Estimating environmental exposures to sulfur dioxide from multiple industrial sources for a case–control study

Jf, Rogers; Gg, Killough; Sj, Thompson; Cl, Addy; McKeown, Robert E.; Dj, Cowen

doi:10.1038/sj.jea.7500059

Cited by 7 publications

(8 citation statements)

References 17 publications

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“…As with other exposure estimators, dispersion modeling is also subject to uncertainty (Sexton et al, 1992 ). Uncertainty in dispersion modeling results was evaluated in a recent study estimating SO 2 levels resulting from emissions from multiple industrial facilities in southeastern Georgia, USA (Rogers et al, 1999 ) . Rogers et al conducted transport model uncertainty analysis and found very little difference in monitored versus modeled pollutant levels when using effective stack height in dispersion modeling.…”

Section: Discussionmentioning

confidence: 99%

Childhood respiratory symptoms, hospital admissions, and long-term exposure to airborne particulate matter

Hrubá¹,

Fabiánovà²,

Koppová³

et al. 2001

J Expo Sci Environ Epidemiol

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The effects of long -term exposure to air pollution on respiratory symptoms and respiratory hospitalization ( for asthma, bronchitis or pneumonia ) were assessed in a cross -sectional study of children ( ages 7 ± 11 years, N = 667 ) living in a moderately industrialized city in Central Slovakia. Individual health, residence and family history data obtained through the CESAR study were coupled, using Geographic Information System ( GIS ) technologies, with total suspended particulate ( TSP ) exposure estimates derived from dispersion modeling of almost all local stationary sources. These data were used to assess, at the intra -city level and child -specific level, the potential for TSP as a risk factor for respiratory disease in children. TSP, PM 10 , and PM 2.5 monitored ambient concentrations are highly correlated in the study location. Modeled TSP concentrations resulting from local source emissions are dominated by a large wood processing facility, suggesting variation in exposures among children. The prevalence of respiratory non -asthmatic symptoms and hospitalizations was associated with increased TSP. No association between long -term exposure to TSP and asthma diagnosis or wheeze symptoms was found. Logistic regression modeling indicated a significant increase in hospital admissions for asthma, bronchitis or pneumonia associated with increasing air pollution ( OR 2.16, CI, 1.01 ± 4.60 ) , doctor -diagnosed bronchitis ( OR 1.53, CI, 1.02 ± 2.30 ) , and parent -reported chronic phlegm ( OR 3.43, CI, 1.64 ± 7.16 ) , expressed as odds for a 15 g / m 3 increase in estimated TSP exposure, and these increases are not due to differences in socio -economic, health care or other identified factors.

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Section: Discussionmentioning

confidence: 99%

Childhood respiratory symptoms, hospital admissions, and long-term exposure to airborne particulate matter

Hrubá¹,

Fabiánovà²,

Koppová³

et al. 2001

J Expo Sci Environ Epidemiol

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“…This classification is analogous to that carried out in Section III by using the Wilcoxon rank sum test for the medians. With the estimators (T (0.2), s W (0.2)) and (T bi (9), s bi (9)), the classification of the variables is equivalent to that obtained by using nonparametric estimators. However, the difference is that X 10 can form a category by itself in the groupings given by the pair of estimators (T (0.2), s W (0.2)).…”

Section: A Central Tendency and Scale Estimatorsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…In [9], a method of estimating exposure to SO 2 was presented. The Gaussian plume atmospheric transport model used in [9] was introduced by [10] and [11], and it obtained the average ground-level concentration of SO 2 over long periods of time. In [9], uncertainty was incorporated into the abovementioned model by multiplying by a lognormally distributed factor.…”

Section: Introductionmentioning

confidence: 99%

“…The Gaussian plume atmospheric transport model used in [9] was introduced by [10] and [11], and it obtained the average ground-level concentration of SO 2 over long periods of time. In [9], uncertainty was incorporated into the abovementioned model by multiplying by a lognormally distributed factor. Additionally, the geometric bias expressed in terms of the predicted-to-observed ratio was used to compare the predicted and observed SO 2 concentrations.…”

Section: Introductionmentioning

confidence: 99%

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Robust Analysis of the Information Obtained From a Set of 12 Years of SO₂ Concentration Measurements

et al. 2020

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In this paper, a robust analysis of SO 2 concentration measurements taken at the Belisario air quality monitoring station, Quito, Ecuador is carried out. The analyzed data contain 12 years of measurements, from January 2008 to December 2019. In addition, this set of measurements was decomposed into variables that represent each year, month, day of the week, and hour of the day in groups of two hours. For the analysis, classic, nonparametric and robust statistical methods were used, and the data were classified based on criteria established by the Quiteño Air Quality Index, taking confidence intervals into account. The results showed that the level of air pollution at the Belisario station due to the SO 2 concentration is acceptable. In addition, the trend in the level of SO 2 concentration decreased over the years studied, with a sharp drop from 2008 to 2012, then a small rise in 2013 and another fall until 2019, presenting decreasing oscillations that tend toward a desirable level of pollution. In this paper, it was shown that the air pollution at the Belisario station due to the concentration of SO 2 in the last 12 years is not harmful to humans, with the measurement precision provided by robust statistical methods. Therefore, it can be concluded that the measures that have been taken by the Quito city council over the last few years are yielding good results.

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Meteorological data source comparison—a case study in geospatial modeling of potential environmental exposure to abandoned uranium mine sites in the Navajo Nation

Girlamo,

Lin,

Hoover

et al. 2023

Environ Monit Assess

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Meteorological (MET) data is a crucial input for environmental exposure models. While modeling exposure potential using geospatial technology is a common practice, existing studies infrequently evaluate the impact of input MET data on the level of uncertainty on output results. The objective of this study is to determine the effect of various MET data sources on the potential exposure susceptibility predictions. Three sources of wind data are compared: The North American Regional Reanalysis (NARR) database, meteorological aerodrome reports (METARs) from regional airports, and data from local MET weather stations. These data sources are used as inputs into a machine learning (ML) driven GIS Multi-Criteria Decision Analysis (GIS-MCDA) geospatial model to predict potential exposure to abandoned uranium mine sites in the Navajo Nation. Results indicate significant variations in results derived from different wind data sources. After validating the results from each source using the National Uranium Resource Evaluation (NURE) database in a geographically weighted regression (GWR), METARs data combined with the local MET weather station data showed the highest accuracy, with an average R2 of 0.74. We conclude that local direct measurement-based data (METARs and MET data) produce a more accurate prediction than the other sources evaluated in the study. This study has the potential to inform future data collection methods, leading to more accurate predictions and better-informed policy decisions surrounding environmental exposure susceptibility and risk assessment.

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Estimating environmental exposures to sulfur dioxide from multiple industrial sources for a case–control study

Cited by 7 publications

References 17 publications

Childhood respiratory symptoms, hospital admissions, and long-term exposure to airborne particulate matter

Childhood respiratory symptoms, hospital admissions, and long-term exposure to airborne particulate matter

Robust Analysis of the Information Obtained From a Set of 12 Years of SO₂ Concentration Measurements

Meteorological data source comparison—a case study in geospatial modeling of potential environmental exposure to abandoned uranium mine sites in the Navajo Nation

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