Introduction
Vegetation fires are estimated to emit ∼2.0 Pg of carbon per
year into the atmosphere , influencing air quality,
weather and climate .
Particulate matter emissions adversely affect human health and mortality
rates , and have a substantial but very
uncertain effect on the Earth's radiative budget .
Climate model intercomparison indicates little
agreement in simulated magnitude and sign of direct radiative forcing
attributed to biomass burning emissions. Smoke plumes are difficult to
observe, characterise and represent in climate models because they contain
chemically and microphysically complex particles that evolve during their
atmospheric lifetimes. Improving the characterisation of emission factors,
aerosol optical properties, mixing effects and microphysical processes in
modelling schemes were identified as key directions narrowing the
uncertainties and reducing model and observational biases
.
Characterisation of smoke aerosols is a fundamentally difficult problem
because of the dynamic nature of combustion particle formation and evolution.
Particulate emission from open biomass burning consists of organic matter,
soot carbon and trace inorganic elements. Different chemical species are not
discretely separate, but are internally mixed . Smoke
particle size distributions are typically bimodal with the bulk of mass
concentrated in the fine mode (aerosols 0.1 to 1 µm in diameter).
Particle production rates, the proportions of different chemical species,
particle size distributions, mixing state and hence optical properties of the
particles vary greatly depending on the fuel type and moisture, a ratio of
flaming versus smouldering combustion, fire intensity, and meteorological
conditions at the time of emission and references
therein.
The complexity of the parametrisation increases with ageing processes.
Emitted smoke particles are lofted to altitudes ranging from hundreds
to thousands of metres, frequently above the planetary boundary layer
, and in some cases to the
lower stratosphere . During the lifetime of
several days to weeks plumes can be transported on regional
or intercontinental scales.
Most of the changes occur within minutes up to a few hours after emission.
Above the flaming zone in cooling plumes particles grow rapidly
in size and mass by condensation and coagulation. During the first
few hours the particle distribution volume median radius
has been reported to increase by up to ∼60 % .
measured a growth in volume median radius from 0.125 to 0.19 µm
in 2 h for a large and intense prescribed burn in North America. Near-source condensation of low pressure vapour organics and secondary production
of inorganic and organic particulate matter are thought to increase smoke
particle size by up to ∼10 % .
The rate of coagulation is approximately proportional to the square of
particle concentration and is, therefore, highest near
to the source. In highly concentrated plumes, however, coagulation can be
important on the timescales of days. Particles continue to grow on these
timescales, but at a much lower rate
. In addition,
water uptake by particles can be significant, in particular at higher
relative humidity and for particles with higher amounts of soluble material.
Inferred hygroscopic growth factors suggest an increase in particle size by
up to ∼15 % .
The above particle growth processes enhance smoke-scattering efficiency. A
number of studies report increases in a ratio of scattering extinction to
total extinction, single scattering albedo (SSA(λ)) in ageing plumes
.
Northern temperate and boreal forest fires are generally large and intense,
with significant contribution of smouldering combustion. Plumes tend to be
less absorbing with larger particles than African savanna or Amazonian forest
emissions, and exhibit large variability in retrieved properties
. Inferred large particle sizes
could be attributed to significant smouldering phase burning, inefficient
combustion in very intense flaming fires and accelerated coagulation rates in
highly concentrated plumes . Even
larger particles, with Rfv of ∼0.25 µm, have
been observed in aged plumes originating from strongly smouldering combustion
of peat fuels .
Observation and retrieval of smoke particle properties by remote sensing
is challenging because of particularly high spatial and temporal variability
in plume occurrence and evolution. Satellite remote sensing provides
global and continuous measurements of Aerosol Optical Thickness (AOT)
with improving accuracy and between-sensor agreement .
However, while methods based on UV absorption or multi-angle retrieval
offer the potential to resolve further aerosol properties, ,
the presence of variable background reflectance over land surfaces
makes routine operational retrieval of aerosol absorption problematic,
especially at low optical thickness. Accordingly, many retrieval schemes
make use of a priori knowledge of properties of characteristic aerosol
types within retrievals.
