Junlin An
Nanjing University of Information Science and Technology
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Science of The Total Environment | 2014
Honglei Wang; Junlin An; Lijuan Shen; Bin Zhu; Chen Pan; Zirui Liu; Xiaohui Liu; Qing Duan; Xuan Liu; Yuesi Wang
In this paper we investigate a severe pollution episode that occurred in the Yangtze River Delta (YRD) region in January 2013. The episode was caused by the combination of anthropogenic emissions and unusual atmospheric circulation, the depression of strong cold air activities and the very unfavorable dispersion. The episode contained three haze events (haze1: Jan. 4-9, haze2: Jan. 10-13, and haze3: Jan. 14-16). In Nanjing, aerosol size distributions from 10nm to 10 μm and chemical components of single particles from 0.2 to 2 μm were measured with a Wide Range Particle Spectrometer (WPS) and a Single Particle Aerosol Mass Spectrometer (SPAMS), respectively. The results indicate that the mean PM2.5 concentrations in the YRD region were greater than 110 μg·m(-3). The highest PM2.5 concentration of 175.6 μg·m(-3) occurred in Nanjing; the other cities had values in the range of 110.8-147.3 μg·m(-3). The average PM2.5 concentrations were 58.3, 122.7, 145.4 and 154.7 μg·m(-3) on clean and haze1, haze2 and haze3 days, respectively. The highest PM2.5 values of 416.5, 415.5 and 300.5 μg·m(-3) in Nanjing occurred during the three haze events. The spectra of the aerosol number concentrations had unimodal distributions on clean and haze days. The maximum surface area peaks were located at 0.5-0.7 μm and had values of 419, 1397, 1309 and 1378 μm(2)·cm(-3)·nm(-1) on clean and haze1, haze2 and haze3 days, respectively. The number concentrations of biomass/biofuel burning-containing particles (biomass), organic carbon-containing particles (OC), elemental carbon-containing particles (EC), nitrate-containing particles (nitrate) and sulfate-containing particles (sulfate) increased significantly during the haze events. The chemical components of the aerosols during the haze1 and haze2 events were similar to those on clean days, and variations were caused by local particle accumulations under poor diffusion conditions. The high EC particle concentration of 24.76% during the haze3 event was impacted by the pollutants transported from surrounding cities. In addition, the different chemical components showed distinct size distributions.
Chemosphere | 2016
Honglei Wang; Junlin An; Mengtian Cheng; Lijuan Shen; Bin Zhu; Yi Li; Yuesi Wang; Qing Duan; Amy P. Sullivan; Li Xia
Half-hourly mass concentrations water-soluble ions (WSIs) and PM2.5 were measured online a Rapid Collector of Fine Particles and Ion Chromatography system (RCFP-IC) and FH62C14 Continuous Particulate Monitor in Nanjing from October 18, 2013 to November 17, 2014. The WSIs concentration ranged from 7.07 to 333.42 μg m(-3) with an annual mean of 76.32 μg m(-3). The WSIs ranked in the order of SO4(2-) > NH4(+) > NO3(-) > Cl(-) > NO2(-) > K(+) > Ca(2+) > Na(+) > Mg(2+). The PM2.5 concentration ranged from 4.00 to 400 μg m(-3) with an annual mean of 83.58 μg m(-3). The concentrations of WSIs varied in the order of winter (115.77 μg m(-3)) > spring (76.10 μg m(-3)) > autumn (63.72 μg m(-3)) > summer (59.75 μg m(-3)), with the highest level in January (123.99 μg m(-3)) and lowest level in August (43.73 μg m(-3)). Different WSIs had distinct diurnal variations. The source analysis of the WSIs in the PCA/APCS mode illustrated that the sources consisted of secondary aerosol, coal combustion, mineral dust, biomass burning, traffic emissions and sea salt. In addition, there were seasonal variations amongst the various sources. The haze formation mechanism was different in summer and winter. The winter was dominated by NH4NO3 (18.56%), (NH4)2SO4 (28.63%), NH4(+) (11.27%), SO4(2-) (18.35%) and NO3(-) (13.13%), and by NH3 (25.93%), (NH4)2SO4 (13.37%), SO4(2-) (15.74%) and NO3(-) (9.97%) in summer. Consequently, the proportions of HCl, HNO3, NH4(+), SO4(2-) and NO3(-) were much larger during haze episodes in winter, while it was dominated by NH4NO3, NH4(+), (NH4)2SO4, SO4(2-) and NO3(-) during summer haze episodes.
