Toxicity screening of air extracts representing different source sectors in the Greater Toronto and Hamilton areas: In vitro oxidative stress, pro-inflammatory response, and toxicogenomic analysis

Abstract

In the present study, the suitability and sensitivity of different in vitro toxicity endpoints were determined to evaluate and distinguish the specific contributions of polycyclic aromatic carbon (PAC) mixtures from various sites in Toronto (Canada), to pulmonary toxicity. Air samples were collected for two-month periods from April 2014 to March 2015 from one location, and from August 2016 to August 2017 from multiple locations reflecting different geographical areas in Toronto, and the Greater Toronto Area, with varying source emissions including background, traffic, urban, industrial and residential sites. Relative concentrations of PACs and their derivatives in these air samples were characterised. In vitro cytotoxicity, pro-inflammatory, and oxidative stress assays were employed to assess the acute pulmonary effects of urban-air-derived air pollutants. In addition, global transcriptional profiling was utilized to understand how these chemical mixtures exert their harmful effects. Lastly, the transcriptomic data and the chemical profiles for each site and season were used to relate the biological response back to individual constituents. Site-specific responses could not be derived; however, the Spring season was identified as the most responsive through benchmark concentration analysis. A combination of correlational analysis and principal component analysis revealed that nitrated and oxygenated polycyclic aromatic hydrocarbons (PAHs) drive the response at lower concentrations while specific PAHs drive the response at the highest concentration tested. Unsubstituted PAHs are the current targets for analysis as priority pollutants. The present study highlights the importance of by-products of PAH degradation in the assessment of risk. The study also demonstrates the usefulness of in vitro toxicity assays to derive meaningful data in support of risk assessment.