Differentiating between Euro 5 gasoline and diesel light-duty engine primary and secondary particle emissions using multivariate statistical analysis of high-resolution mass spectrometry (HRMS) fingerprints

Abstract. Emissions from gasoline and diesel vehicles are predominant anthropogenic sources in ambient air, and their accurate source apportionment is a major concern for air quality policymakers aiming to implement effective strategies to reduce air pollution. Recent studies indicate that particulate matter (PM) emissions from modern cars equipped with the latest after-treatment technologies are mainly related to secondary organic aerosol (SOA) production, particularly in the case of gasoline vehicles. However, distinguishing between emissions from gasoline and diesel vehicles in ambient air remains challenging and is rarely achieved. This study aimed to evaluate the potential of non-targeted-screening (NTS) analyses for determining specific organic molecular markers of primary organic aerosol (POA) and SOA from gasoline and diesel vehicles, which could enhance PM source apportionment efforts. Experiments were conducted using a chassis dynamometer with Euro 5 gasoline and diesel vehicles under three different driving cycles. Exhaust emissions were diluted before being introduced into a potential aerosol mass oxidation flow reactor (PAM-OFR) to simulate atmospheric ageing and SOA formation. Samples were collected both upstream and downstream of the PAM-OFR and analysed using NTS approaches with liquid- and gas-chromatography coupled to quadrupole time-of-flight mass spectrometry (LC- and GC-QToF-MS). The chemical fingerprints obtained were compared using multivariate statistical analyses, including principal component analysis (PCA), hierarchical clustering analysis (HCA), and partial least-square discriminant analysis (PLS-DA). Results revealed specific fingerprints of POA and SOA for each type of vehicle tested and about 10 markers unique to each fraction of diesel and gasoline vehicles. This study demonstrates the promise of combining high-resolution mass-spectrometry-based NTS with advanced multivariate statistical analyses to differentiate between OA fingerprints and to discover specific markers of diesel and gasoline vehicular sources for further use in PM source apportionment studies.