Airborne 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 too...
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Airborne 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-useregression (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 mu m in aerodynamic diameter (PM1.0) were collected in the City of Calgary at 25 sites in August 2010 and 29 sites in January 2011. PM1.0 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 PM1.0 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 PM1.0 components. Summer models performed slightly better than winter models. However, 24 of the 30 PM1.0 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 PMto components. Industrial and commercial zoning were also significant predictors, while traffic indicators and population density had a modest but significant contributi
Up-to-date information on urban air pollution, such as reliable pollution maps, is of great importance for health protection agencies to timely assess the air quality situation and provide advice to the general public...
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ISBN:
(纸本)9781450320276
Up-to-date information on urban air pollution, such as reliable pollution maps, is of great importance for health protection agencies to timely assess the air quality situation and provide advice to the general public. Ultrafine particles (UFPs) are widely spread in urban environments and believed to have severe impact on the human health. However, the lack of spatially resolved data hampers profound evaluation of these effects. In this work, we introduce one of the largest spatially resolved UFP data set available today, with over 25 million measurements to build high-resolution pollution maps for an urban area of 100 km2. The data is collected throughout more than one year using mobile sensor nodes, which are installed on top of public transport vehicles in the city of Zurich, Switzerland. We develop land-useregression models to create pollution maps with a high spatial resolution and study their temporal resolution limit.
Traffic is a major source of air pollutants in urban environments, and exposure to these pollutants may be associated with adverse health effects. However, inconsistencies in observational epidemiological studies may ...
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Traffic is a major source of air pollutants in urban environments, and exposure to these pollutants may be associated with adverse health effects. However, inconsistencies in observational epidemiological studies may be caused by differential measurement errors in various approaches in assessing exposure. We aimed to evaluate a simple method for assessing outdoor air pollutant concentrations in Oslo, Norway, through a land-useregression method. Samples of nitrogen oxides (NOx) were collected in two different weeks using Ogawa passive diffusion samplers simultaneously at 80 locations across Oslo. Independent variables used in subsequent regression models as predictors of the pollutants were derived using the Arc 9 geographic information system (GIS) software. indicators of landuse, traffic, population density, and physical geography were tested. The final regression model yielded an adjusted coefficient of determination (R-2) of 0.77 for nitrogen dioxide (NO2), 0.66 for nitric oxide (NO), and 0.73 for NOx. The results suggest that a good predictive exposure model can be derived from this approach, which can be used to estimate long-term small-area variation in concentrations for individual exposure assessment in epidemiological studies in a highly cost-effective way. These small-area variations in traffic pollution are important since they may have associations with health effects. (C) 2007 Elsevier Ltd. All rights reserved.
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