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Cui J, Li J, Zhang H, Zhang R, Ma W, Zhu Y, Yuan W, Palocz-Andresen M, Zhao Y, Lou Z. Synergistic control potential of flue gas pollutants under Ultra-Low emission standards in waste incineration plants. ENVIRONMENT INTERNATIONAL 2024; 186:108590. [PMID: 38521045 DOI: 10.1016/j.envint.2024.108590] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/15/2024] [Revised: 02/27/2024] [Accepted: 03/19/2024] [Indexed: 03/25/2024]
Abstract
As the dominant waste disposal process, incineration is regarded as the main incentive for the "not-in-my-backyard" syndrome, and faces an inescapable pressures of ultra-low emissions (ULE). Establishing precise response relationships between emission factors (EFs) and full-process influencing factors can provide guidance for the synergistic mitigation of flue gas pollutants (FGPs). In this work, the multi-dimensional EFs of FGPs were identified by initially integrating FGPs concentration monitoring data of existing 1,226 processing lines in China, technologies applied and operational experience (OE), local economic and political characteristics. Significant regional imbalance performance was observed, which EFs in the coastal regions were 3.55-92.39 % lower than those of the inland areas. NOx, SO2, HCl were identified as critical components requiring further reduction under the ULE standards, with exceedance rates recorded at 73.07 %, 38.90 %, and 56.69 %, respectively. An indicative value of 20 mg/m3 for PM is recommended for the control of heavy metals of Cd + Tl and Sb + As + Pb + Cr + Co + Cu + Mn + Ni based on the correlation coefficients of r = 0.28 (p < 0.001) and r = 0.20 (p = 0.002), respectively. Waste composition and OE were quantified as the main contributors of EFs' disparities by the tree-branching controlled variable approach established in this study. Predictive models for FGPs control process and corresponding EFs were constructed. EFs of nine FGPs in 2030 would decrease by 0.97-65.42 %, due to more complex purification processes employed to meet ULE's limitations, such as the application of five-stage processes growing from 45.60 % to 58.28 %. While regional imbalance in EFs-SO2 and EFs-HCl were extended with increases from 25.83 % to 33.07 % and 9.91 % to 32.32 %, respectively, due to the consistent disparities of OE and growing heterogeneity of control policies. Enhancing interregional empirical exchanges, reducing the regional market monopolies, and formulating technical guidelines would be beneficial to synergize the reduction of FGPs emissions and alleviate regional imbalance.
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Affiliation(s)
- Jicui Cui
- Shanghai Engineering Research Center of Solid Waste Treatment and Resource, School of Environmental Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Jiyang Li
- Shanghai Engineering Research Center of Solid Waste Treatment and Resource, School of Environmental Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Haoyu Zhang
- Shanghai Engineering Research Center of Solid Waste Treatment and Resource, School of Environmental Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Ruina Zhang
- Shanghai Environmental Sanitation Engineering Design Institute Co., Ltd, Shanghai 200323, China
| | - Wenchao Ma
- School of Environmental Science and Engineering / Key Laboratory of Efficient Utilization of Low and Medium Grade Energy (MoE) / Tianjin Key Lab of Biomass-wastes Utilization, Tianjin University, Tianjin 300072, China; College of Ecology and Environment, Key Laboratory of Agro-Forestry Environmental Processes and Ecological Regulation of Hainan Province, Hainan University, Haikou 570228, China
| | - Ying Zhu
- Qilu University of Technology (Shandong Academy of Sciences), Advanced Materials Institute, Shandong Engineering Research Centre of Municipal Sludge Disposal, Jinan 250014, China
| | - Wenxiang Yuan
- Shanghai Institute for Design & Research on Environmental Engineering, Shanghai 200232, China
| | | | - Youcai Zhao
- State Key Laboratory of Pollution Control and Resource Reuse, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, China
| | - Ziyang Lou
- Shanghai Engineering Research Center of Solid Waste Treatment and Resource, School of Environmental Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China; Shanghai Institute of Pollution Control and Ecological Security, Shanghai 200092, China.
