Ship-based MAX-DOAS measurements of tropospheric NO<sub>2</sub>, SO<sub>2</sub>, and HCHO distribution along the Yangtze River
In this paper, we present ship-based Multi-Axis Differential Optical Absorption Spectroscopy (MAX-DOAS) measurements of tropospheric trace gases' distribution along the Yangtze River during winter 2015. The measurements were performed along the Yangtze River between Shanghai and Wuhan, cover...
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Copernicus Publications
2018-04-01
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| Series: | Atmospheric Chemistry and Physics |
| Online Access: | https://www.atmos-chem-phys.net/18/5931/2018/acp-18-5931-2018.pdf |
| _version_ | 1819267145823944704 |
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| author | Q. Hong Q. Hong C. Liu C. Liu C. Liu C. Liu C. Liu K. L. Chan Q. Hu Z. Xie Z. Xie Z. Xie Z. Xie H. Liu F. Si J. Liu J. Liu |
| author_facet | Q. Hong Q. Hong C. Liu C. Liu C. Liu C. Liu C. Liu K. L. Chan Q. Hu Z. Xie Z. Xie Z. Xie Z. Xie H. Liu F. Si J. Liu J. Liu |
| author_sort | Q. Hong |
| collection | DOAJ |
| description | In this paper, we present ship-based Multi-Axis Differential Optical
Absorption Spectroscopy (MAX-DOAS) measurements of tropospheric trace gases'
distribution along the Yangtze River during winter 2015. The measurements
were performed along the Yangtze River between Shanghai and Wuhan, covering
major industrial areas in eastern China. Tropospheric vertical column
densities (VCDs) of nitrogen dioxide (NO<sub>2</sub>), sulfur dioxide (SO<sub>2</sub>),
and formaldehyde (HCHO) were retrieved using the air mass factor calculated
by the radiative transfer model. Enhanced tropospheric NO<sub>2</sub> and SO<sub>2</sub>
VCDs were detected over downwind areas of industrial zones over the Yangtze
River. In addition, spatial distributions of atmospheric pollutants are
strongly affected by meteorological conditions; i.e., positive correlations
were found between concentration of pollutants and wind speed over these
areas, indicating strong influence of transportation of pollutants from
high-emission upwind areas along the Yangtze River. Comparison of tropospheric
NO<sub>2</sub> VCDs between ship-based MAX-DOAS and Ozone Monitoring Instrument
(OMI) satellite observations shows good agreement with each other, with a
Pearson correlation coefficient (<i>R</i>) of 0.82. In this study, the
NO<sub>2</sub> ∕ SO<sub>2</sub> ratio was used to estimate the relative contributions
of industrial sources and vehicle emissions to ambient NO<sub>2</sub> levels.
Analysis results of the NO<sub>2</sub> ∕ SO<sub>2</sub> ratio show a higher
contribution of industrial NO<sub>2</sub> emissions in Jiangsu Province, while
NO<sub>2</sub> levels in Jiangxi and Hubei provinces are mainly related to vehicle
emissions. These results indicate that different pollution control strategies
should be applied in different provinces. In addition, multiple linear
regression analysis of ambient carbon monoxide (CO) and odd oxygen (O<sub><i>x</i></sub>)
indicated that the primary emission and secondary formation of HCHO
contribute 54.4 ± 3.7 % and 39.3 ± 4.3 % to the ambient
HCHO, respectively. The largest contribution from primary emissions in winter
suggested that photochemically induced secondary formation of HCHO is reduced
due to lower solar irradiance in winter. Our findings provide an improved
understanding of major pollution sources along the eastern part of the
Yangtze River which are useful for designing specific air pollution control
policies. |
| first_indexed | 2024-12-23T21:12:31Z |
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| institution | Directory Open Access Journal |
| issn | 1680-7316 1680-7324 |
| language | English |
| last_indexed | 2024-12-23T21:12:31Z |
| publishDate | 2018-04-01 |
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| series | Atmospheric Chemistry and Physics |
| spelling | doaj.art-fdf79c6b15914b978eb26c792bc84c4a2022-12-21T17:31:02ZengCopernicus PublicationsAtmospheric Chemistry and Physics1680-73161680-73242018-04-01185931595110.5194/acp-18-5931-2018Ship-based MAX-DOAS measurements of tropospheric NO<sub>2</sub>, SO<sub>2</sub>, and HCHO distribution along the Yangtze RiverQ. Hong0Q. Hong1C. Liu2C. Liu3C. Liu4C. Liu5C. Liu6K. L. Chan7Q. Hu8Z. Xie9Z. Xie10Z. Xie11Z. Xie12H. Liu13F. Si14J. Liu15J. Liu16Key Lab of Environmental Optics and Technology, Anhui Institute of Optics and Fine Mechanics, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, 230031, ChinaThese authors contributed equally to this work.Key Lab of Environmental Optics and Technology, Anhui Institute of Optics and Fine Mechanics, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, 230031, ChinaSchool of Earth and Space Sciences, University of Science and Technology of China, Hefei, 230026, ChinaCAS Center for Excellence in Regional Atmos. Environ., Institute of Urban Environment, Chinese Academy of Sciences, Xiamen, 361021, ChinaAnhui Province Key Laboratory of Polar Environment and Global Change, USTC, Hefei, 230026, ChinaThese authors contributed equally to this