Topdown estimate of anthropogenic emission ... - Wiley Online Library

JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 116, D20305, doi:10.1029/2011JD016215, 2011

Top‐down estimate of anthropogenic emission inventories and their interannual variability in Houston using a mesoscale inverse modeling technique J. Brioude,1,2 S.‐W. Kim,1,2 W. M. Angevine,1,2 G. J. Frost,1,2 S.‐H. Lee,3 S. A. McKeen,1,2 M. Trainer,1 F. C. Fehsenfeld,1,2 J. S. Holloway,1,2 T. B. Ryerson,1 E. J. Williams,1,2 G. Petron,2,4 and J. D. Fast5 Received 5 May 2011; revised 7 July 2011; accepted 23 July 2011; published 19 October 2011.

[1] Texas Air Quality Study field campaigns took place in eastern Texas in August–October

of 2000 and 2006. Several flights of NOAA and NCAR research aircraft were dedicated to characterizing anthropogenic emissions over Houston. We present results from an inverse modeling technique that uses three atmospheric transport models and these aircraft observations to assess and improve existing emission inventories. We used inverse modeling techniques to improve the spatial and temporal emissions’ distribution of CO, NOy, and SO2 predicted by the 4 km resolution U.S. Environmental Protection Agency (EPA) National Emission Inventory (NEI) for 2005. Differences between the prior and posterior inventories are discussed in detail. In September 2006, we found that the prior daytime CO emissions in the Houston urban area have to be reduced by 41% ± 8%. Over the Houston Ship Channel, where industrial emissions are predominant, the prior emissions have to be decreased by 43% ± 5% for CO and 51% ± 5% for NOy. Prior NOy emissions from other major ports around Houston also have to be reduced, probably owing to uncertain nearshore ship emissions in the EPA NEI inventory. Using the measurements from the two field campaigns, we assessed the emissions’ variability between August 2000 and September 2006. Daytime CO emissions from the Houston urban area have decreased by 8% ± 3%, while the NOy emissions have increased by 20% ± 6%. In the Houston Ship Channel, daytime NOy emissions have increased by 13% ± 7%. Our results show qualitative consistencies with known changes in Houston emissions’ sources. Citation: Brioude, J., et al. (2011), Top‐down estimate of anthropogenic emission inventories and their interannual variability in Houston using a mesoscale inverse modeling technique, J. Geophys. Res., 116, D20305, doi:10.1029/2011JD016215.

1. Introduction [2] Publicly available air quality forecasts provide information on pollution that is important for public health, as it may help to reduce the population’s exposure to air pollutants. However, forecasting the occurrence of high‐pollution events is a difficult problem because it requires simultaneous knowledge of meteorological parameters, air chem1 Chemical Sciences Division, Earth System Research Laboratory, NOAA, Boulder, Colorado, USA. 2 Cooperative Institute for Research in Environmental Sciences, University of Colorado at Boulder, Boulder, Colorado, USA. 3 New Mexico Consortium and Earth and Environmental Sciences Division, Los Alamos National Laboratory, Los Alamos, New Mexico, USA. 4 Global Monitoring Division, Earth System Research Laboratory, NOAA, Boulder, Colorado, USA. 5 Atmospheric Sciences and Global Change, Pacific Northwest National Laboratories, Richland, Washington, USA.

Copyright 2011 by the American Geophysical Union. 0148‐0227/11/2011JD016215

istry and emission inventories. In addition to forecasting, emission inventories are used in regulatory and research applications. Major uncertainties in inventories are associated with uncertainties in the spatial distribution of emissions, emission rate estimates, and the temporal variability of emissions on diurnal, daily, and weekly scales. [3] Eastern Texas is regarded as an important study region for air quality. Houston has serious and frequent problems with nonattainment of ozone standards and the resulting likely detrimental impacts on human health. This region contains a variety of pollutant sources, including a large fraction of the U.S. petrochemical industry, fossil‐fueled electric power generation plants, high motor vehicle usage, and extensive shipping traffic. [4] In response to this need, the National Oceanic and Atmospheric Administration (NOAA) organized and led the Texas Air Quality Study (TexAQS) field experiments in August–September 2000 and August–October 2006 across eastern Texas. Brock et al. [2003] and Parrish et al. [2009] gave overviews of these studies. Research aircraft flight

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BRIOUDE ET AL.: MESOSCALE INVERSE MODELING IN HOUSTON

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Figure 1. Portions of the flight tracks of the NOAA WP‐3 aircraft from the TexAQS 2006 field experiment that are used in this study. The inverse modeling technique is applied in the inner domain only. The outer domain is used to remove observations from the analysis that are influenced by pollution from outside the inner domain within 24 h. plans on several days in both years were designed to characterize the emissions upwind and downwind of Houston area. TexAQS findings [e.g., Brock et al., 2003; Ryerson et al., 2003; Wert et al., 2003; Parrish et al., 2009; Washenfelder et al., 2010] indicate that air‐quality chemistry in Houston is in many ways unique when compared with that of other American cities. One reason for Houston’s exceptional pollution is its large concentration of petrochemical facilities, particularly in the Houston Ship Channel; about 50% of the petrochemical refining capacity of the U.S. is located in and around the greater Houston metropolitan area. As discussed by Ryerson et al. [2003], a key characteristic of Houston air‐quality chemistry is the very rapid daily production of ozone in summertime stagnation events, rather than the slow accumulation of ozone over periods of hours or days that is seen in most other cities. [5] Examinations of the data from the TexAQS campaigns have shown that large uncertainties are associated with the U.S. Environmental Protection Agency’s (EPA’s) National Emission Inventory (NEI), resulting in large uncertainties in ozone and aerosol chemical forecasts [e.g., McKeen et al., 2009, and references therein]. NOAA uses the EPA NEI in its national air quality forecast modeling (http://www. weather.gov/aq/). The NEI is a “bottom‐up” inventory that relies predominantly on complex emission calculations based on fuel consumption, source activity, and emission factors for most sectors of the U.S. economy. An independent approach to evaluate or improve emission inventories involves “top‐down” estimates of emissions that combine observations and a model through an inverse modeling technique [e.g., Kim et al., 2006; C. Lee et al., 2011]. In this paper, we use the 2000 and 2006 TexAQS aircraft observations and three different transport models to evaluate a version of the EPA NEI 2005 CO, NOy and SO2 emission inventories (see section 2.2 for details).

[6] Aircraft observations are temporally sparse compared to monitoring data from surface stations and are spatially limited compared to satellite imagery. However, unlike surface stations, aircraft are capable of sampling pollutants both upwind and downwind of industrial and urban sources, and can detect more chemical species than are typically measured at operational surface sites. Unlike satellites, aircraft have a spatial resolution of 1 km or less horizontally and 10 m vertically, with total instrument uncertainties on the order of a few percent. Satellite measurements do not provide adequate vertical coverage of the boundary layer, whereas aircraft can directly sample this region of the atmosphere by doing profiles. Furthermore, aircraft can sample pollutants at different distances downwind of a source, which helps to evaluate model emissions, chemistry, and dynamics. [7] This paper is organized as follows. Details about the observations, models, and inverse modeling techniques are given in section 2. Comparisons between the observations in 2006 and the 2005 EPA NEI inventory are given in section 3. We also use the measurements in 2000 and 2006 to examine the interannual variability of posterior emission inventories in August 2000 and September 2006. Conclusions are presented in section 4.

