The ability of certain components of particulate matter to induce oxidative stress through the generation of reactive oxygen species (ROS) in vivo may be one mechanism accounting for observed linkages between ambient aerosols and adverse health outcomes. A variety of assays have been used to measure this so-called aerosol oxidative potential. We developed a semi-automated system to quantify oxidative potential of filter aqueous extracts utilizing the dithiothreitol (DTT) assay and report here the development of a similar semi-automated system for the ascorbic acid (AA) assay. Approximately 500 PM_2.5 filter samples collected in contrasting locations in the southeastern US were analyzed for a host of aerosol species, along with AA and DTT activities. We present a detailed contrast in findings from these two assays. Water-soluble AA activity was higher in summer and fall than in winter, with highest levels near heavily trafficked highways, whereas DTT activity was higher in winter compared to summer and fall and more spatially homogeneous. AA activity was nearly exclusively correlated with water-soluble Cu (r = 0.70–0.94 at most sites), whereas DTT activity was correlated with organic and metal species. Source apportionment models, positive matrix factorization (PMF) and a chemical mass balance method with ensemble-averaged source impact profiles (CMB-E), suggest a strong contribution from traffic emissions and secondary processes (e.g., organic aerosol oxidation or metals mobilization by secondary acids) to both AA and DTT activities in urban Atlanta. In contrast, biomass burning was a large source for DTT activity, but insignificant for AA. AA activity was not correlated with PM_2.5 mass, while DTT activity co-varied strongly with mass (r = 0.49–0.86 across sites and seasons). Various linear models were developed to estimate AA and DTT activities for the central Atlanta Jefferson Street site, based on the CMB-E sources. The models were then used to estimate daily oxidative potential at this site over the 1998–2009 period. Time series epidemiological analyses were conducted to assess daily emergency department (ED) visits data for the five-county Atlanta metropolitan area based on the estimated 10-year backcast oxidative potential. Estimated AA activity was not statistically associated with any tested health outcome, while DTT activity was associated with ED visits for both asthma or wheeze and congestive heart failure. The findings point to the importance of both organic components and transition metals from biomass burning and mobile sources to adverse health outcomes in this region.

We assess the potential of the water-soluble fraction of atmospheric fine aerosols in the southeastern United States to generate reactive oxygen species (ROS) and identify major ROS-associated emission sources. ROS-generation potential of particles was quantified by the dithiothreitol (DTT) assay and involved analysis of fine particulate matter (PM) extracted from high-volume quartz filters (23 h integrated samples) collected at various sites in different environmental settings in the southeast, including three urban-Atlanta sites, in addition to a rural site. Paired sampling was conducted with one fixed site in Atlanta (Jefferson Street), representative of the urban environment, with the others rotating among different sites, for ~250 days between June 2012 and September 2013 (N=483). A simple linear regression between the DTT activity and aerosol chemical components revealed strong associations between PM ROS-generation potential and secondary organic aerosol (WSOC – water-soluble organic carbon) in summer, and biomass burning markers in winter. Redox-active metals were also somewhat correlated with the DTT activity, but mostly at urban and roadside sites. Positive matrix factorization (PMF) was applied to apportion the relative contribution of various sources to the ROS-generation potential of water-soluble PM_2.5 in urban Atlanta. PMF showed that vehicular emissions contribute uniformly throughout the year (12–25%), while secondary oxidation processes dominated the DTT activity in summer (46%) and biomass burning in winter (47%). Road dust was significant only during drier periods (~12% in summer and fall). Source apportionment by chemical mass balance (CMB) was reasonably consistent with PMF, but with higher contribution from vehicular emissions (32%). Given the spatially large data set of PM sampled over an extended period, the study reconciles the results from previous work that showed only region- or season-specific aerosol components or sources contributing to PM ROS activity, possibly due to smaller sample sizes. The ubiquitous nature of the major sources of PM-associated ROS suggests widespread population exposures to aerosol components that have the ability to catalyze the production of oxidants in vivo.

Nitrogen oxides (NO_x) and ammonia (NH₃) from anthropogenic and biogenic emissions are central contributors to particulate matter (PM) concentrations worldwide. The response of PM to changes in the emissions of both compounds is typically studied on a case-by-case basis, owing in part to the complex thermodynamic interactions of these aerosol precursors with other PM constituents. Here we present a simple but thermodynamically consistent approach that expresses the chemical domains of sensitivity of aerosol particulate matter to NH₃ and HNO₃ availability in terms of aerosol pH and liquid water content. From our analysis, four policy-relevant regimes emerge in terms of sensitivity: (i) NH₃ sensitive, (ii) HNO₃ sensitive, (iii) NH₃ and HNO₃ sensitive, and (iv) insensitive to NH₃ or HNO₃. For all regimes, the PM remains sensitive to nonvolatile precursors, such as nonvolatile cations and sulfate. When this framework is applied to ambient measurements or predictions of PM and gaseous precursors, the “chemical regime” of PM sensitivity to NH₃ and HNO₃ availability is directly determined. The use of these regimes allows for novel insights, and this framework is an important tool to evaluate chemical transport models. With this extended understanding, aerosol pH and associated liquid water content naturally emerge as previously ignored state parameters that drive PM formation.

Background: Two distinctly different types of measurement error are Berkson and classical. Impacts of measurement error in epidemiologic studies of ambient air pollution are expected to depend on error type. We characterize measurement error due to instrument imprecision and spatial variability as multiplicative (i.e. additive on the log scale) and model it over a range of error types to assess impacts on risk ratio estimates both on a per measurement unit basis and on a per interquartile range (IQR) basis in a time-series study in Atlanta. Methods: Daily measures of twelve ambient air pollutants were analyzed: NO₂, NO_x, O₃, SO₂, CO, PM₁₀ mass, PM_2.5 mass, and PM_2.5 components sulfate, nitrate, ammonium, elemental carbon and organic carbon. Semivariogram analysis was applied to assess spatial variability. Error due to this spatial variability was added to a reference pollutant time-series on the log scale using Monte Carlo simulations. Each of these time-series was exponentiated and introduced to a Poisson generalized linear model of cardiovascular disease emergency department visits. Results: Measurement error resulted in reduced statistical significance for the risk ratio estimates for all amounts (corresponding to different pollutants) and types of error. When modelled as classical-type error, risk ratios were attenuated, particularly for primary air pollutants, with average attenuation in risk ratios on a per unit of measurement basis ranging from 18% to 92% and on an IQR basis ranging from 18% to 86%. When modelled as Berkson-type error, risk ratios per unit of measurement were biased away from the null hypothesis by 2% to 31%, whereas risk ratios per IQR were attenuated (i.e. biased toward the null) by 5% to 34%. For CO modelled error amount, a range of error types were simulated and effects on risk ratio bias and significance were observed. Conclusions: For multiplicative error, both the amount and type of measurement error impact health effect estimates in air pollution epidemiology. By modelling instrument imprecision and spatial variability as different error types, we estimate direction and magnitude of the effects of error over a range of error types.