Article pubs.acs.org/est
Comparison of NOx Fluxes Measured by Eddy Covariance to Emission Inventories and Land Use Linsey C. Marr,* Tim O. Moore, Michael E. Klapmeyer, and Myles B. Killar Via Department of Civil and Environmental Engineering, Virginia Tech, 411 Durham Hall, Blacksburg, Virginia 24061, United States S Supporting Information *
ABSTRACT: Uncertainty in emission inventories remains a critical limitation of air quality modeling and management. Using eddy covariance, we measured surface-atmosphere exchange fluxes of nitrogen oxides (NOx) at the neighborhood scale at 13 sites in the Norfolk, Virginia area to estimate emissions, to evaluate official inventories, and to quantify relationships between emissions and land use. Average daytime fluxes ranged from 0.4 μg m−2 s−1 at a site near open water to 9.5 μg m−2 s−1 at a site dominated by vehicle traffic. NOx fluxes were correlated with both road density and medium- plus high-intensity development, confirming that both motor vehicles and sources associated with development are responsible for NOx emissions in urban areas. Spatially averaged NOx fluxes measured by eddy covariance agreed to within 3% with the National Emission Inventory (NEI) but were 2.8 times higher than those in the corresponding grid cell of an emission inventory developed for air quality modeling. These average fluxes were 4.6, 4.5, and 1.7 μg m−2 s−1, respectively. Uncertainty in the inventories appears to be dominated by the nonroad mobile source category. It is especially important to know NOx emissions accurately because in certain photochemical regimes, reducing NOx emissions can exacerbate secondary pollutant formation.
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INTRODUCTION Air pollutant emission inventories are critical tools for improving air quality, but they are known to contain large uncertainties that can limit progress toward scientific and policymaking goals. Emissions from nonpoint sources are especially uncertain because of the dearth of methods for independently verifying them. To estimate such emissions, inventories rely on emission factors, but these are often based on measurements from a very small sample size and may not be representative of the population of sources. Independent estimates suggest that fine particulate matter inventories, for example, may be inaccurate by an order of magnitude.1 A host of techniques has been used to evaluate or estimate emission inventories indirectly, including applying receptor models to observed concentrations,2 comparing air quality model predictions with atmospheric measurements3−7 or satellite observations,8,9 comparing pollutant ratios within the inventory to ambient ones,10 conducting on-road studies for mobile sources,11 and applying inverse methods using threedimensional air quality models,12,13 to highlight just some of the most recent work. However, none of these methods directly measures the flux of emissions to the atmosphere. Eddy covariance, widely used in the biogeosciences to measure surface-atmosphere exchange fluxes, can be employed as a powerful tool for validating emission inventories and identifying missing and/or erroneous components in them.14 Previous work has demonstrated the successful use of eddy covariance towers to measure fluxes in urban locations, such as Chicago, Mexico City, and Edinburgh.15−17 Although towers © 2013 American Chemical Society
enable elevated measurement heights, they are locked into a single location and require infrastructure and permissions that can be difficult to obtain. We have overcome this challenge by employing a van with a telescoping mast as the eddy covariance platform, thereby gaining the advantage of being able to conduct measurements anywhere that is accessible to vehicles and favorable topographically to the method. In a recent pilot study, we demonstrated this approach for measuring anthropogenic fluxes of carbon dioxide (CO2), nitrogen oxides (NOx), and fine particulate matter (PM2.5).14 The objective of this research is to demonstrate a measurement-based method for evaluating emission inventories. Using a mobile eddy covariance system, we measured surface-atmosphere exchange fluxes of NOx during a monthlong field campaign in the Norfolk, Virginia area. Measurements described the temporal and spatial variability of fluxes in an urban area. NOx is of concern because of its role in ozone and secondary aerosol formation and impacts on health and visibility. We compared results with the Environmental Protection Agency’s (EPA) National Emission Inventory (NEI) and with a high-resolution gridded, hourly inventory developed for air quality modeling. These alternative estimates of emissions can provide insight into uncertainties in Received: Revised: Accepted: Published: 1800
August 2, 2012 December 21, 2012 January 14, 2013 January 14, 2013 dx.doi.org/10.1021/es303150y | Environ. Sci. Technol. 2013, 47, 1800−1808
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Figure 1. Map showing 13 sampling locations, excluding the ones in red, spaced across a 12 ×12 km square in the Norfolk area.
