[Variation of O3 Concentration in Different Regions of Beijing from 2006-2015].

O3 continuous monitoring data for the Dingling, Guanyuan, Liulihe, and Qianmen sites from 2006-2015 were analyzed to investigate concentration levels, variation trends, temporal variations, and relationships with precursors and meteorological factors. The results showed that the ten year average concentrations of O3 at the Dingling site were the highest at 65.2 µg·m-3, followed by concentrations at Liulihe (53.4 µg·m-3), Guanyuan (49.6 µg·m-3) and Qianmen (40.4 µg·m-3). The O3 concentrations at Dingling showed a decreasing trend[0.5 µg·(m3·a)-1], while O3 concentrations at Guanyuan[0.9 µg·(m3·a)-1], Liulihe[0.3 µg·(m3·a)-1], and Qianmen[0.3 µg·(m3·a)-1] showed an increasing trend. The highest monthly average concentrations appeared during June and August, and the highest frequency occurred in July (17 times) with average concentrations of 99.8 µg·m-3. The lowest monthly average concentrations appeared during November and February, and the highest frequency occurred in January (14 times) with an average concentration of 16.6 µg·m-3. Notably, the time for the peak concentrations of O3 appeared earlier in the day in recent years. The peak concentrations of O3 appeared at 15:00-16:00 during 2013-2015, which was 1-2 hours earlier than previous years. The heavy air pollution of O3 occurred on 11 days at the Dingling site in 2015, which was ten days more than in 2013, indicating O3 pollution in the downwind suburban regions of Beijing in summer became more and more serious. The concentrations of O3 and NO2 at Dingling showed a positive correlation, while the concentrations of O3 and NO2 at the other sites showed a negative correlation, indicating O3 formation in Dingling was sensitive to NO2 chemistry, while O3 formation at the other sites was sensitive to VOC chemistry. The concentrations of O3 showed a positive correlation with temperature and negative correlations with humidity and surface pressure. Temperature had the greatest influence on O3 concentration, followed by surface pressure and humidity. For cases when daily maximum temperature exceeded 30℃ and relative humidity was between 30% and 70%, the probability of the O3 daily maximum 8 h concentration exceeding 200 µg·m-3 was high, indicating the air quality level reached levels for light pollution and moderate pollution.

[Impact of Gusty Northwesterly Winds on Biological Particles in Winter in Beijing].

Biological aerosol particles play a crucial role in cloud formation and succession of ecosystems and have a large impact on human health. However, the variations in the concentration, composition, and viability of biological particles remain unclear. This study, conducted in January 2013 and January 2015 in Beijing, explores the influence of meteorological conditions on the variations in the concentration and composition of biological particles. Concentrations of biological particles were measured by an online optical detector, waveband integrated bioaerosol sensor (WIBS-4A). The composition of bacterial communities within biological particles was measured by 16S rDNA sequencing. The results showed that the number concentration of biological particles ranged from 2 L-1 to 150 L-1 during winter. The wind could largely influence the concentration and composition of biological particles. During gusty northwesterly winds, when wind speed was above 4 m·s-1 and wind direction was from the northwest (~30°), the concentration increased by one order of magnitude, and the composition of bacterial communities sharply changed. After the passage of gusty winds, the composition gradually changed back to its prior state.

[Characteristics of Ozone Background Concentration in Beijing from 2004 to 2015].

Based on the hourly O3 monitoring data from 2004 to 2015 of Beijing, a comprehensive discussion on the characteristics of O3 concentration at a background station Dingling in Beijing was conducted. The results showed that the annual concentration of O31h was increasing with a growth rate of 4.40 µg·m-3 while the annual concentration of O38h was decreasing with annual average rates of -1.0 µg·m-3 and -1.5 µg·m-3 from May to October in 2004 and 2015. Over the past 3 years, number of O38h severe pollution days increased significantly and the situation of O3 pollution in Beijing became more serious. O3 concentration reached its peak in June in a year and its diurnal peak concentration occurred at about 15:00-18:00 at Dingling station which was 101-1.56 times larger than that in the urban center of Beijing. In different years, the ozone peak concentration at Dingling Station was 1h later than that in the urban center from May to October in diurnal variation and the difference of peak concentration was significantly reduced in recent years, which on the one hand may be related to regional ozone pollution, on the other hand may be related to the expansion of Beijing's urbanization.

[Characterizing Beijing's Airborne Bacterial Communities in PM2.5 and PM1 Samples During Haze Pollution Episodes Using 16S rRNA Gene Analysis Method].

During 8th-14th Jan., 2013, severe particulate matter (PM) pollution episodes happened in Beijing. These air pollution events lead to high risks for public health. In addition to various PM chemical compositions, biological components in the air may also impose threaten. Little is known about airborne microbial community in such severe air pollution conditions. PM2.5 and PM10 samples were collected during that 7-day pollution period. The 16S rRNA gene V3 amplification and the MiSeq sequencing were performed for analyzing these samples. It is found that there is no significant difference at phylum level for PM2.5 bacterial communities during that 7-day pollution period both at phylum and at genus level. At genus level, Arthrobacter and Frankia are the major airborne microbes presented in Beijing winter.samples. At genus level, there are 39 common genera (combined by first 50 genera bacterial of the two analysis) between the 16S rRNA gene analysis and those are found by Metagenomic analysis on the same PM samples. Frankia and Paracoccus are relatively more abundant in 16S rRNA gene data, while Kocuria and Geodermatophilus are relatively more abundant in Meta-data. PM10 bacterial communities are similar to those of PM2.5 with some noticeable differences, i.e., at phylum level, more Firmicutes and less Actinobacteria present in PM10 samples than in PM2.5 samples, while at genus level, more Clostridium presents in PM10 samples. The findings in Beijing were compared with three 16S rRNA gene studies in other countries. Although the sampling locations and times are different from each other, compositions of bacterial community are similar for those sampled at the ground atmosphere. Airborne microbial communities near the ground surface are different from those sampled in the upper troposphere.