Characteristics of ambient volatile organic compounds (VOCs) measured in Shanghai, China.

To better understand the characteristics of ambient abundance of volatile organic compounds (VOCs) in Shanghai, one of the biggest metropolis of China, VOCs were measured with a gas chromatography system equipped with a mass-selective detector (GC/MSD) from July 2006 to February 2010. An intensive measurement campaign was conducted (eight samples per day with a 3 hour interval) during May 2009. The comparison of ambient VOCs collected in different regions of Shanghai shows that the concentrations are slightly higher in the busy commercial area (28.9 ppbv at Xujiaui) than in the urban administrative area (24.3 ppbv at Pudong). However, during the intensive measurement period, the concentrations in the large steel industrial area (28.7 ppbv at Baoshan) were much higher than in the urban administrative area (18 ppbv at Pudong), especially for alkanes, alkenes, and toluene. The seasonal variations of ambient VOC concentrations measured at the Xujiahui sampling site indicate that the VOC concentrations are significantly affected by meteorological conditions (such as wind direction and precipitation). In addition, although alkanes are the most abundant VOCs at the Xujiahui measurement site, the most important VOCs contributing to ozone formation potential (OFP) are aromatics, accounting for 57% of the total OFP. The diurnal variations of VOC concentrations show that VOC concentrations are higher on weekdays than in weekends at the Xujiahui sampling site, suggesting that traffic condition and human activities have important impacts on VOC emissions in Shanghai. The evidence also shows that the major sources of isoprene are mainly resulted from gasoline evaporation at a particular time (06:00-09:00) in the busy commercial area. The results gained from this study provide useful information for better understanding the characteristics of ambient VOCs and the sources of VOCs in Shanghai.

[A Numerical Study of Typical Heavy Air Pollution Episode of PM2.5 in Shanghai].

To analyze the characteristics and formation mechanism of a heavy air pollution episode in Shanghai City from January 23th to January 24th, 2013, the observed data of PM2.5 concentration and ground meteorological data and the WRF-Chem model were collected. The analysis revealed that the synoptic necessary mechanism of the heavy air pollution episode could be characterized by the following patterns: Accompanied with weak cold front activities, the city experienced weak winds (i. e. stable atmosphere) at first and then northerly winds (i. e. pollutant transport process ), causing the continuous increase and maintaining of pollutant concentration. The detailed results are shown as follows: Firstly, the stable atmosphere circulation pattern which lasted for 10 hours was not good for air pollution dispersion, as a result, local PM2.5 concentrations continued to increase and reached severe pollution level and the high concentrations maintained for 7 hours caused by the stable boundary layer (e. g. static surface winds and low level temperature inversion) during nighttime, and the average PM2.5 concentrations during the stable weather process was 172.4 µg · m⁻³. Secondly, the dispersion condition was slightly improved later on with the arrival of a weak cold front, the upstream pollution transportation occurred at the same time, leading to further increase of PM2.5 concentration (up to 280 µg · m⁻³), and the average PM2.5 concentration during the upstream transportation process was 213.6 µg · m⁻³. Numerical simulation with the WRF-Chem model showed that, average contribution of upstream transportation to local PM2.5 concentrations during the episode was 23% . Among them, the contribution during the stable weather and upstream transportation stage was 17.2% and 32.2% . Our results suggested that there were significant differences in the contribution of upstream transportation to the local PM2.5 concentration of Shanghai due to variation of weather conditions. Therefore, the government can design effective emission control strategy in advance taking pollution weather forecasting into account.