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
Related authors
Xiaofei Qin, Hao Li, Jia Chen, Junjie Wei, Hao Ding, Xiaohao Wang, Guochen Wang, Chengfeng Liu, Da Lu, Shengqian Zhou, Haowen Li, Yucheng Zhu, Ziwei Liu, Qingyan Fu, Juntao Huo, Yanfen Lin, Congrui Deng, Yisheng Zhang, and Kan Huang
EGUsphere, https://doi.org/10.5194/egusphere-2025-623, https://doi.org/10.5194/egusphere-2025-623, 2025
Short summary
Short summary
Mercury is a persistent toxic pollutant that has equally important anthropogenic and natural sources. This study developed a quantitative method on separating the anthropogenic and natural contributions of total gaseous mercury. The underlying impacts on the sea-air exchange fluxes of mercury are evaluated. The new method developed in this study can be reproducible in other regions and the findings are innovative in the field of mercury sources and biogeochemical cycles.
Da Lu, Hao Li, Mengke Tian, Guochen Wang, Xiaofei Qin, Na Zhao, Juntao Huo, Fan Yang, Yanfen Lin, Jia Chen, Qingyan Fu, Yusen Duan, Xinyi Dong, Congrui Deng, Sabur F. Abdullaev, and Kan Huang
Atmos. Chem. Phys., 23, 13853–13868, https://doi.org/10.5194/acp-23-13853-2023, https://doi.org/10.5194/acp-23-13853-2023, 2023
Short summary
Short summary
Environmental conditions during dust are usually not favorable for secondary aerosol formation. However in this study, an unusual dust event was captured in a Chinese mega-city and showed “anomalous” meteorology and a special dust backflow transport pathway. The underlying formation mechanisms of secondary aerosols are probed in the context of this special dust event. This study shows significant implications for the varying dust aerosol chemistry in the future changing climate.
Xiaofei Qin, Leiming Zhang, Guochen Wang, Xiaohao Wang, Qingyan Fu, Jian Xu, Hao Li, Jia Chen, Qianbiao Zhao, Yanfen Lin, Juntao Huo, Fengwen Wang, Kan Huang, and Congrui Deng
Atmos. Chem. Phys., 20, 10985–10996, https://doi.org/10.5194/acp-20-10985-2020, https://doi.org/10.5194/acp-20-10985-2020, 2020
Short summary
Short summary
The uncertainties in mercury emissions are much larger from natural sources than anthropogenic sources. A method was developed to quantify the contributions of natural surface emissions to ambient GEM based on PMF modeling. The annual GEM concentration in eastern China showed a decreasing trend from 2015 to 2018, while the relative contribution of natural surface emissions increased significantly from 41 % in 2015 to 57 % in 2018, gradually surpassing those from anthropogenic sources.
Hanzheng Zhu, Yaman Liu, Man Yue, Shihui Feng, Pingqing Fu, Kan Huang, Xinyi Dong, and Minghuai Wang
Atmos. Chem. Phys., 25, 5175–5197, https://doi.org/10.5194/acp-25-5175-2025, https://doi.org/10.5194/acp-25-5175-2025, 2025
Short summary
Short summary
Dust-soluble iron deposition from East Asia plays an important role in the marine ecology of the Northwest Pacific. Using the developed model, our findings highlight a dual trend: a decrease in the overall deposition of soluble iron from dust but an increase in the solubility of the iron itself due to the enhanced atmospheric processing. The study underscores the critical roles of both dust emission and atmospheric processing in soluble iron deposition and marine ecology.
Wenxin Zhang, Yaman Liu, Man Yue, Xinyi Dong, Kan Huang, and Minghuai Wang
Atmos. Chem. Phys., 25, 3857–3872, https://doi.org/10.5194/acp-25-3857-2025, https://doi.org/10.5194/acp-25-3857-2025, 2025
Short summary
Short summary
Understanding long-term organic aerosol (OA) trends and their driving factors is important for air quality management. Our modeling revealed that OA in China increased by 5.6 % from 1990 to 2019, primarily due to a 32.3 % increase in secondary organic aerosols (SOAs) and an 8.1 % decrease in primary organic aerosols (POAs), both largely driven by changes in anthropogenic emissions. Biogenic SOA increased due to warming but showed little response to changes in anthropogenic nitrogen oxide emissions.
Zheng Li, Gehui Wang, Binyu Xiao, Rongjie Li, Can Wu, Shaojun Lv, Feng Wu, Qingyan Fu, and Yusen Duan
EGUsphere, https://doi.org/10.5194/egusphere-2025-654, https://doi.org/10.5194/egusphere-2025-654, 2025
Short summary
Short summary
Gas-to-aerosol partitioning of organics were investigated in Shanghai during 2023 dust storm period. We found the partitioning coefficients (Fp) of WSOCs in DS were comparable to those during a haze episode (HE), and aerosol liquid water content primarily drove Fp variation in HE, while pH was the dominant factor in DS. Moreover, an enhanced light absorption of Asian dust by brown carbon, mainly in coarse mode, formation was revealed.
Xiaofei Qin, Hao Li, Jia Chen, Junjie Wei, Hao Ding, Xiaohao Wang, Guochen Wang, Chengfeng Liu, Da Lu, Shengqian Zhou, Haowen Li, Yucheng Zhu, Ziwei Liu, Qingyan Fu, Juntao Huo, Yanfen Lin, Congrui Deng, Yisheng Zhang, and Kan Huang
EGUsphere, https://doi.org/10.5194/egusphere-2025-623, https://doi.org/10.5194/egusphere-2025-623, 2025
Short summary
Short summary
Mercury is a persistent toxic pollutant that has equally important anthropogenic and natural sources. This study developed a quantitative method on separating the anthropogenic and natural contributions of total gaseous mercury. The underlying impacts on the sea-air exchange fluxes of mercury are evaluated. The new method developed in this study can be reproducible in other regions and the findings are innovative in the field of mercury sources and biogeochemical cycles.
Shuai Wang, Mengyuan Zhang, Hui Zhao, Peng Wang, Sri Harsha Kota, Qingyan Fu, Cong Liu, and Hongliang Zhang
Earth Syst. Sci. Data, 16, 3565–3577, https://doi.org/10.5194/essd-16-3565-2024, https://doi.org/10.5194/essd-16-3565-2024, 2024
Short summary
Short summary
Long-term, open-source, gap-free daily ground-level PM2.5 and PM10 datasets for India (LongPMInd) were reconstructed using a robust machine learning model to support health assessment and air quality management.
