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Featured researches published by Xiaohui Bi.


Environmental Monitoring and Assessment | 2013

Heavy metals in urban ambient PM10 and soil background in eight cities around China

Qiong Wang; Xiaohui Bi; Jianhui Wu; Yufen Zhang; Yin-Chang Feng

The ambient PM10 and background soil samples were collected and analyzed with ICP-AES in eight cities around China to investigate the levels of ten heavy metals (Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, and Pb). The mean concentrations of ten heavy metals in PM10 of the eight cities of China followed the order of Zn > Pb > Mn > Cu > Ni > Cr > Co > V. The metals in the ambient PM10 and soil were compared in each city to evaluate the heavy metal mass fraction from anthropogenic sources in ambient air. The CD values in these cities were all above 0.2, indicating that the ingredients spectrums of PM10 and soil vary markedly. Most heavy metals were enriched in PM10, except Fe and Ti. The results showed that almost all the cities suffer important heavy metal pollution from anthropogenic sources. The eight cities were also grouped according to their similarity in heavy metals of ambient PM10 by cluster analysis to investigate the relationship between the heavy metals and the pollution sources of each city. The conclusion was that the eight cities were divided into three clusters which had similar industrial type and economy scale: the first cluster consisted of Shenzhen, Wuxi, and Guiyang; followed by Jinan and Zhengzhou as the second grouping; and the third group had Taiyuan, Urumqi, and Luoyang.


Science of The Total Environment | 2015

Vertical characteristics of PM2.5 during the heating season in Tianjin, China

Hong Wu; Yufen Zhang; Su-qin Han; Jianhui Wu; Xiaohui Bi; Guo-Liang Shi; Jiao Wang; Qing Yao; Zi-ying Cai; Jingle Liu; Yin-Chang Feng

In this study, PM2.5 samples were collected at four heights (10m, 40m, 120m and 220m) at a meteorological tower in the daytime and nighttime during the heating season in Tianjin, China. The vertical variation and diurnal variability of the concentrations of PM2.5 and main chemical compositions were analyzed in clear days and heavy pollution days. Generally, mass concentrations of PM2.5 and the chemical compositions showed a decreasing trend with increasing height, while mass percentages of SO4(2-), NO3(-) and OC showed an increasing trend with increasing height. Concentrations of ion species and carbon compound in PM2.5 samples in the daytime were higher than those collected at night, which was due to intense human activities and suitable meteorological condition in the daytime. The ratios of NO3(-)/SO4(2-) and OC/EC were also considered, and we have observed that their levels on heavy pollution days were higher than those on clear days. In addition, source apportionments were identified quantitatively using the CMB-iteration model. The results indicated that contributions of secondary ion species increased with increasing height, while contributions of other pollutant sources decreased, and contributions of vehicle exhaust were relatively high on clear days.


Environmental Science and Pollution Research | 2015

Particulate matter mass and chemical component concentrations over four Chinese cities along the western Pacific coast

Hong Xu; Xiaohui Bi; Wei-Wei Zheng; Jianhui Wu; Yin-Chang Feng

China has witnessed rapid economic growth in the past three decades, especially in coastal areas. Particulate matter (PM) pollution is becoming increasingly serious in China’s cities along the western Pacific coast with the rapid development of China’s society and economy. This study analyzed PM (PM10 and PM2.5) in terms of their mass and chemical composition in four coastal Chinese cities. The goal was to study the spatial variation and characteristics of PM pollution in sites under different levels of economic development and in diverse natural environments. A distinct trend for concentrations of PM and related chemical species was observed and increased from south to north in Haikou, Ningbo, Qingdao, and Tianjin. Secondary inorganic aerosols, crustal materials, and organic matter dominated the composition of both PM10 and PM2.5. Crustal materials were the most abundant species in the northern coastal areas because these areas have less vegetation cover and lower humidity than southern coastal areas. The presence of high SO42−/nitrate (NO3−) concentrations indicated that the burning of coals gives significant contributions to PM10 and PM2.5. The differences observed in the characteristics of PM pollution in these coastal cities are probably caused by different levels of industrial and urban development.


Environmental Pollution | 2017

Source apportionment and a novel approach of estimating regional contributions to ambient PM2.5 in Haikou, China ☆

Baoshuang Liu; Tingkun Li; Jiamei Yang; Jianhui Wu; Jiao Wang; Jixin Gao; Xiaohui Bi; Yin-Chang Feng; Yufen Zhang; Haihang Yang

A novel approach was developed to estimate regional contributions to ambient PM2.5 in Haikou, China. In this paper, the investigation was divided into two main steps. The first step: analysing the characteristics of the chemical compositions of ambient PM2.5, as well as the source profiles, and then conducting source apportionments by using the CMB and CMB-Iteration models. The second step: the development of estimation approaches for regional contributions in terms of local features of Haikou and the results of source apportionment, and estimating regional contributions to ambient PM2.5 in Haikou by this new approach. The results indicate that secondary sulphate, resuspended dust and vehicle exhaust were the major sources of ambient PM2.5 in Haikou, contributing 9.9-21.4%, 10.1-19.0% and 10.5-20.2%, respectively. Regional contributions to ambient PM2.5 in Haikou in spring, autumn and winter were 22.5%, 11.6% and 32.5%, respectively. The regional contribution in summer was assumed to be zero according to the better atmospheric quality and assumptions of this new estimation approach. The higher regional contribution in winter might be mainly attributable to the transport of polluted air originating in mainland China, especially from the north, where coal is burned for heating in winter.


Journal of Hazardous Materials | 2016

Atmospheric metallic and arsenic pollution at an offshore drilling platform in the Bo Sea: A health risk assessment for the workers.

