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Comparison of Atmospheric Aerosols between Two Sites over Golden Triangle of Texas
Chiou, P.; Tang, W.; Lin, C. J.; Chu, H. W. & Ho, T. C.
Abstract
Speciated samples of PM2.5 were collected at the Hamshire and Orange sites in
Golden Triangle of Texas by US EPA (Environmental Protection Agency) from July of 2003 to August
of 2005. A total of 269 samples for the Hamshire site and 293 samples for the Orange site with 52
species were measured; however, 22 species were excluded because of too many below-detectionlimit
data. Among the 22 species excluded, 20 species are common to both sites. Due to the laboratory
change about November 1st of 2004 and possible analytical artifacts, phosphorous was excluded as
well. The two data sets were analyzed by positive matrix factorization (PMF) to infer the sources of
PM observed at the two sites. The analysis identified nine common source-related factors: sulfaterich
secondary aerosol, cement/carbon-rich, wood smoke, motor vehicle/road dust, metal processing,
nitrate-rich secondary aerosol, soil, sea salt, and chloride depleted marine aerosol. Sulfate and
nitrate mainly exist as ammonium salts. The sulfate-rich secondary aerosol accounts for 42% and
43% of the PM mass concentrations at the Hamshire and the Orange sites, respectively. The factor
containing highest concentrations of Cl and Na was attributed to sea salt due to the proximity of the
monitoring sites to the Gulf of Mexico. The chloride depleted marine aerosol was related to the sea
salt aerosol but was identified separately due to the chlorine replacement reactions. Basically, the
factors of sulfate, motor vehicle/road dust, nitrate, soil, sea salt, and chloride depleted marine aerosol
at the two sites showed similar chemical composition profiles and seasonal variation reflecting
these six factors were likely to be Golden Triangle regionally related sources. Cement/carbon-rich,
wood smoke, and metal processing factor were likely to be the local sources.
Keywords
Airborne particulate matter, Positive matrix factorization, Receptor modeling, Factor contribution, Factor profiles
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