Article(id=1314917655518208207, tenantId=1146029695717560320, journalId=1234093305789726721, issueId=1314917567911788907, articleNumber=null, orderNo=null, doi=null, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1739203200000, receivedDateStr=2025-02-11, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1791430399063, onlineDateStr=2026-10-08, pubDate=1758297600000, pubDateStr=2025-09-20, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1791430399063, onlineIssueDateStr=2026-10-08, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1791430399063, creator=13701087609, updateTime=1791430399063, updator=13701087609, issue=Issue{id=1314917567911788907, tenantId=1146029695717560320, journalId=1234093305789726721, year='2025', volume='45', issue='9', pageStart='4737', pageEnd='5328', issueExtLink='null', onlineDate='null', pubDate='1758297600000', pubDateStr='2025-09-20', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1791430378177, creator='13701087609', updateTime=1791431890195, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1314923910001418489, tenantId=1146029695717560320, journalId=1234093305789726721, issueId=1314917567911788907, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1314923910001418490, tenantId=1146029695717560320, journalId=1234093305789726721, issueId=1314917567911788907, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=4796, endPage=4806, ext={EN=ArticleExt(id=1314917655962804434, articleId=1314917655518208207, tenantId=1146029695717560320, journalId=1234093305789726721, language=EN, title=Study on vertical distribution characteristics and sources of atmospheric pollutants in the Daya Bay area based on rotor UAV observations, columnId=1234106386020365051, journalTitle=China Environmental Science, columnName=Air Pollution Control, runingTitle=null, highlight=null, articleAbstract=

This study utilized a rotor UAV equipped with multi-pollutant sensors to conduct vertical observations of multiple parameters in the Daya Bay District of Huizhou City, Guangdong Province, from October 12 to 18, 2023, during the morning (9:00), afternoon (15:00), and evening (21:00) each day. The results showed that temperature generally decreased with increasing altitude, with temperature inversion observed at 350 meters during certain periods. Relative humidity exhibited an overall increasing trend with height. NO2 concentrations typically increased with altitude, with significant peaks around 350 meters at specific times. SO2 concentrations exhibited a general decreasing trend, but significant elevations were also observed around 350meters at certain times, suggesting potential NO2 and SO2 pollution transport processes at this altitude. CO concentrations were relatively high at ground level and decreased rapidly with altitude, showing minimal variation above 200meters, indicating that high-altitude CO concentrations were primarily influenced by near-surface emissions and diffusion, with no significant pollution transport processes. PM10 and PM2.5 concentrations were generally low, with slight variations across altitudes. They tended to be higher in the morning and lower in the afternoon and evening, likely due to residual layer accumulation or overnight particle emissions. Backward trajectory analysis revealed that air masses at 0, 300, and 500meters all passed over the Daya Bay Petrochemical Industrial Park. Air masses at 300meters and 500meters originated from northeastern counties and cities of Guangdong Province from October 12 to 14, and from coastal areas of Guangdong and Fujian Provinces from October 16 to 18, indicating that emissions from the Daya Bay Petrochemical Industrial Park and upwind regions contributed to high-altitude pollutant levels in the Daya Bay area. WRF-CAMx model simulations further identified high-concentration zones of NO2 and SO2 upwind of Daya Bay, confirming the existence of high-altitude pollutant transport pathways. The vertical profile characteristics obtained from UAV observations provide valuable insights into the transport patterns of atmospheric pollutants at high altitudes, offering support for regional air quality impact studies.

