The Effects of Potato Presentation on Vegetable Intake in School-Aged Children: A Cross-Over Study.

Vegetables are an essential component of a healthy dietary pattern in children; however, their consumption is often insufficient due to lack of preference. To address this, the influence of combining vegetables (mixed peas and carrots-MPACs) with potatoes, a generally liked food, on overall vegetable consumption among children aged 7-13 years was explored. The research involved a cross-over study design with 65 participants who completed five lunchtime meal conditions, each with different combinations of MPACs and potatoes versus a control (MPACs with a wheat roll). The meals were provided in a cafeteria setting, and plate waste was used to measure vegetable consumption. Anthropometric data and other variables were also measured. Notably, self-reported hunger did not significantly differ between conditions. Meal condition was a significant predictor of MPACs (F = 5.20; p = 0.0005), with MPAC consumption highest when combined with shaped potato faces in the same bowl (+8.77 g compared to serving MPACs and shaped potato faces in separate bowls) and lowest when combined with diced potatoes in the same bowl (-2.85 g compared to serving MPACs and diced potatoes in separate bowls). The overall model for MPAC consumption was influenced by age, height z-score, body fat percentage z-score, and condition (likelihood ratio = 49.1; p < 0.0001). Age had the strongest correlation with vegetable consumption (r = 0.38), followed by male gender, height z-score (r = 0.30), and body fat z-score (r = -0.15). The results highlight the positive impact of combining potatoes with vegetables in school meals, particularly when using shaped potato faces. These findings emphasize the potential of potatoes as a valuable vegetable option in promoting healthier eating habits among children. Additionally, future research could explore the impact of different potato combinations and investigate other factors influencing meal consumption in school settings.

Distributional Validation of Precipitation Data Products with Spatially Varying Mixture Models.

The high mountain regions of Asia contain more glacial ice than anywhere on the planet outside of the polar regions. Because of the large population living in the Indus watershed region who are reliant on melt from these glaciers for fresh water, understanding the factors that affect glacial melt along with the impacts of climate change on the region is important for managing these natural resources. While there are multiple climate data products (e.g., reanalysis and global climate models) available to study the impact of climate change on this region, each product will have a different amount of skill in projecting a given climate variable, such as precipitation. In this research, we develop a spatially varying mixture model to compare the distribution of precipitation in the High Mountain Asia region as produced by climate models with the corresponding distribution from in situ observations from the Asian Precipitation-Highly Resolved Observational Data Integration Towards Evaluation (APHRODITE) data product. Parameter estimation is carried out via a computationally efficient Markov chain Monte Carlo algorithm. Each of the estimated climate distributions from each climate data product is then validated against APHRODITE using a spatially varying Kullback-Leibler divergence measure. Supplementary materials accompanying this paper appear online. Supplementary materials for this article are available at 10.1007/s13253-022-00515-0.

Body shape perception in men and women without obesity during caloric restriction: a secondary analysis from the CALERIE study.

OBJECTIVE: To examine body shape perception in 218 adults without obesity or history of eating disorders during caloric restriction (CR). METHODS: Comprehensive Assessment of Long-term Effects of Reducing Intake of Energy (CALERIE) is a 2-year, randomized clinical trial using a 2:1 assignment (CR, 25% reduction in calories; Control, typical diet). For this secondary analysis, we examined perceived body shape using the Body Shape Questionnaire (BSQ). Analyses of BSQ scores are reported by group, over time, by sex, and by BMI. Data for body fat percentage, symptoms of depression, food cravings, maximal oxygen consumption, and stress were analyzed for their association with BSQ scores. RESULTS: Compared to control, CR reduced BSQ scores. Women tended to have greater concern with body shape than men across all measurement times. There was no difference in change in BSQ scores at 12 or 24 months between those with a BMI < 25 kg/m2 or ≥ 25 kg/m2. Change in body fat percentage was most correlated with change in BSQ score from 0 to 12 (r = 0.39) and 0-24 months (r = 0.38). For change in BSQ score, Akaike/ Bayesian information criterion (AIC/BIC) found that the model of best fit included the following three change predictors: change in body fat percentage, depression symptoms, and food cravings. For 0-12 months, AIC/BIC = 1482.0/1505.6 and for 0-24 months AIC/BIC = 1364.8/1386.5. CONCLUSIONS: CR is associated with reduced concern for body shape in men and women without obesity and with no history of eating disorders. Body shape perception among this sample was complex and influenced by multiple factors. LEVEL OF EVIDENCE: Level I, randomized controlled trial.

Assessing Dimensionality in Dichotomous Items When Many Subjects Have All-Zero Responses: An Example From Psychiatry and a Solution Using Mixture Models.

