Search

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.

Although particulate matter (PM) is composed of various chemicals, investigations regarding the toxicity that results from mixing the substances in PM are insufficient. In this study, the effects of low levels of three PAHs (benz[a]anthracene, benzo[a]pyrene, and dibenz[a,h]anthracene) on Ni toxicity were investigated to assess the combined effect of Ni-PAHs on the environment. We compared the difference in cell mortality and total glutathione (tGSH) reduction between single Ni and Ni-PAHs co-exposure using A549 (human alveolar carcinoma). In addition, we measured the change in Ni solubility in chloroform that was triggered by PAHs to confirm the existence of cation-π interactions between Ni and PAHs. In the single Ni exposure, the dose-response curve of cell mortality and tGSH reduction were very similar, indicating that cell death was mediated by the oxidative stress. However, 10 μM PAHs induced a depleted tGSH reduction compared to single Ni without a change in cell mortality. The solubility of Ni in chloroform was greatly enhanced by the addition of benz[a]anthracene, which demonstrates the cation-π interactions between Ni and PAHs. Ni-PAH complexes can change the toxicity mechanisms of Ni from oxidative stress to others due to the reduction of Ni 2+ bioavailability and the accumulation of Ni-PAH complexes on cell membranes. The abundant PAHs contained in PM have strong potential to interact with metals, which can affect the toxicity of the metal. Therefore, the mixture toxicity and interactions between diverse metals and PAHs in PM should be investigated in the future.

Many studies have demonstrated that heavy metals existing as a mixture in the atmospheric environment cause adverse effects on human health and are important key factors of cytotoxicity; however, little investigation has been conducted on a toxicological study of a metal mixture from atmospheric fine particulate matter. The objective of this study was to predict the combined effects of heavy metals in aerosol by using in vitro human cells and obtain a suitable mixture toxicity model. Arsenic, nickel, and lead were selected for mixtures exposed to A549 human lung cancer cells. Cell proliferation (WST-1), glutathione (GSH), and interleukin (IL)-8 inhibition were observed and applied to the prediction models of mixture toxicity, concentration addition (CA) and independent action (IA). The total mixture concentrations were set by an IC 10 -fixed ratio of individual toxicity to be more realistic for mortality and enzyme inhibition tests. The results showed that the IA model was statistically closer to the observed results than the CA model in mortality, indicating dissimilar modes of action. For the GSH inhibition, the results predicted by the IA and CA models were highly overestimated relative to mortality. Meanwhile, the IL-8 results were stable with no significant change in immune reaction related to inflammation. In conclusion, the IA model is a rapid prediction model in heavy metals mixtures; mortality, as a total outcome of cell response, is a good tool for demonstrating the combined toxicity rather than other biochemical responses.