The Experts below are selected from a list of 309 Experts worldwide ranked by ideXlab platform
Nicholas Angelis - One of the best experts on this subject based on the ideXlab platform.
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Ambient air pollution and emergency department visits in Toronto, Canada
Environmental Science and Pollution Research, 2021Co-Authors: Mieczysław Szyszkowicz, Nicholas AngelisAbstract:To investigate the acute impact of various air Pollutants on various disease groups in the urban area of the city of Toronto, Canada. Statistical models were developed to estimate the relative risk of an emergency department visit associated with ambient air pollution concentration levels. These models were generated for 8 air Pollutants (lagged from 0 to 14 days) and for 18 strata (based on sex, age group, and season). Twelve disease groups extracted from the International Classification of Diseases 10th Revision (ICD-10) were used as health classifications in the models. The qualitative results were collected in matrices composed of 18 rows (strata) and 15 columns (lags) for each air pollutant and the 12 health classifications. The matrix cells were assigned a value of 1 if the association was positively statistically significant; otherwise, they were assigned to a value of 0. The constructed matrices were totalized separately for each air pollutant. The resulting matrices show qualitative associations for grouped diseases, air Pollutants, and their corresponding lagged concentrations and indicate the frequency of statistically significant positive associations. The results are presented in colour-gradient matrices with the number of associations for every combination of patient strata, pollutant, and lag in corresponding cells. The highest number of the associations was 8 (of 12 possible) obtained for the same day exposure to carbon monoxide, nitrogen dioxide, and days with elevated air quality health index (AQHI) values. For carbon monoxide, the number of the associations decreases with the increasing lags. For this air pollutant, there were almost no associations after 8 days of lag. In the case of nitrogen dioxide, the associations persist even for longer lags. The numerical values obtained from the models are provided for every pollutant. The constructed matrices are a useful tool to analyze the impact of ambient air pollution concentrations on public health.
James Mulholland - One of the best experts on this subject based on the ideXlab platform.
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Application of alternative spatiotemporal metrics of ambient air pollution exposure in a time-series epidemiological study in Atlanta
Journal of Exposure Science & Environmental Epidemiology, 2013Co-Authors: Stefanie Ebelt Sarnat, Vlad Isakov, Haluk Ozkaynak, James Mulholland, Mitchel Klein, Jeremy A Sarnat, Howard H. Chang, Paige E TolbertAbstract:Exposure error in studies of ambient air pollution and health that use city-wide measures of exposure may be substantial for Pollutants that exhibit spatiotemporal variability. Alternative spatiotemporal metrics of exposure for traffic-related and regional Pollutants were applied in a time-series study of ambient air pollution and cardiorespiratory emergency department visits in Atlanta, GA, USA. Exposure metrics included daily central site monitoring for particles and gases; daily spatially refined ambient concentrations obtained from regional background monitors, local-scale dispersion, and hybrid air quality models; and spatially refined ambient exposures from population exposure models. Health risk estimates from Poisson models using the different exposure metrics were compared. We observed stronger associations, particularly for traffic-related Pollutants, when using spatially refined ambient concentrations compared with a conventional central site exposure assignment approach. For some relationships, estimates of spatially refined ambient population exposures showed slightly stronger associations than corresponding spatially refined ambient concentrations. Using spatially refined pollutant metrics, we identified socioeconomic disparities in concentration–response functions that were not observed when using central site data. In some cases, spatially refined pollutant metrics identified associations with health that were not observed using measurements from the central site. Complexity and challenges in incorporating modeled pollutant estimates in time-series studies are discussed.
