Amit Marmur

Fine Particle Sources and Cardiorespiratory Morbidity: An Application of Chemical Mass Balance and Factor Analytical Source-Apportionment Methods

Background Interest in the health effects of particulate matter (PM) has focused on identifying sources of PM, including biomass burning, power plants, and gasoline and diesel emissions that may be associated with adverse health risks. Few epidemiologic studies, however, have included source-apportionment estimates in their examinations of PM health effects. We analyzed a time-series of chemically speciated PM measurements in Atlanta, Georgia, and conducted an epidemiologic analysis using data from three distinct source-apportionment methods. Objective The key objective of this analysis was to compare epidemiologic findings generated using both factor analysis and mass balance source-apportionment methods. Methods We analyzed data collected between November 1998 and December 2002 using positive-matrix factorization (PMF), modified chemical mass balance (CMB-LGO), and a tracer approach. Emergency department (ED) visits for a combined cardiovascular (CVD) and respiratory disease (RD) group were assessed as end points. We estimated the risk ratio (RR) associated with same day PM concentrations using Poisson generalized linear models. Results There were significant, positive associations between same-day PM2.5 (PM with aero-dynamic diameter ≤ 2.5 μm) concentrations attributed to mobile sources (RR range, 1.018–1.025) and biomass combustion, primarily prescribed forest burning and residential wood combustion, (RR range, 1.024–1.033) source categories and CVD-related ED visits. Associations between the source categories and RD visits were not significant for all models except sulfate-rich secondary PM2.5 (RR range, 1.012–1.020). Generally, the epidemiologic results were robust to the selection of source-apportionment method, with strong agreement between the RR estimates from the PMF and CMB-LGO models, as well as with results from models using single-species tracers as surrogates of the source-apportioned PM2.5 values. Conclusions Despite differences among the source-apportionment methods, these findings suggest that modeled source-apportioned data can produce robust estimates of acute health risk. In Atlanta, there were consistent associations across methods between PM2.5 from mobile sources and biomass burning with both cardiovascular and respiratory ED visits, and between sulfate-rich secondary PM2.5 with respiratory visits.

Effects of Instrument Precision and Spatial Variability on the Assessment of the Temporal Variation of Ambient Air Pollution in Atlanta, Georgia

Abstract Data from the U.S. Environmental Protection Agency Air Quality System, the Southeastern Aerosol Research and Characterization database, and the Assessment of Spatial Aerosol Composition in Atlanta database for 1999 through 2002 have been used to characterize error associated with instrument precision and spatial variability on the assessment of the temporal variation of ambient air pollution in Atlanta, GA. These data are being used in time series epidemiologic studies in which associations of acute respiratory and cardiovascular health outcomes and daily ambient air pollutant levels are assessed. Modified semivariograms are used to quantify the effects of instrument precision and spatial variability on the assessment of daily metrics of ambient gaseous pollutants (SO2, CO, NOx, and O3) and fine particulate matter ([PM2.5] PM2.5 mass, sulfate, nitrate, ammonium, elemental carbon [EC], and organic carbon [OC]). Variation because of instrument imprecision represented 7–40% of the temporal variation in the daily pollutant measures and was largest for the PM2.5 EC and OC. Spatial variability was greatest for primary pollutants (SO2, CO, NOx, and EC). Population–weighted variation in daily ambient air pollutant levels because of both instrument imprecision and spatial variability ranged from 20% of the temporal variation for O3 to 70% of the temporal variation for SO2 and EC. Wind rose plots, corrected for diurnal and seasonal pattern effects, are used to demonstrate the impacts of local sources on monitoring station data. The results presented are being used to quantify the impacts of instrument precision and spatial variability on the assessment of health effects of ambient air pollution in Atlanta and are relevant to the interpretation of results from time series health studies that use data from fixed monitors.

Evaluation of Fine Particle Number Concentrations in CMAQ

The Community Multiscale Air Quality (CMAQ) model is widely used in air quality management and scientific investigation. Numerous studies have been conducted investigating how well CMAQ simulates fine particle mass concentrations, but relatively few studies have addressed how well CMAQ simulates fine particle number distribution. Accurate simulation of particle number concentrations is important because particle number and surface area concentrations may be directly related to human health and visibility. Simulated fine particle number concentrations derived using CMAQ are compared to measurements to identify problems and to improve model performance. Evaluation is done using measured particle number concentrations in Atlanta, Georgia, from 1/1/1999 to 8/31/2000. While homogeneous binary nucleation mechanism used in CMAQ needs to be modified for better prediction of particle number concentrations, there are also other factors that affect the predicted particle level. Assumed particle size of the primary emissions in CMAQ causes number concentrations to be significantly underestimated, while particle density has a small impact. Assuming particle size distributions by three lognormal modes cannot accurately simulate particles with size less than 0.01 μ m, particularly during nucleation events. An additional mode that accounts for particles smaller than 0.01 μ m can improve the accuracy of the number concentration simulations. Though, the use of the Expectation-Maximization (EM) algorithm to estimate size distribution parameters of measured particles suggests that assumed parameters for the lognormal modes in CMAQ are generally reasonable.

Environmental Risk Assessment: Comparison of Receptor and Air Quality Models for Source Apportionment

ABSTRACT Source apportionment of particulate matter has been commonly performed using receptor models, but studies suggest that the assumptions in receptor models limit the accuracy of results. An alternative approach is the use of three-dimensional source-oriented air quality models. Here, a comparison is conducted between the PM2.5 apportioned from the Chemical Mass Balance (CMB) receptor model using organic tracers as molecular markers with those from the source-based Community Multiscale Air Quality (CMAQ) model. Source apportionment was conducted at sites in the southeastern United States for July 2001 and January 2002. PM2.5 source apportionment results had moderate discrepancies, which originate from different spatial scales, fundamental limitations, and uncertainties of the two models. Results from CMB fluctuated temporally more than real variation due to measurement and source profile errors and uncertainties, whereas those from CMAQ could not capture daily variation well. In addition, results from CMB are mass contributions for the monitoring location, whereas those from CMAQ represent the average mass contributions of the models grid. It is difficult to assess which approach is “better.” Indeed, both models have strengths and limitations, and each models strengths can be utilized to help overcome the other models limitations.