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Air quality impact of particulate matter (PM10) releases from an industrial source

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Abstract

This study is the initial air quality modeling application to investigate the atmospheric dispersion of particulate matter (PM10) released from an industrial plant in a mid-sized city in the northwestern region of Turkey. The study aims to determine how an industrial application might affect air pollution levels in the urban area of that city. Different types of PM10 emission sources at the plant were analyzed to determine their contribution to the overall PM10 level. The main concern of the study was to define whether the PM10 emission from the plant produced any air quality effects on the nearby hospital and downtown area. According to the modeling result, the maximum daily PM10 concentration was observed mostly in the southern part of the plant. The highest daily downwind PM10 was estimated to be 286.3 μg m−3, while the annual mean of downwind PM10 concentration was estimated to be 72.6 μg m−3. It was found that the highest PM10 was emitted from the line source (282.2 μg m−3)—located at the south and southwest of the plant—that has been continuously used for hauling raw materials to the cement plant. In this study, the hauling roads (as a line source) were the hot-spot at the plant and needed serious maintenance for the reduction of PM10 emissions from the plant. In the study area, a sensitive community state facility was subject to be considered an environmental concern since the central hospital complex was located nearby the PM10 source. For this purpose, the American Meteorological Society/Environmental Protection Agency Regulatory Model (AERMOD) was followed to determine whether the plant caused any severe PM10 effect on the central hospital. The results indicate that the maximum hourly PM10 concentrations were predicted as 20 μg m−3 for the central hospital area. Overall, the modeled PM10 concentrations from the source at different time scales and locations did not exceed the national air quality limits in the area.

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References

  • Abu-Allaban, M., & Abu-Qudais, H. (2011). Impact assessment of ambient air quality by cement industry: a case study in Jordan. Aerosol and Air Quality Research, 11(7), 802–810.

    Article  CAS  Google Scholar 

  • Adeniran, J. A., Yusuf, R. O., Fakinle, B. S., & Sonibare, J. A. (2018). Air quality assessment and modelling of pollutants emission from a major cement plant complex in Nigeria. Atmospheric Pollution Research, 10, 257–266.

    Article  CAS  Google Scholar 

  • Afzali, A., Rashid, M., Afzali, M., & Younesi, V. (2017). Prediction of air pollutants concentrations from multiple sources using AERMOD coupled with WRF prognostic model. Journal of Cleaner Production, 166, 1216–1225.

    Article  CAS  Google Scholar 

  • Alberini, A., & Krupnick, A. (1998). Air quality and episodes of acute respiratory illness in Taiwan cities: evidence from survey data. Journal of Urban Economics, 44(1), 68–92.

    Article  Google Scholar 

  • Borge, R., Lumbreras, J., Perez, J., de la Paz, D., & Vedrenne, M. (2014). Emission inventories and modeling requirements for the development of air quality plans. Application to Madrid (Spain). The Science of the Total Environment, 466, 809–819.

    Article  CAS  Google Scholar 

  • Brunekreef, B., & Holgate, S. T. (2002). Air pollution and health. The Lancet, 360(9341), 1233–1242.

    Article  CAS  Google Scholar 

  • Carslaw, D. C. (2019). The Openair manual — open-source tools for analysing air pollution data. Manual for version 2.6-6, University of York.

  • Carslaw, D. C., & Ropkins, K. (2012). Openair — an R package for air quality data analysis. Environmental Modelling and Software, 27-28, 52–61.

    Article  Google Scholar 

  • Chalvatzaki, E., Glytsos, T., & Lazaridis, M. (2015). A methodology for the determination of fugitive dust emissions from landfill sites. International Journal of Environmental Health Research, 25(5), 551–569.

    Article  CAS  Google Scholar 

  • dos Santos Cerqueira, J., de Albuquerque, H. N., & de Sousa, F. D. A. S. (2019). Atmospheric pollutants: modeling with Aermod software. Air Quality, Atmosphere and Health, 12, 21–32.

