Assessment of source-specific health effects associated with an unknown number of major sources of multiple air pollutants: a unified Bayesian approach.

There has been increasing interest in assessing health effects associated with multiple air pollutants emitted by specific sources. A major difficulty with achieving this goal is that the pollution source profiles are unknown and source-specific exposures cannot be measured directly; rather, they need to be estimated by decomposing ambient measurements of multiple air pollutants. This estimation process, called multivariate receptor modeling, is challenging because of the unknown number of sources and unknown identifiability conditions (model uncertainty). The uncertainty in source-specific exposures (source contributions) as well as uncertainty in the number of major pollution sources and identifiability conditions have been largely ignored in previous studies. A multipollutant approach that can deal with model uncertainty in multivariate receptor models while simultaneously accounting for parameter uncertainty in estimated source-specific exposures in assessment of source-specific health effects is presented in this paper. The methods are applied to daily ambient air measurements of the chemical composition of fine particulate matter ([Formula: see text]), weather data, and counts of cardiovascular deaths from 1995 to 1997 for Phoenix, AZ, USA. Our approach for evaluating source-specific health effects yields not only estimates of source contributions along with their uncertainties and associated health effects estimates but also estimates of model uncertainty (posterior model probabilities) that have been ignored in previous studies. The results from our methods agreed in general with those from the previously conducted workshop/studies on the source apportionment of PM health effects in terms of number of major contributing sources, estimated source profiles, and contributions. However, some of the adverse source-specific health effects identified in the previous studies were not statistically significant in our analysis, which probably resulted because we incorporated parameter uncertainty in estimated source contributions that has been ignored in the previous studies into the estimation of health effects parameters.

Part 2. Development of Enhanced Statistical Methods for Assessing Health Effects Associated with an Unknown Number of Major Sources of Multiple Air Pollutants.

A major difficulty with assessing source-specific health effects is that source-specific exposures cannot be measured directly; rather, they need to be estimated by a source-apportionment method such as multivariate receptor modeling. The uncertainty in source apportionment (uncertainty in source-specific exposure estimates and model uncertainty due to the unknown number of sources and identifiability conditions) has been largely ignored in previous studies. Also, spatial dependence of multipollutant data collected from multiple monitoring sites has not yet been incorporated into multivariate receptor modeling. The objectives of this project are (1) to develop a multipollutant approach that incorporates both sources of uncertainty in source-apportionment into the assessment of source-specific health effects and (2) to develop enhanced multivariate receptor models that can account for spatial correlations in the multipollutant data collected from multiple sites. We employed a Bayesian hierarchical modeling framework consisting of multivariate receptor models, health-effects models, and a hierarchical model on latent source contributions. For the health model, we focused on the time-series design in this project. Each combination of number of sources and identifiability conditions (additional constraints on model parameters) defines a different model. We built a set of plausible models with extensive exploratory data analyses and with information from previous studies, and then computed posterior model probability to estimate model uncertainty. Parameter estimation and model uncertainty estimation were implemented simultaneously by Markov chain Monte Carlo (MCMC*) methods. We validated the methods using simulated data. We illustrated the methods using PM2.5 (particulate matter ≤ 2.5 µm in aerodynamic diameter) speciation data and mortality data from Phoenix, Arizona, and Houston, Texas. The Phoenix data included counts of cardiovascular deaths and daily PM2.5 speciation data from 1995-1997. The Houston data included respiratory mortality data and 24-hour PM2.5 speciation data sampled every six days from a region near the Houston Ship Channel in years 2002-2005. We also developed a Bayesian spatial multivariate receptor modeling approach that, while simultaneously dealing with the unknown number of sources and identifiability conditions, incorporated spatial correlations in the multipollutant data collected from multiple sites into the estimation of source profiles and contributions based on the discrete process convolution model for multivariate spatial processes. This new modeling approach was applied to 24-hour ambient air concentrations of 17 volatile organic compounds (VOCs) measured at nine monitoring sites in Harris County, Texas, during years 2000 to 2005. Simulation results indicated that our methods were accurate in identifying the true model and estimated parameters were close to the true values. The results from our methods agreed in general with previous studies on the source apportionment of the Phoenix data in terms of estimated source profiles and contributions. However, we had a greater number of statistically insignificant findings, which was likely a natural consequence of incorporating uncertainty in the estimated source contributions into the health-effects parameter estimation. For the Houston data, a model with five sources (that seemed to be Sulfate-Rich Secondary Aerosol, Motor Vehicles, Industrial Combustion, Soil/Crustal Matter, and Sea Salt) showed the highest posterior model probability among the candidate models considered when fitted simultaneously to the PM2.5 and mortality data. There was a statistically significant positive association between respiratory mortality and same-day PM2.5 concentrations attributed to one of the sources (probably industrial combustion). The Bayesian spatial multivariate receptor modeling approach applied to the VOC data led to a highest posterior model probability for a model with five sources (that seemed to be refinery, petrochemical production, gasoline evaporation, natural gas, and vehicular exhaust) among several candidate models, with the number of sources varying between three and seven and with different identifiability conditions. Our multipollutant approach assessing source-specific health effects is more advantageous than a single-pollutant approach in that it can estimate total health effects from multiple pollutants and can also identify emission sources that are responsible for adverse health effects. Our Bayesian approach can incorporate not only uncertainty in the estimated source contributions, but also model uncertainty that has not been addressed in previous studies on assessing source-specific health effects. The new Bayesian spatial multivariate receptor modeling approach enables predictions of source contributions at unmonitored sites, minimizing exposure misclassification and providing improved exposure estimates along with their uncertainty estimates, as well as accounting for uncertainty in the number of sources and identifiability conditions.