Zijun Zhang, Weiqi Xu, Yi Zhang, Wei Zhou, Xiangyu Xu, Aodong Du, Yinzhou Zhang, Hongqin Qiao, Ye Kuang, Xiaole Pan, Zifa Wang, Xueling Cheng, Lanzhong Liu, Qingyan Fu, Douglas R. Worsnop, Jie Li, and Yele Sun
Atmos. Chem. Phys., 24, 8473–8488, https://doi.org/10.5194/acp-24-8473-2024, https://doi.org/10.5194/acp-24-8473-2024, 2024
Short summary
Short summary
We investigated aerosol composition and sources and the interaction between secondary organic aerosol (SOA) and clouds at a regional mountain site in southeastern China. Clouds efficiently scavenge more oxidized SOA; however, cloud evaporation leads to the production of less oxidized SOA. The unexpectedly high presence of nitrate in aerosol particles indicates that nitrate formed in polluted areas has undergone interactions with clouds, significantly influencing the regional background site.
Shuai Wang, Mengyuan Zhang, Yueqi Gao, Peng Wang, Qingyan Fu, and Hongliang Zhang
Geosci. Model Dev., 17, 3617–3629, https://doi.org/10.5194/gmd-17-3617-2024, https://doi.org/10.5194/gmd-17-3617-2024, 2024
Short summary
Short summary
Numerical models are widely used in air pollution modeling but suffer from significant biases. The machine learning model designed in this study shows high efficiency in identifying such biases. Meteorology (relative humidity and cloud cover), chemical composition (secondary organic components and dust aerosols), and emission sources (residential activities) are diagnosed as the main drivers of bias in modeling PM2.5, a typical air pollutant. The results will help to improve numerical models.
Qiongqiong Wang, Shuhui Zhu, Shan Wang, Cheng Huang, Yusen Duan, and Jian Zhen Yu
Atmos. Chem. Phys., 24, 475–486, https://doi.org/10.5194/acp-24-475-2024, https://doi.org/10.5194/acp-24-475-2024, 2024
Short summary
Short summary
We investigated short-term source apportionment of PM2.5 utilizing rolling positive matrix factorization (PMF) and online PM chemical speciation data, which included source-specific organic tracers collected over a period of 37 d during the winter of 2019–2020 in suburban Shanghai, China. The findings highlight that by imposing constraints on the primary source profiles, short-term PMF analysis successfully replicated both the individual primary sources and the total secondary sources.
Song Gao, Yong Yang, Xiao Tong, Linyuan Zhang, Yusen Duan, Guigang Tang, Qiang Wang, Changqing Lin, Qingyan Fu, Lipeng Liu, and Lingning Meng
Atmos. Meas. Tech., 16, 5709–5723, https://doi.org/10.5194/amt-16-5709-2023, https://doi.org/10.5194/amt-16-5709-2023, 2023
Short summary
Short summary
We optimized and conducted an experimental program for the real-time monitoring of non-methane hydrocarbon instruments using the direct method. Changing the enrichment and specially designed columns further improved the test effect. The results correct the measurement errors that have prevailed for many years and can lay a foundation for the evaluation of volatile organic compounds in the regional ambient air and provide direction for the measurement of low-concentration ambient air pollutants.
Da Lu, Hao Li, Mengke Tian, Guochen Wang, Xiaofei Qin, Na Zhao, Juntao Huo, Fan Yang, Yanfen Lin, Jia Chen, Qingyan Fu, Yusen Duan, Xinyi Dong, Congrui Deng, Sabur F. Abdullaev, and Kan Huang
Atmos. Chem. Phys., 23, 13853–13868, https://doi.org/10.5194/acp-23-13853-2023, https://doi.org/10.5194/acp-23-13853-2023, 2023
Short summary
Short summary
Environmental conditions during dust are usually not favorable for secondary aerosol formation. However in this study, an unusual dust event was captured in a Chinese mega-city and showed “anomalous” meteorology and a special dust backflow transport pathway. The underlying formation mechanisms of secondary aerosols are probed in the context of this special dust event. This study shows significant implications for the varying dust aerosol chemistry in the future changing climate.
Ting Yang, Yu Xu, Qing Ye, Yi-Jia Ma, Yu-Chen Wang, Jian-Zhen Yu, Yu-Sen Duan, Chen-Xi Li, Hong-Wei Xiao, Zi-Yue Li, Yue Zhao, and Hua-Yun Xiao
Atmos. Chem. Phys., 23, 13433–13450, https://doi.org/10.5194/acp-23-13433-2023, https://doi.org/10.5194/acp-23-13433-2023, 2023
Short summary
Short summary
In this study, 130 OS species were quantified in ambient fine particulate matter (PM2.5) collected in urban and suburban Shanghai (East China) in the summer of 2021. The daytime OS formation was concretized based on the interactions among OSs, ultraviolet (UV), ozone (O3), and sulfate. Our finding provides field evidence for the influence of photochemical process and anthropogenic sulfate on OS formation and has important implications for the mitigation of organic particulate pollution.
Meng Wang, Yusen Duan, Zhuozhi Zhang, Qi Yuan, Xinwei Li, Shuwen Han, Juntao Huo, Jia Chen, Yanfen Lin, Qingyan Fu, Tao Wang, Junji Cao, and Shun-cheng Lee
Atmos. Chem. Phys., 23, 10313–10324, https://doi.org/10.5194/acp-23-10313-2023, https://doi.org/10.5194/acp-23-10313-2023, 2023
Short summary
Short summary
Hourly elemental carbon (EC) and NOx were continuously measured for 5 years (2016–2020) at a sampling site near a highway in western Shanghai. We use a machine learning model to rebuild the measured EC and NOx, and a business-as-usual (BAU) scenario was assumed in 2020 and compared with the measured EC and NOx.
Hannah J. Rubin, Joshua S. Fu, Frank Dentener, Rui Li, Kan Huang, and Hongbo Fu
Atmos. Chem. Phys., 23, 7091–7102, https://doi.org/10.5194/acp-23-7091-2023, https://doi.org/10.5194/acp-23-7091-2023, 2023
Short summary
Short summary
We update the 2010 global deposition budget for nitrogen (N) and sulfur (S) with new regional wet deposition measurements, improving the ensemble results of 11 global chemistry transport models from HTAP II. Our study demonstrates that a global measurement–model fusion approach can substantially improve N and S deposition model estimates at a regional scale and represents a step forward toward the WMO goal of global fusion products for accurately mapping harmful air pollution.