    Article  CAS  Google Scholar 

  • EIA Report (2014). Cement Plant EIA Report. Provincial Environmental Agency in Balikesir. Ministry of Environmental and Urbanization, Ankara.

  • EU (2008). Air quality standards. The new air quality directive. The Directive 2008/50/EC, http://ec.europa.eu/environment/air/quality/standards.htm. Accessed 25 Nov 2019.

  • Gomzi, M. (1999). Indoor air and respiratory health in preadolescent children. Atmospheric Environment, 33(24–25), 4081–4086.

    Article  CAS  Google Scholar 

  • Helander, M. L., Savolainen, J., & Ahlholm, J. (1997). Effects of air pollution and other environmental factors on birch pollen allergens. Allergy: European Journal of Allergy & Clinical Immunology, 52(12), 1207–1214.

    Article  CAS  Google Scholar 

  • Kakosimos, K. E., Assael, M. J., Lioumbas, J. S., & Spiridis, A. S. (2011a). Atmospheric dispersion modelling of the fugitive particulate matter from overburden dumps with numerical and integral models. Atmospheric Pollution Research, 2, 24–33.

    Article  Google Scholar 

  • Kakosimos, K. E., Assael, M. J., & Katsarou, A. S. (2011b). Application and evaluation of AERMOD on the assessment of particulate matter pollution caused by industrial activities in the GreaterThessaloniki area. Environmental Technology, 32(6), 593–608.

    Article  CAS  Google Scholar 

  • Kassomenos, A. P., Kelessis, A., Paschalidou, A. K., & Petrakakis, M. (2011). Identification of sources and processes affecting particulate pollution in Thessaloniki, Greece. Atmospheric Environment, 45, 7293–7300.

    Article  CAS  Google Scholar 

  • Kassomenos, A. P., Vardoulakis, S., Chaloulakou, A., Paschalidou, A. K., Grivas, G., Borge, R., & Lumbreras, J. (2014). Study of PM10 and PM2.5 levels in three European cities: analysis of intra and inter urban variations. Atmospheric Environment, 87, 153–163.

    Article  CAS  Google Scholar 

  • Kesarkar, A. P., Dalvi, M., Kaginalkar, A., & Ojha, A. (2007). Coupling of the weather research and forecasting model with AERMOD for pollutant dispersion modeling. A case study for PM10 dispersion over Pune, India. Atmospheric Environment, 41, 1976–1988.

    Article  CAS  Google Scholar 

  • Lakes Environmental Consultant Inc (2016). AERMOD Processor, Version 9.4.0. Lakes Environmental Software, Waterloo, Ontario, Canada.

  • Leone, V., Cervone, G., & Iovino, P. (2016). Impact assessment of PM10 cement plants emissions on urban air quality using the SCIPUFF dispersion model. Environmental Monitoring and Assessment, 188, 499.

    Article  CAS  Google Scholar 

  • Lothongkum, A.W., Seangkiatiyuth, K., & Srapipith, V. (2008). Measuring modeling of particulate dispersion from the cement plants. Better Air Quality Workshop (BAQ), Bangkok.

  • Martonen, T. B., & Schroeter, J. D. (2003). Risk assessment dosimetry model for inhaled particulate matter: I. human subjects. Toxicology Letters, 138(1–2), 119–132.

    Article  CAS  Google Scholar 

  • Monn, C., Alean-Kirkpatrick, P., Künzli, N., Defila, C., Peeters, A., Ackermann-Liebrich, U., Leuenberger, F., & SAPALDİA Team. (1999). Air pollution, climate and pollen comparisons in urban, rural and alpine regions in Switzerland (SAPALDIA study). Atmospheric Environment, 33(15), 2411–2416.

    Article  CAS  Google Scholar 

  • Moshammer, H., & Neuberger, M. (2003). The active surface of suspended particles as a predictor of lung function and pulmonary symptoms in Austrian school children. Atmospheric Environment, 37, 1737–1744.

