Emission inventories of elemental carbon (EC) and organic carbon (OC) contain large uncertainties both in their spatial and temporal distributions for different source types. An inverse model was used to evaluate EC and OC emissions based on 1 year of hourly measurements from the St. Louis–Midwest supersite. The input to the model consisted of continuous measurements of EC and OC obtained for 2002 using two semicontinuous analyzers. High resolution meteorological simulations were performed for the entire time period using the Weather Research and Forecasting Model (WRF). These were used to simulate hourly back trajectories at the measurement site using a Lagrangian model (FLEXPART-WRF). In combination, an Eulerian model (CAMx: The Comprehensive Air Quality Model with Extensions ) was used to simulate the impacts at the measurement site using known emissions inventories for point and area sources from the Lake Michigan Directors Consortium (LADCO) as well as for open burning from the Fire Inventory from NCAR (FINN). By considering only passive transport of pollutants, the Bayesian inversion simplifies to a single least squares inversion. The inverse model combines forward Eulerian simulations with backward Lagrangian simulations to yield estimates of emissions from sources in current inventories as well as from emissions that might be missing in the inventories. The CAMx impacts were disaggregated into separate time chunks in order to determine improved diurnal, weekday and monthly temporal patterns of emissions. Because EC is a primary species, the inverse model estimates can be interpreted directly as emissions. In contrast, OC is both a primary and a secondary species. As the inverse model does not differentiate between direct emissions and formation in the plume of those direct emissions, the estimates need to be interpreted as contributions to measured concentrations. Emissions of EC and OC in the St. Louis region from on-road, non-road, marine/aircraft/railroad (MAR), “other” and point sources were revised slightly downwards on average. In particular, both MAR and point sources had a more pronounced diurnal variation than in the inventory. The winter peak in “other” emissions was not corroborated by the inverse model. On-road emissions have a larger difference between weekday and weekends in the inverse estimates than in the inventory, and appear to be poorly simulated or characterized in the winter months. The model suggests that open burning emissions are significantly underestimated in the inventory. Finally, contributions of unknown sources seems to be from areas to the south of St. Louis and from afternoon and nighttime emissions.
Within fine particulate matter (
EC and OC are prevalent in the USA, with OC making up 20 to 40 %
of
In the Midwest,
The present study is based on continuous, hourly measurements of EC
and OC made during 2002 at the St. Louis–Midwest supersite
Cluster analysis of
In this paper, we study the same year-long hourly time series of EC and OC measured at the St. Louis–Midwest supersite. We seek to obtain improved estimates of the diurnal and monthly emission profiles of specific types of sources by combining forward simulations of EC and OC concentrations from emissions inventories with the measurements using an inverse model. This is carried out for five different source categories as well as for emissions from open burning. In addition, the inverse model uses gridded back trajectories to identify regions that may be missing sources in the inventory. As discussed above, EC is not formed in the atmosphere, but rather emissions are transported until they are removed by deposition such that they can be simulated as passive tracers. In contrast, OC is both emitted and produced in the atmosphere. Our model is focused on transport; consequently, the results for EC can be straightforwardly compared to emission inventories. For OC however, the model does not distinguish between primary OC that is emitted by a source and secondary OC that is created in the plume of that same source. The results are therefore best interpreted in terms of impacts at the measurement site rather than emissions at the source location.
The measurement site is the St. Louis–Midwest supersite which was
funded by the United States Environmental Protection Agency (EPA). It
is located in East St. Louis, approximately 3
Hourly meteorological observations were obtained from Lambert–St. Louis International Airport (KSTL) and St. Louis Downtown Airport
(KCPS) in Cahokia, IL from the Integrated Surface Hourly Data
available from the National Climatic Data Center. KSTL is across the
Mississippi river 24
The Lake Michigan Air Directors Consortium (LADCO) emissions inventory
for 2007 for the Midwest was used as a prior for the inverse model
Domains used for the WRF simulations: large
(D1, 27
Figure
Biogenic emissions in
Emission totals for EC and OC for the Regional domain around
St. Louis by source category for the National Emissions Inventory (NEI),
the LADCO inventory and the least squares inverse model.
Note that OC Inverse totals combine primary emissions and secondary formation.
(MAR
Elemental carbon emissions by source type from the LADCO inventory for the Regional domain in metric tonnes per year, and biogenic tracer emissions in non-dimensional units.