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Development and evaluation of alternative approaches for exposure assessment of multiple air Pollutants in Atlanta, Georgia
Journal of Exposure Science & Environmental Epidemiology, 2013Co-Authors: Kathie L Dionisio, Vlad Isakov, Stefanie Ebelt Sarnat, Jeremy A Sarnat, Lisa K Baxter, Janet Burke, Arlene Rosenbaum, Stephen E Graham, Rich Cook, James MulhollandAbstract:Measurements from central site (CS) monitors are often used as estimates of exposure in air pollution epidemiological studies. As these measurements are typically limited in their spatiotemporal resolution, true exposure variability within a population is often obscured, leading to potential measurement errors. To fully examine this limitation, we developed a set of alternative daily exposure metrics for each of the 169 ZIP codes in the Atlanta, GA, metropolitan area, from 1999 to 2002, for PM_2.5 and its components (elemental carbon (EC), SO_4), O_3, carbon monoxide (CO), and nitrogen oxides (NO_ x ). Metrics were applied in a study investigating the respiratory health effects of these Pollutants. The metrics included: (i) CS measurements (one CS per pollutant); (ii) air quality model results for regional background pollution; (iii) local-scale AERMOD air quality model results; (iv) hybrid air quality model estimates (a combination of (ii) and (iii)); and (iv) population exposure model predictions (SHEDS and APEX). Differences in estimated spatial and temporal variability were compared by exposure metric and pollutant. Comparisons showed that: (i) both hybrid and exposure model estimates exhibited high spatial variability for traffic-related Pollutants (CO, NO_ x , and EC), but little spatial variability among ZIP code centroids for regional Pollutants (PM_2.5, SO_4, and O_3); (ii) for all Pollutants except NO_ x , temporal variability was consistent across metrics; (iii) daily hybrid-to-exposure model correlations were strong ( r >0.82) for all Pollutants, suggesting that when temporal variability of pollutant concentrations is of main interest in an epidemiological application, the use of estimates from either model may yield similar results; (iv) exposure models incorporating infiltration parameters, time-location-activity budgets, and other exposure factors affect the magnitude and spatiotemporal distribution of exposure, especially for local Pollutants. The results of this analysis can inform the development of more appropriate exposure metrics for future epidemiological studies of the short-term effects of particulate and gaseous ambient pollutant exposure in a community.
Stefanie Ebelt Sarnat - One of the best experts on this subject based on the ideXlab platform.
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Application of alternative spatiotemporal metrics of ambient air pollution exposure in a time-series epidemiological study in Atlanta
Journal of Exposure Science & Environmental Epidemiology, 2013Co-Authors: Stefanie Ebelt Sarnat, Vlad Isakov, Haluk Ozkaynak, James Mulholland, Mitchel Klein, Jeremy A Sarnat, Howard H. Chang, Paige E TolbertAbstract:Exposure error in studies of ambient air pollution and health that use city-wide measures of exposure may be substantial for Pollutants that exhibit spatiotemporal variability. Alternative spatiotemporal metrics of exposure for traffic-related and regional Pollutants were applied in a time-series study of ambient air pollution and cardiorespiratory emergency department visits in Atlanta, GA, USA. Exposure metrics included daily central site monitoring for particles and gases; daily spatially refined ambient concentrations obtained from regional background monitors, local-scale dispersion, and hybrid air quality models; and spatially refined ambient exposures from population exposure models. Health risk estimates from Poisson models using the different exposure metrics were compared. We observed stronger associations, particularly for traffic-related Pollutants, when using spatially refined ambient concentrations compared with a conventional central site exposure assignment approach. For some relationships, estimates of spatially refined ambient population exposures showed slightly stronger associations than corresponding spatially refined ambient concentrations. Using spatially refined pollutant metrics, we identified socioeconomic disparities in concentration–response functions that were not observed when using central site data. In some cases, spatially refined pollutant metrics identified associations with health that were not observed using measurements from the central site. Complexity and challenges in incorporating modeled pollutant estimates in time-series studies are discussed.
