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
<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) variati...
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| Autores principales: | , , , , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Copernicus Publications
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/efdbe4c7ec5d41d3aa6911c233436c54 |
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| Sumario: | <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> |
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