Comparative Analysis of PM<sub>2.5</sub> and O<sub>3</sub> Source in Beijing Using a Chemical Transport Model
For many years, Beijing has suffered from severe air pollution. At present, fine particulate matter (PM<sub>2.5</sub>) pollution in the winter and ozone (O<sub>3</sub>) pollution in the summer constitute serious environmental problems. In this study, the combination of a comp...
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MDPI AG
2021-08-01
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author | Wei Wen Song Shen Lei Liu Xin Ma Ying Wei Jikang Wang Yi Xing Wei Su |
author_facet | Wei Wen Song Shen Lei Liu Xin Ma Ying Wei Jikang Wang Yi Xing Wei Su |
author_sort | Wei Wen |
collection | DOAJ |
description | For many years, Beijing has suffered from severe air pollution. At present, fine particulate matter (PM<sub>2.5</sub>) pollution in the winter and ozone (O<sub>3</sub>) pollution in the summer constitute serious environmental problems. In this study, the combination of a comprehensive air quality model with particulate matter source apportionment technology (CAMx-PAST) and monitoring data was used for the high-spatial resolution source apportionment of secondary inorganic components (SNA: SO<sub>4</sub><sup>2</sup><sup>−</sup>, NO<sub>3</sub><sup>−</sup>, and NH<sub>4</sub><sup>+</sup>) in PM<sub>2.5</sub>; their corresponding precursor gases (SO<sub>2</sub>, NO<sub>2</sub>, and NH<sub>3</sub>); and O<sub>3</sub> in the winter and summer over Beijing. Emissions from residents, industry, traffic, agriculture, and power accounted for 54%, 25%, 14%, 5%, and 2% of PM<sub>2.5</sub> in the winter, respectively. In the summer, the emissions from industry, traffic, residents, agriculture, and power accounted for 42%, 24%, 20%, 10%, and 4% of PM<sub>2.5</sub>, respectively. The monthly transport ratio of PM<sub>2.5</sub> was 27% and 46% in the winter and summer, respectively. The regional transport of residential and industrial emissions accounted for the highest proportion of PM<sub>2.5</sub>. The regional transport of emissions had a significant effect on the SO<sub>4</sub><sup>2</sup><sup>−</sup> and NO<sub>3</sub><sup>−</sup> concentrations, whereas SO<sub>2</sub> and NO<sub>2</sub> pollution were mainly affected by local emissions, and NH<sub>4</sub><sup>+</sup> and NH<sub>3</sub> were mainly attributed to agricultural emissions. Industrial and traffic sources were two major emission sectors that contributed to O<sub>3</sub> pollution in Beijing. The monthly transport ratios of O<sub>3</sub> were 31% and 65% in the winter and summer, respectively. The high-spatial resolution regional source apportionment results showed that emissions from Langfang, Baoding, and Tangshan had the greatest impact on Beijing’s air pollution. This work’s methods and results will provide scientific guidance to support the government in its decision-making processes to manage the PM<sub>2.5</sub> and O<sub>3</sub> pollution issues. |
first_indexed | 2024-03-10T08:05:13Z |
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spelling | doaj.art-ad1afcdb246c4bcd97ef19ba2f71b8432023-11-22T11:09:13ZengMDPI AGRemote Sensing2072-42922021-08-011317345710.3390/rs13173457Comparative Analysis of PM<sub>2.5</sub> and O<sub>3</sub> Source in Beijing Using a Chemical Transport ModelWei Wen0Song Shen1Lei Liu2Xin Ma3Ying Wei4Jikang Wang5Yi Xing6Wei Su7School of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing 100083, ChinaSchool of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing 100083, ChinaState Key Laboratory of Severe Weather & Key Laboratory of Atmospheric Chemistry of CMA, Chinese Academy of Meteorological Sciences, Beijing 100081, ChinaNational Meteorological Center, Beijing 100081, ChinaInstitute of Urban Meteorology, China Meteorological