Introduction
Woodsmoke resulting from anthropogenic activities is a widespread air
pollution problem in many parts of the world. For example, residential
woodsmoke is estimated to account for 20 % of total stationary and mobile
polycyclic organic matter emissions, and 50 % of all area-source air-toxic cancer risks according to the 2011 National Air Toxics Assessment in
the US (https://www.epa.gov/national-air-toxics-assessment). It is
reported that around 35 % of total fine particulate matter (PM2.5) emissions in the United
Kingdom came from domestic wood burning in 2015, while road transport only
contributed around 13 % of the total PM2.5 emissions
. In addition to its contribution to regional air quality,
residential woodsmoke may cause significant near-source air-quality impacts
due to relatively low stack heights and low exhaust temperatures. While in
some sense wood burning products may be considered natural substances, the
health effects of wood smoke are found to be comparable to those of
fossil-fuel combustion sources .
Chemicals that are enriched in woodsmoke relative to other sources have been
used to quantify woodsmoke impacts on ambient PM. Among
them, levoglucosan, a sugar anhydride derived from the pyrolysis of the major
wood polymer cellulose, has been used extensively as a molecular marker for
woodsmoke because it is emitted at high concentrations and is relatively stable
in the atmosphere . However, detecting
levoglucosan in PM samples at present requires detailed chemical analysis,
and the related information is not widely available.
Wavelength-dependent responses of the AE-33 Aethalometer to (a) woodsmoke
and (b) diesel plumes. Note that the purpose
of this figure is to reveal the qualitative differences
rather than making a quantitative comparison between the
two types of plumes.
![]()
The widely deployed Aethalometer® has made
possible continuous aerosol light-absorption measurements, commonly referred
to as black carbon (BC), at either two wavelengths (880 and 370 nm) or seven
wavelengths (370, 470, 520, 590, 660, 880 and 950 nm).
first proposed using enhanced light absorption of ambient particulate matter
(PM) at 370 nm relative to 880 nm, due to the presence of light-absorbing
materials from wood combustion near the ultraviolet (UV) range, as a marker for
woodsmoke PM. Figure depicts the distinct responses of a
seven-wavelength Aethalometer (Magee Scientific AE-33) to woodsmoke
(Fig. a) and diesel (Fig. b) plumes, providing
a context for our discussions in this paper. The source of the diesel plume
was a backup diesel generator, and the measurement was conducted in 2015. The
woodsmoke plume data was collected near a residential woodstove source in
early 2016. Note that the purpose of Fig. is to reveal the
qualitative differences between the two sources rather than making a
quantitative comparison.
The wavelength-dependent responses to woodsmoke were clearly shown in
Fig. a. At longer wavelengths, there were virtually no
differences in the signals from the 880 and 950 nm channels. At shorter
wavelengths, the 370 nm channel recorded the highest reading. We refer
to the augmented responses at shorter wavelengths compared to the 880 and
950 nm as “UV enhancement”. In contrast, virtually no
wavelength-dependence (i.e., no UV enhancement) was observed for diesel
exhaust (Fig. b). There are some slight discrepancies among
the different wavelength channels, likely due to the limitations of the
real-time dynamic spot loading correction used by the AE-33 Aethalometer. The
patterns of the wavelength-dependent responses shown in Fig.
were consistent with the findings from several previous studies, which
suggested that UV absorbing compounds are enriched in biomass-combustion PM
but scarce in diesel PM or traffic-related PM
. Broadly, the light-absorbing organic compounds,
referred to as “brown carbon” or BrC, have been shown to strongly absorb
UV .
The concept of DC (also referred to as Delta-C) originated from using the
level of UV enhancement as a marker for woodsmoke PM .
