Sarnia, Ontario, experiences pollutant emissions
disproportionate to its relatively small size. The small size of the city
limits traditional top-down emission estimate techniques (e.g., satellite)
but a low-cost solution for emission monitoring is the mobile MAX-DOAS
(Multi-AXis Differential Optical Absorption Spectroscopy).
Measurements were made using this technique from 21 March 2017 to 23 March 2017
along various driving routes to retrieve vertical column densities (VCDs) of
NO2 and SO2 and to estimate emissions of NOx and SO2 from the Sarnia region. A novel aspect of the current study was the
installation of a NOx analyzer in the vehicle to allow real time
measurement and characterization of near-surface NOx/NO2 ratios
across the urban plumes, allowing improved accuracy of NOx emission
estimates. Confidence in the use of near-surface-measured NOx/NO2
ratios for estimation of NOx emissions was increased by relatively
well-mixed boundary layer conditions. These conditions were indicated by
similar temporal trends in NO2 VCDs and mixing ratios when measurements
were sufficiently distant from the sources. Leighton ratios within
transported plumes indicated peroxy radicals were likely disturbing the
NO–NO2–O3 photostationary state through VOC (volatile organic compound) oxidation. The average
lower-limit emission estimate of NOx from Sarnia was 1.60±0.34th-1 using local 10 m elevation wind-speed measurements. Our
estimates were larger than the downscaled annual 2017 NPRI-reported (National Pollution Release Inventory)
industrial emissions of 0.9 tNOxh-1. Our lower-limit
estimate of SO2 emissions from Sarnia was 1.81±0.83tSO2h-1, equal within uncertainty to the 2017 NPRI downscaled
value of 1.85 tSO2h-1. Satellite-derived NO2 VCDs
over Sarnia from the ozone monitoring instrument (OMI) were lower than
mobile MAX-DOAS VCDs, likely due to the large pixel size relative to the
city's size. The results of this study support the utility of the
mobile MAX-DOAS method for estimating NOx and SO2 emissions in
relatively small, highly industrialized regions, especially when supplemented
with mobile NOx measurements.
Introduction
Differential optical absorption spectroscopy (DOAS) is a remote sensing
technique that quantifies tropospheric trace gases using light spectra and
the unique spectral absorption cross sections of trace gases. DOAS has been
used since its introduction by Platt et al. (1979) to measure
small molecular species including NO2, SO2, OH, BrO, NO3,
NH3, ClO, and others. One advantage of the technique is the potential
for simultaneous quantification of multiple trace gases (e.g., SO2 and
NO2) (Platt et al., 2008). The Multi-AXis DOAS (MAX-DOAS) method allows for sensitive quantification of tropospheric
pollutants by measuring scattered sunlight spectra at multiple viewing
directions and/or elevation angles. Spectra measured at elevation angles
close to horizon-pointing angles have high sensitivity to ground-level gases since
the light paths are longer near the surface
(Hönninger et al., 2004). Ground-based
MAX-DOAS measurements quantify total boundary layer pollution loading by
determining tropospheric vertical column densities (VCDs) of trace gases.
These measurements are, therefore, well suited to measuring total emissions
into an air mass. VCDs are independent of boundary layer height, unlike
mixing ratios, and are spatially averaged (horizontally and vertically) on
the order of a few kilometres along the light path. Ground-based MAX-DOAS
can also retrieve vertical profiles of aerosol extinction and trace gases by
combining MAX-DOAS data with radiative transfer modelling
(Friess
et al., 2006; Heckel et al., 2005; Hönninger et al., 2004; Honninger and
Platt, 2002; Irie et al., 2008; Wagner et al., 2004, 2011).
The recently developed mobile MAX-DOAS technique allows for measurement of
trace-gas emissions from a region of interest by driving the instrument
around the region. The method can estimate emissions on a nearly hourly
basis in a region with a spatial resolution of ∼1km. Mobile
MAX-DOAS has been used to estimate NOx emissions from shipping and
industrial areas (Rivera et al., 2010), power plants
(Wu et al., 2017), and cities
(Ibrahim
et al., 2010; Shaiganfar et al., 2011, 2017); validate satellite and air
quality modelled VCDs
(Dragomir
et al., 2015; Shaiganfar et al., 2015); estimate surface NO2 mixing
ratios from NO2 VCDs (Shaiganfar et
al., 2011); and determine the horizontal variability in trace-gas VCDs
within satellite pixels (Wagner et al., 2010).
Mobile MAX-DOAS is a top-down approach for quantifying real-world
emissions that can be used to validate bottom-up emission inventories
(Shaiganfar et al., 2011).
Sarnia, Ontario, a small Canadian city, experiences pollutant emissions due
to a large number of industrial chemical- and oil-processing facilities;
vehicular exhaust from the Canada-USA international border crossing;
emissions from large ships travelling through the St Clair River; vehicular
traffic; residential heating and other anthropogenic emissions from the city
populace; and transnational air pollution from Ohio, Illinois, and Michigan
(Oiamo et al., 2011). These
sources contribute to increased levels of air pollutants such as NOx,
VOCs (volatile organic compounds), and SO2, which are precursors of PM2.5 and O3 (Ministry of the Environment and Climate Change, 2015).
Traditional top-down methods for quantifying pollutant emissions from
small cities (e.g., satellite monitoring, aircraft studies) are limited by
the small footprint. Additionally, in situ air quality monitoring stations
are limited by the bias towards near-surface emissions and undersampling of
elevated emissions
(Tokarek
et al., 2018).
The mobile MAX-DOAS method has advantages over satellite, aircraft, and
in situ techniques. Major advantages over satellite techniques include (1) the fact that emissions can be estimated without the need for an a priori vertical
profile, (2) accuracy of estimates can increase rather than decrease for
smaller source regions, and (3) emissions may be estimated many times per
day. Satellite retrievals are useful for estimating top-down emissions
on regional and global scales over long periods of time
(Huang
et al., 2014; Kim et al., 2014; Liu et al., 2016; McLinden et al., 2012).
However, accuracy over small regions can be limited by insufficient pixel
resolution due to horizontal averaging and retrieval reliance on modelled
a priori vertical profiles that may not resolve small regions
(Heckel et al., 2011). Aircraft
studies can quantify emissions from cities but are relatively expensive. The
major advantage of emissions estimates using aircraft measurements is that
one can in principle fully characterize the vertical profile of trace-gas
concentration as well as the vertical profile of wind vectors for an
accurate horizontal flux measurement downwind of a source
(Baray
et al., 2018; Gordon et al., 2015). Major advantages of the mobile MAX-DOAS
method over aircraft techniques are that (1) MAX-DOAS VCDs are already
vertically integrated, reducing the uncertainties due to interpolation of
measurements at multiple flight altitudes and (2) MAX-DOAS studies are
logistically easier to conduct. However, one is still left with the
uncertainty in the vertical profile of wind vector fields. The
mobile MAX-DOAS technique is a solution for quantifying pollutant emissions
that complements the aforementioned techniques as well as in situ
monitoring through the ability to observe localized surface-based and
elevated emissions.
