Source apportionment of PM2.5 in Seoul, South Korea and Beijing, China using dispersion normalized PMF

Park, Jieun; Kim, Hyewon; Kim, Youngkwon; Heo, Ji Hoe; Kim, Sang Woo; Jeon, Kwonho; Yi, Seung‐Muk; Hopke, Philip K.

doi:10.1016/j.scitotenv.2022.155056

Cited by 76 publications

(17 citation statements)

References 98 publications

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“…The first factor reflected the group of sources having the highest impact on the ecosystem 9,10 . As can be seen from Tables 1-3, secondary sulfates and nitrates had stronger impact on the snow cover in terms of ionic composition.…”

Section: Resultsmentioning

confidence: 99%

Spatial distribution of anthropogenic tracers in the snow cover of the Southern Baikal region

Molozhnikova

Shikhovtsev

Marinaite

et al. 2022

28th International Symposium on Atmospheric and Ocean Optics: Atmospheric Physics

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This study presents the research results of chemical composition of the snow cover in the Baikal region exposed to aerotechnogenic emissions from pollution sources located in the Irkutsk region and the Republic of Buryatia. The samples of snow cover taken along the Irkutsk–Listvyanka route, in the Selenga River delta and in the territory of the Baikal State Natural Biosphere Reserve were analyzed. The features of the spatial distribution of tracers from large industrial centers in the southern Irkutsk region and the Republic of Buryatia along the prevailing air mass transfers were studied. The percentage of unfavorable meteorological situations in which emissions from regional anthropogenic pollution sources could reach the coast of Lake Baikal was determined based on the calculations of spatial distribution of anthropogenic impurities from large cities being sources of pollution (HYSPLIT). The model of spatial distribution of anthropogenic substances from the large industrial centers of the Baikal region along the prevailing air mass transfers was shown.

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Section: Resultsmentioning

confidence: 99%

Spatial distribution of anthropogenic tracers in the snow cover of the Southern Baikal region

Molozhnikova

Shikhovtsev

Marinaite

et al. 2022

28th International Symposium on Atmospheric and Ocean Optics: Atmospheric Physics

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“…To analyze the sources of PM 10 , PM 2.5 and toxic substances in collected samples, the positive matrix factorization (PMF, version 5.0) receptor model was adopted, provided by U.S. Environmental Protection Agency (EPA). , The PMF model identifies the sources with a higher precision rate and has been employed in many studies …”

Section: Methodsmentioning

confidence: 99%

“…34,35 The PMF model identifies the sources with a higher precision rate and has been employed in many studies. 36…”

Section: Positive Matrix Factorization Modelmentioning

confidence: 99%

Outdoor Health Risk of Atmospheric Particulate Matter at Night in Xi’an, Northwestern China

Ainur

Chen

Sha

et al. 2023

Environ. Sci. Technol.

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The deterioration of air quality via anthropogenic activities during the night period has been deemed a serious concern among the scientific community. Thereby, we explored the outdoor particulate matter (PM) concentration and the contributions from various sources during the day and night in winter and spring 2021 in a megacity, northwestern China. The results revealed that the changes in chemical compositions of PM and sources (motor vehicles, industrial emissions, coal combustion) at night lead to substantial PM toxicity, oxidative potential (OP), and OP/PM per unit mass, indicating high oxidative toxicity and exposure risk at nighttime. Furthermore, higher environmentally persistent free radical (EPFR) concentration and its significant correlation with OP were observed, suggesting that EPFRs cause reactive oxygen species (ROS) formation. Moreover, the noncarcinogenic and carcinogenic risks were systematically explained and spatialized to children and adults, highlighting intensified hotspots to epidemiological researchers. This better understanding of day− night-based PM formation pathways and their hazardous impact will assist to guide measures to diminish the toxicity of PM and reduce the disease led by air pollution.

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“… Abdalmogith and Harrison, 2005 ; Bie et al, 2021 ; Carslaw, 2015 ; Chow et al, 1994 ; Corbin et al, 2018 ; Ding et al, 2020 ; Duan and Tan, 2013 ; Dubey et al, 2012 ; Han et al, 2022 ; Heo et al, 2013 ; Jiang and Zhao, 2021 ; Jin et al, 2016 ; Kang et al, 2012 ; Krall et al, 2021 ; Mondal et al, 2020 ; Pandey et al, 2014 ; Park et al, 2020 ; Park et al, 2022 ; Peng et al, 2016 ; Reff et al, 2009 ; Samae et al, 2022 ; Seo et al, 2020 ; Song et al, 2006 ; Sun et al, 2018 ; Sun et al, 2019 ; Thepnuan et al, 2019 ; Watson, 2004 ; Wei et al, 2019 ; Yadav et al, 2019 ; Zong et al, 2016 ; Zong et al, 2018 .…”

Section: Uncited Referencesunclassified

Local and transboundary impacts of PM2.5 sources identified in Seoul during the early stage of the COVID-19 outbreak

Kim

Jeon

Park

et al. 2022

Atmospheric Pollution Research

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Source apportionment of PM2.5 in Seoul, South Korea and Beijing, China using dispersion normalized PMF

Cited by 76 publications

References 98 publications

Spatial distribution of anthropogenic tracers in the snow cover of the Southern Baikal region

Spatial distribution of anthropogenic tracers in the snow cover of the Southern Baikal region

Outdoor Health Risk of Atmospheric Particulate Matter at Night in Xi’an, Northwestern China

Local and transboundary impacts of PM2.5 sources identified in Seoul during the early stage of the COVID-19 outbreak

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