Source Attribution of Air Pollutant Concentrations and Trends in the

Nov 1, 2013 - Trends apportionment is conducted on 2000–2011 ambient monitoring data from the SEARCH network with NEI emissions data adjusted to imp...
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Source Attribution of Air Pollutant Concentrations and Trends in the Southeastern Aerosol Research and Characterization (SEARCH) Network Charles L. Blanchard,* Shelley Tanenbaum, and George M. Hidy Envair, 526 Cornell Avenue, Albany, California 94706, United States S Supporting Information *

ABSTRACT: A new approach for determining the contributions of emission sources to trends in concentrations of particulate matter and gases is developed using the chemical mass balance (CMB) method and the U.S. EPA’s National Emission Inventory (NEI). The method extends our earlier analysis by using temporally varying emission profiles and includes accounting of primary and secondary particulate organic carbon with an empirical regression calculation. The model offers a potentially important tool for verifying that annual emission reductions by major source category have yielded changes in ambient pollutant concentrations. Using long-term measurements from well-instrumented monitoring sites, observed trends in ambient pollutant concentrations at urban and rural locations can be attributed to emission changes. Trends apportionment is conducted on 2000−2011 ambient monitoring data from the SEARCH network with NEI emissions data adjusted to improve interinventory consistency. The application accounts for major source category influences in southeastern U.S. regional trends; local anomalies are noted. In the SEARCH region, open burning is important as a source of CO and carbonaceous particles. Improved agreement between predicted and measured particulate carbon is obtained by increasing mobile diesel exhaust and area-source particulate carbon emissions by 1 and 20%, respectively, compared with NEI values. The method is general and is applicable to data from any monitoring site that is instrumented for criteria air pollutants, associated gases, and particle composition.

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INTRODUCTION Contemporary air quality management embodies a “weight of evidence” paradigm for planning and implementation of emission reduction programs. This approach includes modeling of emission reductions required to achieve ambient air quality, combined with analysis and interpretation of data from air quality monitoring. Accurate assessment of source contributions to ambient concentrations of air pollutants (source apportionment) is important for effective management, including establishment of “accountability”.1 A major component of accountability is verification that ambient concentrations of pollutants decrease as a function of actions to reduce emissions. Whereas predicting quantitative relationships between specific source emissions and ambient air quality typically requires grid-based dispersion modeling, empirical confirmation is useful retrospectively and involves a comparison between total (gas and particle) species emission trends and corresponding air monitoring trends.2 Quantifying the relationships between emission changes from source categories and changes in ambient air quality using observational data is challenging because multiple emission changes are responsible for trends in ambient concentrations. Our recent advance in receptor modeling using a chemical mass balance (CMB) model for gases and for particle mass and composition3 quantifies relationships between ambient air quality and emissions by source category. This manuscript © 2013 American Chemical Society

introduces annual variation of the emission source profiles, which are linked to emission estimates obtained from the U.S. Environmental Protection Agency (EPA) National Emissions Inventory (NEI). An example trends apportionment is presented using 2000−2011 monitoring data from the Southeastern Aerosol Research and Characterization (SEARCH) network, combined with NEI data adjusted to improve interinventory methodological consistency.

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METHODS Ambient Air Quality Measurements. Hourly measurements of gases and daily measurements of PM2.5 mass and species concentrations were obtained from the SEARCH public archives.4 Annual averages for 2000−2011 were created from the hourly and daily data. The network operations, sampling, and measurement methods for gases (CO, SO2, NO, NOy, HNO3, O3, and NH3) and fine (PM2.5) and coarse (PM10−2.5) particles are described elsewhere.5−11 The network comprises eight extensively instrumented monitoring sites: Pensacola, FL (PNS) and Gulfport, MS (GFP), urban coastal sites on the Received: Revised: Accepted: Published: 13536

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Table 1. Mean Measured Concentrations by Site, 2002−2010, Computed As Means of Annual MeansaI species

BHM

CTR

GFP

JST

OAK

OLF

PNSb

YRK

c

515 15.6 13.8 4.1 12.6 1.4 2.0 0.8 64.2 38.3 17.8 1.6 4.0 1.01 0.05 0.18 0.004 0.10 0.10 0.18

214 11.2 9.1 3.2 4.5 1.0 0.2 0.3 7.8 4.6 0.3 0.5 2.8 0.38 0.03 0.08 0.003 0.06 0.02 0.03

280 10.6 8.4 3.1 4.8 1.0 0.6 0.4 20.5 12.9 2.9 0.6 2.2 0.50 0.04 0.10 0.005 0.06 0.03 0.04

506 14.6 13.2 4.0 11.6 1.5 1.2 0.8 82.3 40.2 25.0 1.2 4.0 0.51 0.04 0.09 0.004 0.06 0.04 0.08

209 10.6 8.5 3.1 3.4 0.9 0.2 0.3 5.3 3.0 0.2 0.5 2.5 0.49 0.05 0.11 0.004 0.06 0.02 0.04

241 10.3 8.4 3.0 4.9 1.0 0.4 0.4 13.2 9.4 1.0 0.6 2.3 0.45 0.04 0.09 0.004 0.06 0.02 0.04

350 11.8 9.4 3.2 7.0 1.0 0.6 0.4 25.6 15.6 4.8 0.7 2.5 0.61 0.06 0.11 0.005 0.09 0.05 0.06

242 12.3 10.7 3.9 6.8 1.5 2.1 0.7 12.4 8.1 0.6 0.5 2.9 0.33 0.03 0.07 0.003 0.05 0.02 0.03

CO (g) PM2.5 massd sum of speciese SO4 SO2 (g) NH4 NH3f (g) NO3 NOy as NO2 (g) NO2 (g) NO (g) EC OC MMOg Alh Sih Tih Kh Cah Feh

