Application of various cytotoxic endpoints for the toxicity prioritization of fine dust (PM2.5) sources using a multi-criteria decision-making approach

  • Injeong Kim
  • Kihong Park
  • KwangYul Lee
  • Minhan Park
  • Heungbin Lim
  • Hanjae Shin
  • Sang Don KimEmail author
Original Paper


Fine dust (PM2.5) is generated from various sources, and many studies have reported on the sources of PM2.5. However, the current research on PM2.5 toxicity based on its sources is insufficient. In this study, we developed a framework for the prioritization of fine dust (PM2.5) sources on the basis of the multi-endpoint toxicities using the multi-criteria decision-making method (MCDM). To obtain the multi-endpoint toxicities of PM2.5 sources, cell mortality, reactive oxygen species (ROS), inflammation and mutagenicity were measured for diesel exhaust particles (DEP), gasoline exhaust particles (GEP), rice straw burning particles (RBP), coal combustion particles (CCP) and tunnel dust particles (TDP). The integrative toxicity score (ITS) of the PM2.5 source was calculated using MCDM, which consist of four steps: (1) defining the decision-making matrix, (2) normalization and weighting, (3) calculating the ITS (linear aggregation) and (4) a global sensitivity analysis. The indicator of cell mortality had the highest weight (0.3780) followed by inflammation (0.2471), ROS (0.2178) and mutagenicity (0.1571). Additionally, the ITS based on the sources contributing to PM2.5 resulted in the following order: DEP (0.89), GEP (0.44), RBP (0.40), CCP (0.23) and TDP (0.06). The relative toxicity index (RTI), which represents the ratio of toxicity due to the difference in sources, increases as the contribution of the highly toxic sources increases. The RTI over 1 is closely associated with an increased contribution from highly toxic sources, such as diesel exhaust, gasoline exhaust and biomass burning. It is necessary to investigate the toxicity of various PM2.5 sources and PM2.5 risk based on the sources.


PM2.5 sources Cytotoxicity Toxicity index Risk assessment Decision-making Objective weighting 



This research was supported by the National Research Foundation (NRF) of Korea (2014M3C8A5030618).


