Served in the Breakout Area on level 2, outside the seminar rooms, where you can also register

“Air pollution causes 29,000 deaths a year in the UK”. How good is this estimate and indeed what does it really mean? What are the main alternatives?

In 2010 COMEAP, the UK Advisory Committee on the Medical Effects of Air Pollutants, published its report on The Mortality Effects on Mortality of Long-term Exposure to Particulate Air Pollution in the UK. Its main result has often been summarised (not entirely accurately) as saying that outdoor air pollution causes the deaths of about 29,000 people per year in the UK. Drawing heavily on COMEAP (2010), this talk will consider various ways of expressing the mortality burden of long-term exposure to outdoor air pollution (e.g. in terms of its effect on annual deaths, on total population survival time, and on life expectancy), from the viewpoints both of accuracy and of ease of communication. The relationship to mortality from short-term exposure (identified using time series rather than cohort studies) will be discussed.

Again drawing on COMEAP (2010), and so using mass of fine particles (PM2.5) as the signature pollutant, the talk will consider also some of the methodological issues involved in estimating mortality burden (e.g. the pollutant as itself causal or as indicator of a mixture; choice of concentration-response function; extrapolation to low concentrations; the role of latency). If time permits I will discuss, at least briefly, some other estimates which are increasingly being used in public debate, and why they differ; and additional methodological issues involved in considering simultaneously the associations with and possible effects of more than one pollutant – an issue which is engaging COMEAP currently, as it considers associations of mortality with long-term exposure to nitrogen dioxide (NO2).

Global Estimation of Air Quality and the Burden of Disease associated with Ambient Air Pollution

Air pollution is a major risk factor for global health, with both ambient and household air pollution contributing substantial components of the overall global disease burden. One of the key drivers of adverse health effects is fine particulate matter ambient pollution (PM2.5) to which an estimated 3 million deaths can be attributed annually. The primary source of information for estimating exposures has been measurements from ground monitoring networks but, although coverage is increasing, there remain regions in which monitoring is limited. Ground monitoring data therefore needs to be supplemented with information from other sources, such as satellite retrievals of aerosol optical depth and chemical transport models. A hierarchical modelling approach for integrating data from multiple sources is proposed allowing spatially-varying relationships between ground measurements and other factors that estimate air quality. Set within a Bayesian framework, the resulting Data Integration Model for Air Quality (DIMAQ) is used to estimate exposures, together with associated measures of uncertainty, on a high resolution grid covering the entire world. Bayesian analysis on this scale can be computationally challenging and here approximate Bayesian inference is performed using Integrated Nested Laplace Approximations. Based on summaries of the posterior distributions for each grid cell, it is estimated that 92% of the world's population reside in areas exceeding the World Health Organization's Air Quality Guidelines. Estimated exposures from the model, produced on a high-resolution grid (10km x 10km) covering the entire globe, are combined with risk estimates to produce a global assessment of exposures to PM2.5 and to estimate the associated burden of disease attributable to air pollution. 

Served in the Breakout Area

Estimating the Health Impacts of Ambient Air Pollution Using a Regional Coupled Model

Scott Archer-Nicholls, Paola Crippa, S. Castruccio, Rajesh Kumar, Ellison Carter, Qingyang Xiao, Yang Liu, Kun Ni, Xudong Yang, Michael Brauer, Mohammad Forouzanfar, Joseph Frostad, Jill Baumgartner, Christine Wiedinmyer

Three-dimensional atmospheric models are an essential tool for investigating the impacts of short-lived pollutants on air quality, human health, atmospheric dynamics and circulation, and climate. Of particular interest for health impacts are the accurate representation of production and loss processes of ozone and fine particulate matter (PM2.5). To be done explicitly, this requires accurate emissions of primary and precursor species, representation of complex multiphase chemistry, and realistic meteorological processes to simulate transport of pollutants and losses through wet and dry deposition. In recent years, the development of ‘’online’’ coupled models, such as the regional Weather Research and Forecasting model with Chemistry (WRF-Chem) have enhanced our capabilities as the chemical and aerosol processes are calculated on same timestep as the dynamical processes, and advected using the same parameterisations. However, large uncertainties still remain, particularly from emissions, limited understanding of processes and ability to process them with reasonable computational cost.

The disease burden due to exposure to ambient pollution can be estimated applying surface O3 and PM2.5 from model output to Integrated Response Functions (IRF), which estimate the increased age-dependent risk of various cardiopulmonary health outcomes for given exposures. The calculated increased risk depends on local baseline health and population data. While these techniques have enabled the estimation of global air pollution disease burden, uncertainties remain high particularly in developing countries, where local demographic and health data may be more limited. In addition, the IRFs are largely derived from cohort studies in Europe and the United States, which may not be representative of much of the world.

We present findings from two case studies using the Weather Research and Forecasting model with chemistry (WRF-Chem) to investigate health impacts from extreme elevated PM2.5 from particular sources in Asia. The first discusses the impacts of residential solid fuel burning for cooking and heating purposes in China, a major source of particulate pollution in much of the developing world. The assesses the short-term health impacts from the extensive fires that occurred in Indonesia in Autumn 2015, one of the most extensive biomass burning seasons recorded. Findings from these studies highlight current sources of uncertainty in using models to assess health burden from atmospheric pollution, and suggestions for further development are discussed.