Ground-based Aerosol Robotic Network (AERONET) sun and sky photometers
, located worldwide, provide
more robust aerosol measurements. The observations are less affected
by the surface reflectance component and are not limited to one or
a few view angles. Retrieved aerosol optical and physical properties
are fundamental in determining dominant aerosol types for various
regions
and have become a benchmark for validating satellite observations
. AERONET aerosol characterisations
have been derived by taking average values from a set of stations
assumed to be representative to a certain region or aerosol type.
This approach approximates complexities and variability in aerosol
properties, but it does not exploit some of the information contained
in individual AERONET observations. For example a record from stations
located at or near boreal forest is likely to include observations
of smoke of various age and origin. Several studies used atmospheric
transport modelling and satellite data to determine the source and
age for a set of AERONET smoke observations, focusing on individual
burning events .
subdivided sets of AERONET stations representing
biomass burning regions into near source and distant ones to explore
the ageing effects on optical and microphysical properties. These
studies stress the need for smoke plume source and age-resolved analysis
methods establishing particle properties for different emission sources
and long-term ageing effects.
This study addresses this research gap by improving the characterisation
and ageing effects of smoke plumes typically attributed to northern
temperate and boreal forests. A new method is presented here allowing
the estimation of age and source for AERONET aerosol observations.
The method is applied with a focus on two aims. The first is to determine
smoke particle microphysical and optical properties for emissions
from different vegetation types. Explicit source attribution offers
additional information content compared to region-based approaches
and could partly address the large variability in aerosol properties
characteristic to the boreal forests. The second aim is to explore
changes in particle properties occurring in plumes over several days
of ageing, complementing existing studies
with independent estimates based on an alternative method and larger
sample.
Data and methods
The source and age estimation for AERONET smoke observations was achieved
by using an air parcel trajectory model and satellite active fire
and AOT observations. For all of the selected AERONET smoke observations
described in Sects. and ,
a set of air parcel back trajectories ending at a range of altitudes
was generated using the HYSPLIT model as specified in Sect. .
Coinciding satellite active fire (Sect. )
and AOT observations (Sect. ) along the
trajectories were used as inputs into the decision tree outlined in
Sect. and Fig. , estimating
source and age for the AERONET observations.
(a) study area, land cover types examined
and locations of AERONET stations used. MODIS MCD12C1 land cover data
products were employed with classes defined by the International Geosphere
Biosphere Programme classification system . Pie
charts inside the circles indicate the origin land cover type of plumes
observed at the AERONET stations as estimated in this study. (b–d)
show total estimate counts per source land cover type, smoke age per day of ageing and year.
AERONET data
The analysis is based on AERONET Level 2.0 version 2 inversion data
products. AERONET CIMEL radiometers are calibrated and continuously
monitored, and data products are cloud screened and undergo robust
standardised processing which enables quantitative
comparative analysis. Direct solar extinction measurements provide
columnar AOT at several wavelengths ranging from 340 to 1640 nm. Combined
direct sun and diffuse sky radiance measurements at four wavelengths
(440, 676, 879 and 1020 nm) are best-fitted with a radiative transfer
model effectively retrieving columnar aerosol size distribution, spectral
complex refractive index, SSA(λ), Ångström Exponent (AE), Absorption
Ångström Exponent (AAE), phase function and precipitable water content
. Retrieved aerosol
optical properties and size distributions are in agreement with independent
in situ aerosol measurements
and have well-defined uncertainties .
The accuracy of optical properties retrieval is increased during moderate and
high aerosol-loading conditions. Level 2 inversions contain SSA retrievals
only at AOT(440) levels of 0.4 or higher. AERONET uncertainty
for individual SSA retrievals in these cases is approximately 0.03 .
Particle fine mode volume median radius has an uncertainty of 0.01 µm,
the spread of the fine mode particle distribution – 0.06.
Estimated uncertainty in asymmetry parameter ranges from 0.015 at AOT(440) to
0.04 at AOT(1020) .
Size distribution parameters and optical properties discussed throughout
this study are consistent with the definitions and units described in .
