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Ningrui Liu, Rajni Oshan, Magali Blanco, Lianne Sheppard, Edmund Seto, Timothy Larson, Elena Austin OCTOBER 24, 2024 Contents Introduction Methods Results and discussion Conclusions Contents Introduction Methods Results and discussion Conclusions Introduction ◼ Health issues of air pollution • Adverse health outcomes: cardiovascular diseases, respiratory diseases, lung cancer, etc. • Global Burden of Disease 2021: Air pollution is the second leading risk factor, accounting for 8.2% of total DALYs. ◼ Mobile monitoring (MM) studies GBD2021, Lancet, 2024, 403: 2162–2203. Apte et al., Science, 2024, 385: 380–385. • Drive vehicles with high-quality instruments through fixed routes • Have higher spatial resolution than regulatory monitoring RESEARCH GAP OF MM • More focus on PM2.5 than ultrafine particles (UFPs), while health impacts depend on size distribution and chemical composition • Multi-pollutant spatiotemporal data from MM have not been fully used in health studies. Introduction ◼ Source apportionment (SA) studies • Use multi-pollutant data to derive the sources • Approach: positive matrix factorization (PMF), PCA, et al. • Many relied on regulatory monitoring data, and few on MM Hopke et al., Sci. Total Environ., 2020, 740: 140091. Hopke et al., Sci. Total Environ., 2022, 819: 153104. Larson et al., Atmos. Environ., 2017, 152: 201-211. RESEARCH GAP OF SA-MM • Few considered particle size distribution • Few classified traffic-related source into different vehicle types • Limited time frames (days to weeks) ◼ Research aims: PMF + MM 1. Characterize emission sources more accurately 2. Assess source-specific air pollution exposures 3. Estimate the annual average emission…

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