Evaluations of NOx and highly reactive VOC emission inventories in Texas

Introduction Conclusions References

order to evaluate emissions of nitrogen oxides (NO x = NO + NO 2 ) and volatile organic compounds (VOCs) in the cities of Houston and Dallas-Fort Worth. We compared the model results with satellite retrievals of tropospheric nitrogen dioxide (NO 2 ) columns and airborne in-situ observations of several trace gases including NO x and a number of VOCs. The model and satellite NO 2 columns agree well for regions with large power 10 plants and for urban areas that are dominated by mobile sources, such as Dallas. However, in Houston, where significant mobile, industrial, and in-port marine vessel sources contribute to NO x emissions, the model NO 2 columns are approximately 50 %-70 % higher than the satellite columns. Similar conclusions are drawn from comparisons of the model results with the TexAQS 2006 aircraft observations in Dallas and Hous- 15 ton. For Dallas plumes, the model-simulated NO 2 showed good agreement with the aircraft observations. In contrast, the model-simulated NO 2 is ∼60 % higher than the aircraft observations in the Houston plumes. Further analysis indicates that the NEI-2005 NO x

Introduction
Texas is the second most populous state in the US, according to 2010 Census data (http://factfinder.census.gov). In addition to large cities, such as Houston, Dallas-Fort Worth, San Antonio, Austin, and El Paso, and numerous fossil-fueled electricity-generating power plants, one of the world's largest petrochemical complexes 5 is located in the Houston metropolitan area, leading to complicated air quality problems in Texas and in Houston, in particular. One of the major pollutants responsible for long-standing air quality issues in Texas is ozone (O 3 ). Ozone, which is strongly enhanced during photochemical smog events, is a regulated pollutant, and US Environment Protection Agency (EPA) ozone standards have consistently been violated in 10 the Houston-Galveston area for decades (http://www.tceq.texas.gov/airquality/sip/).
Ozone in the troposphere is produced by the oxidation of volatile organic compounds (VOCs) with nitrogen oxides (NO x , the sum of nitrogen oxide, NO, and nitrogen dioxide, NO 2 ) acting as a catalyst (Haagen-Smit, 1952). Therefore, to understand the formation of ozone in the troposphere, it is essential to have accurate knowledge about its 15 precursors, NO x and VOCs. Mobile sources in urban areas and coal-burning power plants have been recognized as large sources of NO x (Ryerson et al., 1998;Kim et al., 2006;Bishop and Stedman, 2008;Dallmann and Harley, 2010;Peischl et al., 2010). In Texas, in addition to these two major NO x sources, petrochemical refineries and related industrial activities in the Houston-Galveston metropolitan area have been shown pospheric NO 2 vertical columns with satellite-retrieved columns. The NO x emission inventory is then evaluated by comparing the model simulation of NO 2 with aircraft observations. Because the satellite-retrieved NO 2 columns have uncertainties caused by the application of an air mass factor (Boersma et al., 2004;Kim et al., 2009;Lamsal et al., 2010;Heckel et al., 2011), more definitive conclusions regarding the emission 15 inventory are obtained using other independent observational data sets (e.g. aircraft measurements). Next, the emissions of very reactive VOCs in NEI-2005 are compared with the estimates by Solar Occultation Flux (SOF) measurements . Ethylene and propylene emissions in the NEI-2005 are updated following the SOF observations in Mellqvist et al. (2010). Finally, the model simulations of ozone priate for the comparisons with the satellite data and the nested domain is designed for the comparison with the aircraft observations. The vertical grid was composed of 35 full sigma levels stretching from near surface at about 20 m (the first half sigma level) to the model top (50 hPa). The National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) model analysis data with a horizontal resolu-20 tion of 1 • × 1 • were used as meteorological initial and boundary conditions. The physical parameterizations used in this study were the same as in Lee et al. (2011a), which utilized an urban canopy model within the WRF model and showed excellent model performance in the Houston-Galveston area. The options relevant to chemistry, including chemical initial and boundary conditions and chemical mechanism, were the same 25 as in Kim et al. (2009). The physical and chemical options and the anthropogenic and biogenic emission inventories used in this study are summarized in (International Consortium for Atmospheric Research on Transport and Transformation) field campaign proved the model's capability to simulate the emissions, transport, and transformation of urban plumes originating from New York City (Lee et al., 2011b).
The WRF-Chem model used in this study does not include the NO x emissions from lightning processes. The lightning NO x sources missing in the model may add un-5 certainties in the simulated NO 2 columns (e.g. Huijnen et al., 2010). In this study, however, the NO x emissions from large anthropogenic sources in Texas are approximately ∼factor of 10 larger than lightning NO x emissions (personal communication with O. R. Cooper based on Cooper et al., 2009) and more than 50 % error in those anthropogenic emissions are focused. 10 The simulations were conducted from 26 July 2006 to 6 October 2006 covering the TexAQS-2006 period with a one-way nesting technique (Skamarock et al., 2008). Various modified emission inventories were tested with the default NEI-2005 as a reference. The details are summarized in the next sub-section. 15 The reference emission inventory (NEI05-REF) used for the model simulations was based on the US EPA NEI-2005(US EPA, 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/, with only weekday emissions considered here. Specific details of the inventory are available in the readme.txt 20 file that comes with the emissions data, but some background information about the inventory applies to inventory modifications discussed in the following text.