Common methods for determining the number of latent dimensions underlying an item set include eigenvalue analysis and examination of fit statistics for factor analysis models with varying number of factors. Given a set of dichotomous items, the authors demonstrate that these empirical assessments of dimensionality often incorrectly estimate the number of dimensions when there is a preponderance of individuals in the sample with all-zeros as their responses, for example, not endorsing any symptoms on a health battery. Simulated data experiments are conducted to demonstrate when each of several common diagnostics of dimensionality can be expected to under- or over-estimate the true dimensionality of the underlying latent variable. An example is shown from psychiatry assessing the dimensionality of a social anxiety disorder battery where 1, 2, 3, or more factors are identified, depending on the method of dimensionality assessment. An all-zero inflated exploratory factor analysis model (AZ-EFA) is introduced for assessing the dimensionality of the underlying subgroup corresponding to those possessing the measurable trait. The AZ-EFA approach is demonstrated using simulation experiments and an example measuring social anxiety disorder from a large nationally representative survey. Implications of the findings are discussed, in particular, regarding the potential for different findings in community versus patient populations.

Accurate Prediction of Knee Angles during Open-Chain Rehabilitation Exercises Using a Wearable Array of Nanocomposite Stretch Sensors.

In this work, a knee sleeve is presented for application in physical therapy applications relating to knee rehabilitation. The device is instrumented with sixteen piezoresistive sensors to measure knee angles during exercise, and can support at-home rehabilitation methods. The development of the device is presented. Testing was performed on eighteen subjects, and knee angles were predicted using a machine learning regressor. Subject-specific and device-specific models are analyzed and presented. Subject-specific models average root mean square errors of 7.6 and 1.8 degrees for flexion/extension and internal/external rotation, respectively. Device-specific models average root mean square errors of 12.6 and 3.5 degrees for flexion/extension and internal/external rotation, respectively. The device presented in this work proved to be a repeatable, reusable, low-cost device that can adequately model the knee's flexion/extension and internal/external rotation angles for rehabilitation purposes.

The impact of exercise intensity on neurophysiological indices of food-related inhibitory control and cognitive control: A randomized crossover event-related potential (ERP) study.

Food-related inhibitory control, the ability to withhold a dominant response towards highly palatable foods, influences dietary decisions. Food-related inhibitory control abilities may increase following a bout of aerobic exercise; however, the impact of exercise intensity on both food-related inhibitory control and broader cognitive control processes is currently unclear. We used a high-powered, within-subjects, crossover design to test how relative intensity of aerobic exercise influenced behavioral (response time, accuracy) and neural (N2 and P3 components of the scalp-recorded event-related potential [ERP]) measures of food-related inhibitory and cognitive control. Two hundred and ten participants completed three separate conditions separated by approximately one week in randomized order: two exercise conditions (35% VO2max or 70% VO2max) and seated rest. Directly following exercise or rest, participants completed a food-based go/no-go task and a flanker task while electroencephalogram data were recorded. Linear mixed models showed generally faster response times (RT) and improved accuracy following 70% VO2max exercise compared to rest, but not 35% VO2max; RTs and accuracy did not differ between 35% VO2max exercise and rest conditions. N2 and P3 amplitudes were larger following 70% VO2max exercise for the food-based go/no-go task compared to rest and 35% VO2max exercise. There were no differences between exercise conditions for N2 amplitude during the flanker task; however, P3 amplitude was more positive following 70% VO2max compared to rest, but not 35% VO2max exercise. Biological sex did not moderate exercise outcomes. Results suggest improved and more efficient food-related recruitment of later inhibitory control and cognitive control processes following 70% VO2max exercise.

To play or not to play? The relationship between active video game play and electrophysiological indices of food-related inhibitory control in adolescents.

Sedentary behaviors, such as computer use and sedentary video games, are barriers to physical activity, contribute to overweight and obesity among adolescents, and can adversely affect eating behaviors. Active video games may increase daily physical activity levels among adolescents and improve food-related inhibitory control. We compared the effects of acute bouts of active and sedentary video gaming on event-related potential (ERP) indices of food-related inhibitory control, energy expenditure, and ad libitum eating. In a within-subjects design, 59 adolescent participants (49% female, Mage  = 13.29 ± 1.15) completed two separate counterbalanced, 60-min long video gaming sessions separated by seven days. Immediately after, participants completed two go/no-go tasks with high- and low-calorie images and N2 and P3 ERP amplitudes were measured. Participants also completed a Stroop task and were given high- and low-calorie snacks to consume ad libitum. Results indicated that active relative to sedentary video games significantly increased energy expenditure on multiple measures (e.g., METs, heart rate, kcals burned) and participants consumed more calories after the active compared to the sedentary video game session. N2 amplitudes were larger when participants inhibited to high- compared to low-calorie foods, suggesting that high-calorie foods necessitate increased the recruitment of inhibitory control resources; however, there were non-significant differences for the N2 or P3 amplitudes, accuracy or response times, and Stroop performance between active versus sedentary video game sessions. Overall, sixty minutes of active video gaming increased energy expenditure and food consumption but did not significantly alter neural or behavioral measures of inhibitory control to food stimuli.