2. Methods 2.1. Observations [8] The NCAR Electra aircraft and the NOAA WP‐3 aircraft carrying highly instrumented payloads were used in TexAQS 2000 and 2006, respectively. In our inverse modeling analysis, we used eight flights in 2000, mostly in August, and eight flights in September 2006. (See Table 1 for details on the flights.) All of these were flights dedicated to characterizing emissions and atmospheric chemistry

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Table 1. Flights During TexAQS 2000 and 2006 Used in the Studya Takeoff to Landing (LST)

Date 8 August 2000 20 August 2000 25 August 2000 27 August 2000

11:30–15:45 15:45–21:30 10:30–16:30 11:30–17:45

28 August 2000

10:30–17:00

30 August 2000 1 September 2000

09:30–16:30 08:45–15:00

6 September 2000

09:30–16:00

15 19 20 21 25

2006 2006 2006 2006 2006

09:50–16:20 09:50–16:20 09:55–16:15 09:50–16:25 09:45–16:25

26 September 2006

09:50–16:35

27 September 2006

12:45–17:55

29 September 2006

13:45–20:10

September September September September September

Flight Description Houston urban plume, refineries Houston urban plume Houston emissions’ characterization Houston urban emission, ship channel, refineries Houston urban emission, ship channel, refineries Texas City, ship channel Houston urban emission, ship channel, Texas City Houston urban emission, ship channel Houston emissions’ characterization Houston urban plume, refineries Houston urban plume, ship channel Houston urban plume Houston, Dallas urban plumes and power plants Houston urban plume and industrial sources Houston urban plume and industrial sources, Beaumont–Port Arthur Houston urban plume into nighttime

a

Dates, takeoff and landing times, and descriptions on flight tracks are given. LST, local standard time.

in the Houston area. Figure 1 represents the tracks of the 2006 flights. Other NOAA WP‐3 flights in 2006 were discarded because of poor simulation quality due to large uncertainties in planetary boundary layer (PBL) height, wind direction, and wind speed in the models. The height of the planetary boundary layers during the two nighttime flights in 2006 was poorly represented by our models (see sections 2.3 and 2.5 for further details), and therefore nighttime data were not used in this study. CO on the research aircraft was measured once per second using vacuum ultraviolet resonance fluorescence [Holloway et al., 2000] with an uncertainty of ±(1 ppbv + 0.05*CO). NOy (total reactive nitrogen [Fahey et al., 1986]) was measured by ozone chemiluminescence with an uncertainty of ±(0.20 ppbv + 0.12*NOy) [Ryerson et al., 2000]. SO2 was measured by UV fluorescence with an uncertainty of ±(0.3 ppbv + 0.1*SO2) [Ryerson et al., 1998]. [9] We focus on NOy rather than NOx in our analysis because NOy includes all reactive nitrogen compounds. NOx ( = NO + NO2) can be converted to other NOy components (e.g., HNO3 and organic nitrates) on time scales of a few hours, while NOy is a considered as a conserved tracer under the conditions of this data set (see details below). The conservative tracer assumption for NOy is strengthened by confining the analysis to daytime, when heterogeneous N2O5 hydrolysis is unimportant. Because we do not consider chemical transformations in our modeling, changes in NOy are interpreted as changes in NOx emissions. We refer to “NOx emissions” and “NOy emissions” interchangeably throughout the paper. [10] The flights during the TexAQS experiments were selected to avoid rain clouds or rainy days. In 2006, five flights were associated with clear sky conditions, and three flights had scattered clouds. The measurements were not far

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from the emission sources (up to 150 km, Figure 1), so that secondary production of CO can be neglected. According to Brock et al. [2003] and Neuman et al. [2004], loss of NOy and SO2 can be as large as 20% in power plant plumes after 3 to 4 h of transport. Since the data set used in this analysis include both fresh and older plumes, we estimate that the average chemical loss of SO2 and NOy in the data set is low, ranging from 0% to 20%. It is difficult to estimate how this low bias translates into an uncertainty in surface flux emission. Fresh plumes, being more concentrated when sampled, have larger mixing ratio than older plumes. Therefore, the older plumes that can potentially be affected by NOy and SO2 losses have larger relative uncertainty, but lower mixing ratios than fresh plumes that are less uncertain. Brock et al. [2003] found that the SO2/NOy ratio was conserved with time. On the basis of our results on SO2 and NOy fluxes in section 3, we think that the uncertainty of assuming SO2 and NOy are passive tracers on the surface flux in the posterior is probably lower than other sources of uncertainties described in section 2.5. We assume that CO, NOy and SO2 are passive tracers throughout the paper, regardless of the position of the measurements relative to the source. 2.2. Prior Emission Inventory [11] The a priori emissions are based on the U.S. EPA NEI‐ 2005 national inventory, version 2 [U.S. Environmental Protection Agency, 2010]. The gridded (4 km resolution) and point source hourly emission files used in this study are available electronically at ftp://aftp.fsl.noaa.gov/divisions/ taq/emissions_data_2005/. Since the NOAA‐WP3 aircraft only sampled on weekdays during TexAQS‐2006, the weekday emissions inventory is used as the prior. Specific details of the inventory are described in a document listed with the emissions. Some background information about the inventory is discussed in section 3. [12] Emissions for the four major inventory components (point, mobile on‐road, mobile nonroad, and area) were processed according to EPA recommendations with emissions data available through the U.S. EPA as of October of 2008. Thus portions of the point and area sources, updated within more recent NEI‐2005 releases, are based on NEI‐ 2002 (version 3) emissions [U.S. Environmental Protection Agency, 2008]. The point emissions include U.S. emissions from the Continuous Emissions Monitoring System (CEMS) network for August of 2006, which mainly tracks power plant emissions, but all other point source activity data is from the NEI‐2002 v3 inventory. The mobile on‐road and mobile nonroad U.S. emissions were derived from EPA’s National Mobile Inventory Model (NMIM) [U. S. Environmental Protection Agency, 2005] for the month of July 2005. The mobile on‐road emissions were determined using the EPA’s MOBILE6.2 model, and the mobile nonroad emissions from the NONROAD2005 model. The mobile nonroad emissions were compiled before the more recent NONROAD2008 model became available, thus a priori CO emissions are expected to be too high in the Houston region [U. S. Environmental Protection Agency, 2009]. The area emissions are based entirely on source activity data within the NEI‐2002v3 inventory, so the inventory does not include some area sources within the more recent versions of the NEI‐2005, including open‐ocean

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commercial marine vessels, offshore oil and gas exploration and drilling sources, prescribed burning, and wildfire sources. 2.3. Modeling [13] For the 2006 field campaign, meteorological fields simulated by three different meteorological model configurations were used to drive the FLEXPART Lagrangian particle dispersion model. One meteorological model is the European Centre for Medium‐Range Weather Forecasts (ECMWF) operational data, with a temporal resolution of 3 h (analyses at 00:00, 06:00, 12:00, 18:00 UTC; 3 h forecasts at 03:00, 09:00, 15:00, 21:00 UTC) and 91 vertical levels. Horizontal resolution was 0.36° × 0.36° resolution (108°W–27°W, 9°N–54°N). The other two meteorological models used the Weather Research and Forecasting (WRF) mesoscale research model run using two different configurations. The first configuration used WRF version 2.2 with nested grids at 15 and 5 km horizontal spacing with 60 vertical levels. The model was initialized at 0000 UTC each day with fields from the ECMWF analysis, and boundary conditions were updated from that analysis every 6 h during each 24 h run. The Mellor‐Yamada‐Janjic boundary layer scheme and its associated surface layer were used. The land surface was represented by the five‐layer thermal diffusion formulation. Other significant physics options were Eta‐Ferrier microphysics, RRTM (Rapid Radiative Transfer Model) longwave and Dudhia shortwave radiation. Kain‐Fritsch cumulus was used on the outer domain only. References for these options are given by Skamarock et al. [2005]. The water surface temperatures were derived from satellite data as described by Cheng and Byun [2008] and Cheng et al. [2008] Observation nudging was used on all domains. Wind profiles from three radar wind profilers at LaPorte (between Houston and Galveston Bay), Moody (near Waco), and Beaumont (near the Gulf shore and the Texas‐Louisiana border) were assimilated every hour. Analysis nudging to the 6‐hourly ECMWF analyses was also used on the larger domain only. The second configuration used WRF version 3.1 with nested grids at 20 km and 4 km horizontal spacing with 35 vertical levels. The National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) model analysis data (horizontal resolution of 1° × 1°) with 6 h intervals are used as meteorological initial and boundary conditions. The urban areas in the model domain were remapped using the National Land Cover Data set (NLCD) 2001 for better representation of the urban areas, and associated urban physical processes were taken into account through the single‐layer urban canopy model. The Goddard shortwave radiation scheme and the RRTM longwave radiation scheme are used. Grid‐scale clouds are resolved using the Lin scheme. The YSU boundary layer and Noah LSM schemes are used. References for these options are given by Skamarock et al. [2008]. See the work of S.‐H. Lee et al. [2011] for further details on the model configuration. [14] For 2000, NCEP GFS and operational ECMWF data were of lower quality compared to 2006. Therefore we used the ECMWF Reanalysis interim (ERA‐interim) [Simmons et al., 2007] data for 2000 to run WRF in the same configuration used with NCEP GFS data in 2006; this WRF+ERA‐interim combination is the only model used for