Emission inventory
The four major source components (point, mobile on-road, mobile non-road, and area) were processed according to EPA recommendations with emissions data available from the US EPA as of October 2008. Thus, portions of the point and area source 25 emissions, updated within more recent NEI-2005 releases, were based on the earlier NEI-2002 (version  The NEI05-REF with ethylene and propylene emissions updated following Mellqvist et al. (2010), the development of which is described in Sect. 4.2.1, is denoted as NEI05-VOC. We also generated another emission inventory (NEI05-VOCNOX) from NEI05-VOC that modifies the NO x emissions in the Houston Ship Channel area only. The NEI05-VOCNOX reduces the industrial NO x emissions in the Houston Ship Channel 15 by a factor of 2 and eliminates the port NO x emissions in this region. The rationale for these modifications is given in Sect. 4.

Satellite retrieved NO 2 columns
The retrievals of tropospheric NO 2 columns by instruments on polar-orbiting satellites 20 have been widely used to detect NO x sources, derive emission trends, and evaluate existing emission inventories (e.g. Martin et al., 2003;Beirle et al., 2004;Richter et al., 2005;Kim et al., 2006Kim et al., , 2009Konovalov et al., 2006;van der A, 2008;Zhang et al., 2009;Russell et al., 2010 Instrument on the Aura satellite) instruments (Bovensmann et al., 1999;Levelt et al., 2006). The satellite retrievals of tropospheric NO 2 columns have inherent uncertainties, the largest of which arise from separating the stratospheric and tropospheric contributions and from applying an air mass factor (Richter and Burrows, 2002) to convert slant columns to vertical columns (van Noije et al., 2006;Lamsal et al., 2010;Heckel et al., 2011). In order to understand uncertainties in the satellite retrievals, it is helpful to compare the data sets from various instruments and retrieval groups. In this study, we used SCIAMACHY and OMI retrievals from the University of Bremen (Kim et al., 2009) and other OMI retrievals from the Royal Netherlands Meteorological Institute (KNMI) (Boersma et al., 2004;Boersma et al., 2007;Boersma et al., 2011) and the US Na-10 tional Aeronautics and Space Administration (NASA) (Buscela et al., 2006;Kim et al., 2009). The KNMI provided 2 OMI retrievals. The apparent differences are taken here as an indication of the inherent uncertainty in the retrieval algorithms.
To systematically compare the satellite data with the model results, the WRF-Chem data are projected onto the daily orbital SCIAMACHY and OMI pixels. Because clouds 15 inhibit the satellite from sensing the boundary layer NO 2 , cloudy grid cells are filtered out. Pixels with cloud fraction <0.15 are used in the comparisons of the satellite retrievals with the model, ensuring the same number of samples in each comparison. For the model and OMI satellite comparisons, only fine-resolution scenes with pixel numbers between 20 and 40 are used, so that the model (20 × 20 km 2 ) and satellite 20 resolution (maximum size: 395 km 2 ≈ 30 km (across track) × 13 km (along track)) are similar.

