William F. Christensen

PM source apportionment and health effects: 1. Intercomparison of source apportionment 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 PM2.5 mass source apportionment results are consistent across users and methods, and that todays source apportionment methods are robust enough for application to PM2.5 health effects assessments.

Workgroup Report: Workshop on Source Apportionment of Particulate Matter Health Effects—Intercomparison of Results and Implications

Although the association between exposure to ambient fine particulate matter with aerodynamic diameter < 2.5 μm (PM2.5) and human mortality is well established, the most responsible particle types/sources are not yet certain. In May 2003, the U.S. Environmental Protection Agency’s Particulate Matter Centers Program sponsored the Workshop on the Source Apportionment of PM Health Effects. The goal was to evaluate the consistency of the various source apportionment methods in assessing source contributions to daily PM2.5 mass–mortality associations. Seven research institutions, using varying methods, participated in the estimation of source apportionments of PM2.5 mass samples collected in Washington, DC, and Phoenix, Arizona, USA. Apportionments were evaluated for their respective associations with mortality using Poisson regressions, allowing a comparative assessment of the extent to which variations in the apportionments contributed to variability in the source-specific mortality results. The various research groups generally identified the same major source types, each with similar elemental makeups. Intergroup correlation analyses indicated that soil-, sulfate-, residual oil-, and salt-associated mass were most unambiguously identified by various methods, whereas vegetative burning and traffic were less consistent. Aggregate source-specific mortality relative risk (RR) estimate confidence intervals overlapped each other, but the sulfate-related PM2.5 component was most consistently significant across analyses in these cities. Analyses indicated that source types were a significant predictor of RR, whereas apportionment group differences were not. Variations in the source apportionments added only some 15% to the mortality regression uncertainties. These results provide supportive evidence that existing PM2.5 source apportionment methods can be used to derive reliable insights into the source components that contribute to PM2.5 health effects.

Latent Variable Analysis of Multivariate Spatial Data

Multivariate spatial or geo-referenced data arise naturally in such disciplines as ecology, agriculture, geology, and atmospheric sciences. In practice, interest often lies in modeling underlying structure and representing interrelationships in terms of a smaller number of variables. For such situations, statistical analysis using a latent variable model is proposed. We present a general model that incorporates spatial correlation and potential lagged or shifted dependencies and that can represent subject matter theory or serve as a practical exploratory model. Procedures for model fitting, parameter estimation, inferences, and latent variable prediction are developed without restrictive assumptions on distribution and covariance function forms. The properties and usefulness of the proposed approaches are assessed by asymptotic theory and an extensive simulation study. An example from precision agriculture is also presented.

Predicting Presidential and Other Multistage Election Outcomes Using State-Level Pre-Election Polls

Although much of the media attention during presidential election years focuses on polls tracking popular support for the major candidates, the complicated role played by the Electoral College in this multistage election process must be accounted for in order to address the issue of winning the presidency. State-level pre-election polls are used in a manner that allows the structure of multistage election processes to be addressed directly. We consider frequentist and Bayesian approaches for predicting election outcomes and discuss ways to incorporate such analyses in a course project suitable for undergraduates or graduate students studying statistics. Using state-level pre-election polling data, we consider the U.S. presidential election of 2004 and we also apply this approach to predict the control of the U.S. Senate in 2006. This class exercise has proved to be a useful “capstone project” which requires students to address a complicated problem by synthesizing multiple sources of available data and applying a combination of statistical methods. Using simulation-based approaches for addressing the multistage nature of presidential elections and control-of-Congress processes can be valuable and instructive for students of statistics and political science, and can be beneficial to the media in providing consumers with political news.

Accounting for dependence in a flexible multivariate receptor model

Formulation and evaluation of environmental policy depends on receptor models that are used to assess the number and nature of pollution sources affecting the air quality for a region of interest. Different approaches have been developed for situations in which no information is available about the number and nature of these sources (e.g., exploratory factor analysis) and the composition of these sources is assumed known (e.g., regression and measurement error models). We propose a flexible approach for fitting the receptor model when only partial pollution source information is available. The use of latent variable modeling allows the direct incorporation of subject matter knowledge into the model, including known physical constraints and partial pollution source information obtained from laboratory measurements or past studies. Because air quality data often exhibit temporal and/or spatial dependence, we consider the importance of accounting for such correlation in estimating model parameters and making valid statistical inferences. We propose an approach for incorporating dependence structure directly into estimation and inference procedures via a new nested block bootstrap method that adjusts for bias in estimating moment matrices. A goodness-of-fit test that is valid in the presence of such dependence is proposed. The application of the approach is facilitated by a new multivariate extension of an existing block size determination algorithm. The proposed approaches are evaluated by simulation and illustrated with an analysis of hourly measurements of volatile organic compounds in the El Paso, Texas/Ciudad Juarez, Mexico area.