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our 2000 analysis. Using ERA‐interim in 2006 showed that the results were not very sensitive to the input data of either NCEP GFS or ERA‐interim (results not shown). Figure 2 presents comparisons between boundary layer heights retrieved from six wind profilers in the Houston area and simulated by WRF+ERA‐interim for six different days in 2000. Only the wind profilers with high data quality are used. Lamarque wind profiler has the largest bias (+35%), probably because this site is the closest to the shore and the PBL height variability is the most affected by sea breeze. The other sites have biases ranging from −11% to +7.5%. The average standard deviation is about 20%. Since trajectories between the surface sources and the aircraft measurements in the boundary layer can potentially pass through regions of positive and negative PBL height bias, it is difficult to assess the overall PBL height bias on the chemical mixing ratio simulated by FLEXPART. However, as the onshore biases vary from −11% to +7.5%, one can expect the mean bias to be lower than each individual bias. For instance, the bias combining the PBL height data from the four onshore wind profilers is +2.5%. The WRF+ERA‐interim simulation in 2000 is probably less accurate than the ensemble of three model simulations in 2006. [15] To simulate the transport of passive chemical species, we used the meteorological models described above to drive the FLEXPART Lagrangian particle dispersion model (version 6.2) [Stohl et al., 2005, and references therein]. 20000 back trajectories were released every 20 s along the aircraft flight tracks. The FLEXPART output has a resolution of 0.045° × 0.05°. The output consists of a residence time in the surface layer weighted by the atmospheric density. When this output is combined with a surface flux emission inventory, one can calculate a mixing ratio for each set of trajectories along the aircraft flight track. In this way, FLEXPART is used to linearize the transport processes between the surface and the aircraft, so that an adjoint model is unnecessary to apply an inverse modeling technique (see section 2.4 for further details). [16] FLEXPART simulates the transport over 24 h to focus on local transport. Chemical background concentrations of each species of interest for each flight were removed from the measurements. We defined the chemical background as the lowest mixing ratio in the boundary layer measured upwind of Houston area for each flight. Only the observations in the boundary layer are used in this study. We used additional large‐scale FLEXPART simulations to estimate regional and continental transport of pollution into the Houston area to constrain the observational data to those with local emission influences only (for further details on the large‐scale FLEXPART runs, see the work of Brioude et al. [2007]). [17] The FLEXPART output domain is 97.7°W–91.8°W and 26.81°N–32.12°N (outer domain in Figure 1), covering Texas (outer domain in Figure 1). We applied the inverse modeling on a smaller domain, 96.75°W–93.75°W and 28.565°N–30.815°N (inner domain in Figure 1), to focus on Houston and its surroundings and to reduce the size of the matrices involved in the inversion and the corresponding computation time. Observations that were influenced by transport from outside the inner domain were also removed from the data set (emissions from Dallas and Austin, for instance). Only measurements above background that are

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Figure 2. Comparison between PBL heights observed by five wind profilers in the Houston area and simulated by the WRF model in 2000. Dashed lines represent the 20% error uncertainties. The relative biases are calculated dividing the mean bias error by the mean observed PBL height.

downwind of Houston or other urban areas in the inner domain are kept in the analysis. Since chemical backgrounds are removed from the chemical measurements for each flight, no chemical boundary conditions are used in FLEXPART. Hence, 3343 aircraft segments of 20 s from the TexAQS 2006 campaign analyzed by FLEXPART were selected to be used in the inversion method. [18] In the paper, we call transport model a combination of one of the meteorological model (either ECMWF, WRF2.2+ECMWF, or WRF3.1+GFS) to drive FLEXPART. We consider that each transport model is independent. The ensemble of those three transport models is used in section 3 to estimate model uncertainties. See section 2.5 for further details. The posterior inventories (the resulting inventories after the inversion method is applied) reported here have an

associated mean value and standard deviation that includes the variability of the surface flux, the uncertainty of the method, and the uncertainty from the transport models due to advection. The uncertainty from FLEXPART itself cannot be assessed with this approach, but we believe this uncertainty is small compared to the uncertainties in the meteorological fields. A discussion of the uncertainty in the meteorological models and FLEXPART is given in section 2.5. 2.4. Inverse Modeling Technique [19] Assuming that observations (simulated or measured) and parameters (such as the surface emission flux from an inventory) are stochastic quantities, they can be represented by normal distributions. The probability density function Po

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of the error on the observations assuming Gaussian distributions can be written as 1 T

Po ¼ Ae2"o R

1

"o

;

ð1Þ

with "o ¼ yo hð xÞ;

ð2Þ

x being the emission inventory, and R being the covariance matrix of the measured observation (yo) and the simulated concentrations by a model h. A is a normalization constant. [20] The probability density function Pb of the error on the prior emission inventory can be written as Pb ¼ Ae

12"Tb B1 "b

;

ð3Þ

with "b ¼ x xb

ð4Þ

and B being the covariance matrix of the prior emission inventory xb. The FLEXPART output can be combined with surface fluxes to calculate the mixing ratios at a given receptor. The FLEXPART output is therefore a linear model (H) that, when multiplied by a surface inventory (x), can simulate an observation (y): y ¼ Hx:

ð5Þ

The purpose of our inversion is to find the best estimate of x that minimizes the sum of the errors in the observation ("o) and in the prior inventory ("b). Assuming Gaussian distributions, this is equivalent to minimizing a cost function J for both observation and prior of the form J ¼ Jobs þ Jprior ; ð6Þ 1 1 J ¼ ðyo HxÞT R1 ðyo HxÞ þ ðx xb ÞT B1 ðx xb Þ 2 2

where yo is a vector column of p elements (observations), x is a vector column with m elements (grid cells), H is a matrix of [pxm] elements, R is a matrix of [pxp] elements, and B is a matrix of [mxm] elements. [21] A Gaussian distribution is typically assumed for both observations and prior. The formulation of the resulting cost function J is simple and allows the use of methods that converge quickly to a solution. However, assuming Gaussian distributions allows negative values for surface fluxes and observations above a background value. It is common for passive tracers with no uptakes to discard negative fluxes by reducing the variance of the grid cells where negative fluxes are found during the inverse modeling process [e.g., Stohl et al., 2009]. However, these modifications have no physical basis and can potentially bias the final solution. Above‐ background observations and emission fluxes without uptakes are better represented by lognormal distributions instead of Gaussian distributions. We therefore assume in our inverse modeling that both the above‐background observations and the emissions have a log normal distribution. [22] With the Gaussian assumption, the mode, the mean and the median have the same cost function. Assuming a lognormal distribution for both the observations and prior