Evaluating Relationships Between Sleep and Next-Day Physical Activity in Young Women.

BACKGROUND: To evaluate the relationship between sleep and next-day physical activity (PA) under free-living conditions in women. METHODS: Sleep and PA were measured objectively for 7 consecutive days by accelerometry in 330 young adult women (aged 17-25 y). A structural equation model was used to evaluate the relationship between the driving factor of sleep (total sleep or morning wake time) and the amount of nonsleep sedentary (SED) and moderate to vigorous physical activity (MVPA) each day. RESULTS: With sleep duration as the driving factor, the estimates of ßSED and ßMVPA were -0.415 and -0.093, respectively (P ≤ .05). For every hour slept, a 24.9-minute reduction in SED time and a 5.58-minute reduction in MVPA were observed. With wake time as the driving factor, the estimates of ßSED and ßMVPA were -0.636 and -0.149, respectively. For every wake time that was 1 hour later, a 38.2-minute decrease in SED and a 8.9-minute decrease in MVPA (P ≤ .05) were observed. CONCLUSIONS: Women who wake later or who sleep longer tend to get less MVPA throughout the day. Getting up earlier and going to bed earlier may support behaviors that improve PA and lifestyle.

A case study exploring associations between popular media attention of scientific research and scientific citations.

The association between mention of scientific research in popular media (e.g., the mainstream media or social media platforms) and scientific impact (e.g., citations) has yet to be fully explored. The purpose of this study was to clarify this relationship, while accounting for some other factors that likely influence scientific impact (e.g., the reputations of the scientists conducting the research and academic journal in which the research was published). To accomplish this purpose, approximately 800 peer-reviewed articles describing original research were evaluated for scientific impact, popular media attention, and reputations of the scientists/authors and publication venue. A structural equation model was produced describing the relationship between non-scientific impact (popular media) and scientific impact (citations), while accounting for author/scientist and journal reputation. The resulting model revealed a strong association between the amount of popular media attention given to a scientific research project and corresponding publication and the number of times that publication is cited in peer-reviewed scientific literature. These results indicate that (1) peer-reviewed scientific publications receiving more attention in non-scientific media are more likely to be cited than scientific publications receiving less popular media attention, and (2) the non-scientific media is associated with the scientific agenda. These results may inform scientists who increasingly use popular media to inform the general public and scientists concerning their scientific work. These results might also inform administrators of higher education and research funding mechanisms, who base decisions partly on scientific impact.

Predicting vertical ground reaction force during running using novel piezoresponsive sensors and accelerometry.

Running is a common exercise with numerous health benefits. Vertical ground reaction force (vGRF) influences running injury risk and running performance. Measurement of vGRF during running is now primarily constrained to a laboratory setting. The purpose of this study was to evaluate a new approach to measuring vGRF during running. This approach can be used outside of the laboratory and involves running shoes instrumented with novel piezoresponsive sensors and a standard accelerometer. Thirty-one individuals ran at three different speeds on a force-instrumented treadmill while wearing the instrumented running shoes. vGRF was predicted using data collected from the instrumented shoes, and predicted vGRF were compared to vGRF measured via the treadmill. Per cent error of the resulting predictions varied depending upon the predicted vGRF characteristic. Per cent error was relatively low for predicted vGRF impulse (2-7%), active peak vGRF (3-7%), and ground contact time (3-6%), but relatively high for predicted vGRF load rates (22-29%). These errors should decrease with future iterations of the instrumented shoes and collection of additional data from a more diverse sample. The novel technology described herein might become a feasible way to collect large amounts of vGRF data outside of the traditional biomechanics laboratory.

Functional Data Analyses of Gait Data Measured Using In-Shoe Sensors.

