMF2+σRCDAMF2+σAMF×SCDAMF222,
where σdSCD is the statistical uncertainty on the DOAS
fit (output of QDOAS) and σRCD and σAMF are the
estimated statistical uncertainties using standard errors of the RCD and the
zenith-sky empirical AMF regression, respectively (Eq. A1). To estimate the
upper limit of the nominal uncertainty, AMF and SCD are used as median and
maximum values in the dataset, respectively.
The combined uncertainties of the surface concentration can be calculated
using
σCPan=RCVσVPan2+RCVσVstrat22+RCVσVftrop2+VPan-Vstrat-Vftrop2σR2‾,
where σVpan is the uncertainty of Pandora zenith-sky total
column NO2, (here we use the derived σVCD in Eq. D1),
σVstrat is the uncertainty of the stratospheric NO2
column (estimated using the 1σ standard deviation of the
Vstrat), σVftrop is the uncertainty of the free
troposphere NO2 column (estimated using the 1σ standard deviation
of the Vftrop). RCV is the GEM-MACH calculated surface VMR to PBL
column ratio, and σR is the uncertainty of that ratio
(estimated using the 1σ standard deviation of the RCV). The means
of RCV, VPan, Vstrat, and Vftrop are used in the uncertainty
estimation.
Estimated uncertainties for Pandora zenith-sky total column and
surface NO2.
Besides the UP method, another simple approach to estimate uncertainty is to
compare the data product with another high-quality (lower uncertainty)
coincident datum. For example, if we assume that the Pandora direct-Sun total
column NO2 data can represent the true value, we can estimate the
uncertainty of Pandora zenith-sky total column NO2 by calculating the
1σ standard deviation of their difference (referred to here as the SDD
method):
σVCDZS=σVCDDS-VCDZS.
Similarly, if we assume that the in situ surface NO2 VMR can represent
the true value, the uncertainty of Pandora zenith-sky-based surface NO2
VMR can be given by
σCPan=σCin situ-Cpan.
Also, if there is systematic bias between the two datasets, it can be
removed and the random uncertainty can be calculated by
D5σVCDZS=σVCDDS-k1VCDZS,D6σCPan=σCin situ-k2Cpan,
where k1 and k2 are the slopes in the linear fits with intercept set
to zero (e.g., slopes in Figs. 2 and 6). This method is referred to here as
the unbiased SDD. These three uncertainty estimation methods (UP, SDD, and
unbiased SDD) were all implemented, and the results are summarized in Table D1.
The results show that Pandora zenith-sky total column NO2 data have
a 0.09–0.12 DU uncertainty that is about twice the Pandora direct-Sun total
column nominal accuracy (0.05 DU, at 1σ level). When using the UP
method, for the worst-case scenario, the Pandora zenith-sky total column
NO2 data have a 0.17 DU uncertainty (i.e., using minimum of AMFs to estimate
the upper limit of uncertainty). The estimated Pandora zenith-sky-based
surface NO2 VMR data have uncertainties from 4.8 to 6.5 ppbv. In Eq. (D2),
the contributions of the VPan, VStrat, Vftrop, and RCV
terms to the total uncertainty are 36 %, 2 %, 0.3 %, and 62 %,
respectively. This result indicates that the uncertainty in the Pandora
zenith-sky-based surface NO2 VMR is dominated by the uncertainties of
Pandora zenith-sky total column NO2 and the modelled column-to-surface
conversion ratio (RCV). However, note that this uncertainty budget
depends on the NO2 vertical distributions and hence may vary from site
to site; e.g., in Toronto, tropospheric column NO2 is typically 2–4
times higher than stratospheric column NO2, and thus the contribution
to uncertainty from VPan is much larger than the corresponding
contributions from VStrat and Vftrop. In addition, the uncertainty
of Pandora direct-Sun surface NO2 VMR is also estimated and provided in
Table D1. It shows slightly better results than for zenith-sky-based surface
NO2 VMR.
Author contributions
XZ analyzed the data and prepared the manuscript, with significant
conceptual input from DG, VF, and CM, and critical feedback from all
co-authors. JD, AO, VF, XZ, and SCL operated and managed the Canadian
Pandora network. AL, MDM, and DG performed and analyzed the GEM-MACH
simulations. AC, MT, and MM operated the Pandonia network and provided
critical technical support to the Canadian Pandora measurements and
subsequent data analysis.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
Xiaoyi Zhao was supported by the NSERC Visiting Fellowships in Canadian
Government Laboratories program. We thank Ihab Abboud and Reno Sit for their
technical support of Pandora measurements. We thank NAPS for providing
surface NO2 data. We acknowledge the NASA Earth Science Division for
providing OMI NO2 SPv3.0 data. We also thank Thomas Danckaert,
Caroline Fayt, Michel Van Roozendael, and others from IASB-BIRA for providing the
QDOAS software, the NDACC UV–visible working group for providing NDACC
UV–visible NO2 AMF LUT, and Yushan Su from the Ontario Ministry of the
Environment, Conservation and Parks for providing NAPS Toronto north station
in situ NO2 information. We thank two anonymous referees for their
helpful and insightful comments, which improved the overall quality of this
work.
Review statement
This paper was edited by Robert McLaren and reviewed by Michel Van Roozendael and one anonymous referee.
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