Data selection
This study uses data from AERONET stations positioned within or in
proximity to the Northern boreal and temperate forests. All available
level 2 data with AOT(440) level ≥0.4 from the stations located north
of 45∘ latitude in North America and Asia (Fig. ) and collected at any
time from the year 2002 through 2013 were selected. European observations
were excluded because of higher background AOT levels and more likely
mixing of smoke plumes with urban and industrial aerosol. Temporal
extent of the study was constrained by the start date of MODIS data
availability from both platforms Terra and Aqua in early 2002. AOT
records from the selected AERONET stations indicate generally low aerosol
background levels with sharp spikes in aerosol loading occurring during
the burning season lasting from late spring to early autumn. The pattern
suggests that the majority of level 2 retrievals at AOT(440) levels of ≥0.4)
is a record of biomass burning plumes. Notably, this study is not
fully inclusive or exclusive to the Northern forest emissions both
in terms of vegetation type or geographic extent. Plumes transported
to the selected AERONET locations from areas extending beyond the
region of interest and attributed to a range of land cover types have
been included in the analysis.
Severe burning seasons in 2004 and 2005 in Alaska caused very high
AOT(λ) values ∼(2–5) recorded at Bonanza Creek AERONET
station. The very large (0.2–0.25) Rfv
values retrieved during these events were attributed to
peat fuel combustion . The method used in this
study could not establish ageing properties for these plumes because
of a large number of active fires and persistently elevated AOT levels
in the region. As a result, Bonanza Creek observations for August in 2004 and 2005 have been excluded from the analysis.
Active fire data
As a proxy for fire activity during the period analysed, the MODIS
fire location data set MCD14ML produced by the University of Maryland
and provided by NASA Fire Information for Resource Management System
was used. The data set contains active fire detections from Terra and
Aqua platforms with information of the hotspot location, brightness
temperature at MODIS bands 21 and 31, fire radiative power and detection
confidence .
Fire data processing and selection
Fire inventories compiled for Alaska and
Canada indicate that very large fires
are not numerous, but account for the majority of the total area burned.
found that fires larger than 100 km2 represented
more than 80 % of total area burned. Following this only large wildfire
events which were likely to be strong emission sources were considered in the
analysis. To identify such fires MODIS individual hotspots with 80 %
or more detection confidence were agglomerated into fire events using
the Density-Based Spatial Clustering (DBSCAN) algorithm .
DBSCAN clustering was performed merging individual fire detections
into a single fire object if at least two were found closer together
than 10 km in space and 24 h in time. The objects were iteratively
formed by adding any fire points found within the search radius from
all of the fire detections belonging to the cluster (Fig. ).
Identified fire events were considered as large and selected for the
analysis if the event duration was more than 48 h, and the spatial
bounding box including all points belonging to the event was larger
than 100 km2.
An illustration of the DBSCAN fire pixel segmentation
method. Individual MODIS active fires in north-west Canada, Alaska and the
north-western USA observed during July–August (vertical axis) in 2009.
Colours show separate, large fire objects used in the
analysis. Grey fire pixels were removed as described in Sect. .
Fire and emission source land cover type
The emission source land cover type for each of the fire events was
determined using MODIS MCD12C1 annual land cover type data products, which
employ 17 different land cover classes defined by the International Geosphere
Biosphere Programme . Initially, the land cover type
was identified for each of the active fire pixels within a fire event from
a grid value given in the MCD12C1 product from the corresponding year. The
land cover value occurring most often (mode) was used as a land cover type
identifier for the fire event. This was done for all of the years except 2013
for which MCD12C1 is unavailable and the 2012 product was used instead.
Back trajectories
To link AERONET observations with source regions and to identify the likely
smoke transport pathways, air mass trajectories were computed with the
HYSPLIT (Hybrid Single-Particle Lagrangian Integrated) model
. The HYSPLIT model was run using the Global Data
Assimilation System (GDAS) meteorological archive data for the available
2005–2013 period and The National Center for Environmental Prediction and
The National Center for Atmospheric Research (NCEP/NCAR)
reanalysis data for 2002–2004. For each of the studied AERONET elevated AOT
observations (AOT at 440 nm above 0.4) 7 day back trajectories with
1 h temporal step were generated starting at 16 elevations ranging from 500
to 12 000 m: at 500 m intervals below 4000 and at
1000 m intervals above 4000 m. The uncertainty in the
individual trajectories was assessed, estimating HYSPLIT Model numerical
integration and meteorological data resolution errors. The first was
estimated by computing back trajectory and then forward trajectory from the
back trajectory's end point. The error was assumed to be half of the
horizontal and vertical distance between the initial start and the final end
points. The resolution error and resultant divergence in flow field was
determined generating a grid of 27 (3×3×3) back trajectories
beginning around the initial start point, with horizontal and vertical
offsets given by the estimated numerical error.