Traditionally, DC was calculated by the difference between the 370 and 880 nm
signals, i.e., DC = BC (370 nm) – BC (880 nm), due to the availability
of two-channel Aethalometer models. But the concept is not limited to those
two particular wavelengths. Figure a indicates that woodsmoke
UV enhancement starts appearing at 660 nm, and more enhancement can be
expected at even shorter wavelength (than 370 nm) not available in current
Aethalometer models. Studies show that woodsmoke enhancement peaks at
∼ 300 nm . It is possible
that including shorter wavelengths in future instrumentation would
improve the sensitivity to woodsmoke PM . Another approach
taking advantage of UV enhancement (or wavelength dependence of the aerosol
absorption coefficient in general), as reported by ,
derives light absorption Ångström exponents (AAE, or α) from
multi-wavelength Aethalometer readings. α is close to 1 for traffic
sources, and varies for woodsmoke, but is generally much larger than 1.
Assuming a certain value of α for woodsmoke,
conducted a quantitative analysis
of source contributions to PM. This approach often
requires light-absorption measurements at multiple wavelengths to have a
reliable estimate for α .
showed that using different pairs of wavelengths led to different values of
α for woodsmoke. Since the ambient data to be presented in this paper
were collected by a two-wavelength Aethalometer and given the uncertainties
associated with values of α for woodsmoke, we did not perform a direct
source apportionment analysis similar to that presented by
, but presented a qualitative analysis to be presented
later (Sect. ).
reported a strong correlation between DC and woodsmoke
markers such as levoglucosan during the heating season, and no statistically
significant correlation between DC and vehicle exhaust markers based on field
data collected in Rochester, NY. A follow-up study from the same research
group used DC as an input variable in source apportionment models, where it
was found to play an important role in separating traffic emissions (especially diesel)
from wood combustion emissions .
adopted DC as a woodsmoke marker for their fixed-site measurements in northern
New York State, and revealed temporally and spatially resolved patterns of
woodsmoke PM . However, analyzed data
for DC from an Aethalometer network in the UK and suggested the presence of other
UV absorbing contributors (such as coal burning) to the DC signal. Laboratory
experiments conducted by showed that besides biomass
burning, other sources such as uncontrolled coal (e.g., lignite) and kerosene
combustion in lamps can also lead to high DC values. In addition, some
secondary organic aerosol (SOA) products have also been found to result in UV
enhancement , and increase DC responses.
Motivated by the findings from those previous investigations, we aim to
further evaluate the applications and limitations of using DC as a
qualitative and semi-quantitative woodsmoke marker. Our work is based on
recent joint wintertime measurements of PM2.5 and light-absorptive PM in
woodsmoke-dominated ambient environments and woodsmoke plume environments in
three cities/towns located in the northeastern US. Woodsmoke is known to be
the major PM source during wintertime, and predominant PM source during
winter nighttime, in the three studied cities/towns. Neither heating by coal
nor kerosene lamps are common in this region. Furthermore, SOA formation is
typically slow during wintertime. Our study can be regarded as a “necessary
condition test” for DC serving as a woodsmoke PM marker. In other words, DC
would be deemed an inappropriate marker if it were unable to track woodsmoke
PM patterns even under woodsmoke-dominated environments. The paper is
organized in such a way that we distinguish the ambient and plume
environments by discussing their field measurements and results separately,
as the potential implications based on the two types of environments are
inherently different. Data from multiple locations and different environments
contribute to a more robust evaluation of DC.
Field measurements
Woodsmoke-dominated environments: ambient (Rutland,
Clinton and Lakeview) and plume (Ithaca)
In this paper, we report the results from field measurements conducted in
four sites in three northeastern US cities, i.e., Rutland, VT; Saranac
Lake, NY and Ithaca, NY. Table describes the general
site characteristics.
Descriptions of field measurement sites
Site Name
Environment
Monitoring method
Operation period
Site descriptions
Rutland, VT
ambient
fixed-site
October 2011 to June 2013
Co-located with FEM/FRM at AQS 50-021-0002, no nearby woodsmoke sources
Saranac Lake, NY
Clinton
ambient
December 2014 to April 2015
Located in the backyard of a residential property on Clinton Street, minimal woodsmoke sources
Lakeview
ambient
January to April 2015
Located in the backyard of a residential property on Lakeview Street, no nearby woodsmoke sources
Ithaca, NY
plume
mobile
December 2015to March 2016
Right outside the property lines of woodsmoke sources at downwind direction
Descriptions of air quality instruments deployed in various field measurements.
