Biomedical subjects

Timothy V Larson

Publications and source records attributed to Timothy V Larson.

14 recordsLinked to original sources

PubMed2006

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.

Air Pollutants↗

PubMed2005

PM source apportionment and health effects: 2. An investigation of intermethod variability in associations between source-apportioned fine particle mass and daily mortality in Washington, DC.

Source apportionment may be useful in epidemiological investigation of PM health effects, but variations and options in these methods leave uncertainties. An EPA-sponsored workshop investigated source apportionment and health effects analyses by examining the associations between daily mortality and the investigators' estimated source-apportioned PM(2.5) for Washington, DC for 1988-1997. A Poisson Generalized Linear Model (GLM) was used to estimate source-specific relative risks at lags 0-4 days for total non-accidental, cardiovascular, and cardiorespiratory mortality adjusting for weather, seasonal/temporal trends, and day-of-week. Source-related effect estimates and their lagged association patterns were similar across investigators/methods. The varying lag structure of associations across source types, combined with the Wednesday/Saturday sampling frequency made it difficult to compare the source-specific effect sizes in a simple manner. The largest (and most significant) percent excess deaths per 5-95(th) percentile increment of apportioned PM(2.5) for total mortality was for secondary sulfate (variance-weighted mean percent excess mortality=6.7% (95% CI: 1.7, 11.7)), but with a peculiar lag structure (lag 3 day). Primary coal-related PM(2.5) (only three teams) was similarly significantly associated with total mortality with the same 3-day lag as sulfate. Risk estimates for traffic-related PM(2.5), while significant in some cases, were more variable. Soil-related PM showed smaller effect size estimates, but they were more consistently positive at multiple lags. The cardiovascular and cardiorespiratory mortality associations were generally similar to those for total mortality. Alternative weather models generally gave similar patterns, but sometimes affected the lag structure (e.g., for sulfate). Overall, the variations in relative risks across investigators/methods were found to be much smaller than those across estimated source types or across lag days for these data. This consistency suggests the robustness of the source apportionment in health effects analyses, but remaining issues, including accuracy of source apportionment and source-specific sensitivity to weather models, need to be investigated.

Air Pollutants↗

PubMed2005

PM source apportionment and health effects. 3. Investigation of inter-method variations in associations between estimated source contributions of PM2.5 and daily mortality in Phoenix, AZ.

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.

Air Pollutants↗

PubMed2005

Association between particulate matter and emergency room visits, hospital admissions and mortality in Spokane, Washington.

There is conflicting evidence regarding the association between different size fractions of particulate matter (PM) and cardiac and respiratory morbidity and mortality. We investigated the short-term associations of four size fractions of particulate matter (PM(1), PM(2.5), PM(10), and PM(10-2.5)) and carbon monoxide with hospital admissions and emergency room (ER) visits for respiratory and cardiac conditions and mortality in Spokane, Washington. We used a log-linear generalized linear model to compare daily averages of PM and carbon monoxide with daily counts of the morbidity and mortality outcomes from January 1995 to June 2001. We examined pollution lags ranging from 0 to 3 days and compared our results to a similar log-linear generalized additive model. Effect estimates tended to be smaller and have larger standard errors for the generalized linear model. Overall, we saw no association with respiratory ER visits and any size fraction of PM. However, there was a suggestion of greater respiratory effect from fine PM when compared to coarse fraction. Carbon monoxide was associated with both all respiratory ER visits and visits for asthma at the 3-day lag. We feel that carbon monoxide may be serving as a marker for combustion-derived pollutants, which is one large component of the diverse air pollutant mixture. We also found no association with any size fraction of PM or CO with cardiac hospital admissions or mortality at the 0- to 3-day lag. We found no consistent associations between any size fraction of PM and cardiac or respiratory ER visits or hospital admissions.

Air Pollutants↗

PubMed2005

Exhaled nitric oxide in children with asthma and short-term PM2.5 exposure in Seattle.