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2
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Ye X, Zhang L, Wang X, Lu X, Jiang Z, Lu N, Li D, Xu J. Spatial and temporal variations of surface background ozone in China analyzed with the grid-stretching capability of GEOS-Chem High Performance. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 914:169909. [PMID: 38185162 DOI: 10.1016/j.scitotenv.2024.169909] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Revised: 12/11/2023] [Accepted: 01/02/2024] [Indexed: 01/09/2024]
Abstract
Surface background ozone, defined as the ozone in the absence of domestic anthropogenic emissions, is important for developing emission reduction strategies. Here we apply the recently developed GEOS-Chem High Performance (GCHP) global atmospheric chemistry model with ∼0.5° stretched resolution over China to understand the sources of Chinese background ozone (CNB) in the metric of daily maximum 8 h average (MDA8) and to identify the drivers of its interannual variability (IAV) from 2015 to 2019. The GCHP ozone simulations over China are evaluated with an ensemble of surface and aircraft measurements. The five-year national-mean CNB ozone is estimated as 37.9 ppbv, with a spatially west-to-southeast downward gradient (55 to 25 ppbv) and a summer peak (42.5 ppbv). High background levels in western China are due to abundant transport from the free troposphere and adjacent foreign regions, while in eastern China, domestic formation from surface natural precursors is also important. We find greater importance of soil nitric oxides (NOx) than biogenic volatile organic compound emissions to CNB ozone in summer (6.4 vs. 3.9 ppbv), as ozone formation becomes increasingly NOx-sensitive when suppressing anthropogenic emissions. The percentage of daily CNB ozone to total surface ozone generally decreases with increasing daily total ozone, indicating an increased contribution of domestic anthropogenic emissions on polluted days. CNB ozone shows the largest IAV in summer, with standard deviations (seasonal means) of ∼5 ppbv over Qinghai-Tibet Plateau (QTP) and >3.5 ppbv in eastern China. CNB values in QTP are strongly correlated with horizontal circulation anomalies in the middle troposphere, while soil NOx emissions largely drive the IAV in the east. El Nino can inhibit CNB ozone formation in Southeast China by increased precipitation and lower temperature locally in spring, but enhance CNB in Southwest China through increased biomass burning emissions in Southeast Asia.
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Affiliation(s)
- Xingpei Ye
- Laboratory for Climate and Ocean-Atmosphere Studies, Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing 100871, China
| | - Lin Zhang
- Laboratory for Climate and Ocean-Atmosphere Studies, Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing 100871, China.
| | - Xiaolin Wang
- Laboratory for Climate and Ocean-Atmosphere Studies, Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing 100871, China
| | - Xiao Lu
- School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai, Guangdong, China
| | - Zhongjing Jiang
- Environmental and Climate Sciences Department, Brookhaven National Laboratory, Upton, NY 11973-5000, United States of America
| | - Ni Lu
- Laboratory for Climate and Ocean-Atmosphere Studies, Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing 100871, China
| | - Danyang Li
- Laboratory for Climate and Ocean-Atmosphere Studies, Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing 100871, China
| | - Jiayu Xu
- Laboratory for Climate and Ocean-Atmosphere Studies, Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing 100871, China
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3
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Sun Z, Tan J, Wang F, Li R, Zhang X, Liao J, Wang Y, Huang L, Zhang K, Fu JS, Li L. Regional background ozone estimation for China through data fusion of observation and simulation. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 912:169411. [PMID: 38123088 DOI: 10.1016/j.scitotenv.2023.169411] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/24/2023] [Revised: 12/05/2023] [Accepted: 12/13/2023] [Indexed: 12/23/2023]
Abstract
