Source identification of PM2.5 in Steubenville, Ohio using a hybrid method for highly time-resolved data.

A new source-type identification method, Reduction and Species Clustering Using Episodes (ReSCUE), was developed to exploit the temporal synchronicity typically observed between ambient species in high time resolution fine particulate matter (PM2.5) data to form clusters that vary together. High time-resolution (30 min) PM2.5 sampling was conducted for a month during the summer of 2006 in Steubenville, OH, an EPA designated nonattainment area for the U.S. National Ambient Air Quality Standards (NAAQS). When the data were evaluated, the species clusters from ReSCUE matched extremely well with the source types identified by EPA Unmix demonstrating that ReSCUE is a valuable tool in identifying source types. Results from EPA Unmix show that contributions to PM2.5 are mostly from iron/steel manufacturing (36% ± 9%), crustal matter (33% ± 11%), and coal combustion (11% ± 19%). More importantly, ReSCUE was useful in (i) providing objective data driven guidance for the number of source factors and key fitting species for EPA Unmix, and (ii) detecting tenuous associations between some species and source types in the results derived by EPA Unmix.

Source apportionment of ambient fine particulate matter in Dearborn, Michigan, using hourly resolved PM chemical composition data.

High time-resolution aerosol sampling was conducted for one month during July-August 2007 in Dearborn, MI, a non-attainment area for fine particulate matter (PM2.5) National Ambient Air Quality Standards (NAAQS). Measurements of more than 30 PM2.5 species were made using a suite of semi-continuous sampling and monitoring instruments. Dynamic variations in the sub-hourly concentrations of source 'marker' elements were observed when discrete plumes from local sources impacted the sampling site. Hourly averaged PM2.5 composition data for 639 samples were used to identify and apportion PM2.5 emission sources using the multivariate receptor modeling techniques EPA Positive Matrix Factorization (PMF) v4.2 and EPA Unmix v6.0. Source contribution estimates from PMF and Unmix were then evaluated using the Sustained Wind Instance Method (SWIM), which identified plausible source origins. Ten sources were identified by both PMF and Unmix: (1) secondary sulfate, (2) secondary nitrate characterized by a significant diurnal trend, (3) iron and steel production, (4) a potassium-rich factor attributable to iron/steel slag waste processing, (5) a cadmium-rich factor attributable to incineration, (6) an oil refinery characterized by La/Ce>1 specific to south wind, (7) oil combustion, (8) coal combustion, (9) motor vehicles, and (10) road dust enriched with organic carbon. While both models apportioned secondary sulfate, oil refinery, and oil combustion PM2.5 masses closely, the mobile and industrial source apportionments differed. Analyses were also carried out to help infer time-of-day variations in the contributions of local sources.

The Near-Road Exposures and Effects of Urban Air Pollutants Study (NEXUS): study design and methods.

The Near-Road Exposures and Effects of Urban Air Pollutants Study (NEXUS) was designed to examine the relationship between near-roadway exposures to air pollutants and respiratory outcomes in a cohort of asthmatic children who live close to major roadways in Detroit, Michigan USA. From September 2010 to December 2012 a total of 139 children with asthma, ages 6-14, were enrolled in the study on the basis of the proximity of their home to major roadways that carried different amounts of diesel traffic. The goal of the study was to investigate the effects of traffic-associated exposures on adverse respiratory outcomes, biomolecular markers of inflammatory and oxidative stress, and how these exposures affect the frequency and severity of respiratory viral infections in a cohort of children with asthma. An integrated measurement and modeling approach was used to quantitatively estimate the contribution of traffic sources to near-roadway air pollution and evaluate predictive models for assessing the impact of near-roadway pollution on children's exposures. Two intensive field campaigns were conducted in Fall 2010 and Spring 2011 to measure a suite of air pollutants including PM2.5 mass and composition, oxides of nitrogen (NO and NO2), carbon monoxide, and black carbon indoors and outdoors of 25 participants' homes, at two area schools, and along a spatial transect adjacent to I-96, a major highway in Detroit. These data were used to evaluate and refine models to estimate air quality and exposures for each child on a daily basis for the health analyses. The study design and methods are described, and selected measurement results from the Fall 2010 field intensive are presented to illustrate the design and successful implementation of the study. These data provide evidence of roadway impacts and exposure variability between study participants that will be further explored for associations with the health measures.

Separating the air quality impact of a major highway and nearby sources by nonparametric trajectory analysis.

