[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 Ozone Pollution in Guangzhou: Case Study with the Application of Lagrangian Photochemical Trajectory Model].

A six-day ozone pollution episode in Guangzhou in early October 2018 was analyzed with the application of a Lagrangian photochemical trajectory model to trace the sources of ozone, quantify the contributions of different regions, and evaluate the effects of emission reduction measures targeted at different emission sectors and different precursors on ozone pollution. The results showed that during the ozone pollution episode, the maximum daily 8 h ozone exceeded 160 µg·m-3 and the highest value reached 271 µg·m-3. The average concentrations of nitrogen oxides and volatile organic compounds (VOCs) were (77.7±42.8) µg·m-3 and (71.9±56.2) µg·m-3, respectively. Aromatics and alkenes were the dominant reactive VOCs, with contributions of 38% and 30% to·OH reactivity and 51% and 16% to ozone formation potential, respectively. The ozone pollution in Guangzhou during this episode was affected by three types of air masses, with the primary source regions of Guangzhou, Guangdong Province, and regions outside Guangdong Province. For all three air mass types, ozone production in these source region was controlled by VOCs. Sensitivity tests showed that, in the primary source regions, reducing the emissions of VOCs is more effective than reducing NOx in terms of reducing ozone concentrations. Under the condition of full emission reduction, regulating traffic emissions could substantially reduce ozone levels by 14.6%-21.0% in Guangzhou, which was a more significant reduction than regulating controlled industry (8.4%-15.3%), power plant (0.9%-6.2%) and residential (2.3%-4.7%) emissions. However, the traffic emission reduction is not as effective (induced ozone reduction<10%) when the emissions reduction ratio is lower than 90%. In addition, biogenic emissions in the Pearl River Delta also substantially contributed to the ozone levels under certain circumstances, as indicated by the ozone reduction up to 19% when biogenic emissions were shut off.

[Concentration distribution of metal elements in atmospheric aerosol under different weather conditions in Qingdao Coastal Region].

To know the influence of different weather conditions on the concentration of metal elements in aerosols in the coastal region, total suspended particles (TSP) samples were collected from April to May 2012, and August 2012 to March 2013 in the Qingdao coastal region, and common trace metals were analyzed by using Inductively Coupled Plasma-Mass Spectrometry (ICP-MS) and Inductively Coupled Plasma-Atomic Emission Spectrometry (ICP-AES). The results showed that Al, Ca, Fe, Na, K and Mg were the dominant metal elements in TSP, and the sum of the six elements accounted for 94.2% of the sum of all metals. TSP and metal elements had significant monthly variations, Fe, Al, K, Ca, Mg, Zn, Ba, Mn, Ti, Sr and Li had the highest concentration in November and January, while Be, Sc, Co, Ni and Cr showed the highest value in January. Na had the highest concentration in August, November and February, and the lowest in December. Pb had the highest concentration in January and February, and the lowest in August and December. Enrichment factors indicated that Be, Co, Al, Ca, Fe, K, Mg, Mn, Sr and Ti were mainly affected by natural sources; Li, Cr, Ni, Zn, Ba and Na were affected by natural sources and part of anthropogenic sources; Pb was mainly from anthropogenic sources. Different weather conditions had great impact on TSP and metal elements concentrations, all the measured metals had the highest concentrations in smog except Ti. Compared with the sunny day, the concentration of atmospheric particulate Ti decreased, while the other elements increased by 1 to 4 times in smog. Li, Be, Cr, Ni, Al, Fe, Mg and Mn had little variation in concentration in foggy day, and the concentration of Pb and Na increased considerably. The concentration of Co, Ca and Ti reduced obviously in fog. Except for Cr, Co and Ti, the other elements increased by 1 to 3 times in haze. Most of the elements had the minimal enrichment factors in sunny day, while the other had the maximal enrichment factor in foggy day. Enrichment factors of Ni, Zn, Ba, K, Na, Pb and Sr varied in the order of sunny day < haze day < smog day