Daylight saving time (DST) is a common practice in many countries, in which Official Time (OT) is abruptly shifted 1 hour with respect to solar time on two occasions every year (in fall and spring). All anthropogenic emitting processes tied to OT, like job and school commuting traffic, abruptly change in this moment their timing with respect to solar time, inducing a sudden shift between emissions and the meteorological factors that control the dispersion and transport of air pollutants. Analyzing 13 years of hourly particulate matter (PM10) concentrations measured in Santiago, Chile, we demonstrate that the DST practice has observable nontrivial effects in the PM10 diurnal cycle. The clearest impact is in the morning peak of PM10 during the fall DST change, which occurs later and has on average a significant smaller magnitude in the days after the DST change as compared to the days before it. This decrease in magnitude is most remarkable because it occurs in a period of the year when overall PM10 concentrations increase due to generally worsening of the dispersion conditions. Results are shown for seven monitoring stations around the city, and for the fall and spring DST changes. They show clearly the interplay of emissions and meteorology in conditioning urban air pollution problems, highlighting the role of the morning and evening transitions of the atmospheric boundary layer in shaping the diurnal pattern of urban air pollutant concentrations.Implications: The effects of daylight saving time adjustments on the diurnal cycle of PM 10 measured in Santiago, Chile, have been shown through the analysis of 13 years of hourly concentration data for seven air quality monitoring stations distributed over the city. They constitute an empirical demonstration of the sensitivity of PM 10 levels to modifications in the emission patterns and, as such, help in evaluating emission control measures taken during pollution episodes, as well as in constraining the magnitude and time phases of emission estimates used in numerical dispersion models.