A statistical framework for the apportionment of particulate contaminants and their health effect determination

Air pollution is a complex mixture of diverse substances from anthropogenic and natural sources. These sources in combination with factors such as meteorology and chemical/biological transformations, determine the air pollution concentration and the variation in the physiochemical components across space and time. The identification of these sources is a key element for developing effective and efficient strategies to control and reduce pollution through targeted actions. In addition, air pollution is a major public health concern, being increasingly associated with risk of morbidity and mortality of human populations. Recent evidence points out that mixture of particles from different sources can have a different detrimental contribution on health; this makes the understanding of pollutant sources even more important in order for air quality managers to fully understand the potential health outcomes of pollutant mixtures. While knowledge of the main sources of pollution can be effectively obtained on temporal and/or geographical localised setting, the modelling and the understanding of some aspects of the dynamic and physiochemical processes remain a substantial challenge.

This project focuses primarily upon: (i) the development of a methodological approach for particle matter (PM) source apportionment (SA) which uses nonparametric processes with dependence on dynamic factors (e.g. meteorology) to model the underlying spatial or temporal structure and the distribution of contaminants to identify sources; (ii) the quantification of the impact of apportioned air contaminants upon vulnerable populations; and (iii) the translation of this methodological approach to real-life decision making through the predictions of the health outcomes under changing scenarios of pollution mix as a result of potential policy implementations.

The proposed approach will be tested against the state-of-the-art tools for SA of air pollution, using simulated examples. Evaluation of the adverse responses associated with air particulate sources is reached by comparing two-stage procedures vs joint models for SA and health-effect assessment. We will consider two real case studies: (i) to identify time-varying sources of particles (PM2.5) in Greater London and evaluate their acute effects on respiratory hospital admissions in vulnerable populations (0-14 years, 65+) in a time-series framework; (ii) to disentangle spatially-varying sources of particles (PM2.5) in South East England and evaluate their respiratory chronic effects in the same region in a small-area framework.

By using rigorous and innovative methodologies, we believe that the proposed research (i) will provide scientific evidence of the differential harmful effect of PM chemical components, (ii) will help understand the sources that can be controlled, and (iii) will have the potential to inform air pollution policy implementation and regulation to improve UK population health.

Technical Summary

Particulate matter (PM) is a complex mixture of diverse substances from anthropogenic and natural sources, which vary widely across space and time. Despite being a major public health concern, most studies have linked total PM to health outcomes, while it is now understood that contaminant mixtures deriving from different sources may have differential health effects.

We propose a Bayesian nonparametric approach that, starting from measurements of a wide range of components, estimate sources of PM and evaluate their health effects, through probabilistic clustering. We will account for spatial or temporal and covariate dependency (e.g. meteorology) in the cluster allocation; the link between sources and health outcomes will be evaluated using (i) a two-stage model where the sources identification is separated from the epidemiological model and (ii) a joint model, allowing for the outcome to influence the apportionment of the sources.

Our approach will provide competitive advantages over standard receptor methods, as it incorporates: (i) temporal dynamics in time-series speciation data, (ii) spatial dependence structure in multi-site contaminant data, (iii) missing data accommodation, (iv) quantification of uncertainty associated with source profiles, (v) nonlinear relationship between sources and health outcomes.

We will build an extensive simulation study and consider two real case studies: (i) to identify time-varying sources of PM in Greater London and evaluate their acute effects on respiratory hospital admissions in vulnerable populations; (ii) to disentangle spatially-varying sources of PM in South East England and evaluate their respiratory chronic effects. We believe that the proposed research (i) will provide new scientific evidence of the differential health impact of air pollutant mixtures, (ii) will help understand the sources that can be controlled and (iii) will have the potential to inform policy implementation for improving UK population health.