An uncertainty associated with MAX-DOAS and satellite methods when
estimating NOx emissions from NO2 measurements is the assumptions
concerning the NOx/NO2 relationship in the air mass, which can be
variable both spatially and temporally. The NOx/NO2 ratio is often
assumed to be spatially constant, taken from literature based on the
season, estimated using atmospheric modelling, or occasionally taken from
aircraft measurements when available (Rivera et al., 2010). In this study,
we combined the mobile MAX-DOAS method with simultaneous mobile NOx
measurements (NO, NO2, NOx) to increase knowledge of the
NOx/NO2 ratio in the air mass spatially and temporally in order to
improve the accuracy of the NOx emission estimates obtained from
NO2 measurements. A stationary modular meteorological station was
deployed in the airshed and provided auxiliary meteorological information,
typically a major source of uncertainty in mobile MAX-DOAS emission
estimations. Hourly wind data measured at 10 m elevation above ground level (a.g.l.) were also
available from local, permanent monitoring stations. Vertical wind profiles
were modelled in high resolution (1km×1km) using version 3.9.1 of the
Weather Research and Forecasting model (WRF) centered on Sarnia
(42.9745∘ N, 82.4066∘ W) in an attempt to improve upon emissions
values calculated using near-surface wind speed, since wind speeds are
expected to increase with altitude. However, intercomparison of WRF
modelled winds with measured near-surface winds during the study period
indicated poor model performance (see the Supplement S2.2 for detailed results).
Emissions in this study were therefore calculated using the 10 m measured
winds to provide lower-limit estimates of the hourly emissions.
Our study objectives were to (1) examine the relationship between the
NO2 near-ground mixing ratios and the NO2 tropospheric VCDs, (2) determine NOx and SO2 emissions from the city of Sarnia including
industrial sources, (3) determine the impact of NOx/NO2 variability
on the accuracy of NOx emission estimates, and (4) examine ozone monitoring instrument (OMI) satellite
intrapixel NO2 homogeneity. This study aims to demonstrate the utility
of this method for determining trace-gas emissions and monitoring pollutant
transportation in Sarnia and similar urban and industrial areas.
Location of industrial NOx and SO2 emission
sources and meteorological stations in the Sarnia area.
ExperimentalLocation and instruments
Measurements were conducted in and around the city of Sarnia
(42.9745∘ N, 82.4066∘ W), located in southwestern
Ontario, Canada, at the border with Port Huron, MI, USA (Fig. 1). The routes
driven (Table 1) in the vehicle aimed to capture major NOx and SO2 emission
sources at different distances downwind, dependent on the prevailing wind
conditions. The metro area has a population of ∼72000 (2016
census) and an area of ∼165km2. Sources of air
pollution in this region include emissions from large ships, anthropogenic
emissions from the cities of Sarnia and Port Huron, transport from the
cities of Windsor and Detroit (60 km SW), the St Clair and Belle River
power plants (20 km SSW), oil refineries and chemical industry in Sarnia,
and the cross-border traffic between Canada and the USA along Highway 402. Emissions from ships along the St. Clair River, normally a major
source, were absent during the time of our study since the canal had not
opened for the season.
Daily meteorological conditions, number of routes, and time
period of routes driven. Wind speed from SLEA LaSalle Road; temperature and
relative humidity from portable meteorological station Day 1 and Day 2 and
from Moore Line station Day 2.
DateNumber ofMeasurementAveragePrevailingAverageAverageEmissionrouteslocalwind speedwindtemperaturerelativeareadriventime period(kmh-1)direction(∘C)humidity (%)measured21 Mar 2017410:26–13:1615Westerly1050City of Sarnia22 Mar 2017117:22–17:418Northerly-352City of Sarnia23 Mar 2017211:10–11:5715Southerly142NOVA Chemicalsindustrial facility
A mini-MAX-DOAS instrument (Hoffmann Messtechnik GmbH) measured scattered
sunlight spectra during 3 d: 21 March 2017 to 23 March 2017 (“Days 1 to
3”) while mounted on top of a car in a backwards-pointing direction. The
instrument has a sealed metal box containing entrance optics, UV fibre
coupled with a spectrometer, and electronics. Incident light is focused on a
cylindrical quartz lens (focal length =40mm) into a quartz fibre optic
that transmits light into the spectrometer (Ocean Optics USB2000)
with a field of view of approximately 0.6∘. The spectrometer has a
spectral range of 290–433 nm and a 50 µm wide entrance slit, yielding a
spectral resolution of ∼0.6nm. The spectrometer is cooled
and stabilized by a Peltier cooler. Spectrometer data were transferred to a
laptop computer via USB cable. Spectra were obtained with an integration
time of ∼1min with the continuously repeating sequence of
viewing elevation angles (30, 30, 30, 30, 40,
90∘). The vehicle was driven at a low but safe target speed of 50 kmh-1 when possible to provide a spatial resolution of ∼1km, but speeds were occasionally up to 80 kmh-1 when necessary.
Tropospheric VCDs were estimated from the 30 and 40∘ elevation
angle spectra. The 40∘ spectra allow for verification that aerosol levels
were sufficiently low to determine VCDs without radiative transfer modelling
since VCDs obtained from both angles should be equal within ±15 %
under low to moderate aerosol loading conditions
(Wagner et al., 2010). The cool temperatures in
March aided in this as secondary organic aerosol loading tends to be low in
this season due to an absence of biogenic emissions.
A model 42 chemiluminescence NO–NO2–NOx analyzer (Thermo
Environmental Instruments Inc.) mounted in the vehicle measured NO,
NO2, and NOx (NO+NO2) near-surface mixing ratios. A PTFE
inlet tube (5 m length and I.D.=1/4 in.) was mounted above the front vehicle
window on the passenger side (∼1.5m above ground). The
instrument alternately recorded average NO–NO2–NOx mixing ratios
with a temporal resolution of 1 min. Most of the routes were driven
downwind of Sarnia on rural remote roads with little to no traffic such that
NOx emissions from other vehicles were not a concern. When NOx
from other vehicles was a potential concern, data were filtered out via
careful note taking. The instrument indirectly measures NO2 by
subtracting the NO chemiluminescence signal obtained when air bypasses a
heated molybdenum (Mo) convertor from the successive total NOx
chemiluminescence signal obtained when air passes over the Mo convertor. The
NOx analyzer can overestimate NOx and NO2 due to the
potential contribution of other non-NOx reactive nitrogen oxides
(NOz) other than NO2 that can also be reduced to NO by the Mo
converter (HNO3, HONO, organic nitrates, etc.), leading to an
overestimation (Dunlea et al., 2007). Since this overestimation is
more important in low NOx regions, only data with NOx mixing
ratios >3ppb were used. Mixing ratios of <3ppbNO2 were only measured outside of plume-impacted regions when NO2
VCDs were also low. The potential error in NOx/NO2 ratios is
addressed further in Sect. 3.2. NOx mixing ratios can also have an
error when successive NO and NOx measurements occurred in areas with a
significant temporal gradient in the NOx emissions. Such gradients were
seen due to passing vehicles or localized industrial NOx plumes.
These data were removed based on records of passing vehicles and other local
near-surface sources or whenever the NO2 mixing ratios were reported as
negative. Few data points were removed because the routes driven were
primarily rural roads with extremely low traffic density.
Aura satellite ozone monitoring instrument (OMI) data were obtained for
overpasses of the Sarnia, Ontario, area for Days 1 and 3. Tropospheric
NO2 VCDs are the NASA Standard Product Version 3.0 with AMFs (air-mass factors)
recalculated using the Environment and Climate Change Canada regional air
quality forecast model GEM-MACH. The OMI instrument makes UV–Vis solar
backscatter radiation measurements with a spatial resolution of 13km×24km at nadir and up to 28km×150km at swath edges
(Ialongo et al.,
2014). The NO2 detection limit of OMI is 5×1014molec.cm-2 (Ialongo et al.,
2016). The OMI data used were screened for row anomalies that have affected
OMI data since June 2007
(Boersma et al.,
2007).
