The Denver Aerosol Sources and Health (DASH) Study: Overview and Early Findings

Improved understanding of the sources of air pollution that are most harmful could aid in developing more effective measures for protecting human health. The Denver Aerosol Sources and Health (DASH) study was designed to identify the sources of ambient fine particulate matter (PM(2.5)) that are most responsible for the adverse health effects of short-term exposure to PM (2.5). Daily 24-hour PM(2.5) sampling began in July 2002 at a residential monitoring site in Denver, Colorado, using both Teflon and quartz filter samplers. Sampling is planned to continue through 2008. Chemical speciation is being carried out for mass, inorganic ionic compounds (sulfate, nitrate and ammonium), and carbonaceous components, including elemental carbon, organic carbon, temperature-resolved organic carbon fractions and a large array of organic compounds. In addition, water soluble metals were measured daily for 12 months in 2003. A receptor-based source apportionment approach utilizing positive matrix factorization (PMF) will be used to identify PM (2.5) source contributions for each 24-hour period. Based on a preliminary assessment using synthetic data, the proposed source apportionment should be able to identify many important sources on a daily basis, including secondary ammonium nitrate and ammonium sulfate, diesel vehicle exhaust, road dust, wood combustion and vegetative debris. Meat cooking, gasoline vehicle exhaust and natural gas combustion were more challenging for PMF to accurately identify due to high detection limits for certain organic molecular marker compounds. Measurements of these compounds are being improved and supplemented with additional organic molecular marker compounds. The health study will investigate associations between daily source contributions and an array of health endpoints, including daily mortality and hospitalizations and measures of asthma control in asthmatic children. Findings from the DASH study, in addition to being of interest to policymakers, by identifying harmful PM(2.5) sources may provide insights into mechanisms of PM effect.

Source apportionment of exposure to toxic volatile organic compounds using positive matrix factorization

Data from the Total Exposure Assessment Methodology studies, conducted from 1980 to 1987 in New Jersey (NJ) and California (CA), and the 1990 California Indoor Exposure study were analyzed using positive matrix factorization, a receptor-oriented source apportionment model. Personal exposure and outdoor concentrations of 14 and 17 toxic volatile organic compounds (VOCs) were studied from the NJ and CA data, respectively. Analyzing both the personal exposure and outdoor concentrations made it possible to compare toxic VOCs in outdoor air and exposure resulting from personal activities. Regression analyses of the measured concentrations versus the factor scores were performed to determine the relative contribution of each factor to total exposure concentrations. Activity patterns of the NJ and CA participants were examined to determine whether reported exposures to specific sources correspond to higher estimated contributions from the factor identified with that source. For a subset of VOCs, a preliminary analysis to determine irritancy-based contributions of factors to exposures was carried out. Major source types of toxic VOCs in both NJ and CA appear to be aromatic sources resembling automobile exhaust, gasoline vapor, or environmental tobacco smoke for personal exposures, and automobile exhaust or gasoline vapors for outdoor concentrations.

Reliability of optimal control strategies for photochemical air pollution

This study illustrates how consideration of modeling uncertainties can affect optimal control strategies for urban ozone. Control strategies are investigated for illustrative cases of air parcel trajectories ending at Azusa, CA, and Riverside, CA, on August 28, 1987. The control strategies are designed to achieve a specified air quality target with a given reliability, considering uncertainties in the California Institute of Technology's trajectory model and its inputs, including uncertainties in emissions and in the SAPRC-97 chemical mechanism. A decoupled stochastic optimization scheme is used to solve the chance-constrained programming problem. Least-cost control strategies derived using nominal model inputs and parameter values have low reliability for some target O3 concentrations when uncertainties are taken into account. For the case considered, reducing volatile organic compound (VOC) emissions from motor vehicles is identified as the least-cost approach to meeting O3 targets at Azusa. However, the optimal control strategies for Riverside depend on the target O3 concentrations and the level of reliability required. Consideration of model uncertainty is found to shift the focus from VOC controls to nitrogen oxide controls for the Riverside trajectory.

Ozone Control and Methanol Fuel Use

Methanol fuel use in motor vehicles and stationary combustion has the potential to improve air quality. A modeling study of methanol fuel use in Los Angeles, California, shows that the low chemical reactivity of methanol vapor slows ozone formation and would lead to lower ozone concentrations. Predicted peak ozone levels decreased up to 16 percent, and exposure to levels above the federal standard dropped by up to 22 percent, when pure (M100) methanol fuel use was simulated for the year 2000. Similar results were obtained for 2010. Use of a gasoline-methanol blend (M85) resulted in smaller reductions. Predicted formaldehyde levels and exposure were not increased severely, and in some cases declined, in the simulations of methanol use.