Units are μg m−3 as listed species. bPNS averages are 2000−2009 data. cGases (g) are identified. dAll condensed-phase species are PM2.5 size fractions. eSum of daily PM2.5 SO4, NO3, NH4, EC, OM, and MMO concentrations. f2004−2010. gMMO = sum of Al2O3, SiO2, K2O, CaO, TiO2, and Fe2O3. hElemental mass. IMeasurements of some species were incomplete in 2000, 2001, and 2011. a

Gulf of Mexico; Pensacola − outlying (aircraft) landing field (OLF) and Oak Grove, MS (OAK), nonurban coastal sites near the Gulf; Atlanta, GA − Jefferson Street (JST) and North Birmingham, AL (BHM), urban inland sites; and Yorkville, GA (YRK) and Centreville, AL (CTR), nonurban inland sites. PNS, OAK, and GFP were closed at the beginning of 2010, 2011, and 2012, respectively. All sites have measurements of meteorological parameters and gas-phase species (i.e., criteria pollutants; HNO3 and 24-h NH3 commencing mid-2003) at 10 m height.5,9,11 Measurements of speciated non-methane organic compounds (NMOC) were made at JST through 2008 and by EPA at YRK.12 Anthropogenic NMOC (aNMOC) is defined as the sum of identified NMOC species concentrations excluding known biogenic species, such as isoprene. Particulate measurements include 24-h resolution filter-based or continuous PM2.5 and PM10−2.5 mass concentration, bulk composition of inorganic species (water-soluble ions and elements by X-ray fluorescence), and organic carbon (OC) and elemental carbon (EC) by thermal-optical differentiation.5,7,8,10 PM2.5 OC and EC analyses follow the IMPROVE_A thermal/optical reflectance (TOR) protocol using DRI Model 2001 thermal/ optical carbon analyzers.13,14 SEARCH reports concentrations of aluminum (Al), silicon (Si), potassium (K), calcium (Ca), titanium (Ti), and iron (Fe) as the corresponding masses of the oxides Al2O3, SiO2, K2O, CaO, TiO2, and Fe2O3. Although these elements may exist in different valence states, major metal oxides (MMO) are defined as the sum of the six listed oxides. Spatial and temporal variations of SEARCH measurements of air pollutant concentrations from 1999 to 2010 have been described.2,15 Table 1 summarizes mean concentrations of selected air pollutants by monitoring site. Emission Inventory Trends. Emissions data were obtained from the NEI, which is released at three-year intervals. Recent inventories are the 2002 NEI (version 3, released October 15, 2007),16 the 2005 NEI (version 2, released March 11, 2009),17 the 2008 NEI (version 1.5, released May 16, 2011; version 2,

released April 10, 2012; version 3, released March 1, 2013),18 and the 2011 NEI (version 1, released September 30, 2013).19 The NEI provides annual emissions of pollutants at county, state, and national levels, and, prior to the 2008 NEI, at four levels of source specificity: Tier 1 (13 source categories), Tier 2 (59 categories), Tier 3 (247 categories), and source classification codes (SCC, over 4000 categories). The 2008 NEI continues to report emissions at the SCC and Tier 1 levels but disaggregates by “emission inventory sector” (EIS). The 60 EIS categories do not match the 59 older Tier 2 categories. We reaggregated the 2002 and 2005 NEI Tier 3 emissions into the 60 EISs. Matching the Tier 3 categories required combining the 2008 “biomass” and “other” fuel combustion EISs within each of four source types (electricity generation, commercial, residential, and industrial), and combining the “Solvent - Dry Cleaning” and “Solvent - Consumer & Commercial Solvent Use” EISs. These modifications yielded 55 EISs. The data reported in the NEI range from continuous measurements of SO2 and NOx made at electrical generating units (EGUs) to estimates based on emission testing or on process engineering. Continuous emissions monitoring (CEM) data at facilities subject to EPA programs20 were obtained and summed by year for all reporting facilities located in AL, FL, GA, and MS: 518 units in 2005 and 693 units in 2010, 3.9 × 1015 Btu in 2005, and 4.0 × 1015 Btu in 2010 (1 Btu = 1055.06 J). The CEMs SO2 emissions represented 99% of the NEI total EGU SO2 emissions and the CEMs NOx emissions were 94 to 98% of the NEI total EGU NOx emissions throughout the 1999−2010 period. Since PM2.5 speciation information is not included in the NEI, emissions of EC, OC, and trace elements (e.g., Al, Fe, Si) were estimated by multiplying their fractions in source speciation profiles by NEI PM2.5 mass emissions.21,22 Current versions of 84 composite profiles21 were obtained from EPA’s SPECIATE 4.3 database.23 An EPA mapping of the 84 source 13537

