Articles | Volume 22, issue 24
https://doi.org/10.5194/acp-22-15851-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-22-15851-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
16 Dec 2022
Research article |
| 16 Dec 2022
| 16 Dec 2022
Enhanced natural releases of mercury in response to the reduction in anthropogenic emissions during the COVID-19 lockdown by explainable machine learning
Xiaofei Qin
Center for Atmospheric Chemistry Study, Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP3), National Observations and Research Station for Wetland Ecosystems of the Yangtze Estuary, Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, China
Shengqian Zhou
Center for Atmospheric Chemistry Study, Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP3), National Observations and Research Station for Wetland Ecosystems of the Yangtze Estuary, Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, China
Hao Li
Center for Atmospheric Chemistry Study, Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP3), National Observations and Research Station for Wetland Ecosystems of the Yangtze Estuary, Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, China
Guochen Wang
Center for Atmospheric Chemistry Study, Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP3), National Observations and Research Station for Wetland Ecosystems of the Yangtze Estuary, Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, China
Cheng Chen
Center for Atmospheric Chemistry Study, Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP3), National Observations and Research Station for Wetland Ecosystems of the Yangtze Estuary, Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, China
Chengfeng Liu
Center for Atmospheric Chemistry Study, Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP3), National Observations and Research Station for Wetland Ecosystems of the Yangtze Estuary, Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, China
Xiaohao Wang
State Ecologic Environmental Scientific Observation and Research
Station for Dianshan Lake, Shanghai Environmental Monitoring Center, Shanghai 200030, China
Juntao Huo
State Ecologic Environmental Scientific Observation and Research
Station for Dianshan Lake, Shanghai Environmental Monitoring Center, Shanghai 200030, China
Yanfen Lin
State Ecologic Environmental Scientific Observation and Research
Station for Dianshan Lake, Shanghai Environmental Monitoring Center, Shanghai 200030, China
Jia Chen
State Ecologic Environmental Scientific Observation and Research
Station for Dianshan Lake, Shanghai Environmental Monitoring Center, Shanghai 200030, China
Qingyan Fu
State Ecologic Environmental Scientific Observation and Research
Station for Dianshan Lake, Shanghai Environmental Monitoring Center, Shanghai 200030, China
Yusen Duan
State Ecologic Environmental Scientific Observation and Research
Station for Dianshan Lake, Shanghai Environmental Monitoring Center, Shanghai 200030, China
Kan Huang
CORRESPONDING AUTHOR
Center for Atmospheric Chemistry Study, Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP3), National Observations and Research Station for Wetland Ecosystems of the Yangtze Estuary, Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, China
Institute of Eco-Chongming (IEC), Shanghai 202162, China
IRDR ICoE on Risk Interconnectivity and Governance on Weather/Climate Extremes Impact and Public Health, Fudan University, Shanghai 200433, China
Congrui Deng
CORRESPONDING AUTHOR
Center for Atmospheric Chemistry Study, Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP3), National Observations and Research Station for Wetland Ecosystems of the Yangtze Estuary, Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, China
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Cited
7 citations as recorded by crossref.
- Characteristics of secondary inorganic aerosols and contributions to PM2.5 pollution based on machine learning approach in Shandong Province T. Li et al. 10.1016/j.envpol.2023.122612
- The application of machine learning to air pollution research: A bibliometric analysis Y. Li et al. 10.1016/j.ecoenv.2023.114911
- Significant spatiotemporal changes in atmospheric particulate mercury pollution in China: Insights from meta-analysis and machine-learning H. Wang et al. 10.1016/j.scitotenv.2024.177184
- Application of machine learning to analyze ozone sensitivity to influencing factors: A case study in Nanjing, China C. Zhang et al. 10.1016/j.scitotenv.2024.172544
- Global Distribution of Mercury in Foliage Predicted by Machine Learning L. Chen et al. 10.1021/acs.est.4c00636
- Identifying Driving Factors of Atmospheric N2O5 with Machine Learning X. Chen et al. 10.1021/acs.est.4c00651
- Quantifying the pollution changes and meteorological dependence of airborne trace elements coupling source apportionment and machine learning H. Wang et al. 10.1016/j.scitotenv.2024.174452
7 citations as recorded by crossref.
- Characteristics of secondary inorganic aerosols and contributions to PM2.5 pollution based on machine learning approach in Shandong Province T. Li et al. 10.1016/j.envpol.2023.122612
- The application of machine learning to air pollution research: A bibliometric analysis Y. Li et al. 10.1016/j.ecoenv.2023.114911
- Significant spatiotemporal changes in atmospheric particulate mercury pollution in China: Insights from meta-analysis and machine-learning H. Wang et al. 10.1016/j.scitotenv.2024.177184
- Application of machine learning to analyze ozone sensitivity to influencing factors: A case study in Nanjing, China C. Zhang et al. 10.1016/j.scitotenv.2024.172544
- Global Distribution of Mercury in Foliage Predicted by Machine Learning L. Chen et al. 10.1021/acs.est.4c00636
- Identifying Driving Factors of Atmospheric N2O5 with Machine Learning X. Chen et al. 10.1021/acs.est.4c00651
- Quantifying the pollution changes and meteorological dependence of airborne trace elements coupling source apportionment and machine learning H. Wang et al. 10.1016/j.scitotenv.2024.174452
Latest update: 18 Nov 2024
Short summary
Using artificial neural network modeling and an explainable analysis approach, natural surface emissions (NSEs) were identified as a main driver of gaseous elemental mercury (GEM) variations during the COVID-19 lockdown. A sharp drop in GEM concentrations due to a significant reduction in anthropogenic emissions may disrupt the surface–air exchange balance of Hg, leading to increases in NSEs. This implies that NSEs may pose challenges to the future control of Hg pollution.
Using artificial neural network modeling and an explainable analysis approach, natural surface...
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