Spatiotemporal relationship between particle air pollution and respiratory emergency hospital admissions in Brisbane, Australia

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Abstract

The nature of spatial variation in the relationship between air pollution and health outcomes within a city remains an open and important question. This study investigated the spatial variability of particle matter air pollution and its association with respiratory emergency hospital admissions across six geographic areas in Brisbane, Australia. Data on particles of 10 μm or less in aerodynamic diameter per cubic metre (PM10), meteorological conditions, and daily respiratory emergency hospital admissions were obtained for the period of 1 January 1998 to 31 December 2001. A Poisson generalised linear model was used to estimate the specific effects of PM10 on respiratory emergency hospital admissions for each geographic area. A pooled effect of PM10 was then estimated using a meta-analysis approach for the whole city. The results of this study indicate that the magnitude of the association between particulate matter and respiratory emergency hospital admissions varied across different geographic areas in Brisbane. This relationship appeared to be stronger in areas with heavy traffic. We found an overall increase of 4.0% (95% confidence interval [CI]: 1.1–6.9%) in respiratory emergency hospital admissions associated with an increase of 10 μg /m3 in PM10 in the single pollutant model. The association was weaker but still statistically significant (an increase of 2.6%; 95% CI: 1.0−5.5%) after adjusting for O3, but did not appear to be affected by NO2. The effect estimates of PM10 were generally consistent for three spatial methods used in this study, but appeared to be underestimated if the spatial nature of the data was ignored. Therefore, the spatial variation in the relationship between PM10 and health outcomes needs to be considered when the health impact of air pollution is assessed, particularly for big cities.

Introduction

By the late twentieth century, due primarily to legislative changes, levels of air pollution in developed countries had been reduced substantially and were no longer regarded by many as a health hazard. However, many recent epidemiological studies have demonstrated that exposure to particle air pollution even at a low level has been associated with cardiorespiratory diseases (Atkinson et al., 1999, Braga et al., 2001, Brunekreef et al., 1995, Dab et al., 1996, Hagen et al., 2000, Kuo et al., 2002, Morgan et al., 1998, Moolgavkar et al., 1997, Petroeschevsky et al., 2001, Pope et al., 1995, Samet et al., 2000a, Schwartz, 1996, Wordley et al., 1997). The main approach for assessing this association in these studies is to use the air pollution data from a single monitoring site or the average of observations from a few monitoring sites to represent the whole population of the study area. A common assumption is that the spatial distributions of certain pollutions are homogeneously distributed within a large urban area, especially smaller particulates. Although this assumption was broadly supported by early concentration variability studies (Burton et al., 1996, Suh et al., 1997, Wilson and Suh, 1997), several recent studies found that, in some areas, there may be greater variation within a city than previously reported (Briggs, 2000, Ito et al., 2004, Kim et al., 2005, Pinto et al., 2004, Wilson et al., 2005, Wilson et al., 2006, Wilson and Zawar-Reza, 2006, Zhu et al., 2002). Therefore, there is growing concern about intra-urban variations in air pollution and the impact on the estimates of health effects of air pollution (Brunekreef and Holgate, 2002, Jerrett et al., 2005, Kukkonen et al., 2001, Wilson et al., 2006).

This study has several objectives: 1) to investigate intra-urban variations in particulate matter air pollution less than 10 um in aerodynamic diameter (PM10) in Brisbane, Australia; 2) to examine spatiotemporal relationships between particle air pollution and respiratory emergency hospital admissions; and 3) to assess the impact of different exposure measurements on the estimates of such relationships.

Section snippets

Air pollution

Data on daily averages of particulate matter smaller than 10 μm (PM10) for the period 1 January 1998–31 December 2001 were obtained from the Queensland Environmental Protection Agency (EPA). PM10 was measured using a Tapered Element Oscillating Microbalance (TEOM) method. Four monitoring stations regularly measured PM10 concentrations around urban Brisbane during the study period (Fig. 1). These stations were at the Queensland University of Technology (QUT), Rocklea, Eagle Farm and Springwood.

Results

Table 1 shows the descriptive summary of the incidence rates of respiratory emergency hospital admissions, distribution of age groups, 24-hour PM10 estimated by the three different models and other air pollutant concentrations (24-hour NO2 and 1-hour O3) across the geographic areas in Brisbane. The incidence rates of respiratory emergency hospital admissions ranged from 2313 to 4178 per 100,000 people while the daily mean PM10 ranged from 17 μg/m3 to 20 μg/m3 across different geographical

Discussion

It has been debated whether air pollution data from a single monitoring site or the average of observations from a few monitoring sites is representative of the whole city. This study assessed the spatial variation of PM10 and explored the heterogeneous associations between PM10 and respiratory emergency hospital admissions across different geographic areas, using a multi-site epidemiological study design. The results of this study show that the daily mean PM10 level varied slightly across

Conclusion

The results of this study indicate that the association between particulate matter and respiratory emergency hospital admissions varied across different geographic areas in Brisbane and this relationship appeared to be stronger in areas with heavy traffic. The effect estimates for PM10, however, were generally consistent for three spatial methods used in this study. The findings of this study may have two implications for the risk assessment of air pollution. Firstly, the comparison between

Acknowledgments

We thank the Queensland Environmental Protection Agency, Queensland Health, and the Australian Bureau of Meteorology for providing the data for this study. Assoc. Prof. Shilu Tong is supported by an NHMRC research fellowship. The study was partly funded by the Australian Research Council.

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