Constraining emission inventories with observation data:

This section has two goals: first, to describe temporal trends and NMVOC speciation of some emissions from U.S. sources as estimated from ambient measurements; and second, to compare those estimates with the trends included in emission inventories. These estimates will address on-road vehicles and electric power plants. The U.S. EPA finds that these two sources were responsible for the majority of anthropogenic CO, NOx and SO2 and a large fraction of the NMVOC emitted in the country over the past 25 years.

Temporal trends and evaluation of U.S. power-plant emissions

Power-plant emission inventories for the U.S. are believed to be very accurate, and many detailed hourly emission data are available, because generally these emissions are measured by Continuous Emission Monitoring Systems (CEMS), which are required by the U.S. EPA‘s Acid Rain Program and the NOx Budget Trading Program. The U.S. EPA regularly reports estimated emissions and their trends over the previous decades, generally in annual National Air Quality and Emissions Trends Reports (http://www.epa.gov/ttn/chief/trends/index.html). Comparisons of a recent historical sample of these Trends Reports [U.S. EPA, 1995; 2000; 2003] with an earlier inventory developed for NAPAP [Saeger et al., 1989] and the most recent emission Trends Tables (―1970 – 2006 Average Annual Emissions, All Criteria Pollutants‖, posted July 2007) are shown in Figure 3.22, for NOx and SO2 from fuel combustion in electric utilities. It is clear that there has been little variation in the emission estimates for any given year, with only the NOx emissions in the 1995 Trends Report significantly different (as much as 19% higher) than the later estimates.

According to the inventories in Figure 3.22, over the last 26 years U.S. NOx and SO2

emissions from electric utility power plants have decreased by factors of 2.0 and 1.8, respectively.

The substantial reductions of power-plant SO2 emissions during the 1990‘s and of power-plant NOx

emissions in the past decade are a direct result of pollutant-specific cap-and-trade control strategies mandated by the EPA‘s Acid Rain Program and the NOx Budget Trading Program. The power-plant emissions measured by CEMS have been checked against aircraft flux determinations based on ambient measurements in the downwind emission plumes, and the two sets of observations generally agree with standard deviations of less than ±14% [Peischl et al., 2010]. A larger-scale confirmation of the recent power-plant NOx emission reductions is demonstrated by satellite observations over the Ohio River Valley, a region in the central U.S. with a high density of coal-fired power plants that dominate the NOx emissions [Kim et al., 2006]. Satellite NO2 vertical columns over the Ohio River Valley declined by 40% between 1999 and 2005, a trend consistent with CEMS-reported declines in power-plant NOx emissions. The consistency of the CEMS-based inventory over the years, and the

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correspondence of the CEMS data with aircraft flux and satellite determinations, indicate that these inventories of U.S. power-plant emissions are accurate.

Temporal trends and evaluation of U.S. on-road vehicle emissions

Making accurate estimates of on-road vehicle emissions is much more difficult than the analogous task for power plants. Bottom-up, on-road emission models integrate the product of

emission factors (e.g., g/km driven) for a diverse, constantly evolving vehicle fleet and highly variable activity factors. The different emission models that have been used over the years have yielded results that differ widely in some important respects. Figure 3.22 shows estimated on-road emissions of NOx

and CO from the five references discussed in the previous section plus one additional Trends Report [U.S. EPA, 1990] and compares those estimates to emissions inferred from ambient measurements [Parrish, 2006]. For NOx the emission inventories differ significantly in the temporal trends of the emissions. The most recent evaluation suggests decreasing NOx emissions throughout the 25 years, while the earlier calculations suggest increasing NOx emissions through the decade of the 1990‘s. The emissions inferred from ambient data support the earlier trend estimates. All the emission models and the inferred emissions agree that CO emissions have decreased continually by 3 to 5%/year. However, there is significant disagreement regarding the magnitude of the CO emissions. The most recent inventory is approximately a factor of two larger than emissions inferred from ambient measurements.

Figure 3.22. Annual U.S. emissions from (a) power plants and (b) on-road vehicles from 1980-2007, as estimated in six U.S. inventories and inferred from ambient observations. Units are million metric tons/year. The dates in the annotation indicate the year of publication of the inventory report.

