Source apportionment of 1 h semi-continuous data during the 2005 Study of Organic Aerosols in Riverside (SOAR) using positive matrix factorization

Abstract Positive matrix factorization (PMF2) was used to elucidate sources of fine particulate material (PM2.5) for a study conducted during July and August 2005, in Riverside, CA. One-hour averaged semi-continuous measurements were made with a suite of instruments to provide PM2.5 mass and chemical composition data. Total PM2.5 mass concentrations (non-volatile plus semi-volatile) were measured with an R&P filter dynamic measurement system (FDMS TEOM) and a conventional TEOM monitor was used to measure non-volatile mass concentrations. PM2.5 chemical species monitors included a dual-oven Sunset monitor to measure both non-volatile and semi-volatile carbonaceous material, an ion chromatographic-based monitor to measure sulfate and nitrate and an Anderson Aethalometer to measure black carbon (BC). Gas phase data including CO, NO2, NOx and O3 were also collected during the sampling period. In addition, single-particle measurements were made using aerosol time-of-flight mass spectrometry (ATOFMS). Twenty different single-particle types consistent with those observed in previous ATOFMS studies in Riverside were identified for the PMF2 analysis. Finally, time-of-flight aerosol mass spectrometry (ToF-AMS) provided data on markers of primary and secondary organic aerosol. Two distinct PMF2 analyses were performed. In analysis 1, all the data except for the ATOFMS and ToF-AMS data were used in an initial evaluation of sources at Riverside during the study. PMF2 was able to identify six factors from the data set corresponding to both primary and secondary sources, primarily from automobile emissions, diesel emissions, secondary nitrate formation, a secondary photochemical associated source, organic emissions and Basin transported pollutants. In analysis 2, the ATOFMS and ToF-AMS data were included in the analysis. In the second analysis, PMF2 was able to identify 16 factors with a variety of both primary and secondary factors being identified, corresponding to both primary and secondary material from both anthropogenic and natural sources. Based on relationships with Basin meteorology, the PMF identified source profiles and diurnal patterns in the source concentrations, sources were identified as being of local origin or resulting from transport of pollutants across the Basin due to onshore flow. Good agreement was observed between the PMF2 predicted mass and the FDMS measured mass for both analyses.

[1]  B. Morrical,et al.  Real-Time Analysis of Individual Atmospheric Aerosol Particles: Design and Performance of a Portable ATOFMS , 1997 .

[2]  Semi-continuous mass closure of the major components of fine particulate matter in Riverside, CA , 2008 .

[3]  K. Prather,et al.  Single particle characterization of ultrafine and accumulation mode particles from heavy duty diesel vehicles using aerosol time-of-flight mass spectrometry. , 2006, Environmental science & technology.

[4]  M. Molina,et al.  Secondary organic aerosol formation from anthropogenic air pollution: Rapid and higher than expected , 2006 .

[5]  K. Prather,et al.  Formation of aerosol particles from reactions of secondary and tertiary alkylamines: characterization by aerosol time-of-flight mass spectrometry. , 2001, Environmental science & technology.

[6]  D. Eatough,et al.  Comparison of Speciation Sampler and PC-BOSS Fine Particulate Matter Organic Material Results Obtained in Lindon, Utah, during Winter 2001–2002 , 2008, Journal of the Air & Waste Management Association.

[7]  D. Eatough,et al.  Source Apportionment of One-Hour Semi-Continuous Data Using Positive Matrix Factorization with Total Mass (Nonvolatile plus Semi-Volatile) Measured by the R&P FDMS Monitor , 2008 .

[8]  Harvey Patashnick,et al.  Continuous PM-10 Measurements Using the Tapered Element Oscillating Microbalance , 1990 .

[9]  Measurement of Fine Particulate Matter Nonvolatile and Semi-Volatile Organic Material with the Sunset Laboratory Carbon Aerosol Monitor , 2008, Journal of the Air & Waste Management Association.

[10]  D. R. Worsnop,et al.  Hydrocarbon-like and oxygenated organic aerosols in Pittsburgh: insights into sources and processes of organic aerosols , 2005 .

[11]  Barbara J. Turpin,et al.  Species Contributions to PM2.5 Mass Concentrations: Revisiting Common Assumptions for Estimating Organic Mass , 2001 .

[12]  D. Eatough,et al.  The measurement of PM2.5, including semi-volatile components, in the EMPACT program: results from the Salt Lake City Study , 2003 .

[13]  Delbert J. Eatough,et al.  Semi-volatile secondary organic aerosol in urban atmospheres: meeting a measurement challenge , 2003 .

[14]  Effects of Equilibration Temperature on PM10 Concentrations from the TEOM Method in the Lower Fraser Valley. , 1999, Journal of the Air & Waste Management Association.

[15]  Continuous Determination of PM2.5 Mass, Including Semi-Volatile Species , 2001 .

[16]  P. Paatero Least squares formulation of robust non-negative factor analysis , 1997 .

[17]  Stephan Borrmann,et al.  A New Time-of-Flight Aerosol Mass Spectrometer (TOF-AMS)—Instrument Description and First Field Deployment , 2005 .

[18]  P. Dasgupta,et al.  A continuous analyzer for soluble anionic constituents and ammonium in atmospheric particulate matter. , 2003, Environmental science & technology.

[19]  David A Sodeman,et al.  Determination of single particle mass spectral signatures from light-duty vehicle emissions. , 2005, Environmental science & technology.

[20]  Katrin Fuhrer,et al.  Field-deployable, high-resolution, time-of-flight aerosol mass spectrometer. , 2006, Analytical chemistry.

[21]  P. Hopke,et al.  Measurement of Both Nonvolatile and Semi-Volatile Fractions of Fine Particulate Matter in Fresno, CA , 2006 .

[22]  P. Paatero,et al.  Atmospheric aerosol over Alaska: 2. Elemental composition and sources , 1998 .

[23]  James N. Pitts,et al.  Chemistry of the Upper and Lower Atmosphere: Theory, Experiments, and Applications , 1999 .

[24]  P. Bhave,et al.  Source apportionment of fine particulate matter by clustering single-particle data: tests of receptor model accuracy. , 2001, Environmental science & technology.

[25]  Qi Zhang,et al.  Deconvolution and quantification of hydrocarbon-like and oxygenated organic aerosols based on aerosol mass spectrometry. , 2005, Environmental science & technology.

[26]  K. Prather,et al.  Real-time characterization of individual aerosol particles using time-of-flight mass spectrometry , 1994 .