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inventory, the cost function will have different forms if the estimation criteria is based on the maximum likelihood (i.e., the mode of the distribution), the minimum variance (i.e., the mean of the distribution), or the minimum absolute distance (i.e., the median of the distribution). We decide to minimize the cost function to find the median of the distribution. The cost function can then be written as [Fletcher and Zupanski, 2006] J ¼ Jobs þ Jprior 1 J ¼ ðln yo lnðHxÞÞT R1 ðln yo lnðHxÞÞ 2 1 þ ðlnð xÞ lnðxb ÞÞB1 ðlnð xÞ lnðxb ÞÞ: 2

ð7Þ

Here, R and B are covariance matrices in the lognormal distribution space. In general, the R and B covariance matrices are unknown, especially the off‐diagonal elements. The measurement uncertainties are known and are uncorrelated (based on instrument calibration). The uncertainty in the model is unknown most of the time, and the uncertainty of consecutive simulated observations can potentially be correlated. Usually, R is assumed to be diagonal (no cross correlation between the observation or in the model). A good way to increase the likelihood that R is diagonal is by taking a random subset of observations so each observation is not spatially or temporally close to another. Further comments are given in section 2.5. [23] B is also unknown. However, the values of R and B will strongly affect the final solution. If B is too large compared to R, the solution will not depend on the information in the prior inventory, and the final solution will be dominated by the errors in the observations/models. If R is too large compared to B, the observations will not affect the solution which will be unchanged compare to the prior. The values chosen for B and R can be seen as a balance between the uncertainty of the observations/models and the uncertainty of the prior. [24] To reach a good compromise without using an arbitrary value for R and B, we apply a regularization a on Jprior and calculate J applying an inverse modeling technique for different values of a: J ðÞ ¼ Jobs þ Jprior :

ð8Þ

We first assume an uncertainty of 5% for each observation in R (which includes the uncertainty from the measurements and the model), and an uncertainty of 100% for the prior in B. To find the optimal value for a that balances R and B, we use the L‐curve theory [Hansen, 2001], which states that the optimal value of a should be the maximum curvature between Jobs and Jprior. As shown by Henze et al. [2009], the optimal solution of a is the value that minimizes the sum of the normalized error from the observation and the prior. The observation error is normalized to the value of Jobs at the first iteration, while the prior error is normalized to the value of Jprior for a being small (a = 0.01). The value that minimizes J(a) in Figure 3 is about 2. [25] To minimize J with respect to x (the emission inventory), we apply 3‐D and 4‐D variational least square methods (3DVAR and 4DVAR). Here the term “variational method” refers to finding the best estimate of x by mini-

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Figure 3. Variation of the errors from the observation and prior terms in the inverse modeling technique with a. See section 2.4 for more details.

mizing J on the basis of the spatial (3‐D) and spatial‐ temporal (4‐D) variability of x applied to a top‐down emissions verification. One can use the Jacobian of J to converge to a solution quickly. If the minimum of J is reached, then @J ¼0 @x

ð9Þ

with 0 being a vector of all elements = 0 with the dimension of x. To avoid negative values in the minimization process, one can write @J @J @ lnð xÞ @J 1 ¼ ¼ ¼ 0; @x @ lnð xÞ @x @ lnð xÞ x

ð10Þ

where 1/x never equals zero. Thus @J @J ¼0, ¼ 0; @x @ lnð xÞ

ð11Þ

@J ¼ WR1 ðln yo lnðHxÞÞ þ B1 ðlnð xÞ lnðxb ÞÞ @ lnð xÞ with 2

3

: ð12Þ

6 H ði; jÞxð jÞ 7 7 W ¼6 4X H ði; jÞxð jÞ5 j

i; j

We applied an iterative method to find the best estimate of x, lnðxiþ1 Þ ¼ lnðxi Þ S

dJ d lnðxi Þ

ð13Þ

with S being calculated to minimize J at each iterative step (i.e., the gradient method). We used the function fminbnd from the Matlab software to find S at each iterative step. We don’t use the Hessian of J to improve the convergence method because the Hessian is not necessarily defined.

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Using this iterative method, no negative result is found. Arbitrarily modifying the covariance matrices to avoid negative values is therefore unnecessary. [26] Using all the grid cells available in the prior x, the size of H would be 3343 observations (using the observations of TexAQS 2006) by 3111 surface flux grid cells (number of 4 × 4 km grid cells in the inner domain on Figure 1) in a 3DVAR inversion, or by 37332 surface flux grid cells in a 4DVAR inversion if the surface fluxes are defined every 2 h. To reduce the size of H, we optimize grid cells in the prior that are known for having significant anthropogenic sources, with an average CO flux larger than 0.3 10−9 kg s−1 m−2 and an average NOy flux larger than 0.1 10−9 kg s−1 m−2. Furthermore, a grid cell needs to have more than 5% of the maximum average residence time in the inner domain to be used in the analysis. The grid cells used in the inversion in 2000 can be different than those used in 2006, because of differences in the footprint distribution due to differences in wind regimes for the two years. For instance, the flights in 2000 had more measurements downwind of surface emissions from the western part of the inner domain. In 2006, H has a dimension of 3343 × 745 in our 3DVAR inversion. The 745 grid cells used in the inversion are represented by the colored grid cells in Figures 5–8. The white grid cells are not used in the analysis. Furthermore, because the atmospheric transport was poorly represented during nighttime, and because the measurements during daytime were distributed mostly between 12am and 18pm, we used a variable time step in the 4DVAR inversion. We used a first time step between 7pm and 4am, a second time step between 5am and 8am, and 5 time steps every 2 h between 9am and 6pm, resulting in 7 different time steps in the 4DVAR inversion. Thus, H has a dimension of 3343 × 5215 in our 4DVAR inversion. 2.5. Model and Method Uncertainty 2.5.1. Transport Models Uncertainty [27] It is always a difficult task to assess uncertainties or biases from transport models. Transport model uncertainty originates from wind uncertainty (direction and speed), mesoscale mechanisms in the PBL (ventilation, turbulent mixing, etc …), and chemical removal and/or in situ production [see, e.g., Gerbig et al., 2008]. By using three models (ECMWF, WRF+ECMWF and WRF+GFS) to drive FLEXPART, we assume that the results from those models are independent and unbiased, meaning that they provide a random sample centered on the truth. In September 2006, there was no sea breeze effect or other major mesoscale phenomenon during the flights considered in this paper, so the ECMWF data at 0.36 × 0.36° are as reliable as those from the WRF runs. For 2000, we used only one run (WRF+ERA‐interim), because the operational ECMWF data and GFS data were of lower quality than in 2006 and could potentially bias the results of the ensemble. We have to assume that the results with WRF+ERA‐interim in 2000 are as reliable as the results from the three models in 2006. [28] The WRF runs over Houston have been validated against surface‐based wind profilers and aircraft meteorological measurements [e.g., McKeen et al., 2009; S.‐H. Lee et al., 2011]. The root mean square error of the wind direction near the surface was 40 degrees relative to the observed wind direction, and the timing of sea breeze was well represented.