atmosphere from the surface. The lag was determined by maximizing the covariance between w and χ and was confirmed through calculations based on flow rates and sample tubing geometries in the system. The lag for NOx was 6 s. Fluxes were subjected to stationarity testing through comparison of a period’s 30-min flux and the average of six corresponding 5-min fluxes.15,17,19 Differences of less than 60% were deemed to be indicative of periods of acceptable quality.17,19,20 Quality assurance also included spectral and cospectral analyses. The normalized spectra of temperature and cospectra of temperature with vertical wind velocity had slopes of −2/3 and −4/3 (Figure S2 of the Supporting Information, SI), obeying their respective power decay laws,21 but those of NOx deviated from these slopes at higher frequencies due to the analyzer’s slower response time, nominally ∼1 s and operationally ∼2 s when the residence time in the molybdenum converter was also considered. Losses in fluxes at high frequencies were corrected by assuming spectral similarity with temperature using a method22 that we have applied previously with this same experimental setup.23 Briefly, the integral of the raw cospectrum of temperature (i.e., the flux), which was assumed to be lossless, was calculated up to the attenuation frequency. The value of the normalized integral at the attenuation frequency represented the fraction of flux that was actually captured by the slower analyzers. An attenuation frequency of 0.2 s−1 NOx was determined by visual inspection of spectra and cumulative integrals, and the resulting correction factor was 1.12 averaged over all valid 30-min periods. Accurate quantification of uncertainties from eddy covariance data alone is challenging. Previous efforts dedicated to the estimation of such uncertainties found systematic errors in fluxes of ≤25% and random errors of ≤20%.20,24−29 We estimated uncertainty by calculating the variance of the
inventories and prioritize further development of inventorying methods.
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METHODS Flux Measurements. The mobile Flux Laboratory for the Atmospheric Measurement of Emissions (FLAME) is a customized television news van with a mast that extends to 15 m above ground. During the field campaign, a sonic anemometer (Applied Technologies SATI-3K) and conductive Teflon tubing, in which depositional losses have been shown to be negligible,14 were mounted on a rotating platform on top of the mast. A pump drew air at 20 L min−1 through 14-m long, 1.27-cm diameter PTFE conductive tubing (TELEFLEX T1618−08) down to ground level, and gas analyzers subsampled the air through a custom-designed Teflon manifold. NOx was measured using a fast chemiluminescence analyzer (Eco Physics CLD 88Y), which was calibrated daily in the field. CO2 was measured by infrared absorption (LiCor 7000), but these data had to be discarded due to calibration problems. Quality control and standard postprocessing of the measurements included spike removal, lag correction, coordinate rotation by the planar fit method, linear detrending, and calculation of fluxes according to eq 1, F = w′χ ′ρa M
(1)
where the mass flux F of a trace gas is the time-averaged covariance between the instantaneous deviations of the vertical wind velocity w′ and the mixing ratio χ′ from their respective means over the averaging period, multiplied by the molar density of air ρa, and the molecular mass M of the trace gas.17,18 A positive flux represents net transfer upward into the 1801
