Identification of long-range transport pathways and potential sources of PM2.5 and PM10 in Beijing from 2014 to 2015

Li, Deping; Liu, Jianguo; Zhang, Jiaoshi; Gui, Huaqiao; Du, Peng; Yu, Tongzhu; Wang, Jie; Lu, Yao; Liu, Wenqing; Cheng, Yin

doi:10.1016/j.jes.2016.06.035

Cited by 72 publications

(34 citation statements)

References 65 publications

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“…In order to quantify the influence of Chinese 371 emission for SMA, we calculated Chinese impact of total N (defined as a sum of N mass 372 concentrations in NOX, NOz, and nitrate). Previous studies for PM2.5 transboundary influence have 373 reported that PM2.5 is often transported via the upper layer of the atmosphere [83,84]. Considering 374 vertical changes of long-range transported total N inflow, we present the total N vertical column 375 densities (VCDs) in this study.…”

Section: Seasonal Chinese Emission Impacts On Pm 25mentioning

confidence: 99%

Long-Range Transport Influence on Key Chemical Components of PM2.5 in the Seoul Metropolitan Area, South Korea, during the Years 2012–2016

Bae

Kim

et al. 2019

Atmosphere

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This study identified the key chemical components based on an analysis of the seasonal variations of ground level PM 2.5 concentrations and its major chemical constituents (sulfate, nitrate, ammonium, organic carbon, and elemental carbon) in the Seoul Metropolitan Area (SMA), over a period of five years, ranging from 2012 to 2016. It was found that the mean PM 2.5 concentration in the SMA was 33.7 µg/m 3 , while inorganic ions accounted for 53% of the total mass concentration. The component ratio of inorganic ions increased by up to 61%-63% as the daily mean PM 2.5 concentration increased. In spring, nitrate was the dominant component of PM 2.5 , accounting for 17%-32% of the monthly mean PM 2.5 concentrations. In order to quantify the impact of long-range transport on the SMA PM 2.5 , a set of sensitivity simulations with the community multiscale air-quality model was performed. Results show that the annual averaged impact of Chinese emissions on SMA PM 2.5 concentrations ranged from 41% to 44% during the five years. Chinese emissions' impact on SMA nitrate ranged from 50% (winter) to 67% (spring). This result exhibits that reductions in SO 2 and NO X emissions are crucial to alleviate the PM 2.5 concentration. It is expected that NO X emission reduction efforts in China will help decrease PM 2.5 concentrations in the SMA.

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Section: Seasonal Chinese Emission Impacts On Pm 25mentioning

confidence: 99%

Long-Range Transport Influence on Key Chemical Components of PM2.5 in the Seoul Metropolitan Area, South Korea, during the Years 2012–2016

Bae

Kim

et al. 2019

Atmosphere

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“…Hence CWT method [8], [10], [13] was used with the aiming of producing a geographical overview of emission source areas within the study region. …”

Section: Concentration-weighted Trajectory (Cwt) Methodmentioning

confidence: 99%

Application of PSCF and CWT to Identify Potential Sources of Aerosol Optical Depth in ICIPE Mbita

Zachary¹,

Yin²,

Zacharia³

2018

OALib

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This paper evaluates the effects of long-range transport patterns of air trajectories arriving at a rural ground based station, ICIPE Mbita 1125 meters above mean sea level. Mass concentration data of fine mode AOD, coarse mode AOD and fine mode fraction AOD were combined with back-trajectory cluster analysis. The Potential Source Contribution Function (PSCF) model and Concentration-Weighted Trajectory (CWT) method were used to evaluate the transport pathways and Potential Source Areas (PSA) affecting AOD loadings in western parts of Kenya during wet (MAM) and dry (JJA) seasons. The main sources and paths of advection to source and receptor regions and its relation to AOD concentration were identified. Using these methods, the Geographic Information System (GIS) based software and MeteoInfo was used for query and computation of potential source contribution function and concentration weighted trajectory analyses when the measurement data were included. The results for both PSCF and CPF were sufficient indicators that pollutants originated from two main sources, that is, northeastern and southeast directions from the site.