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Figure 4. Subset of all SO2 observations comparing SO2 2006 aircraft measurements (in black) with SO2 mixing ratios simulated by two of the three models used in this study. The error on the wind speed was about 1 m s−1 [S.‐H. Lee et al., 2011]. These studies conclude that the WRF PBL height has an uncertainty of about 20%. The PBL height is a good proxy for knowing the vertical mixing downwind of a source, as the vertical mixing is strong enough to homogeneously mix the plume throughout the PBL height. Therefore, an uncertainty of 20% in PBL height results in an uncertainty of roughly 20% in the surface flux produced by an inverse modeling technique. Furthermore, a systematic bias of PBL height would bias the results of the emission inventories of CO, NOy and SO2 in the same way. As discussed in section 3, we did not find any evidence of a bias from the PBL height using the ensemble of three transport models in 2006. [29] The CEMS data provide in situ measurements of power plant emissions of CO, NOy and SO2. SO2 is probably the best of these tracers to characterize model uncertainty, as the Parish Power Plant, located southwest of the Houston urban core, is by far the largest emitter of SO2 in the Houston area. The drawback of using a power plant as a metric of model uncertainty is that the pollution is emitted from the stacks with positive buoyancy. The Parish stack emissions are injected into the atmosphere at 300 to 500 m in altitude. Since our FLEXPART back trajectories assume all emissions occur within a surface layer of 100 m depth, the uncertainty of the transport models using power plant emissions can be artificially overestimated. The SO2 fluxes in the posterior (section 3.1.4) are therefore more uncertain than CO and NOy fluxes because the power plants are the only major sources of SO2. [30] Figure 4 shows a subset of all the 2006 observations of SO2 considered in this analysis compared with simulated mixing ratios from two of the three transport models. When the measured and simulated SO2 peaks are collocated, the magnitudes of the simulated and measured SO2 mixing ratios are similar. Measured and simulated peaks are not collocated when the wind direction is significantly in error. In this case, an inverse modeling technique would tend to reduce the emissions from a point source to reduce the absolute error between the measured and simulated observations. We found that the Parish Power Plant flux is underestimated by 20% on average using the ensemble of

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the three models because of uncertainties in wind direction, PBL height, and emission injection above the surface. This analysis shows that each single grid cell in our inventory probably has an uncertainty of about 20% due to the transport models. [31] However, the underestimation of the Parish Power Plant SO2 emissions cannot necessarily be extrapolated to larger surface areas like the urban area or Houston Ship Channel, because over a larger scale, the wind direction uncertainty is reduced. Quantitative comparison between flux emission in the prior and posterior should not be made at the grid spacing resolution of the transport models. Numerical models can be described by an effective resolution at which turbulent and mesoscale phenomenon are realistic compared to observations. Skamarock [2004] showed that the effective resolution of the WRF model is 6 to 7 times the grid spacing resolution. The implication of these findings is that we should compare results of emission inventories at about 6 times the grid spacing of the model, even though single grid cells can potentially have significant information with a small uncertainty. Quantitative comparisons in section 3 are made in regions encompassing dozens of grid cells (one grid cell being at roughly the native resolution of the models). [32] To assess the uncertainty in horizontal wind direction and wind speed over larger areas than a single grid cell, one can compare the distribution of CO/NOy ratio in the surface inventories and compare them with the ratio measured downwind of the urban area and Ship Channel area. The flight tracks were specifically chosen to characterize urban and industrial sources. If the transport in the models is correct, the magnitude and spatial distribution of ratios at the surface should be different over different areas in Houston as shown by the measurements. As shown in section 3.1.3, we found that the simulated ratios were close to those seen in the measurements, confirming that mesoscale atmospheric transport is accurately represented by the models. 2.5.2. Inversion Method Uncertainty [33] It is also difficult to assess the uncertainty from the inverse modeling method itself. For that reason, we applied both 3DVAR and 4DVAR methods and compared the results in section 3. The models performed poorly during nighttime. Therefore, the fluxes reported in this paper are for daytime only (both prior and 3DVAR and 4DVAR posterior inventories). [34] It is known that using one set of observation with a prior will always give the same posterior. It is therefore possible that the solution is not necessarily the best estimate. As explained in section 2.4, we used random subsets of observations for each hour during the day to increase the likelihood of having a diagonal covariance matrix. Each time the inverse modeling technique is applied with a random observation subset, we also apply a random term on the prior to reduce the cross correlation between grid cells in the posterior. The random term has the same magnitude flux as the smallest fluxes in the domain. The cross correlation between grid cells represents the likelihood that a grid cell flux varies owing to the variation of a different grid cell. Figure 5 shows a map of the median of cross correlations of the CO posterior inventory (based on 100 realizations). By using a random prior, the cross correlation of the fluxes from different grid cells on the outside of Houston is reduced

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Figure 5. Cross correlation (r2) between grid cells (a) without a random prior for one model, (b) with a random prior for the same model, and (c) for the three‐model ensemble with a random prior. See section 2.5.2 for details. from 0.9 to 0.35. The grid cells that have significant surface flux have a small cross correlation (this is the majority of the grid cells). Using the three‐model ensemble in addition to the random term, the largest cross correlation is reduced to around 0.1 to 0.2. The cross correlation could explain 5% of the surface flux variation in the Ship Channel, 13% in the Houston urban area, and 11.5% in the Greater Houston area, meaning that the differences between the prior and posteriors are uncertain by at most only 5% in the Ship Channel and 13% in the Houston urban area owing to cross correlation. Those values are included in our calculations of confidence intervals in section 3. Using this random method, we increase the chances of reaching the best estimate of a posterior inventory and reduce error correlation in the posterior.

3. Results and Discussion 3.1. Comparison Between 2006 Posterior and EPA NEI Prior 3.1.1. CO [35] The daytime EPA NEI CO inventory is characterized by large emissions in the urban area of Houston (off scale in Figure 6) and by on‐road emissions, mainly from highways. Lower fluxes are found from small cities around Houston, such as Beaumont to the east, Conroe to the north, and Waller to the northwest, and over major ports like Texas

City, Freeport and Port Arthur (see Figure 6b for locations of each city). [36] The 3DVAR and 4DVAR posterior inventories, averaged for the inversions with the three models, show the same emission pattern as the prior, but with large reductions in CO emissions from the Houston urban area (Figure 6). Differences exist between the two posteriors. The 3DVAR posterior has higher emissions from the small cities around Houston, while the 4DVAR posterior gives higher emissions in the Houston urban area. The 4DVAR posterior has an uncertainty of 15% in the Houston urban area, while the 3DVAR posterior uncertainty is 30%. The uncertainty (1 s standard deviation) is estimated from the ensemble of realizations from a random subset of observations and the random term in the prior (100 realizations per model) for each of the three models (see explanation in section 2.5). The variability is larger over the small cities around Houston, with an uncertainty of 30% for the 4DVAR posterior and 60% for the 3DVAR posterior. The uncertainty of the 3DVAR posterior is likely larger than the 4DVAR because it is more difficult for the inverse modeling technique to converge to a single solution with the lower number of parameters available in the 3DVAR method. [37] The percentage differences in CO emissions between the prior and posteriors (Figure 7, top) clearly show that the emissions reduction is limited to the urban area. Both methods agree on an increase in emissions over the small cities around Houston.

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Figure 6. Daytime surface CO emissions from (a) the EPA NEI prior, (c, d) three‐model average posteriors, and (e, f) the standard deviation of the posterior flux from the three models, using 3DVAR and 4DVAR techniques. (b) Map indicating the regions used throughout the paper and showing the position of major industrial ports and small cities near Houston. [38] As explained in section 2.5, the models have an associated effective resolution of about 6 times the model grid spacing. Any single grid cell will have a numerical uncertainty from the model that can be reduced by averaging the surface fluxes over several grid cells. We report on Table 2 differences between the prior and posteriors for three different regions in Houston (as shown in Figure 6): A large area called Greater Houston, the Houston Urban core Area that is dominated by urban emissions, and the Ship Channel area with strong industrial and traffic emissions. The daytime CO emissions from Greater Houston are reduced from 15 to 19% with the 3DVAR or 4DVAR techniques, with an uncertainty of 5% to 9%. Those uncertainties represent the 95% confidence interval on the difference between the mean prior and mean posterior based on a statistical significance in a t test and include the bias from cross cor-

relations in the posterior. The Urban Area emissions from the prior are reduced by 37% ± 7% to 45% ± 8%, while the emissions from the Ship Channel are reduced by 41% ± 4% to 46% ± 6%. These findings agree with previously published results showing an overestimation of CO flux in the EPA NEI inventory for the Houston area [e.g., McKeen et al., 2009]. 3.1.2. NOy [39] The EPA NEI NOy inventory (Figure 8) shows large emissions in the Ship Channel and in the major ports along the coast, due to industrial point sources, ship traffic, and on‐road emissions. Both posteriors show large emission reductions in the Ship Channel and the major ports. As was the case for CO, the 3DVAR posterior NOy inventory has larger emissions on highways and from the small cities around Houston than the 4DVAR posterior. The percentage

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Figure 7. Differences (%) between the average prior and posterior for (top) CO, (middle) NOy, and (bottom) SO2, using the 3DVAR and 4DVAR techniques. Positive values indicate an increase in posterior emissions relative to the prior. differences in NOy flux between the prior and posterior (Figure 7, middle) clearly demonstrate that the NOy emission reductions are limited to the Ship Channel region and major ports (Freeport, Texas City, Port Arthur) using both methods.