Two 7-day back trajectories ending at Fort McMurray
AERONET station and coinciding fire and Satellite AOT observations.
(a) trajectory ending at 1500 m altitude does pass close to
active fire objects but satellite AOT remains low. (b) trajectory
ending at 3000 m altitude passes close to the fire objects followed
by a sudden increase in observed AOT, indicating the source and age of the
AERONET smoke observation.
Satellite AOT
The two independent satellite AOT data products used are based on
observations from (1) The Moderate-Resolution Imaging Spectrometer
(MODIS) sensors on-board Terra and Aqua platforms and (2) Along Track
Scanning Radiometer (AATSR) sensors flown in succession on ERS-2 and
ENVISAT satellites. While the method can readily be extended to include
further satellite data, the role here is to confirm model tracking
of plume transport from source to AERONET, rather than to add additional
information on aerosol properties. The MODIS collection 5.1 data set
M*D04_L2 is based on the dark target retrieval scheme
and AATSR_SU on the algorithm developed at Swansea University ,
modified under the ESA Aerosol Climate Change Initiative (CCI) .
Both data products provide interpolated AOT at 550 nm with
10km×10 km
pixel size at nadir, which is doubled at the edge of swath for MODIS and
increase only negligibly for AATSR. Because of the wider view angle and two
sensors operational at the same time, The MODIS data set has greater spatial
coverage and temporal resolution. The algorithms perform well compared to
AERONET AOT observations. Validation studies suggest RMSE from 0.1 to 0.2,
and little bias between AERONET and both satellite AOT retrieval schemes
. The product validations and RMSE as
a measure are biased towards low AOT conditions, which constitute the vast
majority of the observations. Discrepancies are larger for the aerosol
loadings (AOT(440) ≥0.4) analysed herein. Estimated MODIS AOT one sigma
expected error bounds increase linearly with AOT ±(0.05+0.15 %AOT). AATSR_SU algorithm retrieves AOT only up to 2.0,
meanwhile MODIS M*D04_L2 product contains AOT values up to 5.0. Notably,
the algorithms do not estimate AOT over opaque plumes near the source and
often reject bright dense smoke as cloud or bright surface
.
Age and source estimation
Smoke source attribution for AERONET observations was performed by
finding coinciding satellite AOT and fire event observations along
the generated trajectories and identifying candidate plume pathways.
Starting at an AERONET station and an observation time, for each trajectory
level spatiotemporal queries were performed, finding any satellite
AOT and fire event observations falling within the trajectory domains
at hourly steps (Fig. ). The location and size of spatial
search windows at each time step was given by the trajectory uncertainty
analysis. Identified AOT observations and proximity to large fire
events served as inputs into the decision tree. A trajectory was selected
as a candidate if the conditions shown in Fig. were
satisfied. When several candidate trajectories were selected, the
trajectories were ranked according to the potential fire source size
and satellite AOT values observed after the trajectory had passed
the source. Finally, the highest ranked candidate was identified as
the source and age estimate.
Statistical methods
Error bars and uncertainties on the quantities stated represent 95 % confidence
intervals derived using the bias-corrected non-parametric bootstrap methods (Efron, 1993).
During the resampling error was modelled as normally distributed AERONET uncertainty
given as 1 standard deviation. The medians of two populations are identified as significantly
different if the bootstrapped 95 % confidence interval on difference in medians
agree in sign.
Results and discussion
From a total of 1337 AERONET observations processed, age and source were
determined for 629. The majority of the identifications are for smoke of up
to 1–2 days of age, and only 6 % are for plumes older than
4 days (Fig. c). The method employed was limited to the
tracking of highly concentrated free tropospheric plumes emitted from fires
larger than 100 km2 and the results are representative of such
events. The sampling bias is particularly severe for very old attributions as
only very dense continental superplumes can be observed after several days of
ageing. However, fire inventories
indicate that large burning
events form the dominant mode of burning in boreal and temporal forests. The
results of our study suggest that the large plumes are accountable for at
least 45 % of AERONET AOT(440) ≥0.4) observations in the
region.