Assessment of mobile source contributions in El Paso by PMF receptor modeling coupled with wind direction analysis.

It is well-known that El Paso is the only border area in Texas that has violated national air quality standards. Mobile source emissions (including vehicle exhaust) contribute significantly to air pollution, along with other sources including industrial, residential, and cross-border. This study aims at separating unobserved vehicle emissions from air-pollution mixtures indicated by ambient air quality data. The level of contributions from vehicle emissions to air pollution cannot be determined by simply comparing ambient air quality data with traffic levels because of the various other contributors to overall air pollution. To estimate contributions from vehicle emissions, researchers employed advanced multivariate receptor modeling called positive matrix factorization (PMF) to analyze hydrocarbon data consisting of hourly concentrations measured from the Chamizal air pollution monitoring station in El Paso. The analysis of hydrocarbon data collected at the Chamizal site in 2008 showed that approximately 25% of measured Total Non-Methane Hydrocarbons (TNMHC) was apportioned to motor vehicle exhaust. Using wind direction analysis, researchers also showed that the motor vehicle exhaust contributions to hydrocarbons were significantly higher when winds blow from the south (Mexico) than those when winds blow from other directions. The results from this research can be used to improve understanding source apportionment of pollutants measured in El Paso and can also potentially inform transportation planning strategies aimed at reducing emissions across the region.

Dependence among randomly acquired characteristics on shoeprints and their features.

Randomly acquired characteristics (RACs), also known as accidental marks, are random markings on a shoe sole, such as scratches or holes, that are used by forensic experts to compare a suspect's shoe with a print found at the crime scene. This article investigates the relationships among three features of a RAC: its location, shape type and orientation. If these features, as well as the RACs, are independent of each other, a simple probabilistic calculation could be used to evaluate the rarity of a RAC and hence the evidential value of the shoe and print comparison, whereas a correlation among the features would complicate the analysis. Using a data set of about 380 shoes, it is found that RACs and their features are not independent, and moreover, are not independent of the shoe sole pattern. It is argued that some of the dependencies found are caused by the elements of the sole. The results have important implications for the way forensic experts should evaluate the degree of rarity of a combination of RACs.

Specific model for the estimation of methane emission from municipal solid waste landfills in India.

The landfill gas (LFG) model is a tool for measuring methane (CH4) generation rates and total CH4 emissions from a particular landfill. These models also have various applications including the sizing of the LFG collection system, evaluating the benefits of gas recovery projects, and measuring and controlling gaseous emissions. This research paper describes the development of a landfill model designed specifically for Indian climatic conditions and the landfill's waste characteristics. CH4, carbon dioxide (CO2), oxygen (O2) and temperature were considered as the prime factor for the development of this model. The developed model was validated for three landfill sites in India: Shillong, Kolkata, and Jaipur. The autocorrelation coefficient for the model was 0.915, while the R(2) value was 0.429.

Forensic bitemark identification: weak foundations, exaggerated claims.

Several forensic sciences, especially of the pattern-matching kind, are increasingly seen to lack the scientific foundation needed to justify continuing admission as trial evidence. Indeed, several have been abolished in the recent past. A likely next candidate for elimination is bitemark identification. A number of DNA exonerations have occurred in recent years for individuals convicted based on erroneous bitemark identifications. Intense scientific and legal scrutiny has resulted. An important National Academies review found little scientific support for the field. The Texas Forensic Science Commission recently recommended a moratorium on the admission of bitemark expert testimony. The California Supreme Court has a case before it that could start a national dismantling of forensic odontology. This article describes the (legal) basis for the rise of bitemark identification and the (scientific) basis for its impending fall. The article explains the general logic of forensic identification, the claims of bitemark identification, and reviews relevant empirical research on bitemark identification-highlighting both the lack of research and the lack of support provided by what research does exist. The rise and possible fall of bitemark identification evidence has broader implications-highlighting the weak scientific culture of forensic science and the law's difficulty in evaluating and responding to unreliable and unscientific evidence.

