姓名：戴启立
职务职称：副教授
生态环境部城市空气颗粒物污染防治重点实验室成员
中国气象局-南开大学大气环境与健康联合实验室成员
天津市软件体验与人机交互重点实验室成员
联系电话：022-85358616
邮箱：daiql@nankai.edu.cn
研究领域：大气环境计量学；大气污染防治
研究兴趣： 围绕空气污染来源问题，主要从事环境大数据挖掘、空气污染因果推断等方面的研究，致力于研发人工智能辅助的空气污染成因诊断与政策评估技术（AI4Air）、揭示并解耦“空气污染-人类活动-天气气候”的复杂关联。诚邀对此感兴趣的同学、同行联系合作！
教育背景
2013.9—2019.6 南开大学beat365官方网站 理学博士（硕博连读）
2016.8—2017.8 莱斯大学（Rice University）市政与环境工程系联合培养博士生
2009.9—2013.6 安徽师范大学beat365官方网站工学学士
科研经历
2023.10 —至今 南开大学beat365官方网站副教授
2019.7—2023.9 南开大学beat365官方网站师资博士后、助理研究员
学术与社会任职
1. SCI期刊《Journal of Hazardous Materials》青年编委（Early Career Editorial Board）
2. SCI期刊《Science of The Total Environment》青年编委（Early Career Editorial Board）
3. 亚洲大洋洲地球科学学会第21届年会（Asia Oceania Geoscience Society, 21th Annual Meeting）分会场召集人
4. 国家自然科学基金、波兰、卡塔尔等多个国内外科学基金项目评审人
5. 天津市第一批生态环境青年科技人才，天津市生态环境局
科研项目
o 技术研发类项目：
1．国家重点研发计划项目（No. 2022YFC3703000），2022—2026, 子课题负责人；
2．国家自然科学基金青年项目，大气颗粒物氧化潜势在排放源和环境受体中的粒径分布及来源解析方法研究，2021—2023，主持；
3．中国博士后科学基金特别资助项目，基于机器学习算法量化排放和气象因素对PM2.5环境浓度贡献的方法研究，2022—2023，主持；
4．中央高校基本科研业务费，贝叶斯推断大气颗粒物溯源方法，2021.01-2021.12，主持；
5．中国博士后科学基金面上项目，基于先验信息约束的大气颗粒物来源解析方法研究，2019—2021，主持；
6．中央高校基本科研业务费，基于数学模型外信息的大气颗粒物源解析结果寻优，2019—2020，主持；
7．国家自然科学基金面上项目，耦合多维先验信息的新型大气颗粒物来源解析方法研究，2022—2025，项目骨干；
8．生态环境部国家大气污染联合攻关中心，细颗粒物和臭氧污染协同防控“一市一策”驻点跟踪研究，2020—2023，项目骨干；
9．天津市科委生态环境治理科技重大专项, 天津市大气复合污染精准解析及防治方案研究, 2018—2021, 项目骨干；
10. 生态环境部国家大气污染联合攻关中心，多模型融合的综合精细化来源解析技术，2017—2019，项目骨干。
围绕环境监测大数据如何支撑国家和地方大气污染精细化管控的问题，依托上述技术研发类项目，持续开展数据驱动的污染成因来源解析与调控效果评估技术研发。相关研究成果服务于G20杭州峰会、成都大运会等多次国家重大活动空气质量保障及区域重污染天气应急管控，并应用于支撑天津、合肥、郑州、驻马店等多个重点城市大气污染防治管理实践。部分技术应用类项目如下：
o 技术应用类项目：
1. 郑州航空港经济综合实验区环境监测站，大气颗粒物源解析项目，2020-2022，主持；
2. 杭州市生态环境监测中心，杭州市PM2.5和O3污染来源综合解析，2023-2024，项目骨干；
3. 合肥市生态环境局，研究合肥市大气污染特征和演变规律，2020-2023，项目骨干；
4. 乌鲁木齐市环保局，乌鲁木齐市PM2.5来源解析研究，2014-2016，项目骨干；
5. 西安市环境监测站，西安市大气颗粒物来源解析研究，2014-2015，项目骨干。
学术论著
发表学术论文100余篇，包括以第一/通讯作者在《中国科学: 地球科学》、Geophysical Research Letters、Environmental Science & Technology (Letters)、Journal of Geophysical Research: Atmospheres、Atmospheric Chemistry and Physics等重要期刊发表论文29篇，其中3篇入选ESI高被引论文；授权国家发明专利1项。累计引用3800余次，谷歌H指数32.（截止2024年）
部分研究/评论文章如下（†为同等贡献，*为通讯作者）：
1. Shao, M., Lv, S., Song, Y., Liu, R., Dai, Q.* (2024) Disentangling the Effects of Meteorology and Emissions from Anthropogenic and Biogenic sources on the Increased Surface Ozone in Eastern China. Atmospheric Research [Link]
2. Song, Y.†, Wu, H.†, Dai, Q.*, Liu, X., Zhang, Y., Feng, Y. (2024). Differentiating Periodic Drivers of Air Quality Changes: A Two-step Decomposition Approach integrating Machine Learning and Wavelet Analysis. Journal of Geophysical Research: Atmosphere, 129(7), e2023JD039658. [Link]
3. Luo, Z., Feng, C., Yang, J., Dai, Q.*, Dai, T., Zhang, Y., Liang, D., Feng, Y. (2024). Assessing emission-driven changes in health risk of source-specific PM2.5-bound heavy metals by adjusting meteorological covariates. Science of the Total Environment, 927: 172038. [Link]
4. Shao, M., Liu, X., Lv, S., Dai, Q.*, Mu, Q. (2024). The importance of local thermal circulations in PM2.5 formation in a river valley: A case study from the lower Yangtze River, China. Journal of Geophysical Research: Atmospheres, 129(2), e2023JD039717. [Link]（城市内河引发的局地环流对空气污染的影响)
