Document Type : Research article

Authors

1 Ph.D, Faculty of Natural Resources Engineering, Isfahan University of technology, Isfahan, Iran.

2 Ph.D, Human Environment and Sustainable Development Research Center, Najafabad Branch, Islamic Azad University, Najafabad, Iran

3 Msc, Department of Environmental Management (HSE), Technical and Engineering Faculty, Islamic Azad University, Zahedan Branch, Zahedan, Iran.

Abstract

Background and Purpose: One of the fundamental problems of air pollution is that it often affects large areas of various land uses, such as cities and agricultural products hundreds of kilometers away from the source of pollutants, or results in cumulative effects with other industries. This research aims to quantify the concentration of pollutants in residential areas around industrial areas.

Materials and Methods: In this study, the AERMOD software was used for modeling air pollutants. This software uses meteorological data, digital elevation models, and information about pollutant sources. It assesses and quantifies air pollution levels related to PM10, SOX, NOx, and CO pollutants in a specific location.

Results: The results of this study indicate that in these 28 population points surrounding the targeted industrial land use, pollutant concentrations in both scenarios with and without background concentrations did not exceed the standard limits for any pollutant. The most significant pollutant in this research was NOx, which showed the slightest difference from the permissible pollution limit. Furthermore, due to the closer proximity of pollutants to environmental standards in this region, increased loading of industrial land uses can lead to various health, economic, and social problems.

Conclusion: The findings of this research demonstrate that to assess and quantify pollutant concentrations in the areas surrounding industrial pollutant points, it is advisable to consider background pollution in addition to modeling point sources for greater accuracy in the direction of sustainable development in such areas.

