[Changes in Ozone Pollution Trend Characteristics and Sensitivity in Jinan from 2015 to 2020].

The surface ozone (O3) spatiotemporal distribution, variations, and its causes in Ji'nan from 2015 to 2020 were revealed based on the air quality monitoring network data and satellite retrievals from the Ozone Monitoring Instrument (OMI). The results showed that the ozone concentration in Ji'nan gradually increased from 2015 to 2020. The annual 90th percentile of the daily maximum 8-h average (MDA8) O3(namely the annual evaluation value) and the MDA8 O3(April-September) increased by 4.8 µg·(m3·a)-1 and 3.8 µg·(m3·a)-1, respectively. The trend of the ozone levels in the high-concentration range increased faster than that in the low-concentration range. The MDA8 in June increased by 7.4 µg·(m3·a)-1, and the rate range of increases was 2.6-3.9 µg·(m3·a)-1 in the cool seasons (December-February); thus, the O3 control in winter cannot be ignored. It is apparent from the diurnal variations in ozone from 2015 to 2020 in April-September that the average ozone levels have risen in recent years. The growth rate in the daytime was higher than that at night. The capacity of photochemical production has been increasing, especially in recent years. Additionally, it is noteworthy that the peak time for ozone levels occurred approximately 1-2 h earlier. The disparity of ozone concentrations among different stations gradually decreased in recent years. Compared with that in 2015, the range of areas with high O3 concentrations in 2019-2020 was further expanded. The significant positive trends in MDA8-90th and MDA8 (April-September) were observed in 16.1% and 22.6% of the monitoring sites in Ji'nan (P<0.05), most of which were located in urban areas and the suburbs close to urban areas. The temporal and spatial changes in ozone in Jinan had been affected by the changes in VOCs and NOx emissions since 2015. Satellite remote sensing data from 2015 to 2020 revealed that the NO2 tropospheric columns (April-September) showed reductions of 20.6%, with a decreasing rate of 0.3×1015 mole·(cm2·a)-1, especially in the urban areas and suburbs. The detected variation trends of tropospheric HCHO were weak and insignificant, which suggested that the decrease in NOx emissions was much greater than the decrease in VOCs emissions, and the gap had become more obvious in the urban areas. With responses to precursor emissions, the chemical sensitivity of O3 formation had been changing. The VOCs-limited regimes continuously decreased, and the mixed NOx/VOCs-sensitive regimes and NOx-limited regimes increased. In general, such an extremely inappropriate control ratio of ozone precursor NOx/VOCs led to an overall trend of slow increasing fluctuations of O3 in Ji'nan. The findings clearly indicate that the reduction of VOCs in Ji'nan was far from sufficient, and strengthening the current control of VOCs emissions is an effective measure to control the growth trend of O3 pollution in Ji'nan in the near future, especially in urban and surrounding suburban areas.

[Ozone Formation and Key VOCs of a Continuous Summertime O3 Pollution Event in Ji'nan].

In the summer of 2019, field measurements of ozone (O3) and its precursors[volatile organic compounds (VOCs) and nitrogen oxides (NOx)] were carried out at an urban site in Ji'nan. We found that the daily maximum 8-hour averages φ(O3) were (103.0±14.5)×10-9. The average φ(NOx) and φ(VOCs), which are ozone precursors, were (16.7±11.3)×10-9and (22.4±9.4)×10-9, respectively. The ·OH reactivity of VOCs was determined (9.6±3.8) s-1. Ji'nan suffered from serious O3 pollution. An observation-constrained chemical box model was deployed to evaluate in situ photochemical O3 production, which indicated that chemical reactions made positive contributions to O3 production rates between 07:00 and 19:00 LT, with the average hourly O3 production rate of 35.6×10-9 h-1. To evaluate the effectiveness of various ozone precursor control strategies in reducing ozone pollution, we combined the observation-based model (OBM) with the relative incremental reactivity (RIR) method. The key indicators that affect the local ozone production rate were identified. Ji'nan was under VOC-limited conditions and the key VOC precursors were alkenes. The O3 formation mechanism changed from the VOC-limited regime in the morning to the transitional regime in the afternoon. Correspondingly, the simulated local O3 production rate was increased from 18.3×10-9 h-1 to 29.6×10-9 h-1. To further explore the role of anthropogenic emissions in ozone pollution, we used the positive matrix factorization (PMF) model to identify the major sources contributing to VOCs. The major sources in Ji'nan were vehicular exhaust and gasoline evaporation, accounting for more than 50% of the observed VOCs. Therefore, constraints on vehicular emissions is the most effective strategy to control O3 pollution in Ji'nan.

[Source apportionment of dioxin-like polychlorinated biphenyls in soil of the modern Yellow River Delta].

The concentrations of 12 dioxin-like polychlorinated biphenyls (PCBs) in twenty two soil samples collected from modern Yellow River Delta were determined by dual capillary GC-ECD associated with GC-MS. Principle component analysis (PCA) was applied in analysing the dioxin-like PCBs data to obtain types, sources, percentages of source contribution and the congener composition of PCBs in the Delta on the congener level. The results indicated that four types of PCBs contamination sources affecting the PCBs distribution pattern were apportioned in the Delta. The first source was water runoff (non-point source), which was from the industrial production and use by the enterprises along the Yellow River, and it contributed 49.6% of the total contamination burden in the Delta, its dioxin-like PCBs profile was similar to native PCBs and Aroclorl221; the second and third types of PCBs sources were point sources, whose percentages of contribution were 15.0% and 10.1% respectively, the composition of the second source was similar to that of Aroclor1221 and Aroclor1242, the composition of the third source was similar to that of Aroclor1260; the fourth source is believed to be non-point source which was from the atmospheric dry and wet precipitation, it contributed 8.4%, its profile have similarity composition of the integration of Aroclor1242, Aroclor1248 and Aroclor1260.