首页|Knowledge-guided machine learning reveals pivotal drivers for gas-to-particle conversion of atmospheric nitrate

Knowledge-guided machine learning reveals pivotal drivers for gas-to-particle conversion of atmospheric nitrate

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Particulate nitrate,a key component of fine particles,forms through the intricate gas-to-particle con-version process.This process is regulated by the gas-to-particle conversion coefficient of nitrate(ε(NO3)).The mechanism between ε(NO3-)and its drivers is highly complex and nonlinear,and can be charac-terized by machine learning methods.However,conventional machine learning often yields results that lack clear physical meaning and may even contradict established physical/chemical mechanisms due to the influence of ambient factors.It urgently needs an alternative approach that possesses transparent physical interpretations and provides deeper insights into the impact of ε(NO3-).Here we introduce a supervised machine learning approach-the multilevel nested random forest guided by theory ap-proaches.Our approach robustly identifies NH4+,SO42-,and temperature as pivotal drivers for ε(NO3-).Notably,substantial disparities exist between the outcomes of traditional random forest analysis and the anticipated actual results.Furthermore,our approach underscores the significance of NH4+during both daytime(30％)and nighttime(40％)periods,while appropriately downplaying the influence of some less relevant drivers in comparison to conventional random forest analysis.This research underscores the transformative potential of integrating domain knowledge with machine learning in atmospheric studies.

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Bo Xu、Haofei Yu、Zongbo Shi、Jinxing Liu、Yuting Wei、Zhongcheng Zhang、Yanqi Huangfu、Han Xu、Yue Li、Linlin Zhang、Yinchang Feng、Guoliang Shi

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State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control,Tianjin Key Laboratory of Urban Transport Emission Research,College of Environmental Science and Engineering,Nankai University,Tianjin,300350,China

CMA-NKU Cooperative Laboratory for Atmospheric Environment-Health Research(CLAER),College of Environmental Science and Engineering,Nankai University,Tianjin,300350,China

Department of Civil,Environmental,and Construction Engineering,University of Central Florida,Orlando,FL,USA

School of Geography Earth and Environment Sciences,University of Birmingham,Birmingham,B15 2TT,UK

State Key Laboratory of Precision Measuring Technology and Instruments,Tianjin Key Laboratory of air Pollutants Monitoring Technology,School of Precision Instrument and Opto-electronics Engineering,Tianjin University,Tianjin,300072,China

Gigantic Technology(Tianjin)Co.,Ltd,Tianjin,300072,China

College of Computer Science,Nankai University,Tianjin,300350,China

China National Environmental Monitoring Centre,Beijing,100012,China

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国家自然科学基金国家重点研发计划Blue Sky Foundation,Tianjin Science and Technology Plan Project中央高校基本科研业务费专项中央高校基本科研业务费专项

420771912022YFC370340018PTZWHZ001206321307263213074

2024

10.1016/j.ese.2023.100333

环境科学与生态技术(英文)

环境科学与生态技术(英文)

ISSN：
年,卷(期)：2024.19(3)
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