5. Guo, F., Bui, A. A., Schulze, B. C., Dai, Q., Yoon, S., Shrestha, S., Wallace, H. W., Sanchez, N. P., Alvarez, S., Erickson, M. H., Sheesley, R. J., Usenko, S., Flynn, J., Griffin, R. J.* (2024). Airmass history, night-time particulate organonitrates, and meteorology impact urban SOA formation rate. Atmospheric Environment, 322: 120362. [Link]
6. Dai, T., Dai, Q.*, Yin, J., Chen, J., Liu, B., Bi, X., Wu, J., Zhang, Y., Feng, Y*. (2024). Spatial source apportionment of airborne coarse particulate matter using PMF-Bayesian receptor model. Science of the Total Environment, 917: 170235. [Link]
7. Dai, T., Dai, Q.*, Bi, X., Wu, J., Liu, B., Zhang, Y., Feng, Y. (2023). Measuring the emission changes and meteorological dependence of source-specific BC aerosol using factor analysis coupled with machine learning. Journal of Geophysical Research: Atmospheres, 128(5), e2023JD038696. [Link]
8. Dai, Q., Dai, T., Hou, L., Li, L., Bi, X., Zhang, Y.*, Feng, Y.*. (2023). Quantifying the impacts of meteorology and emissions on the interannual variation of air pollutants in major Chinese cities in 2015-2021. Science China Earth Sciences, 66(8), 1725–1737. [Link] [中文版：“污染减排与气象因素对我国主要城市2015~2021年环境空气质量变化的贡献评估. 中国科学: 地球科学, 53(8): 1741–1753.” Link]（机器学习气象标准化技术的详细中文介绍及应用）
9. Song, C., Liu, B., Cheng, K., Cole, M., Dai, Q.*, Elliott, R., Shi, Z.*. (2023). Attribution of air quality benefits to Clean Winter Heating Polices in China: Combining machine learning with causal Inference. Environmental Science & Technology, 57, 17707−17717. [Link] (机器学习因果推断评估我国北方冬季清洁供暖政策的环境效果）
10. Dai, Q., Chen, J., Wang, X., Dai, T., Tian, Y., Bi, X., Shi, G., Wu, J., Liu, B., Zhang, Y., Yan, B., Kinney, P. L., Feng, Y.*, Hopke, P. K. (2023). Trends of source apportioned PM2.5 in Tianjin over 2013-2019: impacts of Clean Air Actions. Environmental Pollution,325: 121344. [Link]
11. Ding, J., Dai, Q.*, Fan, W., Lu, M., Zhang, Y., Han, S.*, Feng, Y. (2023). Impact of meteorology and precursor emission changes on O3 variation in Tianjin, China from 2015 to 2021. Journal of Environmental Sciences, 126, 506–516. [Link](ESI高被引论文)
12. Song, C.*, Becagli, S., Beddows, D. C. S., Brean, J., Browse, J., Dai, Q., Dall’Osto, M., Ferracci, V., Harrison, R. M., Harris, N., Li, W., Jones, A. E., Kirchgäßner, A., Kramawijaya, A. G., Kurganskiy, A., Lupi, A., Mazzola, M., Severi, M., Traversi, R., Shi, Z.* (2022). Understanding sources and drivers of size-resolved aerosol in the high Arctic islands of Svalbard using a receptor model coupled with machine learning. Environmental Science & Technology, 56(16), 11189–11198. [Link]
13. Hou, L., Dai, Q.*, Song, C., Liu, B., Guo, F., Dai, T., Li, L., Liu, B., Bi, X., Zhang, Y., Feng, Y. (2022). Revealing drivers of haze pollution by explainable machine learning. Environmental Science & Technology Letters, 9(2), 112–119. [Link](ESI高被引论文, 年度最佳论文奖)
14. Gu Y., Liu B.*, Dai, Q., Zhang, Y., Zhou, M., Feng, Y., Hopke, P. K. (2022). Multiply improved positive matrix factorization for source apportionment of volatile organic compounds during the COVID-19 shutdown in Tianjin, China. Environment International, 158, 106979.
