- Email: [email protected]
Sensitivity of regional ozone concentrations to temporal distribution of emissions
ARTICLE IN PRESS
Atmospheric Environment 38 (2004) 6279–6285 www.elsevier.com/locate/atmosenv
Sensitivity of regional ozone concentrations to temporal distribution of emissions Zhining Taoa,, Susan M. Larsonb, Allen Williamsc, Michael Caugheyc, Donald J. Wuebblesa a
Department of Atmospheric Sciences, University of Illinois at Urbana-Champaign, 2204, Griffith Dr., Champaign, IL 61820, USA b Department of Civil & Environmental Engineering, University of Illinois at Urbana-Champaign c Illinois State Water Survey, 2204 Griffith Dr., Champaign, IL 61820, USA Received 1 April 2004; accepted 12 August 2004
Abstract Temporal representations of emissions bear large uncertainty. Before any costly efforts are undertaken to improve the accuracy of temporal emission profiles, however, the impact of their uncertainty on predictions of surface O3 concentrations should be examined. In this study, a 3-D air quality modeling system was used to probe the sensitivity of regional surface O3 concentrations to temporal allocation of emissions over the continental US. The raw emissions inventory was processed using SMOKE and was segmented into hourly intervals using both ‘‘time-varying’’ and ‘‘uniform’’ temporal profiles of anthropogenic sources. Our simulation results show that, on average and with the grid resolution (90 km) used, regional daytime O3 concentrations are not sensitive to changes in the temporal allocation of emissions, while nighttime O3 concentrations are lower under ‘‘uniform’’ profiles than under ‘‘time-varying’’ profiles. r 2004 Elsevier Ltd. All rights reserved. Keywords: Anthropogenic emissions inventory; Temporalization; Continental US; Photochemical model
1. Introduction Accurate estimation of pollutant emissions is critical to successful photochemical modeling, and to effective ozone (O3) abatement strategies based on those simulations. The emissions inventories are subject to large uncertainties (Placet et al., 2000; Sawyer et al., 2000), including (1) the degree of completeness of the inventory; (2) the quality of emission factor estimates; and (3) the accuracy of the inventory’s temporal and spatial patterns. Determining the sensitivity of air quality model output to the variations in emission input Corresponding author. Tel.: 217 244 1917.
E-mail address: [email protected] (Z. Tao).
will benefit efficient inventory development (Houyoux et al., 2000; Placet et al., 2000). The currently available emissions inventories (e.g., National Emissions Inventory (NEI) 96 and NEI99) contain only annual total and O3-season daily average emissions for the major pollutant classes. Air quality models, however, require emissions at finer temporal resolution. To provide these, the emissions model apportions the longer-term average values into hourly fluxes according to one of a limited number of comparatively simple temporal profiles that specify how many emissions are assigned hourly. These temporal profiles are not based on actual temporal data for a specific time period, but on typical temporal variations for broad categories of sources. A source
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category code specified in the emissions inventory determines which temporal profile is used for each emission source. In most cases, using a generalized profile leads to some degree of errors in modeling ambient concentrations of surface O3 (Placet et al., 2000). The central question is whether or not such errors are large enough to justify the major effort and expense that would be required to develop and assign more accurate temporal patterns to the emission sources in the inventory. To answer this question, we have evaluated the sensitivity of regional surface O3 concentrations to the uncertainties in temporal allocations using a 3-D comprehensive air quality modeling system. Two sets of temporal profiles, one ‘‘time-varying’’ (hourly resolution) and one ‘‘uniform’’ (constant) temporal allocation, are applied in the analysis. Note that the ‘‘uniform’’ profile was chosen as an extreme case, not as a representation of actual emission profiles. If there are no large differences in O3 concentrations between the ‘‘time-varying’’ profiles and the extreme assumption of the ‘‘uniform’’ profiles, this indicates that the error inherent in the ‘‘time-varying’’ profile will not cause excessive errors in predicted O3 concentrations.
2. Air quality modeling system The comprehensive air quality modeling system used in this study has been described elsewhere (Tao, 2003; Tao et al., 2003), and was summarized here for completeness. The modeling system included a regional climate model (RCM) (Liang et al., 2001), an emissions model (EM), and an air quality model (AQM). The 1996 National Emission Trends database (NET96, now designated NEI96) was used as the anthropogenic emission inventory for this study (EPA, 2002). The inventory was processed through SMOKE, an EM (Houyoux et al., 2000), using both ‘‘timevarying’’ and ‘‘uniform’’ profiles. The ‘‘time-varying’’ profiles contained in SMOKE described variations in monthly, weekly, diurnal-weekday, and diurnal-weekend anthropogenic emissions that resolved time-averaged man-made emissions into hourly flux. For each gridcell, each profile yielded different hourly emissions, but when integrated over time, total emissions were the same for both cases throughout the simulation period. It should be noted that neither profiles were applied to calculations of biogenic emissions. Biogenic emissions were estimated using the county-level land use data, and were adjusted based on temperature and light. Due to diurnal variations in temperature and solar radiation, biogenic emissions displayed a diurnal pattern. Combining biogenic emissions with the anthropogenic emissions processed under the ‘‘uniform’’ profiles, resulted in an overall emission distribution that still exhibited diurnal