Site names
PM2.5
Light-absorptive PM
PAH
Others
Rutland, VT
pDR-1500 at 5 min time resolution, 2.5 µm cyclone inlet
AE-21 at 5 min time resolution, 2.5 µm cyclone inlet
not measured
FEM and FRM PM2.5 monitors
Saranac Lake, NY (Clinton and Lakeview)
pDR-1500 at 1 min time resolution, 2.5 µm cyclone inlet
AE-42 at 1 min time resolution, 2.5 µm cyclone inlet
EcoChem PAS2000 at 30 s time resolution
2-D Sonic Anemometer for wind speed and direction
Ithaca, NY
pDR-1500 at 1 s time resolution, 2.5 µm cyclone inlet
AE-33 1 s time resolution, 2.5 µm cyclone inlet
not measured
CO2 probe
Rutland is the third largest city in the state of Vermont with a population
of 16 500, where residential wood combustion is a major source of winter
space heating . According to the 2014 National Emission
Inventory, residential wood combustion (RWC) contributes to approximately
38.6 % of the annual PM2.5 emissions in Rutland County. In
comparison, on-road mobile sources only account for 1.4 %. Considering
the seasonal patterns of various emission sources, it is clear that RWC is
the predominant primary PM2.5 source in Rutland during wintertime. The
ambient air-quality monitoring site in Rutland (EPA AQS site number:
50-021-0002) is one of very few routine monitoring stations in the US
heavily influenced by woodsmoke
(http://dec.vermont.gov/air-quality/monitoring/network/rutland). Even
though Rutland is not a nonattainment area for annual or 24 h PM2.5
National Ambient Air Quality Standards, its PM2.5 design value is among
the highest in New England. The next two sites were located in Saranac Lake,
a rural town of 5400 people in Upstate New York. The 2014 National Emission
Inventory indicated that RWC accounts for approximately 22.4 to 25.4 % of
the annual PM2.5 emissions, while the contribution of on-road mobile
sources is between 2.8 and 3.9 %, which indicated that it is also a
woodsmoke-dominated environment during wintertime. Ambient PM concentrations
are generally low in Ithaca, the final site and a city of 30 500 in Central
New York. While residential wood combustion is not widespread in Ithaca, it
has caused localized air pollution hotspots and complaints against woodsmoke
were filed by affected residents living in the densely populated
neighborhoods. A primary goal for the field measurements in Ithaca was to
capture those hotspots. It is woodsmoke-dominant in nature as we purposefully
sampled woodsmoke plumes.
In short, a common feature for the three cities/towns is that woodsmoke is
the predominant PM source during winter nighttime, and the only known major
source of DC. Furthermore, the Rutland, Clinton and Lakeview sites represent
ambient environments since they captured the mixture of multiple sources, not
dominated by any individual source. By contrast, the mobile monitoring
technique employed in Ithaca was designed to capture individual sources,
thus, representing plume environments.
Table summarizes the major equipment deployed in the
different sites. Detailed descriptions of the experimental methods are
provided in Sects. 2.2 and 2.3.
Ambient Monitoring
Rutland, VT
The Vermont State Department of Environmental Conservation maintains an
air-quality monitoring site in Rutland, VT (43.608056∘ N,
72.982778∘ W; elevation: 179 m, EPA site number: 50-021-0002). This
site is located in the downtown area of Rutland, not adjacent to any known
woodsmoke sources. Routine measurements of PM2.5, O3, CO, SO2, NO,
NO2, VOCs and meteorological variables are conducted.
We deployed a personal DataRAM™ Aerosol Monitor (model pDR-1500,
Thermo Fisher Scientific, USA) and a two-wavelength Aethalometer (370 and
880 nm, model AE-21, Magee Scientific, USA) for continuous monitoring of
PM2.5 and BC, respectively, at the Rutland monitoring
site. Operating at a 5 min time resolution, both pDR-1500 (1 L min-1, no relative humidity (RH) and
temperature correction) and AE-21 (2 L min-1) were equipped with 2.5 µm
sharp-cut cyclone inlets (BGI model SCC 0.732) placed 1.5 m above the roof of
a trailer and ambient air was drawn to the instruments through an aluminum
sample line. The pDR-1500 was running from December 2011 to April 2012,
during which we were able to compare the PM2.5 readings from both
pDR-1500 and the collocated Federal Equivalent Method (FEM) instrument (BAM
1020, Met One, USA). The AE-21 was in operation from October 2011 to 11 June 2013.
