Spatial analysis of air pollution and childhood asthma in Hamilton, Canada: comparing exposure methods in sensitive subgroups.

BACKGROUND: Variations in air pollution exposure within a community may be associated with asthma prevalence. However, studies conducted to date have produced inconsistent results, possibly due to errors in measurement of the exposures. METHODS: A standardized asthma survey was administered to children in grades one and eight in Hamilton, Canada, in 1994-95 (N approximately 1467). Exposure to air pollution was estimated in four ways: (1) distance from roadways; (2) interpolated surfaces for ozone, sulfur dioxide, particulate matter and nitrous oxides from seven to nine governmental monitoring stations; (3) a kriged nitrogen dioxide (NO2) surface based on a network of 100 passive NO2 monitors; and (4) a land use regression (LUR) model derived from the same monitoring network. Logistic regressions were used to test associations between asthma and air pollution, controlling for variables including neighbourhood income, dwelling value, state of housing, a deprivation index and smoking. RESULTS: There were no significant associations between any of the exposure estimates and asthma in the whole population, but large effects were detected the subgroup of children without hayfever (predominately in girls). The most robust effects were observed for the association of asthma without hayfever and NO2LUR OR = 1.86 (95%CI, 1.59-2.16) in all girls and OR = 2.98 (95%CI, 0.98-9.06) for older girls, over an interquartile range increase and controlling for confounders. CONCLUSION: Our findings indicate that traffic-related pollutants, such as NO2, are associated with asthma without overt evidence of other atopic disorders among female children living in a medium-sized Canadian city. The effects were sensitive to the method of exposure estimation. More refined exposure models produced the most robust associations.

Predicting personal nitrogen dioxide exposure in an elderly population: integrating residential indoor and outdoor measurements, fixed-site ambient pollution concentrations, modeled pollutant levels, and time-activity patterns.

Predicting chronic exposure to air pollution at the intra-urban scale has been recognized as a priority area of research for environmental epidemiology. Exposure assessment models attempt to predict and proxy for individuals' personal exposure to ambient air pollution, and there are no studies to date that explicitly attempt to compare and cross-validate personal exposure concentrations with pollutants modeled at the intra-urban level using methods such as interpolated surfaces and land-use regression (LUR) models. This study aimed to identify how well personal exposure to NO(2) (nitrogen dioxide) can be predicted from ambient exposure measurements and intra-urban exposure estimates using LUR and what other factors contribute to predicting variations in personal exposure beyond measured pollutant levels within home. Personal, indoor and outdoor NO(2) were measured in a population of older adults (>65 yr old) living in Hamilton, Canada. Our results show that personal NO(2) was most strongly associated with contemporaneously collected indoor and outdoor concentrations of NO(2). Predicted NO(2) exposures from intra-urban LUR models were not associated with personal NO(2), whereas interpolated surfaces of particulates and ozone were modestly associated. Combinations of variables that best predicted personal NO(2) variability were derived from time-activity diaries, interpolated surfaces of ambient particulate pollutants, and a city wide temporally matched average of NO(2). The nonsignificant associations between personal NO(2) and the modeled ambient NO(2) concentrations suggest that observed associations between NO(2) generated by LUR models and health effects are probably not produced by NO(2), but by other pollutants that follow a similar spatial pattern.

Sources of uncertainty in calculating mortality and morbidity attributable to air pollution.

Quantifying the burden of illness and mortality from air pollution exposure relies on statistical estimates and other assumptions that have inherent uncertainties. Through an intensive study in Hamilton, Canada, this study illustrates for policymakers the sensitivity of health effect estimates to a wide range of possible uncertainties. Dose-response relationships were derived based on pooled and averaged estimates published in the scientific literature from 1997 to 2001. These estimates were applied to local air pollution, mortality, and hospital admissions data for the years 1995-1999. The data were adjusted to reflect uncertainties in the current state of knowledge, including (1) baseline pollution, (2) single versus multipollutant effects, (3) local or pooled estimates, and (4) chronic effects. The estimates of mortality ranged from 96 to 374 annual deaths, while admissions ranged from 139 to 607 respiratory and from 479 to 2000 cardiovascular admissions. Chronic fine particle exposure resulted in 232 annual deaths. As conclusions, first, there should be an effort to reach a consensus on reporting scientific findings from air pollution studies using standardized study designs and reporting formats. Second, given the sensitivity of the estimates to underlying assumptions, an immediate need exists for widely accepted burden of illness and mortality estimation conventions. Third, many areas of air pollution research require considerable refinement before complete estimates can be ascribed.

A land use regression model for predicting ambient concentrations of nitrogen dioxide in Hamilton, Ontario, Canada.

This paper reports on the development of a land use regression (LUR) model for predicting the intraurban variation of traffic-related air pollution in Hamilton, Ontario, Canada, an industrial city at the western end of Lake Ontario. Although land use regression has been increasingly used to characterize exposure gradients within cities, research to date has yet to test whether this method can produce reliable estimates in an industrialized location. Ambient concentrations of nitrogen dioxide (NO2) were measured for a 2-week period in October 2002 at > 100 locations across the city and subsequently at 30 of these locations in May 2004 to assess seasonal effects. Predictor variables were derived for land use types, transportation, demography, and physical geography using geographic information systems. The LUR model explained 76% of the variation in NO2. Traffic density, proximity to a highway, and industrial land use were all positively correlated with NO2 concentrations, whereas open land use and distance from the lake were negatively correlated with NO2. Locations downwind of a major highway resulted in higher NO2 levels. Cross-validation of the results confirmed model stability over different seasons. Our findings demonstrate that land use regression can effectively predict NO2 variation at the intraurban scale in an industrial setting. Models predicting exposure within smaller areas may lead to improved detection of health effects in epidemiologic studies.

A review and evaluation of intraurban air pollution exposure models.

The development of models to assess air pollution exposures within cities for assignment to subjects in health studies has been identified as a priority area for future research. This paper reviews models for assessing intraurban exposure under six classes, including: (i) proximity-based assessments, (ii) statistical interpolation, (iii) land use regression models, (iv) line dispersion models, (v) integrated emission-meteorological models, and (vi) hybrid models combining personal or household exposure monitoring with one of the preceding methods. We enrich this review of the modelling procedures and results with applied examples from Hamilton, Canada. In addition, we qualitatively evaluate the models based on key criteria important to health effects assessment research. Hybrid models appear well suited to overcoming the problem of achieving population representative samples while understanding the role of exposure variation at the individual level. Remote sensing and activity-space analysis will complement refinements in pre-existing methods, and with expected advances, the field of exposure assessment may help to reduce scientific uncertainties that now impede policy intervention aimed at protecting public health.