Yizhen Wu, Juntao Huo, Gan Yang, Yuwei Wang, Lihong Wang, Shijian Wu, Lei Yao, Qingyan Fu, and Lin Wang
Atmos. Chem. Phys., 23, 2997–3014, https://doi.org/10.5194/acp-23-2997-2023, https://doi.org/10.5194/acp-23-2997-2023, 2023
Short summary
Short summary
Based on a field campaign in a suburban area of Shanghai during summer 2021, we calculated formaldehyde (HCHO) production rates from 24 volatile organic compounds (VOCs). In addition, HCHO photolysis, reactions with OH radicals, and dry deposition were considered for the estimation of HCHO loss rates. Our results reveal the key precursors of HCHO and suggest that HCHO wet deposition may be an important loss term on cloudy and rainy days, which needs to be further investigated.
Yu Han, Tao Wang, Rui Li, Hongbo Fu, Yusen Duan, Song Gao, Liwu Zhang, and Jianmin Chen
Atmos. Chem. Phys., 23, 2877–2900, https://doi.org/10.5194/acp-23-2877-2023, https://doi.org/10.5194/acp-23-2877-2023, 2023
Short summary
Short summary
Limited knowledge is available on volatile organic compound (VOC) multi-site research of different land-use types at city level. This study performed a concurrent multi-site observation campaign on the three typical land-use types of Shanghai, East China. The results showed that concentrations, sources and ozone and secondary organic aerosol formation potentials of VOCs varied with the land-use types.
Changqin Yin, Jianming Xu, Wei Gao, Liang Pan, Yixuan Gu, Qingyan Fu, and Fan Yang
Atmos. Chem. Phys., 23, 1329–1343, https://doi.org/10.5194/acp-23-1329-2023, https://doi.org/10.5194/acp-23-1329-2023, 2023
Short summary
Short summary
The particle matter (PM2.5) at the top of the 632 m high Shanghai Tower was found to be higher than the surface from June to October due to unexpected larger PM2.5 levels during early to middle afternoon at Shanghai Tower. We suppose the significant chemical production of secondary species existed in the mid-upper planetary boundary layer. We found a high nitrate concentration at the tower site for both daytime and nighttime in winter, implying efficient gas-phase and heterogeneous formation.
Meng Wang, Yusen Duan, Wei Xu, Qiyuan Wang, Zhuozhi Zhang, Qi Yuan, Xinwei Li, Shuwen Han, Haijie Tong, Juntao Huo, Jia Chen, Shan Gao, Zhongbiao Wu, Long Cui, Yu Huang, Guangli Xiu, Junji Cao, Qingyan Fu, and Shun-cheng Lee
Atmos. Chem. Phys., 22, 12789–12802, https://doi.org/10.5194/acp-22-12789-2022, https://doi.org/10.5194/acp-22-12789-2022, 2022
Short summary
Short summary
In this study, we report the long-term measurement of organic carbon (OC) and elementary carbon (EC) in PM2.5 with hourly time resolution conducted at a regional site in Shanghai from 2016 to 2020. The results from this study provide critical information about the long-term trend of carbonaceous aerosol, in particular secondary OC, in one of the largest megacities in the world and are helpful for developing pollution control measures from a long-term planning perspective.
Xiaoxi Zhao, Kan Huang, Joshua S. Fu, and Sabur F. Abdullaev
Atmos. Chem. Phys., 22, 10389–10407, https://doi.org/10.5194/acp-22-10389-2022, https://doi.org/10.5194/acp-22-10389-2022, 2022
Short summary
Short summary
Long-range transport of Asian dust to the Arctic was considered an important source of Arctic air pollution. Different transport routes to the Arctic had divergent effects on the evolution of aerosol properties. Depositions of long-range-transported dust particles can reduce the Arctic surface albedo considerably. This study implied that the ubiquitous long-transport dust from China exerted considerable aerosol indirect effects on the Arctic and may have potential biogeochemical significance.
Junri Zhao, Weichun Ma, Kelsey R. Bilsback, Jeffrey R. Pierce, Shengqian Zhou, Ying Chen, Guipeng Yang, and Yan Zhang
Atmos. Chem. Phys., 22, 9583–9600, https://doi.org/10.5194/acp-22-9583-2022, https://doi.org/10.5194/acp-22-9583-2022, 2022
Short summary
Short summary
Marine dimethylsulfide (DMS) emissions play important roles in atmospheric sulfur cycle and climate effects. In this study, DMS emissions were estimated by using the machine learning method and drove the global 3D chemical transport model to simulate their climate effects. To our knowledge, this is the first study in the Asian region that quantifies the combined impacts of DMS on sulfate, particle number concentration, and radiative forcings.
Han Zang, Yue Zhao, Juntao Huo, Qianbiao Zhao, Qingyan Fu, Yusen Duan, Jingyuan Shao, Cheng Huang, Jingyu An, Likun Xue, Ziyue Li, Chenxi Li, and Huayun Xiao
Atmos. Chem. Phys., 22, 4355–4374, https://doi.org/10.5194/acp-22-4355-2022, https://doi.org/10.5194/acp-22-4355-2022, 2022
Short summary
Short summary
Particulate nitrate plays an important role in wintertime haze pollution in eastern China, yet quantitative constraints on detailed nitrate formation mechanisms remain limited. Here we quantified the contributions of the heterogeneous N2O5 hydrolysis (66 %) and gas-phase OH + NO2 reaction (32 %) to nitrate formation in this region and identified the atmospheric oxidation capacity (i.e., availability of O3 and OH radicals) as the driving factor of nitrate formation from both processes.
Syuichi Itahashi, Baozhu Ge, Keiichi Sato, Zhe Wang, Junichi Kurokawa, Jiani Tan, Kan Huang, Joshua S. Fu, Xuemei Wang, Kazuyo Yamaji, Tatsuya Nagashima, Jie Li, Mizuo Kajino, Gregory R. Carmichael, and Zifa Wang
Atmos. Chem. Phys., 21, 8709–8734, https://doi.org/10.5194/acp-21-8709-2021, https://doi.org/10.5194/acp-21-8709-2021, 2021
Short summary
Short summary
This study presents the detailed analysis of acid deposition over southeast Asia based on the Model Inter-Comparison Study for Asia (MICS-Asia) phase III. Simulated wet deposition is evaluated with observation data from the Acid Deposition Monitoring Network in East Asia (EANET). The difficulties of models to capture observations are related to the model performance on precipitation. The precipitation-adjusted approach was applied, and the distribution of wet deposition was successfully revised.