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category profiles to 3497 SCCs21,23 was consulted to link the 84 source profiles to the 247 Tier 3 categories and 55 EISs. We aggregated emissions from 55 EISs to smaller numbers of categories, yielding the data summarized in Table S1 (Supporting Information) for 2008. Some pollutants are emitted principally by one or a few sectors, whereas emissions of others are more evenly distributed among sectors. EGUs emitted the majority (87%) of SO2, while open burning (e.g., prescribed burns, wildfires) accounted for 71% of primary OC emissions. The 2008 NEI provides an estimate of biogenic emissions of volatile organic compounds (VOC), which prior NEIs excluded; in 2008, the biogenic source represented 77% of total VOC emissions. The term “VOC” is used here for emissions of gas-phase organic compounds as defined by EPA,24 whereas “NMOC” refers to ambient concentration measurements of low-molecular weight (C2 - C12), gas-phase, non-methane organic compounds. The term “aVOC” is used to designate VOC emissions from anthropogenic sources, including biomass combustion. Because emission estimation procedures are revised between versions of the NEI,25 emission trends cannot be generated from sequential NEIs without consideration of methodological changes. Between versions 1.5 and 2 of the 2008 NEI, EPA shifted from the MOBILE to the MOVES emission model for estimating on-road mobile source emissions.26 Our comparisons of versions 1.5 and 2 of the 2008 NEI indicated that MOVES generated higher emissions of NOx (by ∼40%) and PM2.5 (by ∼ a factor of 2) than previously found by the MOBILE model, consistent with previous reports.27 Our NEI comparisons showed that EGU PM2.5 emissions in the 2008 NEI v2 were lower by almost an order of magnitude than in the 2005 NEI v2, because EPA decreased the EGU condensable PM2.5 emissions component. Previous work has suggested that the 2005 NEI condensable PM2.5 emissions were too high.3 EPA prepares trend estimates from a base-year NEI.25,28 We created an internally consistent regional trend inventory for the years 1999 through 2011 based on the NEIs, CEMs data, SPECIATE, and activity data (Table S2). The regional inventory was developed by state for AL, FL, GA, and MS. Because the SEARCH network extends into FL only at Pensacola, the FL inventory was then restricted to the westernmost 12 counties, denoted NW FL (representing ∼5% of the FL state population). Our study domain is therefore AL, NW FL, GA, and MS. Initial estimates of on-road mobile source annual emissions were computed using MOVES version 2010b with EPA’s default national input database. EPA cautions that this database does not represent the most accurate local information. We therefore determined annual, state-specific emission factors for gasoline and diesel vehicles on a mass-per-unit-energy basis from the MOVES output files. We obtained gasoline and onroad diesel fuel sale volumes by state29 and generated annual, state gasoline and diesel vehicle emissions as the product of fuel sales volumes, energy content of diesel and gasoline fuels, and the MOVES emission factors. Previous studies have used fuelbased inventories as an independent approach to estimating emissions for comparison with national and state inventories.30 Our fuel-based approach uses emission factors from MOVES to incorporate current EPA assessment of the temporal evolution of fleet emissions characteristics and provide a consistent estimation methodology across years (Table S2). Annual nonroad mobile emissions were estimated for nonroad equipment, railroads, and vessels using annual state-

level sales of nonroad, locomotive, and marine diesel fuels,29 in combination with NEI emissions for each of these EISs. The resulting values for nonroad equipment were compared with annual state emissions obtained using the EPA NONROAD version 2008a emission model.31 Our approach generated values with greater interannual variation than the NEI and NONROAD, especially from 2007 to 2009 (Figure S1). In contrast, the 2002 NEI emissions were carried forward into the 2005 NEI for all nonroad equipment, introducing unrealistic constant nonroad emissions from 2002 through 2005. Annual fire emissions were estimated based on 2008 NEI wildfire and prescribed burn emissions scaled by the ratios of the annual acreage of wildfires and prescribed burns relative to their 2008 burn acreage (Table S2). Prescribed burns accounted for ∼95% of the acreage. For comparison, the emissions were recalculated by scaling to the 2011 NEI data. Prescribed burns were ∼90% of the fire emissions based on the 2008 NEI and ∼50% based on the 2011 NEI. Prescribed burns conducted by private parties and state agencies are not reported consistently in national data, even though they represented the majority of the acreage.32 Annual reports of state forestry agencies were consulted33−36 and compared with average annual prescribed burn acreages reported in a national survey.37 Higher fire emissions occurred in 2007 due to wildfires in and near the Okefenokee Swamp in southeastern Georgia and northeastern Florida, and again in 2009 from an increase in prescribed burn acreage in Georgia. Emission trends for eight pollutants are shown in Figure S2. SO2 emissions decreased by about two-thirds, while emissions of NOx, PM2.5, and CO each declined by about 50%. We compared these trends with NEI estimates and another recent trend inventory (Figure S3). The absolute magnitudes of onroad CO and NOx emissions differ among inventories, but the rates of change are comparable. Our fire emissions are higher than those of another inventory,38 apparently because ours are scaled to the 2008 NEI instead of the 2002 and 2005 NEIs. Emissions from prescribed burns are comparable in the 2008 and 2011 NEIs, but state wildfire emissions are up to an order of magnitude greater in the 2011 NEI compared to the 2008 NEI. The 2011 NEI biomass burning emissions are about 50% greater than in our inventory (see “Results”). Model Development and Application. Our earlier work3 did not account for OC in primary (POC) and secondary (SOC) forms. Laboratory measurements and observational evidence suggest that a substantial fraction (∼20−50% annually) of the southeastern PM2.5 OC is produced by atmospheric chemical reactions.39−43 Delineation of the fractions deriving from primary emissions and from secondary formation is difficult from first principles, because the atmospheric chemistry is complex and incompletely understood, and both anthropogenic and natural emission rates are poorly constrained. Primary carbon emissions may be semivolatile, leading to low-volatility species that, in turn, may be oxidized to condensed-phase secondary OC.44 SOC also derives from atmospheric processes involving particle formation or growth from adsorption of organic vapors or from VOC oxidation products.45 Aerosol mass spectrometry46 and watersoluble extraction (WSOC)47 infer POC and SOC. Alternatively, empirical methods have been used to estimate POC and SOC from ambient data. The SEARCH data archive does not include WSOC or aerosol mass spectrometric measurements but has a record of OC and EC obtained by thermal-optical differentiation.5,7,8,10 13538