Other investigators have inferred fuel-based emission factors (grams of pollutant per kg of gasoline or diesel burned) from roadside observations, which can be compared to the emission factors used in emission models. For example, multi-year monitoring programs in several U.S. cities suggest that the average CO and NOx fuel-based emission factors for gasoline vehicles declined by an average of 7 to 9 %/yr since the mid-1990‘s [Ban-Weiss et al., 2008; Bishop and Stedman, 2008; Harley et al., 2005]. These declines are thought to be the result of continual improvements to the emission control systems on U.S. gasoline-powered vehicles. In contrast, NOx emission factors inferred from roadside

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observations of diesel-powered vehicles, which produce relatively little CO, appear to have decreased only slightly over the past decade [Ban-Weiss et al., 2008].

The study by Kim et al. [2006] also examined the northeastern U.S. urban corridor stretching from Washington, DC, in the south to Boston in the north. This region, dominated by mobile source NOx emissions, saw only a slight decrease in satellite-retrieved NO2 vertical columns between 1997 and 2005. This finding suggests that, overall, mobile source NOx emissions have not changed much over this time period. In addition to year-to-year variability of on-road emissions, clear differences between weekday and weekend U.S. urban NOx emissions resulting primarily from motor vehicle use patterns have been documented by in-situ [Harley et al., 2005] and satellite methods [Beirle et al., 2003]. Because of differential control of gasoline and diesel vehicle emissions, the weekly cycle in U.S. on-road NOx emissions is predicted to have changed over the past decade [Harley et al., 2005].

The important lesson from these evaluations is that without the support of direct

measurements (as CEMS provides for power-plant sources), it is not possible to develop bottom-up inventories that are accurate enough for some of the scientific uses to which they are applied.

However, bottom-up inventories are indispensable components of photochemical models, and the foundation upon which our knowledge of pollutants in the atmosphere is based. A continuing process of inventory development is clearly required: testing of emission inventories through top-down evaluations; inventory improvement; evaluation of the top-down tests; and then repeating this process until the top-down tests of the inventories indicate sufficient accuracy for the intended use of the inventory.

Temporal trends and speciation of on-road vehicle VOC emissions

The speciation of ambient hydrocarbon concentrations in several cities across the globe (Figure 3.23) reveals a large degree of similarity. This similarity spans cities in North America and Asia, has remained generally constant over the past two decades in the U.S., and persists over wide ranges of absolute concentrations. A two-part hypothesis most likely explains this similarity. First, gasoline-fuelled vehicle exhaust and the associated evaporative gasoline emissions dominate the ambient hydrocarbon concentrations in all of these urban areas. Second, there is no large difference in the hydrocarbon composition of gasoline and exhaust emissions among these urban areas.

Comparison of datasets collected in U.S. cities over the past three decades indicates that a substantial decrease (something like an order of magnitude) in hydrocarbon emissions has occurred even while total distance travelled by on-road vehicles has nearly tripled. This change is at least partially responsible for the decrease in concentrations between the two U.S. studies in Figure 3.23.

The ambient concentration data suggest that the emission decrease has been larger than indicated by U.S. emission inventories, while the global EDGAR inventory does not capture any significant decrease (Figure 3.24). Evidently, U.S. strategies aimed toward controlling hydrocarbon emissions, based upon automobile catalytic converters, minimization of gasoline evaporation and other vehicle emission control strategies, have been very successful—indeed, more successful than indicated by emission inventories. Unless the emission inventory uncertainties, particularly in the global inventory, can be substantially reduced, retrospective analyses of anthropogenic influences on tropospheric composition will be uncertain to an important degree.

Evaluation of ambient VOC measurements can also provide detailed, critical tests of VOC speciation in inventories. Benzene and acetylene are examined here. Both of these hydrocarbons are in the top ten in terms of ambient concentrations in U.S. cities, and they react slowly in the atmosphere.

Fortin et al. [2005] argued that these species are primarily emitted by on-road vehicles, and they showed that the benzene-to-acetylene ratio is remarkably invariant throughout the U.S. in any given year and exhibits long-term trends in response to VOC emission control measures (Figure 3.25). Before 1994, the ratio increased slowly due to the preferential removal of acetylene by automotive catalytic converters. In 1994, in response to the 1990 U.S. Clean Air Act Amendments, specific benzene control measures were introduced, which reduced the benzene-to-acetylene ratio dramatically in the following decade.

