Literature DB >> 18200928

Comparison of the industrial source complex and AERMOD dispersion models: case study for human health risk assessment.

Keith C Silverman1, Joan G Tell, Edward V Sargent, Zeyuan Qiu.   

Abstract

Air quality models are typically used to predict the fate and transport of air emissions from industrial sources to comply with federal and state regulatory requirements and environmental standards, as well as to determine pollution control requirements. For many years, the U.S. Environmental Protection Agency (EPA) widely used the Industrial Source Complex (ISC) model because of its broad applicability to multiple source types. Recently, EPA adopted a new rule that replaces ISC with AERMOD, a state-of-the-practice air dispersion model, in many air quality impact assessments. This study compared the two models as well as their enhanced versions that incorporate the Plume Rise Model Enhancements (PRIME) algorithm. PRIME takes into account the effects of building downwash on plume dispersion. The comparison used actual point, area, and volume sources located on two separate facilities in conjunction with site-specific terrain and meteorological data. The modeled maximum total period average ground-level air concentrations were used to calculate potential health effects for human receptors. The results show that the switch from ISC to AERMOD and the incorporation of the PRIME algorithm tend to generate lower concentration estimates at the point of maximum ground-level concentration. However, the magnitude of difference varies from insignificant to significant depending on the types of the sources and the site-specific conditions. The differences in human health effects, predicted using results from the two models, mirror the concentrations predicted by the models.

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Year:  2007        PMID: 18200928     DOI: 10.3155/1047-3289.57.12.1439

Source DB:  PubMed          Journal:  J Air Waste Manag Assoc        ISSN: 1096-2247            Impact factor:   2.235


  6 in total

1.  Comparison of stationary and personal air sampling with an air dispersion model for children's ambient exposure to manganese.

Authors:  Florence Fulk; Erin N Haynes; Timothy J Hilbert; David Brown; Dan Petersen; Tiina Reponen
Journal:  J Expo Sci Environ Epidemiol       Date:  2016-05-11       Impact factor: 5.563

Review 2.  A review of AirQ Models and their applications for forecasting the air pollution health outcomes.

Authors:  Gea Oliveri Conti; Behzad Heibati; Itai Kloog; Maria Fiore; Margherita Ferrante
Journal:  Environ Sci Pollut Res Int       Date:  2017-01-04       Impact factor: 4.223

3.  Uncontrolled combustion of shredded tires in a landfill -Part 2: Population exposure, public health response, and an air quality index for urban fires.

Authors:  Ashish Singh; Scott N Spak; Elizabeth A Stone; Jared Downard; Robert Bullard; Mark Pooley; Pamela A Kostle; Matthew W Mainprize; Michael D Wichman; Thomas Peters; Douglas Beardsley; Charles O Stanier
Journal:  Atmos Environ (1994)       Date:  2015-03       Impact factor: 4.798

4.  Dispersion of NO2 and SO2 pollutants in the rolling industry with AERMOD model: a case study to assess human health risk.

Authors:  Mohsen Hesami Arani; Neamatollah Jaafarzadeh; Mehrdad Moslemzadeh; Mohammad Rezvani Ghalhari; Samaneh Bagheri Arani; Mahdiyeh Mohammadzadeh
Journal:  J Environ Health Sci Eng       Date:  2021-06-03

5.  An In Toto Approach to Radon Dispersion Modelling from a South African Gold Mine Tailings.

Authors:  Frank Komati; Martin Ntwaeaborwa; Rian Strydom
Journal:  Int J Environ Res Public Health       Date:  2022-07-05       Impact factor: 4.614

6.  Fast inverse distance weighting-based spatiotemporal interpolation: a web-based application of interpolating daily fine particulate matter PM2:5 in the contiguous U.S. using parallel programming and k-d tree.

Authors:  Lixin Li; Travis Losser; Charles Yorke; Reinhard Piltner
Journal:  Int J Environ Res Public Health       Date:  2014-09-03       Impact factor: 3.390

  6 in total

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