The objective of this study was to evaluate associations between short-term (hourly) exposures to particulate matter with aerodynamic diameters < 2.5 microm (PM2.5) and the fractional concentration of nitric oxide in exhaled breath (FE(NO) in children with asthma participating in an intensive panel study in Seattle, Washington. The exposure data were collected with tapered element oscillation microbalance (TEOM) PM2.5 monitors operated by the local air agency at three sites in the Seattle area. FE(NO) is a marker of airway inflammation and is elevated in individuals with asthma. Previously, we reported that offline measurements of FE(NO) are associated with 24-hr average PM2.5 in a panel of 19 children with asthma in Seattle. In the present study using the same children, we used a polynomial distributed lag model to assess the association between hourly lags in PM2.5 exposure and FE(NO) levels. Our model controlled for age, ambient NO levels, temperature, relative humidity, and modification by use of inhaled corticosteroids. We found that FE(NO) was associated with hourly averages of PM2.5 up to 10-12 hr after exposure. The sum of the coefficients for the lag times associated with PM2.5 in the distributed lag model was 7.0 ppm FE(NO). The single-lag-model FE(NO) effect was 6.9 [95% confidence interval (CI), 3.4 to 10.6 ppb] for a 1-hr lag, 6.3 (95% CI, 2.6 to 9.9 ppb ) for a 4-hr lag, and 0.5 (95% CI, -1.1 to 2.1 ppb) for an 8-hr lag. These data provide new information concerning the lag structure between PM2.5 exposure and a respiratory health outcome in children with asthma.

Age Factors↗

PubMed2005

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 microm (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.

Air Pollutants↗

PubMed2005

Associations between health effects and particulate matter and black carbon in subjects with respiratory disease.

We measured fractional exhaled nitric oxide (FE(NO)), spirometry, blood pressure, oxygen saturation of the blood (SaO2), and pulse rate in 16 older subjects with asthma or chronic obstructive pulmonary disease (COPD) in Seattle, Washington. Data were collected daily for 12 days. We simultaneously collected PM10 and PM2.5 (particulate matter < or = 10 microm or < or = 2.5 microm, respectively) filter samples at a central outdoor site, as well as outside and inside the subjects' homes. Personal PM10 filter samples were also collected. All filters were analyzed for mass and light absorbance. We analyzed within-subject associations between health outcomes and air pollution metrics using a linear mixed-effects model with random intercept, controlling for age, ambient relative humidity, and ambient temperature. For the 7 subjects with asthma, a 10 microg/m3 increase in 24-hr average outdoor PM10 and PM2.5 was associated with a 5.9 [95% confidence interval (CI), 2.9-8.9] and 4.2 ppb (95% CI, 1.3-7.1) increase in FE(NO), respectively. A 1 microg/m3 increase in outdoor, indoor, and personal black carbon (BC) was associated with increases in FE(NO) of 2.3 ppb (95% CI, 1.1-3.6), 4.0 ppb (95% CI, 2.0-5.9), and 1.2 ppb (95% CI, 0.2-2.2), respectively. No significant association was found between PM or BC measures and changes in spirometry, blood pressure, pulse rate, or SaO2 in these subjects. Results from this study indicate that FE(NO) may be a more sensitive marker of PM exposure than traditional health outcomes and that particle-associated BC is useful for examining associations between primary combustion constituents of PM and health outcomes.

Age Factors↗

PubMed2004

Applying open-path FTIR with computed tomography to evaluate personal exposures. Part 1: simulation studies.

This paper presents the theoretical background and the numerical evaluation results obtained using computed tomography coupled with open-path Fourier transform infrared (CT-FTIR) measurements to estimate personal exposures. In this simulation study, we first tested the one-dimensional scenario with a five-beam segment geometry. A series of Gaussian plumes and the corresponding path-integrated concentrations (PICs) were simulated. The personal exposures were estimated as the average of the point estimates calculated from the workers' locations and the concentration profiles reconstructed from the Smooth Basis Function Minimization algorithm. It was found that the running-average PIC updating strategy has similar performance as the spline PIC updating strategy. However, the latter strategy gives delayed estimates of the workers' exposures since it requires additional measurements before and after the time period of interest. In the two-dimensional scenario, we simulated a series of single-mode bivariate Gaussian plumes with a nine-beam radial geometry. The average of the estimated exposures from the CT-FTIR approach was close to the average of the true exposures. The concordance correlation factors between the true and estimated exposures were reasonably good (between 0.50 and 0.58). This study demonstrated that the CT-FTIR approach is feasible for industrial hygiene monitoring.

Algorithms↗

PubMed2004

Applying open-path FTIR with computed tomography to evaluate personal exposures. Part 2: experimental studies.