Emission source
The decision tree used to identify potential smoke
source and age. For every AERONET observation back trajectories arriving at
different altitudes are tested against the conditions defined in the main
part of the decision tree. Any trajectories identified as potential
candidates are ranked (lower part of the diagram), identifying
a single source and age estimate for the AERONET observation.
The most frequent emission source land cover type determined was North American
evergreen needleleaf forest (ENF) constituting more than half of all estimates
(Fig. b). The dominance could indicate larger and more intense fires
characteristic to North American forests compared with Eurasian boreal regions ,
but this may be caused by uneven spatial
distribution of the AERONET stations. Emissions attributed to woody savannas and
open shrubland were split between the continents. All mixed forest plumes
represent Eurasian fires. Sample size inconsistencies and uncertainty in source
attribution should be considered when evaluating the results. Distributions for classes with a small number
of estimates exhibit multimodality (Fig. ) and thus their
summary metrics should be treated with caution. The source estimates
presented here have uncertainties at several levels.
found that MODIS land cover type products were
only accurate in ∼88 % of the cases analysed. In addition,
only the dominant land cover type for a given fire is considered here
ignoring varying proportions of included grids attributed to different
vegetation and intra-grid mixing. Finally, there is likely some unquantified
uncertainty in the back trajectory analysis and source age attribution
presented here. Despite these limitations the emerging patterns suggest
meaningful differences between the plumes attributed to the land cover types
discussed.
Optical and microphysical properties of smoke
attributed to different land cover types (a–c). Maximum satellite
AOT detections along the identified trajectories (d) and fine mode
volume radius distributions attributed to less dense (AOT < 3.5) and very
dense plumes (AOT > 3.5) emitted during day and night hours (e).
Coloured areas indicate kernel density estimates, error bars represent
95 % confidence intervals for median.
Plumes show variability in particle concentrations, as indicated by the
maximum satellite AOT value along the determined trajectory. The estimate is
only an indication of initial plume concentrations because of limitations
retrieving AOT over optically thick plumes and large uncertainties associated
with high AOT retrievals. However, the highest AOT values are typically found
within hours from the source, and therefore are better indicators of the
initial plume concentrations than the downwind AERONET AOT retrievals. Pooled
plume concentration estimates seen in Fig. d exhibit bimodality.
The bulk of all plumes are relatively less dense, with maximum AOT values
lower than 3.5. Approximately a quarter of the plumes indicate extreme
optical thickness with maximum AOT values above the 3.5 value and often close
to or at MODIS saturation value of 5.0. If this interesting feature is not
introduced by sampling biasses or satellite AOT retrieval artefacts, it may
indicate two distinct emission modes.
Grassland and cropland emissions are distinctively less optically thick, in
agreement with observations that flaming grass and shrub fires are less
intense and produce less particles than forest fires .
Notably, Eurasian mixed forest plumes are predominantly in the extremely
concentrated group, open shrubland and wooded savannas emissions shared
between the continents show clear bimodality, meanwhile North American ENF
plumes are primarily less optically thick with a smaller proportion of very
dense plumes. Satellite observations and fire inventories suggest that North
American fires tend to be high intensity crown burns consuming more fuel per
area burned than predominantly surface fires in Eurasian boreal forests.
. The apparent higher
proportion of very dense plumes in Eurasia in this study might be caused by
the small number of AERONET stations on the continent and thus greater
sampling bias. However, our data also indicates that the extreme events tend
to occur north of 50∘ latitude, and in biomes with lower tree cover
density. Thus, it seems unlikely that such events can be explained solely by
variability in intensity of crown and surface fuel combustion. The highest
proportion of total fuel in boreal regions is contained below ground as
organic soils, peat and root material . Notably,
the highly dense plumes attributed to mixed forest fires include well-documented forest and peat bog fires in
Russia in the year 2010. During the events AERONET AOT(500) values as high as 6.4
were recorded in Moscow. This suggests that ground fuel combustion triggered
at least some of the 165 extremely concentrated plumes discussed. Such events
are probably over-represented in this study, in particular on the Eurasian
continent. Nonetheless they comprise at least 12 to 25 % of all
AERONET(440) ≥0.4 observations in the region and period studied and
require further investigation.