Hong Xu; Suqin Han; Xiaohui Bi; Zhijing Zhao; Lei Zhang; Wenjie Yang; Min Zhang; Jing Chen; Jianhui Wu; Yufen Zhang; Yin-Chang Feng

To investigate the ambient metal pollution at the offshore drilling platform in the Bo Sea, which few studies have focused on, PM2.5 samples were collected and ten heavy metals, as well as As, were analyzed. High concentration levels of metals were observed, and the heavy metal pollution was quite serious compared to air quality standards and other marine areas. Back trajectories and wind dependent and PCA analyses showed that the marine sources included ship traffic emissions and corrosive stainless steels from the equipment at the platform as well as industrial emissions from stainless steel production and coal combustion sources, which were transported from the surrounding mainland. Both contributed greatly to the ambient metallic particles at the offshore platform. The Hazard Index values of the metals, which were much less than 1, the Carcinogenic Risk data, which were lower than the EPAs acceptable range, and the fact that the metal concentrations did not the exceed the permissible exposure limits of OSHA, indicated that the health risks from the ambient metallic particles for the oil-drilling workers were not significant.


Journal of Environmental Sciences-china | 2018

Insight into the critical factors determining the particle number concentrations during summer at a megacity in China

Baoshuang Liu; Xiaohui Bi; Jiaying Zhang; Jie Yuan; Zhimei Xiao; Qili Dai; Yin-Chang Feng; Yufen Zhang

To identify the critical factors impacting the number concentration of particles with the aerodynamic diameters less than 2.5μm (PNC2.5), the continuous measurement of PNC2.5, chemical components in PM2.5, gaseous pollutants and meteorological conditions were conducted at an urban site in Tianjin in June 2015. Results indicated that the average PNC2.5 was 2839±2430 dN/dlogDp 1/cm3 during the campaign. Compared to other meteorological parameters, the relative humidity (RH) had the strongest relationship with PNC2.5, with a Pearsons correlation coefficient of 0.53, and RH larger than 30% influenced strongly PNC2.5. The important influence of secondary reactions on PNC2.5 was inferred due to higher correlation coefficients between PNC2.5 and SO42-, NO3-, NH4+ (r=0.78-0.89; p<0.01) and between PNC2.5 and ratios that represent the conversion of nitrogen and sulfur oxides to particulate matter (r=0.42-0.49; p<0.01). Under specific RH conditions, there were even stronger correlations between PNC2.5 and NO3-, SO42-, NH4+, while those between PNC2.5 and EC, OC were relatively weak, especially when RH exceeded 50%. Principal component analysis (PCA) and Pearsons correlation analysis indicated that secondary sources, vehicle emission and coal combustion might be major contributors to PNC2.5. Backward trajectory and potential source contribution function (PSCF) analysis suggested that the transport of air masses originated from these regions around Tianjin (Liaoning, Hebei, Shandong and Jiangsu) influenced critically PNC2.5. The north of Jiangsu, the west of Shandong, and the east of Hebei were distinguished as major potential source-areas of PNC2.5 by PSCF model.


Journal of The Air & Waste Management Association | 2011

Determination of buffering capacity of total suspended particle and its source apportionment using the chemical mass balance approach.

Xiaohui Bi; Yin-Chang Feng; Tan Zhu; Yufen Zhang; Jianhui Wu; Xiang Li

ABSTRACT The samples of total suspended particle (TSP) from sources and TSP in the ambient atmosphere were collected in 2006 at Tianjin, Peoples Republic of China and analyzed for 16 chemical elements, two water-soluble ions, total carbon, and organic carbon. On the basis of the chemical mass balance (CMB) model, the contributions of different TSP sources to the ambient TSP were identified. The results showed that resus-pended dust has the biggest contributions to the concentration of ambient TSP. The buffering capacity of each TSP source was also determined by an analytical chemistry method, and the result showed that the constructive dust (the dust emitted from construction work) had the strongest buffering capacity among the measured sources, whereas the coal combustion dust had the weakest buffering capacity. A calculation formula of the source of buffering capacity of ambient TSP was developed based on the result of TSP source apportionment and the identification of the buffering capacity of each TSP source in this study. The results of the source apportionment of the buffering capacity of ambient TSP indicated that open sources (including soil dust, resuspended dust, and constructive dust) were the dominant sources of the buffering capacity of the ambient TSP. Acid rain pollution in cities in Northern China might become serious with a decrease of open source pollution without reducing acidic sources. More efforts must be made to evaluate this potential risk, and countermeasures should be proposed as early as possible. IMPLICATIONS This study aimed to analyze the sources of buffering capacity of ambient TSP in Tianjin, Peoples Republic of China. The results of this research can provide a valuable reference for the study on acid rain in areas suffering heavy particle pollution.


Atmospheric Environment | 2007

Source apportionment of PM10 in six cities of northern China

Xiaohui Bi; Yin-Chang Feng; Jianhui Wu; Yuqiu Wang; Tan Zhu


Aerosol and Air Quality Research | 2011

Estimation of the Main Factors Influencing Haze, Based on a Long-term Monitoring Campaign in Hangzhou, China

Zhimei Xiao; Yufen Zhang; Sheng-mao Hong; Xiaohui Bi; Li Jiao; Yin-Chang Feng; Yuqiu Wang


Atmospheric Research | 2016

Chemical composition and source apportionment of ambient PM2.5 during the non-heating period in Taian, China

Baoshuang Liu; Na Song; Qili Dai; Rubo Mei; Benhui Sui; Xiaohui Bi; Yin-Chang Feng

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