Air dispersion modeling: A tool for environmental evaluation and improvement

Cora, Mario G.; Hung, Yung Tse

doi:10.1002/tqem.10075

Cited by 16 publications

(12 citation statements)

References 10 publications

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“…7 By using mathematical representations of these factors, air dispersion models can-if sufficient data are available to describe these parameters-provide a more accurate assessment of potential exposure. 8 Although air dispersion modelling has been used extensively for air quality management and regulatory purposes, this approach has rarely been applied to exposure assessment for epidemiological studies, despite proving to be a useful tool in the few studies where modelling has been used. [9][10][11][12][13] Following the findings of the preliminary investigations in Runcorn, a decision was made to further investigate renal effects in this population.…”

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confidence: 99%

Assessment of exposure to mercury from industrial emissions: comparing “distance as a proxy” and dispersion modelling approaches

et al. 2006

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Background: The Runcorn area, north-west England, contains many pollution sources, the health effects of which have been under discussion for over 100 years. Preliminary investigations revealed an excess risk of mortality from kidney disease in people living nearest to several point sources of pollution, using distance as a proxy for exposure. Ongoing epidemiological investigations into the effect of ambient mercury exposure on dose and renal effect required a more refined assessment of exposure. Methods: Atmospheric dispersion modelling was used to assess mercury dispersion from three mercuryemitting sources (including a large chlor alkali plant), based on knowledge of emissions, local meteorology and topography. Results: The model was sensitive to various input parameters, with different dispersion patterns and groundlevel concentrations, and therefore different exposed populations identified when different input parameters were defined. The different approaches to exposure assessment also had an impact on the epidemiological findings. The model output correlated well with weekly monitoring data collected in the local area, although the model underestimated concentrations in close proximity to the chlor alkali plant. The model identified that one point source did not contribute significantly to ground-level mercury concentrations, so that inclusion of this source when using the ''distance as a proxy'' approach led to significant exposure misclassification. Conclusions: The model output indicates that assessment of ambient exposure should give consideration to the magnitude of emissions, point source characteristics, local meteorology and topography to ensure that the most appropriate exposure classification is reached. Even if dispersion modelling cannot be undertaken, these data can be used to inform and improve the distance as a proxy approach, and improve the interpretability of the epidemiological findings.

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confidence: 99%

Assessment of exposure to mercury from industrial emissions: comparing “distance as a proxy” and dispersion modelling approaches

et al. 2006

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“…The Gaussian approach assumes that the simulated concentration values follow a bell-shaped distribution. The concentration of a modeled pollutant is the highest at the center of the plume (the location of the source in this study), and then decreases exponentially approaching zero at the plume edge (Cora and Hung 2003). Thus, the areas beyond the plume edge (10% of the highest value) can be considered as free of the impact of the source.…”

Section: Source Impact Index (Sii) Calculationmentioning

confidence: 98%

“…The areas with SII values higher than or equal to 10% of the highest value were considered to be inside the plume buffer, and the other areas were outside of the plume buffer. The reason we used this definition was that 10% of the highest value is considered as the plume edge by US EPA when simulated by a Gaussian modeling approach, as used in AERMOD (Cora and Hung 2003). The Gaussian approach assumes that the simulated concentration values follow a bell-shaped distribution.…”

Section: Source Impact Index (Sii) Calculationmentioning

confidence: 99%

Loose-coupling an air dispersion model and a geographic information system (GIS) for studying air pollution and asthma in the Bronx, New York City

Maantay

Maroko

2009

International Journal of Environmental Health Research

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This study developed new procedures to loosely integrate an air dispersion model, AERMOD, and a geographic information system (GIS) package, ArcGIS, to simulate air dispersion from stationary sources in the Bronx, New York City, for five pollutants: PM(10), PM(2.5), NO(x), CO, and SO(2). Plume buffers created from the model results were used as proxies of human exposure to the pollution from the sources and they modified the commonly used fixed-distance proximity buffers by considering the realities of air dispersion. The application of the plume buffers confirmed that the higher asthma hospitalization rates were associated with the higher potential exposure to local air pollution. The air dispersion modeling exhibited advantages over proximity analysis and geostatistical methods for environmental health research. The loose integration provides a relatively simple and feasible method for health scientists to take advantage of both air dispersion modeling and GIS by avoiding the need for intensive programming and substantial GIS expertise.

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“…Prediction of impacts of emissions on the receiving environment is important as ambient air quality is critical for public health. Air pollution models may substantially help in assessing air quality and optimizing emission reduction strategies (Cora and Hung 2003;Kumar et al 1999). Several air quality modeling studies have been carried out in the past using Industrial Source Complex (ISC) model (Lorber et al 2000;Bhanarkar et al 2005a, b;Sax and Isakov 2003).…”

Section: Introductionmentioning

confidence: 99%

Emissions of SO2, NO x and particulates from a pipe manufacturing plant and prediction of impact on air quality

Bhanarkar

Majumdar

Nema

et al. 2009

Environ Monit Assess

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Integrated pipe manufacturing industry is operation intensive and has significant air pollution potential especially when it is equipped with a captive power production facility. Emissions of SO(2), NO(x), and particulate matter (PM) were estimated from the stationary sources in a state-of-the-art pipe manufacturing plant in India. Major air polluting units like blast furnace, ductile iron spun pipe facility, and captive power production facility were selected for stack gas monitoring. Subsequently, ambient air quality modeling was undertaken to predict ground-level concentrations of the selected air pollutants using Industrial Source Complex (ISC 3) model. Emissions of SO(2), NO(x), and particulate matter from the stationary sources in selected facilities ranged from 0.02 to 16.5, 0.03 to 93.3, and 0.09 to 48.3 kg h(-1), respectively. Concentration of SO(2) and NO(x) in stack gas of 1,180-kVA (1 KW = 1.25 kVA) diesel generator exceeded the upper safe limits prescribed by the State Pollution Control Board, while concentrations of the same from all other units were within the prescribed limits. Particulate emission was highest from the barrel grinding operation, where grinding of the manufactured pipes is undertaken for giving the final shape. Particulate emission was also high from dedusting operation where coal dust is handled. Air quality modeling indicated that maximum possible ground-level concentration of PM, SO(2), and NO(x) were to the tune of 13, 3, and 18 microg/m(3), respectively, which are within the prescribed limits for ambient air given by the Central Pollution Control Board.

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Air dispersion modeling: A tool for environmental evaluation and improvement

Cited by 16 publications

References 10 publications

Assessment of exposure to mercury from industrial emissions: comparing “distance as a proxy” and dispersion modelling approaches

Assessment of exposure to mercury from industrial emissions: comparing “distance as a proxy” and dispersion modelling approaches

Loose-coupling an air dispersion model and a geographic information system (GIS) for studying air pollution and asthma in the Bronx, New York City

Emissions of SO2, NO x and particulates from a pipe manufacturing plant and prediction of impact on air quality

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