Keywords

1.    Rahmati M H. Moghani V. Vesal M. The Effects of Short-Term Exposure to Air Pollution on Mortality Rates: The Case of Six Metropolitan Areas in Iran. QJER 2020; 20(2): 53-76. (Persian)
2.    Kelishadi R. Moeini R. Poursafa P. et al. Independent association between air pollutants and vitamin D deficiency in young children in Isfahan, Iran. Paediatrics and international child health 2014; 34(1), 50-55. ‏
3.    Cai J. Yu S. Pei Y. et al. Association between airborne fine particulate matter and residents’ cardiovascular diseases, ischemic heart disease and cerebral vascular disease mortality in areas with lighter air pollution in China. International journal of environmental research and public health 2018; 15: 1-17.
4.    Esmaeilzadeh M. Bazrafshan E. Nasrabadi M. Dispersion Modeling of NOx and SO2 Emissions from Tous Gas Power Plant, Mashhad. Health & Environ 2013; 6(1): 77-90. (Persian)
5.    Erfanmanesh M. Afyuni M. Environmental pollution water, soil. 8th ed. Arkan danesh; 2012. P. 115-120. (Persian)
6.    Abbaspour M. Air pollution modeling. First. Sharif University of Technology; 2012. P. 9-12. (Persian)
7.    Shin U. Ucan O. Bayat C. et al. Modeling of SO2 distribution in Istanbul using artificial neural networks. Environmental Modeling & Assessment 2005; 10(2): 135-142.
8.    Seangkiatiyuth K. Surapipth V. Tantrakamapa k. et al. Application of the AERMOD modeling system for environmental impact assessment of NO2 emissions from a cement complex. Environmental Science 2011; 23(6): 931-940.
9.    Kalhor M. Ghalrh Askari S. Bozorgi M. AERMET performance in evaluation of boundary layer parameters and its effect on carbon monoxide concentration outputs in AERMOD model compared to upper air data. Health & Environ 2018; 11(3): 365-376. (Persian)
10.    Gargiulo M. Chiodi A. De Miglio R. et al. An integrated planning framework for the development of sustainable and resilient cities–the case of the InSMART project. Procedia engineering 2017; 198: 444-453.
11.    Bayat R. Ashrafi K. Motlagh M S. et al. Health impact and related cost of ambient air pollution in Tehran. Environmental research 2019; 176: 1-12.
12.    Issaloo A. Shahmoradi B. Bahrami S. editiors. Islamic Azad University-Sanandaj Branch. Proceedings of the third national conference on urban development. 2011 Oct. 26-27. Sanandaj. Iran. Civilica; 2011. (Persian)
13.    Mohammadi M. Grakvandi S. Godarzi Gh. editiors Sharif University of Technology. Proceedings of the 6th national conference on air and noise pollution management. 2018 Jan. 23-24. Tehran. Iran. Civilica; 2018. (Persian)
14.    Clougherty J E. Levy J I. Kubzansky L D. et al. Synergistic effects of traffic-related air pollution and exposure to violence on urban asthma etiology. Environmental health perspectives 2007; 115(8): 1140-1146.‏
15.    Miri M. Ghassoun Y. Dovlatabadi A. et al. Estimate annual and seasonal PM1, PM2.5 and PM10 concentrations using land use regression model. Ecotoxicology and environmental safety 2019; 174(15): 137-145.
16.    Bergstra A D. Brunekreef B. Burdorf A. The effect of industry-related air pollution on lung function and respiratory symptoms in school children. Environmental Health 2018; 17(1): 1-9.
17.    allaji H. Bohloul M.R. Peyghambarzadeh S.M. et al. Measurement of air pollutants concentrations from stacks of petrochemical company and dispersion modeling by AERMOD coupled with WRF model. Int. J. Environ. Sci. Technol 2023; 7217–7236. 
18.    Han L. Zhao J. Gao Y. et al, J. Spatial distribution characteristics of PM2.5 and PM10 in Xi’an City predicted by land use regression models. Sustainable Cities and Society 2020; 61: 1-16.‏
19.    Peykanpour Fard R. Moradi H. Lotfi A. et al. Advancing the mapping of optimal land use structure in industrialized areas: incorporating AERMOD modeling and MCE approach. GeoJournal 2022; 1-17.
20.    Sarwar M T. Maqbool A. Causes and control measures of urban air pollution in China. Environment & Ecosystem Science (EES) 2019; 3(1): 35-36.
21.    Deputy of the organization of Statistics and Information of Iran. Statistical yearbook of Hormozgan province in 1395. Hormozgan: Hormozgan Province Management and Planning Organization; 1396. (Persian)
22.    Shaikh K. Imran U. Khan A. et al. Health risk assessment of emissions from brick kilns in Tando Hyder, Sindh, Pakistan using the AERMOD dispersion modle. SN Applied Sciences 2020; 2(7): 1-11.
23.    Echeverría R.S. Jiménez A.L.A. Barrera M.D.C.T. et al. Nitrogen and sulfur compounds in ambient air and in wet atmospheric deposition at Mexico city metropolitan area. Atmospheric Environment 2023; 292, 119411.
24.    Arefiev N. Terleev V. Badenko V. GIS-based fuzzy method for urban planning. Procedia Engineering 2015; 117(1): 39-44.
25.    Romano G. Dal Sasso P. Liuzzi G.T. et al. Multi-criteria decision analysis for land suitability mapping in a rural area of Southern Italy. Land Use Policy 20115; 48: 131-143.‏
26.    El Baroudy A.A. Mapping and evaluating land suitability using a GIS-based model. Catena 2016;140: 96-140.
27.    Memarbashi E. Azadi H. Barati A.A. et al. Land-use suitability in Northeast Iran: application of AHP-GIS hybrid model. ISPRS International Journal of Geo-Information 2017; 6(12): 396- 410.
28.    kuo Y. Lu S. Tzeng G. et al. Using fuzzy integral approach to enhance site selection assessment–a case study of the optoelectronics industry. Procedia Computer Science 2013; 17: 306-313.
29.    Khavarian-Garmsir A.R. Rezaei M.R. Selection of appropriate locations for industrial areas using GIS-fuzzy methods. A case study of Yazd Township, Iran. Journal of Settlements and Spatial Planning 2013; 6(1): 19-25.‏