Administration, Beijing 100081, ChinaNational Meteorological Center, Beijing 100081, ChinaSchool of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing 100083, ChinaSchool of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing 100083, ChinaFor many years, Beijing has suffered from severe air pollution. At present, fine particulate matter (PM<sub>2.5</sub>) pollution in the winter and ozone (O<sub>3</sub>) pollution in the summer constitute serious environmental problems. In this study, the combination of a comprehensive air quality model with particulate matter source apportionment technology (CAMx-PAST) and monitoring data was used for the high-spatial resolution source apportionment of secondary inorganic components (SNA: SO<sub>4</sub><sup>2</sup><sup>−</sup>, NO<sub>3</sub><sup>−</sup>, and NH<sub>4</sub><sup>+</sup>) in PM<sub>2.5</sub>; their corresponding precursor gases (SO<sub>2</sub>, NO<sub>2</sub>, and NH<sub>3</sub>); and O<sub>3</sub> in the winter and summer over Beijing. Emissions from residents, industry, traffic, agriculture, and power accounted for 54%, 25%, 14%, 5%, and 2% of PM<sub>2.5</sub> in the winter, respectively. In the summer, the emissions from industry, traffic, residents, agriculture, and power accounted for 42%, 24%, 20%, 10%, and 4% of PM<sub>2.5</sub>, respectively. The monthly transport ratio of PM<sub>2.5</sub> was 27% and 46% in the winter and summer, respectively. The regional transport of residential and industrial emissions accounted for the highest proportion of PM<sub>2.5</sub>. The regional transport of emissions had a significant effect on the SO<sub>4</sub><sup>2</sup><sup>−</sup> and NO<sub>3</sub><sup>−</sup> concentrations, whereas SO<sub>2</sub> and NO<sub>2</sub> pollution were mainly affected by local emissions, and NH<sub>4</sub><sup>+</sup> and NH<sub>3</sub> were mainly attributed to agricultural emissions. Industrial and traffic sources were two major emission sectors that contributed to O<sub>3</sub> pollution in Beijing. The monthly transport ratios of O<sub>3</sub> were 31% and 65% in the winter and summer, respectively. The high-spatial resolution regional source apportionment results showed that emissions from Langfang, Baoding, and Tangshan had the greatest impact on Beijing’s air pollution. This work’s methods and results will provide scientific guidance to support the government in its decision-making processes to manage the PM<sub>2.5</sub> and O<sub>3</sub> pollution issues.https://www.mdpi.com/2072-4292/13/17/3457PM<sub>2.5</sub> componentsozonesource apportionmentregional transport |
spellingShingle | Wei Wen Song Shen Lei Liu Xin Ma Ying Wei Jikang Wang Yi Xing Wei Su Comparative Analysis of PM<sub>2.5</sub> and O<sub>3</sub> Source in Beijing Using a Chemical Transport Model Remote Sensing PM<sub>2.5</sub> components ozone source apportionment regional transport |
title | Comparative Analysis of PM<sub>2.5</sub> and O<sub>3</sub> Source in Beijing Using a Chemical Transport Model |
title_full | Comparative Analysis of PM<sub>2.5</sub> and O<sub>3</sub> Source in Beijing Using a Chemical Transport Model |
title_fullStr | Comparative Analysis of PM<sub>2.5</sub> and O<sub>3</sub> Source in Beijing Using a Chemical Transport Model |
title_full_unstemmed | Comparative Analysis of PM<sub>2.5</sub> and O<sub>3</sub> Source in Beijing Using a Chemical Transport Model |
title_short | Comparative Analysis of PM<sub>2.5</sub> and O<sub>3</sub> Source in Beijing Using a Chemical Transport Model |
title_sort | comparative analysis of pm sub 2 5 sub and o sub 3 sub source in beijing using a chemical transport model |
topic | PM<sub>2.5</sub> components ozone source apportionment regional transport |
url | https://www.mdpi.com/2072-4292/13/17/3457 |
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