Na Zhao, Xinyi Dong, Kan Huang, Joshua S. Fu, Marianne Tronstad Lund, Kengo Sudo, Daven Henze, Tom Kucsera, Yun Fat Lam, Mian Chin, and Simone Tilmes
Atmos. Chem. Phys., 21, 8637–8654, https://doi.org/10.5194/acp-21-8637-2021, https://doi.org/10.5194/acp-21-8637-2021, 2021
Short summary
Short summary
Black carbon acts as a strong climate forcer, especially in vulnerable pristine regions such as the Arctic. This work utilizes ensemble modeling results from the task force Hemispheric Transport of Air Pollution Phase 2 to investigate the responses of Arctic black carbon and surface temperature to various source emission reductions. East Asia contributed the most to Arctic black carbon. The response of Arctic temperature to black carbon was substantially more sensitive than the global average.
Kun Zhang, Ling Huang, Qing Li, Juntao Huo, Yusen Duan, Yuhang Wang, Elly Yaluk, Yangjun Wang, Qingyan Fu, and Li Li
Atmos. Chem. Phys., 21, 5905–5917, https://doi.org/10.5194/acp-21-5905-2021, https://doi.org/10.5194/acp-21-5905-2021, 2021
Short summary
Short summary
Recently, high O3 concentrations were frequently observed in rural areas of the Yangtze River Delta (YRD) region under stagnant conditions. Using an online measurement and observation-based model, we investigated the budget of ROx radicals and the influence of isoprene chemistry on O3 formation. Our results underline that isoprene chemistry in the rural atmosphere becomes important with the participation of anthropogenic NOx.
Rui Li, Qiongqiong Wang, Xiao He, Shuhui Zhu, Kun Zhang, Yusen Duan, Qingyan Fu, Liping Qiao, Yangjun Wang, Ling Huang, Li Li, and Jian Zhen Yu
Atmos. Chem. Phys., 20, 12047–12061, https://doi.org/10.5194/acp-20-12047-2020, https://doi.org/10.5194/acp-20-12047-2020, 2020
Xiaofei Qin, Leiming Zhang, Guochen Wang, Xiaohao Wang, Qingyan Fu, Jian Xu, Hao Li, Jia Chen, Qianbiao Zhao, Yanfen Lin, Juntao Huo, Fengwen Wang, Kan Huang, and Congrui Deng
Atmos. Chem. Phys., 20, 10985–10996, https://doi.org/10.5194/acp-20-10985-2020, https://doi.org/10.5194/acp-20-10985-2020, 2020
Short summary
Short summary
The uncertainties in mercury emissions are much larger from natural sources than anthropogenic sources. A method was developed to quantify the contributions of natural surface emissions to ambient GEM based on PMF modeling. The annual GEM concentration in eastern China showed a decreasing trend from 2015 to 2018, while the relative contribution of natural surface emissions increased significantly from 41 % in 2015 to 57 % in 2018, gradually surpassing those from anthropogenic sources.
Cited articles
Aas, K., Jullum, M., and Loland, A.: Explaining individual predictions when
features are dependent: More accurate approximations to Shapley values,
Artificial Intelligence, 298, 103502, https://doi.org/10.1016/j.artint.2021.103502, 2021.
Bahlmann, E., Ebinghaus, R., and Ruck, W.: Development and application of a
laboratory flux measurement system (LFMS) for the investigation of the
kinetics of mercury emissions from soils, J. Environ.
Manag., 81, 114–125, https://doi.org/10.1016/j.jenvman.2005.09.022, 2006.
Bash, J. O. and Miller, D. R.: A note on elevated total gaseous mercury
concentrations downwind from an agriculture field during tilling, Sci. Total
Environ., 388, 379–388, https://doi.org/10.1016/j.scitotenv.2007.07.012, 2007.
Belis, C. A., Karagulian, F., Larsen, B. R., and Hopke, P. K.: Critical
review and meta-analysis of ambient particulate matter source apportionment
using receptor models in Europe, Atmos. Environ., 69, 94–108,
https://doi.org/10.1016/j.atmosenv.2012.11.009, 2013.
Carpi, A. and Lindberg, S. E.: Sunlight-mediated emission of elemental
mercury from soil amended with municipal sewage sludge, Environ.
Sci. Technol., 31, 2085–2091, https://doi.org/10.1021/es960910+, 1997.
Carpi, A., Fostier, A. H., Orta, O. R., dos Santos, J. C., and Gittings, M.:
Gaseous mercury emissions from soil following forest loss and land use
changes: Field experiments in the United States and Brazil, Atmos.
Environ., 96, 423–429, https://doi.org/10.1016/j.atmosenv.2014.08.004, 2014.
Chang, Y., Huang, K., Xie, M., Deng, C., Zou, Z., Liu, S., and Zhang, Y.: First long-term and near real-time measurement of trace elements in China's urban atmosphere: temporal variability, source apportionment and precipitation effect, Atmos. Chem. Phys., 18, 11793–11812, https://doi.org/10.5194/acp-18-11793-2018, 2018.
Chang, Y., Huang, R. J., Ge, X., Huang, X., Hu, J., Duan, Y., Zou, Z., Liu,
X., and Lehmann, M. F.: Puzzling haze events in China during the coronavirus
(COVID-19) shutdown, Geophys. Res. Lett., 47, e2020GL088533, https://doi.org/10.1029/2020GL088533,
2020.
Cheng, I., Xu, X., and Zhang, L.: Overview of receptor-based source apportionment studies for speciated atmospheric mercury, Atmos. Chem. Phys., 15, 7877–7895, https://doi.org/10.5194/acp-15-7877-2015, 2015.
Chong, X., Wang, Y., Liu, R., Zhang, Y., Zhang, Y., and Zheng, W.: Pollution
characteristics and source difference of gaseous elemental mercury between
haze and non-haze days in winter, Sci. Total Environ., 678,
671–680, https://doi.org/10.1016/j.scitotenv.2019.04.338, 2019.
Cole, A. S., Steffen, A., Eckley, C. S., Narayan, J., Pilote, M., Tordon,
R., Graydon, J. A., St Louis, V. L., Xu, X., and Branfireun, B. A.: A Survey
of Mercury in Air and Precipitation across Canada: Patterns and Trends,
Atmosphere, 5, 635–668, https://doi.org/10.3390/atmos5030635, 2014.
Custodio, D., Ebinghaus, R., Spain, T. G., and Bieser, J.: Source apportionment of atmospheric mercury in the remote marine atmosphere: Mace Head GAW station, Irish western coast, Atmos. Chem. Phys., 20, 7929–7939, https://doi.org/10.5194/acp-20-7929-2020, 2020.
