Characteristics of atmospheric single particles during haze periods in a typical urban area of Beijing: A case study in October, 2014.

To investigate the composition and possible sources of particles, especially during heavy haze pollution, a single particle aerosol mass spectrometer (SPAMS) was deployed to measure the changes of single particle species and sizes during October of 2014, in Beijing. A total of 2,871,431 particles with both positive and negative spectra were collected and characterized in combination with the adaptive resonance theory neural network algorithm (ART-2a). Eight types of particles were classified: dust particles (dust, 8.1%), elemental carbon (EC, 29.0%), organic carbon (OC, 18.0%), EC and OC combined particles (ECOC, 9.5%), Na-K containing particles (NaK, 7.9%), K-containing particles (K, 21.8%), organic nitrogen and potassium containing particles (KCN, 2.3%), and metal-containing particles (metal, 3.6%). Three haze pollution events (P1, P2, P3) and one clean period (clean) were analyzed, based on the mass and number concentration of PM2.5 and the back trajectory results from the hybrid single particle Lagrangian integrated trajectory model (Hysplit-4 model). Results showed that EC, OC and K were the major components of single particles during the three haze pollution periods, which showed clearly increased ratios compared with those in the clean period. Results from the mixing state of secondary species of different types of particles showed that sulfate and nitrate were more readily mixed with carbon-containing particles during haze pollution episodes than in clean periods.

Size distribution and seasonal variations of particle-associated organochlorine pesticides in Jinan, China.

The concentrations of organochlorine pesticides (OCPs) in atmospheric particulate matter in Jinan, China, over the period from July 2009 to June 2010, were determined to study their pollution levels, compositions, size distribution and seasonal variations. All target compounds except endosulfan sulfate were detected. The annual average concentration of ∑18 OCPs was 92 ± 82 pg m(-3). Total HCH, total endrin, aldrin, endosulfan compounds and total DDT compounds were the primary components, accounting for approximately 27%, 20%, 16%, 14% and 10% of total OCPs, respectively. The annual mean ng g(-1) concentrations of ∑18 OCPs in PM(2.5), PM(5), PM(10) and TSP were 481 ± 190, 433 ± 161, 414 ± 158 and 264 ± 193, respectively, indicating that most OCPs tend to be strongly absorbed by fine air particles which were strongly related to a potential health risk. Distinct seasonal trends were found in OCPs concentrations with high concentrations appearing in November and March whereas low concentrations appeared in the summer, which were significantly positively correlated with particulate mass concentrations and Air Pollution Index (API). The high OCPs levels could be attributed to the seasonal usage, long-range atmospheric transport as well as adverse meteorological conditions.

A land use regression for predicting NO2 and PM10 concentrations in different seasons in Tianjin region, China.

Land use regression (LUR) model was employed to predict the spatial concentration distribution of NO2 and PM10 in the Tianjin region based on the environmental air quality monitoring data. Four multiple linear regression (MLR) equations were established based on the most significant variables for NO2 in heating season (R2 = 0.74), and non-heating season (R2 = 0.61) in the whole study area; and PM10 in heating season (R2 = 0.72), and non-heating season (R2 = 0.49). Maps of spatial concentration distribution for NO2 and PM10 were obtained based on the MLR equations (resolution is 10 km). Intercepts of MLR equations were 0.050 (NO2, heating season), 0.035 (NO2, non-heating season), 0.068 (PM10, heating season), and 0.092 (PM10, non-heating season) in the whole study area. In the central area of Tianjin region, the intercepts were 0.042 (NO2, heating season), 0.043 (NO2, non-heating season), 0.087 (PM10, heating season), and 0.096 (PM10, non-heating season). These intercept values might imply an area's background concentrations. Predicted result derived from LUR model in the central area was better than that in the whole study area. R2 values increased 0.09 (heating season) and 0.18 (non-heating season) for NO2, and 0.08 (heating season) and 0.04 (non-heating season) for PM10. In terms of R2, LUR model performed more effectively in heating season than non-heating season in the study area and gave a better result for NO2 compared with PM10.

[Seasonal Variation Characteristics and Potential Source Contribution of Sulfate, Nitrate and Ammonium in Beijing by Using Single Particle Aerosol Mass Spectrometry].

Single particle aerosol mass spectrometry (SPAMS) was deployed to continuously observe the aerosol particles of Beijing urban area from 2013-12 to 2014-11, and the hourly average data of sulfate, nitrate and ammonium (SNA) were obtained using the characteristic ion tracer method. The mixing state and size distribution of SNA were analyzed. In addition, based on Hysplit 48 h back air mass trajectory results in combination with Concentration Weighted Trajectory method (CWT), we obtained the seasonal potential source contribution area of SNA. The results showed that the mixture of sulfate, nitrate and ammonium in spring and summer was more stable than that in autumn and winter. The size distribution of sulfate and nitrate was very similar. The size distribution characteristics of SNA followed the order of autumn > summer > spring > winter. The potential source region of SNA had similar spatial distribution characteristics, and the potential source region of SNA was mainly located in Beijing and south areas, especially at Tianjin, Langfang, Hengshui, Baoding and Shijiazhuang.

[Ozone pollution, influence factors and their correlation at urban area in summer].

Based on the observational data of near surface O3, NO, NO2, CO and meteorological factors in the urban area of Ji'nan during summer 2003, the 03 concentrations and their temporal variation characteristics were studied. The correlation between O3 and its precursors (NO, NO2, CO) and related meteorological factors (solar radiation, temperature) was analyzed. The results show that O3 pollution during summer was very serious in Ji'nan, and the levels of O3, NO, NO2, NOx and CO were quite high during the observational period. O3 concentrations were well negatively correlated with NO, NO2, NO, and CO during day time. As to the meteorological factors, O3 concentrations correlated well with solar radiations, but showed no obvious correlation with the temperatures. Consequently, based on the above data and results, a regression equation that relates ozone concentrations observed in the day time to its precursors and solar radiation was constructed. The results show that the calculated values were in good agreement with the observed values.