In order to accurately assess air pollution risks, health studies require spatially resolved pollution concentrations. Land-use regression (LUR) models estimate ambient concentrations at a fine spatial scale. However, spatial effects such as spatial non-stationarity and spatial autocorrelation can reduce the accuracy of LUR estimates by increasing regression errors and uncertainty; and statistical methods for resolving these effects--e.g., spatially autoregressive (SAR) and geographically weighted regression (GWR) models--may be difficult to apply simultaneously. We used an alternate approach to address spatial non-stationarity and spatial autocorrelation in LUR models for nitrogen dioxide. Traditional models were re-specified to include a variable capturing wind speed and direction, and re-fit as GWR models. Mean R(2) values for the resulting GWR-wind models (summer: 0.86, winter: 0.73) showed a 10-20% improvement over traditional LUR models. GWR-wind models effectively addressed both spatial effects and produced meaningful predictive models. These results suggest a useful method for improving spatially explicit models.

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There is an acknowledged need in ecotoxicology for methods that integrate spatial analyses in risk assessment. This has resulted in the emergence of landscape ecotoxicology, a subdiscipline of ecotoxicology. However, landscape ecotoxicology has yet to become common practice in risk assessment due to the underdevelopment of techniques and a lack of standardized methods. In the present study, we demonstrate how geographic information systems (GISs) can serve as a standardized platform to integrate data, assess spatial patterns of ecotoxicological data for multiple species, and assess relationships between chemical mixture exposures and effects on biota for landscape ecotoxicological risks assessment. We use data collected under the Joint Oil Sands Monitoring Program in the Athabasca Oil Sands Region in Alberta, Canada. This dataset is composed of concentrations of contaminants including metals and polycyclic aromatic compounds, and health endpoints measured in 1100 biological samples, including tree swallows, amphibians, gull and tern eggs, plants, and mammals. We present 3 examples using a GIS as a platform and geospatial analysis to: 1) integrate data and assess spatial patterns of contaminant exposure in the region, 2) assess spatial patterns of exposures to complex mixtures, and 3) examine patterns of exposures and responses across the landscape. We summarize the methods used in the present study into a workflow for ease of use. The GIS methods allow researchers to identify hot spots of contamination, use georeferenced monitoring data to derive quantitative exposure‐response relationships, and assess complex exposures with more realism. Environ Toxicol Chem 2019;38:2797–2810. © 2019 SETAC

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