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Figure 3.23. Mixing ratios of VOC species from five measurement programs. The median VOC concentrations in U.S. cities in 1984-88 are taken from Table 2 of Seila et al. [1989], and the mean concentrations in U.S. cities in 1999-2005 are the means of the city mean mixing ratios from Table 2 of Baker et al. [2008]. Seila et al. [1989] did not report CO measurements; the CO concentration for the U.S. cities in 1984-88 is estimated from the median acetylene mixing ratio and the CO-to-acetylene ratio reported by those same authors for a tunnel study conducted in New York City in 1982 [Lonneman et al., 1986].

Figure 3.24. VOC emissions from on-road vehicles in the U.S. estimated by two emission inventories. The 2008 U.S. EPA emissions are from the latest (August, 2008) emission tables posted on the website of the EPA Technology Transfer Network: Clearinghouse for

Inventories & Emission Factors (http://www.epa.gov/ttn/chief/trends/index.html). The EDGAR emissions are from EDGAR 3.2 FT2000 [van Aardenne et al., 2005] for 2000, EDGAR 3.2 [Olivier and Berdowski, 2001] for 1990 and 1995 and EDGAR-HYDE 1.3 [van Aardenne et al., 2001] for 1970, 1980 and 1990. The fossil-fuel consumption emissions of EDGAR-HYDE 1.3 were multiplied by 0.864 to bring them into agreement with the 1990 on-road emissions of EDGAR 3.2. The grey lines indicate exponential trends necessary to account for a factor of 5 or 10 decrease in emissions between 1975 and 2005.

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The measured ambient ratios and their trend can be compared to the VOC speciation in emission inventories. Figure 3.25 shows benzene-to-acetylene emission ratios extracted from three recent U.S. national emission inventories, both for on-road vehicles and inventory totals. The NEI 1996 and 1999 numbers were obtained by applying the SPECIATE software (EPA Clearinghouse for Inventories and Emissions Factors: http://www.epa.gov/ttn/chief/emch/speciation/index.html) to the respective NEI. The 1996 number agrees to within 10% with the 1996 National Toxic Air Pollutant inventory for benzene (http://www.epa.gov/ttn/atw/nata/).

The comparison between the ambient and inventory ratios in Figure 3.25 is quite poor. The inventory ratios for total emissions are a factor of 3 to 4 higher than the ambient values; those for on-road emissions are also significantly higher than observed. Further, the temporal trends are not clearly in agreement. Although the recent trend in the inventory appears to parallel the ambient trend, it is for the wrong reasons. The ambient ratio is believed to have decreased due to reduced benzene emissions, but the inventory benzene emissions have remained approximately constant while the acetylene emissions have increased, particularly in the on-road emissions. In conclusion, the VOC speciation in the U.S. NEI as tested by these two example species appears to be in error by a factor of three to four, and the temporal trend in the inventory emissions is not consistent with the observations. There is a critical need for a re-evaluation of the VOC speciation throughout the U.S. national emission

inventory, and by implication similarly derived inventories worldwide. Correctly interpreted, reliable ambient concentration measurements must be one of the important guides for this re-evaluation.

Figure 3.25. Semi-log plot of temporal trends of observed ambient benzene-to-acetylene ratios from field study data compared to inventory ratios. The colours of the symbols indicate geographic location: U.S. Urban (black), California (red), southeast U.S. (blue), northeast U.S. (green), and Texas (purple). The error bars indicate the 95% confidence limit of the mean. The grey lines indicate estimated ambient trends before and after 1993. The inventory ratios are from three national U.S. inventories.

[Adapted from Figure 2 in Fortin, T. J., et al. (2005), Temporal changes in U.S.

benzene emissions inferred from atmospheric measurements, Environmental Science

& Technology, 39: 1403-1408. Copyright 2005 American Chemical Society.]

FINDING: The integrated study of emissions, forward and inverse modelling, and satellite and ground observations can usefully bound source magnitudes and lead to improved emission inventories.

RECOMMENDATION: Previous studies demonstrate a clear need for the development of an integrated framework of emissions, models, and observations that can be readily applied to

-2.0

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different intercontinental transport situations and can quickly adapt to new technical capabilities as they arise (new satellite retrievals, new network data releases, etc.).

FINDING: Emission events (dust storms, volcanic eruptions, forest fires, etc.) are detectable in many of the observational datasets (aircraft campaigns, ground-station monitoring, satellite retrievals, etc.). It may be possible to quantify the magnitudes of such emission events for some species, by coordinated application of these measurement tools.