Aircraft measurements
During the TexAQS 2006 field campaign, a NOAA WP-3D aircraft was instrumented to measure various gas-and aerosol-phase chemical species, including NO, NO 2 , O 3 , 25 ethylene, propylene, and formaldehyde (HCHO) (Parrish et al., 2009 Parrish et al. (2009). The measurements used here to study the emissions and ozone formation were from WP-3D flights on 13, 19, 25, and 26 September and 5 and 6 October 2006; all were days in which northerly flow dominated in the Houston-Galveston and Dallas-Fort Worth areas and the model performed well in terms of meteorology. The flight paths on those selected days are given in Fig. 2.

5
Overall statistics exhibiting the model performance with respect to meteorological variables measured at surface stations and radar wind profilers for the selected days are summarized in Tables 2 and 3. The mean model biases (root mean square errors) in near-surface temperature, wind speed and wind direction relative to measurements from 10 surface stations are less than 2 • C, 2 m s −1 , and 20 • , respectively. The comparison of the model wind speed and direction with radar wind profiler observations at the middle of the boundary layer height also shows that the mean model biases are less than ∼2 m s −1 and 26 • , respectively. Model boundary layer heights in comparison with those determined by radar wind profilers at Arcola and La Porte are shown in Fig. 3. At both sites, the correlation coefficient between model boundary layer height and wind 15 profiler data is 0.87. The slopes of the linear regression between the model and profiler data indicate that the boundary layer heights agree within 10-20 % on average.