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covariance (i.e., the flux), a statistically rigorous method of estimating random error.30 Emissions from the FLAME’s gasoline-powered generator are carried downwind and, according to footprint calculations, should have minimal impact on the calculated fluxes. Field Campaign. The field campaign took place in Norfolk, Virginia, and also included parts of the surrounding cities of Chesapeake, Portsmouth, and Virginia Beach. The area is designated nonattainment for ozone and often experiences high levels of PM2.5 during the summer. For eddy covariance, a uniform fetch is most desirable, but urban areas are spatially heterogeneous. To overcome this limitation, we followed the recommendation of Schmid et al.31 to use multiple sites within an ecosystem in order to achieve higher spatial averaging power. We focused the field campaign on a 12 ×12 km square, shown in Figure 1, that coincided with the grid cell of highest emissions in the region according to an inventory developed by the Visibility Improvement State and Tribal Association of the Southeast (VISTAS) regional planning organization for modeling air quality. We conducted measurements at 16 sites approximately evenly spaced across the area, but sampling on three days at SL7, SL9, and SL11 was precluded by excessive heat that led to equipment failure. Thus, data were available from the 13 sites shown in Figure 1. The sites encompassed residential/industrial, commercial, and open water land uses, as defined by the U.S. Department of Agriculture. Terrain in the immediate surroundings was flat with low building and tree heights (2 km away, so its emissions likely had no influence on observed fluxes. SL10 was located 500 m southeast and often downwind of the intersection of a number of major highways in this area. Traffic in this location was typically very heavy due to the proximity of downtown Norfolk and industrial operations, and the frequency and fraction of diesel-powered vehicle traffic was especially high (>100 trucks per day). The site was also ∼500 m to the east of a point source which was responsible for 51 tons y−1 of NOx in 2008, according to the NEI. At this location, both vehicle traffic and industrial activities were the likely source of NOx.
and daily emissions were 1.15 times higher on Monday through Thursday compared to the average over the entire week. The latter adjustment factor was derived from an hourly, day-ofweek specific motor vehicle emission inventory of NOx that was based on actual traffic counts.36 To convert emission rates to fluxes, we divided by the area of Norfolk, 139 km.2 The gridded, hourly inventory was generated by the VISTAS regional planning organization37 and was based on point and area source data from state and local agencies, information on fire-related emissions, vehicle miles traveled (VMT), and the MOBILE and NON-ROAD models. In many cases, data were updated from versions submitted to EPA for regulatory purposes to account for missing sources and pollutants and corrections to VMT. We obtained the inventory files from the Air Division of the Virginia Department of Environmental Quality. From this inventory, we extracted hourly emission rates in June from the 12 ×12 km grid cell corresponding to the measurement sites in Norfolk. Because this inventory was created for the year 2002, we adjusted area, on-road mobile, nonroad mobile, and point sources downward by 24%, 17%, 47%, and 76%, respectively, to account for the decrease in NOx emissions between 2002 and 2008 in Virginia, according to the NEI.
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RESULTS NOx Mixing Ratios. As shown in Figure 3, NOx mixing ratios, or concentrations, followed the diurnal pattern typical of
Figure 3. Time series of NOx concentrations and fluxes (average and standard deviation) across sites.
