TY - JOUR
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 - Al-Mamun, Abdullah
AU - Al-Muhtaseb, Ala’a H.
N1 - Publisher Copyright:
© 2018, Springer Nature Switzerland AG.
PY - 2018/11/1
Y1 - 2018/11/1
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.].
KW - AERMOD
KW - Deposition
KW - Dispersion
KW - Iron industry
KW - Particle emission
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U2 - 10.1007/s10661-018-7009-4
DO - 10.1007/s10661-018-7009-4
M3 - Article
C2 - 30338389
AN - SCOPUS:85055073636
SN - 0167-6369
VL - 190
JO - Environmental Monitoring and Assessment
JF - Environmental Monitoring and Assessment
IS - 11
M1 - 655
ER -
