Abstract. Ozone pollution in eastern China has become one of the top environmental issues. Quantifying the temporal trend of surface ozone helps to assess the impacts of the anthropogenic precursor reductions and the likely effects of emission control strategies implemented. In this paper, ozone data collected at the Shangdianzi (SDZ) regional atmospheric background station from 2003 to 2015 are presented and analyzed to obtain the variation in the trend of surface ozone in the most polluted region of China, north of eastern China or the North China Plain. A modified KolmogorovZurbenko (KZ) filter method was performed on the maximum daily average 8 h (MDA8) concentrations of ozone to separate the contributions of different factors from the variation of surface ozone and remove the influence of meteorological fluctuations on surface ozone. Results reveal that the short-term, seasonal and long-term components of ozone account for 36.4, 57.6 and 2.2 % of the total variance, respectively. The long-term trend indicates that the MDA8 has undergone a significant increase in the period of 2003-2015, with an average rate of 1.13 ± 0.01 ppb year −1 (R 2 = 0.92). It is found that meteorological factors did not significantly influence the long-term variation of ozone and the increase may be completely attributed to changes in emissions. Furthermore, there is no significant correlation between the longterm O 3 and NO 2 trends. This study suggests that emission changes in VOCs might have played a more important role in the observed increase of surface ozone at SDZ.

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Understanding the trend of chemical composition of precipitation is of great importance for air pollution control strategies in Northern China. A comprehensive study on the long-term chemical compositions of precipitation was carried out from 2003 to 2014 at the Shangdianzi (SDZ) regional background station in northern China. All samples were analyzed for pH, electrical conductivity and major ions (F, Cl, NO, SO, NH, Mg, Ca, K and Na). The average pH during this period was 4.53±0.35, which is considerably lower than those reported in other background stations in China (Linan, Waliguan and Longfengshan). NH, SO, Ca and NO were the dominant ions in precipitation, with concentrations (volume-weighted mean) of 212.99μeqL, 200.20μeqL, 116.88μeqL and 98.56μeqL, respectively. The ion concentrations at SDZ were much higher than those of other background stations and megacities in China. A significantly increasing trend was observed for NO (7.26%year), and a decreasing trend was observed for SO/NO, suggesting that the precipitation of SDZ evolved from a sulfuric acid type to a mixed type dominated by both sulfuric and nitric acid. The source identification indicated that SO, NO, NH and F were dominated by secondary sources, Mg, Ca and Na mostly originated from natural sources, and K and Cl probably associated with anthropogenic sources. Long-range transport of air masses could influence the acidity, electrical conductivity and ion concentrations of precipitation at SDZ. The higher acidity and ion concentrations mainly occurred in the southerly and westerly trajectory pathways and partially in northwest pathways. Anthropogenic pollutants and crustal sources along these pathways were significant contributors to the chemical composition of precipitation in SDZ.

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Surface particulate matter with an aerodynamic diameter of <2.5 μm (PM2.5) and column-integrated aerosol optical depth (AOD) exhibits substantial diurnal, daily, and yearly variabilities that are regionally dependent. The diversity of these temporal variabilities in urban and rural areas may imply the inherent mechanisms. A novel time-series analysis tool developed by Facebook, Prophet, is used to investigate the holiday, seasonal, and inter-annual patterns of PM2.5 and AOD at a rural station (RU) and an urban station (UR) in Beijing. PM2.5 shows a coherent decreasing tendency at both stations during 2014–2018, consistent with the implementation of the air pollution action plan at the end of 2013. RU is characterized by similar seasonal variations of AOD and PM2.5, with the lowest values in winter and the highest in summer, which is opposite that at UR with maximum AOD, but minimum PM2.5 in summer and minimum AOD, but maximum PM2.5 in winter. During the National Day holiday (1–7 October), both AOD and PM2.5 holiday components regularly shift from negative to positive departures, and the turning point generally occurs on October 4. AODs at both stations steadily increase throughout the daytime, which is most striking in winter. A morning rush hour peak of PM2.5 (7:00–9:00 local standard time (LST)) and a second peak at night (23:00 LST) are observed at UR. PM2.5 at RU often reaches minima (maxima) at around 12:00 LST (19:00 LST), about four hours later (earlier) than UR. The ratio of PM2.5 to AOD (η) shows a decreasing tendency at both stations in the last four years, indicating a profound impact of the air quality control program. η at RU always begins to increase about 1–2 h earlier than that at UR during the daytime. Large spatial and temporal variations of η suggest that caution should be observed in the estimation of PM2.5 from AOD.

We observed significant effects of particulate matter (PM2.5) on cause-specific mortality by applying a time-stratified case-crossover and lag-structure designs in Beijing over a nine-year study period (2005–2013). The year-round odds ratio (OR) was 1.005 on the current day with a 10 μg/m3 increase in PM2.5 for all-cause mortality. For cardiovascular mortality and stroke, the ORs were 1.007 and 1.008 on the current day, respectively. Meanwhile, during a lag of six days, the cumulative effects of haze on relative risk of mortality, respiratory mortality and all-cause mortality was in the range of 2~11%. Moreover, we found a significant seasonal pattern in the associations for respiratory mortality: significant associations were observed in spring and fall, while for all-cause mortality, cardiovascular mortality, cardiac and stroke, significant associations were observed in winter. Moreover, increasing temperature would decrease risks of mortalities in winter taking fall as the reference season. We concluded that in summer, temperature acted as a direct enhancer of air pollutants; while in winter and spring, it was an index of the diameter distribution and composition of fine particles.

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