All Aethalometer data were corrected for filter spot optical loading
saturation effects using the
“binned” approach, first described by , as implemented by
version 7.1 of the Aethalometer “data masher” program .
This correction provides a more robust measurement of the DC metric, since
the optical attenuation for BC at 370 nm is 2.4 times larger than at
880 nm, resulting in a larger loading artifact at the shorter wavelength. If
only BC is present, this results in a negative DC instrument response when
the loading is not corrected for.
Saranac Lake, NY (Clinton and Lakeview)
Both sites in Saranac Lake, i.e., Clinton and Lakeview, were located in the
backyards of residential properties that did not burn wood for either
recreational or heating purposes. Both pDR-1500 (1 L min-1, no RH and
temperature correction) and AE-42 (2 L min-1) were deployed with the same 2.5 µm
sharp-cut cyclone inlets as described in Sect. 2.2.1, mounted 1.83 m
(or 6 feet) above the ground. Both sites were equipped with a 2-D Sonic
Anemometer (model Windsonic, Gill Instruments, UK) for wind speed and
direction. In addition, the Lakeview site also included a Photoelectric
Aerosol Sensor (model PAS2000, EcoChem, USA) for continuous particle-bound
polycyclic aromatic hydrocarbon (PAH) measurement. The operation periods for the three fixed sites are listed
in Table .
Mobile monitoring at Ithaca, NY
As mentioned earlier, we adopted mobile monitoring techniques in Ithaca, NY
to identify air pollution hotspots caused by woodsmoke. Both the Aethalometer
(370, 470, 520, 590, 660, 880 and 950 nm; model AE-33, Magee Scientific,
USA) and the pDR-1500 were equipped with 2.5 µm sharp-cut cyclones (BGI SCC 1.197 cyclone at
2.3 L min-1 for the pDR-1500 and BGI SCC 1.829 cyclone at 5 L min-1 for the
AE-33). The sampling inlets of both instruments were mounted one foot above the
sunroof of a hybrid electric vehicle (HEV). Although the AE-33 employs automated real-time loading compensation
, no post data processing was attempted to
account for the filter loading effect. To account for the filter loading effect,
that correction was not used here since it is not appropriate for mobile
monitoring where different combustion sources are sampled in rapid
succession. Filter loading was kept relatively low to minimize any loading
effects. A flow-through type CO2 probe (model CARBOCAP®
GMP343, Vaisala, Finland) was connected to the outlet of the AE-33 to record the
CO2 level. The pDR-1500 operated without RH correction. RH in
the pDR-1500 sensing chamber was always less than 35 % without additional
sample heating as the instrument was inside a heated vehicle and the chamber
temperature was well above ambient dew point. The pDR-1500 was zeroed prior
to each mobile run. The pDR-1500 and AE-33 both operated at 1 s time
resolution, and the GMP343 at 2 s time resolution to capture individual
woodsmoke plumes.
The mobile monitoring occurred periodically from December 2015 to March 2016.
Assisted by the weather forecast from New York State Department of
Environmental Conservation (NYSDEC) staff, we chose to conduct mobile runs
only during low temperature and low wind-speed conditions, when the local air-quality impacts from woodsmoke were expected to be significant. We made a
total of 20 mobile runs (two in December 2015, seven in January, five in
February and six in March 2016). The monitoring routes were recorded at 1 s
intervals from a Delorme BU-353S4 GPS receiver using Delorme Street Atlas
2015 PLUS software.
At the beginning of the field campaign, we employed the mobile measurements
as an efficient way to survey the air-quality levels in the Ithaca area,
which then enabled us to identify a few recurring hotspots. The rest of the
field campaign focused on those recurring hotspots. Specifically, we parked
the HEV right outside the property lines of residential woodsmoke sources in
the downwind direction, and all instruments were powered primarily by the HEV
battery without self-pollution. The internal combustion engine of the HEV
occasionally turned on to recharge the battery, and caused brief periods of
self-pollution. We recorded those conditions, generally characterized by high
CO2 and low PM2.5 levels, and removed them
from subsequent data analysis.