, authors=Zheng-yang LIU1, 2, Yang LI1, Mei HE2, Yan-qin REN1, Wei TANG1, Rui GAO1, Zhen-hai WU1, Dan-dan ZHANG1, Fang-yun LONG1, Lin ZHANG3, Xiong-feng ZHENG4, Yu-lin HUANG5, Fang BI1, *, authorsList=Zheng-yang LIU, Yang LI, Mei HE, Yan-qin REN, Wei TANG, Rui GAO, Zhen-hai WU, Dan-dan ZHANG, Fang-yun LONG, Lin ZHANG, Xiong-feng ZHENG, Yu-lin HUANG, Fang BI, authorCompany=null, correspAuthors=Fang BI, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1314917656369651924, articleId=1314917655518208207, tenantId=1146029695717560320, journalId=1234093305789726721, language=CN, title=基于旋翼无人机观测的大亚湾区大气污染物垂直分布特征及其来源研究, columnId=1234106388364981004, journalTitle=中国环境科学, columnName=大气污染与控制, runingTitle=null, highlight=null, articleAbstract=

本研究利用搭载多污染物传感器的旋翼无人机,于2023年10月12~18日每日上午(9:00)、下午(15:00)、晚上(21:00)在广东省惠州市大亚湾区开展多项参数垂直观测.观测结果显示,随着高度的上升,温度总体呈现逐渐下降趋势,部分时段在 350m 高度出现逆温现象;相对湿度总体呈现上升趋势;NO2浓度总体呈现逐渐上升趋势,部分时段在350m左右高度有较为显著的浓度抬升现象;SO2浓度总体呈现逐渐下降趋势,但是部分时段在350m左右高度也有显著浓度抬升现象.这表明350m高度可能发生了NO2和SO2污染传输过程.CO浓度在地面相对较高,并随高度上升而迅速降低,200m高度以上浓度差异很小,这表明高空CO浓度主要受近地面排放扩散影响,没有明显的污染传输过程.PM10和PM2.5浓度较低,在各高度的差异较小,且总体呈现“上午高、下午晚上低”的现象,这可能是夜间残留层累积或排放的颗粒物对第2d上午产生了影响.利用后向轨迹模拟分析气团来源,发现0m、300m和500m高度的气团均经过大亚湾石化园区,而且300m和500m高度气团在12~14日来源于广东省东北部县市,在16~18日则来源于广东及福建沿海区域,因此大亚湾区石化园区排放和上风向县市均对大亚湾区高空污染物存在一定贡献.利用WRF~CAMx模型模拟分析区域高空NO2和SO2分布,发现大亚湾区上风向高空存在污染物浓度高值区,表明该区域可能存在高空污染物传输带.通过无人机观测得到的垂直廓线特征,有助于理解大气污染物在高空的传输规律,为区域空气质量影响研究提供了支撑.

, authors=刘正阳1, 2, 李洋1, 贺美2, 任艳芹1, 唐伟1, 高锐1, 毋振海1, 张丹丹1, 龙芳云1, 张林3, 郑雄枫4, 黄雨林5, 毕方1, *, authorsList=刘正阳, 李洋, 贺美, 任艳芹, 唐伟, 高锐, 毋振海, 张丹丹, 龙芳云, 张林, 郑雄枫, 黄雨林, 毕方, authorCompany=null, correspAuthors=毕方, authorNote=

刘正阳(1999-),男,河南郑州人,中国环境科学研究院环境工程硕士研究生,主要从事大气污染控制研究.发表论文2篇..

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刘正阳(1999-),男,河南郑州人,中国环境科学研究院环境工程硕士研究生,主要从事大气污染控制研究.发表论文2篇..

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刘正阳(1999-),男,河南郑州人,中国环境科学研究院环境工程硕士研究生,主要从事大气污染控制研究.发表论文2篇..

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基于旋翼无人机观测的大亚湾区大气污染物垂直分布特征及其来源研究
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刘正阳 1, 2 , 李洋 1 , 贺美 2 , 任艳芹 1 , 唐伟 1 , 高锐 1 , 毋振海 1 , 张丹丹 1 , 龙芳云 1 , 张林 3 , 郑雄枫 4 , 黄雨林 5 , 毕方 1, *
中国环境科学 | 大气污染与控制 2025,45(9): 4796-4806
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中国环境科学 |大气污染与控制 2025 , 45 (9) : 4796 -4806
基于旋翼无人机观测的大亚湾区大气污染物垂直分布特征及其来源研究
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刘正阳(1999-),男,河南郑州人,中国环境科学研究院环境工程硕士研究生,主要从事大气污染控制研究.发表论文2篇..