In order to have an additional comparison to the LADCO prior emissions and the inverse
model results, the 2008 National Emissions Inventory (NEI) version 3 was obtained
from the US Environmental Protection Agency. EC and OC emissions were
available in speciated files for
EC and OC have experienced a downward trend in the US,
with around 1 to 2 % decreases per year
EC and OC emissions from open burning were calculated using the Fire
Inventory from NCAR (FINN) version 1
Open burning emissions of EC and OC for the Large domain for 2002 using the FINN model, which include forest, prescribed and agricultural fires detected by Terra MODIS. Pink lines show the six sectors used in the inverse model, pink dot is the supersite.
In FINN, open burning includes the fires which are detected by Terra
MODIS. These are a combination of forest fires, prescribed burns and
larger agricultural fires, with a minimum burn area of
1
Figure
The meteorological simulations were performed with the Weather
Research and Forecasting (WRF) model version 3.5.1
The model was run with two-way nesting, with the Yonsei University
(YSU) boundary layer scheme, the Kain–Fritsch convective
parameterization, the NOAH land surface scheme, the WSM 3-class simple
ice microphysics scheme, the Dudhia shortwave scheme and the Rapid
Radiation Transfer Model longwave scheme. Individual simulations were
performed lasting 162
Particle back trajectories were calculated from the supersite with
FLEXPART
Emission totals for open burning by geographical sector relative to the measurement site for the FINN model and the least squares inverse model. Also shown are the ratios of the inverse emission estimates to the FINN prior estimates and the fraction of EC or OC at the measurement site that is estimated to be due to open burning. Note that OC Inverse totals combine primary emissions and secondary formation.
Concentration field analysis
The Comprehensive Air Quality Model with Extensions
This study is focused on estimating source contributions from specific source groups based on atmospheric transport and therefore does not use the aerosol module in CAMx. Both EC and OC are simulated as passive tracers with wet and dry deposition. This is adequate for EC, and so the inverse model results can be straightforwardly compared to the emissions inventories. In contrast to EC, there is extensive formation of OC in the atmosphere which is not simulated in our model. This means that the inversion will not distinguish between primary and secondary OC, and that results are therefore best interpreted as impacts at the measurement site rather than as emissions at the source location. It also means that we are not able to evaluate the non-linear interactions of different plumes together.
The least squares inverse model used in the present study was
developed in
Inverse models based on back trajectories alone include
Hourly Eulerian simulations with CAMx were performed for the five different
source groups in the LADCO inventory:
on-road, non-road, MAR, “other” and point sources.
Because we are interested in evaluating the temporal profiles
of the sources, we carry out separate simulations for emissions during
different times of the day and different days of the week.
The time slots were selected based on the diurnal profile used in the
emissions inventory: 11:00 p.m. to 5:00 a.m., 5:00 to
8:00 a.m., 8:00 a.m. to 2:00 p.m., 2:00 to 6:00 p.m., and 6:00 to
11:00 p.m. Days of the week were split into a weekday group and a group
containing Saturdays, Sundays and holidays.
As an example, an hourly time series of concentrations was obtained from a
CAMx simulation with on-road emissions
only between 5:00 to 8:00 a.m. on weekdays.
With 5 source groups, 5 time slots and 2 day types, this means that there
were 50 CAMx simulations.
We are also interested in the annual profile of the emissions, and so
we divide the 50 resulting concentration time
series into 12 months for a total of 600 input time series into the
inverse model. With this method of resolving temporal profiles,
individual time series are used for each temporal interval of
interest. This is in contrast with
The open burning emissions are included in the inversion as six time
series simulated by CAMx for the entire year for the six geographic sectors shown in
Fig.
In addition to the forward Eulerian simulations, we perform backward
Lagrangian simulations of particle back trajectories for each hour of
the measurement campaign. These are mapped onto a polar grid
surrounding the measurement site. The time series from each grid cell
gives an estimate of the concentration at the measurement site that
would be caused by a constant area emission in that cell. We divided
these gridded time series into impacts due to weekdays and weekends,
and also into four time slots during the day: 3:00 to 9:00 a.m.,
9:00 a.m. to 3:00 p.m., 3:00 to 9:00 p.m., and 9:00 p.m. to 3:00 a.m. These
were selected to capture the morning and afternoon rush hours in the
middle of two of the slots, and to differentiate the daytime and
nighttime emissions between those. The polar grid was chosen to have
eighteen 20
The inverse model derives a posterior estimate of emissions based on the Eulerian simulations that used the emissions inventory as a prior. In addition, the inverse model uses the Lagrangian simulations to derive an estimate of sources that may be missing from the inventory. This is done by using the polar grids of residence time analysis that represent the impact that an emission in a given grid cell would have at the measurement site. As all the known sources were already included in the CAMx simulations with the emissions inventory, we use a field of zero prior emissions for the polar grids from the Lagrangian simulations.