Regional background ozone (O3_RBG) is an important component of surface ozone (O3). However, due to the uncertainties in commonly used Chemical Transport Models (CTMs) and statistical models, accurately assessing O3_RBG in China is challenging. In this study, we calculated the O3_RBG concentrations with the CTM - Brute Force Method (BFM) and constrained the results with site observations of O3 with the multiple linear regression (MLR) model. The annual average O3_RBG concentration in China region in 2020 is 35 ± 4 ppb, accounting for 81 ± 5 % of the maximum 8-h average O3 (MDA8 O3). We applied the random forest and Shapley additive explanations based on meteorological standardization techniques to separate the contributions of meteorology and natural emissions to O3_RBG. Natural emissions contribute more significantly to O3_RBG than meteorology in various Chineses regions (30-40 ppb), with higher contributions during the warm season. Meteorological factors show higher contributions in the spring and summer seasons (2-3 ppb) than the other seasons. Temperature and humidity are the primary contributors to O3_RBG in regions with severe O3 pollution in China, with their individual impacts ranging from 30 % to 62 % of the total impacts of all meteorological factors in different seasons. For policy implications, we tracked the contributions of O3_RBG and local photochemical reaction contributions (O3_LC) to total O3 concentration at different O3 levels. We found that O3_LC contribute over 45 % to MDA8 O3 on polluted days, supporting the current Chinese policy of reducing O3 peak concentrations by cutting down precursor emissions. However, as the contribution of O3_RBG is not considered in the policy, additional efforts are needed to achieve the control groal of O3 concentration. As the implementation of stringent O3 control measurements in China, the contribution of O3_RBG become increasingly significant, suggesting the need for attention to O3_RBG and regional joint prevention and control.
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Affiliation(s)
- Zhixu Sun
- School of Environmental and Chemical Engineering, Shanghai University, Shanghai 200444, China; Key Laboratory of Organic Compound Pollution Control Engineering (MOE), Shanghai University, Shanghai 200444, China
| | - Jiani Tan
- School of Environmental and Chemical Engineering, Shanghai University, Shanghai 200444, China; Key Laboratory of Organic Compound Pollution Control Engineering (MOE), Shanghai University, Shanghai 200444, China
| | - Fangting Wang
- School of Environmental and Chemical Engineering, Shanghai University, Shanghai 200444, China; Key Laboratory of Organic Compound Pollution Control Engineering (MOE), Shanghai University, Shanghai 200444, China
| | - Rui Li
- School of Environmental and Chemical Engineering, Shanghai University, Shanghai 200444, China; Key Laboratory of Organic Compound Pollution Control Engineering (MOE), Shanghai University, Shanghai 200444, China
| | - Xinxin Zhang
- School of Environmental and Chemical Engineering, Shanghai University, Shanghai 200444, China; Key Laboratory of Organic Compound Pollution Control Engineering (MOE), Shanghai University, Shanghai 200444, China
| | - Jiaqiang Liao
- School of Environmental and Chemical Engineering, Shanghai University, Shanghai 200444, China; Key Laboratory of Organic Compound Pollution Control Engineering (MOE), Shanghai University, Shanghai 200444, China
| | - Yangjun Wang
- School of Environmental and Chemical Engineering, Shanghai University, Shanghai 200444, China; Key Laboratory of Organic Compound Pollution Control Engineering (MOE), Shanghai University, Shanghai 200444, China
| | - Ling Huang
- School of Environmental and Chemical Engineering, Shanghai University, Shanghai 200444, China; Key Laboratory of Organic Compound Pollution Control Engineering (MOE), Shanghai University, Shanghai 200444, China
| | - Kun Zhang
- School of Environmental and Chemical Engineering, Shanghai University, Shanghai 200444, China; Key Laboratory of Organic Compound Pollution Control Engineering (MOE), Shanghai University, Shanghai 200444, China
| | - Joshua S Fu
- Deparent of Civil and Environmental Engineering, University of Tennessee, Knoxville, TN 37996, USA
| | - Li Li
- School of Environmental and Chemical Engineering, Shanghai University, Shanghai 200444, China; Key Laboratory of Organic Compound Pollution Control Engineering (MOE), Shanghai University, Shanghai 200444, China.
