Measurement report: Spatiotemporal and policy-related variations of PM2.5 composition and sources during 2015–2019 at multiple sites in a Chinese megacity

A thorough understanding of the relationship between urbanization and PM2.5 (fine particulate matter with aerodynamic diameter less than 2.5 µm) variati...

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Autores principales:	X. Feng, Y. Tian, Q. Xue, D. Song, F. Huang, Y. Feng
Formato:	article
Lenguaje:	EN
Publicado:	Copernicus Publications 2021
Materias:	Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/efdbe4c7ec5d41d3aa6911c233436c54
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id	oai:doaj.org-article:efdbe4c7ec5d41d3aa6911c233436c54
record_format	dspace
institution	DOAJ
collection	DOAJ
language	EN
topic	Physics QC1-999 Chemistry QD1-999
spellingShingle	Physics QC1-999 Chemistry QD1-999 X. Feng Y. Tian Y. Tian Q. Xue D. Song F. Huang Y. Feng Measurement report: Spatiotemporal and policy-related variations of PM<sub>2.5</sub> composition and sources during 2015–2019 at multiple sites in a Chinese megacity
description	<p>A thorough understanding of the relationship between urbanization and PM<span class="inline-formula"><sub>2.5</sub></span> (fine particulate matter with aerodynamic diameter less than 2.5 <span class="inline-formula">µ</span>m) variation is crucial for researchers and policymakers to study health effects and improve air quality. In this study, we selected a rapidly developing Chinese megacity, Chengdu, as the study area to investigate the spatiotemporal and policy-related variations of PM<span class="inline-formula"><sub>2.5</sub></span> composition and sources based on long-term observation at multiple sites. A total of 836 samples were collected from 19 sites in winter 2015–2019. According to the specific characteristics, 19 sampling sites were assigned to three layers. Layer 1 was the most urbanized area and referred to the core zone of Chengdu, layer 2 was located in the outer circle of layer 1, and layer 3 belonged to the outermost zone with the lowest urbanization level. The average PM<span class="inline-formula"><sub>2.5</sub></span> concentrations for 5 years were in the order of layer 2 (133 <span class="inline-formula">µ</span>g m<span class="inline-formula"><sup>−3</sup></span>) > layer 1 (126 <span class="inline-formula">µ</span>g m<span class="inline-formula"><sup>−3</sup>)</span> > layer 3 (121 <span class="inline-formula">µ</span>g m<span class="inline-formula"><sup>−3</sup>)</span>. Spatial clustering of the chemical composition at the sampling sites was conducted for each year. The PM<span class="inline-formula"><sub>2.5</sub></span> composition of layer 3 in 2019 was found to be similar to that of the other layers 2 or 3 years ago, implying that urbanization levels had a strong effect on air quality. During the sampling period, a decreasing trend was observed for the annual average concentration of PM<span class="inline-formula"><sub>2.5</sub></span>, especially at sampling sites in layer 1, where the stricter control policies were implemented. The <span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M15" display="inline" overflow="scroll" dspmath="mathml"><mrow class="chem"><msubsup><mi mathvariant="normal">SO</mi><mn mathvariant="normal">4</mn><mrow><mn mathvariant="normal">2</mn><mo>-</mo></mrow></msubsup><mo>/</mo><msubsup><mi mathvariant="normal">NO</mi><mn mathvariant="normal">3</mn><mo>-</mo></msubsup></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="58pt" height="17pt" class="svg-formula" dspmath="mathimg" md5hash="ac2ebf42081ded3b3d88cc0ffc1f61fc"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="acp-21-16219-2021-ie00001.svg" width="58pt" height="17pt" src="acp-21-16219-2021-ie00001.png"/></svg:svg></span></span> mass ratio at most sites exceeded 1 in 2015 but dropped to less than 1 since 2016, reflecting decreasing coal combustion and increasing traffic impacts in Chengdu, and these values can be further supported by temporal variations of the SO<span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M16" display="inline" overflow="scroll" dspmath="mathml"><mrow><msubsup><mi/><mn mathvariant="normal">4</mn><mrow><mn mathvariant="normal">2</mn><mo>-</mo></mrow></msubsup></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="13pt" height="17pt" class="svg-formula" dspmath="mathimg" md5hash="7f75eaded4497f2452ab2c17fc6da474"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="acp-21-16219-2021-ie00002.svg" width="13pt" height="17pt" src="acp-21-16219-2021-ie00002.png"/></svg:svg></span></span> and