[Forum SIS] Seminario PM health effects: responses to source-related exposures. Alessio Pollice

[giovanna jona lasinio]

Il professor Alessio Pollice (DSS Univ. di Bari) terrà, il giorno 13
novembre 2009, un seminario dal titolo PM health effects: responses to
source-related
exposures.

Il seminario si terrà in sala 34 presso il DSPSA dell'università di
Roma "La Sapienza" alle ore 15.

Giovanna Jona lasinio



PM health effects: responses to source-related exposures.
A. Pollice
Dipartimento di Scienze Statistiche “Carlo Cecchi”, Università degli
Studi di Bari.

Abstract

Many studies report associations between ambient PM concentrations and
mortality/morbidity, but PM may originate from various emission source
types and its toxicity may vary depending on its source and chemical
composition.
Pollution source apportionment has to do with the quantification of
the contribution of one or more pollution emitting sources in
determining the chemical composition of fine particulate matter at a
given monitoring station. It is based on considering replicated
measurements of the amounts of different chemical compounds in the
atmosphere at the receptor and possibly at some previously defined
sources. The former can be approximated by the sum of the composition
at the sources weighted by the importance of the sources themselves,
as stated by the chemical mass balance equation. In statistical terms
this equation lends itself to different interpretations if knowledge
of the amounts of chemical compounds at the sources is available or
not. In the first case estimates of the source contributions are
obtained by regression techniques and the problem is referred to as a
chemical mass balance problem in the specialized literature. In the
case that sources are unknown or uncertain, the so called multivariate
receptor models rely on statistical factor analytic techniques to
estimate the source-specific contributions from a large number of
observed chemical concentrations.
Examining the associations between source-apportioned PM and health
outcomes may lead to more efficient and effective control strategies,
rather than with PM mass overall. The representation of unobservable
sources of pollution and the estimation of source-specific
contributions in health effects analysis was addressed in two ways.
The simplest characterization selects a set of tracer species to
represent the known sources, and the measured concentrations of the
tracers are taken as surrogates for the source contributions. This
tracer approach is convenient and easy to implement but considers only
a subset of the measured species and characterizes each source in
terms of a single element. As an alternative a 2-stage plug-in
strategy uses source contributions from a previously estimated
receptor model to assess the impact of pollution sources on health
effects within a Poisson regression model. In both cases the
statistical properties of health effects estimates are not well
understood. In particular, neglecting the uncertainty associated with
estimated contributions can lead to biased and unreliable estimates of
regression coefficients. Structural equation models were also proposed
for assessing source specific health effects using speciated air
pollution data. The approach corresponds to jointly fitting a
multivariate receptor model and a Poisson regression model for the
health outcome given source contributions. Inferences on the health
effects resulting from this joint modelling strategy account for the
uncertainty associated with the exposure. However, due to model
misspecification and/or outliers in the outcome, this approach can
produce a poor estimation of the exposure. Exposure estimation and
inference about the outcome might be properly separated by correcting
the 2-stage plug-in approach within the framework of measurement error
models. For multivariate receptor exposure models a 2-stage Bayesian
method and the use of exposure simulations are proposed.