urban areas. They were highest in the early morning, ∼100 ppb at 7:30, when emissions were plentiful and the mixing height low, and decreased throughout the morning as the boundary layer grew. Concentrations stabilized at 20−30 ppb between 11:00 and 17:30. Concentration time series at individual sites are shown in Figure S5 of the SI. There was substantial spatial variability 1803
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The lowest average fluxes of 0.4 and 0.7 μg m−2 s−1 were found at SL1 and SL14, respectively. The two largest negative 30-min fluxes of the campaign were recorded at SL1 and may be due to this site’s proximity to a large body of water and golf course to the northwest. When winds were from this direction, deposition likely outweighed emissions. The site was also bounded by a rail yard to the south, whose associated emissions may have accounted for positive fluxes when winds were southerly. The next two most negative 30-min fluxes of the campaign were observed at SL5, which was also at the water’s edge. The other site with low fluxes, SL14, was located in a mainly residential area where NOx-generating activities appeared to be minimal. Fluxes vs Land Use. We aimed to determine whether fluxes were strongly correlated with land use/and or demographic characteristics, because if so, then such relationships could be used in an alternative, and simpler, approach to developing inventories. NOx fluxes were correlated with both road density and medium- plus high-intensity development, as shown in Figure 4. The figure includes only a subset of the flux
railroad density in this limited data set. Average fluxes at each site were positively correlated with the fraction of heavy industrial facilities in the area but not with household income, population density, or the fraction of medium industrial facilities in the area. Fluxes vs Emission Inventory. Figure 3 includes lines representing fluxes in the NEI for the city of Norfolk and those derived from a gridded, hourly emission inventory used for air quality modeling by the VISTAS regional planning organization over the same area. From the VISTAS inventory, we extracted emission rates on weekdays in June over the hours of 7:00 to 17:00. Average daytime fluxes were 4.6, 4.5, and 2.4 μg m−2 s−1 according to eddy covariance measurements, the NEI, and the gridded inventory, respectively. The NEI and eddy covariance fluxes agreed to within 4%, well within the uncertainties of the measurement technique. Agreement was much poorer with the VISTAS gridded emission inventory. If the VISTAS inventory were adjusted downward by 30%, to 1.7 μg m−2 s−1, to account for reductions in emissions between 2002 and 2008, then based on changes in the NEI over the same period, the eddy covariance fluxes would be 2.8 times higher than those in the inventory.
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DISCUSSION To our knowledge, this research represents the first time that NOx fluxes have been measured by eddy covariance in an urban area. The observation that net fluxes were mostly positive agrees with previous eddy covariance measurements of CO2, particles, and other pollutants showing that urban areas are sources of emissions.14,16,17,40,43−47 In Norfolk, 30-min daytime fluxes ranged from −7.5 to 22.2 μg m−2 s−1. In forests where anthropogenic influences are minimal, average NOx fluxes have been found to be negative (i.e., downward), and the largest 30min median values were −0.3 and −0.08 μg m−2 s−1,48,49 an order of magnitude smaller than observed in Norfolk in absolute terms. In Greenland, average NOx fluxes above the snowpack reached 4 × 10−4 μg m−2 s−1,50 negligible relative to values observed in Norfolk; emissions were thought to be due to nitrate photolysis within the snowpack. There was large spatial and temporal variability in NOx fluxes across the 13 sampling sites and no distinct temporal pattern overall. Typically, we think of on-road mobile sources, which are expected to dominate NOx emissions in urban areas, as having a marked temporal activity pattern, with strong peaks during the morning and evening rush hours. We previously developed a motor vehicle emission inventory whose temporal allocation was based on actual traffic counts, separated by lightduty vehicles (assumed to be gasoline-powered) and heavyduty (assumed to be diesel-powered) vehicles.36 The activity patterns of the two types of vehicles differed such that emissions from heavy-duty diesel trucks during the late morning hours smoothed out the temporal profile of total motor vehicle emissions of NOx. During the hours corresponding to our flux measurements, 7:00−17:00, hourly emissions in the inventory varied by only 7% (relative standard deviation). Thus, the lack of a distinct temporal pattern in the average flux shown in Figure 3 is consistent with prior findings. The variability in fluxes presents a challenge for modeling air quality at high spatial resolution (i.e., a few kilometers or less). Capturing fine spatial and temporal features in emissions will be needed in order to simulate atmospheric chemistry at the neighborhood scale. This level of accuracy could be important for predicting photochemical ozone formation,51,52 under-
Figure 4. NOx fluxes during each 30-min period versus road density and intensity of development in the corresponding footprint (p ≤ 0.0001 in both cases).
