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2008 年至 2020 年潍坊地区 SO 的时空特征。

Spatio-Temporal Characteristics of SO across Weifang from 2008 to 2020.

机构信息

Chinese Academy of Surveying and Mapping, Beijing 100830, China.

出版信息

Int J Environ Res Public Health. 2021 Nov 20;18(22):12206. doi: 10.3390/ijerph182212206.

DOI:10.3390/ijerph182212206
PMID:34831963
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8624775/
Abstract

China has achieved good results in SO pollution control, but SO pollution still exists in some areas. Analyzing the spatio-temporal distribution of SO is critical for regional SO pollution prevention and control. Compared with existing air pollution studies that paid more attention to PM., NO and O, and focused on the macro scale, this study took the small-scale Weifang city as the research area, analyzed the temporal and spatial changes in SO, discussed the migration trajectory of SO pollution and explored the impact of wind on SO pollution. The results show that the average annual concentration of SO in Weifang has exhibited a downward trend in the past 13 years, showing the basic characteristics of "highest in winter, lowest in summer and slightly higher in spring and autumn", "highest on Sunday, lowest on Thursday and gradually decreasing from Monday to Thursday" and "highest at 9 a.m., lowest at 4 p.m. and gradually increasing from midnight to 9 a.m.". SO concentration showed obvious spatial heterogeneity: higher in the north and lower in the south. In addition, Shouguang, Changyi and Gaomi were seriously polluted. The SO pollution shifted from south to northeast. The clean wind direction (southeast wind and northeast wind) of Weifang city accounted for about 41%, and the pollution wind direction (northwest wind and west wind) accounted for about 7%. Drawing from the multi-scale analysis, vegetation, precipitation, temperature, transport situation and human activity were the most relevant factors. Limited to data collection, more quantitative research is needed to gain insight into the influence mechanism in the future.

摘要

中国在 SO 污染控制方面取得了良好的成果,但在一些地区仍然存在 SO 污染。分析 SO 的时空分布对于区域 SO 污染防治至关重要。与现有的空气污染研究相比,这些研究更关注 PM、NO 和 O,侧重于宏观尺度,本研究以潍坊市为研究区域,分析了 SO 的时空变化,讨论了 SO 污染的迁移轨迹,并探讨了风对 SO 污染的影响。结果表明,潍坊市 SO 的年平均浓度在过去 13 年中呈下降趋势,呈现出“冬季最高、夏季最低、春秋略高”、“周日最高、周四最低、周一至周四逐渐下降”和“上午 9 时最高、下午 4 时最低、午夜至上午 9 时逐渐升高”的基本特征。SO 浓度表现出明显的空间异质性:北部较高,南部较低。此外,寿光、昌邑和高密污染严重。SO 污染从南部转移到东北部。潍坊市的清洁风向(东南风和东北风)约占 41%,污染风向(西北风、西风)约占 7%。从多尺度分析来看,植被、降水、温度、交通状况和人类活动是最相关的因素。受限于数据收集,未来需要进行更多的定量研究,以深入了解影响机制。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8bbe/8624775/c6e600851d31/ijerph-18-12206-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8bbe/8624775/e02d1115490a/ijerph-18-12206-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8bbe/8624775/73ab61e7f751/ijerph-18-12206-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8bbe/8624775/f437ac2698c8/ijerph-18-12206-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8bbe/8624775/78ab1aae2010/ijerph-18-12206-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8bbe/8624775/56c5c36c0108/ijerph-18-12206-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8bbe/8624775/6e60aa9c2cdc/ijerph-18-12206-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8bbe/8624775/4c7ce74af749/ijerph-18-12206-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8bbe/8624775/1f3ae77f91d3/ijerph-18-12206-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8bbe/8624775/97c061c8dfde/ijerph-18-12206-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8bbe/8624775/3dd21cf33542/ijerph-18-12206-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8bbe/8624775/c6e600851d31/ijerph-18-12206-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8bbe/8624775/e02d1115490a/ijerph-18-12206-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8bbe/8624775/73ab61e7f751/ijerph-18-12206-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8bbe/8624775/f437ac2698c8/ijerph-18-12206-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8bbe/8624775/78ab1aae2010/ijerph-18-12206-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8bbe/8624775/56c5c36c0108/ijerph-18-12206-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8bbe/8624775/6e60aa9c2cdc/ijerph-18-12206-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8bbe/8624775/4c7ce74af749/ijerph-18-12206-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8bbe/8624775/1f3ae77f91d3/ijerph-18-12206-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8bbe/8624775/97c061c8dfde/ijerph-18-12206-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8bbe/8624775/3dd21cf33542/ijerph-18-12206-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8bbe/8624775/c6e600851d31/ijerph-18-12206-g011.jpg

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Environ Int. 2021 Jun;151:106416. doi: 10.1016/j.envint.2021.106416. Epub 2021 Mar 2.
3
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Environ Health. 2021 Feb 27;20(1):23. doi: 10.1186/s12940-021-00698-y.
4
Understanding the industrial NO and SO pollutant emissions in China from sector linkage perspective.从部门关联角度理解中国工业氮氧化物和硫氧化物污染物排放。
Sci Total Environ. 2021 May 20;770:145242. doi: 10.1016/j.scitotenv.2021.145242. Epub 2021 Jan 19.
5
Spatiotemporal mapping and assessment of daily ground NO concentrations in China using high-resolution TROPOMI retrievals.利用高分辨率TROPOMI反演数据对中国地面每日一氧化氮浓度进行时空映射与评估
Environ Pollut. 2021 Jan 8;273:116456. doi: 10.1016/j.envpol.2021.116456.
6
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Sci Total Environ. 2021 Feb 20;756:143998. doi: 10.1016/j.scitotenv.2020.143998. Epub 2020 Nov 28.
7
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8
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9
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Environ Pollut. 2020 Dec;267:115639. doi: 10.1016/j.envpol.2020.115639. Epub 2020 Sep 11.
10
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Sci Total Environ. 2021 Feb 25;757:143821. doi: 10.1016/j.scitotenv.2020.143821. Epub 2020 Nov 18.