Dataset Information

Modeling particle exposure in U.S. trucking terminals.

ABSTRACT: Multi-tiered sampling approaches are common in environmental and occupational exposure assessment, where exposures for a given individual are often modeled based on simultaneous measurements taken at multiple indoor and outdoor sites. The monitoring data from such studies is hierarchical by design, imposing a complex covariance structure that must be accounted for in order to obtain unbiased estimates of exposure. Statistical methods such as structural equation modeling (SEM) represent a useful alternative to simple linear regression in these cases, providing simultaneous and unbiased predictions of each level of exposure based on a set of covariates specific to the exposure setting. We test the SEM approach using data from a large exposure assessment of diesel and combustion particles in the U.S.trucking industry. The exposure assessment includes data from 36 different trucking terminals across the United States sampled between 2001 and 2005, measuring PM2.5 and its elemental carbon (EC), organic carbon (OC) components, by personal monitoring, and sampling at two indoor work locations and an outdoor "background" location. Using the SEM method, we predict the following: (1) personal exposures as a function of work-related exposure and smoking status; (2) work-related exposure as a function of terminal characteristics, indoor ventilation, job location, and background exposure conditions; and (3) background exposure conditions as a function of weather, nearby source pollution, and other regional differences across terminal sites. The primary advantage of SEMs in this setting is the ability to simultaneously predict exposures at each of the sampling locations, while accounting for the complex covariance structure among the measurements and descriptive variables. The statistically significant results and high R2 values observed from the trucking industry application supports the broader use of this approach in exposure assessment modeling.

SUBMITTER: Davis ME

PROVIDER: S-EPMC1995567 | biostudies-literature | 2006 Jul

REPOSITORIES: biostudies-literature

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Modeling particle exposure in U.S. trucking terminals.

Davis M E ME Smith T J TJ Laden F F Hart J E JE Ryan L M LM Garshick E E

Environmental science & technology 20060701 13

Multi-tiered sampling approaches are common in environmental and occupational exposure assessment, where exposures for a given individual are often modeled based on simultaneous measurements taken at multiple indoor and outdoor sites. The monitoring data from such studies is hierarchical by design, imposing a complex covariance structure that must be accounted for in order to obtain unbiased estimates of exposure. Statistical methods such as structural equation modeling (SEM) represent a useful ...[more]

PMID: 16856739

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Project description:In recent years, significant attention has been given to polycyclic aromatic hydrocarbons (PAHs) exposures given their mutagenic and carcinogenic properties. However, levels of exposure and the key determinants of exposure are not well defined for the trucking industry. We measured ultrafine particle characteristics at 10 trucking terminals of varying operating size and location in the Northeast region of the United States using particle concentration counter and a surface area analyzer. Multivariate mixed-effects linear regression models were used to assess determinants of the concentration of total bound PAHs (tPAH), the total aerosol active surface area (AS), and the ratio tPAH/AS overall and individually within docks, trucking cabs, and administrative offices. Associations between PAH measures with integrated measures of elemental carbon (EC), organic carbon (OC), and particulate matter (PM)2.5 were assessed by Spearman rank correlation. In adjusted models, tPAH, AS, and tPAH/AS average concentrations (95% confidence interval) were significantly higher in truck cabs compared to office locations (1.26 (ng m-3) (1.18, 1.35); 0.99 (mm2 mm-3) (0.91, 1.08); 0.26 (ng mm-2) (0.18, 0.33), respectively). In the loading dock, AS concentrations were significantly higher than in the office (0.67 (0.61, 0.71), while the tPAH/AS was not (-0.63 (-0.67, -0.58). In each location, average tPAH concentrations were moderately but significantly correlated with EC (r = 0.47-0.63) and with tPAH/AS (r = 0.34-0.40) in the truck cabs and loading docks. In conclusion, key predictors of tPAH, AS, and tPAH/AS within the trucking industry are work location (in particular truck cabs and terminal docks) and terminal characteristics (size). The association of tPAH and tPAH/AS with EC concentrations in dockworkers and pick-up and delivery drivers is consistent with occupational exposure attributable to vehicle exhaust. Therefore, measurement of tPAH, AS, and tPAH/AS to characterize ultrafine particles and bound PAH concentrations provide additional information regarding exposures in the trucking industry not captured by integrated measures by EC, OC, and PM2.5.