20
In Fig. 1   In contrast to the flights over Dallas, NO 2 observations from the 6 daytime flights over Houston deviate substantially from the model predictions. Table 5 summarizes the means of WP-3D NO 2 and the model NO 2 using NEI05-REF for boundary-layer data from these 6 flights within the Houston source box defined in Table 4. Each of the 6 flights shows consistent model overestimates of observed NO 2 over Houston. The Using the WP-3D observations from each downwind transect, the source region contributing to these model-observed discrepancies over Houston can be isolated. The time series in Fig. 8 demonstrates this approach for the 26 September flight. Upwind of Houston and the Ship Channel, where urban mobile sources should dominate NO x emissions (transect T1), the simulated NO 2 agrees well with the aircraft observa-5 tions. However, in transects T2 and T3 downwind of the Houston urban core and Ship Channel, the simulated NO 2 shows substantial deviations from the observations. In particular, in the eastern portions of transects T2 and T3, influenced by sources in the Ship Channel, there are large model over-predictions of NO 2 . On the western side of these transects, where mobile source emissions from the urban core should dominate, the model is in agreement with the observations. This behavior, with good model-observation agreement downwind of the Houston urban core and significant disagreements downwind of the Ship Channel, occurred for each of the 6 daytime WP-3D flights focused on the Houston region. In order to quantify these differences, we separated the transect segments downwind of the Houston urban 15 core from the segments downwind of the Ship Channel for each flight. Figure 9 shows how this separation was carried out for the 26 September 2006 flight. The "urban-only" segments of each transect are denoted by black lines on the maps on the left side of Fig. 9. These segments are obtained by examining linear correlations of CO and NO y (the sum of odd nitrogen species) with CO 2 on each transect. For example, portions of 20 each of the 26 September transects that are over and downwind of the Houston urban core have highly correlated, linear relationships between CO and CO 2 (the black points in the scatter plots on the right side of Fig. 9). These urban-only data correlations have a distinctly larger slope when compared to the transect portions downwind of both Ship Channel itself and the large industrial sources in Mont Belvieu to the north of the Ship 25 Channel (gray points in Fig. 9). A similar separation between the urban-only and mixed urban/industrial portions of each transect is also seen in the NO y :CO 2 correlations (not shown). Examination of transects from all 6 daytime flights over Houston reveals a consistent pattern of distinct urban-core-influenced segments on the west side of each Introduction Houston transect and Ship-Channel-influenced segments on the eastern portions of the transects. Figure 10 summarizes the multi-flight averages of the simulated and the WP-3D observed NO 2 for Dallas, Houston, the Houston urban area, and the Houston Ship Channel area. The definitions of the averaging regions used for the Dallas and Houston 5 areas are the same as those for the satellite-model comparisons ( Table 4). The "Urban" and "Ship Channel" averaging areas were defined for each Houston flight using the procedure described in the preceding paragraph. The picture that emerges from the multi-flight averages ( Fig. 10) is consistent with that seen in the individual 13 and 26 September examples (Figs. 7 and 8). The averages of simulated and observed NO 2 10 are in good agreement over Dallas. For the entire Houston area, model NO 2 is about 60 % higher than the aircraft observations. For the Houston urban-only segments, the model slightly under-predicts the aircraft NO 2 on average. In contrast, the average simulated NO 2 is nearly a factor of 2 higher than the observations for the Ship Channel portions of the Houston flights. 15 The findings from the model-aircraft NO 2 comparison are consistent with those from the model-satellite NO 2 column comparison. In contrast to good agreement over Dallas-Fort Worth, the model simulations of NO 2 over Houston are about 60 % (50 %-70 %) higher than that of the aircraft (satellite) observations. The aircraft data show that most of the model NO 2 overestimate in the Houston source box appears to be 20 driven by the Ship Channel, whereas NO 2 in the urban core appears to be reasonably well represented by the model. In spite of potential uncertainties in the satellite retrievals, this comparison demonstrates that the large-scale view of NO x emissions obtained from the satellite data is consistent with the high-resolution picture offered by the aircraft observations, which pinpoint the areas with emission uncertainties. In the ACPD 11,2011 Evaluations of NO x and highly reactive VOC emission inventories in Texas  The NO x area source sector for the Houston Ship Channel within NEI-2005 is dominated (134.0 tonnes day −1 = 96 %) by port emissions from commercial marine vessels (CMVs) (  (2007) report. Based on the data in Table 7, NO x to SO 2 emission ratios are a factor of six higher for NEI-2005 than in the US EPA (2007) report. Furthermore, NO x /SO 2 emission factor ratios from US EPA (2007) are more than a factor of 10 lower for CMVs using diesel fuel than the diesel fuel emission ratios from NEI-2005 in Table 7. NO x /SO 2 emission 20 factor ratios for CMVs using residual fuel within US EPA (2007) are also a factor of 2 (or more) lower for most ship classes than those in NEI-2005. NO x /SO 2 emission ratios determined from ship plume sampling during TexAQS-2006(Williams et al., 2009) are more consistent with the US EPA (2007) report than with the NEI-2005 port emissions. Though only a few plumes close to port were actually sampled by Williams et al. (2009), 25 emission ratios were similar to the many plumes sampled from similar ships anchored in the Gulf of Mexico. Mean NO x /SO 2 mass emission ratios from these ships range from 0.58 for crude oil tankers to 2.19 for bulk freight carriers.