[40] The spatial distribution of the CO and NOy posterior‐ prior differences is quite different (Figure 7, top and middle). If there were a bias in the inversion resulting from the modeled PBL height or some other meteorological parameter, we would expect the CO and NOy posteriors to be

Table 2. Daytime Average CO Emissions in the Prior (EPA NEI) and Posteriors From 3DVAR and 4DVAR and the Posterior‐Prior Differences Calculated in the Three Regions (Greater Houston, Urban Area, and Ship Channel) Defined in Figure 6ba

Greater Houston Urban area Ship Channel

3DVAR (Daytime Only)


Daytime Average Prior Emissions (10−9 kg m−2 s−1)

Emissions (10−9kg m−2 s−1)

Difference (%)


Difference (%)

2.6 10.7 11.1

2.11 ± 0.23 5.46 ± 0.86 6.0 ± 0.66

−19 ± 9 −45 ± 8 −46 ± 6

2.21 ± 0.13 6.7 ± 0.75 6.5 ± 0.44

−15 ± 5 −37 ± 7 −41 ± 4

a Uncertainty range represents the 95% confidence interval of the difference between the prior EPA NEI and posteriors based on a statistical significance in a t test.

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Figure 8. Same as Figure 6 but for NOy.

biased in the same way and to show consistent spatial changes from prior to posterior. We can conclude that because the posterior‐prior differences in CO and NOy emissions instead have very different spatial distributions, biases in PBL height are not a major issue with the three models used in this analysis. [41] Interestingly, the small cities north of Houston (Waller and Conroe) see an increase in NOy emissions in conjunction with CO emission increases. These emission increases are largely confined to the local area around these cities as well as on the belt of highways surrounding Houston. On the other hand, the large ports show increases in CO accompanied by decreases in NOy, while the southwestern part of the domain shows relatively little change in either CO or NOy. The spatial details of the changes to the prior inventory therefore give us confidence that the inversion methods really are improving the inventory and are not the result of numerical noise. Those NOy and CO emission increases in Waller and Conroe are

probably due to a greater population growth than reported in EPA NEI. See section 3.1.3 for further comments. [42] Compared to the EPA NEI prior, the NOy posterior emissions (values are the averages of the three models for both 3DVAR and 4DVAR calculations) are reduced by 28% ± 6% in the Greater Houston region (Table 3). The Urban Area emission is reduced by 7% ± 4%, while the Ship Channel emission is reduced by 51% ± 6%. [43] The large NOy emission reductions relative to the EPA NEI in the Ship Channel are in agreement with the findings of Kim et al. [2011], who carried out direct comparisons of satellite, aircraft, and ground‐based data with an Eulerian 3‐D chemical‐transport model. The analysis by Kim et al. suggests NOx emissions for the Ship Channel region within the NEI‐2005 inventory are roughly 50% too large. They find that probable sources of this NOy discrepancy are associated with port NOx emissions from commercial marine vessels within the NEI‐2005 area inventory and overestimates of industrial point source NOx

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BRIOUDE ET AL.: MESOSCALE INVERSE MODELING IN HOUSTON Table 3. Same as Table 2 but Valid for NOy Emissions




Daytime Average Prior Emissions (10−9kg m−2 s−1)


Difference (%)


Difference (%)

0.39 0.73 4.2

0.28 ± 0.027 0.67 ± 0.037 2.0 ± 0.25

−29 ± 7 −8 ± 5 −52 ± 6

0.28 ± 0.016 0.69 ± 0.022 2.1 ± 0.21

−28 ± 4 −6 ± 3 −50 ± 5

emissions. They report that NOx to SO2 emission ratios for in‐port vessels in the NEI‐2005 are significantly higher (by a factor of 5) than the observed emission ratios from ship plumes collected during TexAQS 2006 [Williams et al., 2009] and than those from an independent U.S. EPA port inventory for Houston [U.S. Environmental Protection Agency, 2007]. The Ship Channel NOx point source emissions within NEI‐2005 are significantly higher (∼64%) than a point source inventory for 2006 provided by the Texas Commission on Environmental Quality [Kim et al., 2011]. Part of this difference is possibly due to mandated NOx emission reductions between 2002 and 2006 for Ship Channel industrial point sources that are not reported within the CEMS database. Applying these NOx emission differences to the NEI‐2005 inventory results in an estimate consistent with total Ship Channel NOx emissions based on NO2 solar occultation flux observations [Rivera et al., 2010; Kim et al., 2011].

3.1.3. Validations of CO and NOy Posterior Inventories [44] CO/NOy ratios can be used to characterize urban and industrial plumes, as the ratios of CO and NOy emissions fluxes have different distributions for urban or industrial sources. Comparing measured slopes and the ratios of emissions from inventories helps to evaluate the horizontal transport of the models. [45] Figure 9a shows an example of slopes measured downwind of the Houston urban area and of the Ship Channel on one research flight in 2006. Considering all 2006 research flights, the CO/NOy measurements give an average slope of 5 to 6 ppb of CO per ppb of NOy for urban emissions during daytime. In the 4DVAR posterior, the ratio varies from 6.5 to 11.5, with an average value of 7.8 during daytime. In contrast, the prior EPA NEI inventory has an average ratio of 12. Over the Ship Channel, the measured CO/NOy ratio is about 3. In the posterior, the ratio varies from 5 to 2.3, with an average daytime value of 3.2. The

Figure 9. (a) CO/NOy slopes measured by the aircraft in one 2006 research flight, (b) map of this ratio from the average 2006 posterior, (c) diurnal variation in the CO/NOy slope in the urban area, and (d) diurnal variation in the CO/NOy slope in the Ship Channel from the prior (blue line) and posterior (black line) 2006 inventories. 13 of 19

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Figure 10. Time series of measurements (black) and simulations by WRF‐Chem using the EPA NEI inventories (blue line) and optimized inventories (red line) for the 26 September 2006 flight: (top) NO2 and (bottom) CO. prior inventory ratio is about 1 over the Ship Channel. Thus the CO/NOy ratio in the posterior inventory has improved compared to the prior for both urban and Ship Channel emissions. This result confirms that the modeled mesoscale horizontal transport is able to accurately distinguish emissions from the urban and Ship Channel areas. [46] A second validation involves using the information we derive from our inverse modeling calculations to drive a chemical‐transport model and comparing the result to aircraft observations. Figure 10 shows the NO2 and CO time series measured by the NOAA WP‐3 aircraft on 26 September 2006 and simulated by the WRF‐Chem mesoscale chemical‐ transport model, using either the EPA NEI inventory or the optimized CO and NOy inventories discussed in sections 3.1.1 and 3.1.2 (see Kim et al. [2011] for details of the model configuration). The discrepancies between the measurements and WRF‐Chem simulations of NO2 and CO for this flight are reduced by using the optimized inventories to drive the chemical‐transport model. The mean absolute error between the measured mixing ratio of CO and the simulated mixing ratio using the prior and posterior is reduced from 47 to 30 ppbv, respectively. In case of the NO2 mixing ratios, the mean absolute error is reduced from 3.6 ppbv to 2.2 ppbv. The correlations between measured and simulated mixing ratios are similar. Using the optimized CO and NOy emission inventories instead of the EPA NEI‐ 2005 inventory improves the simulation skill of WRF‐Chem. 3.1.4. SO2 [47] SO2 is mainly emitted from power plants, some industrial sources, and ships. In contrast to NOx, SO2 is not emitted in large quantities by on‐road vehicles. Several large SO2 sources are located in the Ship Channel. Another major emitter is the Parish Power Plant for which observed emissions are available in the CEMS database.