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4
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Ni Y, Yang Y, Wang H, Li H, Li M, Wang P, Li K, Liao H. Contrasting changes in ozone during 2019-2021 between eastern and the other regions of China attributed to anthropogenic emissions and meteorological conditions. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 908:168272. [PMID: 37924894 DOI: 10.1016/j.scitotenv.2023.168272] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Revised: 10/09/2023] [Accepted: 10/30/2023] [Indexed: 11/06/2023]
Abstract
Ozone pollution is one of the most severe air quality issues in China that poses a serious threat to human health and ecosystems. During 2019-2021, the maximum daily 8-h average ozone concentrations in eastern China (110-122.5°E, 26-42°N) and the rest of China (ROC) show different decreasing patterns, with ozone concentrations in eastern China decreasing by 14.9 μg/m3, which is much larger than 4.8 μg/m3 in ROC. Here, based on two independent methods, the atmospheric chemical transport model (GEOS-Chem) simulations and the machine learning (ML) model (LightGBM) predictions, the reasons for the differences in ozone changes between eastern China and ROC during the warm season (April to September) are investigated. According to the GEOS-Chem (LightGBM) results, changes in the meteorological conditions contributed to an ozone decrease by 7.3 (6.8) μg/m3 in eastern China due to decreased chemical production and an ozone decrease by 6.8 (7.0) μg/m3 in ROC attributed to the weakened horizontal and vertical advection. With the influence of meteorological factors excluded, the observations show that changes in anthropogenic emissions resulted in an ozone decrease by 7.6 (8.1) μg/m3 in eastern China and an ozone increase by 2.0 (2.2) μg/m3 in ROC, which is primarily induced by the changes in NOx emissions. The surface measurements and satellite retrievals also indicate that the reduction in NOx emissions in ROC is less efficient than that in the more developed eastern China, leading to contrasting changes in ozone concentrations between eastern China and ROC during 2019-2021. Our results highlight the critical need to reduce ozone precursor emissions in the rest regions of China apart from eastern China.
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Affiliation(s)
- Yiqian Ni
- Joint International Research Laboratory of Climate and Environment Change (ILCEC), Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, Nanjing, Jiangsu, China
| | - Yang Yang
- Joint International Research Laboratory of Climate and Environment Change (ILCEC), Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, Nanjing, Jiangsu, China.
| | - Hailong Wang
- Atmospheric Sciences and Global Change Division, Pacific Northwest National Laboratory, Richland, Washington, USA
| | - Huimin Li
- Joint International Research Laboratory of Climate and Environment Change (ILCEC), Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, Nanjing, Jiangsu, China
| | - Mengyun Li
- Joint International Research Laboratory of Climate and Environment Change (ILCEC), Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, Nanjing, Jiangsu, China
| | - Pinya Wang
- Joint International Research Laboratory of Climate and Environment Change (ILCEC), Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, Nanjing, Jiangsu, China
| | - Ke Li
- Joint International Research Laboratory of Climate and Environment Change (ILCEC), Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, Nanjing, Jiangsu, China
| | - Hong Liao
- Joint International Research Laboratory of Climate and Environment Change (ILCEC), Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, Nanjing, Jiangsu, China
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5
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Clifton OE, Schwede D, Hogrefe C, Bash JO, Bland S, Cheung P, Coyle M, Emberson L, Flemming J, Fredj E, Galmarini S, Ganzeveld L, Gazetas O, Goded I, Holmes CD, Horváth L, Huijnen V, Li Q, Makar PA, Mammarella I, Manca G, Munger JW, Pérez-Camanyo JL, Pleim J, Ran L, Jose RS, Silva SJ, Staebler R, Sun S, Tai APK, Tas E, Vesala T, Weidinger T, Wu Z, Zhang L. A single-point modeling approach for the intercomparison and evaluation of ozone dry deposition across chemical transport models (Activity 2 of AQMEII4). ATMOSPHERIC CHEMISTRY AND PHYSICS 2023; 23:9911-9961. [PMID: 37990693 PMCID: PMC10659075 DOI: 10.5194/acp-23-9911-2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/23/2023]