NO<span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M17" display="inline" overflow="scroll" dspmath="mathml"><mrow><msubsup><mi/><mn mathvariant="normal">3</mn><mo>-</mo></msubsup></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="9pt" height="16pt" class="svg-formula" dspmath="mathimg" md5hash="5a2143864edd3f7cf8f1639018917994"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="acp-21-16219-2021-ie00003.svg" width="9pt" height="16pt" src="acp-21-16219-2021-ie00003.png"/></svg:svg></span></span> concentrations. The positive matrix factorization (PMF) model was applied to quantify PM<span class="inline-formula"><sub>2.5</sub></span> sources. A total of five sources were identified, with average contributions of 15.5 % (traffic emissions), 19.7 % (coal and biomass combustion), 8.8 % (industrial emissions), 39.7 % (secondary particles), and 16.2 % (resuspended dust). From 2015 to 2019, a dramatic decline was observed in the average percentage contributions of coal and biomass combustion, but the traffic emission source showed an increasing trend. For spatial variations, the high coefficient of variation (CV) values of coal and biomass combustion and industrial emissions indicated their higher spatial difference in Chengdu. High contributions of resuspended dust occurred at sites with intensive construction activities, such as subway and airport construction. Combining the PMF results, we developed the source-weighted potential source contribution function (SWPSCF) method for source localization. This new method highlighted the influences of spatial distribution for source contributions, and the effectiveness of the SWPSCF method was evaluated.</p>
format	article
author	X. Feng Y. Tian Y. Tian Q. Xue D. Song F. Huang Y. Feng
author_facet	X. Feng Y. Tian Y. Tian Q. Xue D. Song F. Huang Y. Feng
author_sort	X. Feng
title	Measurement report: Spatiotemporal and policy-related variations of PM<sub>2.5</sub> composition and sources during 2015–2019 at multiple sites in a Chinese megacity
title_short	Measurement report: Spatiotemporal and policy-related variations of PM<sub>2.5</sub> composition and sources during 2015–2019 at multiple sites in a Chinese megacity
title_full	Measurement report: Spatiotemporal and policy-related variations of PM<sub>2.5</sub> composition and sources during 2015–2019 at multiple sites in a Chinese megacity
title_fullStr	Measurement report: Spatiotemporal and policy-related variations of PM<sub>2.5</sub> composition and sources during 2015–2019 at multiple sites in a Chinese megacity
title_full_unstemmed	Measurement report: Spatiotemporal and policy-related variations of PM<sub>2.5</sub> composition and sources during 2015–2019 at multiple sites in a Chinese megacity
title_sort	measurement report: spatiotemporal and policy-related variations of pm<sub>2.5</sub> composition and sources during 2015–2019 at multiple sites in a chinese megacity
publisher	Copernicus Publications
publishDate	2021
url	https://doaj.org/article/efdbe4c7ec5d41d3aa6911c233436c54
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spelling	oai:doaj.org-article:efdbe4c7ec5d41d3aa6911c233436c542021-11-04T11:11:19ZMeasurement report: Spatiotemporal and policy-related variations of PM<sub>2.5</sub> composition and sources during 2015–2019 at multiple sites in a Chinese megacity10.5194/acp-21-16219-20211680-73161680-7324https://doaj.org/article/efdbe4c7ec5d41d3aa6911c233436c542021-11-01T00:00:00Zhttps://acp.copernicus.org/articles/21/16219/2021/acp-21-16219-2021.pdfhttps://doaj.org/toc/1680-7316https://doaj.org/toc/1680-7324<p>A thorough understanding of the relationship between urbanization and PM<span class="inline-formula"><sub>2.5</sub></span> (fine particulate matter with aerodynamic diameter less than 2.5 <span class="inline-formula">µ</span>m) variation is crucial for researchers and policymakers to study health effects and improve air quality. In this study, we selected a rapidly developing Chinese megacity, Chengdu, as the study area to investigate the spatiotemporal and policy-related variations of PM<span class="inline-formula"><sub>2.5</sub></span> composition and sources based on long-term observation at multiple sites. A total of 836 samples were collected from 19 sites in winter 2015–2019. According to the specific characteristics, 19 sampling sites were assigned to three layers. Layer 1 was the most urbanized area and referred to the core zone of Chengdu, layer 2 was located in the outer circle of layer 1, and layer 3 belonged to the outermost zone with the lowest urbanization level. The