Abstract
Abstract. A Positive Matrix Factorization receptor model for aerosol pollution source apportionment was fit to a synthetic dataset simulating one year of daily measurements of ambient PM2.5 concentrations, comprised of 39 chemical species from nine pollutant sources. A novel method was developed to estimate model fit uncertainty and bias at the daily time scale, as related to factor contributions. A circular block bootstrap is used to create replicate datasets, with the same receptor model then fit to the data. Neural networks are trained to classify factors based upon chemical profiles, as opposed to correlating contribution time series, and this classification is used to align factor orderings across the model results associated with the replicate datasets. Factor contribution uncertainty is assessed from the distribution of results associated with each factor. Comparing modeled factors with input factors used to create the synthetic data assesses bias. The results indicate that variability in factor contribution estimates does not necessarily encompass model error: contribution estimates can have small associated variability across results yet also be very biased. These findings are likely dependent on characteristics of the data.
Highlights
Air pollution comprised of particulate matter smaller than 2.5μm in aerodynamic diameter (PM2.5) has been associated with a significant increased risk of morbidity and mortality (Dockery et al, 1993; Pope et al, 2002; Peel et al, 2005)
Speciated PM2.5 is quantified including sulfate, nitrate, bulk elemental and organic carbon, trace metals, and trace organic compounds. These speciated PM2.5 data are used as input to a receptor model, Positive Matrix Factorization (PMF), for pollution source apportionment
Having measures of uncertainty associated with the contribution of diesel fuel combustion to PM2.5, at the daily time scale, may lead to more reliable characterization of the role diesel fuel combustion has in daily health effects data
Summary
Air pollution comprised of particulate matter smaller than 2.5μm in aerodynamic diameter (PM2.5) has been associated with a significant increased risk of morbidity and mortality (Dockery et al, 1993; Pope et al, 2002; Peel et al, 2005). Speciated PM2.5 is quantified including sulfate, nitrate, bulk elemental and organic carbon, trace metals, and trace organic compounds. These speciated PM2.5 data are used as input to a receptor model, Positive Matrix Factorization (PMF), for pollution source apportionment. An association will be explored between the individual factor contributions and short-term, adverse health effects, including daily mortality, daily hospitalizations for cardiovascular and respiratory conditions, and measures of poor asthma. Having measures of uncertainty associated with the contribution of diesel fuel combustion to PM2.5, at the daily time scale, may lead to more reliable characterization of the role diesel fuel combustion has in daily health effects data
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