Particle distributions of smoke originating from the land cover type fires discussed
indicate distinctiveness in fine mode volume median radius
and spread of Rfv. Cropland and/or natural vegetation
mosaic and grassland emissions tend to show the smallest particles, with
median Rfv values 0.143 (0.135–0.145) and 0.157 (0.148–0.168) µm. Plumes from mixed
forests generally contain the largest particles followed by open shrubland emissions
having median Rfv values 0.194 (0.184–0.2) and 0.185 (0.176–0.194) µm respectively.
The most numerous ENF smoke observations typically have particles smaller than the emissions
from mixed forests and woody savannas, with determined median Rfv
0.164 (0.16–0.167) µm. A significant difference in Rfv
exists comparing less dense and very dense plumes; estimated median Rfv
are 0.163 (0.16–0.166) and 0.191 (0.184–0.195) µm for the two initial concentration categories.
The width of the fine mode particle distribution is positively related to Rfv, and
classes with larger particles have wider distributions.
These estimates generally agree with published size distributions
for various fuel types. Grass fires are reported to emit smaller particles
than forest fires .
Boreal smoke particles are known to be generally larger Rfv compared to African or
Amazon forest emissions , but also exhibit high
variability in retrieved properties .
The distinctiveness in particle size distributions attributed to the
three forest cover types – ENF, mixed forests and wooded savannas
– is particularly interesting. It indicates significant differences
in fuels consumed, combustion conditions and fire intensities between these
vegetation types which are typically agglomerated under broad extra-tropical or
boreal forest definitions. Differences in median Rfv
could be partly explained by varying proportions of very dense plumes
attributed to these cover types. Wooded savannas, open shrubland and, in particular,
mixed forest emissions are dominated by such events. Inferred particle sizes
in optically thick plumes are close to some of the largest documented values ,
suggesting that they might be produced by smouldering combustion of peat fuels.
Accelerated coagulation rates in highly concentrated plumes and higher particle hygroscopicity may increase
Rfv even further. Smaller particles in ENF could be due to
predominantly flaming combustion of crown fuels, inferred lower initial concentrations and
lower coagulation rates just after emission.
(a) fine mode volume median radius and
estimated age. Points joined by lines show identified paired observations.
(b) fine mode volume radius against the spread of fine mode volume
radius for young
and well-aged plumes.
Notable differences exist in fine mode particle distributions comparing
daytime and night-time emissions. Fire radiative power observations indicate
a strong diurnal cycle in burning intensity with a peak at around 2–4 pm
local time . Results obtained by this study imply
that plumes emitted between 12 pm and 10 pm local time tend to have larger
particle fRfv than the ones emitted between 12 pm and 8 am
(Fig. e). Plumes aged between 12 and 48 h were compared,
expecting higher accuracy in age estimates for such observations, and
minimizing very young smoke bias and long-term ageing effects. Although the
spread is large and overlaps are substantial, the day and night differences
in Rfv are significant for the less concentrated plumes. Daytime
and night-time plumes generally have larger particles, with a median Rfv
value of 0.17 (0.167–0.18) µm, compared to 0.155
(0.148–0.161) µm value for night emissions. A very small
difference exists in SSA(440) values. Day plumes are only insignificantly
more absorbing, estimated median SSA(440) values are 0.948 (0.943–0.956) and
0.956 (0.952–0.962) for the day and night emissions respectively.
reported larger and less absorbing particles produced at
night in Brazil attributed to smouldering combustion. A different diurnal
particle size pattern suggests that larger and more intense flaming daytime
fires in boreal regions generate larger particles than more smouldering
night-time burning.
The differences in optical properties between plumes attributed to
the land cover classes considered are subtle. The variability
in single scattering albedo and its spectral dependence is small.
Inferred median SSA(440) is close to often reported values of 0.95 for
boreal regions
for all land cover types except for the plumes
from cropland and/or natural vegetation mosaic fires, which are more absorbing
and have a median SSA(440) value of 0.9 (0.886–0.916). Notably, in this land cover
class the lowest SSA(440) corresponds to the smallest median
Rfv (Fig. ). Smoke from mixed forests, with
a median SSA(440) value of 0.963 (0.956–0.977), is slightly less absorbing than
ENF, wooded savannas and open shrubland emissions which all have median SSA(440)
values not significantly different from 0.95. These differences in absorption magnitude suggest
some variability in flaming vs. smouldering combustion ratios and
is consistent with observed differences in size distributions. However,
there is little to differentiate between the different forest types
considered here based solely on optical properties.