variations, though considerably damped with respect to the ‘‘time-varying’’ profiles. In general, the average daytime (8 am–8 pm local time) NOx emissions for the ‘‘uniform’’ case were approximately 26% smaller than the counterpart for the ‘‘time-varying’’ case, and the average nighttime (8 pm–8 am local time) NOx emissions under the ‘‘uniform’’ profiles were about 70% greater. Since biogenic sources dominated the non-methane volatile organic compound (NMVOC) emissions (Tao et al., 2003), the effect of two temporal profiles on variations in diurnal NMVOC emissions was much less. Under the ‘‘uniform’’ profiles, the average daytime NMVOC emissions were about 8% lower and the average nighttime emissions were around 34% higher than their counterparts under the ‘‘time-varying’’ profiles. The resulting speciated and gridded hourly emissions were then input into SAQM, an AQM (Chang et al., 1997). SAQM used a terrain-following s-coordinate system. In this current investigation, 15 layers were used in the vertical direction up to 16 km above the surface. The inclusion of the surface-layer-submodel (SLS) gave a 15 m vertical resolution right above the ground. The horizontal grid used in the study covered the entire continental US and the adjacent land and ocean areas with a 90-km resolution. The Lambert map projection was applied. The projection center was 931W and 381N. The lower left corner of the domain was located at 122.891W and 11.661N. Quantitative comparison of SAQM simulations of surface O3 concentrations to observations was conducted in four selected sites, one each in the Southeastern regions, the Northeastern industrial corridor, the San Francisco Bay area, and the central Illinois. The first three sites were located in regions with serious ozone problems. The central Illinois site represented a perturbed rural area. Measured surface ozone concentrations used in the comparison were taken from the Aerometric information retrieval system (AIRS) monitoring network (http://www.epa.gov/aqspubl1/select. html). Several statistical measures (Tesche et al., 1990; Jacobson, 1999) were applied to quantify model performance assessment. These statistical measures were: (1) bias and normalized bias (NB); (2) gross error (GE) and normalized gross error (NGE); and (3) unpaired peak prediction accuracy (UPPA). The results were summarized in Table 1. It can be seen that NB varied from 3% (the San Francisco Bay area site) to 21% (the central Illinois site). The US EPA (1991) suggested a range of 75–15% as an acceptable guideline of model performance for NB. Only the central Illinois site (21%) did not meet the criteria of acceptable model performance with respect to this guideline. When averaged over all four sites, however, NB was 6% and well within the EPA’s criterion. The EPA’s guideline for NGE was 30–35% for an acceptable level of model
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Table 1 SAQM performance assessments at four selected sitesa,b Site
Central Illinois North Georgia East New York San Francisco Bay Area Averagec EPA guideline
Statistics Bias (ppb)
NB (%)
GE (ppb)
NGE (%)
UPPA (%)
13.01 0.90 6.74 0.10 4.74 —
21 5 10 3 6 75–15
19.77 23.27 14.48 13.21 17.68 —
34 38 25 23 30 30–35
16 11 9 6 2 715–20
a
Cutoff value=40 ppb of surface P O3 concentration. P PN P N 1 1 NGE ¼ N1 N Bias ¼ N1 N i¼1 ðPi Oi Þ; NB ¼ N i¼1 ðPi Oi =Oi Þ; GE ¼ N i¼1 ðjPi Oi jÞ; i¼1 ðjPi Oi j=Oi Þ; UPPA ¼ ðPmax Omax =Omax Þ; where N is the number of observations; P and O are the SAQM prediction of the one-hour average surface ozone concentration, and the observed value, respectively; and Pmax and Omax are peak ozone concentrations from the SAQM prediction and the observation, respectively. c Averaged over four sites. b
performance. Compared with this guideline, only the North Georgia site (38%) slightly exceeded the criterion. The spatial averaged NGE was 30%, within the level recommended for model acceptance. The UPPA for four selected sites ranged from 6% to 16%, which all met the EPA criteria for acceptable model performance of 715–20% for UPPA. A complete model evaluation can be found in Tao (2003). Our simulation period began on 10 July 1995 and ended on 20 July 1995. The in-depth analysis focused on the results for the last seven days, allowing three days for model spin-up. We compared differences in surface O3 concentrations, cumulative frequency distributions, peak magnitude, diurnal variations, and peak timing for the two modeling approaches. Using the results under the ‘‘time-varying’’ profiles as reference, four statistic measures, bias, NB, GE, and NGE, were computed to compare the two model simulations.
3. Results and discussions Fig. 1 displayed the weekly average daily maximum O3 (1-h average) distribution across the continental US. Large O3 concentrations (40.08 ppm) were found in the coast of California, the Northeastern states, and the Southeastern regions. More attention was given to these regions in the following analysis. We first included every gridcell-hour surface O3 concentration and, for each gridcell, examined the weekly average bias and GE between results under two emission profiles within the continental US. The results were illustrated in Figs. 2(a) and (b). It can be seen that for most areas of the US, the average bias was within 70.002 ppm, which accounted for o5% of the average daily maximum surface O3 concentrations under the
Fig. 1. Spatial distribution of weekly average daily maximum surface O3 concentrations under the ‘‘time-varying’’ temporal profiles of anthropogenic emissions.
‘‘time-varying’’ profiles. A slightly larger bias (up to 0.008 ppm) was seen in the Southeastern areas where daily maximum concentration was also high (40.12 ppm). The negative biases found in most areas across the country indicated that the model, in general, predicted less surface O3 concentrations using the ‘‘uniform’’ profiles than using the ‘‘time-varying’’ ones when averaging over all hours of the week. The average GE showed a similar spatial pattern across the continental US. It was within 0.004 ppm for the vast areas of the country. It increased to about 0.006 ppm along the coast of California and Northeastern states, and the largest GE (approximately 0.009 ppm) again occurred in the Southeastern region of the nation. We also analyzed the daily bias and GE when every gridcell-hour O3 was considered. The magnitude and spatial distributions of bias and GE in each day were similar to their weekly average counterparts. The largest
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Fig. 2. Weekly average bias and GE of hourly surface O3 concentrations under the ‘‘uniform’’ and ‘‘time-varying’’ temporal profiles of anthropogenic emissions: (a) bias when all hours included; (b) GE when all hours included; (c) bias at nighttime (8 pm–8 am); (d) GE at nighttime; (e) bias during daytime (8 am–8 pm); (f) GE during daytime.
