Martin Cope

An approach for estimating exposure to ambient concentrations.

The degree of certainty in epidemiological studies is probably limited more by estimates of exposure than by any other component. We present a methodology for computing daily pollutant concentration fields that reduces exposure uncertainty and bias by taking account of spatial variation in air quality. This approach, using elliptical influence functions, involves the optimum blending of observations from a monitoring network with gridded pollution fields predicted by the complex air quality model TAPM. Such fields allow more information to be incorporated in the exposure fields used in epidemiological studies, rather than having to assume that ambient exposure is the same across a whole city and/or that individuals remain at the one location for the duration of a study.

Quantifying the health impacts of air pollution under a changing climate—a review of approaches and methodology

Climate change has been predicted to affect future air quality, with inevitable consequences for health. Quantifying the health effects of air pollution under a changing climate is crucial to provide evidence for actions to safeguard future populations. In this paper, we review published methods for quantifying health impacts to identify optimal approaches and ways in which existing challenges facing this line of research can be addressed. Most studies have employed a simplified methodology, while only a few have reported sensitivity analyses to assess sources of uncertainty. The limited investigations that do exist suggest that examining the health risk estimates should particularly take into account the uncertainty associated with future air pollution emissions scenarios, concentration-response functions, and future population growth and age structures. Knowledge gaps identified for future research include future health impacts from extreme air pollution events, interactions between temperature and air pollution effects on public health under a changing climate, and how population adaptation and behavioural changes in a warmer climate may modify exposure to air pollution and health consequences.

The mortality effect of ship-related fine particulate matter in the Sydney greater metropolitan region of NSW, Australia.

This study investigates the mortality effect of primary and secondary PM2.5 related to ship exhaust in the Sydney greater metropolitan region of Australia. A detailed inventory of ship exhaust emissions was used to model a) the 2010/11 concentration of ship-related PM2.5 across the region, and b) the reduction in PM2.5 concentration that would occur if ships used distillate fuel with a 0.1% sulfur content at berth or within 300 km of Sydney. The annual loss of life attributable to 2010/11 levels of ship-related PM2.5 and the improvement in survival associated with use of low-sulfur fuel were estimated from the modelled concentrations. In 2010/11, approximately 1.9% of the region-wide annual average population weighted-mean concentration of all natural and human-made PM2.5 was attributable to ship exhaust, and up to 9.4% at suburbs close to ports. An estimated 220 years of life were lost by people who died in 2010/11 as a result of ship exhaust-related exposure (95% CIβ: 140-290, where CIβ is the uncertainty in the concentration-response coefficient only). Use of 0.1% sulfur fuel at berth would reduce the population weighted-mean concentration of PM2.5 related to ship exhaust by 25% and result in a gain of 390 life-years over a twenty year period (95% CIβ: 260-520). Use of 0.1% sulfur fuel within 300 km of Sydney would reduce the concentration by 56% and result in a gain of 920 life-years over twenty years (95% CIβ: 600-1200). Ship exhaust is an important source of human exposure to PM2.5 in the Sydney greater metropolitan region. This assessment supports intervention to reduce ship emissions in the GMR. Local strategies to limit the sulfur content of fuel would reduce exposure and will become increasingly beneficial as the shipping industry expands. A requirement for use of 0.1% sulfur fuel by ships within 300 km of Sydney would provide more than twice the mortality benefit of a requirement for ships to use 0.1% sulfur fuel at berth.

The Development of the Australian Air Quality Forecasting System: Current Status

The AAQFS is routinely providing highspatial resolution air quality forecasts for guidance for the EPAs in Melbourne and Sydney. Case studies of photochemical smog events in Melbourne and Sydney have given encouraging agreement with observations. Meteorologically the two airsheds present different challenges: in Melbourne it is important to predict the onset and strength of the Port Phillip Bay breeze and the Bass Strait sea breeze; in Sydney is it important to predict the onset and strength of the Tasman sea breeze, the pollution plume trajectory for flow over complex terrain and the effects of local photochemical smog production and inter-regional transport. In the case studied for Sydney there was an additional complication of a synoptic-scale wind surge called the Southerly Buster. For both airsheds, the interaction between synoptic-scale forcing and mesoscale circulations can strongly influence the characteristics of an air pollution event and thus the meteorological model must be able to accurately simulate these interactions. In general, the LCC photochemical mechanism gave better predictions of the 1-hour ozone peak than the GRS mechanism. Improvements to the GRS mechanism and emissions inventory and online modelling of emissions and photochemistry are being developed and implemented. Work on the meteorological model to improve surface winds, soil moisture analysis and boundary-layer height also continues. We have yet to establish the limits of predictability of the system.

Measurement and modelling of pollutant emissions from Hong Kong

Abstract During November 1997 a detailed airborne investigation of air pollution in the Hong Kong region was undertaken. The airborne investigation formed part of a larger study funded by the Hong Kong Environmental Protection Department (EPD) and included the development of a state of the art numerical air quality modelling system to simulate air pollution in the Hong Kong region. The system consisted of a numerical weather prediction module, a prognostic air–chemistry/transport model, an emissions inventory system and a Graphical User Interface for display of results and preparation of simulations. The purpose of the airborne investigations was to provide data on the fluxes of selected pollutants arising from or entering the Hong Kong airshed as a check on the inventory. In addition the aircraft was to provide data on other pollutants of interest particularly with respect to the formation of photochemical smog. This paper describes the inventory data obtained from the aircraft and makes comparisons between the predictions of the model and the aircraft data for one of the days when the aircraft was able to be used to estimate the total fluxes of NMHC and NOx from the study area.