Abstract
A primary sink of air pollutants and their precursors is dry deposition. Dry deposition estimates differ across chemical transport models, yet an understanding of the model spread is incomplete. Here, we introduce Activity 2 of the Air Quality Model Evaluation International Initiative Phase 4 (AQMEII4). We examine 18 dry deposition schemes from regional and global chemical transport models as well as standalone models used for impact assessments or process understanding. We configure the schemes as single-point models at eight Northern Hemisphere locations with observed ozone fluxes. Single-point models are driven by a common set of site-specific meteorological and environmental conditions. Five of eight sites have at least 3 years and up to 12 years of ozone fluxes. The interquartile range across models in multiyear mean ozone deposition velocities ranges from a factor of 1.2 to 1.9 annually across sites and tends to be highest during winter compared with summer. No model is within 50 % of observed multiyear averages across all sites and seasons, but some models perform well for some sites and seasons. For the first time, we demonstrate how contributions from depositional pathways vary across models. Models can disagree with respect to relative contributions from the pathways, even when they predict similar deposition velocities, or agree with respect to the relative contributions but predict different deposition velocities. Both stomatal and nonstomatal uptake contribute to the large model spread across sites. Our findings are the beginning of results from AQMEII4 Activity 2, which brings scientists who model air quality and dry deposition together with scientists who measure ozone fluxes to evaluate and improve dry deposition schemes in the chemical transport models used for research, planning, and regulatory purposes.
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Affiliation(s)
- Olivia E. Clifton
- NASA Goddard Institute for Space Studies, New York, NY, USA
- Center for Climate Systems Research, Columbia Climate School, Columbia University in the City of New York, New York, NY, USA
| | - Donna Schwede
- Office of Research and Development, United States Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Christian Hogrefe
- Office of Research and Development, United States Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Jesse O. Bash
- Office of Research and Development, United States Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Sam Bland
- Stockholm Environment Institute, Environment and Geography Department, University of York, York, UK
| | - Philip Cheung
- Air Quality Research Division, Atmospheric Science and Technology Directorate, Environment and Climate Change Canada, Toronto, Canada
| | - Mhairi Coyle
- United Kingdom Centre for Ecology and Hydrology, Bush Estate, Penicuik, Midlothian, UK
- The James Hutton Institute, Craigiebuckler, Aberdeen, UK
| | - Lisa Emberson
- Environment and Geography Department, University of York, York, UK
| | | | - Erick Fredj
- Department of Computer Science, The Jerusalem College of Technology, Jerusalem, Israel
| | | | - Laurens Ganzeveld
- Meteorology and Air Quality Section, Wageningen University, Wageningen, the Netherlands
| | - Orestis Gazetas
- Joint Research Centre (JRC), European Commission, Ispra, Italy
| | - Ignacio Goded
- Joint Research Centre (JRC), European Commission, Ispra, Italy
| | - Christopher D. Holmes
- Department of Earth, Ocean and Atmospheric Science, Florida State University, Tallahassee, FL, USA
| | - László Horváth
- ELKH-SZTE Photoacoustic Research Group, Department of Optics and Quantum Electronics, University of Szeged, Szeged, Hungary
| | - Vincent Huijnen
- Royal Netherlands Meteorological Institute, De Bilt, the Netherlands
| | - Qian Li
- The Institute of Environmental Sciences, The Robert H. Smith Faculty of Agriculture, Food and Environment, The Hebrew University of Jerusalem, Rehovot, Israel
| | - Paul A. Makar
- Air Quality Research Division, Atmospheric Science and Technology Directorate, Environment and Climate Change Canada, Toronto, Canada
| | - Ivan Mammarella