In studies of gait, continuous measurement of force exerted by the ground on a body, or ground reaction force (GRF), provides valuable insights into biomechanics, locomotion, and the possible presence of pathology. However, gold-standard measurement of GRF requires a costly in-lab observation obtained with sophisticated equipment and computer systems. Recently, in-shoe sensors have been pursued as a relatively inexpensive alternative to in-lab measurement. In this study, we explore the properties of continuous in-shoe sensor recordings using a functional data analysis approach. Our case study is based on measurements of three healthy subjects, with more than 300 stances (defined as the period between the foot striking and lifting from the ground) per subject. The sensor data show both phase and amplitude variabilities; we separate these sources via curve registration. We examine the correlation of phase shifts across sensors within a stance to evaluate the pattern of phase variability shared across sensors. Using the registered curves, we explore possible associations between in-shoe sensor recordings and GRF measurements to evaluate the in-shoe sensor recordings as a possible surrogate for in-lab GRF measurements.

Nano-Composite Foam Sensor System in Football Helmets.

American football has both the highest rate of concussion incidences as well as the highest number of concussions of all contact sports due to both the number of athletes and nature of the sport. Recent research has linked concussions with long term health complications such as chronic traumatic encephalopathy and early onset Alzheimer's. Understanding the mechanical characteristics of concussive impacts is critical to help protect athletes from these debilitating diseases and is now possible using helmet-based sensor systems. To date, real time on-field measurement of head impacts has been almost exclusively measured by devices that rely on accelerometers or gyroscopes attached to the player's helmet, or embedded in a mouth guard. These systems monitor motion of the head or helmet, but do not directly measure impact energy. This paper evaluates the accuracy of a novel, multifunctional foam-based sensor that replaces a portion of the helmet foam to measure impact. All modified helmets were tested using a National Operating Committee Standards for Athletic Equipment-style drop tower with a total of 24 drop tests (4 locations with 6 impact energies). The impacts were evaluated using a headform, instrumented with a tri-axial accelerometer, mounted to a Hybrid III neck assembly. The resultant accelerations were evaluated for both the peak acceleration and the severity indices. These data were then compared to the voltage response from multiple Nano Composite Foam sensors located throughout the helmet. The foam sensor system proved to be accurate in measuring both the HIC and Gadd severity index, as well as peak acceleration while also providing additional details that were previously difficult to obtain, such as impact energy.

Estimation of 3D Ground Reaction Force Using Nanocomposite Piezo-Responsive Foam Sensors During Walking.

This paper describes a method for the estimation of the 3D ground reaction force (GRF) during human walking using novel nanocomposite piezo-responsive foam (NCPF) sensors. Nine subjects (5 male, 4 female) walked on a force-instrumented treadmill at 1.34 m/s for 120 s each while wearing a shoe that was instrumented with four NCPF sensors. GRF data, measured via the treadmill, and sensor data, measured via the NCPF inserts, were used in a tenfold cross validation process to calibrate a separate model for each individual. The calibration model estimated average anterior-posterior, mediolateral and vertical GRF with mean average errors (MAE) of 6.52 N (2.14%), 4.79 N (6.34%), and 15.4 N (2.15%), respectively. Two additional models were created using the sensor data from all subjects and subject demographics. A tenfold cross validation process for this combined data set resulted in models that estimated average anterior-posterior, mediolateral and vertical GRF with less than 8.16 N (2.41%), 6.63 N (7.37%), and 19.4 N (2.31%) errors, respectively. Intra-subject estimates based on the model had a higher accuracy than inter-subject estimates, likely due to the relatively small subject cohort used in creating the model. The novel NCPF sensors demonstrate the ability to accurately estimate 3D GRF during human movement outside of the traditional biomechanics laboratory setting.

Disparity in neural and subjective responses to food images in women with obesity and normal-weight women.

OBJECTIVE: Self-reports tend to differ from objective measurements of food intake, particularly in adults with obesity; however, no studies have examined how neural responses to food (an objective measure) and subjective ratings of food differ by BMI status. This study tested normal-weight women (NWW) and women with obesity (OBW) for group differences in neural indices of attention towards food pictures, subjective ratings of these pictures, and the disparity between objective and subjective measurements. METHODS: Twenty-two NWW (21.8 ± 1.7 kg/m2 ) and 22 OBW (37.0 ± 5.7 kg/m2 ) viewed food and flower pictures while late positive potential amplitude, an event-related potential, was recorded. Participants rated pictures for arousal and valence. RESULTS: Late positive potential amplitude was larger toward food than flower pictures. OBW self-reported flower pictures as more pleasant than food; NWW showed no difference for pleasantness. There were no significant main effects or interactions for arousal. Standardized scores showed that only on subjective, but not objective, measures did OBW compared with NWW disproportionately indicate food pictures as less pleasant than flowers. CONCLUSIONS: Compared with NWW, OBW showed larger discrepancies between neural and subjective reports of attention towards food. Inaccurate self-reports of attention towards food may reduce the efficiency of health interventions.