MAX-DOAS determination of VCDs
Trace-gas differential slant column densities (DSCDs) were obtained using
the DOAS technique (Platt et al., 2008) with the
spectral fitting range of 410–435 nm for NO2 at 293 K and 307.5–318 nm
for SO2 at 293 K. All trace-gas cross sections used were from
Bogumil et al.
(2003). For both gases, spectral fits also included a Fraunhofer reference
spectrum (FRS), ring spectrum created from the FRS, O3 cross sections
at 223 and 297 K, and a 3rd-order polynomial. The NO2 cross section
was included in the SO2 fits. Formaldehyde (HCHO) was not included in
the fits for SO2 as it was expected to be very low and did not affect
the residuals for the SO2 fits. NO2 DSCDs from Day 1 were fit
against a single, same-day FRS obtained in a low-pollutant region near
solar-noon time. These DSCDs were corrected for SCD(FRS) and SCD(solar
zenith angle (SZA)) contributions using the DSCDoffset method
(Wagner et al., 2010). The SCD(FRS) is the
constant tropospheric trace-gas SCD component present in the FRS that causes
an underestimation in the fitted DSCD. The SCD(SZA) is the difference
between the stratospheric trace-gas component in the FRS and the measured
non-zenith spectra. SCD(SZA) varies over time of day (ti), maximizing
overestimation in the DSCD early and late in the day. The sum of SCD(FRS)
and SCD(SZA) is collectively known as the DSCDoffset. The
DSCDoffset(ti)
function was estimated by fitting a 2nd-order polynomial to multiple
pairs of DSCDs of spectra (non-zenith and zenith from the same sequence),
described in detail in Wagner et al. (2010).
The DSCDoffset polynomial is most accurate when successive spectra in
each sequence observe similar mixing ratio fields, and measurements obtained
many data points over most of the daylight hours. However, routes on Days 2
and 3 included driving in and out of both high- and low-NOx regions
within short time periods and thus met neither of the requirements listed
above for the DSCDoffset method. On these days, a second method was
used where NO2 DSCDs were fit against an FRS spectrum obtained close in
time (<25min) along each respective route in a low-pollutant
region. The impacts of SCD(FRS) and SCD(SZA) on the retrieved DSCDs can be
assumed to be negligible since each FRS was from a low-pollutant area and
obtained close in time, respectively. This method was also used for the Day 1 SO2 route since limited data were available but included background
SO2 measurements close in time.
For all routes, trace-gas tropospheric VCDs were determined by assuming a
single scattering event occurred for each photon such that the air-mass
factor (AMF) depended only on the viewing elevation angle, α,
AMFtropα≈1sin(α).
(Brinksma
et al., 2008; Wagner et al., 2010). This “geometric approximation” is most
valid under low to moderate aerosol loading and has been shown to deviate
from the typically more accurate radiative transfer modelling by up to
±20 % under moderate aerosol loading
(Shaiganfar et al., 2011). Day 1 VCDs
were calculated following Eq. (1):
VCDtrop=DSCDmeasα,ti+DSCDoffset(ti)1sin(α,ti)
Days 2 and 3 NOx and Day 1 SO2 VCDs were calculated following Eq. (2):
VCDtrop≈DSCDmeasα,ti1sin(α,ti).
The VCD of SO2 was above detection limit on only two occasions in this
study (both on Day 1), in contrast to NO2. The detection limit of
SO2 is higher than NO2 for several reasons; first, it's
differential cross section is less than that of NO2; second, its absorption features are in the UV wavelength region where scattered
sunlight intensity is much less than that in the visible region. The fast
measurements required in mobile DOAS also allow for limited averaging of spectra
compared to stationary measurements (Davis et al., 2019), where detection of
industrial SO2 plumes is easier. Therefore, SO2 DSCDs were only
above detection limits for Day 1 routes 3 and 4 when the light levels were
highest, and the vehicle observed the combined plumes of the largest
SO2 sources in the area.
Estimating trace-gas emissions from MAX-DOAS VCDs
Trace-gas emission estimates were calculated following a flux integral
approximation Eq. (3):
E=∑iVCDoutflow,i-VCDinflux,iwisinβidsMWAv,
where VCDoutflow,i is the VCD measured at position i along the route s
for distance ds, VCDinflux,i is either the measured influx values or
the estimated background VCD value, wi is the wind speed,
βi is the angle between the driving direction
and the wind direction, MW is the molecular weight of the target gas, and Av
is Avogadro's number. Transect routes were designed to observe both within
and beyond emission-impacted areas since routes encircling the emission
sources were often not possible. Flux integrals were calculated using
portions of the transects impacted only by the Sarnia urban and industrial plume
in cases where plumes from other sources impacted the transect (i.e., Day 1;
USA power plant plumes). In these cases, the endpoints of integration
were chosen judiciously where NO2 VCDs and surface mixing ratios
decreased to a minimum at the edge of the Sarnia emissions. This method
assumes that the wind field and trace-gas emission rates are constant during
the time required to drive a route. The validity of this assumption improves
with decreased time for driving route completion. The Sarnia region is ideal
for this method since a small geographical area contains the majority of the
emissions and is surrounded on three sides by rural regions with low
anthropogenic emissions.
A potential source of uncertainty in mobile MAX-DOAS emission estimates is
variation in the wind fields and/or source emission rates while driving
(Ibrahim
et al., 2010; Wu et al., 2017). Previous studies have estimated wind fields
from local meteorology stations
(Ibrahim et
al., 2010), meteorological models
(Shabbir
et al., 2016; Shaiganfar et al., 2011, 2017), or lidar measurements
(Wu et al., 2017). In our study, wind
field information was obtained from a modular weather station (Nova Lynx
110-WS-25DL-N) that we deployed near one of the driving routes (42.8148,
-82.2381) (Fig. 1) and from meteorological ground stations in the area
(Fig. 1, Table S1, Fig. S1 in the Supplement). The modular weather station measured wind speed
and direction, temperature, relative humidity, and barometric pressure at 2 m above the surface every 30 s. Wind data were available from the
Sarnia-Lambton Environmental Association (SLEA) LaSalle Road
(42.911330, -82.379900) and Moore Line
(42.83954, -82.4208) meteorological stations that
are located near the driving routes (Fig. 1). These stations were surrounded
by flat fallow farmland for at least 4 km on each side and thus should
reflect total boundary layer for plumes transported away from the city more
than the urban stations (Fig. S1). The hourly wind direction data from the
modular and permanent stations exhibited similar values (±10∘)
and trends on Day 1 (Fig. S2). Wind directions for Days 2 and 3 were
obtained by determining the angle of a vector drawn between the geographical
locations of the maximum NO2 VCD enhancements and the industrial
facilities expected to have emitted the plumes. These map-determined
wind directions were consistent (±10∘) with the data from the
station(s) closest to the driving route. Comparison of wind-speed data on
Days 2 and 3 was not possible due to a technical issue with the modular
weather station on these days.