daily bias (0.012 ppm in magnitude) was found on 15 July 1995 and occurred in the Southeastern region (the North Georgia). The largest GE (0.013 ppm) occurred in the Northwest Georgia on 17 July 1995. Since surface O3 is generated through photochemistry, it is informative to examine the difference of daytime (8 am–8 pm) and nighttime (8 pm–8 am) O3 under two emission profiles. The results indicated, not surprisingly, that the average nighttime bias and GE, as displayed in Figs. 2(c) and (d), showed magnitudes and spatial patterns similar to the respective daily ones, suggesting
that the differences in the daily surface O3 concentrations under two emissions temporalizations were mainly due to the differences occurring at night. Nighttime O3 concentrations under the ‘‘time-varying’’ profiles were greater than their counterparts under the ‘‘uniform’’ profiles. This difference derived from the lack of photochemical O3 formation at night and the greater concentrations of nighttime NOx under ‘‘uniform’’ profiles, which tended to titrate more O3. On the other hand, during daytime when photochemical O3 formation occurred, the average bias and GE
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were very small as shown in Figs. 2(e) and (f). The maximum GE was 0.003 ppm, which occurred in the Northeastern region. The non-linear nature of NOx–NMVOCs–O3 system was believed to be responsible for the phenomenon. In contrast to the nighttime situation, the ‘‘uniform’’ profiles assigned fewer emissions at daytime in comparison to the ‘‘time-varying’’ profiles. However, chemistry was slow at night so that large portions of nighttime emissions were still available for the next day O3 generation, which led to small changes in daytime O3 concentration. In order to demonstrate this possibility, we analyzed the diurnal variations of ambient NOx concentrations. As expected, nighttime NOx concentrations under the ‘‘uniform’’ profiles were significantly higher than their counterparts under the ‘‘time-varying’’ profiles. When using the ‘‘uniform’’ profiles, the Southeastern regions and Northeastern states saw approximately 60% higher NOx at night and the coast of California experienced about 50% greater nighttime NOx. At daytime, the discrepancy of ambient NOx concentrations was considerably smaller. The Eastern US regions typically underwent a 10% lower concentration employing the ‘‘uniform’’ profiles. The coast of California also saw approximately 10% difference. The largest change (about 20%) happened in the downwind regions (the Northwestern New Jersey) of the New York City (NYC) metropolitan areas. Averaging over the entire continental US, nighttime NOx concentrations under the ‘‘uniform’’ profiles were about 25% higher, while daytime NOx were only 5% less than the ones under the ‘‘time-varying’’ profiles. We further examined the response of the daily maximum O3 concentration to changes in temporal emission allocation. The weekly average bias for the peak was very small (within 70.001 ppm) across most areas of the continental US, including the Southeastern regions and the coast of California. The fluctuation of daily biases in the above regions was also small (within 70.003 ppm), suggesting that daily peak O3 was not very sensitive to changes in temporalization across the vast areas of the continental US. However, large average bias (up to 0.008 ppm with the daily biases ranging from 0.003 to 0.014 ppm) was found in the NYC metropolitan area. The positive bias can be explained that NYC was NMVOC sensitive (Roselle, 1994; Pierce et al., 1998; Tao et al., 2003), where NOx titration may occur leading to decreased O3 concentrations. Large reduction in NOx emissions, as was the case when comparing daytime emissions under the ‘‘uniform’’ profiles to those under the ‘‘time-varying’’ profiles, may reduce titration and thus increase ambient O3 concentrations. Seeing this relatively large bias in NYC, more studies, preferably with higher resolution, should be conducted to identify the impact of emission temporalization on O3 in complex urban settings like NYC. The average GE for the peak was spatially similar to the average biases
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across the continental US. It was within 0.003 ppm for almost all the areas of the country. It increased to 0.004 ppm in the Northern California and reached its peak (approximately 0.008 ppm) in NYC. We then evaluated the diurnal O3 variations predicted using two emission profiles. As expected, predictions of daytime O3 concentrations agreed well with each other, while predictions of nighttime O3 concentrations were typically 5–15% smaller for the ‘‘uniform’’ case than for the ‘‘time-varying’’ case. The average time reaching the daily maximum O3 concentration was within 71 h in most areas across the continental US, including the high-O3 areas of the Southeastern regions, the Northeastern states, and the coast of California. The NB and NGE (when O3 concentration was more than 0.05 ppm) were analyzed as well. The NB ranged from 10% to 10% for the entire continental US. Most areas experienced 73% of NB. The largest NB (710%) was seen in the portions of the Southeastern states and the Northeastern industrial corridor. The NGE was o5% over vast regions across the continental US. The largest NGE (approximately 12%) occurred where the largest NB was found. Both NB and NGE were small and well within the US EPA’s guideline for acceptable air quality model performance, implying that using the ‘‘uniform’’ temporal profiles would not bring excessive errors and can lead to the same satisfactory AQM results. In addition, we evaluated the correlation coefficient (R) between the results under the two profiles. The background O3 over the US ranged from 0.025 to 0.05 ppm (Altshuller and Lefohn, 1996). Thus we analyzed O3 above 0.05 ppm. We prescribed three bins for low (0.05–0.065 ppm), medium (0.066–0.08 ppm), and high (40.08 ppm) O3. The R-values for low, medium, and high O3 bin were 0.81, 0.87, and 0.98, respectively. Fig. 3 showed the scatter plot of ‘‘uniform’’ vs. ‘‘time-varying’’ O3 in each bin. It can be seen that, when viewed in the space-frequency context, the model predictions for two cases agreed each other quite well, especially for high O3 bin that, coincidently, had the highest R-value. Since high O3 concentrations are most relevant with respect to meeting air quality standard and impacting human & ecosystem health, it is informative to show if the frequency of high O3 (e.g., more than 0.08 ppm) occurrences changes significantly under changing temporalization of emissions. Fig. 4 showed the US continental surface O3 cumulative frequency distributions under two emission profiles. The two frequency distributions matched well, indicating that there was no clear difference in how often high O3 concentrations occurred for these two cases. Under the ‘‘time-varying’’ emission profiles, the grid hours when the surface O3 concentrations were more than 0.08 and 0.12 ppm were 8531 and 175, respectively. These numbers changed to
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[O3]uniform(ppm)
0.14 0.12 0.10 0.08 0.06 0.04 0.04
0.06
0.08
0.10
0.12
0.14
0.16
[O3 ]time-varying (ppm)
Fig. 3. Scatter plot of ‘‘uniform’’ vs. ‘‘time-varying’’ surface O3 concentrations at low (circle), medium (square), and high (triangle) bins. The solid line is the 1:1 line.