    Article  CAS  Google Scholar 

  • NAQI. (2018) National Air Quality Index. Ministry of Environmental and Urbanization, Ankara. http://www.havaizleme.gov.tr/hava.html. Accessed on 18 Nov 2019.

  • Noorpoor, A., & Rahman, H. R. (2015). Application of AERMOD to local scale diffusion and dispersion modeling of air pollutants from cement factory stacks (Case study: Abyek Cement Factory). Pollution, 1(4), 417–426.

    Google Scholar 

  • O’Shaughnessy, P. T., & Altmaier, R. (2011). Use of AERMOD to determine a hydrogen sulfide emission factor for swine operations by inverse modeling. Atmospheric Environment, 45, 4617–4625.

    Article  CAS  Google Scholar 

  • Orloff, K., Kaplan, K., & Kowalski, P. (2006). Hydrogen cyanide in ambient air near a gold heap leach field: measured vs. modeled concentrations. Atmospheric Environment, 40, 3022–3029.

    Article  CAS  Google Scholar 

  • Paine, R. J., Lee, R., Brode, R., Wilson, R., Cimorelli, A., Perry, S., Weil, J., Venkatram, A., & Peters, W. (1999). AERMOD: model formulation and evaluation results. 92nd Annual Conference of Air and Waste Management Association, St. Louis, USA, 1–11.

  • Paine, R., Samani, O., Kaplan, M., Knipping, E., & Kumar. (2015). Evaluation of low wind modeling approaches for two tall-stack databases. Journal of the Air & Waste Management Association, 65, 1341–1353.

    Article  CAS  Google Scholar 

  • Perry, S. G., Cimorelli, A. J., Paine, R. J., Brode, R. W., Weil, J. C., Venkatram, A., Wilson, R. B., Lee, R. F., & Peters, W. D. (2005). AERMOD: a dispersion model for industrial source applications. Part II: model performance against 17 field study databases. Journal of Applied Meteorology, 44, 694–708.

    Article  Google Scholar 

  • Phetrawech, T., & Thepanondh, S. (2017). Evaluation f resuspension of road dust in a cement industial complex area. International Journal of GEOMATE, 12(33), 96–103.

    Google Scholar 

  • Rolph, G., Stein, A., & Stunder, B. (2017). Real-time environmental applications and display system: READY. Environmental Modelling & Software, 95, 210–228.

    Article  Google Scholar 

  • Schulman, L. L., Strimaitis, D. G., & Scire, J. S. (2000). Development and evaluation of the PRIME plume rise and downwash model. Journal of the Air and Waste Management, 50, 378–390.

    Article  CAS  Google Scholar 

  • Seangkiatiyuth, K., Surapipith, V., Tantrakarnapa, K., & Lothongkum, A. W. (2011). Application of the AERMOD modeling system for environmental impact assessment of NO2 emissions from a cement complex. Journal of Environmental Sciences, 23(6), 931–940.

    Article  CAS  Google Scholar 

  • Stein, A. F., Isakov, V., Godowitch, J., & Draxler, R. R. (2007). A hybrid modeling approach to resolve pollutant concentrations in the urban area. Atmospheric Environment, 41, 9410–9426.

    Article  CAS  Google Scholar 

  • Stein, A. F., Draxler, R. R., Rolph, G. D., Stunder, B. J. B., Cohen, M. D., & Ngan, F. (2015). NOAA’s HYSPLIT atmospheric transport and dispersion modeling system. Bull.Amer.Meteor.Soc., 96, 2059–2077.

    Article  Google Scholar 

  • Tartakovsky, D., Broday, D. M., & Stern, E. (2013). Evaluation of AERMOD and CALPUFF for predicting ambient concentrations of total suspended particulate matter (TSP) emissions from a quarry in complex terrain. Environmental Pollution, 179, 138–145.

