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The new version of EPA's positive matrix factorization (EPA PMF) software, 5.0, includes three error estimation (EE) methods for analyzing factor analytic solutions: classical bootstrap (BS), displacement of factor elements (DISP), and bootstrap enhanced by displacement (BS-DISP). These methods capture the uncertainty of PMF analyses due to random errors and rotational ambiguity. To demonstrate the utility of the EE methods, results are presented for three data sets: (1) speciated PM2.5 data from a chemical speciation network (CSN) site in Sacramento, California (2003-2009); (2) trace metal, ammonia, and other species in water quality samples taken at an inline storage system (ISS) in Milwaukee, Wisconsin (2006); and (3) an organic aerosol data set from high-resolution aerosol mass spectrometer (HR-AMS) measurements in Las Vegas, Nevada (January 2008). We present an interpretation of EE diagnostics for these data sets, results from sensitivity tests of EE diagnostics using additional and fewer factors, and recommendations for reporting PMF results. BS-DISP and BS are found useful in understanding the uncertainty of factor profiles; they also suggest if the data are over-fitted by specifying too many factors. DISP diagnostics were consistently robust, indicating its use for understanding rotational uncertainty and as a first step in assessing a solution's viability. The uncertainty of each factor's identifying species is shown to be a useful gauge for evaluating multiple solutions, e.g., with a different number of factors., (Published by Elsevier B.V.)

As part of an EPA-sponsored workshop to investigate the use of source apportionment in health effects analyses, the associations between the participant's estimated source contributions of PM(2.5) for Phoenix, AZ for the period from 1995-1997 and cardiovascular and total nonaccidental mortality were analyzed using Poisson generalized linear models (GLM). The base model controlled for extreme temperatures, relative humidity, day of week, and time trends using natural spline smoothers. The same mortality model was applied to all of the apportionment results to provide a consistent comparison across source components and investigators/methods. Of the apportioned anthropogenic PM(2.5) source categories, secondary sulfate, traffic, and copper smelter-derived particles were most consistently associated with cardiovascular mortality. The sources with the largest cardiovascular mortality effect size were secondary sulfate (median estimate=16.0% per 5th-to-95th percentile increment at lag 0 day among eight investigators/methods) and traffic (median estimate=13.2% per 5th-to-95th percentile increment at lag 1 day among nine investigators/methods). For total mortality, the associations were weaker. Sea salt was also found to be associated with both total and cardiovascular mortality, but at 5 days lag. Fine particle soil and biomass burning factors were not associated with increased risks. Variations in the maximum effect lag varied by source category suggesting that past analyses considering only single lags of PM(2.5) may have underestimated health impact contributions at different lags. Further research is needed on the possibility that different PM(2.5) source components may have different effect lag structure. There was considerable consistency in the health effects results across source apportionments in their effect estimates and their lag structures. Variations in results across investigators/methods were small compared to the variations across source categories. These results indicate reproducibility of source apportionment results across investigative groups and support applicability of these methods to effects studies. However, future research will also need to investigate a number of other important issues including accuracy of results.

During the past three decades, receptor models have been used to identify and apportion ambient concentrations to sources. A number of groups are employing these methods to provide input into air quality management planning. A workshop has explored the use of resolved source contributions in health effects models. Multiple groups have analyzed particulate composition data sets from Washington, DC and Phoenix, AZ. Similar source profiles were extracted from these data sets by the investigators using different factor analysis methods. There was good agreement among the major resolved source types. Crustal (soil), sulfate, oil, and salt were the sources that were most unambiguously identified (generally highest correlation across the sites). Traffic and vegetative burning showed considerable variability among the results with variability in the ability of the methods to partition the motor vehicle contributions between gasoline and diesel vehicles. However, if the total motor vehicle contributions are estimated, good correspondence was obtained among the results. The source impacts were especially similar across various analyses for the larger mass contributors (e.g., in Washington, secondary sulfate SE=7% and 11% for traffic; in Phoenix, secondary sulfate SE=17% and 7% for traffic). Especially important for time-series health effects assessment, the source-specific impacts were found to be highly correlated across analysis methods/researchers for the major components (e.g., mean analysis to analysis correlation, r>0.9 for traffic and secondary sulfates in Phoenix and for traffic and secondary nitrates in Washington. The sulfate mean r value is >0.75 in Washington.). Overall, although these intercomparisons suggest areas where further research is needed (e.g., better division of traffic emissions between diesel and gasoline vehicles), they provide support the contention that PM(2.5) mass source apportionment results are consistent across users and methods, and that today's source apportionment methods are robust enough for application to PM(2.5) health effects assessments.

