Article pubs.acs.org/est
Source Contributions to Atmospheric Gases and Particulate Matter in the Southeastern United States Charles L. Blanchard,* Shelley Tanenbaum, and George M. Hidy Envair, Albany, California 94706, United States S Supporting Information *
ABSTRACT: A new approach for determining the contributions of emission sources to 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 approach apportions combined gasphase and condensed-phase concentrations of individual compounds as well as PM2.5 mass. Because the NEI is used to provide source emission profiles for CMB analysis, the method generates information on the consistency of the NEI with ambient monitoring data. The method also tracks secondary species to primary source emissions, permitting a more complete accounting of the impact of aggregated source types on PM2.5 mass concentrations. An example application is presented using four years of monitoring data collected at eight sites in the Southeastern Aerosol Research and Characterization (SEARCH) network. Including both primary and secondary species, area sources contributed 2.0−3.7 μg m−3 (13−26%), point sources contributed 3.0−4.6 μg m−3 (22−33%), and mobile sources contributed 1.0−6.0 μg m−3 (9−42%) to mean PM2.5 mass concentrations. Whereas the NEI generally accounts for the ambient concentrations of gases and particles, certain anomalies are identified, especially related to carbonaceous compounds and dust.
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matrix factorization (PMF).8,9 PMF reconstructs factor contributions, often interpreted subjectively as source contributions, based on correlations among measured species. PMF does not require source profiles and is potentially capable of identifying previously unknown emission sources, chemical and physical processes, or measurement issues. However, correlations among species arise from atmospheric processes rendering PMF results difficult to interpret as specific source contributions. Receptor models can project future source contributions only by assuming proportional responses to emission changes and constant meteorological conditions. The southeastern U.S. is of interest because of the complexities of sources in the region and meteorological conditions that foster pollution accumulation associated with air mass stagnation, often on a scale covering several states. Large, relatively isolated cities, including Atlanta, Georgia, and Birmingham, Alabama, experience elevated concentrations of pollutants. Air pollution concentrations reported in the region
INTRODUCTION Quantitative source contributions to ambient concentrations of air pollutants (source apportionment) are needed for effective management of air pollution. Source apportionment methods include dispersion and receptor modeling, having complementary strengths and limitations. Dispersion models are based on fundamental physical and chemical processes, in which emissions and meteorological data are used to predict ambient air pollutant concentrations.1,2 Source contributions can be tracked from emissions to receptors and predictions of future concentration can be obtained by altering the emissions data. Extensive input data and computational capability are needed. Model performance evaluation is necessary to establish consistency between model predictions and ambient observations. Receptor models quantify emission contributions to pollutant concentrations by using measured ambient air pollutant concentrations at a receptor site.3 The chemicalmass balance (CMB) methodology4,5 and CMB with organic molecular markers (CMB-MM)6 are deterministic models that reconstruct observed concentrations from a linear combination of emission source profiles subject to assumptions.7 CMB inferences of source contributions are limited to sources whose emission profiles have been included in the model. Alternate receptor models apply statistical approaches, such as positive © 2012 American Chemical Society
Received: Revised: Accepted: Published: 5479
October 7, 2011 March 20, 2012 April 4, 2012 April 4, 2012 dx.doi.org/10.1021/es203568t | Environ. Sci. Technol. 2012, 46, 5479−5488
Environmental Science & Technology
Article
Table 1. NEI 2005 Emissions of Primary Pollutants in AL, NW FL, GA, and MS by Source Category (Thousand Metric Tons Per Year)a category b
point mobile dieselc mobile gasd areae dustf Ag−NH3g sum
CO
NH3
NOx
SO2
VOC
PM2.5
MMOh
EC
OC
TC
fTC
mTC
288 156 4295 1805 0 0 6543
11 1 20 22 0 182 236
495 419 260 58 0 0 1231
1,338 33 4 24 0 0 1400
118 29 429 839 0 0 1415
116 19 6 194 110 3 448
33 6 1 7 67 0 115
4 13 1 12 0 0 30
8 3 3 93 6 1 114
12 16 4 105 6 1 144
9 16 4 0 5 0 35
3 0 0 104 1 1 109
a
Emissions of primary MMO, EC, OC, TC, fossil TC (fTC), and modern TC (mTC) were calculated from PM2.5 emissions and U.S. EPA SPECIATE profiles as described in the text. bTier 1 categories 1 through 7: EGU fuel, industrial fuel, other fuel (excluding residential fuel), chemical, metals, petroleum, other industrial processes. cTier 1 categories for on-road and nonroad mobile, restricted to Tier 3 subcategories identified as diesel or marine residual oil. dTier 1 categories for on-road and nonroad mobile, restricted to Tier 3 subcategories identified as gasoline or aviation jet fuel. eTier 1 categories for solvent use, storage and transport, waste combustion, and miscellaneous, excluding Tier 3 categories identified as paved and unpaved road dust, agricultural operations, livestock production, and fertilizer use. Includes biomass combustion, slash combustion, residential fuel use. fTier 1 category for miscellaneous, restricted to Tier 3 categories identified as paved and unpaved road dust, agricultural operations. gTier 1 category for miscellaneous, restricted to Tier 3 categories identified as livestock production, and fertilizer use. hMMO = Al2O3 + SiO2 + K2O + CaO + TiO2 + Fe2O3. Within MMO, emissions of K as K2O are 6, 2, and 1 thousand metric tons for area, dust, and point sources, respectively (0.01 to 0.08 thousand metric tons for other categories).
