An empirical model for estimating census unit population exposure in areas lacking air quality monitoring

Abstract

This study presents the methods and results of part of the HAPiNZ (Health and Air Pollution in New Zealand) study. A part of this project was to produce accurate measures of pollution exposure for the entire population of New Zealand living in urban areas. Suitable data are limited in most parts of New Zealand with some areas having no monitoring at all. As a result, this project has developed an empirical model to estimate annual exposure values for the whole country down to the census area unit level. This uses surrogate emission indicators and meteorological variables. Data sources used include census data on domestic heating, industrial emissions estimates, vehicle kilometres travelled and meteorological measurements. These were used to calculate annual exposure estimates and were then compared to monitored data for the areas where monitoring data were available. Results show a good association between the model estimates and the monitored data, enabling advanced health effects assessments for the country's entire urban population. Keywords: air pollution, exposure, empirical model, regression. IntroductionLinks between adverse air quality and health are now well established (Brunekreef and Holgate, 2002). This is especially true for cases when pollution concentrations are high as was seen in an event such as the London Smog of 1952 (Bell andDavis, 2001) or more recently in London in 1991 (Anderson et al., 1995). Less clear is the health impact the lower concentrations of pollution might have. To this end, studies have attempted to quantify links between actual concentrations of air pollution and health effects (Kunzli et al., 1997;COMEAP, 1998;Ku¨nzli et al., 2000; WHO, 2001). This is especially true for particulates where it has been suggested there is no level below which there are no significant health impacts, with the WHO (2006) stating ''there is little evidence to suggest a threshold below which no adverse health effects would be anticipated. In fact, the lower range of concentrations at which adverse health effects has been demonstrated is not greatly above the background concentration' ' (WHO, 2006, pp. 275 to 276). Consequently there is an increasing need for accurate estimates of particulate pollution at fine spatial levels to enable identification of potential health effects. In addition, many countries are now adopting air quality guidelines or standards as part of their efforts to manage air quality including the United States, United Kingdom, Australia and New Zealand. Identifying representative sites to locate pollution monitors and an understanding of spatial variation in concentrations of pollution is of significant importance.The principal aim of this study was to develop a method for estimating annual average particulate pollution concentrations for small spatial areal units, where good quality pollution data are sparse. In this particular instance, the area of study is New Zealand. Methods adopted had to account for different sources of particulates, namely domestic heating, traffic and...

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