Driscoll, C. T., Mason, R. P., Chan, H. M., Jacob, D. J., and Pirrone, N.:
Mercury as a global pollutant: sources, pathways, and effects, Environ.
Sci. Technol., 47, 4967–4983, https://doi.org/10.1021/es305071v, 2013.
Fu, X. W., Zhang, H., Lin, C.-J., Feng, X. B., Zhou, L. X., and Fang, S. X.: Correlation slopes of GEM / CO, GEM / CO2, and GEM / CH4 and estimated mercury emissions in China, South Asia, the Indochinese Peninsula, and Central Asia derived from observations in northwestern and southwestern China, Atmos. Chem. Phys., 15, 1013–1028, https://doi.org/10.5194/acp-15-1013-2015, 2015.
Giang, A. and Selin, N. E.: Benefits of mercury controls for the United
States, P. Natl. Acad. Sci. USA, 113, 286–291, https://doi.org/10.1073/pnas.1514395113, 2016.
Gibson, M. D., Haelssig, J., Pierce, J. R., Parrington, M., Franklin, J. E., Hopper, J. T., Li, Z., and Ward, T. J.: A comparison of four receptor models used to quantify the boreal wildfire smoke contribution to surface PM2.5 in Halifax, Nova Scotia during the BORTAS-B experiment, Atmos. Chem. Phys., 15, 815–827, https://doi.org/10.5194/acp-15-815-2015, 2015.
Grange, S. K., Carslaw, D. C., Lewis, A. C., Boleti, E., and Hueglin, C.: Random forest meteorological normalisation models for Swiss PM10 trend analysis, Atmos. Chem. Phys., 18, 6223–6239, https://doi.org/10.5194/acp-18-6223-2018, 2018.
Gustin, M. S., Engle, M., Ericksen, J., Xin, M., Krabbenhoft, D., Lindberg,
S., Olund, S., and Rytuba, J.: New insights into mercury exchange between
air and substrate, Geochim. Cosmochim. Acta, 69, A700–A700, 2005.
Holmes, C. D., Jacob, D. J., Corbitt, E. S., Mao, J., Yang, X., Talbot, R., and Slemr, F.: Global atmospheric model for mercury including oxidation by bromine atoms, Atmos. Chem. Phys., 10, 12037–12057, https://doi.org/10.5194/acp-10-12037-2010, 2010.
Hopke, P. K.: Review of receptor modeling methods for source apportionment,
J. Air Waste Manage., 66, 237–259,
https://doi.org/10.1080/10962247.2016.1140693, 2016.
Horowitz, H. M., Jacob, D. J., Zhang, Y., Dibble, T. S., Slemr, F., Amos, H. M., Schmidt, J. A., Corbitt, E. S., Marais, E. A., and Sunderland, E. M.: A new mechanism for atmospheric mercury redox chemistry: implications for the global mercury budget, Atmos. Chem. Phys., 17, 6353–6371, https://doi.org/10.5194/acp-17-6353-2017, 2017.
Hou, L., Dai, Q., Song, C., Liu, B., Guo, F., Dai, T., Li, L., Liu, B., Bi,
X., Zhang, Y., and Feng, Y.: Revealing Drivers of Haze Pollution by
Explainable Machine Learning, Environ. Sci. Tech. Let.,
9, 112–119, https://doi.org/10.1021/acs.estlett.1c00865, 2022.
Huang, K.: Gaseous elementary mercury and other air pollutants data during COVID-19, Zenodo [data set], https://doi.org/10.5281/zenodo.6654670, 2022.
Huang, S. and Zhang, Y.: Interannual Variability of Air-Sea Exchange of
Mercury in the Global Ocean: The “Seesaw Effect” in the Equatorial Pacific
and Contributions to the Atmosphere, Environ. Sci. Technol.,
55, 7145–7156, https://doi.org/10.1021/acs.est.1c00691, 2021.
Huang, X., Ding, A., Gao, J., Zheng, B., Zhou, D., Qi, X., Tang, R., Wang,
J., Ren, C., Nie, W., Chi, X., Xu, Z., Chen, L., Li, Y., Che, F., Pang, N.,
Wang, H., Tong, D., Qin, W., Cheng, W., Liu, W., Fu, Q., Liu, B., Chai, F.,
Davis, S. J., Zhang, Q., and He, K.: Enhanced secondary pollution offset
reduction of primary emissions during COVID-19 lockdown in China, Natl. Sci.
Rev., 8, nwaa137, https://doi.org/10.1093/nsr/nwaa137, 2021.
Jiskra, M., Sonke, J. E., Obrist, D., Bieser, J., Ebinghaus, R., Myhre, C.
L., Pfaffhuber, K. A., Wangberg, I., Kyllonen, K., Worthy, D., Martin, L.
G., Labuschagne, C., Mkololo, T., Ramonet, M., Magand, O., and Dommergue,
A.: A vegetation control on seasonal variations in global atmospheric
mercury concentrations, Nat. Geosci., 11, 244–250,
https://doi.org/10.1038/s41561-018-0078-8, 2018.
Kumar, I. E., Venkatasubramanian, S., Scheidegger, C., and Friedler, S. A.:
Problems with Shapley-value-based explanations as feature importance
measures, International Conference on Machine Learning (ICML), Electr.
Network, https://arxiv.org/pdf/2002.11097.pdf (last access: 16 December 2022), 13–18 July 2020, Vienna, Austria, 2020.
Li, H., Huang, K., Fu, Q., Lin, Y., Chen, J., Deng, C., Tian, X., Tang, Q.,
Song, Q., and Wei, Z.: Airborne black carbon variations during the COVID-19
lockdown in the Yangtze River Delta megacities suggest actions to curb
global warming, Environ. Chem. Lett., 20, 1–10, https://doi.org/10.1007/s10311-021-01327-3, 2021.
Lindberg, S., Bullock, R., Ebinghaus, R., Engstrom, D., Feng, X. B.,
Fitzgerald, W., Pirrone, N., Prestbo, E., and Seigneur, C.: A synthesis of
progress and uncertainties in attributing the sources of mercury in
deposition, Ambio, 36, 19–32, 2007.
Liu, K., Wang, S., Wu, Q., Wang, L., Ma, Q., Zhang, L., Li, G., Tian, H.,
Duan, L., and Hao, J.: A Highly Resolved Mercury Emission Inventory of
Chinese Coal-Fired Power Plants, Environ. Sci. Technol., 52,
2400–2408, https://doi.org/10.1021/acs.est.7b06209, 2018.