15. Shao, M., Yang, J., Wang, J., Chen, P., Liu, B., Dai, Q.*. (2022). Co-occurrence of surface O3, PM2.5 pollution, and tropical cyclones in China. Journal of Geophysical Research: Atmospheres, 127(14), e2021JD036310. [Link] (近十年来100余次台风过程对我国空气污染的跨区域影响规律及机制探讨，Wiley Top Downloaded Article)
16. Shao, M., Xu, X., Lu, Y., Dai, Q.*. (2022). Spatio-temporally differentiated impacts of temperature inversion on surface PM2.5 in eastern China. Science of The Total Environment, 855: 158785. [Link] (我国东部地区近十年来逆温的时空演变及其对近地面PM2.5浓度的影响)
17. Hopke, P.K.*, Feng, Y., Dai, Q. (2022). Source apportionment of particle number concentrations: A global review. Science of The Total Environment, 819: 153104. [Link]
18. Yang, Y., Liu, B.*, Hua, J., Yang, T., Dai, Q., Wu, J., Feng, Y., Hopke, P. K. (2022). Global review of source apportionment of volatile organic compounds based on highly time-resolved data from 2015 to 2021. Environment International, 165: 107330. [Link]
19. Han, B.*, Yao, T., Li, G., Song, Y., Zhang, Y., Dai, Q.*, Yu, J. (2022). Marginal reduction in surface NO2 attributable to airport shutdown: a machine learning regression-based approach. Environmental Research, 214: 114117. [Link]
20. Dai, Q., Hou, L., Liu, B., Zhang, Y., Song, C., Shi, Z., Hopke, P. K., Feng, Y.*. (2021). Spring Festival and COVID-19 Lockdown: Disentangling PM Sources in Major Chinese Cities. Geophysical Research Letters, 48(11), e2021GL093403. [Link] (Wiley Top Downloaded Article)
21. Hopke, P.K.* and Dai, Q. (2021). Why it makes sense that increased PM2.5 was correlated with anthropogenic combustion-derived water. Proceedings of the National Academy of Sciences the United States of America, 118(19), e2102255118.
22. Ding, J., Dai, Q., Zhang, Y.*, Xu, J., Huangfu, Y., Feng, Y. (2021). Air humidity affects secondary aerosol formation in different pathways. Science of the Total Environment, 759: 143540.
23. Shao, M., Dai, Q.*, Yu, Z., Zhang, Y., Xie, M., Feng, Y. (2021). Responses in PM2.5 and its chemical components to typical unfavorable meteorological events in the suburban area of Tianjin, China. Science of the Total Environment, 788: 147814. [Link]
24. Song, L., Dai, Q.*, Feng, Y., Hopke, P. K. (2021). Estimating uncertainties of source contributions to PM2.5 using moving window evolving dispersion normalized PMF. Environmental Pollution, 286: 117576. [Link]
25. Dai, Q., Ding, J., Hou, L., Li, L., Cai, Z., Liu, B., Song, C., Bi, X., Wu, J., Zhang, Y.*, Feng, Y., Hopke, P. K. (2021). Haze episodes before and during the COVID-19 shutdown in Tianjin, China: Contribution of fireworks and residential burning. Environmental Pollution, 286: 117252. [Link]
26. Dai, Q.*, Ding, J., Song, C., Liu, B., Bi, X., Wu, J., Zhang, Y., Feng, Y.*, Hopke, P. K. (2021). Changes in source contributions to particle number concentrations after the COVID-19 outbreak: Insights from a dispersion normalized PMF. Science of the Total Environment, 759: 143548. [Link]