Fig. 5. Spatial distribution of difference in occurrences of surface O3 concentrations (40.12 ppm) between the ‘‘uniform’’ and ‘‘time-varying’’ temporal profiles over the continental US.
4. Conclusions Cumulative frequency (%)
100 80 60 40 uniform time-varying
20 0 0.07
0.08
0.09
0.10
0.11
0.12
0.13
[O3] (ppm) Fig. 4. Cumulative frequency distributions of surface O3 concentrations (40.08 ppm) for the ‘‘uniform’’ and ‘‘timevarying’’ profiles of anthropogenic emissions across the continental US.
8803 and 206 when the ‘‘uniform’’ temporal profiles were considered. Fig. 5 illustrated the difference of occurrences when O3 concentration was more than 0.12 ppm under the two profiles. Though majority areas did not see changes in occurrences of high O3 when replacing the ‘‘time-varying’’ profiles with the ‘‘uniform’’ profiles, such changes did occur at the scattered spots in the Eastern US. Among those spots, there were 9 gridcells changing from none to having 1 or more occurrences of 0.12 ppm O3 when applying the different temporal profiles. Simulations with a finer resolution should be directed to those regions to justify the impact of emission temporalization on O3 concentration. The regression and frequency distribution analyses indicated that, with the grid resolution used, surface O3 was insensitive to changes in emissions temporal profiles, particularly for high O3.
We investigated the sensitivity of regional surface O3 concentrations to changes in temporal profiles of anthropogenic emissions with a 3-D comprehensive air quality modeling system. Our simulation results showed that regional O3 was not particularly sensitive to changes in emissions temporalization, especially during daytime. The bias and GE of O3 concentrations calculated over all hours of the simulation week, as well as the bias and GE of the daily maximum O3 concentrations over the week, were small in most areas within the continental US. A few areas, e.g., parts of the Southeastern and Northeastern regions where high O3 occurred, underwent somewhat larger O3 changes in response to changes in temporal emission allocation. Further analysis indicated that the values of the averaged bias and GE for all simulation hours were strongly influenced by the bias and GE when only nighttime O3 concentrations were taken into account. During daytime the bias and GE were very small anywhere within our domain. The analyses of the NB and NGE of surface O3 concentrations showed the similar results. We also found that changing temporal profiles of man-made emissions did not significantly alter the daytime variations of O3. The cumulative frequency distributions of O3 concentrations were similar for the two scenarios when O3 concentrations were more than 0.08 ppm. The modeled O3 concentrations above the continental background were highly correlated under the two profiles, especially at high O3 (40.08 ppm) bin. Because this study uses a coarse 90-km grid, the results presented here must be viewed from a regional perspective. Under the typical wind speed, the 90-km resolution represents approximately 3-h travel time. It leads to the artificial elimination of difference associated with transport at a time scale shorter than 3 h. Thus this
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model study does not intend to resolve urban areas and resolve areas immediately downwind of large industrial sources. Simulations at greater resolutions are needed to assess this issue on an urban scale, e.g. for the NYC area. Based upon the result of this investigation, we suggest that the limited monetary and technical resources available for emissions inventory studies should be applied first to refining the inventory of emission sources and factors. Development of more detailed temporalization profiles may be desirable for gaining a better representation of urban-scale O3 where daily peak concentration is the major concern.
Acknowledgments The authors thank Dr. Xinzhong Liang of Illinois State Water Survey (ISWS) for providing the RCM results, Dr. Ho-Chun Huang of ISWS for helpful discussion of SAQM, and the Critical Research Initiative Program of University of Illinois at UrbanaChampaign (UIUC) for funding support. We sincerely acknowledge the National Center for Supercomputing Applications (NCSA) of UIUC for its computer support.
References Altshuller, A.P., Lefohn, A.S., 1996. Background ozone in the planetary boundary layer over the United States. JAWMA 46, 134–141. Chang, J.S., Jin, S., Li, Y., Beauharnois, M., Lu, C.-H., Huang, H.-C., Tanrikulu, S., DaMassa, J., 1997. The SARMAP Air Quality Model: Final Report. Air Resource Board, California EPA, Sacramento, CA. EPA, 1991. Guideline for regulatory applications of the Urban Airshed Model. US Environmental Protection Agency Report EPA-450/4-91-013, EPA, Office of Air Quality
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Planning and Standards, Research Triangle Park, NC, 89pp. EPA, 2002. National Emission Inventory (NEI) Database, http://www.epa.gov/air/data. Houyoux, M.R., Vukovich, J.M., Coats Jr., C.J., Wheeler, N.J.M., 2000. Emission inventory development and processing for the seasonal model for regional air quality (SMRAQ) project. Journal of Geophysical Research 105, 9079–9090. Jacobson, M.Z., 1999. Fundamentals of Atmospheric Modeling. Cambridge University Press, New York, NY, pp. 550–552. Liang, X.-Z., Kunkel, K.E., Samel, A.N., 2001. Development of a regional climate model for US Midwest application. Part I: sensitivity to buffer zone treatment. Journal of Climate 14, 4363–4378. Pierce, T., Geron, C., Bender, L., Dennis, R., Tonnesen, G., Guenther, A., 1998. Influence of increased isoprene emissions on regional ozone modeling. Journal of Geophysical Research 103, 25,611–25,629. Placet, M., Mann, C.O., Gilbert, R.O., Niefer, M.J., 2000. Emissions of ozone precursors from stationary sources: a critical review. Atmospheric Environment 34, 2183–2204. Roselle, S.J., 1994. Effects of biogenic emission uncertainties on regional photochemical modeling of control strategies. Atmospheric Environment 28, 1757–1772. Sawyer, R.F., Harley, R.A., Cadle, S.H., Norbeck, J.M., Slott, R., Bravo, H.A., 2000. Mobile sources critical review: 1998 NARSTO assessment. Atmospheric Environment 34, 2161–2181. Tao, Z., 2003. A summer simulation of source contributions to ground level ozone over the continental United States. Ph.D. dissertation, University of Illinois, Urbana, IL. Tao, Z., Larson, S.M., Wuebbles, D.J., Williams, A., Caughey, M., 2003. A summer simulation of biogenic contributions to ground-level ozone over the continental United States. Journal of Geophysical Research 108 (D14), 4404. Tesche, T.W., Georgopoulos, P., Seinfeld, J.H., Cass, G., Lurmann, F.L., Roth, P.M., 1990. Improvement of procedures for evaluating photochemical models. Report prepared by Radian Corporation for the State of California Air Resources Board, Sacramento, CA.