The NO2 VCD influx (background VCD) was estimated on Day 1 since
measurement was impossible along the western border of Sarnia due to the
road configuration and proximity of industrial emissions. A NO2
VCDinflux=2×1015molec.cm-2 was estimated
based on OMI VCDs of ∼1.5–3.5×1015molec.cm-2 from the area east of Sarnia that are expected to be
similar to the NO2 regime west of Sarnia. These pixels are expected to
be unaffected by other sources. The influx would be expected to be impacted
by vehicular and residential emissions from the small city of Port Huron,
USA, on the west side of the St Clair River (Fig. 1), which has limited
industry but a moderate level of commercial vehicle activity due to
border crossings. A 1st-order emission estimate of vehicular NOx
emissions from Port Huron from daily-reported traffic counts results in an
upper limit of NO2 influx VCD of ∼1×1015molec.cm-2 (see the Supplement S4). True influx would vary along the length
of the measurement transect, depending on what sources are upwind of the
location. Halla
et al. (2011) measured NO2 tropospheric VCDs using MAX-DOAS in a
similar region approximately 70 km southeast of Sarnia. The observed
NO2 VCDs in that study ranged from 0.01×1016 to 1.25×1016molec.cm-2 with a median value of 2×1015molec.cm-2, which is expected to be representative of background NO2
columns in this region. The highest VCD in that study was attributed to the
transport of industrial emissions from the Sarnia area and/or from Detroit,
MI, to the northwest and west of the site, respectively
(Halla et al.,
2011). Based on the range of VCDs from literature, vehicular emission
estimates, and satellite measurements, a background VCD of 2×1015molec.cm-2 is a reasonable estimate, and emissions
sensitivity tests were conducted using influx VCDs of 0.5–3×1015molec.cm-2 (the Supplement S5). In contrast, the NO2
VCDinflux on Days 2 and 3 and SO2 VCDinflux on Day 1 were
determined from the average VCDs measured in the low-pollution area of each
transect.
Determination of NOx emission estimates
from NO2 measurements
NOx emissions were estimated using Eq. (4) from the NO2 flux
integral and the average NOx/NO2 ratio (NOx>3 ppb) measured by the NOx analyzer along the route. The emission values
were then corrected for expected NOx loss during transport using a
NOx lifetime, τ. NOx emission estimates were calculated as
follows:
ENOx=ENO2⋅NOxNO2‾⋅ey/wτ,
where τ is NOx lifetime, w is wind speed, and y is the distance
between the NOx source and the measurement location. For routes where
individual NOx/NO2 ratios deviated significantly from the route
average, the NOx emission estimates were calculated by applying (1) the
route-averaged NOx/NO2 ratio and (2) individual NOx/NO2
ratios associated with each NO2 VCD point by point. Multiple factors
determine NOx lifetime in a plume. A NOx lifetime of 6 h was
used in this study based on considerations given in Sect. 3.3. A
sensitivity analysis was performed varying the lifetimes between 4 and 8 h
(the Supplement S7). The conversion factors used to calculate NOx emissions
for each route can be found in Table S8. The NOx/NO2 ratios are
more fully addressed in Sect. 3.2 and the NOx lifetime is addressed
in Sect. 3.3.
NO2 mixing ratios and NO2 VCDs along routes 1–4
on Day 1 (a–d) and route 1 on Day 2 (e). Uncertainties in measured
NO2 mixing ratios are ±0.5 ppb. Uncertainties in the NO2
VCD are given by σVCD=[(0.25VCD)2+(5×1014molec.cm-2)2]1/2.
Results and discussionRelationship between NO2 VCDs and
NO–NO2–NOx analyzer measurements
Figure 2 shows that the enhancements in NO2 VCDs downwind of Sarnia were
generally associated with NO2 surface mixing ratio enhancements during
Days 1 and 2. This suggests that pollution from Sarnia was well-mixed within
the boundary layer at the measurement locations, typically 14–23 km downwind
of sources (Figs. 3 and 4). However, the ratio of NO2 VCD to NO2
mixing ratio was sometimes variable even during relatively short time
periods when the boundary layer height was expected to be constant (Fig. 2a). This variability was probably due to the presence of multiple NOx
plumes that had originated from sources with different heights (i.e., stacks
and surface sources) and emission rates.
Day 1 driving routes used to estimate NOx emissions from Sarnia: (a) route 1, (b) route 2, and (c) route 3.
NO2 VCDs measured on Day 2 route 1.
In contrast to Days 1 and 2, NO2 VCD enhancements on Day 3 were not
consistently associated with NO2 surface mixing ratio enhancements
(Figs. 5 and 6). A large surface enhancement (NOx=22ppb) was
observed at the location of the VCD NO2 enhancements (∼2.5×1016molec.cm-2) associated with the NOVA Chemicals
industrial plume on route 2 (Figs. 5b and 6b) but not on route 1 (Figs. 5a
and 6a). This discrepancy is likely due to the closer proximity of the
driving route to the source compared with Day 1, combined with limited
vertical mixing of the plume. The relatively long sampling time of the
NOx analyzer with a relatively fast driving speed on this route may
also have led to an underestimation of the true NOx values for this
localized plume.
NOx/NO2 ratios for routes driven.
DateDay's routeMeasurement localNumber ofAverage±1σMediannumbertime periodpoints21 Mar 2017110:26–11:06371.53±0.121.4921 Mar 2017211:22–11:45231.45±0.061.4421 Mar 2017312:09–12:28181.36±0.071.3721 Mar 2017412:34–13:16241.29±0.061.3122 Mar 2017117:22–17:41101.49±0.531.3022 Mar 2017117:22–17:41*91.32±0.081.3023 Mar 2017111:10–11:1951.39±0.091.3923 Mar 2017211:42–11:5791.46±0.171.52
The 22 March 2017 17:22–17:41* data had the peak NO2 plume location
NOx/NO2 value removed.
NO2 mixing ratios and NO2 VCDs measured on Day 3
along (a) driving route 1 and (b) driving route 2. Uncertainties in measured
NO2 mixing ratios are ±0.5 ppb. Uncertainties in the NO2
VCD are given by σVCD=[(0.25VCD)2+(5×1014molec.cm-2)2]1/2.
NO2 VCDs measured on Day 3 along (a) driving route 1 and (b) driving route 2.
NOx/NO2 ratios
The NOx/NO2 ratio is necessary to estimate NOx emissions from
the source, given measurements of NO2 VCDs (Eq. 4). Ratios of
NOx/NO2 (Table 2) measured along the routes on Days 1 and 3 were
within 20 % of the route-averaged value with a relative standard deviation
of less than 12 %. NOx/NO2 ratios tended to increase at
locations associated with transported plume centerlines, as expected due
to an increase in NO emissions from the sources (see Fig. 7), and exhibited
the greatest variability in air masses affected by sources with different
altitudes and emission rates. Day 1, route 1 exhibited variable
NOx/NO2 ratios due to emissions from the power plants across the
river in Michigan, residential and vehicular traffic, and industrial
emissions (Figs. 3a and 7).
NO2 VCDs and NOx/NO2 ratios on Day 1 route 1. Detection of Michigan power plants' plume(s) (left) on east–west transect
and Sarnia plume (right) on north–south transect are highlighted in pink
and blue, respectively. Uncertainties in measured NOx/NO2 ratios
are ±5 % (∼±0.075). Uncertainties in the
NO2 VCD are given by σVCD=[(0.25VCD)2+(5×1014molec.cm-2)2]1/2.
Potential errors may exist in the NOx/NO2 ratio due to the
presence of other NOz species in the air mass (e.g., HNO3, HONO,
NO3, N2O5, organic nitrates, etc.) that are also converted to
NO by the Mo convertor in addition to NO2 (Dunlea et al., 2007).