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S2). Second, the number of fitting species was increased for all profiles to improve separation of source contributions. The CMB methodology is a deterministic receptor model that quantifies emission contributions to ambient air pollutant concentrations at a site by reconstructing observed concentrations as a linear combination of emission source profiles.54−57 The CMB model is intuitive and is expressed as an estimate of species apportionment from sources that are distributed geographically over undefined space, and that are important to receptor concentrations. The model assumes that processes take place that do not appreciably affect the relative concentrations of species. The emission region affecting a receptor is unspecified but has been assumed in previous applications to range from local to regional scales. Formally, the model is

We estimated daily average concentrations of POC and SOC using an empirical multiple regression model employing markers for primary and secondary carbon.48 The regression approach yields estimates of POC and SOC that correlate with, and are of comparable magnitude to, measurements of WSOC in Atlanta.49 For each site, the following equation was developed using stepwise multiple regression OC = a*nsK + b*EC + c*CO + d*O3 + f*SO4

(1)

where OC, EC, SO4, and nsK are daily average concentrations in the PM2.5 size fraction, CO is a daily average of the hourly CO concentrations, and O 3 is the daily 1-h peak O 3 concentration. The terms nsK, EC, and CO are markers for primary carbon deriving from combustion processes, including fresh OC emissions and more aged OC emissions still retaining a correlation with coemitted species; the terms O3 and SO4 are markers for secondary carbon deriving from oxidation of gasphase precursors and highly aged aerosol. Terms for an intercept, previous-day O3, NO3, and NOz (NOy − NO) were initially included but were not statistically significant. The term “nsK” represents nonsoil potassium (K, as K2O) as a tracer of wood smoke.50−52 SEARCH K concentrations are not differentiated by soil and nonsoil portions, so nsK was estimated using iron (Fe) measurements to determine the soil fraction of K (E. Edgerton, Pers. comm.):

C is a vector of chemical concentrations measured at each site, and A is a matrix of source profiles. S is a vector of emission source contributions, which can be computed for each site from C and A using eq 8:58 S = [ATWA]−1 ATWC

(2)

The rationale for eq 2 is that K and Fe in the PM10 size fraction derive predominantly from dust and exhibit a ratio characteristic of the soil and dust affecting air samples at each site. Equation 2 was calculated using site-, year-, and monthspecific averages for the ratio of PM10 K2O/PM10 Fe2O3. Since PM10 sampling frequency was usually less than PM2.5 sampling frequency, monthly averages were used in place of day-specific ratios. The result for JST is comparable to the findings of Pachon et al.,53 but we also obtained a high degree of intersite variability (Table S3). SOC and POC concentrations were then estimated as (3)

Predicted SOC = d*O3 + f*SO4

(4)

(8)

W is a diagonal matrix of weighting factors, often inversely proportional to the variances of the measured species.56 The final values and uncertainties of S were determined iteratively using the effective variance approach, which accounts for uncertainties in both C (Table S5) and A (Table S1):59

nsK = K 2O − {(PM 2.5Fe2O3)*(PM10K 2O/PM10Fe2O3)}

Predicted POC = a*nsK + b*EC + c*CO

(7)

C = AS

σSj2 = [ATW −1A]−1 ,

where Wii = σCi 2 +

∑ (Sj2σAij2) (9)

Because some gases (e.g., SO2, NOx) are reactive in the atmosphere, the combined gas-phase and condensed-phase concentrations of the ambient data were used in C (Table 1, using annual averages by site). The fitting species were SOx (to relate to SO2 emissions), NHx (for NH3 emissions), NOy (for NOx emissions), CO, EC, POC (calculated as described above), K, Al, Si, Fe, and Ca. Since NMOC was measured only at JST and YRK, NMOC was not used as a fitting species, but predictions of aNMOC were made for comparison with ambient measurements at those two locations. The computed source-strength vectors (S) and the emissions matrix (A) were used to calculate emission source contributions to ambient SOx, CO, NHx, NOy, aNMOC, EC, POC, Al2O3, Fe2O3, SiO2, CaO, and K2O at each site for each year with eq 7. Source contributions were determined on a site-specific basis, reflecting expected differences in source influences at different sites. Source profiles (A) are typically normalized by PM2.5 mass (for apportionment of PM2.5) or by VOC mass (for apportionment of VOC). Since both gases and PM species are used in the present analysis, source profiles are not normalized. Instead, A is comprised of emissions (primary SO4, NO3, and NH4 were added to emissions of the gases SO2, NOx, and NH3, respectively). The values in A have units of million metric tons per year, so S is a vector of source strengths with units of μg m−3 per million metric tons emitted per year. CMB applications typically apply a single set of fixed source profiles (A) to generate source contributions (S) independently for each sample, thus generating time-varying predicted concentrations (C). In our application, the annual variation of matrix A, with source profiles that represent emissions from 2000 to 2011, generates time-varying predicted concentrations (eq 7). The fitting process yields a single set of source strengths

The equations return positive values of POC and SOC for all samples, but the sum of predicted POC and SOC is not constrained to reproduce the measured OC. The predicted POC and SOC were therefore rescaled by calculating the daily residuals (residual = predicted POC + predicted SOC − measured OC):48 Rescaled POC = POC − [POC/(POC + SOC)]*residual (5)

Rescaled SOC = SOC − [SOC/(POC + SOC)]*residual (6)

Results for computed POC and SOC are summarized in Figure S4 and indicate that this POC-SOC differentiation varied by site, season, and year. Sensitivity analyses were carried out to estimate model uncertainties associated with the computed POC-SOC splits. The Chemical Mass Balance (CMB) model3 was used with our earlier adaptations plus two additional modifications. First, year-specific emission source profiles were developed to represent annual emission changes from 2000 to 2011 (Figure 13539

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Table 2. Computed Source Strengths and One-Sigma Uncertaintiesa site