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.

Filtered kriging for spatial data with heterogeneous measurement error variances.

When predicting values for the measurement-error-free component of an observed spatial process, it is generally assumed that the process has a common measurement error variance. However, it is often the case that each measurement in a spatial data set has a known, site-specific measurement error variance, rendering the observed process nonstationary. We present a simple approach for estimating the semivariogram of the unobservable measurement-error-free process using a bias adjustment of the classical semivariogram formula. We then develop a new kriging predictor that filters the measurement errors. For scenarios where each site's measurement error variance is a function of the process of interest, we recommend an approach that also uses a variance-stabilizing transformation. The properties of the heterogeneous variance measurement-error-filtered kriging (HFK) predictor and variance-stabilized HFK predictor, and the improvement of these approaches over standard measurement-error-filtered kriging are demonstrated using simulation. The approach is illustrated with climate model output from the Hudson Strait area in northern Canada. In the illustration, locations with high or low measurement error variances are appropriately down- or upweighted in the prediction of the underlying process, yielding a realistically smooth picture of the phenomenon of interest.

Impact of species uncertainty perturbation on the solution stability of positive matrix factorization of atmospheric particulate matter data.

Statistical measures for evaluating the similarity of different source apportionment solutions are proposed. The sensitivity of positive matrix factorization to small perturbations in species measurement uncertainty estimates is examined using fine particulate matter measurements on organic carbon, elemental carbon, ions, and metals at the St. Louis-Midwest Supersite. A perturbed uncertainty matrix is created by multiplying each original uncertainty value by a random multiplier generated from a log-normal distribution with a mean of 1 and a standard deviation (and CV) equal to either 0.25, 0.50, or 0.75. The relative errors in reproducing the average contribution estimates from the perturbed data are generally highest for the gasoline exhaust, with the relative error (expressed as a percentage of the "true" value) exceeding 30% for all three perturbation scenarios. The most stable estimates of average source contribution were associated with secondary sulfate and secondary nitrate, with relative errors always less than 4%. Averaged over all 10 sources, the average values for our measure of relative error for the three scenarios are 8%, 14%, and 17%, respectively. Relative errors associated with day-to-day estimates of source contributions can be more than double the size of the relative errors associated with estimates of average source contributions, with errors for four of 10 source contributions exceeding 30% for the largest-perturbation scenario. The stability of source profile estimates in our simulation varies greatly between sources, with a mean correlation between perturbed gasoline exhaust profiles and the true profile equal to only 59% for the largest-perturbation scenario. The process used for evaluation is a tool that may be used to assess the stability of solutions in source apportionment studies.

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.

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. The findings are consistent with the major source of PM2.5 in the Pittsburgh area being dominated by ammonium sulfate from distant transport and so decoupled from local activity emitting organic pollutants in the metropolitan area. In contrast, the major local secondary sources are dominated by organic material.

Characterization of metals emitted from motor vehicles.

A systematic approach was used to quantify the metals present in particulate matter emissions associated with on-road motor vehicles. Consistent sampling and chemical analysis techniques were used to determine the chemical composition of particulate matter less than 10 microm in aerodynamic diameter (PM10*) and particulate matter less than 2.5 microm in aerodynamic diameter (PM2.5), including analysis of trace metals by inductively coupled plasma mass spectrometry (ICP-MS). Four sources of metals were analyzed in emissions associated with motor vehicles: tailpipe emissions from gasoline- and diesel-powered vehicles, brake wear, tire wear, and resuspended road dust. Profiles for these sources were used in a chemical mass balance (CMB) model to quantify their relative contributions to the metal emissions measured in roadway tunnel tests in Milwaukee, Wisconsin. Roadway tunnel measurements were supplemented by parallel measurements of atmospheric particulate matter and associated metals at three urban locations: Milwaukee and Waukesha, Wisconsin, and Denver, Colorado. Ambient aerosol samples were collected every sixth day for one year and analyzed by the same chemical analysis techniques used for the source samples. The two Wisconsin sites were studied to assess the spatial differences, within one urban airshed, of trace metals present in atmospheric particulate matter. The measurements were evaluated to help understand source and seasonal trends in atmospheric concentrations of trace metals. ICP-MS methods have not been widely used in analyses of ambient aerosols for metals despite demonstrated advantages over traditional techniques. In a preliminary study, ICP-MS techniques were used to assess the leachability of trace metals present in atmospheric particulate matter samples and motor vehicle source samples in a synthetic lung fluid.

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 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.