The Impact of Climate Change on Ozone-Related Mortality in Sydney

Coupled global, regional and chemical transport models are now being used with relative-risk functions to determine the impact of climate change on human health. Studies have been carried out for global and regional scales, and in our paper we examine the impact of climate change on ozone-related mortality at the local scale across an urban metropolis (Sydney, Australia). Using three coupled models, with a grid spacing of 3 km for the chemical transport model (CTM), and a mortality relative risk function of 1.0006 per 1 ppb increase in daily maximum 1-hour ozone concentration, we evaluated the change in ozone concentrations and mortality between decades 1996–2005 and 2051–2060. The global model was run with the A2 emissions scenario. As there is currently uncertainty regarding a threshold concentration below which ozone does not impact on mortality, we calculated mortality estimates for the three daily maximum 1-hr ozone concentration thresholds of 0, 25 and 40 ppb. The mortality increase for 2051–2060 ranges from 2.3% for a 0 ppb threshold to 27.3% for a 40 ppb threshold, although the numerical increases differ little. Our modeling approach is able to identify the variation in ozone-related mortality changes at a suburban scale, estimating that climate change could lead to an additional 55 to 65 deaths across Sydney in the decade 2051–2060. Interestingly, the largest increases do not correspond spatially to the largest ozone increases or the densest population centres. The distribution pattern of changes does not seem to vary with threshold value, while the magnitude only varies slightly.

The Australian Air Quality Forecasting System: Modelling of a Severe Smoke Event in Melbourne, Australia

The AAQFS currently issues twice daily 24–36 hour numerical air quality forecast for the Melbourne and Sydney regions for Victoria EPA and NSW EPA in time to provide reference and guidance for air quality forecasting. One of the potential usages of the AAQFS is to provide guidance in the management of prescribed burns and in alerting the public of smoke impacts on air quality.

Blending multiple nitrogen dioxide data sources for neighborhood estimates of long-term exposure for health research

Exposure to traffic related nitrogen dioxide (NO2) air pollution is associated with adverse health outcomes. Average pollutant concentrations for fixed monitoring sites are often used to estimate exposures for health studies, however these can be imprecise due to difficulty and cost of spatial modeling at the resolution of neighborhoods (e.g., a scale of tens of meters) rather than at a coarse scale (around several kilometers). The objective of this study was to derive improved estimates of neighborhood NO2 concentrations by blending measurements with modeled predictions in Sydney, Australia (a low pollution environment). We implemented the Bayesian maximum entropy approach to blend data with uncertainty defined using informative priors. We compiled NO2 data from fixed-site monitors, chemical transport models, and satellite-based land use regression models to estimate neighborhood annual average NO2. The spatial model produced a posterior probability density function of estimated annual average concentrations that spanned an order of magnitude from 3 to 35 ppb. Validation using independent data showed improvement, with root mean squared error improvement of 6% compared with the land use regression model and 16% over the chemical transport model. These estimates will be used in studies of health effects and should minimize misclassification bias.

The Australian Air Quality Forecasting System: The Use of Green Scenarios of Motor Vehicle Usage as an Educational Tool

Abstract The Australian Air Quality Forecasting System (AAQFS) is one of several newly emerging, high-resolution, numerical air quality forecasting systems. The system is briefly described. A public education application of the air quality impact of motor vehicle usage is explored by computing the concentration and dosage of particulate matter less than 10 µm in aerodynamic diameter (PM10) for a commuter traveling to work between Geelong and Melbourne, Victoria, Australia, under “business-as-usual” and “green” scenarios. This application could be routinely incorporated into systems like AAQFS. Two methodologies for calculating the dosage are described: one for operational use and one for more detailed applications. The Clean Air Research Programme-Personal Exposure Study in Melbourne provides support for this operational methodology. The more detailed methodology is illustrated using a system for predicting concentrations due to near-road emissions of PM10 andapplied in Sydney.

Chapter 7.3 Optimum exposure fields for epidemiology and health forecasting

Abstract Perhaps the weakest aspect of epidemiological studies is the exposure component. How accurate is our assessment of the dose received by a person or population on short- or long-term scales? Most epidemiological analyses involving air quality use data from a fixed location monitor(s), with the implicit assumption that pollutant concentration is spatially uniform across the study domain. This is not the situation in reality, nor do individuals remain at the one location, even over short periods. We have developed a methodology which takes account of spatial variation in air quality and reduces uncertainty in ambient exposure estimates. This approach, using elliptical influence functions, involves the blending of observations from a monitoring network with gridded meteorological and pollution fields predicted by the complex air quality model TAPM. Examples from exposure fields developed on a 1.5 km-spaced grid for each day of a 6-year period (1998–2003) for Brisbane will be shown.