Chemical composition data for fine and coarse particles collected in Phoeni
x, AZ, were analyzed using positive matrix factorization (PMF). The objecti
ve was to identify the possible aerosol sources at the sampling site. PMF u
ses estimates of the error in the data to provide optimum data point scalin
g and permits a better treatment of missing and below-detection-limit value
s. It also applies nonnegativity constraints to the factors. Two sets of fi
ne particle samples were collected by different samplers. Each of the resul
ting fine particle data sets was analyzed separately. For each fine particl
e data set, eight factors were obtained, identified as (1) biomass burning
characterized by high concentrations of organic carbon (OC), elemental carb
on (EC), and K; (2) wood burning with high concentrations of Na, K, OC, and
EC; (3) motor vehicles with high concentrations of OC and EC; (4) nonferro
us smelting process characterized by Cu, Zn, As, and Pb; (5) heavy-duty die
sel characterized by high EC, OC, and Mn; (6) sea-salt factor dominated by
Na and Cl; (7) soil with high values for Al, Si, Ca, Ti, and Fe; and (8) se
condary aerosol with SO4-2 and OC that may represent coal-fired power plant
emissions;
For the coarse particle samples, a five-factor model gave source profiles t
hat are attributed to be (1) sea salt, (2) soil, (3) Fe source/motor vehicl
e, (4) construction (high Ca), and (5) coal-fired power plant. Regression o
f the PM mass against the factor scores was performed to estimate the mass
contributions of the resolved sources. The ma for source's for the fine par
ticles were motor vehicles, vegetation burning factors (biomass and wood bu
rning), and coal-fired power plants. These sources contributed most of the
fine aerosol mass by emitting carbonaceous particles, and they have higher
contributions in winter. for the coarse particles, the major source contrib
utions were soil and construction (high Ca). These sources also peaked in w
inter.