Enhanced natural releases of mercury in response to the reduction in anthropogenic emissions during the COVID-19 lockdown by explainable machine learning
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
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
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
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
Viewed
Total article views: 2,657 (including HTML, PDF, and XML)
HTML
PDF
XML
Total
Supplement
BibTeX
EndNote
2,087
492
78
2,657
193
95
110
HTML: 2,087
PDF: 492
XML: 78
Total: 2,657
Supplement: 193
BibTeX: 95
EndNote: 110
Views and downloads (calculated since 31 Aug 2022)
Cumulative views and downloads
(calculated since 31 Aug 2022)
Total article views: 2,297 (including HTML, PDF, and XML)
HTML
PDF
XML
Total
Supplement
BibTeX
EndNote
1,820
411
66
2,297
158
91
103
HTML: 1,820
PDF: 411
XML: 66
Total: 2,297
Supplement: 158
BibTeX: 91
EndNote: 103
Views and downloads (calculated since 16 Dec 2022)
Cumulative views and downloads
(calculated since 16 Dec 2022)
Total article views: 360 (including HTML, PDF, and XML)
HTML
PDF
XML
Total
Supplement
BibTeX
EndNote
267
81
12
360
35
4
7
HTML: 267
PDF: 81
XML: 12
Total: 360
Supplement: 35
BibTeX: 4
EndNote: 7
Views and downloads (calculated since 31 Aug 2022)
Cumulative views and downloads
(calculated since 31 Aug 2022)
Viewed (geographical distribution)
Total article views: 2,657 (including HTML, PDF, and XML)
Thereof 2,617 with geography defined
and 40 with unknown origin.
Total article views: 2,297 (including HTML, PDF, and XML)
Thereof 2,272 with geography defined
and 25 with unknown origin.
Total article views: 360 (including HTML, PDF, and XML)
Thereof 345 with geography defined
and 15 with unknown origin.
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...