Atmospheric Environment 38 (2004) 6279–6285 www.elsevier.com/locate/atmosenv
Sensitivity of regional ozone concentrations to temporal distribution of emissions Zhining Taoa,, Susan M. Larsonb, Allen Williamsc, Michael Caugheyc, Donald J. Wuebblesa a
Department of Atmospheric Sciences, University of Illinois at Urbana-Champaign, 2204, Griffith Dr., Champaign, IL 61820, USA b Department of Civil & Environmental Engineering, University of Illinois at Urbana-Champaign c Illinois State Water Survey, 2204 Griffith Dr., Champaign, IL 61820, USA Received 1 April 2004; accepted 12 August 2004
Abstract Temporal representations of emissions bear large uncertainty. Before any costly efforts are undertaken to improve the accuracy of temporal emission profiles, however, the impact of their uncertainty on predictions of surface O3 concentrations should be examined. In this study, a 3-D air quality modeling system was used to probe the sensitivity of regional surface O3 concentrations to temporal allocation of emissions over the continental US. The raw emissions inventory was processed using SMOKE and was segmented into hourly intervals using both ‘‘time-varying’’ and ‘‘uniform’’ temporal profiles of anthropogenic sources. Our simulation results show that, on average and with the grid resolution (90 km) used, regional daytime O3 concentrations are not sensitive to changes in the temporal allocation of emissions, while nighttime O3 concentrations are lower under ‘‘uniform’’ profiles than under ‘‘time-varying’’ profiles. r 2004 Elsevier Ltd. All rights reserved. Keywords: Anthropogenic emissions inventory; Temporalization; Continental US; Photochemical model
1. Introduction Accurate estimation of pollutant emissions is critical to successful photochemical modeling, and to effective ozone (O3) abatement strategies based on those simulations. The emissions inventories are subject to large uncertainties (Placet et al., 2000; Sawyer et al., 2000), including (1) the degree of completeness of the inventory; (2) the quality of emission factor estimates; and (3) the accuracy of the inventory’s temporal and spatial patterns. Determining the sensitivity of air quality model output to the variations in emission input Corresponding author. Tel.: 217 244 1917.
E-mail address: [email protected] (Z. Tao).
will benefit efficient inventory development (Houyoux et al., 2000; Placet et al., 2000). The currently available emissions inventories (e.g., National Emissions Inventory (NEI) 96 and NEI99) contain only annual total and O3-season daily average emissions for the major pollutant classes. Air quality models, however, require emissions at finer temporal resolution. To provide these, the emissions model apportions the longer-term average values into hourly fluxes according to one of a limited number of comparatively simple temporal profiles that specify how many emissions are assigned hourly. These temporal profiles are not based on actual temporal data for a specific time period, but on typical temporal variations for broad categories of sources. A source
1352-2310/$ - see front matter r 2004 Elsevier Ltd. All rights reserved. doi:10.1016/j.atmosenv.2004.08.042
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category code specified in the emissions inventory determines which temporal profile is used for each emission source. In most cases, using a generalized profile leads to some degree of errors in modeling ambient concentrations of surface O3 (Placet et al., 2000). The central question is whether or not such errors are large enough to justify the major effort and expense that would be required to develop and assign more accurate temporal patterns to the emission sources in the inventory. To answer this question, we have evaluated the sensitivity of regional surface O3 concentrations to the uncertainties in temporal allocations using a 3-D comprehensive air quality modeling system. Two sets of temporal profiles, one ‘‘time-varying’’ (hourly resolution) and one ‘‘uniform’’ (constant) temporal allocation, are applied in the analysis. Note that the ‘‘uniform’’ profile was chosen as an extreme case, not as a representation of actual emission profiles. If there are no large differences in O3 concentrations between the ‘‘time-varying’’ profiles and the extreme assumption of the ‘‘uniform’’ profiles, this indicates that the error inherent in the ‘‘time-varying’’ profile will not cause excessive errors in predicted O3 concentrations.
