<p>Substantial exhaust emissions from on-road vehicles threaten air quality in Hong Kong. This is particularly severe for pedestrians and tourists who are directly exposed to roadside pollution that can hardly be dispersed within deep street canyons. To mitigate this risk, a series of emission control strategies have been implemented in recent years, but their effectiveness in terms of tourist exposure reduction has yet to be assessed. In this study, we established a comprehensive framework that adapts both statistical and numerical modeling approaches to investigate the nitrogen oxides (NO_x) and fine particulate matter (PM_2.5) concentration reduction achieved by four emission control measures in the road transport sector in Hong Kong. The benefits of tourist exposure were then estimated through the tourist exposure assessment with geotagged social media data from TripAdvisor. Results demonstrate that the four measures together reduced annual territory-wide PM_2.5. and NOx concentrations by approximately 6% and 30%, respectively, at the ambient level. The improvements were more pronounced at roadside locations. Phasing out unqualified diesel commercial vehicles (DCV retirement program) was the most effective measure for air pollution mitigation, whereas the catalytic converter (CC) replacement scheme for liquefied petroleum gas (LPG) vehicles was most effective at minimizing tourist exposure to NO_x pollution. All measures show greater benefits in mitigating tourist exposure than resident exposure. These findings provide quantitative evidence that road traffic emission control measures can significantly reduce the health risks to millions of tourists visiting Hong Kong each year. The integrated framework bridges air quality science, machine learning, and tourism geography, offering a transferable methodology for evaluating emission reduction policies in other high-density Asian tourism destinations and supporting evidence-based urban planning for sustainable tourism development.</p>