Ag-NH3

area

BHM CTR GFP JST OAK OLF PNS YRK

10.6 ± 2.5 4.1 ± 1.0 4.8 ± 1.1 6.1 ± 1.9 3.8 ± 0.9 4.2 ± 1.0 4.0 ± 1.1 15.7 ± 3.2

12.1 ± 4.8 10.4 ± 2.7 8.1 ± 2.5 10.5 ± 4.6 10.1 ± 2.8 9.3 ± 2.6 11.6 ± 3.1 8.7 ± 2.8

dust 10.9 1.7 3.7 2.9 3.2 2.7 4.2 0.9

± ± ± ± ± ± ± ±

1.2 0.3 0.4 0.5 0.4 0.4 0.5 0.3

mobile diesel

mobile gas

point

70.1 ± 10.1 8.3 ± 1.9 19.3 ± 3.3 88.5 ± 12.1 6.1 ± 1.7 14.0 ± 2.5 15.3 ± 3.4 11.0 ± 2.3

106.7 ± 10.6 12.9 ± 3.0 38.0 ± 4.4 118.3 ± 11.8 11.2 ± 2.9 22.9 ± 3.4 55.4 ± 5.4 23.1 ± 3.4

11.7 ± 0.6 5.3 ± 0.3 5.2 ± 0.3 10.8 ± 0.6 4.3 ± 0.2 5.6 ± 0.3 7.0 ± 0.4 7.8 ± 0.4

Units are μg m−3 per million metric tons emitted per year. Source contributions to each pollutant are obtained for each site and year by multiplying the tabled values by annual emissions of the pollutant.

a

minus baseline concentrations of CO were used in the vector of ambient concentrations, C. Sensitivity calculations were carried out by fixing baseline CO concentrations at 120 and 130 μg m−3. The computed source contributions to each compound were used to determine the contribution of each source category “i” to mean annual PM2.5 mass at each site

(S) for each site, which allows the temporal variation of the computed concentrations to depend entirely on the temporal variations of emissions (A) as do regression approaches for comparing ambient concentrations changes to emission changes.2 It is not necessary to fix S, and the model predictions of trends can agree with observed trends more closely by allowing temporal variation in both A and S (eq 7). For application on finer time resolutions (e.g., daily or monthly), meteorologically induced variations could be accommodated by temporal variation of S. For annual resolution data, as used here, year-to-year variations in meteorology are known to affect secondary species concentrations (e.g., O3, SO4) but are less important when considering total concentrations (e.g., SOx). Our approach (in which A but not S varies annually) provides a more explicit comparison of emissions with ambient trends and is more diagnostic of inaccuracies in emission trends. For comparison, we also computed source strengths, S, that varied by year. Additional computations were carried out allowing source strengths to vary by season. Components of ambient concentrations outside the spatial domain (e.g., continental baseline concentrations) potentially contribute to observed ambient concentrations of some species. To estimate baseline concentrations, the source profile matrix, A, was initially modified to include additional columns (intercept terms) for unidentified emissions (row entries were 1 for a species whose baseline was to be estimated and 0 otherwise). First, calculations were carried out to test for baseline contributions of CO. Baseline CO concentrations averaged across sites ranged from 114 to 171 μg m−3 depending on year and averaged 135 μg m−3. To further evaluate baseline CO concentration, we determined monthly minimum daily average CO concentrations for all sites and months. The lowest monthly minima occurred in July or August at rural sites, ranging from 126 to 149 μg m−3 (averages of same-month minima at each site); the annual averages of site monthly minima ranged from 151 (OAK and CTR) to 173 μg m−3 (YRK) at rural sites. Baseline CO concentrations are expected to be relatively constant over the spatial scale of the SEARCH domain and for the time period studied and were therefore set at a fixed value of 140 μg m−3 for all sites and years as in Blanchard et al. (2012).3 CO baseline of ∼140 ± 50 μg m−3 (or about 120 ± 40 ppbv) compares favorably with average CO concentrations of ∼100−170 ppbv as reported from various sites that are distant from local source influences.60 After fixing the CO baseline concentration at 140 μg m−3, detectable baseline concentrations were not found for other species; however, this method may not be able to identify lower baseline concentrations of species such as NOy or OC. Equations 7 and 8 were then applied as written, where ambient

[PM 2.5]i = [SO4 ]*([SOx ]i /∑ [SOx ]) + [NO3] *([NOy ]i /∑ [NOy ]j ) + [EC]i + 1.4*[POC]i + [NH4]*([NHx]i / ∑ [NHx]j ) + [Al 2O3]i + [Fe2O3]i + [SiO2 ]i + [CaO]i + [K 2O]i (10)

where each summation is over the “j = 1 to p” source types. Equation 10 assumes that the relative contribution of each source to the condensed-phase species (e.g., SO4) is the same as its relative contribution to the combined gas-phase and condensed-phase species (e.g., SOx). Although this assumption could be inapplicable if a monitoring site is located closer to one source type than another, the monitoring data would reveal this situation. The scaling factor for primary [OM/OC] is assumed to be 1.4. The actual ambient OM/OC scaling factor is variable and depends on aerosol aging and oxidation products. Recent studies suggest that the average OM/OC scaling factor is higher than 1.4 for SEARCH data.61 OM/OC scaling factors may be higher than 1.4 in aged aerosols containing highly oxidized OC compounds. We assume that the multiplier for secondary organic matter (SOM) is 1.8 instead of 1.4. The total contribution to PM2.5 mass at each site includes the sum of the [PM2.5]i (eq 9), SOM, and “other”. “Other” was computed as Other = PM 2.5mass − {Σ i[PM 2.5]i + SOM}

(11)

Uncertainies for all source contributions were determined as σ 2 total = σ 2 inputs + σ 2 model

(12)