  1. Ahmed, E., Kim, K.-H., Shon, Z.-H., & Song, S.-K. (2015). Long-term trend of airborne particulate matter in Seoul, Korea from 2004 to 2013. Atmospheric Environment,101, 125–133.CrossRefGoogle Scholar
  2. Anderson, J. O., Thundiyil, J. G., & Stolbach, A. (2012). Clearing the air: A review of the effects of particulate matter air pollution on human health. Journal of Medical Toxicology,8, 166–175.CrossRefGoogle Scholar
  3. Bae, S., Pan, X. C., Kim, S. Y., Park, K., Kim, Y. H., Kim, H., et al. (2010). Exposures to particulate matter and polycyclic aromatic hydrocarbons and oxidative stress in schoolchildren. Environmental Health Perspectives,118, 579–583.CrossRefGoogle Scholar
  4. BeruBe, K., Aufderheide, M., Breheny, D., Clothier, R., Combes, R., Duffin, R., et al. (2009). In vitro models of inhalation toxicity and disease. Alternatives to Laboratory Animals,37, 89–141.CrossRefGoogle Scholar
  5. Cachon, B. F., Firmin, S., Verdin, A., Ayi-Fanou, L., Billet, S., Cazier, F., et al. (2014). Proinflammatory effects and oxidative stress within human bronchial epithelial cells exposed to atmospheric particulate matter (PM2.5 and PM > 2.5) collected from Cotonou, Benin. Environmental Pollution,185, 340–351.CrossRefGoogle Scholar
  6. Cassoni, F., Bocchi, C., Martino, A., Pinto, G., Fontana, F., & Buschini, A. (2004). The Salmonella mutagenicity of urban airborne particulate matter (PM2.5) from eight sites of the Emilia-Romagna regional monitoring network (Italy). Science of the Total Environment,324, 79–90.CrossRefGoogle Scholar
  7. Chowdhury, Z., Zheng, M., Schauer, J. J., Sheesley, R. J., Salmon, L. G., Cass, G. R., et al (2007) Speciation of ambient fine organic carbon particles and source apportionment of PM2.5 in Indian cities. Journal of Geophysical Research: Atmospheres, 112, 1–14.Google Scholar
  8. Corsini, E., Budello, S., Marabini, L., Galbiati, V., Piazzalunga, A., Barbieri, P., et al. (2013). Comparison of wood smoke PM2.5 obtained from the combustion of FIR and beech pellets on inflammation and DNA damage in A549 and THP-1 human cell lines. Archives of Toxicology,87, 2187–2199.CrossRefGoogle Scholar
  9. Dergham, M., Lepers, C., Verdin, A., Cazier, F., Billet, S., Courcot, D., et al. (2015). Temporal–spatial variations of the physicochemical characteristics of air pollution particulate matter (PM 2.5–0.3) and toxicological effects in human bronchial epithelial cells (BEAS-2B). Environmental Research,137, 256–267.CrossRefGoogle Scholar
  10. Diakoulaki, D., Mavrotas, G., & Papayannakis, L. (1995). Determining objective weights in multiple criteria problems: The critic method. Computers & Operations Research,22, 763–770.CrossRefGoogle Scholar
  11. Dumax-Vorzet, A. F., Tate, M., Walmsley, R., Elder, R. H., & Povey, A. C. (2015). Cytotoxicity and genotoxicity of urban particulate matter in mammalian cells. Mutagenesis,30, 621–633.CrossRefGoogle Scholar
  12. Gilli, G., Pignata, C., Schilirò, T., Bono, R., La Rosa, A., & Traversi, D. (2007). The mutagenic hazards of environmental PM2.5 in Turin. Environmental Research,103, 168–175.CrossRefGoogle Scholar
  13. Hwang, C. L., & Yoon, K. (1981). Multiple attribute decision making: Methods and applications: A state-of-the-art survey. Berlin: Springer.CrossRefGoogle Scholar
  14. Jeong, C.-H., Wang, J. M., & Evans, G. J. (2016). Source apportionment of urban particulate matter using hourly resolved trace metals, organics, and inorganic aerosol components. Atmospheric Chemistry and Physics Discussions, 1–32.Google Scholar
  15. Jeong J. (1999). Development of evaluation criteria and procedure for the selection of weighting method in multi-criteria decision making. Doctorial thesis, Dongkook University, Seoul.Google Scholar
  16. Jo, E.-J., Lee, W.-S., Jo, H.-Y., Kim, C.-H., Eom, J.-S., Mok, J.-H., et al. (2017). Effects of particulate matter on respiratory disease and the impact of meteorological factors in Busan, Korea. Respiratory Medicine,124, 79–87.CrossRefGoogle Scholar