work.Remote Sensing Technology Institute (IMF), German Aerospace Center (DLR), Oberpfaffenhofen, GermanyKey Lab of Environmental Optics and Technology, Anhui Institute of Optics and Fine Mechanics, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, 230031, ChinaKey Lab of Environmental Optics and Technology, Anhui Institute of Optics and Fine Mechanics, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, 230031, ChinaSchool of Earth and Space Sciences, University of Science and Technology of China, Hefei, 230026, ChinaCAS Center for Excellence in Regional Atmos. Environ., Institute of Urban Environment, Chinese Academy of Sciences, Xiamen, 361021, ChinaAnhui Province Key Laboratory of Polar Environment and Global Change, USTC, Hefei, 230026, ChinaSchool of Earth and Space Sciences, University of Science and Technology of China, Hefei, 230026, ChinaKey Lab of Environmental Optics and Technology, Anhui Institute of Optics and Fine Mechanics, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, 230031, ChinaKey Lab of Environmental Optics and Technology, Anhui Institute of Optics and Fine Mechanics, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, 230031, ChinaCAS Center for Excellence in Regional Atmos. Environ., Institute of Urban Environment, Chinese Academy of Sciences, Xiamen, 361021, ChinaIn this paper, we present ship-based Multi-Axis Differential Optical Absorption Spectroscopy (MAX-DOAS) measurements of tropospheric trace gases' distribution along the Yangtze River during winter 2015. The measurements were performed along the Yangtze River between Shanghai and Wuhan, covering major industrial areas in eastern China. Tropospheric vertical column densities (VCDs) of nitrogen dioxide (NO<sub>2</sub>), sulfur dioxide (SO<sub>2</sub>), and formaldehyde (HCHO) were retrieved using the air mass factor calculated by the radiative transfer model. Enhanced tropospheric NO<sub>2</sub> and SO<sub>2</sub> VCDs were detected over downwind areas of industrial zones over the Yangtze River. In addition, spatial distributions of atmospheric pollutants are strongly affected by meteorological conditions; i.e., positive correlations were found between concentration of pollutants and wind speed over these areas, indicating strong influence of transportation of pollutants from high-emission upwind areas along the Yangtze River. Comparison of tropospheric NO<sub>2</sub> VCDs between ship-based MAX-DOAS and Ozone Monitoring Instrument (OMI) satellite observations shows good agreement with each other, with a Pearson correlation coefficient (<i>R</i>) of 0.82. In this study, the NO<sub>2</sub> ∕ SO<sub>2</sub> ratio was used to estimate the relative contributions of industrial sources and vehicle emissions to ambient NO<sub>2</sub> levels. Analysis results of the NO<sub>2</sub> ∕ SO<sub>2</sub> ratio show a higher contribution of industrial NO<sub>2</sub> emissions in Jiangsu Province, while NO<sub>2</sub> levels in Jiangxi and Hubei provinces are mainly related to vehicle emissions. These results indicate that different pollution control strategies should be applied in different provinces. In addition, multiple linear regression analysis of ambient carbon monoxide (CO) and odd oxygen (O<sub><i>x</i></sub>) indicated that the primary emission and secondary formation of HCHO contribute 54.4 ± 3.7 % and 39.3 ± 4.3 % to the ambient HCHO, respectively. The largest contribution from primary emissions in winter suggested that photochemically induced secondary formation of HCHO is reduced due to lower solar irradiance in winter. Our findings provide an improved understanding of major pollution sources along the eastern part of the Yangtze River which are useful for designing specific air pollution control policies.https://www.atmos-chem-phys.net/18/5931/2018/acp-18-5931-2018.pdf |
| spellingShingle | Q. Hong Q. Hong C. Liu C. Liu C. Liu C. Liu C. Liu K. L. Chan Q. Hu Z. Xie Z. Xie Z. Xie Z. Xie H. Liu F. Si J. Liu J. Liu Ship-based MAX-DOAS measurements of tropospheric NO<sub>2</sub>, SO<sub>2</sub>, and HCHO distribution along the Yangtze River Atmospheric Chemistry and Physics |
| title | Ship-based MAX-DOAS measurements of tropospheric NO<sub>2</sub>, SO<sub>2</sub>, and HCHO distribution along the Yangtze River |
| title_full | Ship-based MAX-DOAS measurements of tropospheric NO<sub>2</sub>, SO<sub>2</sub>, and HCHO distribution along the Yangtze River |
| title_fullStr | Ship-based MAX-DOAS measurements of tropospheric NO<sub>2</sub>, SO<sub>2</sub>, and HCHO distribution along the Yangtze River |
| title_full_unstemmed | Ship-based MAX-DOAS measurements of tropospheric NO<sub>2</sub>, SO<sub>2</sub>, and HCHO distribution along the Yangtze River |
| title_short | Ship-based MAX-DOAS measurements of tropospheric NO<sub>2</sub>, SO<sub>2</sub>, and HCHO distribution along the Yangtze River |
| title_sort | ship based max doas measurements of tropospheric no sub 2 sub so sub 2 sub and hcho distribution along the yangtze river |
| url | https://www.atmos-chem-phys.net/18/5931/2018/acp-18-5931-2018.pdf |
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