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Development and evaluation of alternative approaches for exposure assessment of multiple air Pollutants in Atlanta, Georgia
Journal of Exposure Science & Environmental Epidemiology, 2013Co-Authors: Kathie L Dionisio, Vlad Isakov, Stefanie Ebelt Sarnat, Jeremy A Sarnat, Lisa K Baxter, Janet Burke, Arlene Rosenbaum, Stephen E Graham, Rich Cook, James MulhollandAbstract:Measurements from central site (CS) monitors are often used as estimates of exposure in air pollution epidemiological studies. As these measurements are typically limited in their spatiotemporal resolution, true exposure variability within a population is often obscured, leading to potential measurement errors. To fully examine this limitation, we developed a set of alternative daily exposure metrics for each of the 169 ZIP codes in the Atlanta, GA, metropolitan area, from 1999 to 2002, for PM_2.5 and its components (elemental carbon (EC), SO_4), O_3, carbon monoxide (CO), and nitrogen oxides (NO_ x ). Metrics were applied in a study investigating the respiratory health effects of these Pollutants. The metrics included: (i) CS measurements (one CS per pollutant); (ii) air quality model results for regional background pollution; (iii) local-scale AERMOD air quality model results; (iv) hybrid air quality model estimates (a combination of (ii) and (iii)); and (iv) population exposure model predictions (SHEDS and APEX). Differences in estimated spatial and temporal variability were compared by exposure metric and pollutant. Comparisons showed that: (i) both hybrid and exposure model estimates exhibited high spatial variability for traffic-related Pollutants (CO, NO_ x , and EC), but little spatial variability among ZIP code centroids for regional Pollutants (PM_2.5, SO_4, and O_3); (ii) for all Pollutants except NO_ x , temporal variability was consistent across metrics; (iii) daily hybrid-to-exposure model correlations were strong ( r >0.82) for all Pollutants, suggesting that when temporal variability of pollutant concentrations is of main interest in an epidemiological application, the use of estimates from either model may yield similar results; (iv) exposure models incorporating infiltration parameters, time-location-activity budgets, and other exposure factors affect the magnitude and spatiotemporal distribution of exposure, especially for local Pollutants. The results of this analysis can inform the development of more appropriate exposure metrics for future epidemiological studies of the short-term effects of particulate and gaseous ambient pollutant exposure in a community.
Mieczysław Szyszkowicz - One of the best experts on this subject based on the ideXlab platform.
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Ambient air pollution and emergency department visits in Toronto, Canada
Environmental Science and Pollution Research, 2021Co-Authors: Mieczysław Szyszkowicz, Nicholas AngelisAbstract:To investigate the acute impact of various air Pollutants on various disease groups in the urban area of the city of Toronto, Canada. Statistical models were developed to estimate the relative risk of an emergency department visit associated with ambient air pollution concentration levels. These models were generated for 8 air Pollutants (lagged from 0 to 14 days) and for 18 strata (based on sex, age group, and season). Twelve disease groups extracted from the International Classification of Diseases 10th Revision (ICD-10) were used as health classifications in the models. The qualitative results were collected in matrices composed of 18 rows (strata) and 15 columns (lags) for each air pollutant and the 12 health classifications. The matrix cells were assigned a value of 1 if the association was positively statistically significant; otherwise, they were assigned to a value of 0. The constructed matrices were totalized separately for each air pollutant. The resulting matrices show qualitative associations for grouped diseases, air Pollutants, and their corresponding lagged concentrations and indicate the frequency of statistically significant positive associations. The results are presented in colour-gradient matrices with the number of associations for every combination of patient strata, pollutant, and lag in corresponding cells. The highest number of the associations was 8 (of 12 possible) obtained for the same day exposure to carbon monoxide, nitrogen dioxide, and days with elevated air quality health index (AQHI) values. For carbon monoxide, the number of the associations decreases with the increasing lags. For this air pollutant, there were almost no associations after 8 days of lag. In the case of nitrogen dioxide, the associations persist even for longer lags. The numerical values obtained from the models are provided for every pollutant. The constructed matrices are a useful tool to analyze the impact of ambient air pollution concentrations on public health.
Jeremy A Sarnat - One of the best experts on this subject based on the ideXlab platform.
