Coupling Chemical Transport Model Source Attributions with Positive Matrix Factorization: Application to Two IMPROVE Sites Impacted by Wildfires

Source contributions to total fine particle carbon predicted by a chemical transport model (CTM) were incorporated into the positive matrix factorization (PMF) receptor model to form a receptor-oriented hybrid model. The level of influence of the CTM versus traditional PMF was varied using a weighting parameter applied to an object function as implemented in the Multilinear Engine (ME-2). The methodology provides the ability to separate features that would not be identified using PMF alone, without sacrificing fit to observations. The hybrid model was applied to IMPROVE data taken from 2006 through 2008 at Monture and Sula Peak, Montana. It was able to separately identify major contributions of total carbon (TC) from wildfires and minor contributions from biogenic sources. The predictions of TC had a lower cross-validated RMSE than those from either PMF or CTM alone. Two unconstrained, minor features were identified at each site, a soil derived feature with elevated summer impacts and a feature enriched in sulfate and nitrate with significant, but sporadic contributions across the sampling period. The respective mean TC contributions from wildfires, biogenic emissions, and other sources were 1.18, 0.12, and 0.12 ugC/m3 at Monture and 1.60, 0.44, and 0.06 ugC/m3 at Sula Peak

Source Apportionment of PM_2.5 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

Change in F per 10-μg/m increase in PM in subjects not prescribed ICS therapy

<p><b>Copyright information:</b></p><p>Taken from "Exhaled Nitric Oxide in Children with Asthma and Short-Term PM Exposure in Seattle"</p><p>Environmental Health Perspectives 2005;113(12):1791-1794.</p><p>Published online 8 Aug 2005</p><p>PMCID:PMC1314923.</p><p>This is an Open Access article: verbatim copying and redistribution of this article are permitted in all media for any purpose, provided this notice is preserved along with the article's original DOI.</p> TEOM readings averaged from three sites using GEE model. Error bars indicate 95% confidence intervals

Characterizing Ultrafine Particle Mobile Monitoring Data for Epidemiology

Mobile monitoring is increasingly used to assess exposure to traffic-related air pollutants (TRAPs), including ultrafine particles (UFPs). Due to the rapid spatial decrease in the concentration of UFPs and other TRAPs with distance from roadways, mobile measurements may be non-representative of residential exposures, which are commonly used for epidemiologic studies. Our goal was to develop, apply, and test one possible approach for using mobile measurements in exposure assessment for epidemiology. We used an absolute principal component score model to adjust the contribution of on-road sources in mobile measurements to provide exposure predictions representative of cohort locations. We then compared UFP predictions at residential locations from mobile on-road plume-adjusted versus stationary measurements to understand the contribution of mobile measurements and characterize their differences. We found that predictions from mobile measurements are more representative of cohort locations after down-weighting the contribution of localized on-road plumes. Further, predictions at cohort locations derived from mobile measurements incorporate more spatial variation compared to those from short-term stationary data. Sensitivity analyses suggest that this additional spatial information captures features in the exposure surface not identified from the stationary data alone. We recommend the correction of mobile measurements to create exposure predictions representative of residential exposure for epidemiology

Change in F per 10-μg/m increase in PM () in subjects not prescribed ICS and () in subjects prescribed ICS therapy

<p><b>Copyright information:</b></p><p>Taken from "Exhaled Nitric Oxide in Children with Asthma and Short-Term PM Exposure in Seattle"</p><p>Environmental Health Perspectives 2005;113(12):1791-1794.</p><p>Published online 8 Aug 2005</p><p>PMCID:PMC1314923.</p><p>This is an Open Access article: verbatim copying and redistribution of this article are permitted in all media for any purpose, provided this notice is preserved along with the article's original DOI.</p> TEOM readings were averaged from three central sites (Lynnwood, Lake Forest Park, and Kent) for hourly lags from 1 to 48. Model adjusted for temperature, relative humidity, and age. One-hour averaged PM concentrations ranged from 8.3 μg/m at 3-hr lag to 15.2 at 8-hr lag, suggesting that short time-lag periods rather than peak values may determine this health outcome. Error bars indicate 95% confidence intervals

Impact of Mobile Monitoring Network Design on Air Pollution Exposure Assessment Models