Mineral preservatives in the wood of Stradivari and Guarneri.

Following the futile efforts of generations to reach the high standard of excellence achieved by the luthiers in Cremona, Italy, by variations of design and plate tuning, current interest is being focused on differences in material properties. The long-standing question whether the wood of Stradivari and Guarneri were treated with wood preservative materials could be answered only by the examination of wood specimens from the precious antique instruments. In a recent communication (Nature, 2006), we reported about the degradation of the wood polymers in instruments of Stradivari and Guarneri, which could be explained only by chemical manipulations, possibly by preservatives. The aim of the current work was to identify the minerals from the small samples of the maple wood which were available to us from the antique instruments. The ashes of wood from one violin and one cello by Stradivari, two violins by Guarneri, one viola by H. Jay, one violin by Gand-Bernardel were analyzed and compared with a variety of commercial tone woods. The methods of analysis were the following: back-scattered electron imaging, X-ray fluorescence maps for individual elements, wave-length dispersive spectroscopy, energy dispersive X-ray spectroscopy and quantitative microprobe analysis. All four Cremonese instruments showed the unmistakable signs of chemical treatments in the form of chemicals which are not present in natural woods, such as BaSO4, CaF2, borate, and ZrSiO4. In addition to these, there were also changes in the common wood minerals. Statistical evaluation of 12 minerals by discriminant analysis revealed: a. a difference among all four Cremona instruments, b. the difference of the Cremonese instruments from the French and English antiques, and c. only the Cremonese instruments differed from all commercial woods. These findings may provide the answer why all attempts to recreate the Stradivarius from natural wood have failed. There are many obvious implications with regard to how the green tone wood should be treated, which chould lead to changes in the practice of violin-making. This research should inspire others to analyze more antique violins for their chemical contents.

Multi-site assessment of the precision and reproducibility of multiple reaction monitoring-based measurements of proteins in plasma.

Verification of candidate biomarkers relies upon specific, quantitative assays optimized for selective detection of target proteins, and is increasingly viewed as a critical step in the discovery pipeline that bridges unbiased biomarker discovery to preclinical validation. Although individual laboratories have demonstrated that multiple reaction monitoring (MRM) coupled with isotope dilution mass spectrometry can quantify candidate protein biomarkers in plasma, reproducibility and transferability of these assays between laboratories have not been demonstrated. We describe a multilaboratory study to assess reproducibility, recovery, linear dynamic range and limits of detection and quantification of multiplexed, MRM-based assays, conducted by NCI-CPTAC. Using common materials and standardized protocols, we demonstrate that these assays can be highly reproducible within and across laboratories and instrument platforms, and are sensitive to low mug/ml protein concentrations in unfractionated plasma. We provide data and benchmarks against which individual laboratories can compare their performance and evaluate new technologies for biomarker verification in plasma.

A Univariate Inequality for Medians.

An inequality is provided for medians which is an analog of a theorem due to Karamata, dealing with majorization.

A Note on the Behavior of Least Squares Regression Estimates When Both Variables Are Subject to Error.

For the errors in variables model X = U + V, Y = ßf(U) + W, sufficient conditions are given for the L.S. limiting estimate of ß to satisfy P ( ß ^ / ß < 1 ) = 1 or P ( ß ^ / ß > 1 ) = 1 as the sample size tends to infinity.

A Univariate Extension of Jensen's Inequality.

The result in this paper explains some of the qualitative nature of Jensen's inequality. It is shown that the more disperse the distribution of a random variable is, the smaller is the expectation of any concave function of it. This result can be used to show the inadequacy of some current methods of reporting environmental data by using geometric means, and it extends the result of I. Billick, D. Shier, and C. H. Spiegelman, where symmetry of the error in environmental measurements is assumed.

An Iterative Calibration Curve Procedure.

Calibration curves are an important part of many measurement processes. The user of a fitted calibration curve must know its precision and accuracy. These are determined in a timely fashion using the data iteratively. This paper gives a method that divides the data into training and test groups. The test group is iteratively checked to see that a prechosen nominal confidence interval probability of coverage is met. If on the basis of this check the calibration experiment is completed, the nominal probability level is shown to still be valid.