    Article  CAS  Google Scholar 

  • Tartakovsky, D., Stern, E., & Broday, D. M. (2016). Dispersion of TSP and PM10 emissions from quarries in complex terrain. Science of the Total Environment, 542, 946–954.

    Article  CAS  Google Scholar 

  • Tecer, L. H. (2009). A factor analysis study: air pollution, meteorology, and hospital admissions for respiratory diseases. Toxicological & Environmental Chemistry, 91(7), 1399–1411.

    Article  CAS  Google Scholar 

  • Timonen, K. L., Pekkanen, J., Tittanen, P., & Salonen, R. O. (2002). Effects of air pollution on changes in lung function induced by exercise in children with chronic respiratory symptoms. Journal of Occupational and Environmental Medicine, 59, 129–134.

    Article  CAS  Google Scholar 

  • USEPA (2003). AERMOD: latest features and evaluation results, US Environmental Protection Agency, EPA-454/R-03-003, Office of Air Quality Planning and Standards, June.2003, Research Triangle Park, North Carolina.

  • USEPA (2004). User’s guide for the AMS/EPA regulatory model-AERMOD, US Environmental Protection Agency, EPA-454/B-03-001, Office of Air Quality Planning and Standards, September.2004, Research Triangle Park, North Carolina, USA.

  • USEPA (2009). AERMOD Implementation Guide, US Environmental Protection Agency, Office of Air Quality Planning and Standards, March.2009, Research Triangle Park, North Carolina. USA.

  • USEPA (2018). User’s Guide for the AERMOD Terrain Preprocessor (AERMAP), US Environmental Protection Agency, Office of Air Quality Planning and Standards, April.2018, Research Triangle Park, North Carolina. USA.

  • Westbrook, J. A., & Sullivan, P. S. (2007). Fugitive dust modeling with AERMOD for PM10 emissions from a municipal waste landfill air and waste management association – Guideline on air quality models: applications and FLAG developments. An Specialty Conference of Air and Waste Management Association, Denver, 207–223.

  • WHO (2006). Air quality guidelines for particulate matter, ozone, nitrogen dioxide and sulfur dioxide, Summary of Risk Assessment, WHO/SDE/PHE/OEH/06.02, 2005, Geneva, Switzerland.

  • Wong, G. W. K., Ko, F. W. S., Lau, T. S., Li, S. T., Hui, D., Pang, S. W., Leung, R., Fok, T. F., & Lai, C. K. W. (2000). The temporal relationship between air pollution and hospital admissions for asthmatic children in Hong Kong. Clinical and Experimental Allergy, 31(4), 565–569.

    Article  Google Scholar 

  • Wordley, J., Walters, S., & Ayres, J. (1997). Short-term variations in hospital admissions and mortality and particulate air pollution. Occupational and Environmental Medicine, 54(2), 108–116.

    Article  CAS  Google Scholar 

  • Yazdi, N. M., Arhami, M., Ketabchy, M. & Delavarrafeei, M. (2016). Modeling of cement factory air pollution dispersion by AERMOD. A&WMA’s 109th Annual Conference & Exhibition. Paper# 1276. June 20–23, 2016, New orleans, Louisiana.

  • Zou, B., Zhan, B., Wilson, J. G., & Zeng, Y. (2010). Performance of AERMOD at different time scales. Simulation Modelling and Theory, 18, 612–623.

    Article  Google Scholar 

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Funding

I wish to express my sincere gratitude to Balikesir University (Project No: BAP.2020/001), the Provincial Environmental Agency, and Balikesir Governorship for their support in the completion of this study.

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  1. Department of Environmental Engineering, Balikesir University, 10145, Balikesir, Turkey

    Atilla Mutlu

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  1. Atilla Mutlu
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Mutlu, A. Air quality impact of particulate matter (PM10) releases from an industrial source. Environ Monit Assess 192, 547 (2020). https://doi.org/10.1007/s10661-020-08508-7

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