measurements, 118 out of 184 for which valid footprints could be calculated, and an extremely large negative 30-min flux of −7.5 μg m−2 s−1 at SL1 (Figure S5 of the SI) was excluded from the analysis. While the coefficients of determination (R2) were not high, the p-values were 0.0001 or less in both cases. The adjusted R2 were 0.25 and 0.12, respectively. Fluxes were correlated with both medium-intensity development and highintensity development separately, with similar slopes, so we combined the two. Medium-intensity development is defined as areas with a mixture of constructed materials and vegetation where impervious surfaces account for 50−79% of the total cover. These areas most commonly include single-family housing units. High-intensity development is defined as areas where impervious surfaces account for at least 80% of the total cover. These areas commonly include apartment complexes and commercial/industrial buildings. Road density and mediumplus high-intensity development were only weakly correlated with each other (R2 = 0.05). In a multiple linear regression of NOx flux against both road density and percentage of mediumplus high-intensity development, the adjusted R2 increased to 0.31. Fluxes were not correlated with other land use categories: open water (hypothesized to be a proxy for boating activity), open space, low intensity development, barren land, evergreen forest, pasture/hay, cultivated crops, or woodlands. Only six of the 118 footprints intersected railroad tracks (three at SL1 and three at SL13), and there was no correlation between fluxes and 1804
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Table 1. Emission Inventory by Major Source Category (mg m−2 day−1) in Norfolk Area Compared to Measured Flux NEI 2008 area biogenic mobile on-road mobile nonroad point total a
VISTAS June 2002
VISTAS June 2008
eddy covariance June 2008
emissions
% of total
emissions
% of total
projected emissions
% of total
est. fluxa
8.7 0.4 69.7 148.4 7.2 234
4 0.2 30 63 3 100
34.4 0.4 50.9 47.8 1.1 135
26 0.3 38 36 0.8 100
26.3 0.4 42.3 25.4 0.3 95
28 0.4 45 27 0.3 100
NA NA NA NA NA 241
Over 24 h and all days of the week.
the modeling inventory for VISTAS for June 2002 and projected to June 2008, based on changes in the NEI between 2002 and 2008. The NEI values correspond to the 139 km2 of the city of Norfolk, and the VISTAS values correspond to the 12 ×12 km grid cell that includes most of Norfolk and smaller pieces of three surrounding cities, so the comparison is close but not exact. Fluxes measured by eddy covariance, adjusted to a full 24 h and a weekday average by applying the temporal correction factors described previously, are also shown. Mobile sources, on-road and nonroad combined, are the largest source in both inventories: 93% in the NEI and 65% in VISTAS in 2008. The largest discrepancy in emissions by source category between the two inventories lies in nonroad emissions. Total nonroad emissions are nearly 6 times higher in the NEI than in the VISTAS inventory, even though the geographic areas covered by both inventories include the port and shipping channels. According to the NEI, 50% of emissions in Norfolk come from commercial marine vessels; nonroad diesel-powered equipment accounts for another 8% of the total. The lack of concordance is not surprising because inventories of marine vessel emissions have been shown to contain significant uncertainties.56 The second largest discrepancy is in area source emissions. They are 3 times higher in the VISTAS inventory. It is possible that updating previously missing sources as part of the inventory’s development is responsible for some of the difference. Point sources account for no more than 3% of total emissions in the area, and the selection of sampling locations should have avoided a disproportionate impact of point sources on the flux measurements. These observations, combined with the relatively good agreement between the NEI and 24-h estimated flux based on eddy covariance measurements, suggest that reevaluation of nonroad emissions in the modeling inventory may be prudent. Accurate estimates of NOx in this category are important because model simulations have shown that shipping emissions impact tropospheric ozone, the lifetime of methane, and ultimately radiative forcing.57,58 A new mobile-source emission model, MOVES, has been introduced to replace the MOBILE and NONROAD models and should improve the accuracy of inventories. In an analysis of the Chicago area, predicted onroad NOx emissions were 17% higher with MOVES compared to MOBILE, mainly due to the use of a second-by-second drive cycle with corresponding emission factors instead of average speeds.59 Direct measurements of NOx fluxes by eddy covariance add confidence to the accuracy of the NEI in the Norfolk area but raise questions about a gridded, hourly emission inventory used for air quality modeling. Results confirm that both motor vehicles and nonroad sources associated with development are responsible for NOx emissions. If NOx emissions in the gridded, hourly inventory really are substantially lower than