Time series of elemental and organic carbon at the St. Louis–Midwest supersite for 2002. Measurements are shown in blue, circles show the data points excluded from the analysis by the iteratively reweighted least squares scheme. Green line shows the posterior time series, as produced by the least squares inverse model.
By limiting the input of the model to passive tracers and individual
time series, we can use a least squares simplification developed in
The columns of
The system of equations can be solved with a single step of least
squares using
An iteratively reweighted least squares (IRLS) scheme is used to reduce
the sensitivity of the method to outliers in the data: after solving
for
In a Bayesian framework, uncertainty estimates are required to obtain
the error covariance matrices on the two parts of the cost function.
In the absence of detailed prior information,
The inverse model therefore does not need prior error
estimates, but rather relies on an optimization routine to determine
the values of the regularization parameters in the vector
We estimate uncertainties in the inverse model by two different methods.
The first is to use expert judgment to determine an uncertainty on the
measurements (
An alternative method is to assume that by randomly sampling the data included in the inversion we are randomly sampling both the measurement errors and the simulation errors at the same time. This can be done with the bootstrap algorithm. Although measurement errors are assumed to be uncorrelated in time, meteorological events vary on the order of hours to days. In order to obtain samples that have different meteorological conditions, we perform block-bootstrapping with a block length of 24 h. We therefore perform 100 inversions with random selection with replacement of the days included in the analysis. In this way, the bootstrapping yields an estimate of the combined uncertainty due to measurement errors and due to transport modeling errors.
In outline, we first perform the optimization of the regularization parameters without bootstrapping for each set of sources in turn: for the RTA grids, for the LADCO emissions, for the open burning emissions and for the biogenic tracer. This is repeated to make sure the values are stable. We then use the set of regularization parameters to obtain inverse results with the full data set, and for 100 realizations with block-bootstrapping.
Before presenting the results of the inverse model, this section
presents the results of analyzing wind roses and back trajectories
from the measurement site. Winds come from all directions at the
Lambert–St. Louis International Airport with a predominance for
westerly flow, as shown in the wind roses in Fig.
Figure
We use residence time analysis to display the spatial pattern of wind
transport to the measurement site over the course of 2002, see
Fig.
Top: wind roses for Lambert–St. Louis international airport (KSTL) and Downtown St. Louis airport (KCPS). Bottom: wind roses for hours in the top 10 % of EC concentrations at the supersite using KCPS data, and bottom 10 % of WRF mixing layer height. Color indicates time of day.
Top: probability density function of temperature, water vapor, wind speed and wind direction observations and simulations at KCPS. Bottom: autocorrelation coefficient of observations and simulations as well as of the residual between the two.
Left: residence time analysis of FLEXPART-WRF back trajectories using hourly releases during 2002 showing the origin of air masses arriving at the supersite (diamond). Center: concentration field analysis of EC and OC showing air mass transport associated with peak concentrations. Right: column concentration field analysis of EC and OC showing air mass transport associated with higher column amounts of EC and OC.
Contributions of different types of sources to the average concentration of EC and OC at the St. Louis–Midwest supersite using the LADCO inventory (prior) and the least squares inverse model (posterior).
Concentration field analysis of EC and OC (Fig.
Wind rose analysis and CFA are sensitive to peak concentrations
occurring during situations with very shallow boundary layers and so
we need to expand the methods to be more sensitive to the amount of
pollutant rather than to the peak concentration. This can be done by
calculating a “column CFA”: CFA is carried out with an estimate of
the total column of EC rather than with the surface concentration of
EC. To do this, we assume that EC and OC are mainly in the planetary
boundary layer and that concentrations are well mixed throughout. The
column amount is obtained by multiplying the surface concentration by
the height of the boundary layer. Since we do not have measurements of
the mixing height, we use simulated values from the WRF model. The
two graphs on the right in Fig.