Rationale: Out-of-hospital coronary heart disease death is a major public health problem, but the association with air pollution is not well understood., Objectives: We evaluated the association between daily ambient air pollution levels (particle number concentration [PNC]--a proxy for ultrafine particles [diameter < 0.1 microm], mass of particles with diameter less than 10 microm [PM10]; CO, NO2, and O3) and the occurrence of fatal, nonhospitalized coronary events., Methods: Subjects were 5,144 out-of-hospital fatalities (410-414, International Classification of Diseases-9; 1998-2000) who had been residents of Rome. Hospitalizations during the 3 yr before death were considered to identify comorbidities (e.g., diabetes, hypertension, heart failure, dysrhythmia, chronic obstructive pulmonary disease). Statistical analyses were performed using a case-crossover design., Measurements and Main Results: The association with out-of-hospital coronary deaths was statistically significant for PNC, PM10, and CO. Air pollution on the day of death had the strongest effect (e.g., 7.6% increase [95% confidence interval, 2.0-13.6%]) for an interquartile range of PNC, 27,790 particles/cm3. The 65-74- and 75+-yr age groups were at higher risk than the 35-64-yr age group, and there was a suggestion of effect modification for people with hypertension and chronic obstructive pulmonary disease., Conclusions: Air pollutants originating from combustion processes, including ultrafine particles, are related to fatal, nonhospitalized coronary events. The effect is stronger among people over 65 years of age, but is not limited to a group with a specific comorbidity.

In this study, long-term aerosol particle total number concentration measurements in five metropolitan areas across Europe are presented. The measurements have been carried out in Augsburg, Barcelona, Helsinki, Rome, and Stockholm using the same instrument, a condensation particle counter (TSI model 3022). The results show that in all of the studied cities, the winter concentrations are higher than the summer concentrations. In Helsinki and in Stockholm, winter concentrations are higher by a factor of two and in Augsburg almost by a factor of three compared with summer months. The winter maximum of the monthly average concentrations in these cities is between 10,000 cm(-3) and 20,000 cm(-3), whereas the summer min is approximately 5000-6000 cm(-3). In Rome and in Barcelona, the winters are more polluted compared with summers by as much as a factor of 4-10. The winter maximum in both Rome and Barcelona is close to 100,000 cm(-3), whereas the summer minimum is > 10,000 cm(-3). During the weekdays the maximum of the hourly average concentrations in all of the cities is detected during the morning hours between 7 and 10 a.m. The evening maxima were present in Barcelona, Rome, and Augsburg, but these were not as pronounced as the morning ones. The daily maxima in Helsinki and Stockholm are close or even lower than the daily minima in the more polluted cities. The concentrations between these two groups of cities are different with a factor of about five during the whole day. The study pointed out the influence of the selection of the measurement site and the configuration of the sampling line on the observed concentrations.

A multilinear model was developed for the analysis of the spatial patterns and possible sources affecting haze and its visual effects in the southwestern United States. The data from the project Measurement of Haze and Visual Effects (MOHAVE) collected during the late winter and mid-summer of 1992 at the monitoring sites in four states (i.e., California, Arizona, Nevada and Utah) were used in the study. The three-way data array was analyzed by a four-product-term model. This study makes a direct effort to include wind patterns as a component in the model in order to obtain the information of the spatial patterns of source contributions. The solution is computed using the conjugate gradient algorithm with applied non-negativity constraints. For the winter data set, reasonable solutions contained six sources and six wind patterns. The analysis of summer data required seven sources and seven wind patterns. The ME results are compared to the prior single-species empirical orthogonal function analysis results--and prior work describing the transport pathways.

This work analyzes PM2.5 24-h average concentrations measured every third day at over 300 locations in the eastern United States during 2000. The non-negative factor analytic model, Positive Matrix Factorization, has been enhanced by modeling the dependence of PM2.5 concentrations on temperature, humidity, pressure, ozone concentrations, and wind velocity vectors. The model comprises 12 general factors, augmented by 5 urban-only factors intended to represent excess concentration present in urban locations only. The computed factor components or concentration fields are displayed as concentration maps, one for each factor, showing how much each factor contributes to the average concentration at each location. The factors are also displayed as flux maps that illustrate the spatial movement of PM2.5 aerosol, thus enabling one to pinpoint potential source areas of PM2.5. The quality of the results was investigated by examining how well the model reproduces especially high concentrations of PM2.5 on specific days at specific locations. Delimiting the spatial extent of all such factors that exhibit a clear regional maximum surrounded by an almost-zero outer domain lowered the uncertainty in the computed results.