Increases of NEI-2005 propylene and ethylene emissions using Solar Occultation Flux measurements
Direct and indirect evidence of inventory underestimates of ethylene (C 2 H 4 ) and propylene (C 3 H 6 ) emissions from the petrochemical facilities in the Houston area has previ-5 ously been documented. For example, Wert et al. (2003) showed that C 2 H 4 and C 3 H 6 emissions from two major refineries near Freeport and Sweeny were underestimated by a factor of 50 to 100 when compared to emissions derived from Electra aircraft observations from TexAQS 2000. These underestimates were additionally shown to be responsible for serious HCHO and O 3 under-predictions in a simple plume dispersion 10 model (Wert et al., 2003). Likewise, Jiang and Fast (2004) and Byun et al. (2007) showed much better agreement between model and observed O 3 levels in the Houston region when C 2 H 4 and C 3 H 6 emissions in the Ship Channel area were increased by factors of 6 to 8. The Solar Occultation Flux (SOF) measurements of C 2 H 4 and C 3 H 6 emissions re- inventory significantly under-predicts the observed SOF emissions for both C 2 H 4 and C 3 H 6 . A modified inventory, NEI05-VOC, was generated to assess the impact of the low C 2 H 4 and C 3 H 6 emissions in NEI05-REF on the WRF-Chem simulations. In contrast to across-the-board VOC emission increases over the Ship Channel used in previ- 25 ous studies (e.g. Jiang and Fast;, Byun et al., 2007, the NEI05-VOC included adjustments of activity-specific emission factors related to the petrochemical facilities sampled by the SOF measurements. These modifications used the information within 21219 Introduction the US EPA's SCCs for the major C 2 H 4 and C 3 H 6 point sources and for each of the 14 locations sampled by Mellqvist et al. (2010). Because of ambiguity in how facilities report activity-specific VOC emissions, and to keep the analysis of the emissions from dozens of SCCs tractable, eight broad categories were constructed from analysis of the major SCCs contributing to C 2 H 4 and C 3 H 6 emissions within NEI05-REF. These 5 eight categories are listed in Table 8, along with the SCCs assigned to each category. Many other SCCs with relatively minor emissions are lumped into an "Other" category. For either C 2 H 4 or C 3 H 6 , multiplication factors (M i , i = 1, 8) for the emission categories are then numerically determined to yield a best fit to the linear system: is the matrix of NEI05-REF emissions for source category i and location j , and OBS j are the average SOF observations at location j . Table 9 gives the elements of [A i ,j ] and Other j from NEI05-REF for ethylene. The M i vector for the over-determined system is solved by linear least squares using QR/LQ matrix decomposition from the LAPACK library (SIAM, 1999). In practice some of the M i solution 15 values are negative, yielding a multiplication factor with a non-physical meaning. If a negative M i is calculated, the NEI05-REF emissions from that category are added to the "Other" vector, the number of source categories is reduced by one, and the M i vector in Eq. (1) is solved again. Some remaining positive M i factors make a negligible contribution to the overall goodness of the fit to the observed emissions. In that 20 case, the r-coefficient and RMSE values are calculated with each remaining M i factor to further eliminate unnecessary factors. The resulting 5 best-fit multiplication factors for ethylene are given in Fig. 11a, along with the linear fit to observations. The NEI05-VOC point source ethylene emissions are calculated by multiplying the SCC-specific C 2 H 4 emissions (  Fig. 11b, when the sum of the C 2 H 4 and C 3 H 6 emissions are used as elements of [A i ,j ], a good correlation with the average SOF observations is obtained with 4 multiplication factors. The NEI05-VOC point source propylene emissions are therefore calculated by multiplying the SCC-specific C 2 H 4 plus C 3 H 6 emissions (Table 9) by the factors listed in Fig. 11b.

5
The above fitting procedure updated ethylene and propylene emissions by comparison of the NEI-2005 with the average SOF observations at each of the point source locations studied by Mellqvist et al. (2010). The emission estimates derived from SOF have an estimated uncertainty of 35 % due to the measured variability in the wind direction between the source and the sampling point and also from assumptions of rapid

NO 2
The NO 2 simulations with NEI05-VOCNOX show remarkably good agreement with the observations, whereas the simulated NO 2 with either NEI05-REF or NEI05-VOC shows 20 the discrepancy with the observations as discussed above. of the model NO 2 using NEI05-VOCNOX for the 6 daytime flights is 2.24 ppbv, which is ∼50 % lower than that of NEI05-REF and ∼10 % higher than that of the average WP-3D observations.