  17. Kang, C.-M., Kang, B.-W., Sunwoo, Y., & Lee, H. S. (2008). Application of representative PM 2.5 source profiles for the chemical mass balance study in Seoul. Journal of Korean Society for Atmospheric Environment,24, 32–43.Google Scholar
  18. Kasurinen, S., Jalava, P. I., Happo, M. S., Sippula, O., Uski, O., Koponen, H., et al. (2017). Particulate emissions from the combustion of birch, beech, and spruce logs cause different cytotoxic responses in A549 cells. Environmental Toxicology,32, 1487–1499.CrossRefGoogle Scholar
  19. Kim, Y., & Chung, E.-S. (2014). An index-based robust decision making framework for watershed management in a changing climate. Science of the Total Environment,473, 88–102.CrossRefGoogle Scholar
  20. Krall, J. R., Mulholland, J. A., Russell, A. G., Balachandran, S., Winquist, A., Tolbert, P. E., et al. (2017). Associations between source-specific fine particulate matter and emergency department visits for respiratory disease in four US cities. Environmental Health Perspectives,125, 97.CrossRefGoogle Scholar
  21. Lighty, J. S., Veranth, J. M., & Sarofim, A. F. (2000). Combustion aerosols: Factors governing their size and composition and implications to human health. Journal of the Air and Waste Management Association,50, 1565–1618.CrossRefGoogle Scholar
  22. Maertens, R. M., White, P. A., Rickert, W., Levasseur, G., Douglas, G. R., Bellier, P. V., et al. (2009). The genotoxicity of mainstream and sidestream marijuana and tobacco smoke condensates. Chemical Research in Toxicology,22, 1406–1414.CrossRefGoogle Scholar
  23. Maniya, K., & Bhatt, M. (2010). A selection of material using a novel type decision-making method: Preference selection index method. Materials and Design,31, 1785–1789.CrossRefGoogle Scholar
  24. Maron, D. M., & Ames, B. N. (1983). Revised methods for the Salmonella mutagenicity test. Mutation Research/Environmental Mutagenesis and Related Subjects,113, 173–215.CrossRefGoogle Scholar
  25. Mazzoli-Rocha, F., Fernandes, S., Einicker-Lamas, M., & Zin, W. A. (2010). Roles of oxidative stress in signaling and inflammation induced by particulate matter. Cell Biology and Toxicology,26, 481–498.CrossRefGoogle Scholar
  26. McDonald, J. D., Eide, I., Seagrave, J., Zielinska, B., Whitney, K., Lawson, D. R., et al. (2004). Relationship between composition and toxicity of motor vehicle emission samples. Environmental Health Perspectives,112, 1527–1538.CrossRefGoogle Scholar
  27. Miller, M. R., Shaw, C. A., & Langrish, J. P. (2012). From particles to patients; oxidative stress and the cardiovascular effects of air pollution. Future Cardiology,8, 577–602.CrossRefGoogle Scholar
  28. Monn, C., & Becker, S. (1999). Cytotoxicity and induction of proinflammatory cytokines from human monocytes exposed to fine (PM2.5) and coarse particles (PM10–2.5) in outdoor and indoor air. Toxicology and Applied Pharmacology,155, 245–252.CrossRefGoogle Scholar
  29. Nardo, M., Saisana, M., Saltelli, A., Tarantola, S., Hoffman, A., & Giovannini, E. (2005). Handbook on constructing composite indicators: Methodology and user guide. Paris: OECD publishing.Google Scholar
  30. Rohr, A. C., & Wyzga, R. E. (2012). Attributing health effects to individual particulate matter constituents. Atmospheric Environment,62, 130–152.CrossRefGoogle Scholar
  31. Rutter, A. P., Snyder, D. C., Schauer, J. J., DeMinter, J., & Shelton, B. (2009). Sensitivity and bias of molecular marker-based aerosol source apportionment models to small contributions of coal combustion soot. Environmental Science and Technology,43, 7770–7777.CrossRefGoogle Scholar
  32. Sam, K., Coulon, F., & Prpich, G. (2017). A multi-attribute methodology for the prioritisation of oil contaminated sites in the Niger Delta. Science of the Total Environment,579, 1323–1332.CrossRefGoogle Scholar
  33. Sarnat, J. A., Marmur, A., Klein, M., Kim, E., Russell, A. G., Sarnat, S. E., et al. (2008). Fine particle sources and cardiorespiratory morbidity: An application of chemical mass balance and factor analytical source-apportionment methods. Environmental Health Perspectives,116, 459.CrossRefGoogle Scholar
