Global Effect Factors for Exposure to Fine Particulate Matter

We evaluate fine particulate matter
(PM2.5) exposure–response
models to propose a consistent set of global effect factors for product
and policy assessments across spatial scales and across urban and
rural environments. Relationships among exposure concentrations and
PM2.5-attributable health effects largely depend on location,
population density, and mortality rates. Existing effect factors build
mostly on an essentially linear exposure–response function
with coefficients from the American Cancer Society study. In contrast,
the Global Burden of Disease analysis offers a nonlinear integrated
exposure–response (IER) model with coefficients derived from
numerous epidemiological studies covering a wide range of exposure
concentrations. We explore the IER, additionally provide a simplified
regression as a function of PM2.5 level, mortality rates,
and severity, and compare results with effect factors derived from
the recently published global exposure mortality model (GEMM). Uncertainty
in effect factors is dominated by the exposure–response shape,
background mortality, and geographic variability. Our central IER-based
effect factor estimates for different regions do not differ substantially
from previous estimates. However, IER estimates exhibit significant
variability between locations as well as between urban and rural
environments, driven primarily by variability in PM2.5 concentrations
and mortality rates. Using the IER as the basis for effect factors
presents a consistent picture of global PM2.5-related effects
for use in product and policy assessment frameworks.