Al-Naiema, Ibrahim M.Hettiyadura, Anusha P.S.Wallace, Henry W.Sanchez, Nancy P.Madler, Carter J.Cevik, Basak KarakurtBui, Alexander A.T.Kettler, JoshGriffin, Robert J.Stone, Elizabeth A.2018-11-192018-11-192018Al-Naiema, Ibrahim M., Hettiyadura, Anusha P.S., Wallace, Henry W., et al.. "Source apportionment of fine particulate matter in Houston, Texas: insights to secondary organic aerosols." <i>Atmospheric Chemistry and Physics,</i> 18, (2018) Copernicus Publications: 15601-15622. https://doi.org/10.5194/acp-18-15601-2018.https://hdl.handle.net/1911/103356Online and offline measurements of ambient particulate matter (PM) near the urban and industrial Houston Ship Channel in Houston, Texas, USA, during May 2015 were utilized to characterize its chemical composition and to evaluate the relative contributions of primary, secondary, biogenic, and anthropogenic sources. Aerosol mass spectrometry (AMS) on nonrefractory PM1 (PM  ≤  1µm) indicated major contributions from sulfate (averaging 50% by mass), organic aerosol (OA, 40%), and ammonium (14%). Positive matrix factorization (PMF) of AMS data categorized OA on average as 22% hydrocarbon-like organic aerosol (HOA), 29% cooking-influenced less-oxidized oxygenated organic aerosol (CI-LO-OOA), and 48% more-oxidized oxygenated organic aerosol (MO-OOA), with the latter two sources indicative of secondary organic aerosol (SOA). Chemical analysis of PM2.5 (PM  ≤  2.5µm) filter samples agreed that organic matter (35%) and sulfate (21%) were the most abundant components. Organic speciation of PM2.5 organic carbon (OC) focused on molecular markers of primary sources and SOA tracers derived from biogenic and anthropogenic volatile organic compounds (VOCs). The sources of PM2.5 OC were estimated using molecular marker-based positive matric factorization (MM-PMF) and chemical mass balance (CMB) models. MM-PMF resolved nine factors that were identified as diesel engines (11.5%), gasoline engines (24.3%), nontailpipe vehicle emissions (11.1%), ship emissions (2.2%), cooking (1.0%), biomass burning (BB, 10.6%), isoprene SOA (11.0%), high-NOx anthropogenic SOA (6.6%), and low-NOx anthropogenic SOA (21.7%). Using available source profiles, CMB apportioned 41% of OC to primary fossil sources (gasoline engines, diesel engines, and ship emissions), 5% to BB, 15% to SOA (including 7.4% biogenic and 7.6% anthropogenic), and 39% to other sources that were not included in the model and are expected to be secondary. This study presents the first application of in situ AMS-PMF, MM-PMF, and CMB for OC source apportionment and the integration of these methods to evaluate the relative roles of biogenic, anthropogenic, and BB-SOA. The three source apportionment models agreed that  ∼ 50% of OC is associated with primary emissions from fossil fuel use, particularly motor vehicles. Differences among the models reflect their ability to resolve sources based upon the input chemical measurements, with molecular marker-based methods providing greater source specificity and resolution for minor sources. By combining results from MM-PMF and CMB, BB was estimated to contribute 11% of OC, with 5% primary emissions and 6% BB-SOA. SOA was dominantly anthropogenic (28%) rather than biogenic (11%) or BB-derived. The three-model approach demonstrates significant contributions of anthropogenic SOA to fine PM. More broadly, the findings and methodologies presented herein can be used to advance local and regional understanding of anthropogenic contributions to SOA.engThis work is distributed under the Creative Commons Attribution 4.0 License.Journal articlehttps://doi.org/10.5194/acp-18-15601-2018