Fine particulate matter source apportionment for the Chemical Speciation Trends Network site at Birmingham, Alabama, using Positive Matrix Factorization.

ABSTRACT

The Positive Matrix Factorization (PMF) receptor model version 1.1 was used with data from the fine particulate matter ([PM.sub.2.5]) Chemical Speciation Trends Network (STN) to estimate source contributions to ambient [PM.sub.2.5] in a highly industrialized urban setting in the southeastern United States. Model results consistently resolved 10 factors that are interpreted as two secondary, five industrial, one motor vehicle, one road dust, and one biomass burning sources. The STN dataset is generally not corrected for field blank levels, which are significant in the case of organic carbon (OC). Estimation of primary OC using the elemental carbon (EC) tracer method applied on a seasonal basis significantly improved the model's performance. Uniform increase of input data uncertainty and exclusion of a few outlier samples (associated with high potassium) further improved the model results. However, it was found that most PMF factors did not cleanly represent single source types and instead are "contaminated" by other sources, a situation that might be improved by controlling rotational ambiguity within the model. Secondary particulate matter formed by atmospheric processes, such as sulfate and secondary OC, contribute the majority of ambient [PM.sub.2.5] and exhibit strong seasonality (37 [+ or -] 10% winter vs. 55 [+ or -] 16% summer average). Motor vehicle emissions constitute the biggest primary [PM.sub.2.5] mass contribution with almost 25 [+ or -] 2% long-term average and winter maximum of 29 [+ or -] 11%. [PM.sub.2.5] contributions from the five identified industrial sources vary little with season and average 14 [+ or -] 1.3%. In summary, this study demonstrates the utility of the EC tracer method to effectively blank-correct the OC concentrations in the STN dataset. In addition, examination of the effect of input uncertainty estimates on model results indicates that the estimated uncertainties currently being provided with the STN data may be somewhat lower than the levels needed for optimum modeling results.

INTRODUCTION

Epidemiological studies suggest that ambient particulate matter (PM) has significant associations with adverse respiratory and cardiovascular health effects, (1-5) which prompted the U.S. Environmental Protection Agency (EPA) to promulgate National Ambient Air Quality Standards (NAAQS) in July 1997. The majority of the earlier epidemiological studies focused on linking human exposures to the mass, chemical components, and size of PM. More recent studies have been conducted to understand associations between PM emission sources and human exposure. (6-9) To provide nationally consistent data for the assessment of trends, EPA also established, in association with the PM NAAQS, the fine PM ([PM.sub.2.5]) Chemical Speciation Trends Network (STN) program. (10,11) In this program, 24-hr integrated filter-based samples are collected every 3 or 6 days at each monitoring site and analyzed to determine gravimetric mass and chemical composition, including ions, trace elements, and carbonaceous fractions (i.e., organic carbon [OC] and elemental carbon [EC]).

Various [PM.sub.2.5] source apportionment studies have been conducted to understand the sources and contributions of [PM.sub.2.5] in the southeastern United States. (12-20) These studies demonstrate the applicability of different source apportionment methods and models to testing spatial and temporal representativeness of model results, which supports efforts to develop [PM.sub.2.5] management strategies with greater promise of success for different locations in the southeastern United States. Most of these studies applied the Chemical Mass Balance (CMB) and Positive Matrix Factorization (PMF) models, supported by other observation-driven statistical manipulations (species ratios, normalizations, specific marker additions, and trajectories) to assess or improve the mathematical performance of the models. A few recent studies also compared source apportionment results from different receptor models applied to the same dataset, demonstrating that the resulting differences are due to the inherent uncertainty of the multivariate approach. (21-23) Hence, the estimation of the absolute uncertainty of the receptor model results remains difficult, especially with respect to the limited spatial representativeness of the results across large urban areas (possibly due to the influence of different local sources). A recent focus on the use of PMF for [PM.sub.2.5] source apportionment called for more thorough and transparent documentation to help develop standard protocols and procedures necessary to guide the user along the various decisions in the process, to create results that are consistent and reproducible, and to indicate where future research is needed to improve the model's general applicability. (24) In this paper, we follow this call closely by providing detailed information on the reasoning, sensitivities, and effects of certain decisions made.