Ethylene and propylene
As expected, the modeled ethylene and propylene mixing ratios with NEI05-VOC and 5 NEI05-VOCNOX are much larger than those simulated with NEI05-REF (Figs. 12 and  13, Table 10). The reduction of Houston Ship Channel NO x using the NEI05-VOCNOX inventory decreases the modeled ethylene and propylene mixing ratios compared to using NEI05-VOC, because lower NO 2 levels lead to increased hydroxyl radical (OH) mixing ratios that consequently result in a faster sink for these alkenes.
In TexAQS 2006, the WP-3D had two methods for measuring ethylene: canisters analyzed post-flight by the whole air sampler (WAS) system (Schauffler et al., 1999(Schauffler et al., , 2003 and the continuous measurements by the laser photo-acoustic spectroscopy (LPAS) instrument (de Gouw et al., 2009). The LPAS ethylene measurements were made with much higher frequency than the WAS canisters were sampled, and LPAS 15 data were available on more flights than WAS. On the other hand, the WAS analyzer had higher precision and better sensitivity to ethylene than the LPAS instrument.
On the 26 September flight (Fig. 12), the simulated ethylene with NEI05-VOC or NEI05-VOCNOX sometimes agrees better with the ethylene measurements by WAS and LPAS. There are also occasions in which the model results do not capture the 20 observed peaks of the ethylene plumes, as well as times when the model peaks are much larger than the observed ones. The latter situation is discussed further below. An example of the former situation can be seen in the observed ethylene plumes at 18: [15][16][17][18] UTC, which can be traced back to the Beaumont/Port Arthur area. While the updates to the ethylene emissions in NEI05- 25 VOC/NEI05-VOCNOX were applied to all processes listed in Table 8  SOF observations in and around Houston are not generally applicable to ethylene emissions from petrochemical facilities in other areas. Another possibility is that there are additional processes besides those in Table 8 that lead to high ethylene emissions in Beaumont/Port Arthur, in which case the inventory adjustment procedure discussed in Sect. 4.2.1 would not increase ethylene emissions sufficiently in that region. Figure 13 compares flight averages of the model and the ethylene measurements for the WP-3D flight legs within the boundary layer and the Houston-Galveston source box (defined in Table 4). The model results in Fig. 13 were sampled only at the times and locations in which the measurements were made before calculating averages.
For the WAS measurements of ethylene, the model results with the default emission inventory (NEI05-REF) are consistently 50 %-70 % lower than the observations (Fig. 13). The simulations with NEI05-VOC and the NEI05-VOCNOX agree with the WAS observations within −20 % to +30 %, except for 26 September when the model ethylene with NEI05-VOCNOX (NEI05-VOC) is ∼50 % (65 %) higher than the observations. The overall Houston-area boundary layer averages from the 5 flights where WAS 15 data were available (Table 10) similarly show much better agreement between the WAS ethylene and the model simulations using either NEI05-VOC or NEI05-VOCNOX, with NEI05-VOCNOX providing the smallest overall model-measurement discrepancy. For the LPAS measurements of ethylene, the model runs with NEI05-REF are consistently low by 35 %-64 %, similar to the model-WAS comparison (Fig. 13). The sim-20 ulated ethylene mixing ratios using either NEI05-VOC or NEI05-VOCNOX are persistently higher than the LPAS measurements by 17 %-51 % for most of the days (64 %-78 % above the observation on 26 September). Averages from the 6 Houston flights where boundary-layer LPAS measurements of ethylene were available ( The model simulations with NEI05-VOC and NEI05-VOCNOX show enhanced propylene at the times when the measurements detected the plumes on 26 September (Fig. 12), while the model with NEI05-REF cannot produce elevated mixing ratios of propylene. For all boundary layer Houston flight legs investigated, the model propylene with NEI05-REF is consistently lower by 60 %-90 % than the whole air sampler mea-5 surement, except for 19 September in which the model with NEI05-REF and the observations agree better than the simulations with the other inventories (Fig. 13). On 26 September, the simulated propylene mixing ratios with NEI05-VOC are higher than the measurements, but the simulations with NEI05-VOCNOX agree better with the measurements (Fig. 12). The model-simulated propylene with NEI05-VOC is 30 %-120 % higher than the whole air sampler propylene observations except for 25 September (Fig. 13). The simulations with NEI05-VOCNOX reduce the biases in the model propylene on most days, such that the simulated propylene is 20 %-90 % higher than the observations (except for 25 September). For the 5 flight days with boundary layer WAS data in the Houston source box (Table 10)