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“…By analyzing the transport pathways of particulate matter over Guwahati, located in the Brahmaputra River Valley (BRV), Tiwari et al (2017) found that the turbid air masses transported over Guwahati mostly from the western and southwestern directions contribute to higher PM concentrations, either carrying anthropogenic pollution from the Indo-Gangetic Plains or locally and LRT (long-range transported) dust from BRV and western India, respectively [22]. employed the PSCF and concentration weighted trajectory (CWT) methods to analyze the transport trajectories and potential sources of PM 2.5 and PM 10 in Beijing and demonstrated that, in summer and autumn, the impacts of air pollution are mostly from the south and southeast, while those in spring and winter are influenced from the southeast and north [23]. By analyzing the transport pathways and potential sources of PM 10 pollution in Shanghai, Li et al (2014) reported that there are two potential sources of PM 10 pollution in Shanghai: one is located in the northwest (for example, Hebei and Shandong), while the other is in the southwest (for example, Zhejiang and Jiangxi) [24].…”

Section: Literature Reviewmentioning

confidence: 99%

“…The results reveal that the major potential sources of PM 2.5 in Chengdu are located along the western margin of the Sichuan Basin and in the southeastern cities [26]. Xin et al (2016) adopted daily average PM 10 concentration data and Global Data Assimilation System (GDAS) data to study the transport trajectories that significantly influence PM 10 in Xining and found that atmospheric pollution is easily affected by inland trajectories [27]. Based on nine air quality monitoring locations in Qingdao in winter, analyzed the characteristics of atmospheric pollution and pollutant sources, the results revealed that PM 2.5 is a major urban atmospheric pollutant in Qingdao, and the greatest contributions are from Shanxi, the southern part of Hebei, and the western part of Shandong [23].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Xin et al (2016) adopted daily average PM 10 concentration data and Global Data Assimilation System (GDAS) data to study the transport trajectories that significantly influence PM 10 in Xining and found that atmospheric pollution is easily affected by inland trajectories [27]. Based on nine air quality monitoring locations in Qingdao in winter, analyzed the characteristics of atmospheric pollution and pollutant sources, the results revealed that PM 2.5 is a major urban atmospheric pollutant in Qingdao, and the greatest contributions are from Shanxi, the southern part of Hebei, and the western part of Shandong [23]. Additionally, pollutants such as aerosols from the deserts in Inner Mongolia and the Yellow Sea are also a cause of atmospheric pollution.…”

Section: Literature Reviewmentioning

confidence: 99%

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Transport Pathways and Potential Source Region Contributions of PM2.5 in Weifang: Seasonal Variations

Dai

Liu

et al. 2020

Applied Sciences

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As air pollution becomes progressively more serious, accurate identification of urban air pollution characteristics and associated pollutant transport mechanisms helps to effectively control and alleviate air pollution. This paper investigates the pollution characteristics, transport pathways, and potential sources of PM2.5 in Weifang based on PM2.5 monitoring data from 2015 to 2016 using three methods: Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT), the potential source contribution function (PSCF), and concentration weighted trajectory (CWT). The results show the following: (1) Air pollution in Weifang was severe from 2015 to 2016, and the annual average PM2.5 concentration was more than twice the national air quality second-level standard (35 μg/m3). (2) Seasonal transport pathways of PM2.5 vary significantly: in winter, spring and autumn, airflow from the northwest and north directions accounts for a large proportion; in contrast, in summer, warm-humid airflows from the ocean in the southeastern direction dominate with scattered characteristics. (3) The PSCF and CWT results share generally similar characteristics in the seasonal distributions of source areas, which demonstrate the credibility and accuracy of the analysis results. (4) More attention should be paid to short-distance transport from the surrounding areas of Weifang, and a joint pollution prevention and control mechanism is critical for controlling regional pollution.

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Identification of long-range transport pathways and potential sources of PM2.5 and PM10 in Beijing from 2014 to 2015

Cited by 72 publications

References 65 publications

Long-Range Transport Influence on Key Chemical Components of PM2.5 in the Seoul Metropolitan Area, South Korea, during the Years 2012–2016

Long-Range Transport Influence on Key Chemical Components of PM2.5 in the Seoul Metropolitan Area, South Korea, during the Years 2012–2016

Application of PSCF and CWT to Identify Potential Sources of Aerosol Optical Depth in ICIPE Mbita

Transport Pathways and Potential Source Region Contributions of PM2.5 in Weifang: Seasonal Variations

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