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刘正阳(1999-),男,河南郑州人,中国环境科学研究院环境工程硕士研究生,主要从事大气污染控制研究.发表论文2篇..

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刘正阳1, 2 , 李洋1, 贺美2, 任艳芹1, 唐伟1, 高锐1, 毋振海1, 张丹丹1, 龙芳云1, 张林3, 郑雄枫4, 黄雨林5, 毕方1, *
作者信息
  • 1.中国环境科学研究院,环境基准与风险评估国家重点实验室,北京 100012
  • 2.长江大学资源与环境学院,油气地球化学与环境湖北省重点实验室,湖北 武汉 430100
  • 3.惠州市生态环境局大亚湾经济技术开发区分局,惠州市大亚湾生态环境事务中心,广东 惠州 516008
  • 4.惠州市生态环境局大亚湾经济技术开发区分局,惠州市大亚湾环境科学中心,广东 惠州 516083
  • 5.广州鋆达科技有限公司,广东 广州 511400
通讯作者:
* 责任作者,工程师,
作者简介:

刘正阳(1999-),男,河南郑州人,中国环境科学研究院环境工程硕士研究生,主要从事大气污染控制研究.发表论文2篇..

Study on vertical distribution characteristics and sources of atmospheric pollutants in the Daya Bay area based on rotor UAV observations
Zheng-yang LIU1, 2 , Yang LI1, Mei HE2, Yan-qin REN1, Wei TANG1, Rui GAO1, Zhen-hai WU1, Dan-dan ZHANG1, Fang-yun LONG1, Lin ZHANG3, Xiong-feng ZHENG4, Yu-lin HUANG5, Fang BI1, *
Affiliations
  • 1.State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
  • 2.Hubei Key Laboratory of Petroleum Geochemistry and Environment, College of Resources and Environment, Yangtze University, Wuhan 430100, China
  • 3.Daya Bay Ecological Environment Affairs Center, Daya Bay Economic and Technological Development Zone Branch, Huizhou Ecological Environment Bureau, Huizhou 516008, China
  • 4.Daya Bay Environmental Science Center, Daya Bay Economic and Technological Development Zone Branch, Huizhou Ecological Environment Bureau, Huizhou 516083, China
  • 5.Guangzhou Yunda Technology Co., Ltd., Guangzhou 511400, China
出版时间: 2025-09-20
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本研究利用搭载多污染物传感器的旋翼无人机,于2023年10月12~18日每日上午(9:00)、下午(15:00)、晚上(21:00)在广东省惠州市大亚湾区开展多项参数垂直观测.观测结果显示,随着高度的上升,温度总体呈现逐渐下降趋势,部分时段在 350m 高度出现逆温现象;相对湿度总体呈现上升趋势;NO2浓度总体呈现逐渐上升趋势,部分时段在350m左右高度有较为显著的浓度抬升现象;SO2浓度总体呈现逐渐下降趋势,但是部分时段在350m左右高度也有显著浓度抬升现象.这表明350m高度可能发生了NO2和SO2污染传输过程.CO浓度在地面相对较高,并随高度上升而迅速降低,200m高度以上浓度差异很小,这表明高空CO浓度主要受近地面排放扩散影响,没有明显的污染传输过程.PM10和PM2.5浓度较低,在各高度的差异较小,且总体呈现“上午高、下午晚上低”的现象,这可能是夜间残留层累积或排放的颗粒物对第2d上午产生了影响.利用后向轨迹模拟分析气团来源,发现0m、300m和500m高度的气团均经过大亚湾石化园区,而且300m和500m高度气团在12~14日来源于广东省东北部县市,在16~18日则来源于广东及福建沿海区域,因此大亚湾区石化园区排放和上风向县市均对大亚湾区高空污染物存在一定贡献.利用WRF~CAMx模型模拟分析区域高空NO2和SO2分布,发现大亚湾区上风向高空存在污染物浓度高值区,表明该区域可能存在高空污染物传输带.通过无人机观测得到的垂直廓线特征,有助于理解大气污染物在高空的传输规律,为区域空气质量影响研究提供了支撑.