average PM<span class="inline-formula"><sub>2.5</sub></span> concentrations for 5 years were in the order of layer 2 (133 <span class="inline-formula">µ</span>g m<span class="inline-formula"><sup>−3</sup></span>) > layer 1 (126 <span class="inline-formula">µ</span>g m<span class="inline-formula"><sup>−3</sup>)</span> > layer 3 (121 <span class="inline-formula">µ</span>g m<span class="inline-formula"><sup>−3</sup>)</span>. Spatial clustering of the chemical composition at the sampling sites was conducted for each year. The PM<span class="inline-formula"><sub>2.5</sub></span> composition of layer 3 in 2019 was found to be similar to that of the other layers 2 or 3 years ago, implying that urbanization levels had a strong effect on air quality. During the sampling period, a decreasing trend was observed for the annual average concentration of PM<span class="inline-formula"><sub>2.5</sub></span>, especially at sampling sites in layer 1, where the stricter control policies were implemented. The <span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M15" display="inline" overflow="scroll" dspmath="mathml"><mrow class="chem"><msubsup><mi mathvariant="normal">SO</mi><mn mathvariant="normal">4</mn><mrow><mn mathvariant="normal">2</mn><mo>-</mo></mrow></msubsup><mo>/</mo><msubsup><mi mathvariant="normal">NO</mi><mn mathvariant="normal">3</mn><mo>-</mo></msubsup></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="58pt" height="17pt" class="svg-formula" dspmath="mathimg" md5hash="ac2ebf42081ded3b3d88cc0ffc1f61fc"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="acp-21-16219-2021-ie00001.svg" width="58pt" height="17pt" src="acp-21-16219-2021-ie00001.png"/></svg:svg></span></span> mass ratio at most sites exceeded 1 in 2015 but dropped to less than 1 since 2016, reflecting decreasing coal combustion and increasing traffic impacts in Chengdu, and these values can be further supported by temporal variations of the SO<span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M16" display="inline" overflow="scroll" dspmath="mathml"><mrow><msubsup><mi/><mn mathvariant="normal">4</mn><mrow><mn mathvariant="normal">2</mn><mo>-</mo></mrow></msubsup></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="13pt" height="17pt" class="svg-formula" dspmath="mathimg" md5hash="7f75eaded4497f2452ab2c17fc6da474"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="acp-21-16219-2021-ie00002.svg" width="13pt" height="17pt" src="acp-21-16219-2021-ie00002.png"/></svg:svg></span></span> and NO<span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M17" display="inline" overflow="scroll" dspmath="mathml"><mrow><msubsup><mi/><mn mathvariant="normal">3</mn><mo>-</mo></msubsup></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="9pt" height="16pt" class="svg-formula" dspmath="mathimg" md5hash="5a2143864edd3f7cf8f1639018917994"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="acp-21-16219-2021-ie00003.svg" width="9pt" height="16pt" src="acp-21-16219-2021-ie00003.png"/></svg:svg></span></span> concentrations. The positive matrix factorization (PMF) model was applied to quantify PM<span class="inline-formula"><sub>2.5</sub></span> sources. A total of five sources were identified, with average contributions of 15.5 % (traffic emissions), 19.7 % (coal and biomass combustion), 8.8 % (industrial emissions), 39.7 % (secondary particles), and 16.2 % (resuspended dust). From 2015 to 2019, a dramatic decline was observed in the average percentage contributions of coal and biomass combustion, but the traffic emission source showed an increasing trend. For spatial variations, the high coefficient of variation (CV) values of coal and biomass combustion and industrial emissions indicated their higher spatial difference in Chengdu. High contributions of resuspended dust occurred at sites with intensive construction activities, such as subway and airport construction. Combining the PMF results, we developed the source-weighted potential source contribution function (SWPSCF) method for source localization. This new method highlighted the influences of spatial distribution for source contributions, and the effectiveness of the SWPSCF method was evaluated.</p>X. FengY. TianY. TianQ. XueD. SongF. HuangY. FengCopernicus PublicationsarticlePhysicsQC1-999ChemistryQD1-999ENAtmospheric Chemistry and Physics, Vol 21, Pp 16219-16235 (2021)

Measurement report: Spatiotemporal and policy-related variations of PM<sub>2.5</sub> composition and sources during 2015–2019 at multiple sites in a Chinese megacity

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