Nonparametric Trajectory Analysis (NTA), a receptor-oriented model, was used to assess the impact of local sources of air pollution at monitoring sites located adjacent to highway I-15 in Las Vegas, NV. Measurements of black carbon, carbon monoxide, nitrogen oxides, and sulfur dioxide concentrations were collected from December 2008 to December 2009. The purpose of the study was to determine the impact of the highway at three downwind monitoring stations using an upwind station to measure background concentrations. NTA was used to precisely determine the contribution of the highway to the average concentrations measured at the monitoring stations accounting for the spatially heterogeneous contributions of other local urban sources. NTA uses short time average concentrations, 5 min in this case, and constructed local back-trajectories from similarly short time average wind speed and direction to locate and quantify contributions from local source regions. Averaged over an entire year, the decrease of concentrations with distance from the highway was found to be consistent with previous studies. For this study, the NTA model is shown to be a reliable approach to quantify the impact of the highway on local air quality in an urban area with other local sources.

Application of EPA unmix and nonparametric wind regression on high time resolution trace elements and speciated mercury in Tampa, Florida aerosol.

Intensive ambient air sampling was conducted in Tampa, FL, during October and November of 2002. Fine particulate matter (PM(2.5)) was collected at 30 min resolution using the Semicontinuous Elements in Aerosol Sampler II (SEAS-II) and analyzed off-line for up to 45 trace elements by high-resolution ICPMS (HR-ICPMS). Divalent reactive gaseous mercury and particulate bound mercury were also measured semicontinuously (2 h). Application of the United States Environmental Protection Agency's (EPA) Unmix receptor model on the 30 min resolution trace metals data set identified eight possible sources: residual oil combustion, lead recycling, coal combustion, a Cd-rich source, biomass burning, marine aerosol, general industrial, and coarse dust contamination. The source contribution estimates from EPA Unmix were then run in a nonparametric wind regression (NWR) model, which convincingly identified plausible source origins. When the 30 min ambient concentrations of trace elements were time integrated (2 h) and combined with speciated mercury concentrations, the model identified only four sources, some of which appeared to be merged source profiles that were identified as separate sources by using the 30 min resolution data. This work demonstrates that source signatures that can be captured at 30 min resolution may be lost when sampling for longer durations.

Source region identification using kernel smoothing.

As described in this paper, nonparametric wind regression is a source-to-receptor source apportionment model that can be used to identify and quantify the impact of possible source regions of pollutants as defined by wind direction sectors. It is described in detail with an example of its application to SO2 data from East St. Louis, IL. The model uses nonparametric kernel smoothing methods to apportion the observed average concentration of a pollutant to sectors defined by ranges of wind direction and speed. Formulas are given for the uncertainty of all of the important components of the model, and these are found to give nearly the same uncertainties as blocked bootstrap estimates of uncertainty. The model was applied to data for the first quarter (January, February, and March) of 2003, 2004, and 2005. The results for East St. Louis show that almost 50% of the average SO2 concentration can be apportioned to two 30 degrees wide wind sectors containing a zinc smelter and a brewery; a nearby steel mill did not appearto have a significant impact on SO2 during this period.

A source-to-dose assessment of population exposures to fine PM and ozone in Philadelphia, PA, during a summer 1999 episode.

A novel source-to-dose modeling study of population exposures to fine particulate matter (PM(2.5)) and ozone (O(3)) was conducted for urban Philadelphia. The study focused on a 2-week episode, 11-24 July 1999, and employed the new integrated and mechanistically consistent source-to-dose modeling framework of MENTOR/SHEDS (Modeling Environment for Total Risk studies/Stochastic Human Exposure and Dose Simulation). The MENTOR/SHEDS application presented here consists of four components involved in estimating population exposure/dose: (1) calculation of ambient outdoor concentrations using emission-based photochemical modeling, (2) spatiotemporal interpolation for developing census-tract level outdoor concentration fields, (3) calculation of microenvironmental concentrations that match activity patterns of the individuals in the population of each census tract in the study area, and (4) population-based dosimetry modeling. It was found that the 50th percentiles of calculated microenvironmental concentrations of PM(2.5) and O(3) were significantly correlated with census-tract level outdoor concentrations, respectively. However, while the 95th percentiles of O(3) microenvironmental concentrations were strongly correlated with outdoor concentrations, this was not the case for PM(2.5). By further examining the modeled estimates of the 24-h aggregated PM(2.5) and O(3) doses, it was found that indoor PM(2.5) sources dominated the contributions to the total PM(2.5) doses for the upper 5 percentiles, Environmental Tobacco Smoking (ETS) being the most significant source while O(3) doses due to time spent outdoors dominated the contributions to the total O(3) doses for the upper 5 percentiles. The MENTOR/SHEDS system presented in this study is capable of estimating intake dose based on activity level and inhalation rate, thus completing the source-to-dose modeling sequence. The MENTOR/SHEDS system also utilizes a consistent basis of source characterization, exposure factors, and human activity patterns in conducting population exposure assessment of multiple co-occurring air pollutants, and this constitutes a primary distinction from previous studies of population exposure assessment, where different exposure factors and activity patterns would be used for different pollutants. Future work will focus on incorporating the effects of commuting patterns on population exposure/dose assessments as well as on extending the MENTOR/SHEDS applications to seasonal/annual studies and to other areas in the U.S.