Short-term mobile monitoring campaigns are increasingly used to assess long-term air pollution exposure in epidemiology. Little is known about how monitoring network design features, including the number of stops and sampling temporality, impacts exposure assessment models. We address this gap by leveraging an extensive mobile monitoring campaign conducted in the greater Seattle area over the course of a year during all days of the week and most hours. The campaign measured total particle number concentration (PNC; sheds light on ultrafine particulate (UFP) number concentration), black carbon (BC), nitrogen dioxide (NO2), fine particulate matter (PM2.5), and carbon dioxide (CO2). In Monte Carlo sampling of 7327 total stops (278 sites × 26 visits each), we restricted the number of sites and visits used to estimate annual averages. Predictions from the all-data campaign performed well, with cross-validated R2s of 0.51–0.77. We found similar model performances (85% of the all-data campaign R2) with ∼1000 to 3000 randomly selected stops for NO2, PNC, and BC, and ∼4000 to 5000 stops for PM2.5 and CO2. Campaigns with additional temporal restrictions (e.g., business hours, rush hours, weekdays, or fewer seasons) had reduced model performances and different spatial surfaces. Mobile monitoring campaigns wanting to assess long-term exposure should carefully consider their monitoring designs

Source Attribution of Black Carbon in Arctic Snow

Snow samples obtained at 36 sites in Alaska, Canada, Greenland, Russia, and the Arctic Ocean in early 2007 were analyzed for light-absorbing aerosol concentration together with a suite of associated chemical species. The light absorption data, interpreted as black carbon concentrations, and other chemical data were input into the EPA PMF 1.1 receptor model to explore the sources for black carbon in the snow. The analysis found four factors or sources: two distinct biomass burning sources, a pollution source, and a marine source. The first three of these were responsible for essentially all of the black carbon, with the two biomass sources (encompassing both open and closed combustion) together accounting for >90% of the black carbon

Neighborhood-Scale Spatial Models of Diesel Exhaust Concentration Profile Using 1‑Nitropyrene and Other Nitroarenes

With emerging evidence that diesel exhaust exposure poses distinct risks to human health, the need for fine-scale models of diesel exhaust pollutants is growing. We modeled the spatial distribution of several nitrated polycyclic aromatic hydrocarbons (NPAHs) to identify fine-scale gradients in diesel exhaust pollution in two Seattle, WA neighborhoods. Our modeling approach fused land-use regression, meteorological dispersion modeling, and pollutant monitoring from both fixed and mobile platforms. We applied these modeling techniques to concentrations of 1-nitropyrene (1-NP), a highly specific diesel exhaust marker, at the neighborhood scale. We developed models of two additional nitroarenes present in secondary organic aerosol: 2-nitropyrene and 2-nitrofluoranthene. Summer predictors of 1-NP, including distance to railroad, truck emissions, and mobile black carbon measurements, showed a greater specificity to diesel sources than predictors of other NPAHs. Winter sampling results did not yield stable models, likely due to regional mixing of pollutants in turbulent weather conditions. The model of summer 1-NP had an R² of 0.87 and cross-validated R² of 0.73. The synthesis of high-density sampling and hybrid modeling was successful in predicting diesel exhaust pollution at a very fine scale and identifying clear gradients in NPAH concentrations within urban neighborhoods

Pulmonary Effects of Indoor- and Outdoor-Generated Particles in Children with Asthma-0

<p><b>Copyright information:</b></p><p>Taken from "Pulmonary Effects of Indoor- and Outdoor-Generated Particles in Children with Asthma"</p><p>Environmental Health Perspectives 2005;113(4):499-503.</p><p>Published online 10 Jan 2005</p><p>PMCID:PMC1278493.</p><p>This is an Open Access article: verbatim copying and redistribution of this article are permitted in all media for any purpose, provided this notice is preserved along with the article's original DOI.</p

Approach to Estimating Participant Pollutant Exposures in the Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air)

Most published epidemiology studies of long-term air pollution health effects have relied on central site monitoring to investigate regional-scale differences in exposure. Few cohort studies have had sufficient data to characterize localized variations in pollution, despite the fact that large gradients can exist over small spatial scales. Similarly, previous data have generally been limited to measurements of particle mass or several of the criteria gases. The Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air) is an innovative investigation undertaken to link subclinical and clinical cardiovascular health effects with individual-level estimates of personal exposure to ambient-origin pollution. This project improves on prior work by implementing an extensive exposure assessment program to characterize long-term average concentrations of ambient-generated PM2.5, specific PM2.5 chemical components, and copollutants, with particular emphasis on capturing concentration gradients within cities. This paper describes exposure assessment in MESA Air, including questionnaires, community sampling, home monitoring, and personal sampling. Summary statistics describing the performance of the sampling methods are presented along with descriptive statistics of the air pollution concentrations by city