Liu, K., Wu, Q., Wang, L., Wang, S., Liu, T., Ding, D., Tang, Y., Li, G.,
Tian, H., Duan, L., Wang, X., Fu, X., Feng, X., and Hao, J.:
Measure-Specific Effectiveness of Air Pollution Control on China's
Atmospheric Mercury Concentration and Deposition during 2013–2017,
Environ. Sci. Technol., 53, 8938–8946, https://doi.org/10.1021/acs.est.9b02428, 2019.
Lundberg, S. M. and Lee, S.-I.: A Unified Approach to Interpreting Model
Predictions, 31st Annual Conference on Neural Information Processing Systems
(NIPS), 4–9 December 2017, Long Beach, CA, USA, 2017.
Lundberg, S. M., Nair, B., Vavilala, M. S., Horibe, M., Eisses, M. J.,
Adams, T., Liston, D. E., Low, D. K.-W., Newman, S.-F., Kim, J., and Lee,
S.-I.: Explainable machine-learning predictions for the prevention of
hypoxaemia during surgery, Nature Biomedical Engineering, 2, 749–760,
https://doi.org/10.1038/s41551-018-0304-0, 2018.
Lundberg, S. M., Erion, G., Chen, H., DeGrave, A., Prutkin, J. M., Nair, B.,
Katz, R., Himmelfarb, J., Bansal, N., and Lee, S. I.: From Local
Explanations to Global Understanding with Explainable AI for Trees, Nat. Mach.
Intell., 2, 56–67, https://doi.org/10.1038/s42256-019-0138-9, 2020.
Mangalathu, S., Hwang, S.-H., and Jeon, J.-S.: Failure mode and effects
analysis of RC members based on machine-learning-based SHapley Additive
exPlanations (SHAP) approach, Eng. Struct., 219, 110927,
https://doi.org/10.1016/j.engstruct.2020.110927, 2020.
Mao, H., Cheng, I., and Zhang, L.: Current understanding of the driving mechanisms for spatiotemporal variations of atmospheric speciated mercury: a review, Atmos. Chem. Phys., 16, 12897–12924, https://doi.org/10.5194/acp-16-12897-2016, 2016.
Mazur, M., Mitchell, C. P. J., Eckley, C. S., Eggert, S. L., Kolka, R. K.,
Sebestyen, S. D., and Swain, E. B.: Gaseous mercury fluxes from forest soils
in response to forest harvesting intensity: A field manipulation experiment,
Sci. Total Environ., 496, 678–687, https://doi.org/10.1016/j.scitotenv.2014.06.058, 2014.
Moore, C. and Carpi, A.: Mechanisms of the emission of mercury from soil:
Role of UV radiation, J. Geophys. Res.-Atmos., 110, D24302, https://doi.org/10.1029/2004jd005567,
2005.
Obrist, D., Kirk, J. L., Zhang, L., Sunderland, E. M., Jiskra, M., and
Selin, N. E.: A review of global environmental mercury processes in response
to human and natural perturbations: Changes of emissions, climate, and land
use, Ambio, 47, 116–140, https://doi.org/10.1007/s13280-017-1004-9, 2018.
Outridge, P. M., Mason, R. P., Wang, F., Guerrero, S., and
Heimbürger-Boavida, L. E.: Updated Global and Oceanic Mercury Budgets
for the United Nations Global Mercury Assessment 2018, Environ. Sci. Technol., 52, 11466–11477, https://doi.org/10.1021/acs.est.8b01246, 2018.
Pannu, R., Siciliano, S. D., and O'Driscoll, N. J.: Quantifying the effects
of soil temperature, moisture and sterilization on elemental mercury
formation in boreal soils, Environ. Pollut., 193, 138–146,
https://doi.org/10.1016/j.envpol.2014.06.023, 2014.
Pirrone, N., Cinnirella, S., Feng, X., Finkelman, R. B., Friedli, H. R., Leaner, J., Mason, R., Mukherjee, A. B., Stracher, G. B., Streets, D. G., and Telmer, K.: Global mercury emissions to the atmosphere from anthropogenic and natural sources, Atmos. Chem. Phys., 10, 5951–5964, https://doi.org/10.5194/acp-10-5951-2010, 2010.
Poissant, L., Pilote, M., Constant, P., Beauvais, C., Zhang, H. H., and Xu,
X. H.: Mercury gas exchanges over selected bare soil and flooded sites in
the bay St. Francois wetlands (Quebec, Canada), Atmos. Environ., 38,
4205–4214, https://doi.org/10.1016/j.atmosenv.2004.03.068, 2004.
Qi, Y., Li, Q., Karimian, H., and Liu, D.: A hybrid model for spatiotemporal
forecasting of PM2.5 based on graph convolutional neural network and long
short-term memory, Sci. Total Environ., 664, 1–10,
https://doi.org/10.1016/j.scitotenv.2019.01.333, 2019.
Qin, X., Wang, X., Shi, Y., Yu, G., Zhao, N., Lin, Y., Fu, Q., Wang, D., Xie, Z., Deng, C., and Huang, K.: Characteristics of atmospheric mercury in a suburban area of east China: sources, formation mechanisms, and regional transport, Atmos. Chem. Phys., 19, 5923–5940, https://doi.org/10.5194/acp-19-5923-2019, 2019.
Qin, X., Zhang, L., Wang, G., Wang, X., Fu, Q., Xu, J., Li, H., Chen, J., Zhao, Q., Lin, Y., Huo, J., Wang, F., Huang, K., and Deng, C.: Assessing contributions of natural surface and anthropogenic emissions to atmospheric mercury in a fast-developing region of eastern China from 2015 to 2018, Atmos. Chem. Phys., 20, 10985–10996, https://doi.org/10.5194/acp-20-10985-2020, 2020.
Qin, X., Zhou, S., Li, H., Wang, G., Wang, X., Fu, Q., Duan, Y., Lin, Y.,
Huo, J., Huang, K., and Deng, C.: Simulation of Spatiotemporal Trends of
Gaseous Elemental Mercury in the Yangtze River Delta of Eastern China by an
Artificial Neural Network, Environ. Sci. Tech. Let., 9, 205–211,
https://doi.org/10.1021/acs.estlett.1c01025, 2022.
Selin, N. E., Jacob, D. J., Park, R. J., Yantosca, R. M., Strode, S.,
Jaegle, L., and Jaffe, D.: Chemical cycling and deposition of atmospheric
mercury: Global constraints from observations, J. Geophys. Res.-Atmos., 112, D02308,
https://doi.org/10.1029/2006jd007450, 2007.