Ageing effects on particle distributions
Optical and microphysical properties of young and
aged plumes. Less dense (AOT < 3.5) and very dense (AOT > 3.5) plumes
binned per day of ageing (a–b), plumes under 24 h old compared
with smoke aged for more than 72 h (c–d). Error bars represent
95 % confidence intervals for median. Numbers on the distributions
represent high CI, median value and low CI values.
Significant differences exist comparing AERONET retrieved particle size
distributions of young and well-aged plumes. Figure a shows fine
mode volume median radius (Rfv) plotted against the age
estimates. Notably, less dense plumes dominate young estimates, while
optically thick large plumes are more frequently detected after several days
of ageing. Because of the sampling bias and inherent large differences in
size distributions between less dense and very concentrated plumes, the
ageing effects for the two smoke categories are compared separately. The low
number of very old estimates only allow meaningful comparison of plumes of up
to 4 days old, and estimates older than 72 h were pooled together.
Distributions of plumes binned into groups per day of ageing indicate
a steady increase in median Rfv for the first 3 days, and
only slightly larger particles for plumes aged for more than 3 days. A
similar pattern is observed for both plume density categories. Plumes in the
oldest age category have median Rfv values larger by 0.02
(0.009–0.028) and 0.022 (0.012–0.036) µm compared to young smoke
of up to a day old for the less dense and very dense plumes respectively.
This difference would equate to a ∼0.007 (0.003–0.012) µm flat growth rate in median Rfv per day.
The trajectory analysis indicates that in several cases the same plume was
transported over more than one AERONET station, allowing to infer changes in
plume properties between the two observations. Unfortunately, only 13 of such
events were identified preventing a more robust analysis
(Fig. a). In 10 out of the 13 cases older particles are larger,
while three of the pairs suggest a decrease in Rfv between the
observations. The median Rfv change rate is 0.0075 (-0.001 to
0.03) µm per day. The estimate agrees well with the growth rate
suggested by differences in particle distributions between young and aged
plumes. However, it has large confidence intervals due to the low number of
paired observations and uncertainty in individual AERONET retrievals.
Published smoke particle growth magnitude and timescales vary and are difficult to compare
due to differences in measuring time interval, techniques, sampled fuels, fire size,
intensity, combustion phase and smoke age. Rapid increases by as much as 0.08 µm
have been reported ,
suggesting instantaneous Rfv growth rates
of ∼0.04 µm per hour.
concluded in their review that aged smoke particle distributions typically
have Rfv larger by 0.025 µm. Importantly, most of the
particle growth is attributable to condensation and coagulation happens during the first few hours.
The AERONET records typically do not include observations of truly
fresh smoke within seconds or minutes after the emission. Consequently,
our results are for young to well-aged smoke, which is already transformed by the rapid initial growth and
has generally large particles. The changes in particle size distributions inferred in this study happen over the course of days,
suggesting that in thick continental plumes particles continue to grow. The principle mechanism
driving size increase on such timescales is thought to be
coagulation .
The spread of Rfv does not exhibit significant
dependence on estimated age. Generally, distributions with smaller
Rfv tend to have a narrower width (Fig. b). The
relationship between Rfv and fine mode spread, however,
is stronger for young smoke and is not evident for the aged smoke observations. This
indicates that while particle sizes increase in ageing plumes, the
spread of the fine mode volume radius does not change systematically.
suggested that condensation narrows the particle distribution, while
growth due to coagulation does not alter the width significantly. The lack of significant changes
in fine mode spread suggests that the inferred particle growth is attributable to coagulation.
Changes in optical properties
The Ångström exponent (AE) tends to be lower for older plumes
(Fig. b). In well-aged very dense plumes median AE changes by
-0.15 (-0.036 to -0.26) from a 1.74 (1.64–1.82) value for young
plumes. The difference is smaller and not significant for the less dense
plumes, for which median AE changes by -0.06 (0.002 to -0.15) from median
value of 1.89 (1.86–1.92) estimated for young plumes. Decreasing wavelength
dependence of AOT corresponds to inferred particle growth and is an expected
result. Changes in median AE, however, are not as consistent as differences
in median Rfv between the plume age and density categories. This
variability could be due to uncertainties in AERONET inversions, insufficient
sample size for some land cover classes, or they could reflect effects of
differences in particle chemistry affecting AE besides particle size.