Results and discussions
Evaluation of pDR against BAM
As mentioned in Sect. 2.2.1, we collocated a pDR-1500 with BAM 1020, which
is a FEM PM2.5 sampler, from December 2011 to April 2012 at the Rutland
site. Figure illustrates the comparisons of 24 h average
(Fig. a), nighttime (22:00 to 06:00 LT, local time) average (Fig. b),
hourly (Fig. c) and hourly nighttime-only (Fig. d)
PM2.5 from the two instruments. The main reason to present the nighttime
results was that PM during that period almost exclusively came from woodsmoke
sources in Rutland. Therefore, Fig. not only presents the
overall comparisons between the two instruments (Fig. a and c),
but also how their readings correlated for
woodsmoke-dominated environments (Fig. b and d).
Note that the apparent horizontal lines in Fig. c and d
result from the 1 µg m-3 resolution of the
hourly BAM readings.
Comparisons between PM2.5 values
from BAM 1020 (FEM) and pDR-1500 in terms of (a) 24 h
average, (b) Nighttime (22:00 to 06:00 LT) average, (c) hourly
average and (d) nighttime hourly average. The apparent
horizontal lines in (c) and (d) result from the
1 µg m-3 resolution of the hourly BAM readings.
![]()
Comparisons between BAM 1020 (y) and pDR-1500 (x) from
December 2011 to April 2012 in Rutland, VT. The values inside the parentheses
represent the corresponding one standard deviation.
Regression
r2
Daily average
y=1.082(±0.023)⋅x+2.12(±0.33)
0.956
Nighttime average
y=1.095(±0.022)⋅x+2.04(±0.32)
0.960
Hourly average
y=1.063(±0.007)⋅x+2.63(±0.10)
0.895
Nighttime hourly average
y=1.040(±0.011)⋅x+2.67(±0.16)
0.903
Table lists the metrics for the regressions. Overall, we found
a good agreement between the two instruments. The coefficients of
determination, r2, ranged from 0.895 to 0.960. As expected, the daily and
nighttime multi-hour averages (0.956 and 0.960, respectively) showed better
correlations than hourly and nighttime hourly averages (0.895 to 0.903,
respectively). For the hourly data plots, we observed the BAM noise at the
origin where pDR-1500 reads very low and the BAM PM is 2 ± 5 µg m-3.
In general, the comparison results gave us confidence in deploying
pDR-1500 for other woodsmoke studies.
Two-week moving average DC (i.e., BC (370 nm)-BC (880 nm)),
BC (880 nm), and PM2.5 values measured at the
Rutland site from October 2011 to June 2013.
![]()
Diurnal plots (i.e., averaged into 24 hours) of (a) DC
(i.e., BC (370 nm)-BC (880 nm)) and BC (880 nm), and (b) PM2.5
values measured at the Rutland site from October 2011 to June 2013.
![]()
The FRM sampler (model 2025 PM2.5 Sequential Air Sampler
w/VSCC, R&P, USA) at the Rutland site operates every third day so that we
did not include the FRM data in the comparisons. The PM2.5
Continuous Monitor Comparability Assessment at the site reported
PM2.5, FEM =0.97 PM2.5, FRM + 1.76 (R=0.97)
for Year 2011 and PM2.5, FEM = 1.07 PM2.5, FRM + 0.74 (R=0.92)
for Year 2012
(https://www.epa.gov/outdoor-air-quality-data/pm25-continuous-monitor-comparability-assessments).
