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Dive into the research topics where Yangfeng Wang is active.

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Featured researches published by Yangfeng Wang.


Atmospheric Pollution Research | 2013

Characteristics of visibility and particulate matter (PM) in an urban area of Northeast China

Hujia Zhao; Huizheng Che; Xiaoye Zhang; Yanjun Ma; Yangfeng Wang; Hong Wang; Yaqiang Wang

The visibility data from 2010 to 2012 were obtained at Shenyang in Northeast China and the relations between visibility, PM mass concentration and meteorological variables were statistically analyzed. These results demonstrate that the monthly–averaged visibility over Shenyang was higher in March and September with values of approximately 19.0±4.3 km and 17.1±4.3 km, respectively. Low visibility over Shenyang occurred in January at approximately 11.0±4.7 km. Among the meteorological variables considered, wind speed was the main meteorological factor that influenced visibility and PM mass concentrations. The relation between visibility and PM indicates that fine particles are already a main source of pollutants, the existence of which is the most important factor in the deterioration of visibility in an urban area of Northeast China. The study also shows an obvious diurnal variation and weekend effects of visibility and PM, which are mainly caused by human activities. Results of this study highlight the significant impact of fine particles on air pollution and visibility in an urban area of Northeast China.


International Journal of Environmental Research and Public Health | 2017

The Relationship of PM Variation with Visibility and Mixing-Layer Height under Hazy/Foggy Conditions in the Multi-Cities of Northeast China

Hujia Zhao; Huizheng Che; Yanjun Ma; Yangfeng Wang; Hongbin Yang; Yuche Liu; Yaqiang Wang; Hong Wang; Xiaoye Zhang

The variations of visibility, PM-mass concentration and mixing-layer height (MLH) in four major urban/industry regions (Shenyang, Anshan, Benxi and Fushun) of central Liaoning in Northeast China are evaluated from 2009 to 2012 to characterize their dynamic effect on air pollution. The annual mean visibilities are about 13.7 ± 7.8, 13.5 ± 6.5, 12.8 ± 6.1 and 11.5 ± 6.8 km in Shenyang, Anshan, Benxi and Fushun, respectively. The pollution load (PM × MLH) shows a weaker vertical diffusion in Anshan, with a higher PM concentration near the surface. High concentrations of fine-mode particles may be partially attributed to the biomass-burning emissions from September in Liaoning Province and surrounding regions in Northeast China as well as the coal burning during the heating period with lower MLH in winter. The visibility on non-hazy fog days is about 2.5–3.0 times higher than that on hazy and foggy days. The fine-particle concentrations of PM2.5 and PM1.0 on hazy and foggy days are ~1.8–1.9 times and ~1.5 times higher than those on non-hazy foggy days. The MLH declined more severely during fog pollution than in haze pollution. The results of this study can provide useful information to better recognize the effects of vertical pollutant diffusion on air quality in the multi-cities of central Liaoning Province in Northeast China.


Journal of meteorological research | 2018

Characteristics of Boundary Layer Structure during a Persistent Haze Event in the Central Liaoning City Cluster, Northeast China

Xiaolan Li; Yangfeng Wang; Lidu Shen; Hongsheng Zhang; Hujia Zhao; Yunhai Zhang; Yanjun Ma

The characteristics of boundary layer structure during a persistent regional haze event over the central Liaoning city cluster of Northeast China from 16 to 21 December 2016 were investigated based on the measurements of particulate matter (PM) concentration and the meteorological data within the atmospheric boundary layer (ABL). During the observational period, the maximum hourly mean PM2.5 and PM10 concentrations in Shenyang, Anshan, Fushun, and Benxi ranged from 276 to 355 μg m–3 and from 378 to 442 μg m–3, respectively, and the lowest hourly mean atmospheric visibility (VIS) in different cities ranged from 0.14 to 0.64 km. The central Liaoning city cluster was located in the front of a slowly moving high pressure and was mainly controlled by southerly winds. Wind speed (WS) within the ABL (< 2 km) decreased significantly and WS at 10-m height mostly remained below 2 m s–1 during the hazy episodes, which was favorable for the accumulation of air pollutants. A potential temperature inversion layer existed throughout the entire ABL during the earlier hazy episode [from 0500 Local Time (LT) 18 December to 1100 LT 19 December], and then a potential temperature inversion layer developed with the bottom gradually decreased from 900 m to 300 m. Such a stable atmospheric stratification further weakened pollutant dispersion. The atmospheric boundary layer height (ABLH) estimated based on potential temperature profiles was mostly lower than 400 m and varied oppositely with PM2.5 in Shenyang. In summary, weak winds due to calm synoptic conditions, strong thermal inversion layer, and shallow atmospheric boundary layer contributed to the formation and development of this haze event. The backward trajectory analysis revealed the sources of air masses and explained the different characteristics of the haze episodes in the four cities.