extension of the AL-GA border to the Gulf of Mexico) were retained along with AL, GA, and MS data. Electric generating unit (EGU) emissions data are available from continuous emissions monitoring (CEM) at facilities subject to EPA programs.12 CEM data were obtained for all facilities located in AL, FL, GA, and MS and aggregated by year. The 2005 CEM data were 0.8% higher and 1.4% lower than the 2005 NEI estimates of EGU SO2 and NOx emissions respectively within the four states indicating consistency between CEM and NEI values. Fuel-based estimates of mobile-source emissions are 3% and 6% higher respectively than the 2005 NEI mobile-source NOx and PM2.5 emission estimates.13 The fuel-based estimates indicate that 27% of mobile-source NOx is emitted by gasoline vehicles compared with 37% in the NEI.13 The SCC category “Open Fires” potentially double-counts emissions included in other source categories.14 “Open Fires” appears within “Miscellaneous” (Tier 1 category 14), “Other combustion” (Tier 2 category 2), “Other” (Tier 3 category 99) and accounts for 17% of U.S. national PM2.5 emissions. Because PM2.5 speciation information is not included in the NEI, primary 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.14,15 Current versions of 84 composite profiles15 were obtained from EPA’s SPECIATE 4.2 database.16 An EPA mapping of the 84 source category profiles to 3497 SCCs15,16 was acquired and the 84 source profiles were mapped to the 247 Tier 3 categories consistent with the mapping to the SCC level. Some profiles were applied to multiple categories, for example, the inorganic chemical manufacturing profile (number 92039) was applied to all three Tier 3 inorganic chemical manufacturing categories.15 The largest PM2.5 sources lacking speciation profiles are offroad diesel-powered mobile equipment, road construction dust, marine vessels, gasoline-powered boats, and railroad locomotives; for nonroad diesel emissions, the recommended substitution is the heavy-duty diesel vehicle (HDDV) exhaust profile (92035).15 The “prescribed burning” profile (92059) was used to represent “Open Fires”.14 Particulate total carbon (TC) emissions from each of the 84 source profiles were identified as predominantly fossil or modern in origin based on engineering judgment. For example, mobile-source emissions
often approach or exceed applicable U.S. Environmental Protection Agency (EPA) standards. Receptor models have previously been used to apportion the mass of particulate matter less than 2.5 μm aerodynamic diameter (PM2.5) at sites in the southeastern U.S. and elsewhere. This article develops a new approach to estimating emission source contributions to PM2.5 and gases by extending the CMB methodology and applying it to four years of monitoring data from eight sites in the Southeastern Aerosol Research and Characterization (SEARCH) network. The approach apportions PM2.5 mass and gases plus their reaction products, for example NOy, SOx (SO2 plus sulfate aerosol [SO4]), NHx (ammonia [NH3] plus ammonium aerosol [NH4]), and CO. A second new feature is the use of the EPA National Emission Inventory (NEI) to provide emission source profiles. As a consequence, the method generates information on the consistency of the NEI with ambient monitoring data. The source apportionments are expressed in relation to NEI source categories, making the information directly relevant to air quality management. The method is general and is applicable to data from any well-instrumented air-monitoring site.
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DATA Emissions. The EPA NEI provides annual-average emissions of criteria pollutants [CO, SO2, oxides of nitrogen (NOx), PM2.5 mass, volatile organic compounds (VOC)] plus NH3. Volatile organic compound refers to compounds having 1 to 12 carbon atoms (C1−C12) and a vapor pressure greater than 0.15 mmHg.10 Emissions are summarized at county, state, and national levels, and at four levels of source specificity: Tier 1 (13 source categories), Tier 2 (59 categories), Tier 3 (247 categories), and source classification codes (SCC, 4270 categories). The degree of source aggregation increases from SCC to Tier 3 to Tier 1, but all emissions are included at each level of aggregation. The inventory used here is the 2005 NEI version 2, release date March 11, 2009.11 Tier 3 emission estimates were obtained for all counties in AL, FL, GA, and MS, and checked against EPA’s state-level summaries. Because the two SEARCH monitoring sites in FL are located in its westernmost county, 2005 NEI Tier 3 emission estimates from the western 12 FL counties (approximately, the area west of an 5480
dx.doi.org/10.1021/es203568t | Environ. Sci. Technol. 2012, 46, 5479−5488
Environmental Science & Technology
Article
Table 2. Mean Mass Concentrations by Site Determined from 2004−2007 SEARCH Data (μg m−3).a site
CO
SO2
SOxb
NOyc
NO3
NH3
NH4d
NHxe
Cf
aCg
OC
EC
MMOh,i
BHM CTR GFP JST OAK OLF PNS YRK S.E.j
548.3 199.0 301.5 577.1 226.1 247.8 352.1 244.9 8.5
14.3 5.4 4.8 12.7 4.0 5.9 9.5 8.4 0.4
17.3 7.4 6.8 15.7 6.2 8.2 11.6 10.9 0.7
68.9 7.9 21.8 86.4 5.7 13.7 26.7 13.0 1.2
0.76 0.31 0.43 0.82 0.31 0.37 0.43 0.75 0.02
2.1 0.3 0.6 1.2 0.2 0.4 0.7 2.2 0.1
1.5 1.1 1.0 1.6 1.0 1.0 1.1 1.6 0.03
3.6 1.4 1.6 2.8 1.2 1.4 1.7 3.8 0.1
na na na 109.7 na na na 88.3 6.6
na na na 71.3 na na na 15.0 2.3
4.6 3.1 2.3 4.3 2.8 2.6 2.5 3.3 0.1
1.9 0.6 0.7 1.4 0.6 0.7 0.7 0.6 0.04
1.1 0.4 0.4 0.5 0.4 0.4 0.5 0.3