[48] The spatial distribution in the posterior SO2 inventories didn’t change significantly compared to the prior (Figure 11). The standard deviation of the posteriors is quite small, except for the surface fluxes outside Houston where emissions are small. Figure 7 (bottom) shows that the differences between the prior and the posteriors in the urban area and the Ship Channel are small. [49] Notably, the Parish Power Plant is significantly reduced in the 3DVAR (−45% ± 9%) and the 4DVAR (−30% ± 8%) posteriors. The Parish power plant emissions in the prior are based on July and August 2006 averages of CEMS data. The CEMS database shows that using July/ August 2006 data overestimates September 2006 SO2 emissions by 10% on average, and that the September temporal variability in Parish SO2 emissions is about 19%. Therefore, the 4DVAR posterior actually underestimates the true Parish SO2 emissions by 20% ± 8%. As explained in section 2.5, the inversion’s underestimate of the emissions from an isolated point source is expected owing to the effective resolution of the transport models. The reason that the 3DVAR posterior underestimates Parish emissions more than the 4DVAR posterior could be that having a better representation of diurnal emissions with the 4DVAR technique helps the method to converge to a better solution. [50] The differences between the SO2 prior and posterior are small in the urban and Ship Channel areas and are quite different than the differences in CO and NOy emissions (Figure 7). If a bias in the PBL existed over Houston, the SO2 emissions in the posterior would have been reduced by the same magnitude as those for CO and NOy. However, potential bias could exist in the northwestern part of the domain, where posterior CO, NOy and SO2 all increase. Uncertainties in trajectories could also contribute to an overestimation of the surface contribution of the northern part and underestimate the contribution of the urban area

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Figure 11. Same as Figure 6 but for SO2.

and Ship Channel, which would result in an increase in surface flux emission in the northern part of the domain. [51] The SO2 emission in Greater Houston area is reduced by 20% ± 7% in the 3DVAR, mainly owing to the Parish Power Plant emission underestimates (Table 4). As explained above, inversions for a point source are associated with large uncertainties and are unreliable. The differences in emission between the prior and posterior over the urban area and the Ship Channel are small (less than 6%, Table 4), indicating that the inverse modeling didn’t significantly change the prior. While in port, most large ships switch to fuels that do not have high SO2 emissions. Therefore, a reduction in the Ship Channel SO2 emissions of a magnitude similar to that seen for NOy was not expected. 3.2. Interannual Variability (2000 and 2006) [52] In this section, the 4DVAR posteriors calculated using the measurements during TexAQS 2000 and TexAQS 2006 are compared. The EPA NEI 2005 prior is used in the

inverse modeling of both the 2000 and 2006 campaigns. We ignore the 3DVAR posterior results because of their larger uncertainty and to simplify the analysis. The differences between the 2000 posterior and the EPA NEI 2005 prior are not treated in this section to avoid redundancy with section 3.1. Here we analyze and interpret only the differences in the posterior daytime emissions between August 2000 and September 2006. These differences are assumed to result only from emission variations between those two time periods and not from differences in model or observation uncertainties. SO2 is not used in the analysis because the instrument functioned properly for only three flights during the 2000 campaign, and there are not enough observations available to significantly constrain a SO2 solution using a 4DVAR technique. [53] Figure 12 shows the CO and NOy posterior inventories in August 2000 and September 2006. There was a reduction in CO emissions in the urban area (−8% ± 3%) from 2000 to 2006 (Table 5). The NOy emission increased

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BRIOUDE ET AL.: MESOSCALE INVERSE MODELING IN HOUSTON Table 4. Same as Table 2 but Valid for SO2 Emissions




Daytime Average Prior Emissions (10−9kg m−2 s−1)


Difference (%)


Difference (%)

0.30 0.36 2.08

0.23 ± 0.021 0.37 ± 0.007 1.98 ± 0.02

−20 ± 7 +4 ± 2 −5 ± 1

0.29 ± 0.01 0.37 ± 0.004 2.17 ± 0.04

−4 ± 3 +3 ± 1 +6 ± 2

by 20% ± 6% in the urban area and by 13% ± 7% in the Ship Channel. The difference in CO emissions in the Ship Channel is statistically negligible. [54] The CO reduction in the Houston urban area predicted by the inverse modeling is at least qualitatively consistent with declines in CO emissions resulting from pollution controls on gasoline vehicles. Nationwide, the light‐duty

vehicle fuel‐based emission factor for CO (i.e., the mass of CO emitted per volume of fuel burned) decreased by 60% from 2000 to 2006 [Bishop and Stedman, 2008]. During the same time period, gasoline fuel consumption in the State of Texas increased by only 10% (see Tables 6a–6c). Gasoline vehicles are the main on‐road emitters of CO, as the CO emissions from diesel vehicles are relatively low

Figure 12. Fluxes in the posterior in August 2000 and September 2006 for (a, b) CO and (c, d) NOy. (e, f) Differences (%) in CO and NOy fluxes between 2000 and 2006. A positive value indicates an increase in emissions from 2000 to 2006. 16 of 19

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Table 5. Differences in the Posterior CO and NOy Emissions Between 2000 and 2006 Calculated for Greater Houston, Urban Area, and Ship Channela


CO

NOy

−4% ± 5% −8% ± 3% 0%

+10% ± 6% +20% ± 6% +13% ± 7%

a The surface emission maps are shown in Figure 12. Uncertainty range represents the 95% confidence interval of the difference between the posterior in August 2000 and September 2006 based on a statistical significance in a t test.

[McGaughey et al., 2004]. Therefore, on‐road emissions of CO have decreased by 50%. The fraction of CO emissions from gasoline vehicles in the urban area box is unknown and the CO emission variation from other sectors is unknown. Quantitative comparisons with our results are therefore not possible, but our result (reduction of 8% ± 2%) shows qualitative consistency with known on‐road emission variation. [55] In contrast, the inverse modeling predicts that NOy emissions in the urban area increased by 20% from 2000 to 2006. Table 6a shows that the fraction of diesel vehicles increased in Houston from 2000 and 2006, and that this ratio is larger in the Houston urban area than in the State of Texas as a whole. U.S. emission regulations for diesel vehicles were less restrictive than for gasoline vehicles during this time period. At the same time, increases in diesel fuel consumption were quite large relative to gasoline (+10% for gasoline and +44% for diesel for the State of Texas). Overall, the NOy emissions from on‐road sources increased 10% from 2000 to 2006. According to Dallmann and Harley [2010], on‐road sources account for 75% of total NOy emissions nationally, and we would expect on‐road sources to similarly dominate the NOy emissions in the Houston urban area. Assuming that the nonroad emissions have also increased by the same amount, the emissions of NOy in the Houston urban area increased by 10%, which is in agreement with our results. [56] We also analyzed 5 Texas Commission on Environmental Quality (TCEQ) surface stations that have NOx measurements between August 2000 and September 2006. We found a difference in concentration between August 2000 and September 2006 of +2% to +40% (average value of +20%). This difference is associated with a trend in NOx concentration of −3 to −6% per year, and an average difference in NOx concentration between the months of August and September of +21 to +52%. Between August 2000 and September 2006, the PBL height was lower by 17%, according to the meteorological models used here. Assuming either no impact on concentration from the PBL height change, or instantaneous vertical mixing that results in a

