Dispersion and deposition estimation of fugitive iron particles from an iron industry on nearby communities via AERMOD

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Abstract

Emission of fugitive iron particles from anthropogenic sources can have significant effects on the human health and the environment. In this study, a regulatory air pollutant dispersion model (AERMOD) was implemented to predict the dispersion and deposition of fugitive iron particles towards a mid-sized residential area in Sultanate of Oman. The performance of the model was validated using air, soil, and dust fall samples. PM10 was found as the most abundant iron particles in the soil samples. The results showed that the maximum daily concentration level of fugitive iron particles simulated through AERMOD was 7.19 μg/m3. Statistical analysis, including fractional bias (FB), normalized mean square error (NMSE), and predicted/observed ratio (Pred./Obs.), showed a reliable agreement in accuracy and precision between the datasets (for air samples FB = 0.024, NMSE = 0.001, Pred./Obs. = 0.976; for dust fall samples FB = −0.004, NMSE = 0.000, Pred./Obs. = 1.004). However, uncertainties and differences were from the external sources, such as other industries in the region. The results presented that the concentration levels were below the national and international guidelines proposed by the US Environmental Protection Agency (USEPA) and Omani Ambient Air Quality Standards (OAAQS). The methodology followed and the developed dispersion model can be generalized to other industries from which the dispersion of fugitive metal particles need to be evaluated as a potential route for human exposure. [Figure not available: see fulltext.].

Original languageEnglish
Article number655
JournalEnvironmental Monitoring and Assessment
Volume190
Issue number11
DOIs
Publication statusPublished - Nov 1 2018

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Iron
iron
Mean square error
Air
Industry
Dust
dust
Soils
Air quality standards
soil air
industry
Environmental Protection Agency
anthropogenic source
ambient air
Statistical methods
air quality
statistical analysis
iron industry
pollutant dispersion
particle

Keywords

  • AERMOD
  • Deposition
  • Dispersion
  • Iron industry
  • Particle emission

ASJC Scopus subject areas

  • Environmental Science(all)
  • Pollution
  • Management, Monitoring, Policy and Law

Cite this

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title = "Dispersion and deposition estimation of fugitive iron particles from an iron industry on nearby communities via AERMOD",
abstract = "Emission of fugitive iron particles from anthropogenic sources can have significant effects on the human health and the environment. In this study, a regulatory air pollutant dispersion model (AERMOD) was implemented to predict the dispersion and deposition of fugitive iron particles towards a mid-sized residential area in Sultanate of Oman. The performance of the model was validated using air, soil, and dust fall samples. PM10 was found as the most abundant iron particles in the soil samples. The results showed that the maximum daily concentration level of fugitive iron particles simulated through AERMOD was 7.19 μg/m3. Statistical analysis, including fractional bias (FB), normalized mean square error (NMSE), and predicted/observed ratio (Pred./Obs.), showed a reliable agreement in accuracy and precision between the datasets (for air samples FB = 0.024, NMSE = 0.001, Pred./Obs. = 0.976; for dust fall samples FB = −0.004, NMSE = 0.000, Pred./Obs. = 1.004). However, uncertainties and differences were from the external sources, such as other industries in the region. The results presented that the concentration levels were below the national and international guidelines proposed by the US Environmental Protection Agency (USEPA) and Omani Ambient Air Quality Standards (OAAQS). The methodology followed and the developed dispersion model can be generalized to other industries from which the dispersion of fugitive metal particles need to be evaluated as a potential route for human exposure. [Figure not available: see fulltext.].",
keywords = "AERMOD, Deposition, Dispersion, Iron industry, Particle emission",
author = "Hamid Omidvarborna and Mahad Baawain and Mohamed Almamun and Ala Al-Muhtaseb",
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T1 - Dispersion and deposition estimation of fugitive iron particles from an iron industry on nearby communities via AERMOD

AU - Omidvarborna, Hamid

AU - Baawain, Mahad

AU - Almamun, Mohamed

AU - Al-Muhtaseb, Ala

PY - 2018/11/1

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N2 - Emission of fugitive iron particles from anthropogenic sources can have significant effects on the human health and the environment. In this study, a regulatory air pollutant dispersion model (AERMOD) was implemented to predict the dispersion and deposition of fugitive iron particles towards a mid-sized residential area in Sultanate of Oman. The performance of the model was validated using air, soil, and dust fall samples. PM10 was found as the most abundant iron particles in the soil samples. The results showed that the maximum daily concentration level of fugitive iron particles simulated through AERMOD was 7.19 μg/m3. Statistical analysis, including fractional bias (FB), normalized mean square error (NMSE), and predicted/observed ratio (Pred./Obs.), showed a reliable agreement in accuracy and precision between the datasets (for air samples FB = 0.024, NMSE = 0.001, Pred./Obs. = 0.976; for dust fall samples FB = −0.004, NMSE = 0.000, Pred./Obs. = 1.004). However, uncertainties and differences were from the external sources, such as other industries in the region. The results presented that the concentration levels were below the national and international guidelines proposed by the US Environmental Protection Agency (USEPA) and Omani Ambient Air Quality Standards (OAAQS). The methodology followed and the developed dispersion model can be generalized to other industries from which the dispersion of fugitive metal particles need to be evaluated as a potential route for human exposure. [Figure not available: see fulltext.].

AB - Emission of fugitive iron particles from anthropogenic sources can have significant effects on the human health and the environment. In this study, a regulatory air pollutant dispersion model (AERMOD) was implemented to predict the dispersion and deposition of fugitive iron particles towards a mid-sized residential area in Sultanate of Oman. The performance of the model was validated using air, soil, and dust fall samples. PM10 was found as the most abundant iron particles in the soil samples. The results showed that the maximum daily concentration level of fugitive iron particles simulated through AERMOD was 7.19 μg/m3. Statistical analysis, including fractional bias (FB), normalized mean square error (NMSE), and predicted/observed ratio (Pred./Obs.), showed a reliable agreement in accuracy and precision between the datasets (for air samples FB = 0.024, NMSE = 0.001, Pred./Obs. = 0.976; for dust fall samples FB = −0.004, NMSE = 0.000, Pred./Obs. = 1.004). However, uncertainties and differences were from the external sources, such as other industries in the region. The results presented that the concentration levels were below the national and international guidelines proposed by the US Environmental Protection Agency (USEPA) and Omani Ambient Air Quality Standards (OAAQS). The methodology followed and the developed dispersion model can be generalized to other industries from which the dispersion of fugitive metal particles need to be evaluated as a potential route for human exposure. [Figure not available: see fulltext.].

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