Commuters may be exposed to increased levels of traffic-related air pollution owing to close proximity to traffic-emissions. We collected in-vehicle and roof-top air pollution measurements over 238 commutes in Montreal, Toronto, and Vancouver, Canada between 2010 and 2013. Voice recordings were used to collect real-time information on traffic density and the presence of diesel vehicles and multivariable linear regression models were used to estimate the impact of these factors on in-vehicle pollutant concentrations (and indoor/outdoor ratios) along with parameters for road type, land use, and meteorology. In-vehicle PM2.5 and NO2 concentrations consistently exceeded regional outdoor levels and each unit increase in the rate of encountering diesel vehicles (count/min) was associated with substantial increases (>100%) in in-vehicle concentrations of ultrafine particles (UFPs), black carbon, and PM2.5 as well as strong increases (>15%) in indoor/outdoor ratios. A model based on meteorology and the length of highway roads within a 500 m buffer explained 53% of the variation in in-vehicle UFPs; however, models for PM2.5 (R(2) = 0.24) and black carbon (R(2) = 0.30) did not perform as well. Our findings suggest that vehicle commuters experience increased exposure to air pollutants and that traffic characteristics, land use, road types, and meteorology are important determinants of these exposures.

h i g h l i g h t s PM 1.0 -components displayed significant intra-urban variability in Calgary, Alberta. Land-use regression models were developed for 30 elements in summer and winter. 12 elements had models with R 2 > 0.7 in both seasons; 24 had R 2 > 0.5 in both seasons. Industrial sources were major predictors, as well as traffic, land-use, and wind. Interspecies dependencies were similar for measured and modeled pollutant data. a b s t r a c tAirborne particulate matter has been associated with cardiovascular and respiratory morbidity and mortality, and there is evidence that metals may contribute to these adverse health effects. However, there are few tools for assessing exposure to airborne metals. Land-use regression modeling has been widely used to estimate exposure to gaseous pollutants. This study developed seasonal land-use regression (LUR) models to characterize the spatial distribution of trace metals and other elements associated with airborne particulate matter in Calgary, Alberta.Two-week integrated measurements of particulate matter with <1.0 mm in aerodynamic diameter (PM 1.0 ) were collected in the City of Calgary at 25 sites in August 2010 and 29 sites in January 2011. PM 1.0 filters were analyzed using inductively-coupled plasma mass spectrometry. Industrial sources were obtained through the National Pollutant Release Inventory and their locations verified using Google Maps. Traffic volume data were obtained from the City of Calgary and zoning data were obtained from Desktop Mapping Technologies Incorporated. Seasonal wind direction was incorporated using wind rose shapes produced by Wind Rose PRO3, and predictor variables were generated using ArcMap-10.1. Summer and winter LUR models for 30 PM 1.0 components were developed using SAS 9.2.We observed significant intra-urban gradients for metals associated with airborne particulate matter in Calgary, Alberta. LUR models explained a high proportion of the spatial variability in those PM 1.0 components. Summer models performed slightly better than winter models. However, 24 of the 30 PM 1.0 related elements had models that were either good (R 2 > 0.70) or acceptable (R 2 > 0.50) in both seasons. Industrial point-sources were the most influential predictor for the majority of PM 1.0 components. Industrial and commercial zoning were also significant predictors, while traffic indicators and population density had a modest but significant contribution for most elements. Variables incorporating wind direction were also significant predictors. These findings contrast with LUR models for PM and gaseous pollutants in which traffic indicators are typically the most important predictors of ambient concentrations.These results suggest that airborne PM components vary spatially with the distribution of local industrial sources and that LUR modeling can be used to predict local concentrations of these airborne BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/ 4.0/). Atmospheric Environment 106 (2015) 165e177 elements. These models will support ...

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System-representative commuter air pollution exposure data were collected for the metro systems of Toronto, Montreal, and Vancouver, Canada. Pollutants measured included PM (PM = particulate matter), PM, ultrafine particles, black carbon, and the elemental composition of PM. Sampling over three weeks was conducted in summer and winter for each city and covered each system on a daily basis. Mixed-effect linear regression models were used to identify system features related to particulate exposures. Ambient levels of PM and its elemental components were compared to those of the metro in each city. A microenvironmental exposure model was used to estimate the contribution of a 70 min metro commute to daily mean exposure to PM elemental and mass concentrations. Time spent in the metro was estimated to contribute the majority of daily exposure to several metallic elements of PM and 21.2%, 11.3% and 11.5% of daily PM exposure in Toronto, Montreal, and Vancouver, respectively. Findings suggest that particle air pollutant levels in Canadian metros are substantially impacted by the systems themselves, are highly enriched in steel-based elements, and can contribute a large portion of PM and its elemental components to a metro commuter's daily exposure.

A multi-index drought (MID) model was developed to combine the strengths of various drought indices for agricultural drought risk assessment on the Canadian prairies, as related to spring wheat crop yield. The model automatically selects and combines optimum drought indices derived from the preceding and current months as they become available to better match the conditions (both spatially and temporally) where they work well. The cross-validation results showed that (1) the prediction accuracy of the MID model is better than (or occasionally equal to) using any single drought index for all modelling stages, (2) drought indices derived from the recharge period are useful for early drought risk detection, (3) model prediction accuracy improved as the growing season progressed with the most accurate assessments at the beginning of August, and (4) the model performed best in the more arid locations in the southern prairies, which tend to have a more variable precipitation regime. The model assessment results provide the spatial intensity distribution of possible drought progression and recession before and during the growing season, and can be used with complementary information in agricultural drought risk management and mitigation strategies.