2. Air quality modeling system The comprehensive air quality modeling system used in this study has been described elsewhere (Tao, 2003; Tao et al., 2003), and was summarized here for completeness. The modeling system included a regional climate model (RCM) (Liang et al., 2001), an emissions model (EM), and an air quality model (AQM). The 1996 National Emission Trends database (NET96, now designated NEI96) was used as the anthropogenic emission inventory for this study (EPA, 2002). The inventory was processed through SMOKE, an EM (Houyoux et al., 2000), using both ‘‘timevarying’’ and ‘‘uniform’’ profiles. The ‘‘time-varying’’ profiles contained in SMOKE described variations in monthly, weekly, diurnal-weekday, and diurnal-weekend anthropogenic emissions that resolved time-averaged man-made emissions into hourly flux. For each gridcell, each profile yielded different hourly emissions, but when integrated over time, total emissions were the same for both cases throughout the simulation period. It should be noted that neither profiles were applied to calculations of biogenic emissions. Biogenic emissions were estimated using the county-level land use data, and were adjusted based on temperature and light. Due to diurnal variations in temperature and solar radiation, biogenic emissions displayed a diurnal pattern. Combining biogenic emissions with the anthropogenic emissions processed under the ‘‘uniform’’ profiles, resulted in an overall emission distribution that still exhibited diurnal
variations, though considerably damped with respect to the ‘‘time-varying’’ profiles. In general, the average daytime (8 am–8 pm local time) NOx emissions for the ‘‘uniform’’ case were approximately 26% smaller than the counterpart for the ‘‘time-varying’’ case, and the average nighttime (8 pm–8 am local time) NOx emissions under the ‘‘uniform’’ profiles were about 70% greater. Since biogenic sources dominated the non-methane volatile organic compound (NMVOC) emissions (Tao et al., 2003), the effect of two temporal profiles on variations in diurnal NMVOC emissions was much less. Under the ‘‘uniform’’ profiles, the average daytime NMVOC emissions were about 8% lower and the average nighttime emissions were around 34% higher than their counterparts under the ‘‘time-varying’’ profiles. The resulting speciated and gridded hourly emissions were then input into SAQM, an AQM (Chang et al., 1997). SAQM used a terrain-following s-coordinate system. In this current investigation, 15 layers were used in the vertical direction up to 16 km above the surface. The inclusion of the surface-layer-submodel (SLS) gave a 15 m vertical resolution right above the ground. The horizontal grid used in the study covered the entire continental US and the adjacent land and ocean areas with a 90-km resolution. The Lambert map projection was applied. The projection center was 931W and 381N. The lower left corner of the domain was located at 122.891W and 11.661N. Quantitative comparison of SAQM simulations of surface O3 concentrations to observations was conducted in four selected sites, one each in the Southeastern regions, the Northeastern industrial corridor, the San Francisco Bay area, and the central Illinois. The first three sites were located in regions with serious ozone problems. The central Illinois site represented a perturbed rural area. Measured surface ozone concentrations used in the comparison were taken from the Aerometric information retrieval system (AIRS) monitoring network (http://www.epa.gov/aqspubl1/select. html). Several statistical measures (Tesche et al., 1990; Jacobson, 1999) were applied to quantify model performance assessment. These statistical measures were: (1) bias and normalized bias (NB); (2) gross error (GE) and normalized gross error (NGE); and (3) unpaired peak prediction accuracy (UPPA). The results were summarized in Table 1. It can be seen that NB varied from 3% (the San Francisco Bay area site) to 21% (the central Illinois site). The US EPA (1991) suggested a range of 75–15% as an acceptable guideline of model performance for NB. Only the central Illinois site (21%) did not meet the criteria of acceptable model performance with respect to this guideline. When averaged over all four sites, however, NB was 6% and well within the EPA’s criterion. The EPA’s guideline for NGE was 30–35% for an acceptable level of model
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Table 1 SAQM performance assessments at four selected sitesa,b Site
Central Illinois North Georgia East New York San Francisco Bay Area Averagec EPA guideline
Statistics Bias (ppb)
NB (%)
GE (ppb)
NGE (%)
UPPA (%)
13.01 0.90 6.74 0.10 4.74 —
21 5 10 3 6 75–15
19.77 23.27 14.48 13.21 17.68 —
34 38 25 23 30 30–35
16 11 9 6 2 715–20
a
Cutoff value=40 ppb of surface P O3 concentration. P PN P N 1 1 NGE ¼ N1 N Bias ¼ N1 N i¼1 ðPi Oi Þ; NB ¼ N i¼1 ðPi Oi =Oi Þ; GE ¼ N i¼1 ðjPi Oi jÞ; i¼1 ðjPi Oi j=Oi Þ; UPPA ¼ ðPmax Omax =Omax Þ; where N is the number of observations; P and O are the SAQM prediction of the one-hour average surface ozone concentration, and the observed value, respectively; and Pmax and Omax are peak ozone concentrations from the SAQM prediction and the observation, respectively. c Averaged over four sites. b
performance. Compared with this guideline, only the North Georgia site (38%) slightly exceeded the criterion. The spatial averaged NGE was 30%, within the level recommended for model acceptance. The UPPA for four selected sites ranged from 6% to 16%, which all met the EPA criteria for acceptable model performance of 715–20% for UPPA. A complete model evaluation can be found in Tao (2003). Our simulation period began on 10 July 1995 and ended on 20 July 1995. The in-depth analysis focused on the results for the last seven days, allowing three days for model spin-up. We compared differences in surface O3 concentrations, cumulative frequency distributions, peak magnitude, diurnal variations, and peak timing for the two modeling approaches. Using the results under the ‘‘time-varying’’ profiles as reference, four statistic measures, bias, NB, GE, and NGE, were computed to compare the two model simulations.
3. Results and discussions Fig. 1 displayed the weekly average daily maximum O3 (1-h average) distribution across the continental US. Large O3 concentrations (40.08 ppm) were found in the coast of California, the Northeastern states, and the Southeastern regions. More attention was given to these regions in the following analysis. We first included every gridcell-hour surface O3 concentration and, for each gridcell, examined the weekly average bias and GE between results under two emission profiles within the continental US. The results were illustrated in Figs. 2(a) and (b). It can be seen that for most areas of the US, the average bias was within 70.002 ppm, which accounted for o5% of the average daily maximum surface O3 concentrations under the
Fig. 1. Spatial distribution of weekly average daily maximum surface O3 concentrations under the ‘‘time-varying’’ temporal profiles of anthropogenic emissions.