- Institute for Atmospheric and Earth System Research/Physics, Faculty of Science, University of Helsinki, Helsinki, Finland
| | - Giovanni Manca
- Joint Research Centre (JRC), European Commission, Ispra, Italy
| | - J. William Munger
- School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
- Department of Earth and Planetary Sciences, Harvard University, Cambridge, MA, USA
| | | | - Jonathan Pleim
- Center for Environmental Measurement and Modeling, United States Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Limei Ran
- Natural Resources Conservation Service, United States Department of Agriculture, Greensboro, NC, USA
| | - Roberto San Jose
- Computer Science School, Technical University of Madrid (UPM), Madrid, Spain
| | - Sam J. Silva
- Department of Earth Sciences, University of Southern California, Los Angeles, CA, USA
| | - Ralf Staebler
- Air Quality Research Division, Atmospheric Science and Technology Directorate, Environment and Climate Change Canada, Toronto, Canada
| | - Shihan Sun
- Earth and Environmental Sciences Programme, Faculty of Science, The Chinese University of Hong Kong, Hong Kong, China
| | - Amos P. K. Tai
- Earth and Environmental Sciences Programme, Faculty of Science, The Chinese University of Hong Kong, Hong Kong, China
- State Key Laboratory of Agrobiotechnology, The Chinese University of Hong Kong, Hong Kong, China
- Institute of Environment, Energy and Sustainability, The Chinese University of Hong Kong, Hong Kong, China
| | - Eran Tas
- The Institute of Environmental Sciences, The Robert H. Smith Faculty of Agriculture, Food and Environment, The Hebrew University of Jerusalem, Rehovot, Israel
| | - Timo Vesala
- Institute for Atmospheric and Earth System Research/Physics, Faculty of Science, University of Helsinki, Helsinki, Finland
- Institute for Atmospheric and Earth System Research/Forest Sciences, Faculty of Agriculture and Forestry, University of Helsinki, Helsinki, Finland
| | - Tamás Weidinger
- Department of Meteorology, Institute of Geography and Earth Sciences, Eötvös Loránd University, Budapest, Hungary
| | - Zhiyong Wu
- ORISE Fellow at Center for Environmental Measurement and Modeling, United States Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Leiming Zhang
- Air Quality Research Division, Atmospheric Science and Technology Directorate, Environment and Climate Change Canada, Toronto, Canada
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Tan W, Wang H, Su J, Sun R, He C, Lu X, Lin J, Xue C, Wang H, Liu Y, Liu L, Zhang L, Wu D, Mu Y, Fan S. Soil Emissions of Reactive Nitrogen Accelerate Summertime Surface Ozone Increases in the North China Plain. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023; 57:12782-12793. [PMID: 37596963 DOI: 10.1021/acs.est.3c01823] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/21/2023]
Abstract
Summertime surface ozone in China has been increasing since 2013 despite the policy-driven reduction in fuel combustion emissions of nitrogen oxides (NOx). Here we examine the role of soil reactive nitrogen (Nr, including NOx and nitrous acid (HONO)) emissions in the 2013-2019 ozone increase over the North China Plain (NCP), using GEOS-Chem chemical transport model simulations. We update soil NOx emissions and add soil HONO emissions in GEOS-Chem based on observation-constrained parametrization schemes. The model estimates significant daily maximum 8 h average (MDA8) ozone enhancement from soil Nr emissions of 8.0 ppbv over the NCP and 5.5 ppbv over China in June-July 2019. We identify a strong competing effect between combustion and soil Nr sources on ozone production in the NCP region. We find that soil Nr emissions accelerate the 2013-2019 June-July ozone increase over the NCP by 3.0 ppbv. The increase in soil Nr ozone contribution, however, is not primarily driven by weather-induced increases in soil Nr emissions, but by the concurrent decreases in fuel combustion NOx emissions, which enhance ozone production efficiency from soil by pushing ozone production toward a more NOx-sensitive regime. Our results reveal an important indirect effect from fuel combustion NOx emission reduction on ozone trends by increasing ozone production from soil Nr emissions, highlighting the necessity to consider the interaction between anthropogenic and biogenic sources in ozone mitigation in the North China Plain.