Figure
The inverse model decomposes the measurement time series as the sum of
the contributions from different source groups. If these are
sufficiently well separated spatially and temporally it is possible to
estimate the contribution of individual source groups to the average
concentration at the site. In our current case, there is a certain
level of overlap between the different source categories, as can be
seen in Fig.
Pearson's correlation coefficient squared for simulated time series of EC and OC for the complete time series as well as for the subset of points included in the inversion after the Iteratively Reweighted Least Squares (IRLS) procedure. The full inverse time series is the sum of the CAMx posterior and the impacts due to the gridded back trajectories.
Figure
Whereas EC behaves as a tracer species from source to receptor, OC is
due to the combination of transport from source to receptor and
formation in the atmosphere during transport. Because this paper
only considers transport, we expect the model results to underestimate
average concentrations: the prior time series represents 60 % of
the average OC concentration.
As discussed in Sect.
Normalized time series of biogenic precursor concentrations were included in the analysis. Because the units are non-dimensional, the results from the inverse model give an indication of the fraction of EC or OC that correlates with these emissions, without giving an estimate of the emissions themselves. As expected, none of the biogenic precursors contributed to the EC time series in the inversion, and these were therefore left out of the EC inversions. For OC, we tested different biogenic components and found that condensable gases category 5 “CG5” yielded the best inverse time series of OC compared to the measured time series. The model was therefore run just with this species as an input. The model estimated that 4 % of simulated OC at the measurement site is associated with emissions of CG5.
The biogenic tracer serves to highlight that the posterior estimate does not differentiate between direct emissions at the source and chemical formation inside a plume associated with those direct emissions. The biogenic emissions are in the gas phase, and the model obtains an estimate of OC concentrations that results from them. The same applies for the individual source categories. For example the 19 % of simulated impacts from the “other” category are the sum of both direct emissions and chemical formation resulting from those emissions. A finer grained study using an aerosol module would be required to deconvolve these two processes.
We used both Monte Carlo error propagation and bootstrapping to estimate
the uncertainties in the emissions estimates.
Figure
The results of the Monte Carlo error propagation are included in the Supplement. The uncertainties vary between 1.5 and 3 % except for open burning where they are 6 %. These are noticeably lower than the bootstrapping estimates as well as what we expect from knowing about emission inventories and from the values of the regularization parameters that were determined from the inversion themselves. These suggest that using block-bootstrapping provides a better estimate of the uncertainties.
Bootstrapped estimates of uncertainties in inverse EC emissions
by source group:
histograms show the distribution of emission estimates, scatter plots show
the cross-correlation of the estimates.
CV
Monthly and diurnal temporal pattern of emissions of EC and OC for on-road emissions by weekday (green, WD) and weekend (blue, SSH) for St. Louis and the surrounding area. LADCO inventory results shown with solid symbols, Inverse model results shown with thin line. Shading shows the 90 % confidence interval in the inverse model results based on 100 bootstrapped inversions. Note that OC posterior totals combine primary emissions and secondary formation.
The results for OC are included in the Supplement. The bootstrapped standard deviations are between 5 and 10 % of the mean contributions for all emission categories except for open burning where they are 18 %. This suggests that the emissions estimates are robust with respect to uncertainties in the model inputs.
As described in Sect.
Figure
In the prior for both EC and OC, weekday and weekend emissions are
very similar, and there is only a slight annual variation from
a maximum in the winter to a minimum in the summer months. The
posterior levels for EC are similar to the emissions prior during the
summer months for weekdays, but weekends are significantly lower.
During fall and winter, the posterior emissions are very low, which
is why the total emission levels shown in Table
The diurnal emissions profile of on-road EC shows a sharp increase starting at 6:00 a.m., and a peak at 3:00–4:00 p.m. followed by a gradual decline until midnight. There is a large contrast with the posterior. For weekdays EC follows the diurnal trend but has significantly lower emission levels, and has a strong reduction during the afternoon rush hour. For weekends, there is very little diurnal variation of emissions. The OC posteriors follow the diurnal profile of the priors much more closely, with slightly higher emissions during the day and lower emissions on weekends than in the prior. It would therefore seem that OC on-road emissions are better represented in the models than EC on-road emissions.