‘‘time-varying’’ profiles. A slightly larger bias (up to 0.008 ppm) was seen in the Southeastern areas where daily maximum concentration was also high (40.12 ppm). The negative biases found in most areas across the country indicated that the model, in general, predicted less surface O3 concentrations using the ‘‘uniform’’ profiles than using the ‘‘time-varying’’ ones when averaging over all hours of the week. The average GE showed a similar spatial pattern across the continental US. It was within 0.004 ppm for the vast areas of the country. It increased to about 0.006 ppm along the coast of California and Northeastern states, and the largest GE (approximately 0.009 ppm) again occurred in the Southeastern region of the nation. We also analyzed the daily bias and GE when every gridcell-hour O3 was considered. The magnitude and spatial distributions of bias and GE in each day were similar to their weekly average counterparts. The largest
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Fig. 2. Weekly average bias and GE of hourly surface O3 concentrations under the ‘‘uniform’’ and ‘‘time-varying’’ temporal profiles of anthropogenic emissions: (a) bias when all hours included; (b) GE when all hours included; (c) bias at nighttime (8 pm–8 am); (d) GE at nighttime; (e) bias during daytime (8 am–8 pm); (f) GE during daytime.
daily bias (0.012 ppm in magnitude) was found on 15 July 1995 and occurred in the Southeastern region (the North Georgia). The largest GE (0.013 ppm) occurred in the Northwest Georgia on 17 July 1995. Since surface O3 is generated through photochemistry, it is informative to examine the difference of daytime (8 am–8 pm) and nighttime (8 pm–8 am) O3 under two emission profiles. The results indicated, not surprisingly, that the average nighttime bias and GE, as displayed in Figs. 2(c) and (d), showed magnitudes and spatial patterns similar to the respective daily ones, suggesting
that the differences in the daily surface O3 concentrations under two emissions temporalizations were mainly due to the differences occurring at night. Nighttime O3 concentrations under the ‘‘time-varying’’ profiles were greater than their counterparts under the ‘‘uniform’’ profiles. This difference derived from the lack of photochemical O3 formation at night and the greater concentrations of nighttime NOx under ‘‘uniform’’ profiles, which tended to titrate more O3. On the other hand, during daytime when photochemical O3 formation occurred, the average bias and GE
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were very small as shown in Figs. 2(e) and (f). The maximum GE was 0.003 ppm, which occurred in the Northeastern region. The non-linear nature of NOx–NMVOCs–O3 system was believed to be responsible for the phenomenon. In contrast to the nighttime situation, the ‘‘uniform’’ profiles assigned fewer emissions at daytime in comparison to the ‘‘time-varying’’ profiles. However, chemistry was slow at night so that large portions of nighttime emissions were still available for the next day O3 generation, which led to small changes in daytime O3 concentration. In order to demonstrate this possibility, we analyzed the diurnal variations of ambient NOx concentrations. As expected, nighttime NOx concentrations under the ‘‘uniform’’ profiles were significantly higher than their counterparts under the ‘‘time-varying’’ profiles. When using the ‘‘uniform’’ profiles, the Southeastern regions and Northeastern states saw approximately 60% higher NOx at night and the coast of California experienced about 50% greater nighttime NOx. At daytime, the discrepancy of ambient NOx concentrations was considerably smaller. The Eastern US regions typically underwent a 10% lower concentration employing the ‘‘uniform’’ profiles. The coast of California also saw approximately 10% difference. The largest change (about 20%) happened in the downwind regions (the Northwestern New Jersey) of the New York City (NYC) metropolitan areas. Averaging over the entire continental US, nighttime NOx concentrations under the ‘‘uniform’’ profiles were about 25% higher, while daytime NOx were only 5% less than the ones under the ‘‘time-varying’’ profiles. We further examined the response of the daily maximum O3 concentration to changes in temporal emission allocation. The weekly average bias for the peak was very small (within 70.001 ppm) across most areas of the continental US, including the Southeastern regions and the coast of California. The fluctuation of daily biases in the above regions was also small (within 70.003 ppm), suggesting that daily peak O3 was not very sensitive to changes in temporalization across the vast areas of the continental US. However, large average bias (up to 0.008 ppm with the daily biases ranging from 0.003 to 0.014 ppm) was found in the NYC metropolitan area. The positive bias can be explained that NYC was NMVOC sensitive (Roselle, 1994; Pierce et al., 1998; Tao et al., 2003), where NOx titration may occur leading to decreased O3 concentrations. Large reduction in NOx emissions, as was the case when comparing daytime emissions under the ‘‘uniform’’ profiles to those under the ‘‘time-varying’’ profiles, may reduce titration and thus increase ambient O3 concentrations. Seeing this relatively large bias in NYC, more studies, preferably with higher resolution, should be conducted to identify the impact of emission temporalization on O3 in complex urban settings like NYC. The average GE for the peak was spatially similar to the average biases
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across the continental US. It was within 0.003 ppm for almost all the areas of the country. It increased to 0.004 ppm in the Northern California and reached its peak (approximately 0.008 ppm) in NYC. We then evaluated the diurnal O3 variations predicted using two emission profiles. As expected, predictions of daytime O3 concentrations agreed well with each other, while predictions of nighttime O3 concentrations were typically 5–15% smaller for the ‘‘uniform’’ case than for the ‘‘time-varying’’ case. The average time reaching the daily maximum O3 concentration was within 71 h in most areas across the continental US, including the high-O3 areas of the Southeastern regions, the Northeastern states, and the coast of California. The NB and NGE (when O3 concentration was more than 0.05 ppm) were analyzed as well. The NB ranged from 10% to 10% for the entire continental US. Most areas experienced 73% of NB. The largest NB (710%) was seen in the portions of the Southeastern states and the Northeastern