Johnson M.,Water Air and Climate Change Bureau |
Isakov V.,U.S. Environmental Protection Agency |
Touma J.S.,U.S. Environmental Protection Agency |
Mukerjee S.,U.S. Environmental Protection Agency |
Ozkaynak H.,U.S. Environmental Protection Agency
Atmospheric Environment | Year: 2010
Cohort studies designed to estimate human health effects of exposures to urban pollutants require accurate determination of ambient concentrations in order to minimize exposure misclassification errors. However, it is often difficult to collect concentration information at each study subject location. In the absence of complete subject-specific measurements, land-use regression (LUR) models have frequently been used for estimating individual levels of exposures to ambient air pollution. The LUR models, however, have several limitations mainly dealing with extensive monitoring data needs and challenges involved in their broader applicability to other locations. In contrast, air quality models can provide high-resolution source-concentration linkages for multiple pollutants, but require detailed emissions and meteorological information. In this study, first we predicted air quality concentrations of PM 2.5, NO x, and benzene in New Haven, CT using hybrid modeling techniques based on CMAQ and AERMOD model results. Next, we used these values as pseudo-observations to develop and evaluate the different LUR models built using alternative numbers of (training) sites (ranging from 25 to 285 locations out of the total 318 receptors). We then evaluated the fitted LUR models using various approaches, including: 1) internal "Leave-One-Out-Cross-Validation" (LOOCV) procedure within the "training" sites selected; and 2) "Hold-Out" evaluation procedure, where we set aside 33-293 tests sites as independent datasets for external model evaluation. LUR models appeared to perform well in the training datasets. However, when these LUR models were tested against independent hold out (test) datasets, their performance diminished considerably. Our results confirm the challenges facing the LUR community in attempting to fit empirical response surfaces to spatially- and temporally-varying pollution levels using LUR techniques that are site dependent. These results also illustrate the potential benefits of enhancing basic LUR models by utilizing air quality modeling tools or concepts in order to improve their reliability or transferability. © 2010.
Johnson M.,Water Air and Climate Change Bureau |
Macneill M.,Water Air and Climate Change Bureau |
Grgicak-Mannion A.,University of Windsor |
Nethery E.,Water Air and Climate Change Bureau |
And 4 more authors.
Journal of Exposure Science and Environmental Epidemiology | Year: 2013
Regulatory monitoring data and land-use regression (LUR) models have been widely used for estimating individual exposure to ambient air pollution in epidemiologic studies. However, LUR models lack fine-scale temporal resolution for predicting acute exposure and regulatory monitoring provides daily concentrations, but fails to capture spatial variability within urban areas. This study coupled LUR models with continuous regulatory monitoring to predict daily ambient nitrogen dioxide (NO 2) and particulate matter (PM 2.5) at 50 homes in Windsor, Ontario. We compared predicted versus measured daily outdoor concentrations for 5 days in winter and 5 days in summer at each home. We also examined the implications of using modeled versus measured daily pollutant concentrations to predict daily lung function among asthmatic children living in those homes. Mixed effect analysis suggested that temporally refined LUR models explained a greater proportion of the spatial and temporal variance in daily household-level outdoor NO 2 measurements compared with daily concentrations based on regulatory monitoring. Temporally refined LUR models captured 40% (summer) and 10% (winter) more of the spatial variance compared with regulatory monitoring data. Ambient PM 2.5 showed little spatial variation; therefore, daily PM 2.5 models were similar to regulatory monitoring data in the proportion of variance explained. Furthermore, effect estimates for forced expiratory volume in 1 s (FEV 1) and peak expiratory flow (PEF) based on modeled pollutant concentrations were consistent with effects based on household-level measurements for NO 2 and PM 2.5. These results suggest that LUR modeling can be combined with continuous regulatory monitoring data to predict daily household-level exposure to ambient air pollution. Temporally refined LUR models provided a modest improvement in estimating daily household-level NO 2 compared with regulatory monitoring data alone, suggesting that this approach could potentially improve exposure estimation for spatially heterogeneous pollutants. These findings have important implications for epidemiologic studies-in particular, for research focused on short-term exposure and health effects. © 2013 Nature America, Inc. All rights reserved.
Jokinen C.,Public Health Agency of Canada |
Edge T.A.,National Water Research Institute |
Ho S.,Public Health Agency of Canada |
Koning W.,Environment Canada |
And 10 more authors.
Water Research | Year: 2011
Campylobacter spp., Salmonella enterica, and Escherichia coli O157:H7 isolated from 898 faecal, 43 sewage, and 342 surface water samples from the Oldman River were characterized using bacterial subtyping methods in order to investigate potential sources of contamination of the watershed. Among these pathogens, Campylobacter spp. were the most frequently isolated from faecal, sewage, and surface water samples (266/895, 11/43, and 91/342, respectively), followed by Salmonella (67/898, 8/43, and 29/342, respectively), and E. coli O157:H7 (16/898, 2/43, and 8/342, respectively). Salmonella Rubislaw was the most common serovar isolated from water. This serovar was also isolated from two wild bird species. Most other serovars isolated from water were either not isolated from animals or were isolated from multiple species. E. coli O157:H7 was predominantly isolated from cattle. The most common phage-types of this pathogen from cattle were also the most common among water isolates, and there were exact pulsed field gel electrophoresis and comparative genomic fingerprint matches between cattle, sewage, and water isolates. Campylobacters were commonly isolated from surface waters and faeces from most animal species. Restriction fragment length polymorphism of the Campylobacter flaA gene identified several location and host species-specific (cattle, goose, pig) fingerprints. Molecular subtyping of these bacterial pathogens shows considerable promise as a tool for determining the sources of faecal pollution of water. © 2010.