Prediction uncertainty depends on both the input uncertainties and on the formulation of the model. The input uncertainties (σ2inputs) were determined by propagation of effective variance errors (eq 9) through eq 10.55 Model formulation uncertainties (σ2model) were computed from the variances of the source strengths obtained by fitting the CMB model based on alternate choices of POC and baseline CO concentrations. 13540

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Predicted − Observed EC = α*Diesel EC + β*Area EC

RESULTS Model Evaluation. The computed source strengths (eq 3) are listed in Table 2, with further detail in Table S6. Variations among sites reflect differing degrees of source influence at different locations, with, for example, up to 1 order of magnitude higher mobile source influence at JST and BHM than at the rural sites. The uncertainties (σtotal) were 5−6% of the source strengths for point sources, 9−41% of the mobile gasoline, mobile diesel, and dust source strengths, and 20−36% of the area and ag-NH3 source strengths (Table S5). On a percentage basis, the lowest uncertainties were for point sources because SO2 emissions are measured well and σmodel is small compared to σinput. Higher uncertainties (20−33%) resulted for area source strengths due to their sensitivity to modeling parametrizations (e.g., background CO, POC). Uncertainties for dust sources are intermediate (9−22%) because the ambient measurements constrain the dust contributions even though dust emissions are not well quantified. Seasonal variation of source strengths is shown in Table S7. The ag-NH3 and dust source strengths were highest in summer, whereas mobile source strengths were highest in winter and autumn. When CMB fits were carried out with annual variation in both A and S (Table S8), the predicted concentrations more closely followed the year-to-year variations in measured concentrations (Figure S15). Because some of the interannual variation in measured concentrations may result from site-specific conditions or local pollution episodes, fitting the model by year de-emphasizes long-term regional trends and potentially introduces statistical artifacts. Since the ambient POC and SOC concentrations were computed from empirical calculations (eqs 5 and 6; Figure S4), we assessed the sensitivities of the source contributions to the POC-SOC computational procedure by altering the fractions of computed POC and SOC. For each site and year, POC was increased by 50% and decreased by 50% (maximum changes that did not yield negative concentrations of either POC or SOC), and the source contributions were recalculated (Table S9). The 50% decrease in computed POC resulted in 26−48% decreases in the area source contributions and 78−146% increases in SOM. Assumed increases in POC increased area source contributions and decreased SOM. The contributions of other sectors were less affected: the point sector source contributions changed by 1.7 to 3.0%, the diesel contributions changed by 1.8 to 11.6%, and the mobile gasoline contributions changed by 0.7 to 3.8% (Table S9). Thus, the principal uncertainty associated with the empirical estimation of POC and SOC is the split between area source contributions (largely biomass combustion) and SOC. The predicted and measured CO, SOx, NOy, and NHx concentrations generally agreed within uncertainties, whereas larger differences between predictions and measurements were observed for EC, POC, and MMO (Figures S5−S12). The model overpredicted EC at JST but underpredicted EC elsewhere and underpredicted POC at all sites. The model predicted TC better than either EC or POC (Figure S10), suggesting that the splits between EC and OC in the measurements and inventory were incommensurate. To test the discrepancies, we regressed the differences between observed and predicted EC and POC values against the computed source contributions, which yielded the following equations:

(13)

Predicted − Observed POC = α*Diesel POC + β*Area POC

(14)

The regressions were carried out using all site-years for each of the above equations. The resulting coefficients were α = 0.432 ± 0.042 and β = −1.855 ± 0.165 in eq 13 and α = −1.595 ± 0.238 and β = 0 (−0.019 ± 0.049) in eq 14. These results indicate that the agreement between predicted and observed EC and POC could be improved across sites and years by adjusting emissions by factors of 1-α and 1-β; the adjustments would reduce diesel EC emissions by 43% and increase diesel POC emissions and area-source EC emissions by factors of 2.6 and 2.9, respectively. Relative to the values used in the emission trend inventory, these changes would increase diesel TC (EC + POC) emissions by 1% and areasource TC emissions by 20%. Emission adjustments of this magnitude from the CMB analysis are consistent with the assumed one-sigma emission uncertainties of ±20% or greater. The proposed increase in area-source TC emissions is qualitatively consistent with the 2011 NEI, which reports ∼50% higher OC emissions than we have calculated by using the 2008 NEI as our base year. The 2011 NEI revisions may represent either higher emission factors or more fires. We did not adjust trace species, such as K, because they did not account for as much PM2.5 mass as EC and POC in eq 8. Uncertainties in the split between EC and OC in diesel and area-source emissions are expected. The changes implied by eqs 13 and 14 would increase the diesel emissions OC/EC ratio from 0.28:1 to 1.3:1 and would decrease the area-source OC/ EC ratio from 8.2:1 to 2.9:1. A diesel OC/EC ratio of 1.3:1 is consistent with values reported in the literature: 1.3:1 pre1992,62 0.6:1 heavy-duty and 0.9:1 light-duty from 1996 to 2000,63 0.97:1 pre-1998,64 0.64:1 light-duty and 1.1−1.3:1 medium and heavy-duty 1995−96 California,65 1.6:1 light-duty and 0.82−1.5:1 medium and heavy-duty according to SPECIATE profiles,23 and 2.9:1 in 2004 engines and 2.3:1 in 2007 engines. 66 OC/EC ratios reported for biomass combustion vary depending on type of vegetation, wetness, and stage of combustion, among other factors, and include the following: 0.6−2:1 dry and 15−130:1 wet,67 2.4−12.8:1 open burning,60 3−6:1 agricultural burn and 15:1 forest fire,23 and 8−56:1 with 13.8 ± 12.1 for samples from coastal plains.68 A wide range of values for biomass carbon emissions appears to be possible so that values derived from multiple observations near local and regional fires are most appropriate for testing emission inventory estimates. Lower OC/EC ratios appear more characteristic of controlled than of uncontrolled burns. The agreement between predicted and observed concentrations improves after implementing eqs 13 and 14 (Figures S13− S14). For MMO (Figure S12), Al, Ca, Fe, K, and Si, the model predicts averages but not year-to-year variations. Improved agreement between measured and predicted MMO in toto was found than for the individual elements comprising MMO. This result suggests the additional possibility that the SPECIATE soil source profiles are not representative of crustal material in the study region. Paved road dust exhibits compositional variations among Atlanta, Birmingham, and other cities.69 Source Apportionment. Declining ambient SOx concentrations were consistent with emission reductions, mainly by 13541