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Affiliation(s)
- Wanshan Tan
- School of Atmospheric Sciences, Sun Yat-sen University, and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, Guangdong 519082, People's Republic of China
- Guangdong Provincial Observation and Research Station for Climate Environment and Air Quality Change in the Pearl River Estuary, Zhuhai, Guangdong 519082, People's Republic of China
- Key Laboratory of Tropical Atmosphere-Ocean System, Ministry of Education, Zhuhai, China, Zhuhai, Guangdong 519082, People's Republic of China
| | - Haolin Wang
- School of Atmospheric Sciences, Sun Yat-sen University, and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, Guangdong 519082, People's Republic of China
- Guangdong Provincial Observation and Research Station for Climate Environment and Air Quality Change in the Pearl River Estuary, Zhuhai, Guangdong 519082, People's Republic of China
- Key Laboratory of Tropical Atmosphere-Ocean System, Ministry of Education, Zhuhai, China, Zhuhai, Guangdong 519082, People's Republic of China
| | - Jiayin Su
- School of Atmospheric Sciences, Sun Yat-sen University, and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, Guangdong 519082, People's Republic of China
- Guangdong Provincial Observation and Research Station for Climate Environment and Air Quality Change in the Pearl River Estuary, Zhuhai, Guangdong 519082, People's Republic of China
- Key Laboratory of Tropical Atmosphere-Ocean System, Ministry of Education, Zhuhai, China, Zhuhai, Guangdong 519082, People's Republic of China
| | - Ruize Sun
- School of Atmospheric Sciences, Sun Yat-sen University, and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, Guangdong 519082, People's Republic of China
- Guangdong Provincial Observation and Research Station for Climate Environment and Air Quality Change in the Pearl River Estuary, Zhuhai, Guangdong 519082, People's Republic of China
- Key Laboratory of Tropical Atmosphere-Ocean System, Ministry of Education, Zhuhai, China, Zhuhai, Guangdong 519082, People's Republic of China
| | - Cheng He
- School of Atmospheric Sciences, Sun Yat-sen University, and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, Guangdong 519082, People's Republic of China
- Guangdong Provincial Observation and Research Station for Climate Environment and Air Quality Change in the Pearl River Estuary, Zhuhai, Guangdong 519082, People's Republic of China
- Key Laboratory of Tropical Atmosphere-Ocean System, Ministry of Education, Zhuhai, China, Zhuhai, Guangdong 519082, People's Republic of China
| | - Xiao Lu
- School of Atmospheric Sciences, Sun Yat-sen University, and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, Guangdong 519082, People's Republic of China
- Guangdong Provincial Observation and Research Station for Climate Environment and Air Quality Change in the Pearl River Estuary, Zhuhai, Guangdong 519082, People's Republic of China
- Key Laboratory of Tropical Atmosphere-Ocean System, Ministry of Education, Zhuhai, China, Zhuhai, Guangdong 519082, People's Republic of China
| | - Jintai Lin
- Laboratory for Climate and Ocean-Atmosphere Studies, Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing 100871, People's Republic of China
| | - Chaoyang Xue
- Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
- Laboratoire de Physique et Chimie de l'Environnement et de l'Espace (LPC2E), CNRS-Université Orléans-CNES, CEDEX 2 Orléans 45071, France
| | - Haichao Wang
- School of Atmospheric Sciences, Sun Yat-sen University, and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, Guangdong 519082, People's Republic of China
- Guangdong Provincial Observation and Research Station for Climate Environment and Air Quality Change in the Pearl River Estuary, Zhuhai, Guangdong 519082, People's Republic of China
- Key Laboratory of Tropical Atmosphere-Ocean System, Ministry of Education, Zhuhai, China, Zhuhai, Guangdong 519082, People's Republic of China
| | - Yiming Liu
- School of Atmospheric Sciences, Sun Yat-sen University, and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, Guangdong 519082, People's Republic of China