27. Amouei Torkmahalleh, M., Akhmetvaliyeva, Z., Omran, A.D., Darvish Omran, F., Kazemitabar, M., Naseri, M., Naseri, M., Sharifi, H., Malekipirbazari, M., Kwasi Adotey, E., Gorjinezhad, S., Eghtesadi, N., Sabanov, S., Alastuey, A., de Fátima Andrade, M., Buonanno, G., Carbone, S., Cárdenas-Fuentes, D.E., Cassee, F.R., Dai, Q., Henríquez, A., Hopke, P.K., Keronen, P., Khwaja, H.A., Kim, J., Kulmala, M., Kumar, P., Kushta, J., Kuula, J., Massagué, J., Mitchell, T., Mooibroek, D., Morawska, L., Niemi, J.V., Ngagine, S.H., Norman, M., Oyama, B., Oyola, P., Öztürk, F., Petäjä, T., Querol, X., Rashidi, Y., Reyes, F., Ross-Jones, M., Salthammer, T., Savvides, C., Stabile, L., Sjöberg, K., Söderlund, K., Sunder Raman, R., Timonen, H., Umezawa, M., Viana, M., Xie, S. (2021). Global Air Quality and COVID-19 Pandemic: Do We Breathe Cleaner Air? Aerosol and Air Quality Research, 21, 200567. [Link]（空气污染对全球性干预的的响应统计）
28. Hopke, P.K.*, Dai, Q., Li, L., Feng, Y. (2020). Global review of recent source apportionments for airborne particulate matter. Science of the Total Environment, 740: 140091. [Link](ESI高被引论文)
29. Dai, Q., Liu, B., Bi, X., Wu, J., Liang, D., Zhang, Y., Feng, Y.*, Hopke, P. K.*. (2020). Dispersion normalized PMF provides insights into the significant changes in source contributions to PM2.5 after the COVID-19 outbreak. Environmental Science & Technology, 54(16), 9917–9927. [Link]
30. Dai, Q., Hopke, P.K.*, Bi, X., Feng, Y.*. (2020). Improving apportionment of PM2.5 using multisite PMF by constraining G-values with a priori information. Science of the Total Environment, 736: 139657. [Link]
31. Bi, X., Dai, Q., Wu, J., Zhang, Q., Zhang, W., Luo, R., Cheng, Y., Zhang, J., Wang, L., Yu, Z., Zhang, Y.,Tian, Y., & Feng, Y.*. (2019). Characteristics of the main primary source profiles of particulate matter across China from 1987 to 2017. Atmospheric Chemistry and Physics, 19(5), 3223–3243. [Link]
32. Dai, Q., Schulze, B. C., Bi, X., Bui, A. A. T., Guo, F., Wallace, H. W., Sanchez, N. P., Flynn, J. H., Lefer, B. L., Feng, Y.*, Griffin, R. J. (2019). Seasonal differences in formation processes of oxidized organic aerosol near Houston, TX. Atmospheric Chemistry and Physics, 19(14), 9641–9661. [Link]
33. Dai, Q., Bi, X.*, Huangfu, Y., Yang, J., Li, T., Khan, J., Song, C., Xu, J., Wu, J., Zhang, Y., Feng, Y. (2019). A size-resolved chemical mass balance (SR-CMB) approach for source apportionment of ambient particulate matter by single element analysis. Atmospheric Environment, 197, 45–52. [Link]
34. Dai, Q., Bi, X.*, Song, W., Li, T., Liu, B., Ding, J., Xu, J., Song, C., Yang, N., Schulze, B., Zhang, Y., Feng, Y., Hopke, P. K. (2019). Residential coal combustion as a source of primary sulfate in Xi’an, China. Atmospheric Environment, 196, 66–76. [Link]
35. Schulze, B.C.*, Wallace, H., Bui, A., Flynn, J., Erickson, M., Alvarez, S., Dai, Q., Usenko, S., Sheesley, R., Griffin, R. (2018). The impacts of regional shipping emissions on the chemical characteristics of coastal submicron aerosols near Houston, TX. Atmospheric Chemistry and Physics, 18(19), 14217–14241. [Link]
36. Dai, Q., Bi, X.*, Liu, B., Li, L., Ding, J., Song, W., Bi, S., Schulze, B., Song, C., Wu, J., Zhang, Y., Feng, Y., Hopke, P. K. (2018). Chemical nature of PM2.5 and PM10 in Xi’an, China: Insights into primary emissions and secondary particle formation. Environmental Pollution, 240, 155–166. [Link]
荣誉与奖励
1. 入选天津市第一批青年科技人才（2024）
2.《中国科学》杂志社2023年度优秀封面奖（2024）
3. 天津市第十七届青年教师教学大赛校内选拔赛工科组一等奖（南开大学，2023）
4. 中国气象局2023年度气象科技成果评价“良好”等级（第二完成人，2023）
5. Wiley Top Downloaded Article（美国地球物理学会Journal of Geophysical Research: Atmospheres期刊, 2022）
6. 美国化学会ES&T Letters期刊2022年度最佳论文奖（Best Paper Award，2022）；
7. Wiley Top Downloaded Article（美国地球物理学会Geophysical Research Letters期刊, 2021）
8. 第四届环境污染与健康国际会议最佳报告奖（ES&T and ES&T Letters期刊冠名，2018）；
9. 中国环境科学学会大气环境分会年会优秀报告奖（2017）。