  34. Schwarze, P. E., Totlandsdal, A. I., Låg, M., Refsnes, M., Holme, J. A., Øvrevik, J. (2013). Inflammation-related effects of diesel engine exhaust particles: Studies on lung cells in vitro. BioMed Research International, 2013 , 1–13.CrossRefGoogle Scholar
  35. Steenhof, M., Gosens, I., Strak, M., Godri, K. J., Hoek, G., Cassee, F. R., et al. (2011). In vitro toxicity of particulate matter (PM) collected at different sites in the Netherlands is associated with PM composition, size fraction and oxidative potential-the RAPTES project. Particle and Fibre Toxicology,8, 26.CrossRefGoogle Scholar
  36. Straif, K., Cohen, A., & Samet, J. (2013). Air pollution and cancer. IARC Scientific Publication 161.Google Scholar
  37. Tal, T. L., Simmons, S. O., Silbajoris, R., Dailey, L., Cho, S.-H., Ramabhadran, R., et al. (2010). Differential transcriptional regulation of IL-8 expression by human airway epithelial cells exposed to diesel exhaust particles. Toxicology and Applied Pharmacology,243, 46–54.CrossRefGoogle Scholar
  38. Tecer, L. H., Alagha, O., Karaca, F., Tuncel, G., & Eldes, N. (2008). Particulate matter (PM2.5, PM10-2.5, and PM10) and children’s hospital admissions for asthma and respiratory diseases: A bidirectional case-crossover study. Journal of Toxicology and Environmental Health, Part A,71, 512–520.CrossRefGoogle Scholar
  39. Totlandsdal, A. I., Låg, M., Lilleaas, E., Cassee, F., & Schwarze, P. (2015). Differential proinflammatory responses induced by diesel exhaust particles with contrasting PAH and metal content. Environmental Toxicology,30, 188–196.CrossRefGoogle Scholar
  40. Valavanidis, A., Vlachogianni, T., Fiotakis, K., & Loridas, S. (2013). Pulmonary oxidative stress, inflammation and cancer: Respirable particulate matter, fibrous dusts and ozone as major causes of lung carcinogenesis through reactive oxygen species mechanisms. International Journal of Environmental Research and Public Health,10, 3886–3907.CrossRefGoogle Scholar
  41. Vattanasit, U., Navasumrit, P., Khadka, M. B., Kanitwithayanun, J., Promvijit, J., Autrup, H., et al. (2014). Oxidative DNA damage and inflammatory responses in cultured human cells and in humans exposed to traffic-related particles. International Journal of Hygiene and Environmental Health,217, 23–33.CrossRefGoogle Scholar
  42. Wang, J.-J., Jing, Y.-Y., Zhang, C.-F., & Zhao, J.-H. (2009). Review on multi-criteria decision analysis aid in sustainable energy decision-making. Renewable and Sustainable Energy Reviews,13, 2263–2278.CrossRefGoogle Scholar
  43. Wang, Y.-M., & Luo, Y. (2010). Integration of correlations with standard deviations for determining attribute weights in multiple attribute decision making. Mathematical and Computer Modelling,51, 1–12.CrossRefGoogle Scholar
  44. Yang, L., Liu, G., Lin, Z., Wang, Y., He, H., Liu, T., et al. (2016). Pro-inflammatory response and oxidative stress induced by specific components in ambient particulate matter in human bronchial epithelial cells. Environmental Toxicology,31, 923–936.CrossRefGoogle Scholar
  45. Zheng, M., Wang, F., Hagler, G., Hou, X., Bergin, M., Cheng, Y., et al. (2011). Sources of excess urban carbonaceous aerosol in the Pearl River Delta Region. China Atmospheric Environment,45, 1175–1182.CrossRefGoogle Scholar

Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  • Injeong Kim
    • 1
    • 2
  • Kihong Park
    • 1
  • KwangYul Lee
    • 1
  • Minhan Park
    • 1
  • Heungbin Lim
    • 3
  • Hanjae Shin
    • 4
  • Sang Don Kim
    • 1
    • 2
    Email author
  1. 1.School of Earth Sciences and Environmental EngineeringGwangju Institute of Science and TechnologyGwangjuRepublic of Korea
  2. 2.Center for Chemicals Risk AssessmentGwangju Institute of Science and TechnologyGwangjuRepublic of Korea
  3. 3.Department of Industrial Plant Science and TechnologyChungbuk National UniversityCheongjuRepublic of Korea
  4. 4.R&D HeadquarterKT&GDaejeonRepublic of Korea

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