- Guangdong Provincial Observation and Research Station for Climate Environment and Air Quality Change in the Pearl River Estuary, Zhuhai, Guangdong 519082, People's Republic of China
- Key Laboratory of Tropical Atmosphere-Ocean System, Ministry of Education, Zhuhai, China, Zhuhai, Guangdong 519082, People's Republic of China
| | - Lei Liu
- College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730000, People's Republic of China
| | - Lin Zhang
- Laboratory for Climate and Ocean-Atmosphere Studies, Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing 100871, People's Republic of China
| | - Dianming Wu
- Key Laboratory of Geographic Information Science (Ministry of Education), School of Geographic Sciences, East China Normal University, Shanghai 200241, People's Republic of China
- Institute of Eco-Chongming (IEC), Shanghai 202162, People's Republic of China
| | - Yujing Mu
- Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
- University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
| | - Shaojia Fan
- School of Atmospheric Sciences, Sun Yat-sen University, and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, Guangdong 519082, People's Republic of China
- Guangdong Provincial Observation and Research Station for Climate Environment and Air Quality Change in the Pearl River Estuary, Zhuhai, Guangdong 519082, People's Republic of China
- Key Laboratory of Tropical Atmosphere-Ocean System, Ministry of Education, Zhuhai, China, Zhuhai, Guangdong 519082, People's Republic of China
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Li Y, Sha Z, Tang A, Goulding K, Liu X. The application of machine learning to air pollution research: A bibliometric analysis. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2023; 257:114911. [PMID: 37154080 DOI: 10.1016/j.ecoenv.2023.114911] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Revised: 03/27/2023] [Accepted: 04/10/2023] [Indexed: 05/10/2023]
Abstract
Machine learning (ML) is an advanced computer algorithm that simulates the human learning process to solve problems. With an explosion of monitoring data and the increasing demand for fast and accurate prediction, ML models have been rapidly developed and applied in air pollution research. In order to explore the status of ML applications in air pollution research, a bibliometric analysis was made based on 2962 articles published from 1990 to 2021. The number of publications increased sharply after 2017, comprising approximately 75% of the total. Institutions in China and United States contributed half of all publications with most research being conducted by individual groups rather than global collaborations. Cluster analysis revealed four main research topics for the application of ML: chemical characterization of pollutants, short-term forecasting, detection improvement and optimizing emission control. The rapid development of ML algorithms has increased the capability to explore the chemical characteristics of multiple pollutants, analyze chemical reactions and their driving factors, and simulate scenarios. Combined with multi-field data, ML models are a powerful tool for analyzing atmospheric chemical processes and evaluating the management of air quality and deserve greater attention in future.
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Affiliation(s)
- Yunzhe Li
- Beijing Key Laboratory of Farmland Soil Pollution Prevention and Remediation, College of Resources and Environmental Science, China Agricultural University, Beijing 100193, China
| | - Zhipeng Sha
- Beijing Key Laboratory of Farmland Soil Pollution Prevention and Remediation, College of Resources and Environmental Science, China Agricultural University, Beijing 100193, China
| | - Aohan Tang
- Beijing Key Laboratory of Farmland Soil Pollution Prevention and Remediation, College of Resources and Environmental Science, China Agricultural University, Beijing 100193, China.
| | - Keith Goulding
- Sustainable Soils and Crops, Rothamsted Research, Harpenden AL5 2JQ, UK
| | - Xuejun Liu
- Beijing Key Laboratory of Farmland Soil Pollution Prevention and Remediation, College of Resources and Environmental Science, China Agricultural University, Beijing 100193, China
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