Journal of meteorological research | 2018

Comparison of Two Air Pollution Episodes over Northeast China in Winter 2016/17 Using Ground-Based Lidar

Yanjun Ma; Hujia Zhao; Yunsheng Dong; Huizheng Che; Xiaoxiao Li; Ye Hong; Xiaolan Li; Hongbin Yang; Yuche Liu; Yangfeng Wang; Ningwei Liu; Cuiyan Sun

This study analyzes and compares aerosol properties and meteorological conditions during two air pollution episodes in 19–22 (E1) and 25–26 (E2) December 2016 in Northeast China. The visibility, particulate matter (PM) mass concentration, and surface meteorological observations were examined, together with the planetary boundary layer (PBL) properties and vertical profiles of aerosol extinction coefficient and volume depolarization ratio that were measured by a ground-based lidar in Shenyang of Liaoning Province, China during December 2016–January 2017. Results suggest that the low PBL height led to poor pollution dilution in E1, while the high PBL accompanied by low visibility in E2 might have been due to cross-regional and vertical air transmission. The PM mass concentration decreased as the PBL height increased in E1 while these two variables were positively correlated in E2. The enhanced winds in E2 diffused the pollutants and contributed largely to the aerosol transport. Strong temperature inversion in E1 resulted in increased PM2.5 and PM10 concentrations, and the winds in E2 favoured the southwesterly transport of aerosols from the North China Plain into the region surrounding Shenyang. The large extinction coefficient was partially attributed to the local pollution under the low PBL with high ground-surface PM mass concentrations in E1, whereas the cross-regional transport of aerosols within a high PBL and the low PM mass concentration near the ground in E2 were associated with severe aerosol extinction at high altitudes. These results may facilitate better understanding of the vertical distribution of aerosol properties during winter pollution events in Northeast China.


IOP Conference Series: Earth and Environmental Science | 2017

In situ measurement of atmospheric particles mass concentration in Anshan

Yangfeng Wang; Yanjun Ma; Zhongyan Lu; Hongxu Ma; Yunhai Zhang; Ningwei Liu; Ye Hong; Xiaolan Li

The changes of the atmospheric particles mass concentration, their pollution condition, and their relationships to visibility have been studied by using the continuous concentration data of monitoring instrument GRIMM180 from Anshan air component monitoring station in 2014. The results show that the mean mass concentrations about PM10 and PM2.5 are respectively 0.077 mg/m3 and 0.049 mg/m3, and their daily average concentration has a large variation range. The daily variation of the mass concentration is characterized by a pronounced double-peak pattern, with peak concentration during 7:00~9:00 AM and 21:00~22:00 PM, and low during 3:00~5:00 and 14:00~16:00. The ratio above air quality standard about PM10 daily average concentration is 9.07%, and the atmospheric particles exist mainly in the form of fine particles. The atmospheric particles mass concentration and the visibility show negative correlation, and the finer the particles are, the more they affect visibility.


Atmospheric Environment | 2013

Aerosol optical properties over urban and industrial region of Northeast China by using ground-based sun-photometer measurement

Hujia Zhao; Huizheng Che; Xiaoye Zhang; Yanjun Ma; Yangfeng Wang; Xuxin Wang; Chuang Liu; Bo Hou; Haochi Che


Atmospheric Research | 2015

Temporal variability of the visibility, particulate matter mass concentration and aerosol optical properties over an urban site in Northeast China

Hujia Zhao; Huizheng Che; Yanjun Ma; Xiangao Xia; Yangfeng Wang; Peng Wang; Xianchu Wu


Atmospheric Research | 2017

Temporal and spatial analyses of particulate matter (PM10 and PM2.5) and its relationship with meteorological parameters over an urban city in northeast China

Xiaolan Li; Yanjun Ma; Yangfeng Wang; Ningwei Liu; Ye Hong


Journal of Atmospheric and Solar-Terrestrial Physics | 2015

Application of aerosol optical properties to estimate aerosol type from ground-based remote sensing observation at urban area of northeastern China

Huizheng Che; Hujia Zhao; Yunfei Wu; Xiangao Xia; Jun Zhu; Oleg Dubovik; V. Estellés; Yanjun Ma; Yangfeng Wang; Hong Wang; Yaqiang Wang; Xiaoye Zhang; Guangyu Shi


Aerosol and Air Quality Research | 2015

Fine Mode Aerosol Optical Properties Related to Cloud and Fog Processing over a Cluster of Cities in Northeast China

Huizheng Che; Hujia Zhao; Xiangao Xia; Yunfei Wu; Jun Zhu; Yanjun Ma; Yangfeng Wang; Hong Wang; Yaqiang Wang; Xiaoye Zhang; Guangyu Shi

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Yanjun Ma

China Meteorological Administration

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Hujia Zhao

China Meteorological Administration

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Huizheng Che

China Meteorological Administration

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Xiaoye Zhang

China Meteorological Administration

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Ye Hong

China Meteorological Administration

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Xiaolan Li

China Meteorological Administration

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Yaqiang Wang

China Meteorological Administration

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Hong Wang

Nanjing University of Information Science and Technology

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Ningwei Liu

China Meteorological Administration

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Hongbin Yang

China Meteorological Administration

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