standing secondary aerosol formation, and predicting individuals’ exposure to air pollution. Eddy covariance adds a relatively direct method to the suite of techniques available for estimation of emissions, but as with any method, it has limitations. Two uncertainties associated with the experimental approach may introduce bias into the interpretation of measured fluxes as emissions. First, fluxes represent net surface-atmosphere exchange and so incorporate losses due to deposition. Second, the lack of nighttime measurements precludes direct comparisons with inventories over a complete 24-h period. Also, storage of nighttime emissions followed by flushing of them in the morning hours could lead to overestimation of actual emissions.53 The WPL correction for the effect of humidity fluctuations on density54 was not applied because detailed characterization of the NOx analyzer’s response to fluctuations in humidity is still needed. A limitation of this study in particular is the small size of the data set. Ideally, a longer period of measurements at a single site, such as one month, would be used to enable robust fitting using the planar fit method. In future field work, it will be important for a mobile eddy covariance laboratory to spend many days at each site in order to capture a larger set of measurements across the broad spectrum of atmospheric conditions and improve the power of the analysis. Inventory. NOx emissions in the grid cell of the inventory used for air quality modeling were 1.9 times lower than observed fluxes. Developing a gridded, hourly emission inventory for air quality modeling is a complex task that requires numerous inputs and processing steps. Point and area source emissions in this inventory were based on the 1999 NEI, projected forward to 2002, and mobile source emissions were developed using the MOBILE6 and NONROAD 2002 models. There was some overlap in data sources between this gridded inventory and the NEI; the reasons for the disagreement are explored below. Historically, emission inventories have been shown to contain large uncertainties. Confidence in the accuracy of NOx emissions in the NEI is medium to high for point and mobile sources but low for area and biogenic sources.55 According to the NEI for Norfolk, area, biogenic, mobile, and point sources accounted for 4%, 0.2%, 30%, 63%, and 3% of total NOx emissions, respectively, as shown in Table 1. The relatively good agreement between observed fluxes and the NEI, combined with confidence in the accuracy of point and on-road mobile source emissions, suggests that the sum of area and nonroad emissions in the inventory may be reasonably accurate. One caveat in this analysis is that observed fluxes are specific to the summertime, while the NEI is an annual average. Probing the emission inventories in more detail can provide further insight into their uncertainties. Table 1 lists daily emissions by major source category in the NEI for 2008 and in 1805
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actual emissions, then the discrepancy may have serious repercussions for air quality modeling and decision-making based on those results. It is especially important to know NOx emissions accurately because in certain photochemical regimes, reducing NOx emissions can actually exacerbate secondary pollutant formation.
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ASSOCIATED CONTENT
* Supporting Information S
Site descriptions. Footprint statistics. NOx concentrations and fluxes at individual sites. This material is available free of charge via the Internet at http://pubs.acs.org.
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AUTHOR INFORMATION
Corresponding Author
*Phone: (540) 231-6071; fax: (540) 231-7916; e-mail: lmarr@ vt.edu. Notes
The authors declare no competing financial interest.
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ACKNOWLEDGMENTS This research was supported by the National Science Foundation (CBET-0547107 and CBET-0715162) and a Virginia Tech NSF Advance seed grant. We thank the Department of the Air Force for their sponsorship of Major Klapmeyer; however, the views expressed in this work are those of the authors and do not necessarily reflect the official policy or position of the Air Force, the Department of Defense, or the U.S. Government. We also thank H. Rakha, J. Bryson, the Virginia Tech Transportation Institute, M. Kiss, G. Stella, M. Stephanski, the City of Norfolk, and the Woolard family.
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