DC as a qualitative marker for woodsmoke PM
Figure shows the two-week moving average for DC, BC (880 nm),
and PM2.5 values measured at the Rutland site from October 2011
to June 2013. DC is strongly linked to the season, with highest values in the
winter months and much lower values during the summer months. The summertime
DC was close to zero, and the non-zero values could be attributed to Canadian
forest fire events typically taking place during summer months
and other recreational biomass burning
activities. DC, BC (880 nm) and PM2.5 all peaked in winter months, when
they showed very similar temporal trends. This is as expected since a
fraction of woodsmoke PM is BC and woodsmoke sources led to high PM2.5
concentrations in heating seasons. Nevertheless, unlike DC, the
concentrations of BC (880 nm) and PM2.5 were also significant on occasion
in the summertime, likely driven by traffic and other emission sources. This
comparison supports DC as a qualitative woodsmoke marker.
Diurnal profiles of absorption Ångström
exponents (AAE) derived from the two-wavelength Aethalometer
data measured at the Rutland site from October 2011 to June 2013.
The recommended AAE values for traffic and woodsmoke, respectively,
by are also marked.
![]()
Diurnal PM2.5 vs. DC (i.e., BC (370 nm)-BC (880 nm)) averaged
over the wintertime operation periods for (a) the Rutland site, and over the
entire operation periods for (b) the Clinton site and (c) the Lakeview site,
respectively, into 24 h.
![]()
PM2.5 vs. DC relationships from two reoccurring woodsmoke
sources based on the plume measurements conducted in Ithaca, NY. Data are
reported as 5-second averages. The dates are expressed in YYYY/MM/DD. The
values inside the parentheses represent the corresponding one standard
deviation.
![]()
Semi-quantitative relationship between DC (µg m-3)
and PM2.5 (µg m-3) in woodsmoke-dominated ambient
environments. The values inside the parentheses represent the corresponding
one standard deviation.
Site
Regression
r2
Rutland, VT
PM2.5=10.1(±0.90)⋅ DC +7.28(±0.60)
0.852
Saranac Lake, NY
Clinton
PM2.5=16.3(±1.14)⋅ DC +4.33(±0.52)
0.903
Lakeview
PM2.5=15.3(±0.74)⋅ DC +3.90(±0.31)
0.951
The relationship between DC / BC and HDD, both presented as
monthly averaged values based on Rutland data. DC / BC is proposed
as a woodsmoke PM emission indicator. The values inside the
parentheses represent the corresponding one standard deviation.
![]()
Figure illustrates the diurnal variations in DC, BC (880 nm)
and PM2.5 concentrations, for both summer months (July to September 2012)
and winter months (December 2012 to March 2013) at Rutland. As
expected, DC showed a strong diurnal pattern in the winter months, elevated
during nighttime and peaking around 22:00 LT, and little variation during the
summer months. The diurnal patterns of BC (880 nm) persisted over seasons,
but driven by woodsmoke sources in the winter months and likely by traffic
sources in the summer months. The wintertime PM2.5 exhibited a strong
diurnal pattern, driven by woodsmoke sources, and less significant but still
noticeable diurnal pattern in the summertime, driven by traffic sources,
which were not as dominant as woodsmoke sources in Rutland, VT. The nighttime
enhancement in pollutant concentrations due to changes in the atmospheric
boundary layer also contributed to the diurnal patterns both in summertime
and wintertime. This comparison further supports DC as a qualitative
woodsmoke marker. As mentioned earlier, previous studies found that SOA
products may result in DC signals . If SOA
formation were significant, we would expect that PM2.5 and/or DC would
peak around mid-day. The distinct diurnal pattern illustrated in
Fig. is more consistent with a strong influence of local
emissions. Moreover, the seasonal trend shown in Fig.
indicates that DC peaked during wintertime when SOA production is low and
approached zero during summertime when SOA production is expected to be high.
Therefore, both the diurnal and seasonal patterns indicate that SOA is not
likely to be a main driver for DC in Rutland.