Science of The Total Environment | 2016
Xingna Yu; Jia Ma; K. Raghavendra Kumar; Bin Zhu; Junlin An; Jiaqi He; Mei Li
Aerosol optical properties including aerosol scattering coefficient (σsp), absorption coefficient (σap), single scattering albedo (SSA), PM2.5 mass concentration and their relationship with meteorological factors were measured and analyzed from 1st March to 30th April 2011. The observations were conducted at an urban-industrial site in Nanjing located in the Yangtze River Delta (YRD) region, China. The averaged σsp at 550 nm is found to be 329.3±321.6 Mm(-1) and occurred most frequently within the range of 100-200 Mm(-1). σap at 532 nm is measured to be 28.1±17.6 Mm(-1), which is remarkably smaller than most of the measurements made over megacities in China and nearly equivalent to the observations made at rural sites in the YRD. The mean SSA at 532 nm is 0.89±0.08 with the occurrence frequency of higher SSA (>0.9) accounting for about 60%, indicating that scattering-related aerosols are dominant over Nanjing during the study period. A bi-peak diurnal variation of σap and σsp is found with the maximum during late at night and early morning and the minimum in the afternoon, but the diurnal pattern of SSA is just the opposite to this. Significant correlation coefficients are noticed between different aerosol optical properties. σsp and σap showed a clear negative correlation with wind speed, temperature, and visibility. During the study period, the lower northwesterly and southeasterly winds are attributed to an increase in the aerosol optical properties. The σsp and σap increased significantly during severe haze and dust episodes caused by the accumulation of pollutants from both local and regional sources under favorable weather conditions.
Archives of Environmental Contamination and Toxicology | 2017
Junlin An; Junxiu Wang; Yuxin Zhang; Bin Zhu
Volatile organic compounds (VOCs) were collected continuously during June–August 2013 and December 2013–February 2014 at an urban site in Nanjing in the Yangtze River Delta. The positive matrix factorization receptor model was used to analyse the sources of VOCs in different seasons. Eight and seven sources were identified in summer and winter, respectively. In summer and winter, the dominant sources of VOCs were vehicular emissions, liquefied petroleum gas/natural gas (LPG/NG) usage, solvent usage, biomass/biofuel burning, and industrial production. In summer, vehicular emissions made the most significant contribution to ambient VOCs (38%), followed by LPG/NG usage (20%), solvent usage (19%), biomass/biofuel burning (13%), and industrial production (10%). In winter, LPG/NG usage accounted for 36% of ambient VOCs, whereas vehicular emissions, biomass/biofuel burning, industrial production and solvent usage contributed 30, 18, 9, and 6%, respectively. The contribution of LPG/NG usage in winter was approximately four times that in summer, whereas the contribution from biomass/biofuel burning in winter was more than twice that in summer. The sources related to vehicular emissions and LPG/NG usages were important. Using conditional probability function analysis, the VOC sources were mainly associated with easterly, northeasterly and southeasterly directions, pointing towards the major expressway and industrial area. Using the propylene-equivalent method, paint and varnish (23%) was the highest source of VOCs in summer and biomass/biofuel burning (36%) in winter. Using the ozone formation potential method, the most important source was biomass/biofuel burning (32% in summer and 47% in winter). The result suggests that the biomass/biofuel burning and paint and varnish play important roles in controlling ozone chemical formation in Nanjing.
Atmospheric Environment | 2010
Changjie Cai; Fuhai Geng; Xuexi Tie; Qiong Yu; Junlin An
Atmospheric Environment | 2014
Junlin An; Bin Zhu; Honglei Wang; Yongyu Li; Xu Lin; Hui Yang
Journal of Atmospheric Chemistry | 2009
Fuhai Geng; Changjie Cai; Xuexi Tie; Qiong Yu; Junlin An; Li Peng; Guangqiang Zhou; Jianming Xu
Atmospheric Environment | 2015
Honglei Wang; Bin Zhu; Lijuan Shen; Honghui Xu; Junlin An; Guoqiang Xue; Jinfei Cao
Atmospheric Research | 2014
Honglei Wang; Bin Zhu; Lijuan Shen; Junlin An; Yan Yin; Hanqing Kang
Atmospheric Environment | 2015
Junlin An; Honglei Wang; Lijuan Shen; Bin Zhu; Jianan Zou; Jinhui Gao; Hanqing Kang