However, these errors are smaller than might be expected due to the presence
of the error in both the numerator and the denominator of the ratio,
NOx/NO2=(NO+NO2)/NO2, thus partially offsetting
each other. For example, at an apparent NOx/NO2 ratio of 1.40
(average in Table 2), a 10 % and 30 % error in the reported NO2 due
the presence of other NOz species gives rise to errors of only -2.6 %
and -6.6 % in the measured NOx/NO2 ratio, respectively.
Mathematically, the error in the NOx/NO2 ratio becomes larger as the
percentage of NO in the total NOx increases. However, since most of the
interfering NOz species are generated photochemically, or only at night
(NO3, N2O5) increasing with reaction time and distance away
from the source, the percentage of interfering species is smaller at higher
values of total NO and NOx. Under significantly intense photochemical
conditions in the Mexico City metropolitan area (MCMA) field campaign in 2003, the interference in
the chemiluminescence monitors resulted in average NO2 concentrations
being 22 % higher than those determined from spectroscopic measurements
(Dunlea et al., 2007), which would give rise to an error in the
NOx/NO2 ratio of <5 %. In the current study we estimate
that the resultant negative bias in the measured NOx/NO2 ratio
does not exceed -5 % for several reasons: (i) we filter out low-NOx
data (<3ppb), (ii) the emission integral is dominated by regions
with high NOx that are spatially and temporally close to the sources,
and (iii) photochemistry was reduced during this spring campaign. The
uncertainty that arises from potential errors in the NOx/NO2 ratio
is insignificant compared to other errors (see Table S9). It is
also worth noting that NO2 measurements by the NOx analyzer are
not directly used for the calculation of emissions; only the
NOx/NO2 ratio is used.
Previous mobile MAX-DOAS studies have relied on literature estimates of the
NOx/NO2 ratio
(Shabbir et
al., 2016; Shaiganfar et al., 2011) or estimated the ratio from a Leighton
ratio calculated using local air quality station data
(Ibrahim et
al., 2010). In regions with many pollutant sources throughout (e.g.,
megacities), this ratio is expected to be horizontally and vertically
inhomogeneous. The ratio can therefore be challenging to estimate and can
increase the uncertainty in the NOx emission estimate. Estimation of
NOx/NO2 ratios from near-surface monitoring stations can be
problematic because the ratios are applied to a VCD but may reflect only
local emissions (e.g., nearby vehicular exhaust) rather than the total
boundary layer. In this study, NOx data impacted by local emissions
were removed. Also, the Sarnia emissions were expected to be well mixed compared to
the surface since most of the transects were driven sufficiently far from
the sources. Therefore, the near-surface NOx/NO2 ratios should be
representative for the altitude range of the dispersed NOx plume(s).
This hypothesis is supported by the similarity between the NO2 surface
and VCD temporal trends during the study, especially on Days 1 and 2 (Fig. 2).
NOx lifetime
Various lifetimes of NOx, τ, have been used in previous mobile
MAX-DOAS studies for the calculation of NOx emissions from NO2
measurements: 6 h in Germany (Ibrahim et al., 2010), 5 h in Delhi
(Shaiganfar et al., 2011), 5 h in China (Wu et al., 2017), and 3 h in summer and 12 h in
winter in Paris (Shaiganfar et al., 2017). In Beirle et al. (2011), the daytime
lifetime of NOx was quantified by analyzing the downwind patterns of
NO2 measured by satellite instruments and shown to vary from
∼4h in low- to midlatitude locations (e.g., Riyadh, Saudia
Arabia) to ∼8h in northern locations in wintertime (e.g.,
Moscow, Russia). In a follow-up study, Valin et al. (2013) showed that one
cannot assume that τ is independent of wind speed and derived values
of τ from the satellite observations over Riyadh to be 5.5 to 8 h,
corresponding to OH levels of 5–8×106molec.cm-3 at high
and low wind speeds.
Multiple factors determine NOx lifetime in a plume, including season
(e.g., insolation) (Liu et al., 2016);
latitude; wind-driven dilution
(Nunnermacker
et al., 2000; Valin et al., 2013); NOx emission rate and initial
dilution
(Nunnermacker et
al., 2000); temperature; hydroxyl radical levels (OH); and precursors to OH
including O3, H2O, and HONO. Very importantly, the daytime
lifetime of NOx is a nonlinear function of the NOx concentration
itself, having longer lifetimes at high and low concentrations with the
shortest lifetimes at intermediate NOx concentrations due to the impact
on OH levels in a nonlinear feedback on its own lifetime (Valin et al.,
2013). The NOx lifetime is ultimately dependent on the OH levels since
this dictates the loss rate of NO2 to its terminal sink (NO2+OH→HNO3). However, the presence of VOCs in the urban plume, which are
catalytically oxidized forming O3 in the presence of NOx and
HOx (OH+HO2), can decrease the NOx lifetime due to their
acceleration of the conversion of NO to NO2 via peroxy radical
reactions (RO2⚫+NO→NO2+RO⚫). Therefore, NOx
lifetimes can vary both spatially and temporally
(Liu et al., 2016), even within the same plume
(Valin et al., 2013). Underestimation of the true NOx lifetime leads to
overestimation of the NOx emissions, while an overestimate leads to an
underestimation of the emissions.
While photolysis of HONO is often the major source of OH in the morning
boundary layer (Platt et al., 1980; Alicke et al., 2002), midday production
of OH via photolysis of O3 and subsequent reaction of O (1D) with
water is frequently the dominant source of OH. Assuming O(1D) is in
steady state, it can be shown that when ozone photolysis is the main source
of OH, the product of the mixing ratios of H2O and O3 is
proportional to the production rate of OH. In this study, the
[H2O]⋅[O3] product was calculated using surrounding station
measurements (see the Supplement S8.1). The [H2O]⋅[O3] product
indicates that midday OH production under the spring conditions for Days 1
and 2 is only 10 %–25 % of the expected OH production under warmer, more
humid summer conditions, presuming that O3 photolysis predominates.
This might suggest OH levels were lower in our study than during summer, and
hence NOx lifetimes longer. However, we assume this with caution as the
HONO production is not known nor are the loss rates of OH.
As mentioned, the presence of VOCs can decrease the lifetime of NOx under
conditions where NOx is sufficiently high to dominate the peroxy radical
reaction path. To test for the presence of VOCs in the plumes (in the
absence of measurements), Leighton ratios, φ (Leighton,
1961), were calculated at locations of maximum NO2 VCD associated with
Sarnia plumes. Leighton ratios were calculated following Eq. (5) (see
the Supplement S8.2 for details):
ϕ=jNO2NO2k8NOO3,
where jNO2 is the NO2 photolysis rate, k8 is the
temperature-dependent rate constant for the reaction between NO and O3.
Leighton ratios equal to 1.0 indicate that NO, NO2, and O3 are in
steady state with no significant interference from other species, while
ratios of φ greater than 1.0 imply the role of other peroxy radical
species (e.g., RO2, HO2) in the conversion of NO to NO2
(Finlayson-Pitts and Pitts, 2000). The NO2/NO ratios were obtained from
the NOx analyzer measurements; O3 mixing ratios were obtained from
local monitoring stations during the same daytime periods as the transects.
