David A. Olson

Traffic and Meteorological Impacts on Near-Road Air Quality: Summary of Methods and Trends from the Raleigh Near- Road Study

Abstract A growing number of epidemiological studies conducted worldwide suggest an increase in the occurrence of adverse health effects in populations living, working, or going to school near major roadways. A study was designed to assess traffic emissions impacts on air quality and particle toxicity near a heavily traveled highway. In an attempt to describe the complex mixture of pollutants and atmospheric transport mechanisms affecting pollutant dispersion in this near-highway environment, several real-time and time-integrated sampling devices measured air quality concentrations at multiple distances and heights from the road. Pollutants analyzed included U.S. Environmental Protection Agency (EPA)-regulated gases, particulate matter (coarse, fine, and ultrafine), and air toxics. Pollutant measurements were synchronized with real-time traffic and meteorological monitoring devices to provide continuous and integrated assessments of the variation of near-road air pollutant concentrations and particle toxicity with changing traffic and environmental conditions, as well as distance from the road. Measurement results demonstrated the temporal and spatial impact of traffic emissions on near-road air quality. The distribution of mobile source emitted gas and particulate pollutants under all wind and traffic conditions indicated a higher proportion of elevated concentrations near the road, suggesting elevated exposures for populations spending significant amounts of time in this microenvironment. Diurnal variations in pollutant concentrations also demonstrated the impact of traffic activity and meteorology on near-road air quality. Time-resolved measurements of multiple pollutants demonstrated that traffic emissions produced a complex mixture of criteria and air toxic pollutants in this microenvironment. These results provide a foundation for future assessments of these data to identify the relationship of traffic activity and meteorology on air quality concentrations and population exposures.

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.

Trueness, precision, and detectability for sampling and analysis of organic species in airborne particulate matter

Recovery, precision, limits of detection and quantitation, blank levels, calibration linearity, and agreement with certified reference materials were determined for two classes of organic components of airborne particulate matter, polycyclic aromatic hydrocarbons and hopanes, using typical sampling and gas chromatography/mass spectrometry analysis methods. These determinations were based on initial method proficiency tests and on-going internal quality control procedures. Recoveries generally ranged from 75% to 85% for all target analytes and collocated sample precision estimates were generally better than 20% for polycyclic aromatic hydrocarbons and better than 25% for hopanes. Results indicated substantial differences in data quality between the polycyclic aromatic hydrocarbons and hopanes. Polycyclic aromatic hydrocarbons demonstrated better collocated precision, lower method detection limits, lower blank levels, and better agreement with certified reference materials than the hopanes. The most serious area of concern was the disagreement between measured and expected values in the standard reference material for hopanes. With this exception, good data quality was demonstrated for all target analytes on all other data quality indicators.

Predicting Thermal Behavior of Secondary Organic Aerosols

Volume concentrations of secondary organic aerosol (SOA) are measured in 139 steady-state, single precursor hydrocarbon oxidation experiments after passing through a temperature controlled inlet. The response to change in temperature is well predicted through a feedforward Artificial Neural Network. The most parsimonious model, as indicated by Akaikes Information Criterion, Corrected (AIC,C), utilizes 11 input variables, a single hidden layer of 4 tanh activation function nodes, and a single linear output function. This model predicts thermal behavior of single precursor aerosols to less than ±5%, which is within the measurement uncertainty, while limiting the problem of overfitting. Prediction of thermal behavior of SOA can be achieved by a concise number of descriptors of the precursor hydrocarbon including the number of internal and external double bonds, number of methyl- and ethyl- functional groups, molecular weight, and number of ring structures, in addition to the volume of SOA formed, and an indicator of which of four oxidant precursors was used to initiate reactions (NOx photo-oxidation, photolysis of H2O2, ozonolysis, or thermal decomposition of N2O5). Additional input variables, such as chamber volumetric residence time, relative humidity, initial concentration of oxides of nitrogen, reacted hydrocarbon concentration, and further descriptors of the precursor hydrocarbon, including carbon number, number of oxygen atoms, and number of aromatic ring structures, lead to over fit models, and are unnecessary for an efficient, accurate predictive model of thermal behavior of SOA. This work indicates that predictive statistical modeling methods may be complementary to descriptive techniques for use in parametrization of air quality models.