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Published November 22, 2021 | Published
Journal Article Open

Dominant Contributions of Secondary Aerosols and Vehicle Emissions to Water-Soluble Inorganic Ions of PM_(2.5) in an Urban Site in the Metropolitan Hangzhou, China


Water soluble inorganic ions (WSIIs) are important components in PM_(2.5) and could strongly affect the acidity and hygroscopicity of PM_(2.5). In order to achieve the seasonal characteristics and determine the potential sources of WSIIs in PM_(2.5) in Hangzhou, online systems were used to measure hourly mass concentrations of WSIIs (SO₄²⁻, NO₃⁻, NH₄+, Cl⁻, Na+, K+, Ca²+ and Mg²+) as well as PM_(2.5), NO₂ and SO₂ at an urban site for one month each season (May, August, October, December) in 2017. Results showed that the hourly mass concentrations of PM_(2.5) during the whole campaign varied from 1 to 292 μg·m⁻³ with the mean of 56.03 μg·m⁻³. The mean mass concentration of WSIIs was 26.49 ± 20.78 μg·m⁻³, which contributed 48.28% to averaged PM2.5 mass. SNA (SO₄²⁻, NO₃⁻ and NH₄⁺) were the most abundant ions in PM_(2.5) and on average, they comprised 41.57% of PM2.5 mass. PM_(2.5), NO₂, SO₂ and WSIIs showed higher mass concentrations in December, possibly due to higher energy consumption emissions, unfavorable meteorological factors (e.g., lower wind speed and temperature) and regional transport. Results from PCA models showed that secondary aerosols and vehicle emissions were the dominant sources of WSIIs in the observations. Our findings highlight the importance of stronger controls on precursor (e.g., SO₂ and NO₂) emissions in Hangzhou, and show that industrial areas should be controlled at local and regional scales in the future.

Additional Information

© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Received: 14 October 2021 / Revised: 6 November 2021 / Accepted: 14 November 2021 / Published: 19 November 2021. (This article belongs to the Special Issue Advances in Air Quality Data Analysis and Modeling) This work was partially supported by the Department of Science and Technology of China (Nos. 2018YFC0213506, 2018YFC0213503, and 2016YFC0202702), National Research Program for Key Issues in Air Pollution Control in China (No. DQGG0107), and National Natural Science Foundation of China (Nos. 21577126 and 41561144004). Part of this work was also supported by the "Zhejiang 1000 Talent Plan" and Research Center for Air Pollution and Health in Zhejiang University. Pengfei Li is supported by National Natural Science Foundation of China (No. 22006030), Initiation Fund for Introducing Talents of Hebei Agricultural University (412201904), and Hebei Youth Top Q15 Fund (BJ2020032). Author Contributions. S.Y. designed this study and X.C. and S.Y. wrote the manuscript. C.X. and S.Y. contributed to observations and data analyses, C.X., W.L., Z.L., Y.Z., M.L., W.L., P.L. and J.H.S. contributed to the discussions. S.Y. contributed to the manuscript and supervised the research. All authors have read and agreed to the published version of the manuscript. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement. Data that support the findings of this study are available from the corresponding author upon request. The authors declare no conflict of interest.

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