Values of jNO2 were estimated using SLEA Moore Line station solar
irradiance data (Fig. 1; Table S1) and solar zenith angle following the
method in Wiegand and Bofinger
(2000).
Calculated Leighton ratios for selected plume maximums on
Day 1 and Day 2.
DateLocalJNO2Solar irradianceSolar zenithO3 mixingMeasured NO2/NOCalculated Leightontime(×10-3s-1)(Wm-2)angleratio (ppb)(ppbppb-1)ratio*21 Mar 201711:005.2356435181.71.6121 Mar 201711:305.6560040232.21.7621 Mar 201712:156.4467543232.22.0122 Mar 201717:282.7130023100.50.44
* Note that Leighton ratios, φ, could be biased high by
as much as +20 % from the NOz component of NOy measured by
the NOx analyzer but likely much lower due to it being a fresh
urban and industrial NOx plume.
Table 3 shows Leighton ratios calculated at the locations of maximum
NO2 VCD enhancements. Calculated Leighton ratios were significantly
greater than 1 (φ=1.7–2.3) at peak NOx locations on Day 1
(Table 3). Even if we consider a potential bias of +20 % in the
NO2 measurements by the NOx analyzer for reasons outlined in
Sect. 3.2 (highly unlikely in a fresh NOx plume), a +20 % bias in
the Leighton ratio would still give φ=1.4–1.9. We interpret
this as an indication that significant levels of peroxy radicals were
present in the plume, presumably from VOC oxidation by the OH radical. This
is consistent with high VOC emissions from the petrochemical facilities in
Sarnia, with emission rates >300tyr-1 each for four
of the top six industrial NOx emitters in Sarnia
(Environment and Climate Change Canada, 2018d). The Day 2
Leighton ratio of less than 1.0 in Table 3 suggests a relatively fresh plume
(only 4 km downwind of a facility) that had not come to photostationary
state.
Thus we have indications that OH production may be lower than summer time
leading to longer NOx lifetimes and we have indications that VOC oxidation
in the plume may be significant leading to shorter NOx lifetimes than air
masses where the photostationary state in NOx is valid. Without
further information, we have opted to assume a central NOx lifetime
of ∼6h. Sensitivity calculations were conducted for
NOx emission estimates using a range of lifetimes of 4–8 h
(the Supplement S7). Varying the lifetime from ±2h changed the
emission estimates by <15 % for all routes except for Day 1 route 1 due to low wind speeds during that route (30 % change).
For the calculation of SO2 emissions, SO2 was assumed to have a
sufficiently long lifetime in the boundary layer so as to be conserved
between the emission and measurement location. Note that cloud processing of
SO2 was assumed to be negligible since SO2 measurements were
completed on a mostly cloud-free day.
Lower-limit NOx emission estimates from 10 m elevation
wind speeds.
DateEmissionDaily routeLower-limit NOxNPRI NOxsourcenumber(th-1)(th-1)21 Mar 2017Sarnia11.6±0.80.921 Mar 2017Sarnia21.2±0.50.921 Mar 2017Sarnia31.4±0.50.922 Mar 2017Sarnia11.5±0.60.922 Mar 2017Sarnia1*2.2±0.80.923 Mar 2017Nova Chem10.27±0.10.1423 Mar 2017Nova Chem20.29±0.10.14
* Calculated using individual NOx/NO2 ratios.
Emission estimatesEmission estimates of Sarnia
The VCDs measured are shown in Figs. 3–6, while the NOx emissions
calculated using Eqs. (3) and (4) are shown in Table 4. The values of
VCDinflux required for the calculations were typically determined from
measurements of VCD in low-pollution transect areas. However, the
VCDinflux on Day 2 was not determined in this way since these DSCDs
were close to zero within error (Figs. 2 and 4). The VCDinflux is
expected to be low on Day 2 because the north wind direction indicates that
the air masses originated from over Lake Huron. These low values were
probably due to low light levels during measurement, insufficiently long
integration times (low signal to noise ratio), and NO2 background VCD
values below the instrument's limit of detection. A low value of
VCDinflux=0.5(±0.5)×1015molec.cm-2 was
therefore assumed.
The emissions were calculated in two ways: (i) using a route-average
NOx/NO2 ratio value for each route estimate and (ii) using
individual NOx/NO2 ratios co-located with each VCD measurement.
For Day 1 route 1, the route average NOx/NO2 ratio was 1.53±0.12ppbppb-1 with the difference between the calculated
emission rates using the two methods being only 3 %. Day 1 routes 2–4
exhibited small variability in NOx/NO2 (Table 2), and the variation
in the NOx/NO2 ratio impacted emission estimates by less than
5 %.
Lower-limit estimates of NOx emissions from Sarnia on
Day 1 and Day 3 and 2016 NPRI emissions. The 22:1* NOx emission
estimate used individual NOx/NO2 ratio values for each VCDs rather
than a single average ratio.
However, the difference between emission estimates calculated using
individual NOx/NO2 ratios versus a route-averaged value can be
nontrivial, as observed with the Day 2 route 1. Day 2 had consistent
northerly wind conditions, and east–west transects were driven south of
Sarnia to capture the urban plume and background regions to the east (Fig. 4). The resultant Sarnia NOx emission using the first method is
consistent with the first three Day 1 emission estimates but the application
of the second method (individual NOx/NO2 ratios collocated with
each VCD) increased the emission estimate by ∼50 % (Table 4
and Fig. 8). The NOx/NO2 ratio was generally consistent with the
averaged value of 1.3 (maximum NOx/NO2 removed) but increased to 3
in the region of maximum NO2 VCD enhancements 7 km south of the NOVA
Chemicals facility (Table 2). The calculated Leighton ratio for this peak
NOx/NO2 ratio location is less than 1 (see Sect. 3.3 and Table 3). The
Leighton ratio suggests the plume from the NOVA Chemical facility had
significant NO that had not had sufficient time to come to a photostationary
state. The emission estimate using individual NOx/NO2 ratios is
considered the more accurate value for this route compared to the emission
value calculated using the route-averaged ratio.
The importance of measuring the local NOx/NO2 ratio is also
illustrated by observing variation in the ratio due to the impact of the
Michigan power plant plumes, apparent in the Day 1 route 1 east–west
transect (Fig. 3a). The NOx/NO2 ratio along this transect
increased to ∼1.7 (Fig. 7), higher than the maximum
NOx/NO2 ratio observed in the north–south transect downwind of
Sarnia. A higher ratio is somewhat unexpected because the distance between
the source and receptor measurement for the power plant source was greater
than the source–receptor distance for the Sarnia sources. Thus, the
power plant plume would have been expected to be more aged, but the results
suggest that the power plant plumes had a slower conversion of NO to
NO2 perhaps due to higher initial mixing ratios of NOx (Nunnermacker et
al., 2000). Very high NO mixing ratios in a power plant plume (i.e.,
>40ppb) could completely titrate the ambient O3 in the air
entrained into the plume, an observation previously seen in power plant
plumes (Brown et al., 2012).