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Figure 1. Trends in source contributions to annual-average PM2.5 mass concentrations at the SEARCH JST site. “Other” is the difference between measured PM2.5 mass and the sum of source contributions and includes unmeasured PM2.5 species and particle-bound water in addition to prediction error. The computation implements the adjustment to mobile diesel and area source EC and OC emissions (eqs 13 and 14). Estimated uncertainties are equivalent on a percent basis to the uncertainties in source strengths listed in Table S6.

EGUs, as previously reported.2,70 Declining ambient concentrations of NOy were due to decreasing contributions from mobile diesel, mobile gasoline, and point sources (Figure S6). From 2000 to 2011, the on-road mobile-source contributions to NOy decreased by 41 and 49 μg m−3 at BHM and JST, respectively, and by 3−15 μg m−3 at other sites. The pointsource contributions to NOy decreased by 1.6−3.8 μg m−3 at urban sites and by 1.5−2.7 μg m−3 at nonurban sites. Declining mobile gasoline contributions led to decreasing mean annual aNMOC and CO concentrations at urban sites (Figure S7). Because the JST NMOC composition is similar to NMOC composition at other locations where motor vehicle emissions dominate, but with higher fractions of ethane and propane,48 emissions from commercial and residential use of natural gas or natural-gas fueled vehicles may have affected the observed JST aNMOC trend. The observed NMOC and aNMOC trends at YRK and other Atlanta-area PAMS sites exhibit little or no trend.48 From 2000 to 2011, point source emission reductions yielded reductions of mean annual PM2.5 mass concentrations of 2 to 3 μg m−3 at inland sites and 1−2 μg m−3 at coastal sites (Figures 1 and S16; Table S10). Mobile source emission reductions yielded additional reductions of 1−3 μg m−3 in mean annual PM2.5 mass concentrations. Area-source contributions declined 0.3−0.5 μg m−3 due to declining emissions from area sources other than fires (Figure S2). SOM contributions did not exhibit trends (Figure 1).

concentrations were for EC and POC, which can be rationalized by the proposed adjustments. With the exception of point sources (for SO2) and agriculture (for NH3), higher emissions were distributed among several sources of interest. All categories of sources, both local and domain-wide, influence both urban and rural sites. As reported for other regions, CO and VOC are by far the most abundant pollutants (by mass) in the SEARCH domain. Anthropogenic VOC is about 25% of total VOC emissions and primary PM2.5 emissions are about 12% of the gases by mass. Emissions from open burning, vegetation and soils, agricultural activities, and dust remained relatively constant from 2000 to 2011. Fire emissions of CO and PM2.5 were sustained. Most of the PM2.5 mass from fires is carbonaceous, if OC is stated as OM. Application of the regression model for separating POC and SOC gives a reasonably “realistic” apportionment of OC among sources. SOM (1.8*SOC) annually averages about 2−3 μg m−3, an important but limited fraction of mean annual PM2.5 mass concentrations ranging from 8−18 μg m−3 during 2000−2011. While the apportionment indicates that mean annual POC concentrations exceed mean SOC concentrations, SOC may exceed POC concentrations at rural sites during the warmest months due to increased seasonal emissions and more active atmospheric oxidation chemistry. SOC mass concentration is relatively constant across urban and rural sites, suggesting predominance of natural sources of condensed material. Our method does not compute SOC concentrations directly from VOC emissions or ambient VOC concentrations, but uncertainties in this computation largely affect the calculated split between area-source contributions (dominated by fire) and SOC. The multipliers of POC and SOC for primary organic particulate matter (POM) and SOM remain subjective, with further study needed. Our approach is suited to long-term averages, because emissions seldom are available for fine temporal resolution.

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DISCUSSION The NEIs with additional information enabled us to estimate annual emissions by source sector, accounting for changes in methodology, especially for emissions from motor vehicles and open burning. The CMB results were generally consistent with the emission trends and showed a decline in ambient pollutant concentrations coinciding with emission reductions. The principal inconsistencies between predicted and measured 13542