Formaldehyde and O 3
The The model peaks with the NEI05-VOC and NEI05-VOCNOX do not represent the full extent of the formaldehyde plumes (Fig. 12), implying that the spatial and temporal representation of highly reactive VOC in the inventory need further improvement. Some of the HCHO peaks that the model does not capture (18: [15][16][17][18][19][20] 19:15 UTC) are also missing in the modeled ethylene time series; as described above, these plumes originated in the Beaumont/Port Arthur area. We expect rapid HCHO production from plumes containing elevated ethylene, and given the under-prediction of ethylene mixing ratios in these plumes, it is not surprising that the model misses the elevated HCHO here as well. 25 The model-simulated boundary-layer formaldehyde mixing ratios with NEI05-VOC and NEI05-VOCNOX averaged over Houston source area (see Table 4 ton Ship Channel is larger than that calculated for the whole Houston area (Fig. 13); the simulated formaldehyde with NEI05-REF over the Houston Ship Channel is about 25 %-57 % lower than the observations for the 6 flights. With NEI05-VOCNOX, the simulated formaldehyde over Houston Ship Channel agrees with the observations within ∼30 % for the same flights. On average, the model formaldehyde with NEI05-REF 10 (3.54 ppbv) is 43 % lower than the observations (6.20 ppbv) over the Houston Ship Channel (Table 10), while the 6-flight average HCHO over the region using NEI05-VOCNOX (5.12 ppbv) is only 14 % lower than the observations. Overall, the model is much better at capturing the plumes of ethylene, propylene, and formaldehyde in the Ship Channel when the updated VOC and NO x emission inventory is used.  Figure 12 shows a number of discrepancies between the model and the observed O 3 on 26 September. Simulated O 3 using any of the inventories is generally lower than the observations everywhere, but most prominently in the regions outside the Ship Channel plumes. The observed O 3 shows a number of peaks superimposed on top 10 of a rising background, all of which are missing in the various simulations. As was seen with HCHO, the O 3 peaks at 18: [15][16][17][18] UTC, due to plumes originating in the Beaumont/Port Arthur area, are totally missing in the model. As with HCHO, it appears that the large underestimate of ethylene in these plumes is an indication that reactive VOC emissions for the upwind area are not correct, so it can 15 be expected that the model will not produce adequate ozone.
Low biases in modeled CO and NO x are seen in what appear to be urban plumes detected in transects upwind of Houston on 26 September (not shown in Fig. 12). These plumes occur throughout the later transects downwind of Houston, suggesting that some of the underestimates of HCHO and O 3 might be due to an underestimate of 20 upwind urban emissions. The inverse modeling analysis of Brioude et al. (2011) found that NEI-2005 emissions of CO and NO x needed to be increased for suburban areas north and west of Houston, and they suggested that growth in the suburban population could have changed the spatial distribution of emissions relative to those reported in NEI-2005. 25 The causes of the observed rising O 3 background are unclear, though it appears to be the result of photochemistry. Increases in the O 3 baseline values are not seen in NO x or alkenes (Fig. 12) 18:50 UTC correspond to periods of model underestimates of HCHO, although NO x and alkene levels in this period appear to be reasonably represented. Because of ozone's longer lifetime and variety of sources, simulations of ozone will naturally be more difficult. One possibility is that the magnitude, daily and hourly variability, and horizontal and vertical transport of other local or more distant pollution sources, such 5 as prescribed burning and wildfires, may not be well represented in the model. Table 11 summarizes the results of linear fits between the WP-3D O 3 (= x) and the model O 3 simulations (= y) for all boundary layer data of the 6 daytime flights within the Houston source region defined in Table 4. The slopes and correlation coefficients in the linear fits using the simulations with the NEI05-VOC tend to improve from those with the NEI05-REF, except for 13 September 2006 when the model O 3 performance is poor, possibly because the model boundary heights are higher than the radar profiler observations (Fig. 3). The slopes and correlation coefficients in the linear fits using the simulations with NEI05-VOCNOX consistently increase from those with NEI05-VOC for all daytime flights, although the slopes are still much less than 1;  Figure 14 demonstrates that the model has better capability of simulating O 3 plumes in Houston Ship Channel with the NEI05-VOCNOX compared to those with the NEI05-REF and NEI05-VOC.