The east–west transect appears to have captured approximately half of the
power plant plume since the NO2 VCDs and the NO2 mixing ratios
increase from background to a plateau at a maximum (Fig. 2a). A preliminary
estimation of the NOx and SO2 emissions from the power plants can
be determined by scaling up the flux integral from the appropriate section
of the east–west transect by a factor of 2. While this is highly
uncertain, we do this to make a 1st-order estimate of the power plant
plumes on the US side of the border. In this case, we have used
VCDinflux=2–3×1015molec.cm-2 for NOx
and zero for SO2 since the background region SO2 DSCDs were at
or below detection limits. The NOx estimate used individual
NOx/NO2 ratios because the NOx/NO2 ratio was
significantly higher in the plume than outside the plume. This illustrates
the importance of in situ instruments of NOx/NO2, especially when
close to the source where plume NOx/NO2 ratios can be variable
(Valin et al., 2013). Given the above assumptions, a tentative 1st-order
estimate of the total emissions from the power plants are 0.31–0.46 tNOxh-1 and 0.77 tSO2h-1, respectively. The
hourly emissions of the power plants from reported 2015 annual values are
0.74 tNOxh-1 and 2.56 tSO2h-1
(United States EPA, 2018). Our hourly estimates are
only preliminary since only half of the plume (approximately) was captured
by the measurement transect.
The NOx emission estimates from Sarnia from Day 1 are consistent
within 25 % and are consistent with the Day 2 estimates within the
calculated error of approximately ±45 % (Fig. 8, Table 4). Some
variability between the emission estimates is expected due to wind data
uncertainties, NOx/NO2 vertical profile variability, errors
introduced by using a constant VCDinflux and NOx lifetime, and
temporal variations in emissions from the source.
Conversion of the hourly-measured emissions to annual emissions would
require knowledge and application of daily, weekly, and seasonal emission
profiles, which is beyond the scope of this work. The mobile MAX-DOAS
emission estimates are reported in units of tonnes per hour since routes
were completed within <40min. Events such as flaring can
significantly increase the instantaneous emission rate but are excluded from
the annual emission inventory data. However, there was no reported flaring
during the measurement period (Ontario Ministry of the Environment, Conservation and Parks – MOECC, personal communication, 2017). NOx
emissions from petrochemical facilities, excluding flaring, typically have
low variability during periods of continuous operation. According to
Ryerson
et al. (2003), variation in average hourly NOx emissions from a
petrochemical facility reported by industry (continuous emissions monitoring system data) was <10 %
from an average of the hourly-averaged emissions over 11 d in Houston,
Texas. However, this trend may be different for the chemical industry. A
1st-order comparison to the 2017 National Pollution Release Inventory
(NPRI) values (downscaled by assuming constant emissions) was made to
determine whether our measured Sarnia emissions are reasonable. The NPRI
value is the sum of the NOx emissions from the top nine industrial
emitters of NOx in Sarnia, whose emissions would have been captured
along the driving routes. The NPRI requires significant point-source
industrial facilities to report their pollutant emissions, but the method of
estimating emissions can vary by facility (Environment and Climate Change Canada, 2015). The
NPRI emission value does not include mobile and area sources from the Sarnia
region. Thus, the NPRI emission inventory values for Sarnia would be
expected to be smaller than our measured emissions because of this
exclusion. The measured NOx emissions are larger than the 2017 NPRI
value but not statistically so (Fig. 8; Table 4). The exception is the Day 1
route 1* value, which is statistically higher. The average of the four
NOx emission estimates from Sarnia is greater than the 2017 NPRI value.
These results demonstrate that our measured emission rates are reasonable.
Future mobile MAX-DOAS studies could focus on determining diurnal trends in
emissions by driving multiple routes at as many times of the day as possible
on multiple days, seasons, and weekdays or weekends. Measurements of vertical
wind profiles could reduce emission uncertainty to allow for identification of
temporal trends by comparing same-day measurements.
Average emission estimates from mobile MAX-DOAS using 10 m wind speeds and from NPRI.
Apart from NOx, we were also able to estimate SO2 emissions from
the Sarnia urban and industrial region during one route when the SO2 DSCDs
were detectable, Day 1 route 3 (Table 5). Our SO2 emission estimate
using the 10 m wind speed is consistent within error with the 2017 NPRI
value (Table 5). We expect our SO2 emission estimate to be closer to
the NPRI values compared to the NOx estimates because SO2
emissions from area and mobile sources in Sarnia are expected to be small
relative to industrial sources (Ministry of the Environment and
Climate Change, 2017). Since ships were not operating in the St Clair River
at this time of year, shipping emissions of SO2 were absent. Thus
SO2 plumes in this region are localized to the major industrial
emission sources. Therefore, the VCDs from the areas unaffected by the
Sarnia plumes are representative of background values, VCDinflux. While
the mobile MAX-DOAS was able to capture these plumes (Fig. 9), only one of seven
local monitoring stations (LaSalle Road, Fig. S1) observed elevated levels
of SO2 during this period. The undersampling by stations is due to the
highly localized nature of the SO2 plumes that are from stacks where
the plume is frequently elevated above the surface. These results illustrate
the complementary nature of mobile MAX-DOAS and in situ measurements and the
importance of monitoring techniques that can capture localized plumes
independent of the wind direction.
SO2 VCDs along route for emission estimate (Day 1
route 3).
Emission estimates of NOVA Chemicals industrial facility
NOx emissions were opportunistically measured from a single facility on
Day 3 because the southerly wind directions isolated this plume
(Environment and Climate Change Canada, 2018b) from other
industrial sources in Sarnia. The plume originated from Nova Chemicals (labelled Nova Chem), the
second highest emitter of NOx in the region in 2017. These conditions
allowed us to test the mobile MAX-DOAS method in isolating a single plume.
The wind direction on Day 3 indicated that the air masses originated from
rural areas south of Sarnia and the VCDinflux was expected to be low,
∼1×1015molec.cm-2.
The emission estimates of NOx from the two routes on Day 3 from the
NOVA Chemicals industrial site (Tables 4 and 5) are consistent with each
other within 10 %. The consistency increases confidence in fitting the
spectra in each transect against a local FRS and removing influx using the
average background VCDs rather than using the DSCDOffset method
in this case. The use of background VCDs is appropriate because
vehicular traffic upwind of the measurement transect is minimal in the local
area. Upwind emissions were unlikely to have contributed significantly to
the total measured emissions. The emission estimates from NOVA Chemicals are
larger than the 2017 NPRI values (Tables 4 and 5). This comparison merely
indicates that the mobile MAX-DOAS values are reasonable given that there
was likely diurnal variability and the measurements were taken only during a
single hour on a single day.
Day 1 NO2 VCDs from OMI satellite VCDs and
mobile MAX-DOAS route 4. OMI satellite pixels closest to Sarnia were
measured at ∼18:00 local time. Semiopaque rectangles
centered on the coloured dots (indicating satellite VCD value) indicate the
spatial extent of the pixel.
Comparison of OMI satellite and MAX-DOAS VCDs
The satellite and MAX-DOAS NO2 VCDs on Day 1 exhibit similar spatial
trends in the simple sense that NO2 VCDs increase towards the south
from the background regions north of Sarnia (Fig. 10). This trend is
probably due to a combination of emissions from USA power plants, the
Detroit area, and Sarnia. The NO2 VCD of the pixel containing the
majority of the Sarnia industrial facilities is comparable to rural area
VCDs to the northwest of Sarnia. Only 1/8 of the Sarnia pixel's
footprint region is likely to be impacted by Sarnia emissions, and the
remainder observes mostly rural to semirural regions. The OMI pixel from
Day 3 (Fig. 11) containing Sarnia exhibits a minimal increase in NO2 VCD (1–2×1015molec.cm-2) compared to the surrounding
background regions (Fig. 11). In contrast, the mobile MAX-DOAS measurements
observed VCD enhancements of up to 1×1016molec.cm-2
within this pixel. The averaging due by the large pixel size (24km×84km) causes underestimation of the maximum VCDs.