In contrast to particle size distributions and AE, differences in
SSA(λ) are only subtle. SSA(λ) is only slightly higher in
plumes aged for more than 3 days compared to young smoke
(Fig. c). The variability is negligibly small between the two
plume density categories. Combined median SSA(440) insignificantly increases
from a ∼0.95 value for young smoke to ∼0.96 for the
oldest plumes. Mie theory predicts that particle radius growth due to
coagulation should generally result in smaller absorption and higher
scattering efficiency, hence an increase in SSA(λ)
. Such an increase in ageing smoke has been observed in
Brazil , West Africa
, Spain
and North America . Notably, most of the aforementioned
studies were analysing inherently more absorbing smoke with typically smaller
particles compared to emissions from the sources explored in this study. It
is possible that most of the scattering enhancement happens during the rapid
evolution phase within a few hours from the emission and thus can not be
observed by the method employed in this study. Cropland and/or natural
vegetation plumes are significantly more absorbing, but with only 34
estimates a meaningful analysis of ageing effects is not achievable.
The asymmetry parameter is higher in well-aged plumes (Fig. d),
indicating lower back-scatter due to larger Rfv.
The change is consistent across the wavelengths and is similar between the smoke
concentration groups. For combined plumes at 440 nm the median asymmetry
parameter is higher by 0.038 (0.027–0.055) in well-aged plumes. At longer
wavelengths the asymmetry parameter is lower, but the difference in median values
between the young and aged plumes is larger.
Conclusions
This study presents an analysis of ageing effects on smoke particle
size distribution and optical properties in regions associated with
large variability in aerosol characteristics. A new method was developed
allowing the source and age attribution for AERONET aerosol observations,
and applied to data from stations located in or near northern temperate
and boreal forests in North America and Asia.
The results show that plume properties vary with the determined source
vegetation type as defined by the land cover classification scheme.
Notable variability exists not only when comparing emissions from
grasslands, croplands and forests, but also different forest types.
Plumes from mixed forests are generally the most concentrated and contain the largest particles
with a median Rfv 0.193 µm, followed by emissions from wooded
savannas and shrubland. Smoke attributed to evergreen needleleaf forest
fires has lower initial concentrations and exhibit smaller particles than other forest emissions, having
median Rfv value 0.164 µm which is close to the Rfv
values of 0.143 and 0.157 µm observed in plumes from cropland
and grass fires. These differences appear to be partly influenced by the frequent occurrence
of extremely concentrated plumes in some land cover types. Such events
tend to have very large particles, and at least in some cases originate from peat fires.
Day emissions show significantly larger particles compared to night-time smoke.
Absorption properties do not exhibit significant variability.
Estimated median SSA(440) values range from 0.95
to 0.97 for all of the sources considered in this study with the notable
exception of cropland and natural vegetation plumes which are substantially
more absorbing with SSA(440) value of 0.9.
Plumes older than 3 days have higher median Rfv values by
∼0.02 µm compared to smoke aged for less than a day.
This suggests a ∼0.007 µm increase in Rfv
for the first 3 days of ageing. Median growth rate derived from the 13
cases when the same plume was observed at two AERONET stations is remarkably
similar (0.0075 µm). However it is not significant and highly
sensitive to uncertainties in AERONET Rfv retrievals. No
significant shift in fine mode spread in well-aged plumes is observed,
suggesting that the growth is driven by coagulation.
In contrast to size distributions, smoke SSA(λ) do not differ significantly
with plume age. Well-aged plumes are only slightly less absorbing. The Ångström exponent
is lower while the asymmetry parameter is higher in older plumes,
reflecting the increase in particle Rfv.
The method and results presented here allow the microphysical and
optical characterisation of particulate emissions from different vegetation
types to be improved. Estimated ageing effects provide information
on long term particle evolution and active processes in large and
dense plumes. These independent estimates are based on a large sample, compare favourably
with existing estimates and refine growth rates obtained by different
methods.