The main goal of this study was to conduct source apportionment of [PM.sub.2.5] using a large dataset from a highly industrialized urban STN site. The North Birmingham site (designated NBHM from here on) has a large amount of heavy producing industry, which is not typical for most other urban agglomerations in the southeastern United States (which are dominated by transportation and business services), and it was chosen partly for that reason. The NBHM site was also chosen because (1) it is in an EPA-designated nonattainment area for [PM.sub.2.5]; (2) it was part of some of the above-mentioned investigations, and so provides a certain history of [PM.sub.2.5] characterization; (3) it was the subject of a detailed "evidence-building" study that investigated source impacts from strictly observational concentration ratio analyses; (25) (4) it is sampled at a higher frequency (every third day instead of every sixth), which produces a larger dataset resulting in increased statistical robustness; and (5) it employs the MetOne SASS sampler, which is the most widely used sampler in the STN.

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The 3-yr average (2002-2004) of the annual average [PM.sub.2.5] mass concentration is 17.5 [micro]g x [m.sup.-3] for NHBM, (25) which clearly exceeds the current NAAQS for [PM.sub.2.5] of 15 [micro]g x [m.sup.-3] and therefore forces the state of Alabama to develop a state implementation plan (SIP) for effective reduction of ambient [PM.sub.2.5] concentrations. The SIP must be submitted to EPA by April 2008. Demonstrating the feasibility and utility of STN data, the PMF receptor model (26) is applied here in a systematic, comparative way to examine issues such as OC blank correction (here indirect and retroactive), uncertainty, and outlier treatment, as well as to build confidence in its results, help identify local primary sources, and estimate their contributions to the observed ambient [PM.sub.2.5] with acceptable uncertainty.

METHOD AND APPROACH

[PM.sub.2.5] filter samples integrated over 24-hr (12:00 p.m. to 12:00 p.m.) have been collected at the NBHM site every third day since January 2001. The NBHM site (Aerometric Information Retrieval System [AIRS] code 010730023, latitude 33.553056, longitude 86.814900) is located in North Birmingham at 3009 28th Street, Birmingham, AL 35207, which is in Jefferson County, and is categorized as being in an urban and center city setting. Figure 1 and Table A-1 (Appendixes Table A-1 through A-4 and Figure 1-A are provided as supplemental material available online only at http://secure.awma.org/journal/pdfs/2008/1/10.3155-1047-3289.58.1.27_supplmaterial.pdf) provide a combined overview of the site's location and the different emission sources surrounding it. Three steel pipe manufacturers; two large coking operations; a mineral wool plant; an asphalt batching plant; and fugitive dust sources from coal and coke storage yards, limestone quarries, and metal fabricating operations dominate the industrial activities within 10 km of the site. (25) The site is also near three major interstate highways, two large state highways, local roads with heavy traffic, significant railroad traffic with diesel locomotives serving the nearby manufacturing plants, and the Birmingham International Airport (BHM) approximately 5 km to the east-northeast. BHM is Alabama's largest airport, serving more than 3 million travelers annually with more than 160 arrivals and departures daily, most between 6:00 a.m. and 6:00 p.m.

The [PM.sub.2.5] samples were collected on Teflon, nylon, and quartz fiber filters at 6.7 L/min downstream from individual sharp cut cyclones (SCCs) of a SASS aerosol speciation sampler (MetOne Instruments). Gravimetric and elemental mass concentrations were determined from the Teflon filters, with a total of 48 elements measured via energy-dispersive X-ray fluorescence (XRF). The nylon filters were used for the analysis of cations (ammonium [[NH.sub.4.sup.+]], sodium [N[a.sup.+]], and potassium [[K.sup.+]]) and anions (sulfate [S[O.sub.4.sup.2-]] and nitrate [N[O.sub.3.sup.-]]) by ion chromatography. The quartz fiber filters were used for OC and EC applying the thermal optical transmittance (TOT) method. (27)

Initial Data Screening