RECOMMENDATION: Emphasis should be placed on identifying unique signatures of episodic emission releases in the observational record and their subsequent transport (e.g., tracking dust storms) in order to improve source quantification and modelling of long-range processing and deposition.

FINDING: Emission inventories only provide information about primary releases of pollutants;

they do not directly estimate secondary species such as ozone and secondary organic aerosol.

RECOMMENDATION: Careful study of the large-scale relationships between primary precursor emissions (e.g., emission ratios, speciation profiles) and observed secondary species (from satellites, campaigns, networks) can advance our understanding of the mechanisms of formation of the secondary species and our ability to select effective mitigation options for the primary species.

References

Abbot, D. S., et al. (2003), Seasonal and interannual variability of North American isoprene emissions as determined by formaldehyde column measurements from space, Geophysical Research Letters, 30(17):

1886-1889.

Akimoto, H. (2003), Global air quality and pollution, Science, 302: 3.

Akimoto, H., et al. (2006), Verification of energy consumption in China during 1996-2003 by using satellite observational data, Atmospheric Environment, 40(40): 7663-7667.

Allen, D., et al. (2004), Evaluation of pollutant outflow and CO sources during TRACE-P using model-calculated, aircraft-based, and measurements of pollution in the troposphere (MOPITT)-derived CO concentrations, Journal of Geophysical Research, 109: D15S03.

Andreae, M. O., and P. Merlet (2001), Emission of trace gases and aerosols from biomass burning, Global Biogeochemical Cycles, 15(4): 955-966.

Andres, R. J., and A. D. Kasgnoc (1998), A time-averaged inventory of subaerial volcanic sulfur emissions, Journal of Geophysical Research, 103(D19): 25251-25261.

Arellano, A. F., et al. (2004), Top-down estimates of global CO sources using MOPITT measurements, Geophysical Research Letters, 31(L01104).

Baker, A. K., et al. (2008), Measurements of nonmethane hydrocarbons in 28 United States cities, Atmospheric Environment, 42: 170-182.

Ban-Weiss, G. A., et al. (2008), Carbonyl and nitrogen dioxide emissions from gasoline- and diesel-powered motor vehicles, Environmental Science & Technology, 42(11): 3944-3950.

Barkley, M. P., et al. (2008), Net ecosystem fluxes of isoprene over tropical South America inferred from Global Ozone Monitoring Experiment (GOME) observations of HCHO columns, Journal of Geophysical Research, 113(D20304).

Beirle, S., et al. (2003), Weekly cycle of NO2 by GOME measurements: a signature of anthropogenic sources, Atmospheric Chemistry and Physics, 3: 2225-2232.

Bergamaschi, P., et al. (2000), Inverse modeling of the global CO cycle; 1. Inversion of CO mixing ratios, Journal of Geophysical Research, 105(D2): 19.

125

Bergamaschi, P., et al. (2005), Inverse modeling of national and European CH4 emissions using the atmospheric zoom model TM5, Atmospheric Chemistry and Physics, 5: 2431-2460.

Bergamaschi, P., et al. (2007), Satellite chartography of atmospheric methane from SCIAMACHY onboard ENVISAT: 2. Evaluation based on inverse model simulations, Journal of Geophysical Research, 112(D02304).

Bergamaschi, P., et al. (2009), Inverse modeling of global and regional CH4 emissions using SCIAMACHY satellite retrievals, Journal of Geophysical Research, 114(D22301).

Bishop, G. A., and D. H. Stedman (2008), A decade of on-road emissions measurements, Environmental Science & Technology, 42(5): 1651-1656.

Blond, N., et al. (2007), Intercomparison of SCIAMACHY nitrogen dioxide observations, in situ measurements and air quality modeling results over Western Europe, Journal of Geophysical Research, 112(D10311).

Bloom, A. A., et al. (2010), Large-scale controls of methanogenesis inferred from methane and gravity spaceborne data, Science, 327: 322-325.

Boersma, K. F., et al. (2005), Estimates of lightning NOx production from GOME satellite observations, Atmospheric Chemistry and Physics, 5(9): 2311-2331.

Boersma, K. F., et al. (2008), Intercomparison of SCIAMACHY and OMI tropospheric NO2 columns: observing the diurnal evolution of chemistry and emissions from space, Journal of Geophysical Research, 113(D16S26).

Bond, T. C., et al. (2004), A technology-based global inventory of black and organic carbon emissions from combustion, Journal of Geophysical Research, 109(D12403).

