Ram Vedantham

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 (PM2.5) and ozone (O3) 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 PM2.5 and O3 were significantly correlated with census-tract level outdoor concentrations, respectively. However, while the 95th percentiles of O3 microenvironmental concentrations were strongly correlated with outdoor concentrations, this was not the case for PM2.5. By further examining the modeled estimates of the 24-h aggregated PM2.5 and O3 doses, it was found that indoor PM2.5 sources dominated the contributions to the total PM2.5 doses for the upper 5 percentiles, Environmental Tobacco Smoking (ETS) being the most significant source while O3 doses due to time spent outdoors dominated the contributions to the total O3 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.

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 Agencys (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.

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.

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.

Combining continuous near–road monitoring and inverse modeling to isolate the effect of highway expansion on a school in Las Vegas

The impact of a highway expansion on a school adjacent to the highway is investigated with a novel method called the Sustained Wind Incidence Method (SWIM). SWIM falls under the broad group of environmental forensics methods where measured concentration data are used to identify possible contributors such as a point, line or a sectional source. SWIM helps to identify potential sources by highlighting spatial domains associated with the markers unique to potential contributors. In this study, SWIM is used to identify sources of traffic related emissions. The marker used to measure the impact of the traffic due to expansion is black carbon (BC), a key traffic related emission mostly associated with large vehicles (>12 m in length), collected both before and after the expanded lanes were open for use. Using this method, multiple source domains may be simultaneously identified. For this study, the data collection site was situated at the school about 20 meters from the sound wall (7 meters high) separating the school and the highway. SWIM results show that the road expansions may have impacted the traffic patterns of the nearby non–highway feeder road and on–ramp (adjacent to the sound wall) traffic to the highway. This sector showed a surprisingly larger change than the highway in the observed increase in their relative contribution to the receptor site. Some domains (apportioned sector) show a dramatic increase ranging roughly from 10% to 50% in relative contributions. Using the output from SWIM and knowledge of local contributors, a local source landscape is painted.