Taken together, these results suggest that future research should seek to clarify the monthly profiles and the possibility of higher emissions during the summer rather than the winter. Furthermore, the posterior suggests that the diurnal profile could be improved as well as the difference between weekdays and weekends. It is possible that accuracy of the wind transport in the models is a function of the time of day, which could be a factor in the greater discrepancy between the prior and the posterior in the late afternoon. Finally, the large difference between the prior and the posterior could be the result of uncertainties in the current spatial distribution of the emissions.
In contrast to the on-road emissions, the non-road posterior emissions
follow the prior much more closely as can be seen in
Fig.
For EC, the model suggests that there is a greater decrease in emissions on weekends than is currently represented in the inventory. The diurnal profile of the posterior follows that of the prior more closely than for the on-road emissions, although there is again a sharp reduction of emissions in the posterior during the afternoon. The weekend emissions follow the diurnal profile, but are closer to 50 % lower than weekdays compared with 30 % lower in the priors.
For OC, the summer peak in the posterior is double that in the prior.
We also see an enhancement of around 50 % during daylight hours.
An estimate of 40 % of OC at the site being due to secondary
formation
Monthly and diurnal temporal pattern of emissions of EC and
OC for non-road emissions by weekday and weekend for the St. Louis
region, see Fig.
Monthly and diurnal temporal pattern of emissions of EC and
OC for marine/aircraft/railroad (MAR) emissions by weekday and
weekend for the St. Louis region, see Fig.
Monthly and diurnal temporal pattern of emissions of EC and
OC for “other” emissions by weekday and weekend for the St. Louis
region, see Fig.
The temporal profile of the MAR emissions (marine/aircraft/railroad)
are shown in Fig.
The diurnal profile is flat in the prior, but the posterior suggests that there is a definite diurnal profile with emissions of EC at night lower than daytime levels by up to 50 %. There is less difference in the OC profile, but it still suggests that the diurnal activity profile should be reconsidered.
Other emissions are shown in Fig.
The diurnal profile of the “other” category follows those of the on-road emissions. For EC, the profile is similar although the emissions are much lower, and there is a reduction on weekends of morning emissions. For OC we see low posterior emissions at night and increased emissions during the day, as was the case for non-road emissions.
Finally, we see the temporal profiles for point sources in
Fig.
Monthly and diurnal temporal pattern of emissions of EC and
OC for point source emissions by weekday and weekend for the
St. Louis region, see Fig.
The diurnal profile of the point sources is rather flat throughout the day in the prior. As for the MAR sources, the model suggests that there is a reduction in EC emissions between midnight and sunrise. There does also seem to be a slight reduction in EC emissions in the posterior on weekends compared with weekdays. The large swings in the estimates of monthly OC emissions mean that the diurnal profile should also be considered with caution. These swings are mostly contained within the 90 % confidence range displayed in the figure which suggests that they are not statistically significant. At a minimum, we can say that EC emissions from point sources seem to be reliably characterized in the inventory and the model, but that more research is needed for the OC impacts.
As discussed in Sect.
Section
Table
As discussed in Sect.
The inverse model combines emission estimates using Eulerian (CAMx) and Lagrangian (FLEXPART-WRF) simulations. Polar grids of residence time analysis calculated using back trajectories are used to estimate emission sources that could be missing in the LADCO emissions inventory. The polar gridded emissions have zero prior and represent a way of decomposing the residual between the CAMx posterior and the measurements into a spatial emission signal. The inverse model includes separate grids for 3:00 to 9:00 a.m., 9:00 a.m. to 3:00 p.m., 3:00 to 9:00 p.m. and 9:00 p.m. to 3:00 a.m., as well as for weekdays and weekends, for a total of eight grids.
Note that the FLEXPART-WRF simulations do not include deposition, and
that secondary OC formation is not included either. Both of these limitations
would impact the estimation of actual emission amounts from the inverse model.
In this section, we therefore report only impacts of different source regions on
concentrations at the measurement site, which are not affected by deposition
and include estimated impacts of both primary emissions and in-plume secondary formation.
As will be discussed in Sect.
Contributions to the average 2002 concentration of EC and OC in the inverse time series from the residence time analysis grids.
Total contribution to the average concentration of EC and OC in the inverse time series from the residence time analysis grids by time of day for weekdays (WD) and weekends (SSH).
Emissions of EC and OC in the Regional domain by source type for the 2008 NEI, the 2007 LADCO inventory and the posterior estimate based on using LADCO as a prior. Inverse results are shown for the entire year (2002), along with annualized emissions for January–April (JFMA), May–August (MJJA) and September–December (SOND).