This paper presents the experimental evaluation results of using computed tomography coupled with OP-FTIR (CT-FTIR) measurement to estimate personal exposures. Experimental data were collected inside a ventilation chamber with a remote controlled robot as a surrogate for a real human. While the robot moved inside the chamber, a tracer gas (carbon monoxide) was released from a line source. A personal sampling device measured the true exposure on the robot. The estimated personal exposures were calculated from both the area sampling array data and the CT-FTIR measurements along with the information about the robot's locations in real time. The location information was obtained by applying image analysis on recorded digital videotapes. The average slopes of the regression lines between the true and estimated exposures was 0.76 with 1 included in the 95% confidence interval. The concordance correlation factor (CCF) between the true and the CT-FTIR estimated exposures was 0.52, which was similar to the findings from previous simulation studies. Kriging the area sampling array data with an exponential algorithm instead of a liner algorithm improved the CCF value from 0.60 to 0.75. This suggests that using a different basis function for the SBFM algorithm might improve the performance of our estimation approach. Based on the sensitivity and specificity analysis of the experimental data, we demonstrated that this approach is suitable as a warning system.

Algorithms↗

PubMed2004

An analysis of the association between respiratory symptoms in subjects with asthma and daily air pollution in Spokane, Washington.

The association between respiratory symptoms and ambient levels of particulate matter (PM) air pollution has been the focus of several panel studies. The majority of studies focused only on PM10, were conducted for relatively short periods, reported peak flow data, and involved children with asthma. The goal of our study was to evaluate the effect of particulate matter of various size fractions (PM10, PM2.5, PM1.0, and PM coarse fraction) on respiratory symptoms in both adults and children with asthma monitored over many months. Daily diary data on respiratory symptoms and medication use were collected. Air pollution data were collected by the local air agency and Washington State University. Data were collected in Spokane, WA, a semiarid city with diverse sources of particulate matter, including motor vehicles, woodstoves, agricultural burning, resuspended road dust, and dust storms. Sixteen adults and nine children living in Spokane participated in the study. The majority of adult subjects participated for over 1 yr and the children were studied for over 8 mo. In the children, we found a strong association between cough and PM10, PM2.5, PM coarse fraction, and PM1.0(p < .05). Sputum production and runny nose were associated with PM10and coarse fraction. However, no association was found between the presence of any respiratory symptom any PM metric in the adult subjects. These positive associations between various metrics of PM and respiratory symptoms in children suggest that children are more sensitive than adults to the effects of increased levels of PM air pollution or that the central site monitor was more representative for children who spend more time outdoors than adults. These findings also suggest that both larger and smaller particles can aggravate asthma symptoms.

Adolescent↗

PubMed2004

Analysis of ambient particle size distributions using Unmix and positive matrix factorization.

Hourly averaged particle size distributions measured at a centrally located urban site in Seattle were analyzed through the application of bilinear positive matrix factorization (PMF) and Unmix to study underlying size distributions and their daily patterns. A total of 1051 samples each with 16 size intervals from 20 to 400 nm were obtained from a differential mobility particle sizer operating between December 2000 and February 2001. Both PMF and Unmix identify four similar underlying factors in the size distributions. Factor 1 is an accumulation mode particle size spectrum that shows a regular nocturnal pattern, and factor 2 is a larger particle distribution. Factor 3 is assigned as a traffic-related particle distribution, based on its correlations with accompanying gas-phase measurements, and has a regular weekday-high rush-hour pattern. Factor 4 is a traffic-related particle size distribution that has a regular rush-hour pattern on weekdays as well as weekends. Conditional probability functions (CPF) were computed using wind profiles and factor contributions. The results of CPF analysis suggest that these factors are correlated with surrounding particle sources of wood burning, secondary aerosol, diesel emissions, and motor vehicle emissions.

Aerosols↗

PubMed2003

Source apportionment of PM2.5 at an urban IMPROVE site in Seattle, Washington.

