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.

The spatial configuration of cities and its relationship to the urban environment has recently been the subject of empirical, theoretical and policy research. Because of the disciplines involved, relevant articles are scattered over a large number of journals. The objective of this paper is to put the issues in perspective by reviewing the basic concepts and relationships involved, and to evaluate critically the current state of knowledge about urban form, energy utilisation and the environment. The scope of the paper is limited to urban transport energy use and the associated emissions. Suggestions for further progress in the field are offered, with emphasis placed on integrated urban models as useful and policy-sensitive analytical tools.

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The authors address two research questions: (1) Are populations with lower socioeconomic status, compared with people of higher socioeconomic status, more likely to be exposed to higher levels of particulate air pollution in Hamilton, Ontario, Canada? (2) How sensitive is the association between levels of particulate air pollution and socioeconomic status to specification of exposure estimates or statistical models? Total suspended particulate (TSP) data from the twenty-three monitoring stations in Hamilton (1985–94) were interpolated with a universal kriging procedure to develop an estimate of likely pollution values across the city based on annual geometric means and extreme events. Comparing the highest with the lowest exposure zones, the interpolated surfaces showed more than a twofold increase in TSP concentrations and more than a twentyfold difference in the probability of exposure to extreme events. Exposure estimates were related to socioeconomic and demographic data from census tract areas by using ordinary least squares and simultaneous autoregressive (SAR) models. Control for spatial autocorrelation in the SAR models allowed for tests of how robust specific socioeconomic variables were for predicting pollution exposure. Dwelling values were significantly and negatively associated with pollution exposure, a result robust to the method of statistical analysis. Low income and unemployment were also significant predictors of exposure, although results varied depending on the method of analysis. Relatively minor changes in the statistical models altered the significant variables. This result emphasizes the value of geographical information systems (GIS) and spatial statistical techniques in modelling exposure. The result also shows the importance of taking spatial autocorrelation into account in future justice – health studies.

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Cities and metropolitan regions face several challenges including urban sprawl, auto dependence and congestion, and related environmental and human health effects. Examining the spatial characteristics of daily household activity-travel behavior holds important implications for understanding and addressing urban transportation issues. Research of this sort can inform development of urban land use policy that encourages the use of local opportunities, potentially leading to reduced motorized travel. This article examines the potential household activity-travel response to a planned metropolitan polycentric hierarchy of activity centers. Behavioral observations have been drawn from an activity-travel survey conducted in the Portland, Oregon, metropolitan area during the mid-1990s. Evidence presented from exploratory analysis indicates an urban/suburban differential, with less daily travel and smaller activity spaces for urban households. Investigation of the travel reduction potential of the proposed land-use strategy suggests that location effects could be offset by adjustments to household sociodemographic and mobility characteristics. Copyright 2006 Blackwell Publishing.