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Application of alternative spatiotemporal metrics of ambient air pollution exposure in a time-series epidemiological study in Atlanta
Journal of Exposure Science & Environmental Epidemiology, 2013Co-Authors: Stefanie Ebelt Sarnat, Vlad Isakov, Haluk Ozkaynak, James Mulholland, Mitchel Klein, Jeremy A Sarnat, Howard H. Chang, Paige E TolbertAbstract:Exposure error in studies of ambient air pollution and health that use city-wide measures of exposure may be substantial for Pollutants that exhibit spatiotemporal variability. Alternative spatiotemporal metrics of exposure for traffic-related and regional Pollutants were applied in a time-series study of ambient air pollution and cardiorespiratory emergency department visits in Atlanta, GA, USA. Exposure metrics included daily central site monitoring for particles and gases; daily spatially refined ambient concentrations obtained from regional background monitors, local-scale dispersion, and hybrid air quality models; and spatially refined ambient exposures from population exposure models. Health risk estimates from Poisson models using the different exposure metrics were compared. We observed stronger associations, particularly for traffic-related Pollutants, when using spatially refined ambient concentrations compared with a conventional central site exposure assignment approach. For some relationships, estimates of spatially refined ambient population exposures showed slightly stronger associations than corresponding spatially refined ambient concentrations. Using spatially refined pollutant metrics, we identified socioeconomic disparities in concentration–response functions that were not observed when using central site data. In some cases, spatially refined pollutant metrics identified associations with health that were not observed using measurements from the central site. Complexity and challenges in incorporating modeled pollutant estimates in time-series studies are discussed.
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Development and evaluation of alternative approaches for exposure assessment of multiple air Pollutants in Atlanta, Georgia
Journal of Exposure Science & Environmental Epidemiology, 2013Co-Authors: Kathie L Dionisio, Vlad Isakov, Stefanie Ebelt Sarnat, Jeremy A Sarnat, Lisa K Baxter, Janet Burke, Arlene Rosenbaum, Stephen E Graham, Rich Cook, James MulhollandAbstract:Measurements from central site (CS) monitors are often used as estimates of exposure in air pollution epidemiological studies. As these measurements are typically limited in their spatiotemporal resolution, true exposure variability within a population is often obscured, leading to potential measurement errors. To fully examine this limitation, we developed a set of alternative daily exposure metrics for each of the 169 ZIP codes in the Atlanta, GA, metropolitan area, from 1999 to 2002, for PM_2.5 and its components (elemental carbon (EC), SO_4), O_3, carbon monoxide (CO), and nitrogen oxides (NO_ x ). Metrics were applied in a study investigating the respiratory health effects of these Pollutants. The metrics included: (i) CS measurements (one CS per pollutant); (ii) air quality model results for regional background pollution; (iii) local-scale AERMOD air quality model results; (iv) hybrid air quality model estimates (a combination of (ii) and (iii)); and (iv) population exposure model predictions (SHEDS and APEX). Differences in estimated spatial and temporal variability were compared by exposure metric and pollutant. Comparisons showed that: (i) both hybrid and exposure model estimates exhibited high spatial variability for traffic-related Pollutants (CO, NO_ x , and EC), but little spatial variability among ZIP code centroids for regional Pollutants (PM_2.5, SO_4, and O_3); (ii) for all Pollutants except NO_ x , temporal variability was consistent across metrics; (iii) daily hybrid-to-exposure model correlations were strong ( r >0.82) for all Pollutants, suggesting that when temporal variability of pollutant concentrations is of main interest in an epidemiological application, the use of estimates from either model may yield similar results; (iv) exposure models incorporating infiltration parameters, time-location-activity budgets, and other exposure factors affect the magnitude and spatiotemporal distribution of exposure, especially for local Pollutants. The results of this analysis can inform the development of more appropriate exposure metrics for future epidemiological studies of the short-term effects of particulate and gaseous ambient pollutant exposure in a community.