Project description:BackgroundAirports may represent significant sources of secondhand smoke (SHS) exposure for both travelers and employees. While previously common smoking rooms have largely disappeared from US airports, smoking continues to occur outdoors at terminal entrances. SHS may be especially high at arrival areas, since they oftentimes are partially enclosed by overhead departures, creating stagnant microenvironments. This study assessed particulate matter <2.5 microns in diameter (PM2.5), a common surrogate for SHS, at airport terminal locations to evaluate both outdoor exposure risk and possible indoor drift of SHS from outdoor sources.MethodsA convenience sample of nine airport terminal arrival areas in the US state of Florida was surveyed between February and July 2018. PM2.5 levels were assessed outdoors and indoors at terminal entrances and at control areas far into terminal interiors. We also examined the impact of smoking location on SHS exposure by correlating cigarette and passing vehicle counts with PM2.5 levels at terminals with contrasting proximity of designated smoking locations to terminal entrances.ResultsAlthough outdoor PM2.5 levels (mean 17.9, SD 6.1 µg/m3) were significantly higher than indoors (p < 0.001), there was no difference between indoor areas directly inside terminal entrances and areas much further interior (mean 8.8, SD 2.6 vs mean 8.5, SD 3.0 µg/m3, p=0.49). However, when smoking areas were in close proximity to terminal entrances, the number of lit cigarettes and vehicular traffic per minute predicted 70% of the variance of PM2.5 levels (p < 0.001), which was attributable mostly to the cigarette number (β = 0.83; 95% CI (0.55 to 1.11); p < 0.001). This effect was not observed at smoking areas further away.ConclusionPM2.5 data did not suggest indoor drift from outside smoking. Nevertheless, absolute exposure outdoors was high and correlated with the location of designated smoking areas. Further studies are needed to examine the effect of microclimate formation on exposure risk.

Project description:BackgroundExposure to polycyclic aromatic hydrocarbon (PAH) has been linked to various adverse health outcomes. Personal PAH exposures are usually measured by personal monitoring or biomarkers, which are costly and impractical for a large population. Modeling is a cost-effective alternative to characterize personal PAH exposure although challenges exist because the PAH exposure can be highly variable between locations and individuals in non-occupational settings. In this study we developed models to estimate personal inhalation exposures to particle-bound PAH (PB-PAH) using data from global positioning system (GPS) time-activity tracking data, traffic activity, and questionnaire information.MethodsWe conducted real-time (1-min interval) personal PB-PAH exposure sampling coupled with GPS tracking in 28 non-smoking women for one to three sessions and one to nine days each session from August 2009 to November 2010 in Los Angeles and Orange Counties, California. Each subject filled out a baseline questionnaire and environmental and behavior questionnaires on their typical activities in the previous three months. A validated model was used to classify major time-activity patterns (indoor, in-vehicle, and other) based on the raw GPS data. Multiple-linear regression and mixed effect models were developed to estimate averaged daily and subject-level PB-PAH exposures. The covariates we examined included day of week and time of day, GPS-based time-activity and GPS speed, traffic- and roadway-related parameters, meteorological variables (i.e. temperature, wind speed, relative humidity), and socio-demographic variables and occupational exposures from the questionnaire.ResultsWe measured personal PB-PAH exposures for 180 days with more than 6 h of valid data on each day. The adjusted R2 of the model was 0.58 for personal daily exposures, 0.61 for subject-level personal exposures, and 0.75 for subject-level micro-environmental exposures. The amount of time in vehicle (averaging 4.5% of total sampling time) explained 48% of the variance in daily personal PB-PAH exposure and 39% of the variance in subject-level exposure. The other major predictors of PB-PAH exposures included length-weighted traffic count, work-related exposures, and percent of weekday time.ConclusionWe successfully developed regression models to estimate PB-PAH exposures based on GPS-tracking data, traffic data, and simple questionnaire information. Time in vehicle was the most important determinant of personal PB-PAH exposure in this population. We demonstrated the importance of coupling real-time exposure measures with GPS time-activity tracking in personal air pollution exposure assessment.

Dataset Information

Modeling particle exposure in U.S. trucking terminals.

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Modeling particle exposure in U.S. trucking terminals.

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