Bousquet, P., et al. (2006), Contribution of anthropogenic and natural sources to atmospheric methane variability, Nature, 443: 439-443.

Butler, T. M., et al. (2004), Mass balance inverse modeling of methane in the 1990s using a Chemistry Transport Model, Atmospheric Chemistry and Physics, 4: 2561-2580.

Carmichael, G. R., et al. (2003), Evaluating regional emission estimates using the TRACE-P observations, Journal of Geophysical Research, 108(D21): 8810-8826.

Carn, S. A., et al. (2004), Fire at Iraqi sulfur plant emits SO2 clouds detected by Earth Probe TOMS, Geophysical Research Letters, 31(L19105).

Carn, S. A., et al. (2007), Sulfur dioxide emissions from Peruvian copper smelters detected by the Ozone Monitoring Instrument, Geophysical Research Letters, 34(L09801).

Chang, D., and Y. Song (2010), Estimates of biomass burning emissions in tropical Asia based on satellite-derived data, Atmospheric Chemistry and Physics, 10: 2335-2351.

Chen, Y., and R. G. Prinn (2006), Estimation of anthropogenic methane emissions between 1996 and 2001 using a three-dimensional global chemical transport model, Journal of Geophysical Research, 111(D10307).

Ciais, P., et al. (1995), Partitioning of ocean and land uptake of CO2 as inferred by δ¹³C measurements from NOAA Climate Monitoring and Diagnostics Laboratory Global Air Sampling Network, Journal of Geophysical Research, 100: 5051-5070.

CIESIN (2005), Gridded population of the world, version 3 (GPWv3), Center for International Earth Science INformation Network (CIESIN), USA.

Clarke, A. D., et al. (2004), Size distributions and mixtures of dust and black carbon aerosol in Asian outflow:

Physiochemistry and optical properties, Journal of Geophysical Research, 109(D15S09).

Clarke, L., et al. (2007), Scenarios of Greenhouse Gas Emissions and Atmospheric Concentrations, 154 pp, Office of Biological & Environmental Research, U.S. Department of Energy, Washington, DC.

Clerbaux, C., et al. (2008), Carbon monoxide pollution from cities and urban areas observed by the Terra/MOPITT mission, Geophysical Research Letters, 35(L03817).

126

Dalsøren, S. B., et al. (2009), Update on emissions and environmental impacts from the international fleet of ships: The contribution from major ship types and ports, Atmospheric Chemistry and Physics, 9(6):

2171-2194.

Dalsøren, S. B., et. al. (2007), Environmental impacts of the expected increase in sea transportation, with a particular focus on oil and gas scenarios for Norway and Northwest Russia, Journal of Geophysical Research, 112(D02310).

Dickerson, R. R., et al. (2002), Analysis of black carbon and carbon monoxide observed over the Indian Ocean:

implications for emissions and photochemistry, Journal of Geophysical Research, 107(D19): 8017-8027.

Dubovik, O., et al. (2008), Retrieving global aerosol sources from satellites using inverse modeling, Atmospheric Chemistry and Physics, 8: 209-250.

Duncan, B. N., et al. (2003), Interannual and seasonal variability of biomass burning emissions constrained by satellite observations, Journal of Geophysical Research, 108(D2): 4100-4121.

EC/JRC/PBL (2010), Emission Database for Global Atmospheric Research (EDGAR), release version 4.1, European Commission, Joint Research Centre, Netherlands Environmental Assessment Agency, http://edgar.jrc.ec.europa.eu

EMEP (2006), Transboundary acidification, eutrophication and ground level ozone in Europe since 1990 to 2004, 93 pp, European Monitoring and Evaluation Programme.

http://www.emep.int/publ/reports/2006/status_report_1_2006_ch.pdf

Enting, I. G., and J. V. Mansbridge (1989), Seasonal sources and sinks of atmospheric CO2, Direct inversion of filter data, Tellus, Series B - Chemical and Physical Meteorology, 41B: 111-126.

Etiope, G., and R. W. Klusman (2010), Microseepage in drylands: Flux and implications in the global atmospheric source/sink budget of methane, Global and Planetary Change, 72(4): 265-274.

Eyers, C. J., et al. (2004), AERO2k global aviation emissions inventories for 2002 and 2025, Technical Report,

Eyers, C. J., et al. (2004), AERO2k global aviation emissions inventories for 2002 and 2025, Technical Report,

Im Dokument Emission Inventories and Projections (Seite 145-0)