Summary and conclusions
In this study, we evaluate the NO were noted by Rivera et al. (2010), but the emissions from the ships in the port were not believed to dominate the total NO x emissions in this area. Industrial ethylene and propylene emissions in the NEI05-REF are greatly underestimated relative to the estimates using SOF measurements  in the Houston Ship Channel during the period of study. When the NEI-2005 emis-15 sions of these two species are increased, by using the SOF measurements to adjust sources associated with the petrochemical industry, the model simulations of ethylene, propylene, and formaldehyde are substantially improved in comparison with the WP-3D measurements. But remaining model-observation disagreements for these species indicate that further understanding of the spatial distribution and temporal variability of 20 reactive VOCs is required for better model simulations. In particular, the representation of other reactive VOCs in the NEI-2005 besides ethylene and propylene in the Houston area may need to be investigated.
To examine the impact of updating both VOC and NO x emissions on improving model-measurement agreement, we generated another modified version of NEI-2005, that more effort is still required to understand the formation and transport mechanisms of O 3 in Houston.
Acknowledgements. The authors would like to thank Bryan Lambeth from the Texas Commission on Environmental Quality (TCEQ) for providing the surface observation data and the La Porte wind profiler data. The authors thank Jim Corbett and Jordan Silberman for assistance 10 with the ship emission analysis. The authors would like to thank TCEQ for support of the evaluation of the emission inventory using satellite observations. NOAA Health of Atmosphere Program supports this study. Some of the satellite retrievals used in this study were funded by the University of Bremen and the European Union through the ACCENT project. The Dutch-Finnish built OMI is part of the NASA EOS Aura satellite payload. The OMI project is managed 15 by NIVR and KNMI in the Netherlands.

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Interactive Discussion
Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 3798, 2005. Nam, J., Kimura, Y., Vizuete, W., Murphy, C., and Allen, D. T.: Modeling the impacts of emission events on ozone formation in Houston, Texas, Atmos. Environ., 40, 5329-5341, 2006. National Aeronautics and Space Administration: OMI Algorithm Theoretical Basis Document, Volume IV -OMI Trace Gas Algorithms, K. Chance ed., ATBD-OMI-04, Version 2.0, avail-Introduction     Mellqvist et al. (2010) and for the eight emission categories given in Table 8 Table 4). T1-T4 represent transect 1-4. Vertical profiles of potential temperature, water vapor mixing ratio, and NO 2 measured by the WP-3D at a point "P" on the map are shown (top right). The WRF-Chem model and WP-3D NO 2 for the segments of the flight are compared (bottom).
ACPD 11,2011 Evaluations of NO x and highly reactive VOC emission inventories in Texas  Table 4). T1-T3 represent transects 1-3. Vertical profiles of potential temperature, water vapor 7 mixing ratio, and NO 2 measured at a point "P" on the map are shown (top right). The WRF-8 Chem model and WP-3D NO 2 for segments of the flight are compared (bottom).  Table 4). T1-T3 represent transects 1-3. Vertical profiles of potential temperature, water vapor mixing ratio, and NO 2 measured at a point "P" on the map are shown (    WP-3D and model NO 2 averaged for Dallas, Houston, Houston urban, and Houston Ship Channel sources. Filled (unfilled) bar represents WP-3D (WRF-Chem model) NO 2 . Temporal variability (standard deviation) of columns is shown as error bars. 2 (6) day flight data are used for Dallas (Houston). "Dallas" and "Houston" boxes are the same as the source boxes for satellite-model comparison (Table 4). "H Urban" means the average over the flight segments influenced by urban sources in Houston. "H Ship Channel" denotes the average over the flight segments influenced by industrial and commercial marine vessel sources in the Houston Ship Channel region. Houston Ship Channel flights are defined as in Fig. 9 for 6 daytime flights. Number of samples in "H Urban" and "H Ship Channel" is about 10 % of that in "Houston".
ACPD 11,2011 Evaluations of NO x and highly reactive VOC emission inventories in Texas Scatter plots of simulated and observed O 3 data below 1 km above ground level from 6 daytime WP-3D flights that are influenced by the sources in Houston Ship Channel. Houston Ship Channel flights are defined as in Fig. 9 for the same 6 daytime flights.