Figure
Figure
In this section we compare the emissions in metric tonnes per year of
the different source types from the inverse model with the NEI 2008
and the LADCO inventory. Table
Overall, the LADCO inventory is slightly larger than the NEI for both EC and OC. For EC, the on-road emissions are 50 % larger, and the MAR emissions are 25 % larger while the remaining categories are similar. For OC, the largest category by far in both inventories are the “other” sources which are 17 % higher in the LADCO inventory. These include residential wood and waste combustion, non-vehicle road emissions and food cooking (estimates of agricultural burning are high in the NEI but low in the LADCO inventory). OC emissions from on-road, non-road, MAR and point sources are all increased by up to a factor of 2 in the LADCO inventory compared with the NEI.
The posterior emissions are calculated from the model as departures
from the LADCO prior. As discussed in Sect.
Also shown in Fig.
A least squares inverse model was used to estimate emissions of elemental carbon and organic carbon using hourly data for 2002 from the St. Louis–Midwest supersite, and uncertainty estimates were obtained by running the model multiple times using block-bootstrapping. The model provided information on the diurnal pattern of the emissions, the difference between weekdays and weekends and the annual variation on a month by month basis. The inversion was based on the 2007 LADCO inventory for the following source types: on-road, non-road, marine/aircraft/railroad (MAR), “other” and point sources.
There are two important limitations in our modeling. The first is that we do not include deposition in the FLEXPART back trajectories. This means that we cannot obtain emissions directly from the residence time analysis grids but instead we obtain results for the contributions of sources towards EC or OC concentrations at the measurement site. For EC, which is a passive tracer, we performed a sensitivity test on the impact of deposition using forward simulations with CAMx. The emissions based on the FLEXPART inversion were used as input into CAMx and two sets of simulations were performed: one set without deposition, and a second set with both wet and dry deposition. Wet and dry deposition in the model reduced the EC concentration at the site by 4 % on average over the whole year. The main reason this number is low is that most of the impacts are due to fairly local emissions (within 100 to 200 km). Overall, this shows that neglecting deposition in FLEXPART has a minor impact on the results.
The second limitation in our modeling is that we do not include
secondary formation of OC.
There is considerable formation of OC in the atmosphere
The inverse emission estimates were in agreement with the LADCO inventory for most of the source types, with a slight downward revision of the emission totals. The main discrepancies suggested by the model are as follows: (1) on-road emissions were poorly represented during the winter and on weekends. Although the results for winter remain as an outstanding question, there is a clear need to update the diurnal profile for weekends. (2) Non-road emissions need to account for actual use of agricultural equipment, which was done by LADCO but is not carried out by default in MOVES. (3) MAR and point sources do not at present have much diurnal variation in the emissions. Although their diurnal profiles are smoother than on-road and non-road emissions, the model suggests that there is a discernible drop in nighttime emissions. (4) Other emissions from the inverse model matched the inventory during the summer but not during the winter. As with on-road emissions, more research is required to constrain the sources of the discrepancy and to improve the simulations of these impacts.
In addition to these findings, the inverse model identified impacts from open burning at the measurement site, and suggests that emissions of EC and OC should be increased in the FINN model.
Finally, gridded back trajectories suggest that most of the impacts missing from the emission inventories are due to transport from the quadrants southeast and southwest of the measurement site. The contributions to the average EC and OC concentrations at the measurement site from these sources are approximately twice as large during the late afternoon and early nighttime (3:00 p.m. to 3:00 a.m.) as they are earlier in the day (3:00 a.m. to 3:00 p.m.).
The United States Environmental Protection Agency (EPA) funded the EC and OC measurements used in this analysis through cooperative agreement R-82805901-0, and the analysis through grant number RD-83455701. Its contents are solely the responsibility of the grantee and do not necessarily represent the official views of the EPA. Further, the EPA does not endorse the purchase of any commercial products or services mentioned in the publication. We thank the staff of the St. Louis–Midwest fine-particle supersite for their assistance in data collection. We are also grateful to the US EPA for making the National Emissions Inventory available, and to the US National Climatic Data Center for the meteorological data. We wish to thank the three anonymous reviewers for their thoughtful and careful reviews which helped improve the paper. Edited by: K. Carslaw