无人机观测  /  垂直分布特征  /  大气污染物  /  来源分析

This study utilized a rotor UAV equipped with multi-pollutant sensors to conduct vertical observations of multiple parameters in the Daya Bay District of Huizhou City, Guangdong Province, from October 12 to 18, 2023, during the morning (9:00), afternoon (15:00), and evening (21:00) each day. The results showed that temperature generally decreased with increasing altitude, with temperature inversion observed at 350 meters during certain periods. Relative humidity exhibited an overall increasing trend with height. NO2 concentrations typically increased with altitude, with significant peaks around 350 meters at specific times. SO2 concentrations exhibited a general decreasing trend, but significant elevations were also observed around 350meters at certain times, suggesting potential NO2 and SO2 pollution transport processes at this altitude. CO concentrations were relatively high at ground level and decreased rapidly with altitude, showing minimal variation above 200meters, indicating that high-altitude CO concentrations were primarily influenced by near-surface emissions and diffusion, with no significant pollution transport processes. PM10 and PM2.5 concentrations were generally low, with slight variations across altitudes. They tended to be higher in the morning and lower in the afternoon and evening, likely due to residual layer accumulation or overnight particle emissions. Backward trajectory analysis revealed that air masses at 0, 300, and 500meters all passed over the Daya Bay Petrochemical Industrial Park. Air masses at 300meters and 500meters originated from northeastern counties and cities of Guangdong Province from October 12 to 14, and from coastal areas of Guangdong and Fujian Provinces from October 16 to 18, indicating that emissions from the Daya Bay Petrochemical Industrial Park and upwind regions contributed to high-altitude pollutant levels in the Daya Bay area. WRF-CAMx model simulations further identified high-concentration zones of NO2 and SO2 upwind of Daya Bay, confirming the existence of high-altitude pollutant transport pathways. The vertical profile characteristics obtained from UAV observations provide valuable insights into the transport patterns of atmospheric pollutants at high altitudes, offering support for regional air quality impact studies.

UAV observation  /  vertical distribution characteristics  /  atmospheric pollutants  /  source analysis
刘正阳, 李洋, 贺美, 任艳芹, 唐伟, 高锐, 毋振海, 张丹丹, 龙芳云, 张林, 郑雄枫, 黄雨林, 毕方. 基于旋翼无人机观测的大亚湾区大气污染物垂直分布特征及其来源研究. 中国环境科学, 2025 , 45 (9) : 4796 -4806 .
Zheng-yang LIU, Yang LI, Mei HE, Yan-qin REN, Wei TANG, Rui GAO, Zhen-hai WU, Dan-dan ZHANG, Fang-yun LONG, Lin ZHANG, Xiong-feng ZHENG, Yu-lin HUANG, Fang BI. Study on vertical distribution characteristics and sources of atmospheric pollutants in the Daya Bay area based on rotor UAV observations[J]. China Environmental Science, 2025 , 45 (9) : 4796 -4806 .
  • 国家重点研发计划项目(2023YFC3706105)
  • 大亚湾区空气质量精细化管理项目(GDHR-2022-013)
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  • 接收时间:2025-02-11
  • 首发时间:2026-10-08
  • 出版时间:2025-09-20
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  • 收稿日期:2025-02-11
基金
国家重点研发计划项目(2023YFC3706105)
大亚湾区空气质量精细化管理项目(GDHR-2022-013)
作者信息
    1.中国环境科学研究院,环境基准与风险评估国家重点实验室,北京 100012
    2.长江大学资源与环境学院,油气地球化学与环境湖北省重点实验室,湖北 武汉 430100
    3.惠州市生态环境局大亚湾经济技术开发区分局,惠州市大亚湾生态环境事务中心,广东 惠州 516008
    4.惠州市生态环境局大亚湾经济技术开发区分局,惠州市大亚湾环境科学中心,广东 惠州 516083
    5.广州鋆达科技有限公司,广东 广州 511400

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* 责任作者,工程师,
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