Slemr, F., Brunke, E.-G., Ebinghaus, R., and Kuss, J.: Worldwide trend of atmospheric mercury since 1995, Atmos. Chem. Phys., 11, 4779–4787, https://doi.org/10.5194/acp-11-4779-2011, 2011.
Soerensen, A. L., Jacob, D. J., Streets, D. G., Witt, M. L. I., Ebinghaus,
R., Mason, R. P., Andersson, M., and Sunderland, E. M.: Multi-decadal
decline of mercury in the North Atlantic atmosphere explained by changing
subsurface seawater concentrations, Geophys. Res. Lett., 39, L21810,
https://doi.org/10.1029/2012gl053736, 2012.
Steenhuisen, F. and Wilson, S. J.: Development and application of an updated
geospatial distribution model for gridding 2015 global mercury emissions,
Atmos. Environ., 211, 138–150,
https://doi.org/10.1016/j.atmosenv.2019.05.003, 2019.
Stirnberg, R., Cermak, J., Kotthaus, S., Haeffelin, M., Andersen, H., Fuchs, J., Kim, M., Petit, J.-E., and Favez, O.: Meteorology-driven variability of air pollution (PM1) revealed with explainable machine learning, Atmos. Chem. Phys., 21, 3919–3948, https://doi.org/10.5194/acp-21-3919-2021, 2021.
Streets, D. G., Devane, M. K., Lu, Z. F., Bond, T. C., Sunderland, E. M.,
and Jacob, D. J.: All-Time Releases of Mercury to the Atmosphere from Human
Activities, Environ. Sci. Technol., 45, 10485–10491,
https://doi.org/10.1021/es202765m, 2011.
Streets, D. G., Horowitz, H. M., Lu, Z., Levin, L., Thackray, C. P., and
Sunderland, E. M.: Global and regional trends in mercury emissions and
concentrations, 2010–2015, Atmos. Environ., 201, 417–427,
https://doi.org/10.1016/j.atmosenv.2018.12.031, 2019.
Sun, Y., Chen, C., Zhang, Y., Xu, W., Zhou, L., Cheng, X., Zheng, H., Ji,
D., Li, J., Tang, X., Fu, P., and Wang, Z.: Rapid formation and evolution of
an extreme haze episode in Northern China during winter 2015, Scientific
Reports, 6, 27151, https://doi.org/10.1038/srep27151, 2016.
Tang, Y., Wang, S., Wu, Q., Liu, K., Wang, L., Li, S., Gao, W., Zhang, L., Zheng, H., Li, Z., and Hao, J.: Recent decrease trend of atmospheric mercury concentrations in East China: the influence of anthropogenic emissions, Atmos. Chem. Phys., 18, 8279–8291, https://doi.org/10.5194/acp-18-8279-2018, 2018.
Vu, T. V., Shi, Z., Cheng, J., Zhang, Q., He, K., Wang, S., and Harrison, R. M.: Assessing the impact of clean air action on air quality trends in Beijing using a machine learning technique, Atmos. Chem. Phys., 19, 11303–11314, https://doi.org/10.5194/acp-19-11303-2019, 2019.
Wang, C., Feng, L., and Qi, Y.: Explainable deep learning predictions for
illness risk of mental disorders in Nanjing, China, Environ. Res.,
202, 111740, https://doi.org/10.1016/j.envres.2021.111740,
2021.
Wang, G. C., Huang, K., Fu, Q. Y., Chen, J., Huo, J. T., Zhao, Q. B., Duan, Y. S., Lin, Y. F., Yang, F., Zhang, W. J., Li, H., Xu, J., Qin, X. F., Zhao, N., and Deng, C. R.: Response of PM2.5-bound elemental species to emission variations and associated health risk assessment during the COVID-19 pandemic in a coastal megacity, J. Environ. Sci., 122, 115–127, https://doi.org/10.1016/j.jes.2021.10.005, 2022a.
Wang, G. C., Chen, J., Xu, J., Yun, L., Zhang, M. D., Li, H., Qin, X. F.,
Deng, C. R., Zheng, H. T., Gui, H. Q., Liu, J. G., and Huang, K.:
Atmospheric Processing at the Sea-Land Interface Over the South China Sea:
Secondary Aerosol Formation, Aerosol Acidity, and Role of Sea Salts, J.
Geophys. Res.-Atmos., 127, e2021JD036255, https://doi.org/10.1029/2021jd036255, 2022b.
Wang, S. F., Feng, X. B., Qiu, G. L., Fu, X. W., and Wei, Z. Q.:
Characteristics of mercury exchange flux between soil and air in the heavily
air-polluted area, eastern Guizhou, China, Atmos. Environ., 41,
5584–5594, https://doi.org/10.1016/j.atmosenv.2007.03.002, 2007.
Wang, X., Lin, C.-J., and Feng, X.: Sensitivity analysis of an updated bidirectional air–surface exchange model for elemental mercury vapor, Atmos. Chem. Phys., 14, 6273–6287, https://doi.org/10.5194/acp-14-6273-2014, 2014.
Wang, X., Lin, C.-J., Yuan, W., Sommar, J., Zhu, W., and Feng, X.: Emission-dominated gas exchange of elemental mercury vapor over natural surfaces in China, Atmos. Chem. Phys., 16, 11125–11143, https://doi.org/10.5194/acp-16-11125-2016, 2016.
Wanninkhof, R.: Relationship between wind speed and gas exchange over the
ocean revisited, Limnol. Oceanogr.-Meth., 12, 351–362,
https://doi.org/10.4319/lom.2014.12.351, 2014.
Wen, M., Wu, Q., Li, G., Wang, S., Li, Z., Tang, Y., Xu, L., and Liu, T.:
Impact of ultra-low emission technology retrofit on the mercury emissions
and cross-media transfer in coal-fired power plants, J. Hazard.
Mater., 396, 122729, https://doi.org/10.1016/j.jhazmat.2020.122729, 2020.
Wu, Q., Tang, Y., Wang, L., Wang, S., Han, D., Ouyang, D., Jiang, Y., Xu,
P., Xue, Z., and Hu, J.: Impact of emission reductions and meteorology
changes on atmospheric mercury concentrations during the COVID-19 lockdown,
Sci. Total Environ., 750, 142323,
https://doi.org/10.1016/j.scitotenv.2020.142323, 2021.