Identification of Sarnia-only emissions without error due to horizontal
averaging or inclusion of other sources may require satellite measurements
with nadir-viewing pixels centered on Sarnia and/or extremely large
averaging times.
Day 3 NO2 VCDs from OMI satellite and
mobile MAX-DOAS route 1. OMI pixels shown were measured at ∼18:00 local time. Semiopaque rectangle centered on the coloured dots
indicates the spatial extent of the pixel.
Uncertainties in this study and recommended improvements for
mobile MAX-DOAS measurements
Many of the factors that increased the uncertainty in the emission values in
this study can be significantly reduced in future through relatively small
changes in the method. The many factors have been addressed in the Supplement (Sect. S7) and summarized in Table S9. Ideally accurate
horizontal flux measurements would require knowledge of the vertical and
horizontal profile of pollutant concentrations as well as the vertical and
horizontal profile of wind vectors. Lack of knowledge of the vertical
profile of wind speed increases uncertainty in mobile MAX-DOAS emission
estimates since elevated plumes and well-mixed plumes are transported by
winds with typically higher speeds than those near the surface. Future
studies could focus on reducing uncertainty by using measurements from
sodar, lidar, tall towers, balloon soundings, or a radio acoustic
meteorological profiler. In this study, uncertainty was increased (18 %–30 %
based on sensitivity analysis; see Supplement S5 and S7) because driving
routes could not always include measurements along influx regions (Day 1)
due to road proximity to sources or obstructions to the viewing field.
Future experiments could measure influx values while stationary at multiple
locations along the upwind region chosen for an unobstructed viewing field.
Very low background trace-gas levels also resulted in SO2 DSCDs that
were below detection limit most of the time, while being occasionally below
detection limit for NO2 (Fig. 2e). A spectrometer with higher
sensitivity giving lower detection limits could solve this issue. Increased
averaging of spectra would also improve detectability but at the expense of
worse spatial resolution, unless measurements can be made at a slower
driving speed. Uncertainty in the NOx lifetime was a small contribution
to uncertainty in this study (up to ±12 %) because the distances
and transport times between source and measurement locations were relatively
small (<25km). The exception was Day 1 route 1 where uncertainty
was up to 30 % due to low wind speeds. The error contribution of NOx
lifetime could be nontrivial if driving routes are far from the sources
(e.g., large cities). This error could also be nontrivial if the lifetime
that one assumes does not account for the multiple factors discussed in
Sect. 3.3. Bias in the emission estimates from an incorrect lifetime could
be avoided by determining NOx lifetimes from photochemical modelling
or, for large cities, satellite observations
(Beirle et al., 2011) but taking into
account wind speeds (Valin et al., 2013).
Conclusions
In this study, we combined mobile MAX-DOAS techniques with mobile NOx
measurements and a modular meteorological station to measure emissions of
NOx and SO2 from the Sarnia region, a relatively small
urban and industrial city. Trace-gas VCDs were determined using the
DSCDoffset method (Wagner et al., 2010) or
by fitting measured spectra against a route-local low-pollution spectrum.
Both methods provided good results, which suggest that the first method is
ideal if there are many hours of measurements and the second method is
ideal when short routes contain low-pollution regions. Average lower-limit
mobile MAX-DOAS emissions of NOx from Sarnia were measured to be 1.60±0.34th-1 using 10 m elevation measured wind speeds. The
estimates were larger than the downscaled 2017 NPRI-reported industrial
emissions of 0.9 th-1 (Environment and Climate
Change Canada, 2018b) but the NPRI estimate excludes area and mobile
emissions. Our lower-limit SO2 emission measurement for Sarnia was 1.81±0.83th-1 using 10 m wind speeds, which is equal
within uncertainty to the 2017 NPRI value of 1.85 th-1
(Environment and Climate Change Canada, 2018c). Our average
lower-limit NOx emission measurement from the NOVA Chemicals facility
was 0.28±0.06th-1, the same order of magnitude as the
2016 NPRI value of 0.14 th-1 (Environment and
Climate Change Canada, 2018a).
Simultaneous measurements of NO–NO2–NOx improved the accuracy of
NOx emission estimates when plumes of varying ages were observed. The
NOx results from Days 1 and 2 suggest that accurate mobile MAX-DOAS
NOx emission measurements from routes that observe plumes with
differing ages require accurate knowledge of the localized NOx/NO2
ratio.
The variability in the ratio of the NO2 VCDs and mixing ratios
indicates that surface NO2 mixing ratios cannot be reliably estimated
from NO2 VCDs and boundary layer height alone when pollution is emitted
from sources of varying heights and chemical composition. A
NOx analyzer can be an essential component of mobile MAX-DOAS NO2 measurements. The addition of this instrument allows the method to
characterize the boundary layer fully and accurately estimate NOx emissions from NO2 measurements when multiple NOx sources are
present and when transects are sufficiently distant from the sources.
The modular meteorological station improved knowledge of local wind
essential to identify time periods of low temporal variability, ensuring low
error due to wind estimation. These time periods would have been difficult
to identify with only hourly-averaged or modelled wind data. Accurate
knowledge of the vertical wind profile would significantly enhance the
accuracy of the mobile MAX-DOAS emission estimates. Future studies could
obtain vertical wind profiles using sodar, lidar, windRASS, and radiosonde
on a weather balloon or local aircraft soundings.
Mobile MAX-DOAS measurements identified significant OMI intrapixel
inhomogeneity and observed industrial pollution enhancements that were
poorly captured by the in situ ground stations. These results suggest that
mobile MAX-DOAS has clear advantages in similar industrial regions over
other remote sensing techniques used for estimating emissions (e.g., using
aircraft or satellite): higher spatial resolution, the potential for
multiple emission estimates per day (i.e., observations of diurnal trends),
and much lower operational costs. Mobile MAX-DOAS is a top-down low-cost
solution for validating bottom-up inventories that complements in situ
monitoring and has significant utility in smaller regions with significant
emissions where satellite applications are limited. Future mobile MAX-DOAS
studies in such regions can focus on measuring temporal trends in emissions.
Data availability
The MAX-DOAS data collected from this study are publicly available with the following DOI: 10.5683/SP2/8C4CLX (Davis and McLaren, 2019).
The supplement related to this article is available online at: https://doi.org/10.5194/acp-19-13871-2019-supplement.
Author contributions
ZYWD conceived of and organized the field campaign with aid from RM. ZYWD, SB,
AK, WF, CC, and RM carried out the experiments in Sarnia. CAM modelled
conditions for the satellite retrievals of NO2 in the region of Sarnia
and provided useful advice. ZYWD and RM prepared the manuscript, with
contributions from all co-authors.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
This study was completed with collaborative support from the Ontario Ministry
of the Environment and Climate Change (MOECC). Zoe Y. W. Davis would like to acknowledge support from the York University Faculty of Graduate Studies and would like to thank Barry Duffey at the Ontario
Ministry of Environment and Climate Change for his support at the project
start. We also thank Tony Munoz of MOECC for his continued support of our
research.
Financial support
This research has been supported by the Natural Sciences and Engineering Research Council of Canada (Discovery grant no. RGPIN-2018-05898) and Collaborative Research and Training Experience (grant no. 398061-2011).
Review statement
This paper was edited by Robert Harley and reviewed by two anonymous referees.
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