Table 6a. Vehicle Population Data for the State of Texas and Houston Urban Area Reported by the Texas Transport Institute State of Texas Gasoline Diesel

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95% 5%

Houston Urban Area, 2000 92.7% 7.3%

Houston Urban Area, 2006 92% 8%

Table 6b. Texas State Fuel Consumption in Thousands of U.S. Gallons in 2000 and 2006 Taken From the U.S. Federal Highway Administration Statistics

Gasoline Diesel

State of Texas, 2000

State of Texas, 2006

10,428,049 2,824,792

11,473,456 4,053,917

proportional response in concentration from changes in PBL height, we estimate that NOx emissions have been increased by +3 to +20% between August 2000 and September 2006, in qualitative agreement with our results. This large difference between the months of August and September is probably due to an increase in traffic emissions when schools resume in September. For instance, according to TCEQ estimates of vehicle miles of travel in Houston, heavy‐duty vehicle traffic increased by 20% in 2005 and 6% in 2006 between nonschool and in‐school seasons. [57] The inverse modeling indicates that NOy emissions in the Ship Channel increased by 13% ± 7% from 2000 to 2006. The Ship Channel region contains a mix of industrial point sources, on‐road and nonroad vehicle traffic, and shipping sources. According to the U.S. Department of Transportation (http://www.bts.gov/publications/americas_ freight_transportation_gateways/2009/highlights_of_top_ 25_freight_gateways_by_shipment_value/port_of_houston/ html/figure_01.html), the import of ship traffic in Houston has increased by 24% from 86 million short tons to 107 million short tons, accounting for a significant part of the Ship Channel NOy emission increase found between the 2000 and 2006 posteriors. The increase in ship activity probably was associated with an increase in point source activity and on‐road emissions (particularly heavy trucks burning diesel fuel), although emission regulations partially offset increased emissions from those sectors. In EPA‐NEI 2005 inventory, point sources account for about 37% of the total NOy emissions in the Ship Channel [Kim et al., 2011]. On the basis of the TexAQS 2000 and 2006 aircraft observations of individual point source plumes, NOy point source emissions were estimated to have decreased by about 50% from 2000 to 2006 [Washenfelder et al., 2010]. Therefore, in order to explain the overall NOy emissions increase in the Ship Channel indicated by the inverse modeling, the NOy emissions from on‐road and nonroad emitters would have to increase by at least 50% to compensate for the reduction of emissions from point sources. TCEQ stations suggest that large differences in emission are common between August and September, probably due to differences in diesel traffic between August and September. However, about 65% of the point sources in the Ship Channel are not included in the CEMS data, and therefore their emissions are much more uncertain. These sources are also not consistently controlled in the same way Table 6c. Emission Factors for NOx for Gasoline and Diesel Vehicles in 2000 and 2006 From Dallmann and Harley [2010] −1

Gasoline (g kg ) Diesel (g kg−1)

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2000

2006

8.3 41.6

5 35

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Table 7. Daytime CO/NOy Ratios in the Houston Urban Area and Ship Channel From the 2000 and 2006 Posteriors

Urban area Ship Channel

August 2000

September 2006

Difference (%)

9.6 ± 2.1 3.7 ± 1

7.8 ± 0.9 3.2 ± 0.5

−20 −14

as power generation. Nonroad NOy emissions are also known to be more uncertain [e.g., see Dallmann and Harley, 2010]. Finally, the partitioning between point sources, on‐road, nonroad, and shipping emissions is probably also uncertain. [58] In the Ship Channel, the posterior inventory CO/NOy ratio decreased by 14% from 2000 to 2006, from a ratio of 3.7 to a ratio of 3.2 (Table 7). In the urban area, the CO/NOy ratio declined by 20%. As discussed above, this finding is at least qualitatively consistent with the changes in the population of Houston’s on‐road mobile sources, with less restrictive emission regulations on mobile source NOy than CO, and with larger increases in diesel emissions relative to gasoline. A direct analysis of isolated TexAQS 2000 and 2006 aircraft transects downwind of the Houston urban area finds an average reduction in the slope of the CO‐NOy correlation of about 30% (an example of the correlation for one such transect in 2006 is shown in Figure 9a). This direct analysis agrees with the inverse modeling results within the uncertainties. It is not possible to directly interpret the correlation slopes in 2000 downwind of the Ship Channel, because the predominance of southerly winds during TexAQS 2000 prevents a clear chemical signature from Ship Channel sources.

4. Conclusions [59] Houston, Texas, is known for having serious problems with nonattainment of ozone standards. NOAA conducted two aircraft campaigns in 2000 and 2006 to characterize the anthropogenic emissions of Houston and assess existing inventories. In this paper we incorporated these aircraft observations in inverse modeling techniques (3DVAR and 4DVAR applied to a top‐down emissions verification) that used three transport models to estimate and improve emission inventories for Houston and its surroundings in August 2000 and September 2006, with the EPA NEI‐2005 inventory serving as a prior. Using three different transport models allowed us to estimate the contribution of the model uncertainty to the posterior estimate. [60] We found that the EPA NEI‐2005 inventory overestimated the 2006 CO flux in the Houston urban core area by 41% ± 8% and by 43% ± 5% in the Ship Channel compared to the posterior inventory calculated with the aircraft measurements in 2006 (values are the averages of the three models for both 3DVAR and 4DVAR calculations, and the uncertainty ranges represent the 95% confidence interval of the difference between the prior EPA NEI and posteriors based on a statistical significance in a t test). The EPA NEI inventory overestimated the NOy flux in the Ship Channel. The large NEI overestimate in the Ship Channel is probably due to NOx emission biases in the shipping and industrial point source sectors. The inverse modeling analysis indicates that 2006 NOy emissions from other ports

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near Houston (Freeport, Texas City, and Port Arthur) were also overestimated by the NEI‐2005. When compared to the measurements, Houston’s CO/NOy ratios from the posterior inventories were an improvement over those in the NEI‐2005 prior. [61] Comparing the posterior inventories in August 2000 and September 2006, we analyzed the emission variation between those two periods. We found that CO emissions over the Houston urban area declined by 8% ± 3%, while the NOy emissions increased by 20% ± 6%. The results of our calculations show qualitative consistencies with known changes in Houston emissions sources and measurements from TCEQ surface stations. CO emission reductions have resulted from improved CO controls in gasoline vehicles. Houston’s NOy emissions from on‐road vehicles increased by at least 10% from 2000 to 2006, owing to a large increase in diesel traffic and less‐restrictive regulation of NOy emissions from heavy‐duty diesel vehicles than of CO emissions from light‐duty gasoline vehicles. Furthermore, larger concentrations in NOx are found in TCEQ surface stations between the months of August and September, probably owing to differences in diesel traffic. In the Ship Channel, NOy emissions increased by 13% ± 7%. The increase in NOy emissions is likely due to increases in shipping activity, associated heavy truck traffic, and industrial point source output, which are partly compensated by a reduction in point source NOy emissions. [62] This study demonstrates the power of combining high‐frequency, high‐precision aircraft observations with a well‐characterized inverse modeling technique. The results presented here provide a constraint on complex bottom‐up calculations of emission inventories. We conclude that inverse modeling approaches are useful tools in developing or improving emission inventories and can be complementary to the standard research and regulatory methods currently employed to understand the impact of anthropogenic emissions on air quality. [63] Acknowledgments. We thank Andreas Stohl for providing some of the FLEXPART runs using the operational ECMWF data. We thank Chris Kite for providing TCEQ estimates of vehicle miles of travel in Houston. The ERA‐interim data for this study are from the Research Data Archive, which is maintained by the Computational and Information Systems Laboratory at the National Center for Atmospheric Research. The original data are available from the RDA (http://dss.ucar.edu) in data set number ds627.0.

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