Figure depicts the diurnal profiles of AAE (also known as
α), derived from the two-wavelength AE-21 (i.e., 370 and 880 nm)
data in Rutland, for both summer months (July to September 2012) and winter
months (December 2012 to March 2013). Overall, the values of α in the
winter months (ranging from 1.37 to 1.76) are much greater than those in the
summer months (ranging from 0.93 to 1.24). recommended
values of α for traffic and woodsmoke as 0.9 and 1.68, respectively,
by comparing the source apportionment of equivalent black carbon using the
Aethalometer model originally proposed by with 14C measurements of the elemental
carbon fraction from several locations and campaigns across Switzerland.
Those α values are also marked in Fig. . Therefore,
Fig. suggests, qualitatively, that woodsmoke PM dominates
during the winter months, while traffic (or fossil fuel combustion) PM is a
major source of PM during the summer months, which is consistent with the
findings based on the emission inventory described earlier.
It is worth mentioning that both Figs. and
indicate that the woodsmoke activities are small but non-zero during the
summer months, especially during nighttime. This phenomena will be
investigated in a future study.
DC as a semi-quantitative marker for<?xmltex \hack{\break}?> woodsmoke PM
Under woodsmoke-dominated environments we were studying, woodsmoke is the
leading source of PM2.5. Thus, we explored in this section
the relationships between measured PM2.5 and DC to assess
whether DC can be used as a semi-quantitative predictor of woodsmoke
PM2.5, for both ambient and plume environments. We used the
term “semi-quantitative” for two reasons. One is that both highly
time-resolved PM2.5 and BC measurements contain significant
uncertainties. The other reason is that DC cannot be quantitatively
interpreted as an exact amount of a specific compound unless the mixture of
UV-absorbing species remains constant enough and an average absorption
cross section can be assumed.
Ambient environments (Rutland, Clinton<?xmltex \hack{\break}?> and Lakeview)
Figure depicts PM2.5 vs. DC for the three ambient
sites, where we averaged all the hourly data (binned by hours of the day,
i.e., 24 data points), over the wintertime operation periods for Rutland and
over the entire operation periods for Clinton and Lakeview, respectively. The
slopes derived from the linear regressions represent Δ(Ambient
PM2.5) / ΔDC. Table presents the linear
regression results with all correlation coefficients of determination
exceeding 0.85, which indicates strong positive correlations between changes
in DC and changes in ambient PM2.5 changes at the three sites. The most
plausible explanation is that DC is an indicator of woodsmoke PM, which
typically have a strongly diurnal pattern, considering that wood burning and
traffic are the only two major local PM emission sources, and that wood
burning is typically the dominant source of DC in ambient atmosphere. DC
signals only occur in the presence of wood burning. Furthermore, Fig. 5
suggests that averaging stationary PM and BC data over a long period of time
(e.g., over a winter month or longer in a fixed location) may lead to an
average absorption cross section, i.e., a constant Δ(Ambient
PM2.5) / ΔDC, even though PM composition and the resulting
absorption cross section may vary with time.
Furthermore, the regression coefficients for Clinton and Lakeview, the two
ambient sites in Saranac Lake, NY, were very similar, suggesting that the
Δ(Ambient PM2.5) / ΔDC is reproducible for
similar ambient environments. However, the same relationship did not hold true for
the different environment of Rutland. The inclusion of two heating seasons
for the Rutland site, compared to one season in Clinton and Lakeview, may
have also contributed to the discrepancy.
Plume environments (Ithaca)
Figure presents the PM2.5–DC relationships from two
reoccurring woodsmoke sources based on the plume measurements, reported as
5 s moving averages, that were conducted in Ithaca, NY.
Figure a–d characterized Source 1 and
Fig. e–f characterized Source 2. Both sources were woodstoves
as the configurations of the exterior stacks were consistent with this type
of heating equipment. We estimated the background PM2.5 concentrations
for each day, and the values were ∼ 3 µg m-3. Thus, we
only included data points with PM2.5 concentrations larger than
5 µg m-3 in Fig. in order to capture the
plume signals. The slopes derived from the linear regressions represent
Δ(Woodsmoke PM2.5) / ΔDC, as we conducted sampling
in woodsmoke plume environments.