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(7) Edgerton, E. S.; Hartsell, B. E.; Saylor, R. D.; Jansen, J. J.; Hansen, D. A.; Hidy, G. M. The Southeastern Aerosol Research and Characterization Study: Part 2 − filter-based measurements of PM2.5 and PMcoarse mass and composition. J. Air Waste Manage. Assoc. 2005, 55, 1527−1542. (8) Edgerton, E. S.; Hartsell, B. E.; Saylor, R. D.; Jansen, J. J.; Hansen, D. A.; Hidy, G. M. The Southeastern Aerosol Research and Characterization Study, part 3: continuous measurements of fine particulate matter mass and composition. J. Air Waste Manage. Assoc. 2006, 56, 1325−1341. (9) Edgerton, E. S.; Saylor, R. D.; Hartsell, B. E.; Jansen, J. J.; Hansen, D. A. Ammonia and ammonium measurements from the southeastern United States. Atmos. Environ. 2007, 41, 3339−3351. (10) Edgerton, E. S.; Casuccio, G. S.; Saylor, R. D.; Lersch, T. L.; Hartsell, B. E.; Jansen, J. J.; Hansen, D. A. Measurements of OC and EC in coarse particulate matter in the southeastern United States. J. Air Waste Manage. Assoc. 2009, 59, 78−90. (11) Saylor, R.; Edgerton, E. S.; Hartsell, B. E.; Baumann, K.; Hansen, D. A. Continuous gaseous and total ammonia measurements from the southeastern aerosol research and characterization (SEARCH) study. Atmos. Environ. 2010, 44, 4994−5004. (12) Blanchard, C. L.; Hidy, G. M.; Tanenbaum, S. NMOC, ozone, and organic aerosol in the southeastern states, 1999−2007: 1. Spatial and temporal variations of NMOC concentrations and composition in Atlanta, Georgia. Atmos. Environ. 2010, 44, 4827−4839, DOI: 10.1016/j.atmosenv.2010.08.036. (13) Chow, J. C.; Watson, J. G.; Chen, L.-W. A.; Chang, M. C. O.; Robinson, N. F.; Trimble, D. L.; Kohl, S. D. The IMPROVE_A temperature protocol for thermal/optical carbon analysis: Maintaining consistency with a long-term database. J. Air Waste Manage. Assoc. 2007, 57 (9), 1014−1023. (14) Chow, J. C.; Watson, J. G.; Robles, J.; Wang, X. L.; Chen, L.-W. A.; Trimble, D. L.; Kohl, S. D.; Tropp, R. J.; Fung, K. K. Quality assurance and quality control for thermal/optical analysis of aerosol samples for organic and elemental carbon. Anal. Bioanal. Chem. 2011, 401 (10), 3141−3152, DOI: 10.1007/s00216-011-5103-3. (15) Blanchard, C. L.; Tanenbaum, S.; Hidy, G. M. The Southeastern Aerosol Research and Characterization (SEARCH) study: spatial variations and chemical climatology, 1999−2010. J. Air Waste Manage. Assoc. 2013, 63, 260−275, DOI: 10.1080/10962247.2012.749816. (16) U.S. Environmental Protection Agency (EPA) Criteria Pollutant Emissions Summary Files website. http://www.epa.gov/ttn/chief/net/ 2002inventory.html (accessed May 1, 2008). (17) U.S. Environmental Protection Agency (EPA) Criteria Pollutant Emissions Summary Files website. http://www.epa.gov/ttn/chief/net/ 2005inventory.html (accessed April 28, 2009); ftp://ftp.epa.gov/ EmisInventory/2005_nei/tier_summaries/tier_05v2/ (accessed May 21, 2010). (18) U.S. Environmental Protection Agency (EPA) Criteria Pollutant Emissions Summary Files website. http://www.epa.gov/ttn/chief/net/ 2008inventory.html (accessed April 9, 2011, June 1, 2011, February 23, 2012, October 29, 2012, June 14, 2013). (19) U.S. Environmental Protection Agency (EPA) 2011 National Emissions Inventory Data. http://www.epa.gov/ttn/chief/net/ 2011inventory.html (accessed October 1, 2013). (20) U.S. Environmental Protection Agency (EPA) Continuous Emissions Monitoring Fact Sheet website. http://www.epa.gov/ airmarkets/emissions/continuous-factsheet.html; http:// camddataandmaps.epa.gov/gdm/index.cfm?fuseaction=emissions. wizard (accessed May 21, 2010). (21) Reff, A.; Bhave, P.; Simon, H.; Pace, T. S.; Pouliot, G. A.; Mobley, D.; Houyoux, M. Emissions inventory of PM2.5 trace elements across the United States. Environ. Sci. Technol. 2009, 43, 5790−5796. (22) Chow, J. C.; Watson, J. G.; Lowenthal, D. H.; Chen, L. A.; Motallebi, N. PM2.5 source profiles for black and organic carbon emission inventories. Atmos. Environ. 2011, 45, 5407−5414. (23) U.S. Environmental Protection Agency (EPA) SPECIATE website. http://www.epa.gov/ttn/chief/software/speciate/ (accessed August 21, 2012).

Annual apportionment of ambient trends to emission sector trends typically suffices for accountability assessment. For other applications (e.g., epidemiology), finer temporal resolution is needed. Ensemble approaches can provide daily resolution source apportionments that minimize or eliminate nonpositive source impacts and capture day-to-day variability.71,72 Ensemble-trained CMB source profiles potentially provide a means for improving the local representativeness or the temporal resolution of our emission-based profiles.

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ASSOCIATED CONTENT

S Supporting Information *

Summary of information used for estimating annual emissions, annual emissions, comparison of data from emission inventories, computed source strengths and uncertainties, changes in computed source contributions to PM2.5 mass concentrations resulting from changes in calculated ambient POC and SOC concentrations, mean monthly calculated POC and SOC concentrations, and apportionment of species trends from CMB model predictions compared with observed mean annual concentrations. This material is available free of charge via the Internet at http://pubs.acs.org.

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AUTHOR INFORMATION

Corresponding Author

*Phone (510) 525-6231. E-mail: [email protected]. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS This study was sponsored by Southern Company. Eric Edgerton and colleagues at Atmospheric Research and Analysis, Inc. made the measurements, verified the data, and provided data sets. We thank John Jansen, Stephanie Shaw, and three anonymous reviewers for insightful review and discussion, John Watson, Roger McClellan, L.-W. Anthony Chen, and Allen Schaeffer for advice on emissions and emission inventories, and Jia Xing for providing alternative emission trends data.

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REFERENCES

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dx.doi.org/10.1021/es402876s | Environ. Sci. Technol. 2013, 47, 13536−13545