Temporal stability of land use regression models for traffic-related air pollution
Highlights
► Land use regression (LUR) models of traffic air pollution can be extended over time. ► LUR model application is improved for forecasting vs hind-casting scenarios. ► Epidemiologic studies can reliably apply LUR models over ∼7 year time periods.
Introduction
Land use regression (LUR) models are now frequently used as a cost-effective approach for assessing intra-urban air pollution contrasts (Briggs et al., 1997; Hoek et al., 2008; Kashima et al., 2009; Allen et al., 2011). Typically, air pollutants are measured at multiple sites and associated with potentially predictive geographic attributes (e.g., land use, population density, traffic patterns) to build regression models that can be rendered in a Geographic Information System (GIS) to predict concentrations at unmeasured locations (Henderson et al., 2007). Once a LUR model is developed, individual exposure to air pollution can be estimated by geocoding addresses of interest (e.g. subject's residential addresses, schools, etc.) and determining the modeled concentrations at those locations (Henderson et al., 2007).
Many epidemiological studies have adopted LUR models for exposure assessment over periods of different years. In most cases, models were developed over a single year and extrapolated forward (Morgenstern et al., 2008; Ryan et al., 2008) or backward (Brauer et al., 2008; Cesaroni et al., 2008; Clark et al., 2010; Karr et al., 2009a,b; Morgenstern et al., 2006, 2008; Slama et al., 2007) in time to assess the exposures of individual study subjects during specific time periods of interest. In these studies, the gap between the time of the exposure estimate and the LUR model development ranged from 0 to 7 years. Most studies have directly applied models over time without any adjustments, and therefore do not account for temporal variability in exposures due to long-term changes in air pollutant concentrations. The few studies that have accounted for these changes, have used yearly calibration (Molter et al., 2010) or by monthly adjustment (Brauer et al., 2008; Clark et al., 2010; Gan et al., 2011; MacIntyre et al., 2011; Slama et al., 2007). Such approaches may be inadequate because they typically rely on measurements at fewer locations than used to develop the LUR models themselves, and assume that temporal changes are constant across the study area. More generally, spatial prediction models derived from continuous measurements may be better at describing temporal trends but typically are more limited in spatial resolution. Interpolation methods such as kriging, relying on deterministic and stochastic geostatistical techniques (Jerrett et al., 2005), have mainly been used at the regional and national scales (Bell, 2006; Liao et al., 2006). However, as ordinary kriging does not account for factors such as terrain or localized patterns (Jerrett et al., 2005), it cannot reveal small-scale spatial variation (Aguilera et al., 2007).
Land use and traffic emissions change over time. Therefore spatial contrasts of pollutant concentrations predicted from these characteristics may not remain stable. This instability may lead to exposure misclassification, which can affect epidemiological results. For example, if pollutant concentrations decrease more rapidly at some locations than others, exposure estimates based on these initial concentrations will have greater misclassification with respect to concentrations over the long-term. Accordingly, our objective was to evaluate the validity of applying LUR models to periods that are temporally distinct from the time for which the model was built.
Specifically, we evaluated the temporal stability of a LUR model to retrospectively and prospectively estimate subjects' chronic exposure to nitrogen oxides. In Vancouver, LUR models for NO and NO2 were developed in 2003 (Henderson et al., 2007) and have been applied in several epidemiological analyses (Brauer et al., 2008; Clark et al., 2010; Gan et al., 2011; MacIntyre et al., 2011). In this study, we made new measurements in 2010 update the model. The temporal stability of both the original and new LUR models over the 7-year period was evaluated by comparing model predictions with measured spatial contrasts between 2003 and 2010. Findings have specific relevance to longitudinal studies where the exposure period of interest spans several years. In addition, we also sought to enhance the 2010 models by including new variables that were not tested previously.
Section snippets
Materials and methods
In 2003, models were built for NO, NO2, fine particulate matter (PM2.5) and light absorbance (ABS) (Henderson et al., 2007). Using the same methods developed for the 2003 models, we repeated NO and NO2 measurements, updated the input predictor variables, and constructed new models (referred to herein as the 2010 models).
NO and NO2 measurements
Table 2 summarizes descriptive statistics of measured NO and NO2 concentrations in 2010. The NO concentrations ranged from 2.5 to 49.8 ppb, and were log-normally distributed (n = 116) with a geometric mean (geometric standard deviation (gsd)) of 13.6 (1.9) ppb. The NO2 concentrations ranged from 3.1 to 17.6 ppb, and were normally distributed with an arithmetic mean (sd) of 10.9 (3.3) ppb.
Quality control showed no major bias or error. The NO and NO2 measurements from co-located Ogawa samplers
Discussion
The primary motivation of this study was to evaluate the temporal stability of LUR models when they are used to retrospectively or prospectively estimate long-term exposure in epidemiological studies. The 2003 LUR model explained as much variability in the 2010 measurements (forecasting) as it did in the 2003 measurements, indicating no loss of applicability of the model when prospectively predicting spatial contrasts over a 7-year period. Our 2010 LUR model explained as much variability in the
Conclusion
In this study, LUR models were able to predict concentrations in either forecasting or hindcasting. For study areas where pollutant concentrations are decreasing over time, the predictive power of a local LUR model is likely to be retained or improved in forecasting, but decreased in hindcasting applications. The findings can inform the assignment of exposure in studies of the health effects of chronic exposure to air pollution.
Acknowledgments
This work was supported by AllerGen NCE Inc. (The Allergy, Genes and Environment Network), a member of the Network of Centres of Excellence Canada Program.
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