The multivariate receptor models Positive Matrix Factorization (PMF) and Unmix were used along with the EPA's Chemical Mass Balance model to deduce the sources of PM2.5 at a centrally located urban site in Seattle, WA. A total of 289 filter samples were obtained with an IMPROVE sampler from 1996 through 1999 and were analyzed for 31 particulate elements including temperature-resolved fractions of the particulate organic and elemental carbon. All three receptor models predicted that the major sources of PM2.5 were vegetative burning (including wood stoves), mobile sources, and secondary particle formation with lesser contributions from resuspended soil and sea spray. The PMF and Unmix models were able to resolve a fuel oil combustion source as well as distinguish between diesel emissions and other mobile sources. In addition, the average source contribution estimates via PMF and Unmix agreed well with an existing emissions inventory. Using the temperature-resolved organic and elemental carbon fractions provided in the IMPROVE protocol, rather than the total organic and elemental carbon, allowed the Unmix model to separate diesel from other mobile sources. The PMF model was able to do this without the additional carbon species, relying on selected trace elements to distinguish the various combustion sources.

Air Pollutants↗

PubMed2002

Comparison of light scattering devices and impactors for particulate measurements in indoor, outdoor, and personal environments.

Short-term monitoring of individual particulate matter (PM) exposures on subjects and inside residences in health effect studies have been sparse due to the lack of adequate monitoring devices. The recent development of small and portable light scattering devices, including the Radiance nephelometer (neph) and the personal DataRAM (pDR) has made this monitoring possible. This paper evaluates the performance of both the passive pDR and neph (without any size fractionation inlet) against measurements from both Harvard impactors (HI2.5) and Harvard personal environmental monitors (HPEM2.5) for PM2.5 in indoor, outdoor, and personal settings. These measurements were taken at the residences and on the person of nonsmoking elderly subjects across the metropolitan Seattle area and represent a wide range of light scattering measurements directly related to exposures and health effects. At low PM levels, nephs provided finer resolution and more precise measurements (precision = 3-8% and uncertainty = 2.8 x 10(-7) m(-1) or <1 microg/m3) than the pDRs. The unbiased precision of pDRs above 10 microg/m3 is around 5% (with an unbiased uncertainty of 4.4 microg/m3). The 24-h average responses of the pDR and neph, as compared to 24-h integrated gravimetric measurements, are not affected by indoor sources of PM. When regressed against 24-h gravimetric measurements, nephs showed higher coefficients of determination (R2 = 0.81-0.93) than pDRs (R2 = 0.77-0.84). The default mass calibration on the pDRs generally overestimated indoor HI2.5 measurements by 56%. When carried by subjects, the pDR overestimated the HPEM2.5 measurements by approximately 27%. Collocated real-time indoor nephs and pDRs at diverse residential sites had varied coefficients of determination across homes (R2 = 0.75-0.96), and the difference between pDR and neph responses increased during cooking hours. This difference was larger during baking or frying episodes than during other cooking or cleaning activities. Relative humidity, ranging between 25% and 64% indoors in our study, was not a significant factor affecting the differences in neph or pDR response. In summary, for nonsmoking residences, the mass scattering efficiency (m2/g) of a stationary indoor neph on a 24-h basis does not vary by residence, including residences with and without cooking activities. This is also true forthe pDR. These same stationary indoor pDRs and nephs correlate well with each other, even on a 10-min basis, in the absence of indoor source activities. The fact that these activities comprised a relatively small percentage (cooking + cleaning = 2.3%) of the overall sampling time meant that the overall correlation between these two instruments for all time periods was good. However, when examining the cooking and cleaning periods separately, the correlation was not very good. Thus, during these short-term PM episodes, the 24-h average calibrations versus gravimetric mass should be used with caution. Both devices should be potentially useful in future exposure assessment and health effects studies.

Air Pollution, Indoor↗

PubMedDate not supplied

Ambient woodsmoke and associated respiratory emergency department visits in Spokane, Washington.

Three multivariate receptor algorithms were applied to seven years of chemical speciation data to apportion fine particulate matter to various sources in Spokane, Washington. Source marker compounds were used to assess the associations between atmospheric concentration of these compounds and daily cardiac hospital admissions and/or respiratory emergency department visits. Total carbon and arsenic had high correlations with two different vegetative burning sources and were selected as vegetative burning markers, while zinc and silicon were selected as markers for the motor vehicle and airborne soil sources, respectively. The rate of respiratory emergency department visits increased 2% for a 3.0 microg/m3 interquartile range change in a vegetative burning source marker (1.023, 95% CI 1.009-1.038) at a lag of one day. The other source markers studied were not associated with the health outcomes investigated. Results suggest vegetative burning is associated with acute respiratory events.

Air Pollutants↗