Wu, Q. R., Wang, S. X., Li, G. L., Liang, S., Lin, C. J., Wang, Y. F., Cai,
S. Y., Liu, K. Y., and Hao, J. M.: Temporal Trend and Spatial Distribution
of Speciated Atmospheric Mercury Emissions in China During 1978–2014,
Environ. Sci. Technol., 50, 13428–13435,
https://doi.org/10.1021/acs.est.6b04308, 2016.
Wu, Q. R., Wang, S. X., Liu, K. Y., Li, G. L., and Hao, J. M.:
Emission-Limit-Oriented Strategy To Control Atmospheric Mercury Emissions in
Coal-Fired Power Plants toward the Implementation of the Minamata
Convention, Environ. Sci. Technol., 52, 11087–11093,
https://doi.org/10.1021/acs.est.8b02250, 2018.
Xin, M. and Gustin, M. S.: Gaseous elemental mercury exchange with low
mercury containing soils: Investigation of controlling factors, Appl.
Geochem., 22, 1451–1466, https://doi.org/10.1016/j.apgeochem.2007.02.006, 2007.
Xu, J., Wang, Q. Z., Deng, C. R., McNeill, V. F., Fankhauser, A., Wang, F.
W., Zheng, X. J., Shen, J. D., Huang, K., and Zhuang, G. S.: Insights into
the characteristics and sources of primary and secondary organic carbon:
High time resolution observation in urban Shanghai, Environ. Pollut.,
233, 1177–1187, https://doi.org/10.1016/j.envpol.2017.10.003, 2018.
Xu, J., Chen, J., Shi, Y. J., Zhao, N., Qin, X. F., Yu, G. Y., Liu, J. M.,
Lin, Y. F., Fu, Q. Y., Weber, R. J., Lee, S. H., Deng, C. R., and Huang, K.:
First Continuous Measurement of Gaseous and Particulate Formic Acid in a
Suburban Area of East China: Seasonality and Gas-Particle Partitioning, Acs
Earth and Space Chemistry, 4, 157–167, https://doi.org/10.1021/acsearthspacechem.9b00210,
2020.
Xu, X., Liao, Y., Cheng, I., and Zhang, L.: Potential sources and processes affecting speciated atmospheric mercury at Kejimkujik National Park, Canada: comparison of receptor models and data treatment methods, Atmos. Chem. Phys., 17, 1381–1400, https://doi.org/10.5194/acp-17-1381-2017, 2017.
Yang, J., Wen, Y., Wang, Y., Zhang, S., Pinto, J. P., Pennington, E. A.,
Wang, Z., Wu, Y., Sander, S. P., Jiang, J. H., Hao, J., Yung, Y. L., and
Seinfeld, J. H.: From COVID-19 to future electrification: Assessing traffic
impacts on air quality by a machine-learning model, P. Natl. Acad. Sci. USA, 118, e2102705118, https://doi.org/10.1073/pnas.2102705118, 2021.
Yin, X., Kang, S., de Foy, B., Ma, Y., Tong, Y., Zhang, W., Wang, X., Zhang, G., and Zhang, Q.: Multi-year monitoring of atmospheric total gaseous mercury at a remote high-altitude site (Nam Co, 4730 m a.s.l.) in the inland Tibetan Plateau region, Atmos. Chem. Phys., 18, 10557–10574, https://doi.org/10.5194/acp-18-10557-2018, 2018.
Yu, Y., He, S., Wu, X., Zhang, C., Yao, Y., Liao, H., Wang, Q., and Xie, M.:
PM2.5 elements at an urban site in Yangtze River Delta, China: High
time-resolved measurement and the application in source apportionment,
Environ. Pollut., 253, 1089–1099, https://doi.org/10.1016/j.envpol.2019.07.096, 2019.
Zhang, L., Wang, S. X., Wang, L., and Hao, J. M.: Atmospheric mercury concentration and chemical speciation at a rural site in Beijing, China: implications of mercury emission sources, Atmos. Chem. Phys., 13, 10505–10516, https://doi.org/10.5194/acp-13-10505-2013, 2013.
Zhang, L., Wang, S., Wu, Q., Wang, F., Lin, C.-J., Zhang, L., Hui, M., Yang, M., Su, H., and Hao, J.: Mercury transformation and speciation in flue gases from anthropogenic emission sources: a critical review, Atmos. Chem. Phys., 16, 2417–2433, https://doi.org/10.5194/acp-16-2417-2016, 2016a.
Zhang, Y., Jacob, D. J., Horowitz, H. M., Chen, L., Amos, H. M.,
Krabbenhoft, D. P., Slemr, F., St Louis, V. L., and Sunderland, E. M.:
Observed decrease in atmospheric mercury explained by global decline in
anthropogenic emissions, P. Natl. Acad. Sci. USA, 113, 526–531,
https://doi.org/10.1073/pnas.1516312113, 2016b.
Zheng, B., Tong, D., Li, M., Liu, F., Hong, C., Geng, G., Li, H., Li, X., Peng, L., Qi, J., Yan, L., Zhang, Y., Zhao, H., Zheng, Y., He, K., and Zhang, Q.: Trends in China's anthropogenic emissions since 2010 as the consequence of clean air actions, Atmos. Chem. Phys., 18, 14095–14111, https://doi.org/10.5194/acp-18-14095-2018, 2018.
Zhong, S., Zhang, K., Wang, D., and Zhang, H.: Shedding light on “Black
Box” machine learning models for predicting the reactivity of HO radicals
toward organic compounds, Chem. Eng. J., 405, 126627,
https://doi.org/10.1016/j.cej.2020.126627, 2021.
Zhu, J., Wang, T., Talbot, R., Mao, H., Hall, C. B., Yang, X., Fu, C., Zhuang, B., Li, S., Han, Y., and Huang, X.: Characteristics of atmospheric Total Gaseous Mercury (TGM) observed in urban Nanjing, China, Atmos. Chem. Phys., 12, 12103–12118, https://doi.org/10.5194/acp-12-12103-2012, 2012.
Zhu, W., Sommar, J., Li, Z., Feng, X., Lin, C.-J., and Li, G.: Highly
elevated emission of mercury vapor due to the spontaneous combustion of
refuse in a landfill, Atmos. Environ., 79, 540–545,
https://doi.org/10.1016/j.atmosenv.2013.07.016, 2013.
Zhu, W., Lin, C.-J., Wang, X., Sommar, J., Fu, X., and Feng, X.: Global observations and modeling of atmosphere–surface exchange of elemental mercury: a critical review, Atmos. Chem. Phys., 16, 4451–4480, https://doi.org/10.5194/acp-16-4451-2016, 2016.
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...
Altmetrics
Final-revised paper
Preprint