Overall, we observed a dominant set of correlated measurements, likely
representing the average woodstove combustion conditions on each day. In
both Fig. c and f, “Condition 2” marked
data points that define a different correlation are plotted with different
symbols and a separate regression line. Each “Condition 2” line consisted
of plume data recorded continuously. Possibly, during those conditions the
woodstove combustion had been disturbed for some reasons (such as reloading
the stove) for both sources 1 and 2, thus significant deviation from the
average conditions (denoted as “Condition 1” on both
Fig. c and f). For both Condition 1 and
Condition 2, the correlations are generally strong. PM vs. DC slopes vary
significantly for individual sources (from 3 to 9.6 for source 1, and from
7.4 to 28.6 for source 2). Even for the same source, the slopes can change
considerably during different operating conditions. Our analysis also
suggests that the PM2.5–DC relationships can be potentially utilized to
distinguish different combustion and operating conditions of woodsmoke
sources. It is expected that cleaner burns would have a larger slope, i.e.,
less organic aerosol per unit woodsmoke PM . In other words, the different combustion conditions lead
to different chemical compositions and absorption cross sections, which can
be potentially captured by high time resolution light absorption-measurements.
However, further studies are needed to link the PM2.5–DC
relationships to specific conditions.
DC and heating degree days
Heating degree days (HDD), counted as the number of degrees that the daily
average ambient temperature (F) is below 65∘ F, have been shown to be a
better way to estimate energy use for space heating than actual temperature,
as most homes or facilities are maintained at a temperature above 65∘ F. In a
woodsmoke-dominated environment, we expected more woodsmoke with higher HDD.
We calculated the monthly average HDD for Rutland using the temperature data
recorded at the weather station located in the Rutland–Southern Vermont
Regional Airport (KRUT). In our analysis, DC / BC was adopted as a
semi-quantitative woodsmoke emission indicator.
The rationale to use DC / BC, rather than DC directly, was to take BC as a
dilution indicator to normalize DC. Even though the absolute values of DC
change with meteorological conditions, DC / BC should be driven by the amount
of woodsmoke PM emissions generated, not woodsmoke PM concentrations.
Figure illustrates the relationship between DC / BC and HDD,
both presented as monthly averaged values. We observed a relatively strong
linear relationship between DC / BC, which is an indicator for woodsmoke PM
emissions, and HDD, which is a surrogate for space heating energy use. In
other words, Fig. reveals not only a qualitative
relationship (i.e., the colder the weather, the more woodsmoke PM), but also a
potentially semi-quantitative relationship linking space heating energy and
woodsmoke PM emissions. Note that the proportionality between DC / BC and HDD
will vary from place to place, depending on various factors such as fraction
of heating obtained from biomass, and types of biomass fuels burned.
Conclusions
We presented the results from the joint wintertime measurements of
PM2.5 and light-absorptive PM in woodsmoke-dominated ambient
and plume environments in three northeastern US cities/towns, where other
types of sources contributing to DC such as uncontrolled coal and kerosene
burnings are usually rare. Our main conclusion is that DC can be a useful
woodsmoke PM marker, both qualitatively and semi-quantitatively.
As a qualitative marker, DC can track the diurnal and seasonal woodsmoke PM
patterns, approaching zero in the summertime, reaching highest values in the
wintertime, and peaking during winter nights.
As a semi-quantitative marker, we showed strong linear relationships between
PM2.5 and DC in the ambient environments, and the resulting
nearly constant Δ(Ambient PM2.5) / ΔDC values can potentially
estimate woodsmoke contributions to PM2.5.
The PM2.5 vs. DC relationship has shown to be reproducible
for similar ambient environments (like the Clinton and Lakeview sites in
Saranac Lake, NY). Nevertheless, the same relationship did not hold true for
different environments (like Rutland, VT). In other words, the relationship
depends on the environment and combustion conditions.
This paper also presented other potentially interesting findings. The
PM2.5–DC relationships can be utilized to distinguish
different combustion and operating conditions of woodsmoke sources and the
semi-quantitative relationship between DC vs. HDD could link space heating
energy and woodsmoke PM emissions. Those findings could have important
implications and applications in air-quality management. However, as
elaborated in the paper, further studies are needed to elucidate those
findings.