Modeling and prediction for multivariate spatial factor analysis

Factor analysis of multivariate spatial data is considered. A systematic approach for modeling the underlying structure of potentially irregularly spaced, geo-referenced vector observations is proposed. Statistical inference procedures for selecting the number of factors and for model building are discussed. We derive a condition under which a simple and practical inference procedure is valid without specifying the form of distributions and factor covariance functions. The multivariate prediction problem is also discussed, and a procedure combining the latent variable modeling and a measurement-error-free kriging technique is introduced. Simulation results and an example using agricultural data are presented.

Silent Functional Magnetic Resonance Imaging (fMRI) of Tonotopicity and Stimulus Intensity Coding in Human Primary Auditory Cortex

Objectives The aims of this study were to determine the feasibility of obtaining auditory cortex activation evoked by pure tones presented at threshold and suprathreshold hearing levels, to evaluate tonotopicity of the primary auditory cortex, and to determine the effect of stimulus intensity on auditory cortex activation using silent functional magnetic resonance imaging (fMRI).

Objectively Measured Sleep Patterns in Young Adult Women and the Relationship to Adiposity

Purpose. The purpose of this study was to examine the relationship between sleep patterns and adiposity in young adult women. Design. Cross-sectional. Setting. The study took place at two Mountain West region universities and surrounding communities. Subjects. Subjects were 330 young adult women (20.2 ± 1.5 years). Measures. Sleep and physical activity were monitored for 7 consecutive days and nights using actigraphy. Height and weight were measured directly. Adiposity was assessed using the BOD POD. Analysis. Regression analysis, between subjects analysis of variance, and structural equation modeling were used. Results. Bivariate regression analysis demonstrated that sleep efficiency was negatively related to adiposity and that the 7-day standard deviations of bedtime, wake time, and sleep duration were positively related to adiposity (p < .05). Controlling for objectively measured physical activity strengthened the relationship between sleep duration and adiposity by 84% but had a statistically negligible impact on all other relationships that were analyzed. However, multivariate structural equation modeling indicated that a model including sleep efficiency, sleep pattern inconsistency (latent variable consisting of the 7-day standard deviations of bedtime, wake time, and sleep duration), and physical activity was the best for predicting percent body fat. Conclusion. Inconsistent sleep patterns and poor sleep efficiency are related to adiposity. Consistent sleep patterns that include sufficient sleep may be important in modifying risk of excess body fat in young adult women.

Evaluation of Auditory Cortex Activation by Using Silent FMRI

PURPOSE To evaluate auditory cortex activation evoked by pure-tone stimulus using silent functional magnetic resonance imaging (FMRI). MATERIAL AND METHODS Nine volunteers with normal hearing as determined with pure-tone audiometry were studied. Auditory cortex activation was evoked by pure-tone stimuli presented monaurally at 1,000, 2,000, and 4,000 Hz. Images of auditory cortex activation were obtained using silent and conventional FMRI techniques. Heschls gyrus activation was evaluated by the number of voxels exceeding a predetermined significance level (P <.0001). RESULTS In both techniques, all subjects showed activation in the Heschls gyrus. Silent FMRI detected more activation in all studied frequencies compared with the conventional FMRI. The observed difference in the Heschls gyrus activation between the techniques reached statistical significance for 1,000 Hz frequency (P <.05). CONCLUSIONS The amount of Heschls gyrus activation detected with silent FMRI is greater than that of conventional FMRI. Silent FMRI technique can be used to acquire functional images of the auditory cortex without the confounding effects of scanner noise.

Apportionment of Ambient Primary and Secondary Fine Particulate Matter at the Pittsburgh National Energy Laboratory Particulate Matter Characterization Site Using Positive Matrix Factorization and a Potential Source Contributions Function Analysis

Abstract Fine particulate matter (PM2.5) concentrations associated with 202 24-hr samples collected at the National Energy Technology Laboratory (NETL) particulate matter (PM) characterization site in south Pittsburgh from October 1999 through September 2001 were used to apportion PM2.5 into primary and secondary contributions using Positive Matrix Factorization (PMF2). Input included the concentrations of PM2.5 mass determined with a Federal Reference Method (FRM) sampler, semi-volatile PM2.5 organic material, elemental carbon (EC), and trace element components of PM2.5. A total of 11 factors were identified. The results of potential source contributions function (PSCF) analysis using PMF2 factors and HYSPLIT-calculated back-trajectories were used to identify those factors associated with specific meteorological transport conditions. The 11 factors were identified as being associated with emissions from various specific regions and facilities including crustal material, gasoline combustion, diesel combustion, and three nearby sources high in trace metals. Three sources associated with transport from coal-fired power plants to the southeast, a combination of point sources to the northwest, and a steel mill and associated sources to the west were identified. In addition, two secondary-material-dominated sources were identified, one was associated with secondary products of local emissions and one was dominated by secondary ammonium sulfate transported to the NETL site from the west and southwest. Of these 11 factors, the four largest contributors to PM2.5 were the secondary transported material (dominated by ammonium sulfate) (47%), local secondary material (19%), diesel combustion emissions (10%), and gasoline combustion emissions (8%). The other seven factors accounted for the remaining 16% of the PM2.5 mass.