industrial corridor. The NGE was o5% over vast regions across the continental US. The largest NGE (approximately 12%) occurred where the largest NB was found. Both NB and NGE were small and well within the US EPA’s guideline for acceptable air quality model performance, implying that using the ‘‘uniform’’ temporal profiles would not bring excessive errors and can lead to the same satisfactory AQM results. In addition, we evaluated the correlation coefficient (R) between the results under the two profiles. The background O3 over the US ranged from 0.025 to 0.05 ppm (Altshuller and Lefohn, 1996). Thus we analyzed O3 above 0.05 ppm. We prescribed three bins for low (0.05–0.065 ppm), medium (0.066–0.08 ppm), and high (40.08 ppm) O3. The R-values for low, medium, and high O3 bin were 0.81, 0.87, and 0.98, respectively. Fig. 3 showed the scatter plot of ‘‘uniform’’ vs. ‘‘time-varying’’ O3 in each bin. It can be seen that, when viewed in the space-frequency context, the model predictions for two cases agreed each other quite well, especially for high O3 bin that, coincidently, had the highest R-value. Since high O3 concentrations are most relevant with respect to meeting air quality standard and impacting human & ecosystem health, it is informative to show if the frequency of high O3 (e.g., more than 0.08 ppm) occurrences changes significantly under changing temporalization of emissions. Fig. 4 showed the US continental surface O3 cumulative frequency distributions under two emission profiles. The two frequency distributions matched well, indicating that there was no clear difference in how often high O3 concentrations occurred for these two cases. Under the ‘‘time-varying’’ emission profiles, the grid hours when the surface O3 concentrations were more than 0.08 and 0.12 ppm were 8531 and 175, respectively. These numbers changed to
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[O3]uniform(ppm)
0.14 0.12 0.10 0.08 0.06 0.04 0.04
0.06
0.08
0.10
0.12
0.14
0.16
[O3 ]time-varying (ppm)
Fig. 3. Scatter plot of ‘‘uniform’’ vs. ‘‘time-varying’’ surface O3 concentrations at low (circle), medium (square), and high (triangle) bins. The solid line is the 1:1 line.
Fig. 5. Spatial distribution of difference in occurrences of surface O3 concentrations (40.12 ppm) between the ‘‘uniform’’ and ‘‘time-varying’’ temporal profiles over the continental US.
4. Conclusions Cumulative frequency (%)
100 80 60 40 uniform time-varying
20 0 0.07
0.08
0.09
0.10
0.11
0.12
0.13
[O3] (ppm) Fig. 4. Cumulative frequency distributions of surface O3 concentrations (40.08 ppm) for the ‘‘uniform’’ and ‘‘timevarying’’ profiles of anthropogenic emissions across the continental US.
8803 and 206 when the ‘‘uniform’’ temporal profiles were considered. Fig. 5 illustrated the difference of occurrences when O3 concentration was more than 0.12 ppm under the two profiles. Though majority areas did not see changes in occurrences of high O3 when replacing the ‘‘time-varying’’ profiles with the ‘‘uniform’’ profiles, such changes did occur at the scattered spots in the Eastern US. Among those spots, there were 9 gridcells changing from none to having 1 or more occurrences of 0.12 ppm O3 when applying the different temporal profiles. Simulations with a finer resolution should be directed to those regions to justify the impact of emission temporalization on O3 concentration. The regression and frequency distribution analyses indicated that, with the grid resolution used, surface O3 was insensitive to changes in emissions temporal profiles, particularly for high O3.
We investigated the sensitivity of regional surface O3 concentrations to changes in temporal profiles of anthropogenic emissions with a 3-D comprehensive air quality modeling system. Our simulation results showed that regional O3 was not particularly sensitive to changes in emissions temporalization, especially during daytime. The bias and GE of O3 concentrations calculated over all hours of the simulation week, as well as the bias and GE of the daily maximum O3 concentrations over the week, were small in most areas within the continental US. A few areas, e.g., parts of the Southeastern and Northeastern regions where high O3 occurred, underwent somewhat larger O3 changes in response to changes in temporal emission allocation. Further analysis indicated that the values of the averaged bias and GE for all simulation hours were strongly influenced by the bias and GE when only nighttime O3 concentrations were taken into account. During daytime the bias and GE were very small anywhere within our domain. The analyses of the NB and NGE of surface O3 concentrations showed the similar results. We also found that changing temporal profiles of man-made emissions did not significantly alter the daytime variations of O3. The cumulative frequency distributions of O3 concentrations were similar for the two scenarios when O3 concentrations were more than 0.08 ppm. The modeled O3 concentrations above the continental background were highly correlated under the two profiles, especially at high O3 (40.08 ppm) bin. Because this study uses a coarse 90-km grid, the results presented here must be viewed from a regional perspective. Under the typical wind speed, the 90-km resolution represents approximately 3-h travel time. It leads to the artificial elimination of difference associated with transport at a time scale shorter than 3 h. Thus this
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model study does not intend to resolve urban areas and resolve areas immediately downwind of large industrial sources. Simulations at greater resolutions are needed to assess this issue on an urban scale, e.g. for the NYC area. Based upon the result of this investigation, we suggest that the limited monetary and technical resources available for emissions inventory studies should be applied first to refining the inventory of emission sources and factors. Development of more detailed temporalization profiles may be desirable for gaining a better representation of urban-scale O3 where daily peak concentration is the major concern.
Acknowledgments The authors thank Dr. Xinzhong Liang of Illinois State Water Survey (ISWS) for providing the RCM results, Dr. Ho-Chun Huang of ISWS for helpful discussion of SAQM, and the Critical Research Initiative Program of University of Illinois at UrbanaChampaign (UIUC) for funding support. We sincerely acknowledge the National Center for Supercomputing Applications (NCSA) of UIUC for its computer support.
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